Combining implementation and data sciences to accelerate evidence integration into healthcare – ImpleMATE

Combining implementation and data sciences to... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/14-1184" }, "headline": "Combining implementation and data sciences to accelerate evidence integration into healthcare –...", "datePublished": "2025-10-30T11:14:57", "dateModified": "2025-10-30T11:14:57", "author": [ { "@type": "Person", "name": "Jeffery Chan" }, { "@type": "Person", "name": "Janna Hastings" }, { "@type": "Person", "name": "Gabriella Tiernan" }, { "@type": "Person", "name": "Jeannie Paterson" }, { "@type": "Person", "name": "Nigel Lovell" }, { "@type": "Person", "name": "Luc Betbeder-Matibet" }, { "@type": "Person", "name": "Patrick Kin Man Tung" }, { "@type": "Person", "name": "Sarah Pink" }, { "@type": "Person", "name": "Debora Lanzeni" }, { "@type": "Person", "name": "Thomasina Donovan" }, { "@type": "Person", "name": "Andrew Milat" }, { "@type": "Person", "name": "Louisa Jorm" }, { "@type": "Person", "name": "Carolyn Mazariego" }, { "@type": "Person", "name": "Chi Zhang" }, { "@type": "Person", "name": "Guillaume Fontaine" }, { "@type": "Person", "name": "Stephanie Best" }, { "@type": "Person", "name": "Georgina Kennedy" }, { "@type": "Person", "name": "Susan Michie" }, { "@type": "Person", "name": "Frank Lin" }, { "@type": "Person", "name": "The ImpleMATE Network" }, { "@type": "Person", "name": "Natalie Taylor" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background The translation of research evidence into routine healthcare practice is often slow and inconsistent, even timely implementation can significantly improve patient outcomes. While implementation science offers strategies to close this gap, current approaches are frequently manual, fragmented, and poorly integrated within healthcare systems. To address these challenges, we propose ImpleMATE – an AI-powered implementation science platform designed to streamline implementation efforts. Grounded in the Learning Health System (LHS) model, ImpleMATE aims to establish a continuous, data-driven cycle of learning and improvement in implementation practice. Methods ImpleMATE will be developed through a co-design and co-production approach rooted in human-centred design principles. Development will proceed through four key activities: (1) establishing a data processing pipeline and building an implementation-focused ontology; (2) creating and validating an AI system to extract implementation knowledge, structure the ontology, and support implementation solution delivery; (3) designing an interactive web application to deliver AI-powered decision support and streamline implementation processes; and (4) developing an evaluation framework to assess platform’s effectiveness and plan for national integration. These activities align with three core components of the LHS model: converting data into knowledge, translating knowledge into practice, and feeding implementation process and outcome data back into the system for continuous learning. The platform will be underpinned by strong ethical and governance frameworks to ensure data privacy, transparency, and responsible AI use. Discussion ImpleMATE aims to transform the adoption of evidence-based innovations in healthcare by embedding trustworthy AI into the core of implementation practice. Through the integration of structured ontologies, real-time AI reasoning, and an interactive user interface, the platform will provide tailored solutions to support implementation efforts. Designed as a dynamic learning system, ImpleMATE will evolve with user input and real-world data, offering a scalable, ethically grounded solution to accelerate and enhance implementation across healthcare settings. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-1184", "name": "Combining implementation and data sciences to accelerate evidence..." } } ] } Home Browse Combining implementation and data sciences to accelerate evidence... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Chan J, Hastings J, Tiernan G et al. Combining implementation and data sciences to accelerate evidence integration into healthcare – ImpleMATE [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1184 ( https://doi.org/10.12688/f1000research.169999.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Study Protocol Combining implementation and data sciences to accelerate evidence integration into healthcare – ImpleMATE [version 1; peer review: 2 approved with reservations] Jeffery Chan https://orcid.org/0000-0002-7521-9504 1 , Janna Hastings 2-4 , Gabriella Tiernan 1 , [...] Jeannie Paterson 5 , Nigel Lovell 6,7 , Luc Betbeder-Matibet 8 , Patrick Kin Man Tung 8 , Sarah Pink 9 , Debora Lanzeni 10 , Thomasina Donovan https://orcid.org/0000-0002-0127-0091 11 , Andrew Milat 12,13 , Louisa Jorm 14 , Carolyn Mazariego 1 , Chi Zhang 1 , Guillaume Fontaine https://orcid.org/0000-0002-7806-814X 15-17 , Stephanie Best 18,19 , Georgina Kennedy 20-22 , Susan Michie 23 , Frank Lin 20,24-26 , The ImpleMATE Network , Natalie Taylor 1 Jeffery Chan https://orcid.org/0000-0002-7521-9504 1 , Janna Hastings 2-4 , [...] Gabriella Tiernan 1 , Jeannie Paterson 5 , Nigel Lovell 6,7 , Luc Betbeder-Matibet 8 , Patrick Kin Man Tung 8 , Sarah Pink 9 , Debora Lanzeni 10 , Thomasina Donovan https://orcid.org/0000-0002-0127-0091 11 , Andrew Milat 12,13 , Louisa Jorm 14 , Carolyn Mazariego 1 , Chi Zhang 1 , Guillaume Fontaine https://orcid.org/0000-0002-7806-814X 15-17 , Stephanie Best 18,19 , Georgina Kennedy 20-22 , Susan Michie 23 , Frank Lin 20,24-26 , The ImpleMATE Network , Natalie Taylor 1 PUBLISHED 30 Oct 2025 Author details Author details 1 Implementation to Impact (i2i), School of Population Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia 2 Institute for Implementation, Science in Health Care, University of Zurich, Zurich, Swaziland 3 School of Medicine, University of St. Gallen, St. Gallen, Switzerland 4 Swiss Institute of Bioinformatics, Lausanne, Switzerland 5 Centre for AI and Digital Ethics, The University of Melbourne, Melbourne, Victoria, Australia 6 Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, New South Wales, Australia 7 Tyree Institute of Health Engineering (IHealthE), University of New South Wales, Sydney, New South Wales, Australia 8 Research Technology Services, University of New South Wales, Sydney, New South Wales, Australia 9 Emerging Technologies Lab, Monash University, Melbourne, Victoria, Australia 10 Department of Design, Monash University, Melbourne, Victoria, Australia 11 Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia 12 Agency for Clinical Innovation, Sydney, New South Wales, Australia 13 School of Public Health, University of Sydney, Sydney, New South Wales, Australia 14 Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia 15 Ingram School of Nursing, McGill University, Montréal, Canada 16 Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Montréal, Canada 17 Centre for Implementation Research, Ottawa Hospital Research Institute, Ottawa, Canada 18 Australian Genomics, Melbourne, Victoria, Australia 19 Melbourne School of Health Sciences, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Victoria, Australia 20 Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia 21 Ingham Institute of Applied Medical Research, Liverpool, Sydney, New South Wales, Australia 22 Maridulu Budyari Gumal (SPHERE) Cancer Clinical Academic Group, Sydney, New South Wales, Australia 23 Centre for Behaviour Change, University College London, London, UK 24 Department of Medical Oncology, Prince of Wales Hospital, Sydney, New South Wales, Australia 25 Garvan Institute of Medical Research, Sydney, New South Wales, Australia 26 NHMRC Clinical Trials Centre, University of Sydney, Sydney, New South Wales, Australia Jeffery Chan Roles: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Janna Hastings Roles: Conceptualization, Methodology, Writing – Review & Editing Gabriella Tiernan Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Writing – Review & Editing Jeannie Paterson Roles: Conceptualization, Methodology, Writing – Review & Editing Nigel Lovell Roles: Conceptualization, Methodology, Writing – Review & Editing Luc Betbeder-Matibet Roles: Conceptualization, Methodology, Writing – Review & Editing Patrick Kin Man Tung Roles: Conceptualization, Methodology, Writing – Review & Editing Sarah Pink Roles: Conceptualization, Methodology, Writing – Review & Editing Debora Lanzeni Roles: Conceptualization, Methodology, Writing – Review & Editing Thomasina Donovan Roles: Conceptualization, Methodology, Writing – Review & Editing Andrew Milat Roles: Conceptualization, Methodology, Writing – Review & Editing Louisa Jorm Roles: Conceptualization, Methodology, Writing – Review & Editing Carolyn Mazariego Roles: Conceptualization, Methodology, Writing – Review & Editing Chi Zhang Roles: Conceptualization, Methodology, Writing – Review & Editing Guillaume Fontaine Roles: Methodology, Writing – Review & Editing Stephanie Best Roles: Conceptualization, Methodology, Writing – Review & Editing Georgina Kennedy Roles: Methodology, Writing – Review & Editing Susan Michie Roles: Conceptualization, Methodology, Writing – Review & Editing Frank Lin Roles: Conceptualization, Methodology, Writing – Review & Editing Natalie Taylor Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Health Services gateway. This article is included in the Artificial Intelligence and Machine Learning gateway. Abstract Background The translation of research evidence into routine healthcare practice is often slow and inconsistent, even timely implementation can significantly improve patient outcomes. While implementation science offers strategies to close this gap, current approaches are frequently manual, fragmented, and poorly integrated within healthcare systems. To address these challenges, we propose ImpleMATE – an AI-powered implementation science platform designed to streamline implementation efforts. Grounded in the Learning Health System (LHS) model, ImpleMATE aims to establish a continuous, data-driven cycle of learning and improvement in implementation practice. Methods ImpleMATE will be developed through a co-design and co-production approach rooted in human-centred design principles. Development will proceed through four key activities: (1) establishing a data processing pipeline and building an implementation-focused ontology; (2) creating and validating an AI system to extract implementation knowledge, structure the ontology, and support implementation solution delivery; (3) designing an interactive web application to deliver AI-powered decision support and streamline implementation processes; and (4) developing an evaluation framework to assess platform’s effectiveness and plan for national integration. These activities align with three core components of the LHS model: converting data into knowledge, translating knowledge into practice, and feeding implementation process and outcome data back into the system for continuous learning. The platform will be underpinned by strong ethical and governance frameworks to ensure data privacy, transparency, and responsible AI use. Discussion ImpleMATE aims to transform the adoption of evidence-based innovations in healthcare by embedding trustworthy AI into the core of implementation practice. Through the integration of structured ontologies, real-time AI reasoning, and an interactive user interface, the platform will provide tailored solutions to support implementation efforts. Designed as a dynamic learning system, ImpleMATE will evolve with user input and real-world data, offering a scalable, ethically grounded solution to accelerate and enhance implementation across healthcare settings. READ ALL READ LESS Keywords Artificial intelligence, data science, implementation science, natural language processing, large language model, healthcare Corresponding Author(s) Natalie Taylor ( [email protected] ) Close Corresponding author: Natalie Taylor Competing interests: No competing interests were disclosed. Grant information: The project is part-funded by a UNSW Research Infrastructure Scheme: Supporting Collaborative Research 2025 and UNSW-UTS Trustworthy Digital Society; and supported by an MRFF Rapid Applied Research Translation grant (RARUR00125). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Chan J et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Chan J, Hastings J, Tiernan G et al. Combining implementation and data sciences to accelerate evidence integration into healthcare – ImpleMATE [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1184 ( https://doi.org/10.12688/f1000research.169999.1 ) First published: 30 Oct 2025, 14 :1184 ( https://doi.org/10.12688/f1000research.169999.1 ) Latest published: 30 Oct 2025, 14 :1184 ( https://doi.org/10.12688/f1000research.169999.1 ) Background Health research has progressed at an unprecedented pace. These advances have led to a deeper understanding of disease mechanisms, 1 the development of more targeted therapies, 2 and the generation of high-quality evidence for improving health outcomes. 3 Despite rapid scientific advances in health and medical research, the integration of evidence-based innovations into routine healthcare remains a persistent challenge. 4 , 5 Evidence indicates that it can take on average between 7-17 years for evidence to be fully integrated into standard care. 6 – 8 The slow uptake of research findings into practice is particularly problematic in fast-moving fields like cancer genetics and genomics where breakthroughs have transformed cancer care 5 , 9 and enabled more precise diagnostics and targeted therapies that can improve survival and reduce mortality. 10 – 12 This implementation gap significantly impedes timely and effective translation of emerging evidence into improved patient outcomes. The integration of breakthrough into everyday clinical practice continues to face complex, multifaceted barriers. 4 , 13 – 21 Implementation science has emerged as a key discipline to bridge this evidence-to-practice gap. Evidence-driven implementation strategies can drive effective system and practice change, improve health outcomes, and reduce costs. 22 – 24 For example, tailored implementation strategies including actions plans, education, use of clinical opinion leaders, local, external, and country champions for site support, audit and feedback, and reminder strategies were used to implement nurse-initiated protocols to manage fever, hyperglycaemia, and swallowing after stroke in 64 hospitals across 17 countries, leading to significant reductions in death and disability. 25 The effectiveness of tailored implementation strategies, addressing context-specific implementation barriers and facilitators, is further supported by other large scale systematic reviews and meta-analyses. 26 , 27 For example, a systematic review of 28 studies indicated social support was a key strategy associated with successful implementation of guidelines to ensure patients at high risk of hereditary cancer receive genetic testing and counselling. 28 Implementation science can inform us about contextual factors including barriers, facilitators, and strategies that influence the adoption, adaptation, and sustainability of evidence-based interventions in real-world settings. In addition to identifying factors associated with successful implementation, optimizing implementation effectiveness beyond a single study requires identifying the mechanisms of change: the processes through which implementation strategies impact care delivery. 29 For example, through mediation analysis within a randomised controlled trial to improve measurement-based care implementation in youth mental health, it was found that a leader-focused implementation strategy improved implementation climate by increasing leaders’ use of implementation leadership, and improved clinician fidelity to a clinical intervention by enhancing implementation climate. 30 In the genetics space, a mixed-methods process evaluation was conducted on seven Australian hospitals, alongside a hybrid type III trial to improve Lynch syndrome detection. The study found that tailored theory-driven strategies, including education, training, and MDT strategies, may better support Lynch syndrome detection practices. 31 Whilst these complex trials of strategies and analyses of mechanistic effects help to advance the science of implementation, this work relies heavily on collection and manual data analysis of diverse data sources such as implementation literature, live project data, surveys, interviews, meeting records/documentation, and outcome data. Synthesising this wealth of information across thousands of studies to understand patterns of implementation success for different interventions and contexts is an infeasible challenge. Currently, identifying and applying effective implementation strategies is often a manual and resource-intensive process. These approaches are not only inefficient and hindered by the underutilization of existing knowledge, but also susceptible to bias and inconsistency, limiting their scalability and impact. As a result, health systems often duplicate efforts, reinvent strategies, and fail to capitalize on prior learnings. 32 – 35 Implementation science also lacks dedicated infrastructure within health systems to systematically support the adoption of evidence-based practices. 36 , 37 Missing infrastructure includes effective and safe data sharing capabilities, local, national, and international communities of practice, availability of practical educational resources, and access to implementation expertise for training and coaching support. 38 – 40 There is an urgent need for a more scientific, structured, and integrated approach to make health system implementation smarter. If we do not act, we risk continuing to waste time rehashing unsuccessful implementation approaches, with healthcare delivery continuing to lag behind the pace of innovation, leaving patients without timely access to the full benefits of cutting-edge research. To address these persistent challenges, we propose an implementation research and practice platform: ImpleMATE – combining ImpleMentation and dATa sciences to advance the speed of evidence integration into hEalthcare. ImpleMATE harnesses the power of artificial intelligence (AI) and integrates expert-guided feedback mechanisms to directly address these interrelated implementation research and practice, and health system inefficiencies through automatic knowledge (i.e., concept) curation, synthesis, transfer, and continuous improvement. The potential of combining implementation and data sciences Implementation science provides the theories, frameworks, and tools to support practical evidence translation and help advance our understanding how and why interventions succeed or fail in real-world settings, 41 while data science offers the computational power and analytical techniques to process large, complex datasets efficiently. Combining these two disciplines presents an opportunity to accelerate the implementation process, reduce inefficiencies, and minimise human error in decision-making processes. A data science approach can support the design and development of an end-to-end data pipeline that automates the data collection from diverse sources, performs data screening, cleansing, preprocessing, storage, analysis, and retrieval. 42 This pipeline can accommodate both structured (e.g., survey data, practice change data) and unstructured data (e.g., literature, interview data). Data screening processes validate the extent to which the incoming data falls within the project scope. Keyword matching, topic modelling, and AI-based semantic analysis can be used for this purpose. 43 , 44 Data cleansing involves handling missing data, deduplication, and format standardisation, ensuring data quality and consistency. Preprocessing can include de-identification to comply with ethical and privacy standards, as well as indexing, which involves tagging or annotating key concepts, 45 such as clinical terms, interventions, and strategies, within the data. These concepts are organized using an ontology, which is a structured representation of knowledge that defines the concepts and their relationships. 46 Ontologies have been widely used in different fields, including behavioural science, 47 – 49 enabling consistent interpretation of data, and supporting more accurate and meaningful data integration, retrieval, and analysis. Depending on data type and volume, storage solutions may include relational databases, data warehouses, or cloud-based data lakes. Analytical models, including statistical analysis and machine learning algorithms, can be applied to uncover patterns, generate insights, and support context-specific decision-making. Predictive models can forecast the likely success of different implementation strategies by drawing on historical data and contextual variables. 50 Information retrieval techniques can help ensure that users access relevant findings through dashboards or query interfaces, delivering actionable insights when and where they are needed. This systematic approach enhances data usability, reproducibility, and scalability in implementation research and practice. The integration of AI, particularly natural language processing (NLP), within this data pipeline offers additional potential. NLP enables automated identification and extraction of implementation-relevant concepts and their interrelationships from unstructured data sources such as literature, reports, and meeting notes. 48 , 51 – 53 Transformer-based large language models (LLMs), such as encoder-only models like Bidirectional Encoder Representations from Transformers (BERT), 54 or decoder-only models such as Generative Pre-trained Transformer (GPT), 55 Mistral, 56 and LLaMA, 57 can perform this task with potentially high accuracy. 51 Depending on the availability of labelled data, these models can be fine-tuned to improve task-specific performance. Prompting strategies, such as zero-shot, 58 few-shot, 59 and chain-of-thought (CoT) prompting, 60 can be used to control and guide the models’ outputs. NLP can also be leveraged to rapidly synthesise findings from large volumes of implementation literature. 61 By extracting and clustering key concepts, this can help to identify common implementation barriers, facilitators, and effective strategies across studies and contexts. The ability of NLP to understand semantic relationships between concepts, recognize synonyms, and interpret context allows for a more nuanced synthesis of evidence. For instance, NLP can cluster similar implementation determinants under shared categories, or link strategies to specific outcomes across different settings. 62 , 63 However, the implementation landscape is inherently complex with interrelated, dynamic, and context-specific factors. Across the hundreds of thousands of reported implementation studies conducted in the past, present, and ongoing, clinical and research teams are striving to capture the wealth of information generated in real time. This information, collected on a spectrum from real-world quality improvement settings to rigorously designed implementation trials, is highly varied in terms of structure and quality, and often difficult to consolidate into a single, coherent data capture framework. To meaningfully address this complexity and generate reliable, actionable strategies, a platform that enables systematic and structured data capture from live implementation projects, integrated with NLP approach, holds significant promise. This kind of platform has the potential to enhance efficiency, enable real-time feedback and adaptation, and improve the accuracy and relevance of implementation insights. While AI is not a standalone solution and requires thorough testing and a robust governance framework for its own effectiveness and to mitigate harmful bias to optimise an implementation effort in comparison to standard approaches, the potential lies in helping to manage complexity, support timely decision-making, and ultimately improve the design and execution of evidence-informed implementation strategies. As implementation science continues to evolve, integrating data science – including AI – offers a transformative approach to accelerating knowledge translation, avoiding redundant efforts, and systematically capturing and reusing implementation knowledge. This synergy has the potential to reshape how evidence is collected, synthesised and applied across healthcare systems. Aim and research questions This project aims to pioneer the world’s first expert-guided and AI-powered implementation science platform – ImpleMATE – to revolutionize implementation research and practice. ImpleMATE integrates AI with a continuous feedback and improvement mechanism inspired by the Learning Health System (LHS) model, 64 directly targeting key inefficiencies in implementation science and healthcare delivery. We will initially focus on cancer genetics and genomics, and once proof of concept is established, we will look to extend to other areas. The LHS concept for ImpleMATE consists of three components – “Data to Knowledge”, “Knowledge to Implementation Practice”, and “Implementation Practice to Data” forming a learning and improvement cycle as shown in Figure 1 . The research questions are posed around these three components, with evaluation and ongoing planning for health system integration at the core: Figure 1. ImpleMATE learning and improvement cycle. Data to Knowledge (D2K) – knowledge curation, identification, extraction, and representation: 1) What are the knowledge sources and the types of the sources? 2) How can implementation knowledge be curated from the sources? 3) What is the optimal approach for managing process data from multiple sources with varying formats? 4) What essential implementation-related information, entity interrelationships, data organisation, and representation are required in an ontology? 5) What governance frameworks are required to ensure testing, transparency, accountability, and ethical use of curated knowledge to ensure trust in the platform? 6) How do we identify and extract the implementation-related information using AI? In particular, which AI models should be used? 7) What is the accuracy of the identification and extraction tasks, and how can bias in these tasks be detected and mitigated? 8) What is the relevance of the curated knowledge, and how is this reliably measured? Knowledge to Implementation Practice (K2IP) – knowledge transfer and implementation process organisation through an interactive web application : 1) Who are the target users and what is the best approach for effectively transferring knowledge to users through an interactive web application? 2) What components are included in an implementation process and how are the components to be organised in an interactive web application to drive optimal approaches to implementation? 3) How to effectively retrieve the most relevant information from an ontology based on a user’s query? 4) How should the retrieved information be synthesised using AI to answer the query? 5) How can an interactive web application be integrated with backend AI for real-time inference to support a live implementation effort? Implementation Practice to Data (IP2D) – feedback and improvement mechanism: 1) Where in the platform should feedback loops be integrated and how can they improve implementation? 2) What should be included in the feedback loop? 3) How often should the feedback be provided? 4) How is the feedback process integrated into governance framework? System Evaluation for Integration (SE) : 1) How can this platform be integrated into the existing healthcare system? 2) What should an evaluation framework for national rollout look like? Methods Our approach to develop ImpleMATE combines (1) co-design: focusing on collaborative, human-centred design of solutions to address the pre-identified problem of the interrelated implementation science and health systems inefficiencies; and (2) co-production: focusing on operationalisation of the solutions informed by user experience. 65 – 68 This process will actively engage stakeholders including clinicians, implementation scientists, change managers, healthcare professionals, consumers, decision-makers, and data scientists. To ensure robust project governance, three dedicated groups will be established: a Steering Committee, a Principle Setting End User Committee, and a System Integration Advisory Group. Development will proceed through four core activities aligned with the research questions: Activity 1 will address data handling and ontology design; Activity 2 will focus on AI model development; Activity 3 will involve web application creation; and Activity 4 will evaluate outcomes and develop a roadmap for national system integration. Activities 1 and 2 will form a core Component 1 – Learning Implementation System to enable continue learning of implementation knowledge, and Activity 3 will form a core Component 2 – Interactive Web Application (including both 1 st and 2 nd levels of support) to facilitate knowledge transfer. An overview of the ImpleMATE structure and its four activities is presented in Figure 2 . Figure 2. An overview of ImpleMATE structure. Activity 1. Design data handling process to organize incoming data and a structured implementation ontology Address research questions: D2K Q1-5 Handle incoming data and ensure data compatibility with large language models (LLMs) We will focus on curating two types of data sources. The first data source will be published implementation literature: A data pipeline will curate implementation literature from PubMed research database through an application programming interface (API). The initial focus will be on published cancer genetics/genomics studies that used two key frameworks (collectively cited >9000 times for use in implementation studies, 69 – 71 and recently combined 72 ) to support evaluation and fine-tuning of LLMs: the Theoretical Domains Framework (TDF) 70 for individual behaviour change, and the Consolidated Framework for Implementation Research (CFIR) 73 for organizational and policy factors affecting implementation. Studies using alternative frameworks, other healthcare areas, and/or with unstructured implementation data will be our next focus. To ensure the literature is within the scope, we will use two approaches: (1) keyword searches in titles and abstracts, and (2) topic modelling with both statistical and machine learning (ML) approaches such as Latent Dirichlet Allocation (LDA) 74 and BERTopic 75 with embeddings to capture semantic representation of the literature for topic classification and filtering. Regular meetings with clinicians and implementation scientists will guide keyword and topic selection. The second data source will be existing and ongoing project data (e.g., surveys, interviews, meeting records). Initially, this platform will maximise the use of available data and resources across existing cancer genetics/genomics implementation projects in our project team. The data will/has been stored in a secure institutional university data repository. The collected data will come in various formats, including PDFs, text files, audio recordings, and spreadsheets. To standardise the input for AI processing and ensure compatibility across downstream tasks, all data will be converted into plain text. Prior to any further processing, we will apply and compare de-identification techniques to remove personally identifiable information, in accordance with the State and Federal Australian Privacy Laws. For literature data, segmentation techniques (e.g., using regular expressions) will be employed to identify and separate sections, ensuring the input stays within the token limits (i.e., amount of content LLMs can process) required by LLMs and adheres to their specific formatting requirements. For project data, we will assess and address missing data and duplication. This preprocessing step is critical to ensure data sanitisation, privacy compliance, and security, while also preserving the integrity and accuracy of the data representation when deploying LLM-based solutions. An implementation schema to guide the development of ontology To support structured knowledge extraction and synthesis, we will define a comprehensive implementation schema in collaboration with stakeholders. This schema will capture key entities, their interrelationships, and the overall data structure. It will include categories such as implementation study characteristics, intervention characteristics, implementation determinants (i.e., factors influencing implementation success), strategies, and outcomes, including costs. For instance, study characteristics may include the study setting, design, target population, and geographic location. Interventions may involve, for example, whole body MRI scanning to more accurately detect cancers in patients with LiFraumini syndrome. 76 Implementation determinants may include system level factors such as workflow integration, changes to existing approaches for cancer prevention in these patients, and access challenges for patients in remote locations, and individual clinician factors including remembering to use this new approach, skills to interpret results, and trust in the technology. These factors can be aligned to established implementation frameworks (e.g., TDF and CFIR), allowing for a structured identification of barriers and facilitators. Strategies may include specific methods (e.g., system mapping and workflow redesign, prompts, audit and feedback) used to promote the adoption and integration of interventions into practice. Outcomes (which can be aligned to frameworks such as Proctor’s Outcomes Framework 77 ) may include measures such as fidelity, acceptability, and sustainability, as well as service level, clinical and economic outcomes, including associated implementation costs. 78 , 79 The schema will be developed through an iterative and collaborative process that involves gathering stakeholder feedback, refining entity definitions, and structuring the schema to reflect real-world implementation scenarios. This is to provide a structured framework for describing and managing an ontology to represent the implementation knowledge. Throughout this process, stakeholders will be actively involved in reviewing and validating the framework, including assessments of inter-rater reliability to ensure consistency and clarity in the defined categories. Consumers will be engaged early to ensure that the ontology reflects patient priorities and is grounded in real-world healthcare experiences. This co-design approach ensures that the ontology is not only technically rigorous and accurate, but also practical, contextually meaningful, and sustainable over time. The schema will be developed in alignment with the Open Biomedical Ontologies (OBO) Foundry principles, 80 ensuring the framework is interoperable, transparent, and adheres to accepted standards for ontology development. The implementation schema will support the identification and organisation of information that addresses critical implementation questions about what works, why, in which settings, for whom, and at what cost. An example of the implementation science ontology is illustrated in Figure 3 . Figure 3. An example of implementation science ontology. Ethical sharing of project data and responsible use of AI To develop a system that relies on both the sharing of research and live project data, and the synthesis of that data using AI, it is essential to establish a solid and sustained foundation for data privacy, security, and the responsible use of AI. To achieve this, we will establish a Principle Setting End User Committee, which will both inform the AI governance framework and act as the conduit to the wider group of relevant consumers, researchers, and clinicians. The End User Committee will coordinate and lead a series of workshop-style focus groups to present proposed data journey pathways and collaboratively explore approaches to optimise data access while maintaining privacy standards. The goal is to meet both legal requirements and public expectations, establishing and maintaining a social license for data use, and promoting an approach to AI that is fair, inclusive and trustworthy. The focus groups will also explore and refine principles for AI transparency and accountability, ensuring that the AI technologies employed are understandable, explainable, and ethically guided. The End User Committee will remain engaged throughout the project to support the iterative design, development, and deployment of the platform, ensuring its ethical integrity is maintained during the development. In addition, we will conduct targeted interviews to gather diverse stakeholder perspectives on how best to ethically manage and share implementation project data, and how to ensure the responsible application of AI in this domain. Insights from these qualitative investigations will be analysed to develop a set of ImpleMATE Ethics Principles. These principles will be reviewed with the committee to establish consensus and will inform the development of comprehensive data and AI governance frameworks. This ensures transparency in data processing, clarifies the full data journey, and supports accountability for how AI and analytics are used in the project, ultimately reinforcing public trust and supporting ethical innovation in implementation science. Activity 2. Develop and validate an AI system to automate ontology creation and deliver real-time solutions to implementation challenges Address research questions: D2K Q6-8, IP2D Q1-4 AI model selection and implementation concept extraction To enable automated, real-time identification and mapping of implementation science concepts, we will develop and validate an AI-driven system that aligns with the ontology defined in Activity 1. Implementation science experts will first analyze preprocessed data to create a gold-standard dataset by manually annotating concepts including study characteristics, barriers, facilitators, strategies, and outcomes defined in the ontology framework. We will start with 1,000 datasets, each representing an individual document, including published literatures and project data. These datasets will be used to evaluate transformer-based models for their capacity to extract and structure relevant information. Given the diverse and heterogeneous nature of implementation science concepts, ranging from study characteristics and outcomes to more nuanced elements such as barriers, facilitators, and strategies, these datasets will support the exploratory phase of initial model validation and performance comparisons across concept categories. Our approach will involve two core development pathways. First, we will test and fine-tune a concept extractor such as BERT model for Named Entity Recognition (NER) and Relation Extraction (RE), enabling the system to accurately identify discrete implementation concepts and their relationships. Second, we will collaboratively design and evaluate prompt engineering techniques tailored to pretrained, locally hosted transformer models such as Mistral 56 and LLaMA. 57 These models will be explored using a range of strategies, including zero-shot and few-shot learning 58 , 59 as well as CoT reasoning, 60 to determine their effectiveness in extracting relevant information from varied, context-rich data sources. Examples of prompts for extracting implementation concepts are shown in Table 1 . The use of locally hosted models is to ensure that sensitive health and implementation project data remain secure and confidential. Hosting models on secure infrastructure reduces the risk of data exposure, supports compliance with privacy legislation and ethical standards, and ensures that all processing occurs within controlled environments. 81 Table 1. Prompt examples for building the ontology and assisting implementation concept identification and extraction. Implementation concepts Prompts Settings You are an expert in health system analysis. Read the case report and identify the settings in which the intervention or strategy was implemented. The settings could include the type of institution (e.g., hospital, community health center), the geographic location (e.g., city, rural area), the organizational context (e.g., public or private sector), and any other relevant environmental factors that describe where the implementation occurred. Please list and explain each identified setting. Implementation barriers You are an implementation scientist. Read the case report and identify any barriers to the successful implementation of the intervention or strategy discussed. These barriers can be related to organizational, systemic, financial, technological, or human factors, among others. Please list and explain each identified barrier. Return NA if no barrier is identified. Implementation strategies You are a clinical nurse consultant. Read the case report and identify the strategies that were used to overcome challenges or improve the implementation of the intervention or strategy. These strategies can include actions related to enhancing clinical workflows, engaging multidisciplinary teams, optimizing resource use, and more. Please list and explain each identified strategy. As LLM models learn from increasing volumes of annotated data, their performance in identifying and extracting implementation concepts is expected to improve, enabling them to autonomously construct a comprehensive ontology. This continuous learning process will contribute to the dynamic evolution of the ontology and enable the system to deliver real-time, context-specific implementation insights. These insights will form the backbone of the interactive web application developed in Activity 3, allowing users to query and retrieve relevant strategies and evidence-based recommendations efficiently, and supporting the implementation process. To ensure accuracy and practical relevance, the outputs of each model will be systematically compared against expert-annotated datasets. Through this evaluation process, we will determine whether a single model or an ensemble approach provides the most reliable and generalisable performance. This effort will also serve to validate the overall feasibility and robustness of a computational curation pipeline, demonstrating its potential to support scalable, automated knowledge synthesis in implementation science. AI performance evaluation metrics, quality assessment and monitoring To ensure the reliability, accuracy, and continuous improvement of the AI system, we will implement a structured framework for evaluation and quality monitoring. Standard performance metrics, including precision (the proportion of correctly identified entities among those predicted), recall (the proportion of relevant entities successfully identified), and F1-score (the harmonic mean of precision and recall), will be used to quantitatively assess the accuracy and effectiveness of concept extraction. Beyond this surface-level matching, we will evaluate semantic equivalence between AI-generated outputs and expert annotations to account for cases where the same concept is expressed using different wordings. This semantic comparison ensures that meaning is preserved even when the linguistic form differs, which is especially important for interpreting complex implementation science data. The AI system’s adaptability to new or previously unseen concepts will be closely monitored. A reserved validation subset from the gold-standard dataset will serve as ground truth for evaluating prompt performance. We will systematically test multiple prompt formulations, comparing their outputs against the ground truth using the defined metrics. In addition to performance, prompt quality will be assessed based on relevance and the occurrence of hallucination, which is defined as content not grounded in the data source. An iterative refinement strategy will be employed, using the LLMs to suggest and optimize its own prompts to generate candidate prompts. The final prompt selection will be based on both effectiveness and generalizability across diverse implementation science contexts. Regular qualitative assessments will be conducted to identify emerging themes, assess the specificity and generalisability of the model, and highlight areas for improvement. Manual reviews of the evolving ontology, led by implementation scientists, will ensure that AI-generated outputs remain consistent with domain expertise and responsive to real-world practice needs. These evaluations will inform targeted adjustments, such as fine-tuning or prompt control, through a feedback mechanism integrated into the ImpleMATE structure and embedded within the broader governance framework, ensuring that insights from performance monitoring and expert review are systematically incorporated into decision-making processes. This iterative approach will drive continuous system improvement, ensuring the AI remains robust, accurate, and contextually relevant in addressing real-world implementation challenges. Activity 3. Create and test an interactive web application to support implementation decision-making and streamline implementation processes Address research questions: K2IP Q1-5, IP2D Q1-4 An AI-generated decision aid, and an implementation coaching and data sharing platform The interactive web application developed in this project will provide two integrated levels of support for implementation practice. The target users include implementation researchers, clinicians, change managers, and other professionals engaged in evidence translation efforts across healthcare settings. The 1 st level will offer an AI-generated decision aid, while the 2 nd level will deliver a more advanced and comprehensive implementation coaching and data sharing platform. An AI-generated decision aid – This will allow users to input queries related to their implementation context or challenges they are facing. For example, users might ask, “How can I implement X intervention in my setting?” or “Which strategies could help me address a specific barrier?” The system will analyse each query through a text processing pipeline, matched against the ontology created in earlier stages of the project. The ontology will serve as a knowledge base, enabling the retrieval of relevant concepts and their contextual relationships. The semantic meaning of the query will be interpreted by the local LLM, which synthesises the retrieved information and generates a tailored, evidence-based response that aligns with the user’s context. Basic and comprehensive queries will be prepared and regularly submitted to the decision aid for testing to ensure relevance of the retrieved concepts and relationships, as well as accuracy of the AI-generated responses. This supports iterative improvement in the model’s performance and the quality of outputs. An implementation coaching and data sharing platform – this provides more comprehensive support that functions as a central hub for implementation professionals and clients. It will enable users to securely log in, communicate with implementation support teams, access coaching and training resources, and share data collected during real-world implementation projects. Users will be able to upload a wide range of data, including surveys, meeting records/documentation, process maps, focus group transcripts, cost analyses, and health system outcome metrics. The platform will support implementation scientists to analyse this data through AI-enabled tools such as automatic qualitative coding and strategy mapping, matched against established implementation frameworks like CFIR and ERIC. This functionality will offer deeper insights into the determinants, strategies, and outcomes relevant to their specific projects. The platform will include a feedback mechanism that allows users to report perceived inaccuracies in AI-generated responses. These observations will inform further refinement of the system through prompt engineering or fine-tuning of the underlying models. The data and insights generated through this platform will be fed back into the Learning Implementation System, expanding and continuously improving the ontology. The web application itself will be initially developed and validated in a secure local environment before being deployed to a secure web server. The LLM will be accessed through a secure and encrypted channel to ensure data privacy and compliance with relevant legal and ethical standards. To support broader scalability, the platform will be designed with flexible access levels to accommodate the varying needs of different user groups. This architecture ensures that the system can serve as both a lightweight decision aid for individual users and a full-featured collaborative platform for research teams and institutional partners. Examples of user scenarios and the levels of support available to them are described in Table 2 . Examples of the web application is shown in Figure 4 . Table 2. Examples of users and queries for ImpleMATE. Type of user Requirement 1 st level – Query for implementation challenge 2 nd level – Comprehensive implementation support (all data collected fed into Learning Implementation System) Hospital cardiac department manager To identify the most effective strategies for implementing remote pulse oximetry. What are the optimal strategies to implement remote pulse oximetry, and what key barriers hinder patient adoption? Interacts with ImpleMATE for guided implementation support as part of a continued professional development project. Hospital CEOs and change management team To scale and evaluate a precision oncology clinic in routine healthcare. How can we effectively integrate a precision oncology clinic into existing workflows to improve patient outcomes in hospital settings? • Change managers receive ImpleMATE support to collect context specific data. • Provide analysis on operational changes, resource allocations, and policy amendments. • Support to develop strategies. Research team Understand evidence to introduce whole-body MRI cancer screening in Li-Fraumeni Syndrome. How effective is whole-body MRI screening, and what behavior changes are needed to support its implementation? ImpleMATE-guided structured data collection to enable analysis and evaluation. Department of Health and Aged Care To run a national campaign to promote HPV self-collection in rural and remote settings. What contextual and cultural factors influence the successful implementation of HPV self-collection in rural and remote settings? • Campaign staff to get ImpleMATE training to collect context specific data. • Co-design strategies with ImpleMATE coaching team. Figure 4. Examples of the web application. Establish the acceptability of the interactive web application To evaluate and ensure the acceptability of the interactive web application, we will employ ethnographic methods grounded in human-centred design principles. 82 , 83 These include in-depth structured interviews, contextual inquiry, shadowing, and direct observation of end users in their natural work environments. This approach allows us to capture not only what users say they need, but also what they do in practice, revealing tacit knowledge, workflow constraints, informal processes, and contextual nuances often missed by traditional research methods. Ethnographic insights will inform iterative co-design sessions where users actively contribute to shaping the application’s interface, features, and functionality. Through participatory design workshops and usability testing cycles, we will evaluate interface intuitiveness, interaction patterns, and overall user satisfaction, applying core user experience (UX) design principles such as simplicity, responsiveness, consistency, and accessibility. 84 – 86 These methods will help assess users’ experiences and expectations of existing implementation training and coaching methods, identifying gaps and unmet needs. Findings will directly inform platform design to ensure alignment with users’ day-to-day practices, digital literacy levels, organisational cultures, and time/resource constraints. Additionally, we will develop a structured change management and transition strategy in collaboration with users to support platform adoption. This will include tailored onboarding resources and continuous feedback loops to refine the platform post-deployment. The overall goal is to ensure the platform is not only functional and acceptable but also seamlessly embedded into routine workflows, enabling long-term engagement and trust. Activity 4. Design an evaluation framework and roadmap for national integration Address research questions: SE Q1-2 An evaluation framework for ImpleMATE To evaluate the effectiveness of ImpleMATE and its integration into health systems, we will develop a comprehensive evaluation framework that captures both system-level impact and end-user outcomes. A key component of this evaluation will be the identification of relevant data interfaces such as API that can reflect patient-level outcomes influenced by implementation strategies generated or supported through ImpleMATE. These outcomes may include changes in care pathways, access to evidence-based interventions, and improvements in clinical decision-making resulting from the platform’s decision aid or coaching platform. We will collaborate with our multidisciplinary partners, including implementation scientists, clinicians, data scientists, patients, and healthcare managers, to co-develop a set of key performance indicators (KPIs). These KPIs will be designed to assess ImpleMATE’s success from multiple perspectives: implementation effectiveness, patient outcomes, clinical practice, and system-wide performance. The metrics selected will be guided by the quintuple aim of healthcare improvement: enhancing patient outcomes (e.g., earlier diagnosis, timely treatment, patient-reported satisfaction), improving provider experience (e.g., reduced cognitive burden, support for decision-making), improving health equity, achieving cost-effectiveness, and optimizing resource use (e.g., increased speed and quality of evidence-based intervention uptake, reduced duplication of implementation effort, improved allocation of resources). Exploring a national roadmap to integrate ImpleMATE The successful implementation of ImpleMATE hinges on its seamless integration into existing healthcare systems in a way that enhances, rather than disrupts, current clinical and organisational workflows. Achieving this requires close collaboration with state-wide health entities and organisations to ensure the platform meets the operational, technical, and regulatory needs of healthcare providers. To support this integration, ImpleMATE will be hosted on a secure cloud-based infrastructure designed to meet healthcare-grade data security and privacy standards. Early in the project, and in alignment with the work of the Principle Setting End User Committee, we will establish a System Integration Advisory Group. This group will bring together technical experts, health service executives, clinicians, IT professionals, and implementation scientists to guide the strategic integration of ImpleMATE across diverse health settings. Using a process map–guided interview approach, 87 , 88 we will systematically document and analyse current workflows, information systems, and decision-making structures. This will help identify integration points, organisational readiness, potential barriers, and facilitators. Insights from this process will inform the development of tailored integration strategies that are technically feasible and contextually appropriate. Study status We have established the ImpleMATE Network, comprising multidisciplinary experts who will form the core of the project’s governance structure. Partnerships have been initiated with collaborators across IT, research technology, cloud infrastructure, and data and AI security to support the development of a secure platform for hosting the web application. Engagements with hospitals and government agencies are also underway to support the co-design process and plan for integration of ImpleMATE into existing healthcare systems. Feasibility assessments and evaluations of locally hosted LLMs for implementation concept identification and extraction have been completed. In parallel, we have explored and shortlisted tools for developing the web application. The first version of the implementation science ontology has also been developed. Discussion ImpleMATE is designed to address the persistent inefficiencies in translating evidence-based healthcare innovations into routine practice by integrating the strengths of implementation science and data science, enhanced through AI. We will build a structured, user-informed platform to accelerate the integration of evidence into healthcare systems at scale. Beginning with the development of a comprehensive ontology to organize implementation knowledge, and advancing to the automation of knowledge extraction using LLMs, ImpleMATE has the potential to transform how individuals and organizations identify, access, and apply implementation strategies in real time. The AI-generated decision aid, underpinned by a robust and expert-informed ontology, is to ensure that decision support is not only AI-powered but also context-aware and aligned with the practical realities of healthcare settings. The platform supports users in navigating unique implementation challenges, making it a powerful resource for implementation practitioners, change managers, healthcare professionals and managers, health innovation and health system researchers, and others engaged in implementation efforts. ImpleMATE is conceived not as a static solution but as a dynamic learning system. The coaching and data-sharing platform facilitates two-way interactions, enabling users to access tailored implementation support and implementation teams to analyse data through AI-enabled tools to streamline implementation. Our commitment to responsible and ethical AI underpins the ImpleMATE framework. AI transparency, ethical data sharing, and user trust have been prioritized through continuous engagement with a diverse group of stakeholders, including clinicians, consumers, policy-makers, and technical experts. As AI-generated recommendations increasingly influence clinical workflows, we will ensure that decision support is explainable as far as possible, with full transparency about the algorithms and data used in each part of the system. This approach helps maintain trust, ensuring that recommendations are not only grounded in evidence but also aligned with the practical realities of healthcare settings and governed by human oversight. While ImpleMATE presents significant opportunities to transform healthcare implementation, we acknowledge key risks that must be actively managed to ensure safe, effective, and ethical adoption. First, data security is a core concern, and it is addressed in Activity 1 through secure data storage, encryption protocols, controlled access approvals, and adherence to established standards and best practices, in collaboration with partners to ensure privacy. Second, the accuracy of AI outputs due to complex implementation scenarios and models’ performance is another concern. Activities 2 and 3 involve mitigation strategies including continuous validation, expert feedback, and human review processes to fine-tune models and adjust effective prompts to maintain alignment with evidence-based practices. Third, the risk of building and maintaining user trust and acceptance is supported across all activities by co-design approaches, human-centred design principles, and transparent, expert-informed AI recommendations. Fourth, the challenge of navigating regulatory and legal risks is addressed by actively consulting with relevant authorities and following responsible AI practices that comply with data protection and privacy laws. This will be integrated into all the activities. Finally, ensuring successful implementation and adoption, ImpleMATE prioritizes inclusive co-design with stakeholders and consumers, builds a robust integration framework, and establishes clear evaluation metrics to proactively identify and address adoption barriers. As part of this, we will use the co-designed evaluation framework developed in Activity 4 to design a trial that evaluate ImpleMATE within the health system. To inform this evaluation, we conducted a PubMed search on clinical genomics implementation, identifying over 1,500 articles published since 2002, with nearly 90% appearing in the last decade. This trend shows no signs of slowing. As a starting point, we will focus the evaluation on cancer genetics and genomics. This will enable us to assess whether more effective and efficient implementation is achieved in comparison to standard approaches. Ultimately, ImpleMATE is designed to form a sustainable learning loop, where real-world data and human input (i.e., human-in-the-loop) continuously inform and improve AI performance. This process ensures that AI inferencing is transparent, grounded in evidence, and aligned with human decision-making. Combining technological innovation with scientific rigor and ethical responsibility, we anticipate that ImpleMATE will offer a transformative, scalable solution to accelerate implementation of evidence-based healthcare innovations to benefit patients. Data availability No data associated with this article. Software availability No software associated with this article. Acknowledgements In addition to the named authors, the following individuals from The ImpleMATE Network also contributed at the time of this publication: Shuang Liang, Jason Grebely, Ursula Sansom-Daly, Katherine Tucker, Geoffrey Delaney, David Goldstein, David Thomas, David Ziegler, Rebecca Ivers, Jennifer Yu, Sze-Yuan Ooi, Peter Croucher, Christine Chaffer, Mary-Anne Young, Patrick Bolton, Claire Wakefield, Elizabeth Palmer, Adam Jaffe, Jackie Curtis, Kim Delbaere, Raghu Lingham, John Fletcher, Bradley Hastings, Barney Tan, Eva Kimonis, Andrea Torres Robles, Carl Power, Fang Chen, Kun Yu, Luis Ortiz Arguedas, Mohammad Islam, Noemi Fuentes Bolanos, Pramod Nathan, Sarah Tesmer, Tracey O’Brien, and Kathryn Leaney. References 1. Hassan M, Awan FM, Naz A, et al. : “Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review,” (in eng). Int. J. Mol. Sci. Apr 22 2022; 23 (9). PubMed Abstract | Publisher Full Text | Free Full Text 2. Satam H, Joshi K, Mangrolia U, et al. : “Next-Generation Sequencing Technology: Current Trends and Advancements,” (in eng). Biology (Basel). Jul 13 2023; 12 (7). PubMed Abstract | Publisher Full Text | Free Full Text 3. Green ED, Gunter C, Biesecker LG, et al. : “Strategic vision for improving human health at The Forefront of Genomics,” (in eng). Nature. Oct 2020; 586 (7831): 683–692. PubMed Abstract | Publisher Full Text | Free Full Text 4. Ginsburg GS, Phillips KA: “Precision Medicine: From Science To Value,” (in eng). Health Aff (Millwood). 2018; 37 (5): 694–701. PubMed Abstract | Publisher Full Text | Free Full Text 5. Neta G, Sanchez MA, Chambers DA, et al. : “Implementation science in cancer prevention and control: a decade of grant funding by the National Cancer Institute and future directions,” (in eng). Implement. Sci. Jan 8 2015; 10 : 4. PubMed Abstract | Publisher Full Text | Free Full Text 6. Rubin R: “It Takes an Average of 17 Years for Evidence to Change Practice-the Burgeoning Field of Implementation Science Seeks to Speed Things Up,” (in eng). JAMA. Apr 25 2023; 329 (16): 1333–1336. PubMed Abstract | Publisher Full Text 7. Williams A, Lennox L, Harris M, et al. : Supporting translation of research evidence into practice—the use of Normalisation Process Theory to assess and inform implementation within randomised controlled trials: a systematic review. 8. Morris ZS, Wooding S, Grant J: “The answer is 17 years, what is the question: understanding time lags in translational research,” (in eng). J. R. Soc. Med. Dec 2011; 104 (12): 510–520. PubMed Abstract | Publisher Full Text | Free Full Text 9. Best S, Roberts MC, Taylor N: “We Need to Stand Together on the Shoulders of Giants: Consolidating Effective Approaches for Translating Genomics into Practice with Implementation Science,” (in eng). Public Health Genomics. 2024; 27 (1): 12–15. PubMed Abstract | Publisher Full Text 10. Georgiou CL, Kee D, Papadopoulos L, et al. : Two years of the Australian Rare Cancer Portal: a national referral service for rare cancer information and research. Proc. Am. Soc. Clin. Oncol. 2022; 40 : e18581. Publisher Full Text 11. Ozdemir BA, Karthikesalingam A, Sinha S, et al. : “Research activity and the association with mortality,” (in eng). PLoS One. 2015; 10 (2): e0118253. PubMed Abstract | Publisher Full Text | Free Full Text 12. Downing A, Morris EJ, Corrigan N, et al. : “High hospital research participation and improved colorectal cancer survival outcomes: a population-based study,” (in eng). Gut. Jan 2017; 66 (1): 89–96. PubMed Abstract | Publisher Full Text | Free Full Text 13. Chambers DA, Feero WG, Khoury MJ: “Convergence of Implementation Science, Precision Medicine, and the Learning Health Care System: A New Model for Biomedical Research,” (in eng). JAMA. May 10 2016; 315 (18): 1941–1942. PubMed Abstract | Publisher Full Text | Free Full Text 14. Taylor N, Best S, Martyn M, et al. : A transformative translational change programme to introduce genomics into healthcare: a complexity and implementation science study protocol. BMJ Open. 2019; 9 (3): e024681. PubMed Abstract | Publisher Full Text | Free Full Text 15. Best S, Stark Z, Phillips P, et al. : “Clinical genomic testing: what matters to key stakeholders?," (in eng). Eur. J. Hum. Genet. Jul 2020; 28 (7): 866–873. PubMed Abstract | Publisher Full Text | Free Full Text 16. Best S, Stark Z, Brown H, et al. : “The leadership behaviors needed to implement clinical genomics at scale: a qualitative study,” (in eng). Genet. Med. Aug 2020; 22 (8): 1384–1390. PubMed Abstract | Publisher Full Text | Free Full Text 17. Best S, Long JC, McPherson E, et al. : Deep inside the genomics revolution: on the frontlines of care. Transforming healthcare with qualitative research. Rapport F, Braithwaite J, editors. Routledge Studies in Health and Social Welfare: Routledge; 2021; pp. 167–178. 18. Best S, Long JC, Gaff C, et al. : “Investigating the Adoption of Clinical Genomics in Australia. An Implementation Science Case Study,” (in eng). Genes (Basel). Feb 23 2021; 12 (2). PubMed Abstract | Publisher Full Text | Free Full Text 19. Best S, Brown H, Stark Z, et al. : “Teamwork in clinical genomics: A dynamic sociotechnical healthcare setting,” (in eng). J. Eval. Clin. Pract. Dec 2021; 27 (6): 1369–1380. PubMed Abstract | Publisher Full Text 20. Best S, Long JC, Gaff C, et al. : Organizational perspectives on implementing complex health interventions: clinical genomics in Australia. 21. Brown HL, Sherburn IA, Gaff C, et al. : “Structured approaches to implementation of clinical genomics: A scoping review,” (in eng). Genet. Med. Jul 2022; 24 (7): 1415–1424. PubMed Abstract | Publisher Full Text 22. Taylor N, Lawton R, Slater B, et al. : The demonstration of a theory-based approach to the design of localized patient safety interventions. 23. Taylor N, Lawton R, Moore S, et al. : Collaborating with front-line healthcare professionals: the clinical and cost effectiveness of a theory based approach to the implementation of a national guideline. 24. Kang Y-J, Killen J, Caruana M, et al. : The predicted impact and cost-effectiveness of systematic testing of people with incident colorectal cancer for Lynch syndrome. Med. J. Aust. 2020; 212 (2): 72–81. PubMed Abstract | Publisher Full Text | Free Full Text 25. Middleton S, Dale S, McElduff B, et al. : Translation of nurse-initiated protocols to manage fever, hyperglycaemia and swallowing following stroke across Europe (QASC Europe): A pre-test/post-test implementation study. Eur. Stroke J. 2023; 8 (1): 132–147. PubMed Abstract | Publisher Full Text | Free Full Text 26. Fontaine G, Vinette B, Weight C, et al. : Effects of implementation strategies on nursing practice and patient outcomes: a comprehensive systematic review and meta-analysis. Implement. Sci. 2024/09/30 2024; 19 (1): 68. PubMed Abstract | Publisher Full Text | Free Full Text 27. Baker R, Camosso-Stefinovic J, Gillies C, et al. : Tailored interventions to address determinants of practice. Cochrane Database Syst. Rev. 2015; 2015 (4): CD005470. PubMed Abstract | Publisher Full Text | Free Full Text 28. Morrow A, Chan P, Tucker KM, et al. : “The design, implementation, and effectiveness of intervention strategies aimed at improving genetic referral practices: a systematic review of the literature,” (in eng). Genet. Med. Dec 2021; 23 (12): 2239–2249. PubMed Abstract | Publisher Full Text | Free Full Text 29. Lewis CC, Klasnja P, Powell BJ, et al. : “From Classification to Causality: Advancing Understanding of Mechanisms of Change in Implementation Science,” (in eng). Front. Public Health. 2018; 6 : 136. PubMed Abstract | Publisher Full Text | Free Full Text 30. Williams NJ, Ehrhart MG, Aarons GA, et al. : Improving measurement-based care implementation in youth mental health through organizational leadership and climate: a mechanistic analysis within a randomized trial. 31. Morrow A, Baffsky R, Tucker K, et al. : Improving Lynch syndrome detection: a mixed-methods process evaluation of a hybrid type III effectiveness-implementation trial. BMC Health Serv. Res. 2024/12/05 2024; 24 (1): 1552. PubMed Abstract | Publisher Full Text | Free Full Text 32. Morrow A, Baffsky R, Tucker K, et al. : Improving Lynch syndrome detection: a mixed-methods process evaluation of a hybrid type III effectiveness-implementation trial. BMC Health Serv. Res. 2024; 24 (1): 1–19. Publisher Full Text 33. Morrow A, Chan P, Tiernan G, et al. : “Building capacity from within: qualitative evaluation of a training program aimed at upskilling healthcare workers in delivering an evidence-based implementation approach,” (in eng). Transl. Behav. Med. 2022; 12 (1). PubMed Abstract | Publisher Full Text | Free Full Text 34. Hoffmann TC, Glasziou PP, Boutron I, et al. : “Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide,” (in eng). BMJ. Mar 7 2014; 348 : g1687. PubMed Abstract | Publisher Full Text 35. Juckett LA, Bunger AC, McNett MM, et al. : Leveraging academic initiatives to advance implementation practice: a scoping review of capacity building interventions. 36. Cowie J, Nicoll A, Dimova ED, et al. : The barriers and facilitators influencing the sustainability of hospital-based interventions: a systematic review. BMC Health Serv. Res. 2020/06/28 2020; 20 (1): 588. PubMed Abstract | Publisher Full Text | Free Full Text 37. Morrow A, Tyedmers E, Debono D, et al. : “Trials and tribulations: a qualitative exploration of researcher perspectives on navigating the challenges of health system implementation research,” (in eng). BMJ Open. Jan 15 2025; 15 (1): e087926. PubMed Abstract | Publisher Full Text | Free Full Text 38. Taylor N, Liang S, McKay S, et al. : Developing an AI-enabled web platform to collect, analyse, synthesise, and accelerate implementation research. presented at the The European Implementation Event. Newcastle, UK: 4-6 Jun, 2025. 39. Raine R, Fitzpatrick R, Barratt H, et al. : Health Services and Delivery Research. Challenges, solutions and future directions in the evaluation of service innovations in health care and public health. Southampton (UK): NIHR Journals Library; 2016. 40. Brownson RC, Cabassa LJ, Drake BF, et al. : Closing the gap: advancing implementation science through training and capacity building. 41. Fontaine G, Mooney M, Porat-Dahlerbruch J, et al. : Advancing the selection of implementation science theories, models, and frameworks: a scoping review and the development of the SELECT-IT meta-framework. Implement. Sci. 2025/05/28 2025; 20 (1): 24. PubMed Abstract | Publisher Full Text | Free Full Text 42. Mumuni A, Mumuni F: Automated data processing and feature engineering for deep learning and big data applications: A survey. J. Inf. Intell. 2025/03/01/2025; 3 (2): 113–153. Publisher Full Text 43. Taherdoost H, Madanchian M: Artificial Intelligence and Sentiment Analysis: A Review in Competitive Research. Computers. 12 (2). Publisher Full Text 44. Valle F, Osella M, Caselle M: “Multiomics Topic Modeling for Breast Cancer Classification,” (in eng). Cancers (Basel). Feb 23 2022; 14 (5). PubMed Abstract | Publisher Full Text | Free Full Text 45. Scheibner J, Ienca M, Kechagia S, et al. : “Data protection and ethics requirements for multisite research with health data: a comparative examination of legislative governance frameworks and the role of data protection technologies,” (in eng). J. Law Biosci. Jan-Jun 2020; 7 (1): lsaa010. PubMed Abstract | Publisher Full Text | Free Full Text 46. Arp R, Smith B, Andrew D: Spear, Building Ontologies with Basic Formal Ontology. The MIT Press; 2015. 47. Michie S, West R, Finnerty AN, et al. : Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology [version 2; peer review: 2 approved]. Wellcome Open Res. 2021; 5 (123): 123. PubMed Abstract | Publisher Full Text | Free Full Text 48. Michie S, Thomas J, Johnston M, et al. : The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation. 49. Marques MM, Wright AJ, Corker E, et al. : “The Behaviour Change Technique Ontology: Transforming the Behaviour Change Technique Taxonomy v1,” (in eng). Wellcome Open Res. 2023; 8 : 308. Publisher Full Text 50. Sarker IH: “Machine Learning: Algorithms, Real-World Applications and Research Directions,” (in eng). SN Comput. Sci. 2021; 2 (3): 160. PubMed Abstract | Publisher Full Text | Free Full Text 51. Chan J, Lin F, Tran M, et al. : Systematic and AI-assisted curation of knowledge ontology to support rapid learning in implementation science. presented at the Society for Implementation Research Collaboration (SIRC). Sept, 2024; pp. 26–28. 52. Dennstädt F, Zink J, Putora PM, et al. : Title and abstract screening for literature reviews using large language models: an exploratory study in the biomedical domain. Syst. Rev. 2024/06/15 2024; 13 (1): 158. PubMed Abstract | Publisher Full Text | Free Full Text 53. Dennstädt F, Windisch P, Filchenko I, et al. : Application of a general LLM-based classification system to retrieve information about oncological trials. medRxiv. 2024. 2024.12.03.24318390. Publisher Full Text 54. Devlin J, Chang M-W, Lee K, et al. : BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. North American Chapter of the Association for Computational Linguistics. 2019. 55. Brown TB, Mann B, Ryder N, et al. : Language Models are Few-Shot Learners. arXiv [cs.CL],. 2020; 2020 . Reference Source 56. Jiang AQ, Sablayrolles A, Mensch A, et al. : Mistral 7B. arXiv [cs.CL],. 2023; 2023 . Reference Source 57. Touvron H, Lavril T, Izacard G, et al. : LLaMA: Open and Efficient Foundation Language Models. arXiv [cs.CL]. 2023; 2023 . Reference Source 58. Harry Caufield J, Hegde H, Emonet V, et al. : Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning. Bioinformatics. 2024; 40 (3): btae104. PubMed Abstract | Publisher Full Text | Free Full Text 59. Cao L, Sun J, Cross A: “An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontology-Enhanced Large Language Models: Development Study,” (in eng). JMIR Med. Inform. Dec 18 2024; 12 : e60665. PubMed Abstract | Publisher Full Text | Free Full Text 60. Wei J, Wang X, Schuurmans D, et al. : Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv [cs.CL],. 2023; 2023 . Reference Source 61. Hussain SA, Sezgin E, Krivchenia K, et al. : “A natural language processing pipeline to synthesize patient-generated notes toward improving remote care and chronic disease management: a cystic fibrosis case study,” (in eng). JAMIA Open. Jul 2021; 4 (3): ooab084. PubMed Abstract | Publisher Full Text | Free Full Text 62. Scaccia JP, Scott VC: 5335 days of Implementation Science: using natural language processing to examine publication trends and topics. Implement. Sci. 2021/04/26 2021; 16 (1): 47. PubMed Abstract | Publisher Full Text | Free Full Text 63. Lossio-Ventura JA, Gonzales S, Morzan J, et al. : “Evaluation of clustering and topic modeling methods over health-related tweets and emails,” (in eng). Artif. Intell. Med. Jul 2021; 117 : 102096. PubMed Abstract | Publisher Full Text | Free Full Text 64. Olsen L, Aisner D, McGinnis JM: Institute of Medicine. The Learning Healthcare System: Workshop Summary. Washington (DC): National Academies Press; 2007. 65. Vargas C, Whelan J, Brimblecombe J, et al. : “Co-creation, co-design, co-production for public health - a perspective on definition and distinctions,” (in eng). Public Health Res Pract. Jun 15 2022; 32 (2). PubMed Abstract | Publisher Full Text 66. Kilfoy A, Hsu T-CC, Stockton-Powdrell C, et al. : An umbrella review on how digital health intervention co-design is conducted and described. 67. Göttgens I, Oertelt-Prigione S: “The Application of Human-Centered Design Approaches in Health Research and Innovation: A Narrative Review of Current Practices,” (in eng). JMIR Mhealth Uhealth. Dec 6 2021; 9 (12): e28102. PubMed Abstract | Publisher Full Text | Free Full Text 68. Co-design toolkit. (accessed 17 June 2025). Reference Source 69. Michie S, Johnston M, Abraham C, et al. : “Making psychological theory useful for implementing evidence based practice: a consensus approach,” (in eng). Qual. Saf. Health Care. Feb 2005; 14 (1): 26–33. PubMed Abstract | Publisher Full Text | Free Full Text 70. Cane J, O’Connor D, Michie S: Validation of the theoretical domains framework for use in behaviour change and implementation research. Implement. Sci. 2012/04/24 2012; 7 (1): 37. PubMed Abstract | Publisher Full Text | Free Full Text 71. Damschroder LJ, Aron DC, Keith RE, et al. : Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement. Sci. 2009/08/07 2009; 4 (1): 50. PubMed Abstract | Publisher Full Text | Free Full Text 72. Birken SA, Powell BJ, Presseau J, et al. : Combined use of the Consolidated Framework for Implementation Research (CFIR) and the Theoretical Domains Framework (TDF): a systematic review. 73. Damschroder LJ, Reardon CM, Opra Widerquist MA, et al. : The updated Consolidated Framework for Implementation Research based on user feedback. Implement. Sci. 2022/10/29 2022; 17 (1): 75. PubMed Abstract | Publisher Full Text | Free Full Text 74. Blei D, Ng A, Jordan M: Latent Dirichlet Allocation. J. Mach. Learn. Res. 2003; 3 (993-1022). 75. Maarten Grootendorst: BERTopic: Neural topic modeling with a class-based TF-IDF procedure. 76. Nagabushan S, Fuentes-Bolanos N, Lau LMS, et al. : HGG-50. LOW GRADE GLIOMA CAUGHT IN TRANSFORMATION: UTILITY OF COMPREHENSIVE GENOMIC PROFILING IN PEDIATRIC LI FRAUMENI SYNDROME ASSOCIATED HIGH-GRADE GLIOMA. Neuro-Oncology. 2024; 26 (Supplement_4). Publisher Full Text 77. Proctor E, Silmere H, Raghavan R, et al. : “Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda,” (in eng). Admin. Pol. Ment. Health. Mar 2011; 38 (2): 65–76. PubMed Abstract | Publisher Full Text | Free Full Text 78. Donovan T, Abell B, McPhail SM, et al. : Development of an instrument (Cost-IS) to estimate costs of implementation strategies for digital health solutions: a modified e-Delphi study. Implement. Sci. 2025/03/07 2025; 20 (1): 13. PubMed Abstract | Publisher Full Text | Free Full Text 79. Donovan T, Carter HE, McPhail SM, et al. : Challenges and recommendations for collecting and quantifying implementation costs in practice: a qualitative interview study. Implement. Sci. Commun. 2024/10/11 2024; 5 (1): 114. PubMed Abstract | Publisher Full Text | Free Full Text 80. Smith B, Ashburner M, Rosse C, et al. : “The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration,” (in eng). Nat. Biotechnol. Nov 2007; 25 (11): 1251–1255. PubMed Abstract | Publisher Full Text | Free Full Text 81. Dennstädt F, Hastings J, Putora PM, et al. : Implementing large language models in healthcare while balancing control, collaboration, costs and security. NPJ Digit. Med. 2025/03/06 2025; 8 (1): 143. PubMed Abstract | Publisher Full Text | Free Full Text 82. Vaughan L, Pink S, Duque M, et al. : The sum is the realisation of the parts: interdisciplinary perspectives on care. 83. Strengers Y, Hurtado MD, Mortimer M, et al. : Smart Homes for Seniors: Intelligent Home Solutions for Independent Living. 84. Pushpakumar R, Sanjaya K, Rathika S, et al. : Human-Computer Interaction: Enhancing User Experience in Interactive Systems. E3S Web of Conferences. 2023; 399 : 07/12. Publisher Full Text 85. Harst L, Wollschlaeger B, Birnstein J, et al. : “Evaluation is Key: Providing Appropriate Evaluation Measures for Participatory and User-Centred Design Processes of Healthcare IT,” (in eng). Int. J. Integr. Care. Jun 21 2021; 21 (2): 24. PubMed Abstract | Publisher Full Text | Free Full Text 86. Hui SLT, See SL: Enhancing User Experience Through Customisation of UI Design. Procedia Manuf. 2015/01/01/2015; 3 : 1932–1937. Publisher Full Text 87. Morrow A, Steinberg J, Chan P, et al. : “In person and virtual process mapping experiences to capture and explore variability in clinical practice: application to genetic referral pathways across seven Australian hospital networks,” (in eng). Transl. Behav. Med. Aug 11 2023; 13 (8): 561–570. PubMed Abstract | Publisher Full Text | Free Full Text 88. Best S, Long JC, Braithwaite J, et al. : “Standardizing variation: Scaling up clinical genomics in Australia,” (in eng). Genet. Med. Feb 2023; 25 (2): 100109. PubMed Abstract | Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 30 Oct 2025 ADD YOUR COMMENT Comment Author details Author details 1 Implementation to Impact (i2i), School of Population Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia 2 Institute for Implementation, Science in Health Care, University of Zurich, Zurich, Swaziland 3 School of Medicine, University of St. Gallen, St. Gallen, Switzerland 4 Swiss Institute of Bioinformatics, Lausanne, Switzerland 5 Centre for AI and Digital Ethics, The University of Melbourne, Melbourne, Victoria, Australia 6 Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, New South Wales, Australia 7 Tyree Institute of Health Engineering (IHealthE), University of New South Wales, Sydney, New South Wales, Australia 8 Research Technology Services, University of New South Wales, Sydney, New South Wales, Australia 9 Emerging Technologies Lab, Monash University, Melbourne, Victoria, Australia 10 Department of Design, Monash University, Melbourne, Victoria, Australia 11 Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia 12 Agency for Clinical Innovation, Sydney, New South Wales, Australia 13 School of Public Health, University of Sydney, Sydney, New South Wales, Australia 14 Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia 15 Ingram School of Nursing, McGill University, Montréal, Canada 16 Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Montréal, Canada 17 Centre for Implementation Research, Ottawa Hospital Research Institute, Ottawa, Canada 18 Australian Genomics, Melbourne, Victoria, Australia 19 Melbourne School of Health Sciences, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Victoria, Australia 20 Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia 21 Ingham Institute of Applied Medical Research, Liverpool, Sydney, New South Wales, Australia 22 Maridulu Budyari Gumal (SPHERE) Cancer Clinical Academic Group, Sydney, New South Wales, Australia 23 Centre for Behaviour Change, University College London, London, UK 24 Department of Medical Oncology, Prince of Wales Hospital, Sydney, New South Wales, Australia 25 Garvan Institute of Medical Research, Sydney, New South Wales, Australia 26 NHMRC Clinical Trials Centre, University of Sydney, Sydney, New South Wales, Australia Jeffery Chan Roles: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Janna Hastings Roles: Conceptualization, Methodology, Writing – Review & Editing Gabriella Tiernan Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Writing – Review & Editing Jeannie Paterson Roles: Conceptualization, Methodology, Writing – Review & Editing Nigel Lovell Roles: Conceptualization, Methodology, Writing – Review & Editing Luc Betbeder-Matibet Roles: Conceptualization, Methodology, Writing – Review & Editing Patrick Kin Man Tung Roles: Conceptualization, Methodology, Writing – Review & Editing Sarah Pink Roles: Conceptualization, Methodology, Writing – Review & Editing Debora Lanzeni Roles: Conceptualization, Methodology, Writing – Review & Editing Thomasina Donovan Roles: Conceptualization, Methodology, Writing – Review & Editing Andrew Milat Roles: Conceptualization, Methodology, Writing – Review & Editing Louisa Jorm Roles: Conceptualization, Methodology, Writing – Review & Editing Carolyn Mazariego Roles: Conceptualization, Methodology, Writing – Review & Editing Chi Zhang Roles: Conceptualization, Methodology, Writing – Review & Editing Guillaume Fontaine Roles: Methodology, Writing – Review & Editing Stephanie Best Roles: Conceptualization, Methodology, Writing – Review & Editing Georgina Kennedy Roles: Methodology, Writing – Review & Editing Susan Michie Roles: Conceptualization, Methodology, Writing – Review & Editing Frank Lin Roles: Conceptualization, Methodology, Writing – Review & Editing Natalie Taylor Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The project is part-funded by a UNSW Research Infrastructure Scheme: Supporting Collaborative Research 2025 and UNSW-UTS Trustworthy Digital Society; and supported by an MRFF Rapid Applied Research Translation grant (RARUR00125). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 30 Oct 2025, 14:1184 https://doi.org/10.12688/f1000research.169999.1 Copyright © 2025 Chan J et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Chan J, Hastings J, Tiernan G et al. Combining implementation and data sciences to accelerate evidence integration into healthcare – ImpleMATE [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1184 ( https://doi.org/10.12688/f1000research.169999.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 30 Oct 2025 Views 0 Cite How to cite this report: Pham TD. Reviewer Report For: Combining implementation and data sciences to accelerate evidence integration into healthcare – ImpleMATE [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1184 ( https://doi.org/10.5256/f1000research.187409.r428532 ) The direct URL for this report is: https://f1000research.com/articles/14-1184/v1#referee-response-428532 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 30 Dec 2025 Tuan D. Pham , Queen Mary University of London,, London, UK Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.187409.r428532 The manuscript presents ImpleMATE, an AI-powered implementation science platform aimed at accelerating the translation of research evidence into healthcare practice. The concept integrates implementation science, data science, and artificial intelligence (AI) within a Learning Health System (LHS) framework to ... Continue reading READ ALL The manuscript presents ImpleMATE, an AI-powered implementation science platform aimed at accelerating the translation of research evidence into healthcare practice. The concept integrates implementation science, data science, and artificial intelligence (AI) within a Learning Health System (LHS) framework to establish continuous feedback and improvement cycles. The project will employ co-design and co-production approaches, involving stakeholders in healthcare and data science to develop a structured ontology, an AI-driven knowledge extraction system, and an interactive web platform to support implementation decision-making. The proposed system aspires to address the long-standing issue of delayed evidence integration into clinical practice, particularly focusing initially on cancer genetics and genomics. Strengths Innovative Integration: The paper successfully combines implementation science and data science into a coherent conceptual framework, bridging theoretical and applied domains. Comprehensive Structure: The manuscript is systematically organized around the LHS cycle, enhancing readability and logical flow. Methodological Transparency: Detailed methodological descriptions are provided for data processing, ontology development, AI system design, and evaluation planning. Ethical Considerations: The inclusion of dedicated ethical governance (e.g., End User Committee and ImpleMATE Ethics Principles) demonstrates foresight and responsible innovation. Stakeholder Involvement: The co-design and co-production approach aligns well with human-centred design principles and strengthens translational potential. Major Issues Empirical Validation Plan: The protocol outlines AI development and ontology creation but lacks sufficient detail on how empirical validation will be quantitatively measured beyond standard metrics (precision, recall, F1-score). The authors may include a validation study design detailing sample sizes, validation datasets, and expected benchmarks compared with existing manual or semi-automated approaches. Clarify how “success” in the AI’s performance will be defined statistically and pragmatically. Evaluation Framework Specificity: The evaluation framework for national integration is conceptually described but remains too generic. Define specific, measurable KPIs (e.g., reduction in implementation time, accuracy of decision support recommendations, improvements in patient outcomes). Include a timeline or phase plan (e.g., pilot, scale-up, evaluation). Data Governance and Security Implementation: While ethical principles are well discussed, there is limited technical detail on how privacy, consent, and data security will be enforced, particularly for sensitive genomic data. Add a technical appendix or methods subsection specifying: Data encryption standards, federated data storage options, access control and logging mechanisms, governance under relevant data protection acts (e.g., GDPR, Australian Privacy Principles). Ontology Development and Interoperability: The proposed ontology (Figure 3, page 9) is conceptual but requires clearer definition of ontology scope and interoperability mechanisms with existing biomedical ontologies (e.g., SNOMED CT, OBO Foundry ontologies). The authors should describe the mapping process to established terminologies, including ontology evaluation criteria such as completeness, consistency, and usability across multiple healthcare domains. AI Transparency and Bias Mitigation: The manuscript notes the risk of bias and hallucination in LLMs but provides limited description of bias monitoring mechanisms. Specify procedures for bias auditing, model retraining protocols, and inclusion of diverse datasets to reduce cultural or contextual bias in implementation recommendations. Lack of Preliminary Pilot Data: Although it is a protocol, references to completed “feasibility assessments” and “first version of ontology” suggest early work has been done. However, no preliminary outcomes or examples are provided. Please summarize these preliminary findings to support feasibility and establish proof-of-concept credibility. Minor Comments Figures and Visual Clarity: Figures 1–4 are informative but could benefit from clearer legends and scalable resolution for digital publication. Terminology Consistency: Ensure consistent use of abbreviations (e.g., “LLMs,” “AI-powered implementation science platform”) across sections. Formatting: The Methods section is lengthy; consider condensing repetitive explanations of co-design principles to improve readability. Discussion Section: Add a brief comparison to existing AI-enabled implementation platforms (if any) to highlight novelty and justify distinctiveness of ImpleMATE. Is the rationale for, and objectives of, the study clearly described? Yes Is the study design appropriate for the research question? Yes Are sufficient details of the methods provided to allow replication by others? Partly Are the datasets clearly presented in a useable and accessible format? Not applicable Competing Interests: No competing interests were disclosed. Reviewer Expertise: AI and data science in medicine and life science. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Pham TD. Reviewer Report For: Combining implementation and data sciences to accelerate evidence integration into healthcare – ImpleMATE [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1184 ( https://doi.org/10.5256/f1000research.187409.r428532 ) The direct URL for this report is: https://f1000research.com/articles/14-1184/v1#referee-response-428532 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: N Y. Reviewer Report For: Combining implementation and data sciences to accelerate evidence integration into healthcare – ImpleMATE [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1184 ( https://doi.org/10.5256/f1000research.187409.r440859 ) The direct URL for this report is: https://f1000research.com/articles/14-1184/v1#referee-response-440859 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 16 Dec 2025 Yogeesh N , Government First Grade College, Tumkur, India Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.187409.r440859 Summary This manuscript presents a protocol for developing ImpleMATE , an AI-supported implementation science platform that uses an ontology + LLM-based extraction to support implementation planning and learning health system cycles. The topic is important and timely, and ... Continue reading READ ALL Summary This manuscript presents a protocol for developing ImpleMATE , an AI-supported implementation science platform that uses an ontology + LLM-based extraction to support implementation planning and learning health system cycles. The topic is important and timely, and the overall concept is clear. Responses to the core questions Rationale and objectives: Yes — the problem statement and aims are clear. Study design appropriate: Partly — the plan is reasonable for a protocol, but the evaluation design is not yet described in a testable way. Methods replicable: Partly — the workflow is described at a high level, but key operational details are missing. Datasets accessible: Not applicable (protocol) — no dataset/software is provided. Major comments (must be addressed to be scientifically sound) Evaluation plan is not sufficiently defined The manuscript mentions KPIs and broad goals, but it does not specify a clear evaluation study design. Must add: primary outcomes, sample/setting, comparison condition (if any), study type (pilot/pre-post/mixed methods), and a short analysis plan. Insufficient methodological detail for replication (AI + annotation) The protocol states that expert annotation will be used, but does not describe document inclusion criteria, annotation guidelines, inter-rater reliability targets, adjudication, and train/test splits. Must add: clear dataset selection rules, annotation process, reliability metrics, and evaluation metrics for extraction/recommendation performance. LLM safety/traceability needs stronger operational detail Hallucination is acknowledged, but there is no detailed mitigation approach. Must add: evidence-grounded outputs (citations to source passages/ontology nodes), human-in-the-loop review points, and explicit limitations that the system supports implementation planning (not clinical decision-making). Data governance and ethics need more concrete procedures Governance principles are described, but practical details for using real-world project data are limited. Must add: consent/ethics pathway, de-identification workflow, access control, audit logging, and data retention policies. Reproducibility and sharing plan is missing The paper states no associated data/software. For an AI platform protocol, this weakens reproducibility. Must add: at least an intention to share the ontology, sample de-identified/synthetic examples, and evaluation scripts (or provide controlled-access details). Minor comments Please check an affiliation line that appears incorrect (“Zurich, Swaziland”). Improve consistency in terminology across sections (platform vs decision aid vs coaching tool). Recommendation Major revisions. The idea is strong, but the manuscript requires clearer evaluation design, replicable methods, and stronger AI/data governance detail to be scientifically rigorous. Is the rationale for, and objectives of, the study clearly described? Yes Is the study design appropriate for the research question? Partly Are sufficient details of the methods provided to allow replication by others? Partly Are the datasets clearly presented in a useable and accessible format? Not applicable Competing Interests: No competing interests were disclosed. Reviewer Expertise: Related I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT N Y. Reviewer Report For: Combining implementation and data sciences to accelerate evidence integration into healthcare – ImpleMATE [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1184 ( https://doi.org/10.5256/f1000research.187409.r440859 ) The direct URL for this report is: https://f1000research.com/articles/14-1184/v1#referee-response-440859 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 30 Oct 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 30 Oct 25 read read Yogeesh N , Government First Grade College, Tumkur, India Tuan D. Pham , Queen Mary University of London,, London, UK Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Pham T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 Dec 2025 | for Version 1 Tuan D. Pham , Queen Mary University of London,, London, UK 0 Views copyright © 2026 Pham T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript presents ImpleMATE, an AI-powered implementation science platform aimed at accelerating the translation of research evidence into healthcare practice. The concept integrates implementation science, data science, and artificial intelligence (AI) within a Learning Health System (LHS) framework to establish continuous feedback and improvement cycles. The project will employ co-design and co-production approaches, involving stakeholders in healthcare and data science to develop a structured ontology, an AI-driven knowledge extraction system, and an interactive web platform to support implementation decision-making. The proposed system aspires to address the long-standing issue of delayed evidence integration into clinical practice, particularly focusing initially on cancer genetics and genomics. Strengths Innovative Integration: The paper successfully combines implementation science and data science into a coherent conceptual framework, bridging theoretical and applied domains. Comprehensive Structure: The manuscript is systematically organized around the LHS cycle, enhancing readability and logical flow. Methodological Transparency: Detailed methodological descriptions are provided for data processing, ontology development, AI system design, and evaluation planning. Ethical Considerations: The inclusion of dedicated ethical governance (e.g., End User Committee and ImpleMATE Ethics Principles) demonstrates foresight and responsible innovation. Stakeholder Involvement: The co-design and co-production approach aligns well with human-centred design principles and strengthens translational potential. Major Issues Empirical Validation Plan: The protocol outlines AI development and ontology creation but lacks sufficient detail on how empirical validation will be quantitatively measured beyond standard metrics (precision, recall, F1-score). The authors may include a validation study design detailing sample sizes, validation datasets, and expected benchmarks compared with existing manual or semi-automated approaches. Clarify how “success” in the AI’s performance will be defined statistically and pragmatically. Evaluation Framework Specificity: The evaluation framework for national integration is conceptually described but remains too generic. Define specific, measurable KPIs (e.g., reduction in implementation time, accuracy of decision support recommendations, improvements in patient outcomes). Include a timeline or phase plan (e.g., pilot, scale-up, evaluation). Data Governance and Security Implementation: While ethical principles are well discussed, there is limited technical detail on how privacy, consent, and data security will be enforced, particularly for sensitive genomic data. Add a technical appendix or methods subsection specifying: Data encryption standards, federated data storage options, access control and logging mechanisms, governance under relevant data protection acts (e.g., GDPR, Australian Privacy Principles). Ontology Development and Interoperability: The proposed ontology (Figure 3, page 9) is conceptual but requires clearer definition of ontology scope and interoperability mechanisms with existing biomedical ontologies (e.g., SNOMED CT, OBO Foundry ontologies). The authors should describe the mapping process to established terminologies, including ontology evaluation criteria such as completeness, consistency, and usability across multiple healthcare domains. AI Transparency and Bias Mitigation: The manuscript notes the risk of bias and hallucination in LLMs but provides limited description of bias monitoring mechanisms. Specify procedures for bias auditing, model retraining protocols, and inclusion of diverse datasets to reduce cultural or contextual bias in implementation recommendations. Lack of Preliminary Pilot Data: Although it is a protocol, references to completed “feasibility assessments” and “first version of ontology” suggest early work has been done. However, no preliminary outcomes or examples are provided. Please summarize these preliminary findings to support feasibility and establish proof-of-concept credibility. Minor Comments Figures and Visual Clarity: Figures 1–4 are informative but could benefit from clearer legends and scalable resolution for digital publication. Terminology Consistency: Ensure consistent use of abbreviations (e.g., “LLMs,” “AI-powered implementation science platform”) across sections. Formatting: The Methods section is lengthy; consider condensing repetitive explanations of co-design principles to improve readability. Discussion Section: Add a brief comparison to existing AI-enabled implementation platforms (if any) to highlight novelty and justify distinctiveness of ImpleMATE. Is the rationale for, and objectives of, the study clearly described? Yes Is the study design appropriate for the research question? Yes Are sufficient details of the methods provided to allow replication by others? Partly Are the datasets clearly presented in a useable and accessible format? Not applicable Competing Interests No competing interests were disclosed. Reviewer Expertise AI and data science in medicine and life science. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Pham TD. Peer Review Report For: Combining implementation and data sciences to accelerate evidence integration into healthcare – ImpleMATE [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1184 ( https://doi.org/10.5256/f1000research.187409.r428532) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1184/v1#referee-response-428532 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 N Y. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 16 Dec 2025 | for Version 1 Yogeesh N , Government First Grade College, Tumkur, India 0 Views copyright © 2025 N Y. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Summary This manuscript presents a protocol for developing ImpleMATE , an AI-supported implementation science platform that uses an ontology + LLM-based extraction to support implementation planning and learning health system cycles. The topic is important and timely, and the overall concept is clear. Responses to the core questions Rationale and objectives: Yes — the problem statement and aims are clear. Study design appropriate: Partly — the plan is reasonable for a protocol, but the evaluation design is not yet described in a testable way. Methods replicable: Partly — the workflow is described at a high level, but key operational details are missing. Datasets accessible: Not applicable (protocol) — no dataset/software is provided. Major comments (must be addressed to be scientifically sound) Evaluation plan is not sufficiently defined The manuscript mentions KPIs and broad goals, but it does not specify a clear evaluation study design. Must add: primary outcomes, sample/setting, comparison condition (if any), study type (pilot/pre-post/mixed methods), and a short analysis plan. Insufficient methodological detail for replication (AI + annotation) The protocol states that expert annotation will be used, but does not describe document inclusion criteria, annotation guidelines, inter-rater reliability targets, adjudication, and train/test splits. Must add: clear dataset selection rules, annotation process, reliability metrics, and evaluation metrics for extraction/recommendation performance. LLM safety/traceability needs stronger operational detail Hallucination is acknowledged, but there is no detailed mitigation approach. Must add: evidence-grounded outputs (citations to source passages/ontology nodes), human-in-the-loop review points, and explicit limitations that the system supports implementation planning (not clinical decision-making). Data governance and ethics need more concrete procedures Governance principles are described, but practical details for using real-world project data are limited. Must add: consent/ethics pathway, de-identification workflow, access control, audit logging, and data retention policies. Reproducibility and sharing plan is missing The paper states no associated data/software. For an AI platform protocol, this weakens reproducibility. Must add: at least an intention to share the ontology, sample de-identified/synthetic examples, and evaluation scripts (or provide controlled-access details). Minor comments Please check an affiliation line that appears incorrect (“Zurich, Swaziland”). Improve consistency in terminology across sections (platform vs decision aid vs coaching tool). Recommendation Major revisions. The idea is strong, but the manuscript requires clearer evaluation design, replicable methods, and stronger AI/data governance detail to be scientifically rigorous. Is the rationale for, and objectives of, the study clearly described? Yes Is the study design appropriate for the research question? Partly Are sufficient details of the methods provided to allow replication by others? Partly Are the datasets clearly presented in a useable and accessible format? Not applicable Competing Interests No competing interests were disclosed. Reviewer Expertise Related I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) N Y. Peer Review Report For: Combining implementation and data sciences to accelerate evidence integration into healthcare – ImpleMATE [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :1184 ( https://doi.org/10.5256/f1000research.187409.r440859) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1184/v1#referee-response-440859 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "Combining implementation and data sciences...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/14-1184/v1" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/14-1184/v1&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/14-1184/v1" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Chan J et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/14-1184/v1/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/14-1184", templates : { twitter : "Combining implementation and data sciences to accelerate evidence.... Chan J et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/14-1184/v1" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/169999/187409") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "187409"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "438788": 0, "438789": 0, "438786": 0, "438787": 0, "438784": 0, "438785": 0, "430422": 0, "430423": 0, "430420": 0, "430421": 0, "440862": 0, "440863": 0, "440860": 0, "430428": 0, "440861": 0, "430429": 0, "440858": 0, "430426": 0, "440859": 14, "430427": 0, "430424": 0, "440857": 0, "430425": 0, "440866": 0, "440864": 0, "440865": 0, "428526": 0, "428527": 0, "428525": 0, "428534": 0, "428532": 17, "428533": 0, "428530": 0, "428531": 0, "428528": 0, "428529": 0, "438782": 0, "438783": 0, "438780": 0, "438781": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "b05f977c-e33d-492d-860e-21286645eb40"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });