Leveraging implementation science to improve uptake of an ED decision support aid for heart failure patients

Leveraging implementation science to improve uptake of an ED decision support aid for heart failure patients | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Leveraging implementation science to improve uptake of an ED decision support aid for heart failure patients View ORCID Profile Katherine A. Vilain , View ORCID Profile Stacy L. Farr , View ORCID Profile Danielle M. Olds , Kathryn L. Istas , Maria A. Alvarenga , View ORCID Profile John A. Spertus doi: https://doi.org/10.1101/2025.11.19.25340621 Katherine A. Vilain 1 Saint Luke’s Hospital , Kansas City, MO 2 University of Missouri at Kansas City Healthcare Institute for Innovations in Quality MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katherine A. Vilain For correspondence: kvilain{at}saint-lukes.org Stacy L. Farr 1 Saint Luke’s Hospital , Kansas City, MO 2 University of Missouri at Kansas City Healthcare Institute for Innovations in Quality PhD, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stacy L. Farr Danielle M. Olds 1 Saint Luke’s Hospital , Kansas City, MO 2 University of Missouri at Kansas City Healthcare Institute for Innovations in Quality PhD, MPH, RN Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Danielle M. Olds Kathryn L. Istas 1 Saint Luke’s Hospital , Kansas City, MO 2 University of Missouri at Kansas City Healthcare Institute for Innovations in Quality DrPH, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria A. Alvarenga 3 University Health Truman Medical Center , Kansas City, MO MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site John A. Spertus 1 Saint Luke’s Hospital , Kansas City, MO 2 University of Missouri at Kansas City Healthcare Institute for Innovations in Quality MD, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John A. Spertus Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Changing clinical practice is difficult. An innovative risk stratification process to triage patients presenting to the Emergency Department (ED) with heart failure (HF) exacerbation who were safe for discharge, CodeHF, was inconsistently used in an ED, despite strong evidence of its benefits. Understanding barriers and facilitators to its use is needed to improve adoption. The objective of this study was to understand barriers and facilitators to CodeHF use in routine practice and address determinants of adoption. Methods A qualitative study was conducted in a four-hospital healthcare system to explore CodeHF implementation and use after three years’ use of CodeHF as part of routine clinical care. The Tailored Implementation in Chronic Diseases (TICD) determinants was used to guide semi-structured interviews and organize themes identified through open coding. Themes were then mapped to Expert Recommendations for Implementing Change (ERIC) strategies. Results Interviews (n=20) conducted with ED, HF, and hospitalist clinicians; administrative leaders; and researchers revealed five barriers (lack of initial buy-in, validity concerns, lack of clarity around purpose/target, communication gaps, and liability/malpractice concerns) and three facilitators (prompt patient follow-up, decision support, nurse championship). ERIC strategies were adapted to optimize future CodeHF use, and novel strategies were proposed to supplement those from ERIC. Recommendations were created to share with stakeholders and support future implementation efforts. Conclusions Systematically evaluating the implementation of novel evidence-based treatment strategies can identify barriers and facilitators that can then be mapped to refined implementation strategies. This study exemplifies a pragmatical approach to improve uptake, sustained use, and scalability of an evidence-based intervention for future sites seeking to implement ED pathways for risk-stratifying patients with HF. What is known CodeHF, an algorithmic decision support aid (DSA) for emergency department (ED) physicians triaging disposition for patients experiencing heart failure exacerbation, is underutilized at a large health system despite evidence of clinical and process quality improvements associated with its use. Different implementation science tools can be used to assess barriers to uptake of an intervention or to identify potential strategies to improve uptake. What the study adds In addition to barriers found in other ED-based qualitative studies of similar DSAs, this study identifies interdepartmental communication gaps and use of a novel implementation path that ED physicians felt was imposed onto them, hindering initial buy-in to use. The study provides a novel example of how to use multiple implementation science tools to plan qualitative interviews, organize coded themes, and tailor strategies to address identified barriers, by mapping identified determinants from the Tailored Implementation in Chronic Diseases framework to known implementation strategies from the Expert Recommendation for Implementing Change (ERIC) framework. Five additional strategies are suggested for the ERIC framework: (1) clarify roles and responsibilities, (2) de-implement interventions or intervention components, (3) demonstrate responsiveness to reported barriers and needs, (4) automate processes, and (5) leverage acceptable implementation pathways. Introduction The Hospital Readmissions Reduction Program (HRRP) incentivizes hospitals to reduce heart failure (HF) readmissions. 1 While many efforts to reduce readmission rates have failed, most have focused on risk stratifying patients at the time of inpatient discharge for closer follow-up. 2 – 6 An alternative approach is to more efficiently triage Emergency Department (ED) patients with a history of HF to determine who can be safely discharged home. Using a well-validated risk model that was subsequently used to reduce readmissions, a decision support aid (DSA), CodeHF, was developed and shown to be effective at safely improving discharge rates, with more efficient ED care and fewer 30-day representations. 7 , 8 Despite its demonstrated potential to improve the efficiency of HF care in the ED, it was incompletely adopted at a metro area four-hospital healthcare system. During the first 13 months of use, of 1100 eligible encounters, clinicians activated the CodeHF protocol in only 149 (13.5%) cases, with marked variability across sites and physicians. 9 To better understand why use was low and inconsistent, despite positive process and outcome improvements, a qualitative study of multiple stakeholders was conducted. The study goals were to leverage contemporary implementation science frameworks to identify barriers and facilitators to optimizing CodeHF use. Tailored recommendations were then created to support future CodeHF implementations. This report sought to model methods for developing DSA implementation strategies. Methods The CodeHF Strategy The main component of CodeHF is the prospective use of a validated risk score to estimate an ED HF patient’s 7-day mortality. 8 , 9 Prior to implementation, ED physicians and HF cardiologists defined a threshold of risk (in this case, a 7-day mortality risk of ≤1%) at which they were comfortable considering discharge from the ED to home, as opposed to hospital admission. Other components of CodeHF for patients discharged home included (1) an educational video for patients and caregivers, and (2) a standardized process for scheduling outpatient cardiology follow-up within 7 days of ED discharge. During its initial implementation, when CodeHF was used, it was associated with a higher rate of ED discharges home, a lower likelihood of those discharged home returning to the ED within 30 days, decreased short stay (<48 hours) admissions, greater proportions of discharged patients having clinic follow-up within 7-days, and shortened times between ED presentation and discharge. Importantly, marked inter-physician variability and low overall CodeHF use was noted. 9 A better understanding of its inconsistent use was needed to improve care within the health system and to support future use of CodeHF at other institutions. Study Design and Setting To understand why CodeHF use was lower than expected, a qualitative descriptive approach was selected to develop an understanding of the experiences of clinician users and non-users and describe the barriers and facilitators encountered during initial implementation. The study followed Standards for Reporting Qualitative Research (SRQR) reporting guidelines. 10 Interviews were conducted in person and over video conferencing between November 2021 and January 2023 by a team of investigators trained in qualitative methods, health services research, implementation science, and clinical medicine. This project was designated as Quality Improvement by the local Institutional Review Board and all participants provided verbal consent. This study was funded by the Healthcare Institute for Innovations in Quality, University of Missouri—Kansas City. The first two participants identified were ED CodeHF physician champions. Targeted and snowball sampling identified additional key personnel for interviews. These included other ED physicians and nurses, cardiologists, hospital leadership, IT staff and leadership, and researchers involved in the initial CodeHF implementation. In addition to transcribed interviews, interview notes, and email messages between the researchers and three interviewed ED physicians were included as source material. Theoretical Framework The Tailored Implementation for Chronic Diseases (TICD) determinants framework was used to guide the creation of semi-structured interview guides and organize the qualitative analyses. 11 The TICD is a framework for identifying and classifying determinants - barriers and facilitators - to implementing interventions for chronic disease care. It has seven domains: individual health professional factors; patient factors; professional interactions; incentives and resources; capacity for organizational change; social, political, and legal factors; and guideline characteristics. Interview Procedures Interview questions were based on selected TICD domains to ensure all potential barriers and facilitators were probed among individuals who participated in implementing or were intended end users of the tool. Interview guides were customized to four groups: clinician users (e.g., ED physicians and nurses) and nonusers (e.g., hospitalists and HF specialists who received admitted patients after ED triage); IT members who programmed the order set and created usage reports; hospital administrators; and the researchers involved in the original implementation. Interview guides were developed over several iterations among the qualitative research team. After the first interview, minor modifications were made to wording and question order to optimize interview flow. A common set of interview items is shown in Table 1 paired with relevant TICD domains. Interviews were recorded digitally, with additional digital notes taken. No financial incentives were provided to participants. Interviews were conducted until multiple participants from each of the four groups were included and thematic saturation was achieved. View this table: View inline View popup Table 1. Examples of TICD domains & determinants paired to interview guide items, by interviewee role in CodeHF Data Analysis Interview recordings were transcribed by Rev.com and de-identified for analysis within Dedoose software. 12 Open coding was used to explore CodeHF barriers and facilitators, and a codebook based on emergent findings was developed by four members of the research team who reviewed interview transcripts. Two members of the coding team independently coded each interview, with discrepancies resolved through group discussions with all coders. Codes were grouped into categories of factors that facilitated or inhibited CodeHF use. These were shared with and approved by a subset of interviewees as a form of member checking. Categories were mapped to TICD domains. Implementation Strategy Mapping and Tailoring Recommendations The overall study process is illustrated in Figure 1 . The Expert Recommendations for Implementing Change (ERIC) summarizes implementation strategies from published literature. 13 In the final phase of this study, ERIC strategies were examined for potential alignment within each TICD category and the identified determinants. Additional strategies were also identified, as needed. After determinants were mapped to strategies, tailored recommendations were generated in the context of planning for CodeHF re-implementation at the study site and to guide future implementation at other institutions. Download figure Open in new tab Figure 1. Study Process Map The figure illustrates the overall study process with phase chronologically linked using arrows, and colors reflecting the implementation framework(s) informing the phase. The process begins with the Tailored Implementation for Chronic Diseases (TICD) Domains and moves to the development of the semi-structured interview guides, proceeding through the dark blue icons, where the processes and products were informed by the TICD. After mapping facilitator and barrier categories identified through open coding back to the TICD, they were paired with (mapped to) Expert Recommendations for Implementing Change (ERIC) Strategies. The lighter grey icons are informed by convergent use of the TICD and ERIC. Hexagons represent implementation science frameworks. The predesigned process icon (rectangle with vertical borders) represents products or outcomes of a process. The multidocument icon represents processes or products of a similar type. The magnetic disc icon represents a process. Results Participants Twenty interviews were conducted with 24 participants – 17 individual semi-structured interviews, and 3 focus groups with 2, 2, and 3 participants each ( Table 2 ). Participants were 64% male. Ten were physicians (5 ED, 1 internal medicine, and 4 cardiologists); four were nurses (1 ED, 1 cardiology, and 2 nurse educators). The remaining eight represented IT (3) and hospital (2) leadership, along with researchers (3). View this table: View inline View popup Download powerpoint Table 2. Interviewee characteristics Themes Coding revealed eight major factors associated with barriers and facilitators to optimal CodeHF use. Mapping to TICD domains revealed at least one example from each major domain. Facilitators Three facilitators were identified, including the seven-day follow-up appointment, ED throughput, and the role of nurses in the workflow. Table 3 shows example quotes for each theme, along with the TICD domain and determinant deemed most closely associated with each theme. View this table: View inline View popup Table 3. Facilitator Themes and Supporting Quotes with Alignment to TICD Determinants Facilitator Theme 1: Seven-Day Follow-up Appointments The most-cited facilitator across all interviews was the establishment of a new process whereby patients discharged using the CodeHF DSA are contacted within 48 hours to schedule an outpatient cardiology follow-up visit within 7 days. Interviewees universally appreciated this aspect of the program and felt that it directly addressed a concern of discharging patients to home from the ED. Facilitator Theme 2: ED Throughput While initial concerns that the CodeHF program would be time consuming, early analyses of the pilot project demonstrated faster ED ‘door-in to door-out’ time. Faster ED throughput was perceived as a very positive impact from ED physicians’ perspective, even among CodeHF ‘non-users’. This benefit was potentially undermined by the trade-off that while overall ED time was reduced, CodeHF required more physician-patient communication. Both concepts were mentioned by one ED physician user: “I think it does reduce the actual time. It increases the time, I’m guessing, that doctors have to spend in the room.” Facilitator Theme 3: Role of Nurses in CodeHF In contrast to the perceptions of many ED physicians, ED nurses generally showed enthusiasm around the potential benefits of CodeHF on processes and patients. They found that CodeHF aligned with similar initiatives that they valued and cited no difficulties or misgivings about incorporating CodeHF into their workflow. ED nurses strongly valued anything that helped their patients, and accepted physician leadership on what was most appropriate. Barriers Identified barriers included ownership of and buy-in to the CodeHF project, concerns about scientific validity of the mortality risk score and evidence of effectiveness of the pathway, technological errors and limitations, communication gaps, and liability/malpractice concerns. Table 4 shows example quotes for each theme, along with the TICD domain and determinant deemed most closely associated with that theme. View this table: View inline View popup Table 4. Barrier Themes and Supporting Quotes Aligned to TICD Determinants Barrier Theme 1: Ownership & Buy-in The foundational barrier to optimal CodeHF use was a low sense of ownership by ED physicians, based on distrust around its origins as a QI project. ED physician investigators ushered CodeHF implementation through a novel pathway that allowed any hospital staff member to apply for QI project support, which circumvented the usual processes of project advocacy by the ED’s Evidence-based Practice Team (EPT). While the EPT agreed to implement the project, there was resistance by its members because it did not originate from within the EPT committee. Barrier Theme 2: Validity of the Score Some ED physicians expressed skepticism about the CodeHF evidence base. Concerns around validity included the comprehensiveness or accuracy of the risk score, which at the time did not incorporate results of a blood test commonly used in the U.S. for heart failure (i.e., B-type natriuretic peptide [BNP]); inappropriateness of use in patients with alternative potential diagnoses; the small size and design of the original health system pilot study; and the generalizability of a tool developed and validated in Canada. This skepticism was exacerbated when the estimated mortality risk did not align with their clinical assessment. This concern persisted even after efforts to highlight that the score was intended to supplement, not replace, clinical judgement. Barrier Theme 3: Technology Errors and Limitations A part of the implementation strategy was to provide periodic reports of CodeHF use and outcomes. However, errors in the feedback reports occurred and users felt they were not getting appropriate credit for using CodeHF. In addition, limitations of the electronic heath record precluded lab values automatically populating the mortality risk calculator, requiring additional steps for manual data input. Barrier Theme 4: Communication Gaps Cross-departmental communication gaps compounded the initial reticence for using CodeHF. During the initial implementation of CodeHF the focus was on ED physicians, rather than on those receiving admitted patients. Unfamiliarity with the risk score by admitting hospitalists and cardiologists gave ED physicians the perception that it lacked value, particularly among ED physicians initially skeptical of CodeHF. While subsequent educational efforts outside of the ED were pursued, a lack of centralized communication around the project, and the large number of hospitalists to educate, likely contributed to their lack of familiarity with CodeHF. Another major communication gap, likely underscoring Barrier Theme 2, occurred between the original project initiators and the end users of CodeHF. A researcher stated: “We explained to the group that the CodeHF score should be treated as any other biomarker and merely an additional piece of information, but not the sole deciding factor.” Barrier Theme 5: Concerns about liability & malpractice ED physicians dissatisfied with CodeHF also cited concerns about potential malpractice risks associated with its use, particularly in the event of a patient death with a score suggesting the patient could be discharged home. In contrast, others felt that documenting a patient’s risk for safe discharge could be helpful in litigation, should an adverse outcome occur. Recommendation Strategies Table 5 provides tailored recommendations targeting each of the identified barriers and facilitators. These were developed with ERIC strategies and supplemental strategies, as appropriate. The third column of the table shows the relevant ERIC strategy to which barriers were mapped, with the proposed additions in bold font, while the fourth shows tailored recommendations. Novel strategies proposed include Leveraging accepted implementation pathways , Automating processes , Demonstrating responsiveness to reported barriers , Deimplementation of unvalued components , and Clarifying roles and responsibilities . View this table: View inline View popup Table 5. TICD domains, ERIC strategies, and tailored recommendations by theme Barriers identified in this analysis came from all domains of the TICD, suggesting CodeHF optimization will require simultaneous use of interacting strategies to address the interconnected obstacles. Adopting multiple strategies may comprehensively address the identified barriers. Facilitators, such as enthusiastic CodeHF support by ED nurses, rapid cardiology follow-up of discharged patients, and its positive impact on ED throughput, should be leveraged. The most critical recommendation for implementing CodeHF is encouraging ED clinicians to lead its design and launch through the approved pathway of bringing evidence to practice (e.g., an ED EPT in this health system). Discussion Changing clinical practice is difficult, particularly in a fast-paced, high-pressure setting, such as the ED. A qualitative evaluation was conducted to understand why an effective protocol had variable adoption by ED physicians to improve re-implementation and support implementation at future sites. By interviewing multiple CodeHF stakeholders, three facilitators to CodeHF use were identified: better outpatient follow-up for discharged HF patients, improved ED throughput, and nursing support. Offsetting these facilitators, five significant barriers were identified: insufficient initial buy-in from the ED; skepticism of clinical evidence supporting the pathway; IT challenges; communication; and liability concerns. Lessons learned can guide re-implementation at this health system and support future sites seeking to improve the efficiency, safety, and patient-centeredness of managing HF exacerbations. The identified themes generally align with other implementation evaluations of ED provider-facing DSAs. Fujimori et al. (2022) explored the acceptance, barriers, and facilitators of implementing AI-based decision support in the ED, 14 which highlighted that while these tools are generally well-received, challenges included concerns about over-reliance on algorithms for diagnosis, which was paralleled in the CodeHF study by confusion about the tool’s intended use as a support rather than a replacement for clinical acumen. Similar to CodeHF, a study by Billah and colleagues assessing ED physicians’ determinants to hypothetical use of two patient risk-stratification tools (SynDA for syncope and Chest Pain Choice) included concerns of medicolegal risk, lack of need, and skepticism about the tools’ validity. Facilitators included positive attitudes towards shared decision making and improved patient follow-up. An important insight was the means by which CodeHF was introduced. Although the project was selected, designed, and led by an ED physician, it did not follow the institution’s established EPT pathway. This barrier may be unique to this implementation as we were unable to find another qualitative analysis of DSA implementation that identified the mode of translating evidence into practice as a barrier. However, there is evidence that shifting implementation leadership to the ED and using their preferred process reflects distributing information and decision-making authority to those most affected by the implementation. 15 Communication gaps between departments also did not appear to be a theme identified in other published DSA implementation studies. This study should be interpreted in the context of several potential limitations. Members of the interviewing team work closely with the research staff interviewed. Where possible, the team member who did not have a previous working relationship with the research staff led the focus groups and interviews, but the fact that they were researchers may have biased respondents’ comments. Second, while thematic saturation was achieved, other determinants and themes may have arisen had further probing been conducted or a broader group of participants been included. Third, findings of this study may not be generalizable to other settings. Clinical cultures differ and unique sets of barriers and facilitators might be relevant to different EDs. However, many findings align with similar investigations of barriers to ED support tools. While this evaluation addresses a unique protocol implementation, the methods are easy to reproduce in other settings. Moreover, by pre-defining implementation framework and strategy lists, this study can model an approach for identifying barriers and facilitators to refine strategies for addressing identified determinants. Specifically, the TICD framework was used as a guide for developing interview questions and for coding categorization, then mapped to ERIC strategies. Similar linking between implementation tools or frameworks has been done in other studies. For example, Waltz describes the development of the Consolidated Framework for Implementation Research (CFIR)-ERIC Implementation Strategy Matching Tool, which involved self-identified implementation experts mapping ERIC strategies to specific CFIR-based barriers. 16 ERIC is a mutable list, so when no strategy appeared relevant to the CodeHF determinant, new strategies were created, as others have done. 17 These identifying, mapping, and tailoring-and-creating processes allowed development of a tailored recommendation report for re-implementation with high confidence in its depth and breadth. Conclusion This study demonstrates the utility of implementation science frameworks and tools in tailoring strategies for improving clinical DSA uptake. Use of the TICD to develop interview guides and categorize resultant codes facilitated comprehensive examination of the many sources of facilitators and barriers – structural, cognitive, interpersonal, intervention-specific, etc. -- in interviews. Similarly, the ERIC list was useful for identifying potential strategies for overcoming barriers to revive CodeHF at the study site and to prevent future implementations from repeating the challenges encountered in this experience. Lessons learned from this method of linking the TICD domains to ERIC implementation strategies to create context-specific re-implementation recommendations may be applicable to other health systems seeking to improve the value of healthcare. Data Availability Because transcribed interviews contain potentially identifying information, illustrative quotes in the manuscript have been selected or altered to obscure the speaker's identity. Full interview transcripts are therefore not available. Sources of Funding No external funding supported the activities involved in the production of this research or manuscript. Disclosures The authors have no relevant disclosures. References 1. ↵ Hospital Readmissions Reduction Program (HRRP) Fiscal Year 2025 Fact Sheet . In: (CMS) CfMM , editor. qualitynet.cms.gov , 2024 . 2. ↵ Zhou H , Della PR , Roberts P , Goh L , Dhaliwal SS . Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review . BMJ Open 2016 ; 6 : e011060 . OpenUrl Abstract / FREE Full Text 3. Aubert CE , Folly A , Mancinetti M , Hayoz D , Donze J . Prospective validation and adaptation of the HOSPITAL score to predict high risk of unplanned readmission of medical patients . Swiss Med Wkly 2016 ; 146 : w14335 . OpenUrl PubMed 4. Cooksley T , Nanayakkara PW , Nickel CH et al. Readmissions of medical patients: an external validation of two existing prediction scores . QJM 2016 ; 109 : 245 – 8 . OpenUrl CrossRef PubMed 5. 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Alvarenga , John A. Spertus medRxiv 2025.11.19.25340621; doi: https://doi.org/10.1101/2025.11.19.25340621 Share This Article: Copy Citation Tools Leveraging implementation science to improve uptake of an ED decision support aid for heart failure patients Katherine A. Vilain , Stacy L. Farr , Danielle M. Olds , Kathryn L. Istas , Maria A. Alvarenga , John A. 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