Practice-Based Insights into Adult Genetics: High Diagnostic Yield, Demographic Determinants, and Patterns of Test Utilization in Over 7,000 Patients

Practice-Based Insights into Adult Genetics: High Diagnostic Yield, Demographic Determinants, and Patterns of Test Utilization across 10,000 Patient Encounters | 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 Practice-Based Insights into Adult Genetics: High Diagnostic Yield, Demographic Determinants, and Patterns of Test Utilization across 10,000 Patient Encounters Jessica I Gold , Yehuda Elkaim , Stephanie Asher , Anna Raper , Courtney Condit , Zoe Bogus , Isaac Elysee , Laura Hennessy , Emma Kennedy , Teresa Chai , Stacey Cohen , Brielle N. Gehringer , Shannon M Gray , Ala Streater , Eamon Toye , Colleen Kripke , Katherine L Nathanson , Mersedeh Rohanizadegan , Staci Kallish Do , View ORCID Profile Theodore G Drivas doi: https://doi.org/10.1101/2025.10.09.25337579 Jessica I Gold 1 Division of Clinical Genetics, Department of Pediatrics, Cohen Children’s Medical Center , Northwell Health, Great Neck, NY, 11021, USA 2 Institute of Health System Science, Feinstein Institutes for Medical Research , Northwell Health, Manhasset, NY, 11030, USA MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yehuda Elkaim 3 Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA BS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephanie Asher 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA MS CGC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna Raper 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA MS CGC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Courtney Condit 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA 5 Kaiser Permanente, Tysons Corners Medical Center , McLean, VA, 22102, USA MS CGC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zoe Bogus 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA MS CGC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Isaac Elysee 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA MS CGC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Laura Hennessy 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA MS CGC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emma Kennedy 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA MS CGC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Teresa Chai 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA MS CGC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stacey Cohen 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA MS CGC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brielle N. Gehringer 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA MS CGC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shannon M Gray 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA MS CGC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ala Streater 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA BS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eamon Toye 3 Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA BS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Colleen Kripke 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA DPT MSA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katherine L Nathanson 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA 6 Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mersedeh Rohanizadegan 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Staci Kallish Do 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Theodore G Drivas 4 Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA 7 Department of Genetics, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA, 19104, USA MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Theodore G Drivas For correspondence: theodore.drivas{at}pennmedicine.upenn.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Precision medicine increasingly relies on genetic testing for individualized care, yet the practice patterns of genetic evaluation in adults remain under-characterized and few guidelines exist to inform appropriate testing. We analyzed eight years of electronic health record (EHR)-linked data from a high-volume adult genetics clinic (9,867 visits) within a major academic health system to define referral patterns, test utilization, and genetic testing outcomes across indications and demographic groups. Genetic testing was ordered for 52% of all new patients, with significant indication-specific variation in ordering propensity. Overall, 24% of all tests returned a diagnostic result. Diagnostic yield differed markedly by testing modality: whole-exome sequencing yielded diagnoses in 40% of patients, whereas next-generation sequencing panels, the most frequently ordered testing type, yielded diagnoses in only 16%. Although diagnostic yield declined with increasing patient age it remained above 17% in every age stratum, with the yield of exome/genome sequencing remaining above 30% across all age groups. Outcomes varied significantly by referral indication, with some yielding high rates of pathogenic findings, and others more often yielding negative or uncertain results. Operational factors materially shaped practice: test type and laboratory utilization shifted significantly over time in step with payer and testing lab policy changes. Together, these practice-based data establish adult genetics as a high-yield and important clinical domain. Our findings provide actionable evidence to inform age- and indication-aware triage, guide workforce planning, promote adult-focused practice guidelines, and advance the integration of genomic medicine into routine adult patient care. Introduction Precision medicine, often contingent upon genetic diagnosis, is becoming increasingly common in adult clinical care. 1 Health systems are now ordering millions of genetic tests, from single-gene assays to exome and genome sequencing. 2 However, primary-care and internal-medicine-trained clinicians report lower preparedness than pediatric colleagues in interpreting genetic test results, the adult-focused genetics workforce is extremely limited, and adult-specific, evidence-based guidelines for genetics evaluation and testing are sparse. 3 – 6 This mismatch is compounded by limited systematic knowledge of the adult genetics landscape – who is seen, what is tested, and with what yield – hindering workforce planning, guideline development, and the effective integration of genetics into routine adult medical care. Studies of genetic testing in the general adult patient population – principally through exome and genome sequencing – are still limited in number and size but consistently report meaningful diagnostic yields ranging from 17-30% across diverse settings and indications, comparable to those reported in pediatric populations. 7 – 10 In fact, in our recent study of critically ill young adults, genomic diagnoses were both common and actionable, with one quarter of adults aged 18-40 harboring a genetic diagnosis causal for their ICU presentation. 11 Together these studies highlight the diagnostic utility and actionability of genetic testing in adult patients, but only very few studies have documented how testing is actually deployed in routine adult practice. 3 , 6 Against this backdrop, most of what we know about general adult genetics evaluation and testing practices comes from a very small number of longitudinal clinic series and workforce perspectives. The only two large single-center studies of adult genetics clinical practices published to-date describe scope and referral patterns but provide limited detail on testing behavior or diagnostic outcomes. 12 , 13 Additional service-delivery reports have documented pronounced wait times for adult genetics appointments and confirm unmet demand and operational strain, but they do not link who is seen to what is tested and what is found. 14 , 15 Together, these observations underscore a pivotal moment for adult medical genetics: demand is rising and the clinical value is clear, yet we still lack a consolidated, practice-level picture of how adult genetics clinics organize care, deploy tests, and deliver results across indications. To address this gap, we analyze eight years of electronic health record (EHR)-linked data from a high-volume adult genetics clinic to map its catchment and patient mix, quantify testing use by demographics and clinical indications, track the evolution of test modalities and laboratory partnerships in the context of payer policy, EMR integration, and telehealth adoption, and to connect these choices to genetic testing outcomes – pathogenic findings, variants of uncertain significance, and negative results. By tracing the full pathway from referral to result, we aim to generate the practice-level evidence needed to guide workforce planning, inform guideline development, and integrate genetics more effectively into routine adult clinical care. Subjects, Materials, and Methods Study population definition and data sources This study was approved by the University of Pennsylvania’s Institutional Review Board (protocol #850058). We obtained from the Epic Clarity database the complete list of all 9,867 patient visits at the UPHS Perelman Center for Advanced Medicine’s Adult Genetics Clinic (Department Code 209) over an eight year period from 10/1/2016 to 9/30/2024, along with accompanying visit demographic information including patient age, EHR-reported gender, EHR-reported race and ethnicity, home address, and financial class for the visit. This information was joined with the internal clinical databases maintained by the UPHS Adult Genetics Clinic documenting visit indications, whether genetic testing was sent, and the results of genetic testing. After joining, 4,264 patient visits had at least one missing data field, all of which were resolved by manual chart review. Clinical indications for patient visits were collapsed into 21 broad indication groups ( Table S1 ) by a panel of genetics providers blinded to both patient demographic information and outcomes of genetic testing. Latitudes and longitudes were obtained from patient home addresses using the tool Geocodio. View this table: View inline View popup Table S1. Indication to Indication Group Mappings, with Visit Counts Genetic Variant Classification Genetic test results were classified as “pathogenic” if the variant identified was classified as pathogenic/likely pathogenic by the testing lab – for autosomal recessive conditions, two variants in the same gene were required with at least one classified as pathogenic/likely pathogenic – and was consistent with the patient’s presentation as determined by the clinical team at the time of result disclosure. Results were classified as “carrier” if a single pathogenic/likely pathogenic variant was identified in a gene associated with only autosomal recessive disease and no other pathogenic result had been reported. Results were classified as “candidate gene” if the testing lab had reported out only a variant in a candidate gene, i.e., a gene not definitively known to be associated with human disease, but suspicious enough to warrant reporting. Results were classified as variants of uncertain significance (VUS) if the lab reported only VUS(s). Lastly, genetic test reports that identified only likely-benign/benign variants or identified no causative variants, or whole exome sequencing (WES) and whole genome sequencing (WGS) results revealing only a secondary finding, were considered negative. Definitions of race and ethnicity Race and ethnicity of the study population were extracted as reported in the EHR. These data likely represent a combination of self-reported and clinician/staff-assigned identities. Prior studies suggest that, although inaccuracies exist, EHR-derived race and ethnicity is highly concordant with self-reported data. 16 Race and ethnicity represent two entirely separate demographic identifiers, however, due to small sample sizes, we chose to combine race and ethnicity identifiers as follows. Any patient with an EHR-derived ethnicity of “Hispanic or Latino,” regardless of race, was collapsed into a single category, “Hispanic or Latino of any race” (n=339). For all patients without an EHR-derived ethnicity of “Hispanic or Latino,” race/ethnicity categories were defined as follows. Patients with an EHR-derived race listed as “other” (n=136), “some other race” (n=106), “unknown” (n=780), or “patient declined” (n=57) were listed as “Other” (n=1,079). Patients with an EHR-derived race listed as “Black” (n=396) or “Black or African American” (n=349) were listed as “Black or African American” (n=745). Patients with an EHR-derived race listed as “American Indian” (n=6) or “American Indian or Alaskan Native” (n=12) were listed as “American Indian or Alaskan Native” (n=18). Patients with an EHR-derived race listed as “Pacific Island” (n=4) or “Native Hawaiian or Other Pacific Islander” (n=2) were listed as “Native Hawaiian or Other Pacific Islander” (n=6). Patients with an EHR-derived race listed as “Asian” (n=266) or “East Indian” (n=20) were listed as “Asian” (n=286). Patients with an EHR-derived race listed as “White” (n=7,384) were listed as “White” (n=7,384). Statistical analysis Statistical analyses were carried out, as indicated, adjusting for covariates as described in the figure and table legends. For all analyses, two-sided p-values less than 0.05 were considered nominally significant. For analyses making multiple statistical comparisons, a Bonferroni-corrected p-value < 0.05 is used to determine significance, as described in the figure legends, with raw, unadjusted p-values reported in the text for transparency. All analyses were performed with R software, version 4.2.2. 17 Results Demographics of the UPHS Adult Genetics Clinic We first evaluated geographic and demographic characteristics of the 9,867 unique patient visits for 7,766 unique patients to the University of Pennsylvania Health System (UPHS) Adult Genetics Clinic between October 1, 2016, and September 30, 2024. The majority of patients originated from the greater Philadelphia metropolitan area ( Figure 1A ). While most patients clustered in or near Philadelphia, we observed substantial reach into surrounding regions, including parts of New Jersey, Delaware, and southeastern Pennsylvania. A smaller but notable number of patients traveled from further locations such as Baltimore, Washington D.C., and New York City, highlighting the clinic’s regional draw. Patients ranged widely in age at time of visit, from adolescence to older adulthood, but the overall distribution skewed younger, with the highest density of visits occurring among adults under 50 years of age ( Figure 1B ). Two distinct peaks were observed: one around age 30 years and another around age 60 years, in both male and female patients, suggesting different referral patterns or clinical needs across the age spectrum. Download figure Open in new tab Figure 1. Demographics of the UPHS Adult Genetics Clinic Demographics information for all 9,867 patient visits to the UPHS Adult Genetics Clinic are shown for the time period from October 1, 2016, to September 30, 2024. (A) A heatmap of patient home addresses is displayed over a geographic map of the Delaware Valley region, with major U.S. cities labeled for orientation. The inset provides a magnified view of the City of Philadelphia. The color scale indicates the number of unique patient visits per hexagonal unit. (B) Histogram illustrating the distribution of patient ages at time of visit, divided by EHR-reported sex. (C) EHR-reported patient sex for the study cohort. (D) EHR-reported patient race for the study cohort defined as reported in Subjects, Materials, and Methods . (E) EHR-reported patient ethnicity for the study cohort defined as reported in Subjects, Materials, and Methods . (F) Percent of visits conducted by each provider type across the entire study cohort (G) Visit type, divided by new vs. follow up visit, and by in-person and telehealth, for each patient visit. (H) EHR-reported patient insurance class at time of visit for the study cohort. The clinic saw more female than male patients overall (60.4% vs. 39.6%) ( Figure 1C ). The majority of patients self-identified as White (77.1%), with the next most frequent groups being “Other” (11.9%) and Black or African American (7.8%) ( Figure 1D ). Hispanic or Latino patients of any race comprised 3.4% of visits ( Figure 1E ). These proportions are consistent with known racial disparities in access to genetic services, as we have previously reported, 18 and highlight an ongoing need for improved outreach and equity in adult genetics care. The vast majority of patient visits were conducted by a genetics-trained physician (77.5%), with only 12.3% conducted by a genetic counselor and 10.1% conducted by a nurse practitioner ( Figure 1F ). The majority of visits were for new patient consultations, both in person or via telehealth, which together accounted for 79.3% of all encounters ( Figure 1G ). Follow-up visits comprised the remaining 20.7%, again including both in-person and telehealth formats. While most visits overall were conducted in person, telehealth accounted for 13.8% of all visits, with 8.1% of all visits being for new consultations by telehealth and 5.7% for follow-up by telehealth ( Figure 1G ). A sharp rise in telehealth visits was observed beginning in 2020, coinciding with the onset of the COVID-19 pandemic and local stay-at-home orders ( Figure S1 ). 19 This rise was seen for both new patient and follow-up visits. Although telehealth use declined in the years that followed, with most new patient visits returning to in-person formats, a small but substantial proportion of follow-up visits continued to be conducted via telehealth, highlighting its enduring role in the delivery of adult genetics care for established patients. Most patients were commercially insured (75.4%), followed by those with Medicare (15.1%) and Medicaid (8.6%) ( Figure 1H ). Only 0.9% of visits were self-pay. Download figure Open in new tab Figure S1. Visit types, over time Type of visit (new patient vs follow up, in person vs telehealth) to the UPHS Adult Genetics Clinic is shown over time in 6-month intervals from October 1, 2016, to September 30, 2024. A sharp rise in telehealth visits is observed beginning in 2020, coinciding with the COVID-19 pandemic and local stay-at-home orders. Distribution and temporal trends in indications for clinic visits Patients were referred to the UPHS Adult Genetics Clinic for a diverse array of clinical indications ( Figure 2A ). The most common categories included cancer genetics (22.0%), connective tissue disorders (16.6%), multisystem disorders (16.5%), hypermobility (11.8%), lysosomal storage disorders (5.8%), and vascular malformation disorders (4.63%). Referrals for hypermobility declined sharply and significantly over the study period (p<1e-04; OR 0.77 [0.76-0.79]), beginning in late 2019 ( Figure 2B ). This trend aligns with the implementation of new criteria for evaluation in our clinic in early 2020 which aimed to prioritize patients with more complex or multisystem presentations and reduce volume from lower-yield isolated hypermobility evaluations, for which there is no known genetic cause. 20 In contrast, as hypermobility referrals decreased, the clinic was able to accommodate patients with a broader range of referral indications. Significant increases were observed in referrals for cancer genetics (p<1e-04; OR 1.07 [1.06-1.08]), multisystem disorders (p<1e-04; OR 1.04 [1.03-1.05]), lysosomal storage disorders (p=1.2e-04; OR 1.04 [1.02-1.06]), chromosomal disorders (p<1e-04; OR 1.06 [1.03-1.09]), renal disorders (p<1e-04; OR 1.08 [1.05-1.11]), lung disorders (p<1e-04; OR 1.09 [1.05-1.13]), and gastrointestinal disorders (p=1.52e-03; OR 1.13 [1.05-1.22]) during this time period. These increases suggest an intentional and effective broadening of the clinic’s scope to better serve patients with diverse genetic conditions requiring evaluation by a genetics-trained provider. Download figure Open in new tab Figure 2. Comparison of Demographic Characteristics and Visit Types Across Clinical Indications (A) Indications for all 9,867 unique patient visits to the UPHS Adult Genetics Clinic from October 1, 2016, to September 30, 2024 are displayed. The percentage of each indication, relative to the total, is shown in the legend “Indication Group, Percent of Total”. (B) Patient visit indications are shown over time in 6-month intervals from October 1, 2016, to September 30, 2024. Indications are color-coded as shown in the legend “Indication Group, Percent of Total.” Indications with a significant decrease in their proportion over time (Hypermobility, Connective tissue disorder) are marked with a blue asterisk in the legend, while those with a significant increase (Cancer genetics, Multisystem disorder, Lysosomal storage, Chromosomal disorder, Renal disorder, Lung disorder, Gastrointestinal disorder) are marked with a red asterisk. Statistical significance was determined by logistic regression accounting for multiple testing across all 21 indication groups. (C) Dot plots with overlaid boxplots show patient age at the time of visit for each indication group, ordered by median age, across the study period. Each dot represents an individual patient, while boxplots display the median (solid line) and interquartile range (box and whiskers). Indications with a mean patient age significantly above the clinic-wide average (Lysosomal storage disorder, Lung disorder, Cancer genetics, Vascular malformation, Connective tissue disorder, Renal disorder) are marked with red asterisks; those significantly below (Cardiac disorders, Hypermobility, ID/DD/ASD, Chromosomal disorder, General genetics, Multisystem disorder) are marked with blue asterisks. Statistical significance was determined by linear regression adjusting for EHR-reported sex, race, insurance class, and visit type, with Bonferroni correction applied for multiple testing across all 21 indication groups. (D) Stacked bar charts display the EHR-reported race of patients for each indication group across the study period. Indications with a significantly higher proportion of a given racial group, compared to the overall clinic average, are marked with an asterisk. Statistical significance was determined by multinomial logistic regression, adjusting for sex, insurance class, visit type, and age at visit. After Bonferroni correction, patients of Other race were found to be significantly overrepresented among referrals for Hypermobility, while patients of Black or African American race were significantly underrepresented. Hispanic or Latino patients were underrepresented in referrals for cardiac and lung disorders. Patients of Other race were significantly underrepresented in referrals for Cancer genetics and Renal disorders. (E) Stacked bar charts display the proportion of male and female patients for each indication group across the study period. Indications with a significantly higher proportion of female patients compared to the overall clinic average (Hypermobility) are marked with an asterisk in the pink bar, while those with a significantly higher proportion of male patients compared to the overall clinic average (Connective tissue disorder, Lung disorder) are marked with an asterisk in the blue bar. Statistical significance was determined by logistic regression, adjusting for age, race, insurance class, and visit type, with Bonferroni correction applied for multiple testing across all 21 indication groups. (F) Stacked bar charts display the proportion of new patient visits and follow-up visits, further subdivided by in-person and telemedicine encounters, for each indication group across the study period. Indications with a significantly higher proportion of follow up visits (in-person or telemedicine) compared to the overall clinic average (Lysosomal storage disorder, Multisystem disorder, Metabolic disorder, Mitochondrial disease), are marked with an asterisk in the orange bar, while those with a significantly higher proportion of new patient visits (in-person or telemedicine) compared to the overall clinic average (General genetics, Neuromuscular disprder, Renal disorder, Vascular malformation disorder, Hypermobility, Lung disorder) are marked with an asterisk in the purple bar. Statistical significance was determined by logistic regression, adjusting for EHR-reported sex, race, insurance class, and age at visit, with Bonferroni correction applied for multiple testing across all 21 indication groups. Significant variation in patient age, race, and sex across clinical indication groups Significant demographic differences were observed among patients referred for different clinical indications ( Figure 2C–F ). Patients referred for lysosomal storage disorders and lung disorders had the highest median ages (56 and 55 years, respectively), while those referred for hypermobility and cardiac disorders had the lowest (31 and 30 years, respectively, Figure 2C ). After adjusting for EHR-reported sex, race, insurance class, and visit type, patients referred for cancer genetics (p<1e-04; beta 6.79 [5.46,8.12]), connective tissue disorders (p<1e-04; beta 2.10 [0.79,3.41]), lung disorders (p<1e-04; beta 5.97 [2.54,9.39]), lysosomal storage disorders (p<1e-04; beta 9.18 [7.10,11.25]), renal disorders (pe4.5e-03; beta 3.49 [0.74,6.24]), and vascular malformation disorders (3.0e-03; beta 2.70 [0.64,4.75]) were significantly older than the overall clinic population. In contrast, patients referred for cardiac disorders (p=3.6e-02; beta −7.85 [-15.10,-0.61]), chromosomal disorders (p<1e-04; beta −6.35 [−9.07,-3.64]), general genetics (1.7e-04; beta −4.05 [−6.70,−1.40]), hypermobility (p<1e-04; beta -6.28 [-7.78,-4.78]), ID/DD/ASD (Intellectual Disability/Developmental Delay/Autism Spectrum Disorders, p<1e-04; beta -8.92 [−12.55,−5.28]), and multisystem disorders (p<1e-04; beta −2.47 [−3.98,-0.96]) were significantly younger than the overall clinic population, after correcting for multiple comparisons. Examining EHR-reported patient race by indication group ( Figure 2D ), we found that patients evaluated for hypermobility were significantly more likely to have an EHR-reported race of Other (p<1e-04; OR 1.09[1.06-1.13]) and significantly less likely to have an EHR-reported race of Black or African American (p<1e-04; OR 0.95 [0.93-0.96]), by logistic regression after adjusting for EHR-reported sex, age, insurance class, visit type, and after correcting for multiple comparisons. Hispanic or Latino patients were significantly less likely to be evaluated for cardiac disorders (p=4.1e-04; OR 0.92[0.89-0.96]) and lung disorders (p=4.1e-04; OR 0.92[0.89-0.96]), while patients of Other race were significantly less likely to be evaluated for cancer genetics (p<1e-04; OR 0.95[0.93-0.97]) and renal disorders (p=2.3e-03; OR 0.93[0.89-0.96]). Significant differences in EHR-reported patient sex were also identified by indication group ( Figure 2E ). Patients referred for connective tissue disorders (p<1e-04; OR 1.51 [1.33–1.71]) and lung disorders (p=2.1e-03; OR 1.83 [1.32–2.53]) were significantly more likely to be male, while those referred for hypermobility were significantly less likely to be male (p<1e-04; OR 0.24 [0.20–0.28]), by logistic regression after adjusting for EHR-reported race, age, insurance class, visit type, and correcting for multiple comparisons. Comparing visit type by indication group ( Figure 2F ) by logistic regression, adjusted for EHR-reported sex, race, age, and insurance class, we found that patients seen for certain clinical indications were significantly more likely to be seen for follow up visits (across both in person and telehealth visits) compared to the overall clinic population: lysosomal storage disorder (p<1e-04; OR 1.74[1.62-1.87]), multisystem disorders (p=0.043; OR 1.27[1.19-1.35), metabolic disorder (p<1e-04; OR 1.24[1.14-1.34]), and mitochondrial disease (p=5.7e-03; OR 1.23[1.11-1.35]). On the other hand, patients seen for lung disorders (p<1e-04; OR 1.12[1.07-1.17]), hypermobility (p<1e-04; OR 1.09[1.07-1.12]), vascular malformation disorders (p<1e-04; OR 1.09[1.06-1.12]), renal disorders (p<1e-04; OR 1.09[1.06-1.13]), neuromuscular disorders (p=1.0e-03; OR 1.08[1.04-1.13]), and general genetics (p=0.049; OR 1.06[1.03-1.09]) were significantly more likely to be seen for new patient visits (both in person and telehealth) compared to the overall clinic population. Higher genetic testing utilization among older patients and male patients We next compared the likelihood of genetic testing being sent following evaluation. Overall, we found that genetic testing was completed in 51.9% of all new patients, and in 8.0% of all follow up patients during the study period ( Figure 3A ). Among new patient visits, male patients were significantly more likely than females to undergo genetic testing (p=2.39e-03; OR 1.17[1.05-1.29]), even after adjusting for EHR-reported race, age, insurance class, and clinical indication ( Figure 3B ). Interestingly, we also found a significant positive correlation between patient age at time of new patient evaluation and likelihood of completing genetic testing (p=4.07e-12; OR 1.01[1.01-1.02], with the OR reflecting the per-year change in odds of undergoing genetic testing after evaluation), even after adjusting for EHR-reported race, sex, insurance class, and clinical indication ( Figure 3C ). No significant differences were seen in genetic testing rates across different race/ancestry groups after adjusting for multiple hypothesis testing ( Figure 3D ). Download figure Open in new tab Figure 3. Patterns of Genetic Testing Utilization by Visit Type, Demographics, and Clinical Indication In all panels, bars indicate the proportion of visits for which testing was sent (green) versus not sent (pink). Percent labels, in green, above bars show the percent of visits for which testing was sent for each subgroup. Sample sizes for each subgroup are shown below the horizontal axis labels for panels B-E. (A) Marimekko plot showing the percent of visits during which genetic testing was sent or not sent by visit type (New Patient Visit vs. Follow-up). The height of each row represents the proportion of all visits accounted for by each visit type; the width of each column represents the percent of visits, within each visit type, with or without testing sent. A stacked bar plot to the left shows the overall distribution of visit types, for reference, with New Patient Visits in purple and Follow-up visits in orange. (B) Percent of new patient visits during which testing was sent, stratified by EHR-reported sex. The p-value indicates the result of logistic regression comparing the odds of testing sent by sex, adjusted for clinical indication group, age, race, and insurance class. (C) Percent of new patient visits during which testing was sent, stratified by patient age (plotted in discrete age ranges). The p-value indicates the association between age (modeled as a continuous variable) and the odds of testing being sent, from logistic regression adjusted for clinical indication group, race, sex, and insurance class. (D) Percent of new patient visits during which testing was sent, stratified by EHR-reported race. No significant differences were found when comparing each group to the largest group (EHR-reported White race) by logistic regression adjusted for indication group, sex, age, and insurance class, accounting for multiple testing across all 6 groups. (E) Percent of new patient visits during which testing was sent, stratified by clinical indication group. Indications with a significantly higher odds of testing compared to the overall clinic average (Neuromuscular disorder, ID/DD/ASD) are marked with an asterisk in the green bar, while those with a significantly lower odds of testing compared to the overall clinic average (Multisystem disorder, Lysosomal storage disorder, Chromosomal disorder, Hypermobility) are marked with an asterisk in the pink bar. Statistical significance was determined by logistic regression, adjusting for age, race, sex, and insurance class, with Bonferroni correction applied for multiple testing across all 21 indication groups. Significant differences in genetic testing utilization by clinical indication group Significant differences were seen across referral indications in the propensity to complete genetic testing after new-patient evaluation by logistic regression adjusted for age, race/ethnicity, sex, and insurance class ( Figure 3E ). Relative to the clinic-wide new-visit average, testing was more likely to be ordered for ID/DD/ASD (p<1e-04; OR 2.69[1.71-4.21]), and neuromuscular disorders (p<1e-04; OR 3.23[2.02-5.17]). In contrast, testing was less likely to be ordered for chromosomal disorders (p<1e-04; OR 0.36[0.27-0.49]), hypermobility (p<1e-04; OR 0.06[0.04-0.07]), lysosomal storage disorders (p<1e-04; OR 0.39[0.28-0.55]), and multisystem disorders (p<1e-04; OR 0.41[0.35-0.48]). Our data do not allow us to assess whether lower genetic test ordering rates in these groups reflects prior confirmatory testing obtained before referral to our clinic, but we suspect that many new patients being referred for chromosomal disorders or known multisystem disorders already carried a molecular diagnosis and testing was not required. Distribution and temporal trends in testing modalities and testing lab utilization Between October 1, 2016, and September 30, 2024, patterns of genetic testing modalities and laboratory utilization shifted ( Figure 4A-B ). Of the 4,245 tests sent during this period, the most common type of test sent was NGS panels (62.84%), followed by single gene (15.23%), single site (10.80%), whole exome sequencing (3.69%), and chromosomal microarray (CMA, 2.68%). Over time, the ordering of NGS panels (p=4.2e-05; OR 1.03 [1.02–1.04]) and whole genome sequencing (WGS, p=3.2e-03; OR 1.35 [1.13–1.71]) increased significantly, whereas significant decreases were observed in the ordering of CMA (p=8.8e-07; OR 0.90 [0.86–0.94]), single site testing (p=1.5e-04; OR 0.96 [0.94–0.98]), and metabolic testing (p=1.4e-03; OR 0.92 [0.87–0.97]). Download figure Open in new tab Figure 4. Patterns and Temporal Trends in Genetic Testing Modalities and Laboratory Utilization (A) Type of testing sent for all 4,245 tests sent during the study period. Percentages relative to the total are shown in the legend “Testing Type, Percent of Total.” (B) Type of testing sent is shown over time in 6-month intervals from October 1, 2016, to September 30, 2024. Testing types are color-coded as shown in the legend “Testing Type, Percent of Total.” Testing types significantly increasing in utilization over time (NGS Panel, WGS) are marked with a red asterisk in the legend, while those significantly decreasing in utilization over time (Single Site, CMA, Metabolic) are marked with a blue asterisk. Statistical significance was determined using separate binary logistic regression models for each test type, comparing the odds of using that test type (versus all other tests) as a function of time, accounting for multiple testing across all 13 testing types. (C) Testing laboratories used for all 4,245 tests sent during the study period. Percentages relative to the total are shown in the legend “Lab Choice, Percent of Total.” (D) Testing lab choice is shown over time in 6-month intervals from October 1, 2016, to September 30, 2024. Testing labs are color-coded as shown in the legend “Lab Choice, Percent of Total.” Testing labs significantly increasing in utilization over time (Ambry) are marked with a red asterisk in the legend, while those significantly decreasing in utilization over time (UAB, Prevention, Penn, Fulgent, CTGT, ARUP) are marked with a blue asterisk. Statistical significance was determined using binary logistic separate regression models for each testing lab, comparing the odds of using that test type (versus all other labs) as a function of time, accounting for multiple testing across all 34 testing labs. Testing was sent to 34 different laboratories ( Figure 4C ), with three laboratories performing 83.01% of all testing during the study period; 1,945 tests were performed at Invitae (45.77%), 931 were performed at Ambry Genetics (21.95%), and 648 were performed at GeneDx (15.29%). As with the types of tests ordered, temporal shifts were also observed in which genetic testing laboratories were used ( Figure 4D ). Over the study period, the ordering of testing from Ambry increased significantly (p=5.23e-08; OR 1.05 [1.03–1.06]), while significant decreases were observed in testing ordered from ARUP (p=3.19-06; OR 0.90 [0.87–0.94]), CTGT (p=4.8e-06; OR 0.59 [0.46–0.72]), Fulgent (p=7.86-09; OR 0.62 [0.52–0.72]), Prevention (p=1.85-09; OR 0.75 [0.68–0.82]), University of Alabama (UAB, p=1.39-07; OR 0.49 [0.36–0.62]), and the University of Pennsylvania (Penn, p=3.88-04; OR 0.91 [0.86–0.96]). No statistically significant differences were observed in the utilization of other laboratories during this time. Year-to-year shifts in testing laboratories closely tracked changes in billing, insurance, and ordering policies ( Figure 4D ). For example, Blueprint Genetics introduced a $250 capped-cost policy for self-pay NGS panels in November 2018, which led to a sharp increase in orders, but discontinued the policy in February 2021, after which orders declined. Variantyx offered no-cost reflex from any NGS panel to genome sequencing between January 2022 and February 2023, producing a similar rise and fall in orders during that period. Ambry worked to integrate test ordering directly into the UPHS electronic medical record in January 2020, resulting in a large increase in testing volume, and GeneDx became in-network with Independence Blue Cross in February 2023, likewise driving a substantial increase in tests ordered. High rate of positive findings across all patients Overall, a high rate of pathogenic findings was observed across all testing sent during the study period ( Figure 5A ). 24.3% of the 4,245 tests sent between October 1, 2016 and September 30, 2024 resulted in a pathogenic, diagnostic finding. Another 3.8% returned pathogenic carrier results, and 13.6% returned inconclusive (VUS) results. Download figure Open in new tab Figure 5. Genetic Testing Results by Patient and Test Characteristics In all panels, bars indicate the percentage of patients with each genetic testing result classification: pathogenic (blue), variant of uncertain significance (VUS; orange), carrier (yellow), candidate gene (aqua), or negative (gray). Percent labels in blue above bars show the percent of patients who received a pathogenic result for each subgroup. Sample sizes for each subgroup are shown below the horizontal axis labels for panels B–F. (A) Pie chart showing the overall distribution of results for all 4,245 genetic tests sent during the study period. Percentages for each result classification are shown in the legend. (B) Percent of patients receiving each testing result classification, stratified by EHR-reported sex. The p-values indicate the result of logistic regression comparing the odds of receiving a pathogenic (in blue) or VUS (in orange) result by sex, adjusted for patient age, race, insurance class, clinical indication group, and visit type. No significant difference was found for pathogenic results; however, males were significantly more likely than females to receive a VUS result. (C) Percent of patients receiving each testing result classification, stratified by patient age at visit (plotted in discrete age ranges). The p-value indicates the association between age (modeled as a continuous variable) and the odds of receiving a pathogenic (in blue) or VUS (in orange) result from logistic regression adjusted for sex, race, insurance class, clinical indication group, and visit type. Older patients were significantly less likely to receive a pathogenic result and significantly more likely to receive a VUS. (D) Percent of patients receiving each testing result classification, stratified by EHR-reported race. Hispanic or Latino patients of any race were significantly more likely to receive pathogenic results compared to patients of EHR-reported White race; no other significant differences were found for any racial group, relative to patients with EHR-reported White race, for the odds of receiving either a pathogenic (in blue) or VUS (in orange) result by logistic regression adjusted for age, sex, insurance class, clinical indication group, and visit type, accounting for multiple testing across all 6 groups. (E) Percent of patients receiving each testing result classification, stratified by type of testing, ordered by likelihood of receiving a pathogenic result. No formal statistical comparisons were performed for this panel. (F) Percent of patients receiving each testing result classification, stratified by clinical indication group, ordered by likelihood of receiving a pathogenic result. Indications where genetic testing had a significantly higher odds of identifying a pathogenic result compared to the overall clinic average (Chromosomal disorder, Vascular malformation disorder, Renal disorder, Multisystem disorder) are marked with an asterisk in the blue bar, while those with a significantly lower odds of identifying a pathogenic result compared to the overall clinic average (Cancer genetics, Connective tissue disorder, General genetics, Immune/hematologic disorder, Hypermobility) are marked with an asterisk in the grey bar. Indications where genetic testing had a significantly higher odds of identifying a VUS compared to the overall clinic average (Connective tissue disorder, Immune/hematologic disorder) are marked with a hash mark in the orange bar. Statistical significance was determined by logistic regression, adjusting for age, race, sex, and insurance class, with Bonferroni correction applied for multiple testing across all 21 indication groups. The percentage of patients receiving each testing result was stratified by EHR-reported sex ( Figure 5B ). The rate of pathogenic findings did not vary significantly between EHR-reported sex; 25.0% of females and 23.2% of males received a pathogenic result. However, males were significantly more likely than females to receive a VUS result, with 15.6% of testing sent in male patients and 12.0% of testing sent in female patients resulting with one or more VUSs (p=0.002; OR 1.34 [1.11-1.61], results of logistic regression adjusted for age, sex, insurance class, clinical indication group, and visit type). The percentage of patients receiving each testing result was also stratified by EHR-reported race and ethnicity ( Figure 5D ). The rate of pathogenic findings was 40.0% for American Indian or Alaskan Native patients (n=5), 38.1% among Hispanic or Latino patients (n=155), 27.4% for patients of Other race/ethnicity (n=372), 25.4% for Asian patients (n=138), 24.5% for Black or African American patients (n=331), 23.1% for White patients (n=2,986), and 0% for Native Hawaiian or Other Pacific Islander patients (n=3). Hispanic or Latino of any race were significantly more likely to receive pathogenic results than White patients (p=0.001, OR 1.82[1.26-2.61]) by logistic regression adjusted for age, sex, insurance class, clinical indication group, and visit type; no other significant differences in pathogenic result rate were observed by EHR-reported race/ethnicity. The rate of VUS findings was 20.0% for American Indian or Alaskan Native patients, 5.7% among Hispanic or Latino patients, 13.9% for patients of other race/ethnicity, 15.8% for Asian patients, 16.4% for Black or African American patients, 13.7% for White patients, and 0% for Native Hawaiian or Other Pacific Islander patients. No significant differences were observed for VUS rate across patients of different racial/ethnic groups. Significant decrease in positive testing rate with increasing patient age When analyzing results of genetic testing by patient age, the percentage of patients receiving each testing result classification differed significantly ( Figure 5C ). Older patients were significantly less likely to receive a pathogenic result overall (p=2.93e-09; OR 0.98[0.98-0.99], results of logistic regression adjusted for age, sex, insurance class, clinical indication group, and visit type, OR reflects the per-year change in odds). Pathogenic results were identified in 37.2% of patients aged 18-25 years (n=479), 30.1% of patients 26-35 years (n=893), 23.8% of patients 36-45 years (n=748), 17.6% of patients 46-55 years (n=619), 19.5% of patients 56-65 years (n=677), 17.8% of patients 66-75 years (n=477), and 17.9% of patients >75 years (n=106). Older patients were also nominally significantly more likely to receive a VUS result (p=0.015; OR 1.01[1.00-1.02], results of logistic regression adjusted for age, sex, insurance class, clinical indication group, and visit type, OR reflects the per-year change in odds). VUS results were identified in 11.9% of patients aged 18-25 years, 11.2% of patients 26-35 years, 13.0% of patients 36-45 years, 17.0% of patients 46-55 years, 16.7% of patients 56-65 years, 13% of patients 66-75 years, and 8.5% of patients >75 years. Variation in diagnostic yield across genetic testing modalities Large differences in diagnostic yield were observed across different genetic testing modalities ( Figure 5E ). The testing modalities with the highest percentage of pathogenic findings were single site testing (43.5%, n=448), karyotype (43.3%, n=60), whole exome sequencing (WES, 39.7%, n = 156), single gene testing (36.7%, n=635), and chromosomal microarray (CMA, 30.4%, n=112). Next-generation sequencing (NGS) panels, by far the most common test ordered (n=2,584) had the second lowest diagnostic rate (16.2% diagnostic rate) of all testing modalities, followed only by metabolic testing (6.2% diagnostic rate, n=64). The rate of VUS results also varied between testing modalities. The testing types most likely to result in only VUSs were mitochondrial sequencing (30.8%), NGS panels (18.6%), whole exome sequencing (11.5%), and CMA (9.8%). Significant indication-specific variation in genetic test outcomes There were also significant differences in genetic testing results depending on the clinical indication for testing ( Figure 5F ). Indications with the highest likelihood of receiving a pathogenic result included ID/DD/ASD (52.3% pathogenic rate, n=65), chromosomal disorders (50.8% pathogenic rate, n=61), vascular malformation disorders (48.8% pathogenic rate, n=248), skin/tooth disorders (47.8% pathogenic rate, n=46), renal disorders (46.7% pathogenic rate, n=152), and multisystem disorders (43.8% pathogenic rate, n=370). Conversely, indications with the lowest likelihood of receiving a pathogenic result included hypermobility disorders (9.0% pathogenic rate, n=78), immune/hematologic disorders (9.7% pathogenic rate, n=113), and lung disorders (13.8% pathogenic rate, n=109). Additionally, certain clinical indications for testing were characterized by a high likelihood of receiving only VUS results. These included immune/hematologic disorders (38.1% VUS rate), connective tissue disorders (22.9% VUS rate, n=802), bone/skeletal disorders (20.5% VUS rate, n=122), and mitochondrial disease (20.4% VUS rate, n=49). Genetic variants identified span a diverse set of genes Sixty-nine unique chromosomal aneuploidies and variants in 514 unique single genes were identified in the patient cohort ( Figure 6 ). The most frequently identified single-gene variants were in ACVRL1 (65 pathogenic variants and 5 VUSs identified), FBN1 (38 pathogenic variants, 28 VUSs), NF1 (57 pathogenic variants, 7 VUSs); ENG (39 pathogenic variants, 7 VUSs), FLCN (37 pathogenic variants, 6 VUSs), SDHB (39 pathogenic variants, 4 VUSs), and PKD1 (26 pathogenic variants, 11 VUSs). The only recurrent chromosomal aneuploidies identified were 45,X in 16 individuals, 47,XXY in 7 individuals, del(15)(q11.2) in 3 individuals, and del(15)(q11q13) and del(22)(q12.3q13.1) each in 2 individuals. Download figure Open in new tab Figure 6. Distribution of Variants Identified by Gene and Clinical Indication Group Circular bar plot showing how often genetic testing identified a pathogenic variant (blue bars) or a variant of uncertain significance (VUS; red bars) in each gene, grouped and color-coded by clinical indication group. For each gene, the blue bar height above the baseline represents the total number of pathogenic variants reported, while the red bar height extending below the baseline represents the total number of VUSs. Each sector is shaded and labeled to show the clinical indication for the visit during which testing was ordered. Reference rings mark variant count intervals for both pathogenic and VUS counts, and gene names appear adjacent to their respective bars. WES/WGS patients differ demographically from those receiving other tests Altogether 168 individuals referred to our clinic during the study period ultimately underwent WES or WGS testing as part of their evaluation – these broad genetic testing modalities are often inaccessible to adult patients due to insurance denials, 21 and we sought to identify differences in patient demographics between those patients undergoing WES/WGS (n=168) and those undergoing any other type of testing (n=4,077). Significant differences were observed in clinical indication for referral between the two groups (p=5.51e-98; χ² = 522.5, Figure S3A-B ). By residual analysis, this difference was primarily driven by an overrepresentation of individuals referred for ID/DD/ASD, general genetics evaluation, and mitochondrial disease among the WES/WGS group. No significant differences were observed in EHR-reported race or sex between the WES/WGS group and other testing group ( Figure S3C-F ). There was a significant difference seen in insurance classes between the two groups (p=1.56e-07; χ² = 34.5), with Medicaid insurance being overrepresented in the WES/WGS group by residual analysis ( Figure S3G-H ). Lastly, patients undergoing WES/WGS were significantly younger (median age 31.0 years) than those who underwent any other genetic testing (median age 43.6 years, p=3.35e-21 by Wilcoxon rank-sum test, Figure S3I ). Download figure Open in new tab Figure S2. Distribution of Patient Visits by Clinical Indication and Indication Group Circular bar plot displaying the number of patient visits for each unique clinical indication, grouped and color-coded by indication group, across the study period. Indications are arranged around the circle by indication group, with each indication uniquely assigned to one group. Each blue bar represents a specific indication (e.g., “Marfan syndrome”), and bar height reflects the number of unique patient visits for that indication. For counts greater than 20, bar heights are transformed using the formula: height = 20 + (n)^0.333, where n is the visit count, to improve visualization. Reference rings denote visit counts, with breaks at 5, 10, 20, 25, 100, and 500. Indication names are shown adjacent to their respective bars. Download figure Open in new tab Figure S3. Characteristics of Patients Who Received WES/WGS vs. Other Genetic Tests Each panel shows the distribution of patient characteristics stratified by whether whole exome/genome sequencing (WES/WGS, n = 168) was sent (panels A, C, E, G, bottom half of I) or another type of genetic test was sent (n = 4,077, panels B, D, F, H, top half of I). Percentages for each subgroup are indicated in the adjacent legends. (A-B) Proportion of patients by clinical indication group for the visit. (C-D) Proportion of patients by EHR-reported race. (E-F) Proportion of patients by EHR-reported sex. (G-H) Proportion of patients by financial class (insurance type) at the time of the visit. (I) Distribution of patient ages at time of visit. Chi-square analysis comparing WES/WGS to other genetic tests was performed for each patient characteristic in panels A-H. Significant differences were observed for clinical indication (panels A–B) and financial class (panels G–H) only. In those panels, asterisks in the WES/WGS figures indicate categories that were significantly overrepresented among WES/WGS patients, based on residual analysis. Differences in patient age distribution were assessed by Wilcoxon rank-sum test in panel I, where a significant difference was seen with patient ages skewing younger in the WES/WGS group (p=3.35e-21). High diagnostic yield of WES/WGS across all adult patient age groups WES/WGS tests had a very high diagnostic yield overall ( Figure 7A ). Of the 168 WES/WGS tests completed in the study period, 39.3% returned with a diagnostic pathogenic finding. Inconclusive results were identified in 19.6% of individuals undergoing WES/WGS, including variants of uncertain significance (10.7%), variants indicating unaffected carrier status (7.7%), and variants in candidate genes (1.2%). Altogether, only 41.1% of all WES/WGS tests were completely negative. The diagnostic yield of WES/WGS did not differ based on EHR-reported sex, age, or race ( Figure 7B-D ). Specifically, we observed no significant differences in the odds of identifying pathogenic (p= 0.85; OR 1.00 [0.96–1.03]) or VUS (p=0.80; OR 1.01 [0.95–1.06]) results with increasing patient age, with diagnostic yield above 40% even in patients greater than 55 years old. Comparing the yield of WES/WGS by referral indication ( Figure 7E ), we observed a significantly increased likelihood of obtaining pathogenic results in patients undergoing WES/WGS for ID/DD/ASD compared to all other referral indications (p= 1.46e-04; OR 7.07 [2.70–20.79] by logistic regression). Download figure Open in new tab Figure 7. WES/WGS Results by Patient and Visit Characteristics Results of all 168 WES/WGS tests sent over the study period, stratified by patient and visit characteristics. In all panels, bars indicate the percentage of pathogenic (blue), variant of uncertain significance (VUS; orange), carrier (yellow), candidate gene (aqua), or negative (gray) results. Percent labels in blue above bars show the percent of patients who received a pathogenic result for each subgroup. Sample sizes for each subgroup are shown below the horizontal axis labels for panels B-E. (A) Pie chart showing the overall distribution of results for all 168 patients who underwent WES/WGS during the study period. Percentages for each result classification are shown in the legend. (B) Percent of patients receiving each WES/WGS result classification, stratified by EHR-reported sex. No significant associations were found between sex and the odds of receiving a pathogenic (in blue) or VUS (in orange) result from logistic regression adjusted for age, race, insurance class, and clinical indication group. (C) Percent of patients receiving each WES/WGS result classification, stratified by patient age at visit (plotted in discrete age ranges). No significant associations were found between age (modeled as a continuous variable) and the odds of receiving a pathogenic (in blue) or VUS (in orange) result from logistic regression adjusted for insurance class and clinical indication group. (D) Percent of patients receiving each WES/WGS result classification, stratified by EHR-reported race. No significant differences were found for any racial group, relative to patients with EHR-reported White race, for the odds of receiving either a pathogenic (in blue) or VUS (in orange) result by logistic regression adjusted for age, insurance class, and clinical indication group, accounting for multiple testing across all 6 groups. (E) Percent of patients receiving each WES/WGS result classification, stratified by clinical indication group, ordered by likelihood of receiving a pathogenic result. Indications where WES/WGS testing had a significantly higher odds of identifying a pathogenic result compared to the overall WES/WGS average (ID/DD/ASD) are marked with an asterisk. No significant differences were found for any indication group for the odds of receiving a VUS result. Significance was determined by logistic regression adjusted for age, sex, race, and insurance class, accounting for multiple testing across all 21 groups. Discussion Even as precision and genomic medicine gain traction in adult care, the landscape of adult genetics practice – who is evaluated, how testing is deployed, and what diagnoses result – have remained poorly characterized. Our findings offer a practice-level view of adult medical genetics that spans the full path from referral to result. Across eight years, the clinic evaluated a wide range of adults, ranging in age from young to late life and presenting with diverse clinical indications. Testing was ordered for the majority of new patients and, critically, diagnostic yield was high across the lifespan; yield exceeded 17% in every age band and remained high even in older adults. Exome/genome sequencing was particularly informative, delivering consistently high diagnostic yield without attenuation with age. Together, these results counter the lingering perception that molecular genetic diagnosis is chiefly a pediatric enterprise and underscore the value of genomic evaluation in adult medicine when the prior probability is appropriate. Beyond the overall yield of genetic testing demonstrated in our findings, results of testing varied systematically by indication. Chromosomal disorders, vascular malformations, renal disease, and multisystem presentations were significantly more likely to yield pathogenic findings on genetics testing; referrals for joint hypermobility were most likely yield negative testing results; immune/hematologic and connective tissue disorders were significantly more likely to yield uncertain findings. These differences support indication-aware testing pathways: there should be a low threshold to send panel-based testing for indications where we demonstrate a high likelihood of such testing to yield positive results. On the other hand, exome/genome-first (or rapid escalation to exome/genome) testing approaches are defensible for indications where broad genomic testing demonstrates high yields, while targeted panels or single-gene testing may be used, but judiciously, for lower-yield indications unless specific features raise the likelihood that a genetic condition may be present. The factors that make a genetic diagnosis more likely in these low-yield clinical indications remain to be worked out in future studies specifically examining this question. At the same time, the preponderance of inconclusive outcomes – e.g., higher VUS rates in connective tissue and immune/hematologic evaluations – offers a second design lever, highlighting where pretest counseling should anticipate indeterminate results to appropriately calibrate patient and referring-provider expectations and interpretations. Beyond the overall yield of genetic testing demonstrated in our findings, results of testing varied systematically by indication. Chromosomal disorders, vascular malformations, renal disease, and multisystem presentations were significantly more likely to yield pathogenic findings on genetics testing; referrals for joint hypermobility were most likely to yield negative testing results; immune/hematologic and connective tissue disorders were significantly more likely to yield uncertain findings. Results also varied strikingly by testing modality. NGS panels – by far the most commonly ordered test type, accounting for 63% of all testing – had one of the lowest diagnostic yields at 16%, whereas whole exome sequencing yielded pathogenic findings in 40% of cases despite comprising fewer than 4% of tests ordered. This mismatch between utilization and yield suggests that the current testing landscape is shaped as much by insurance coverage, ordering infrastructure, and laboratory pricing as it is by diagnostic efficiency – a conclusion reinforced by the sharp inflection points in laboratory utilization we observed in response to capped-cost policies, EMR integration, and payer network changes. These differences support indication-aware testing pathways: there should be a low threshold to send panel-based testing for indications where we demonstrate a high likelihood of such testing to yield positive results. On the other hand, exome/genome-first (or rapid escalation to exome/genome) testing approaches are defensible not only for indications where broad genomic testing demonstrates high yields, but also as a strategy to improve the overall diagnostic efficiency of genetic testing in adults, particularly given that WES/WGS yield remained high and did not attenuate with patient age. While targeted panels and single-gene testing retain a clear role — particularly for lower-yield indications and familial cascade testing — the factors that distinguish diagnostically productive from unproductive testing in these lower-yield settings remain to be defined, and future studies addressing this question will be essential for more effective resource allocation. At the same time, the preponderance of inconclusive outcomes – e.g., higher VUS rates in connective tissue and immune/hematologic evaluations – offers a second design lever, highlighting where pretest counseling should anticipate indeterminate results to appropriately calibrate patient and referring-provider expectations and interpretations. We also observed demographic differences by indication, a pattern not emphasized in previous adult genetics studies. Connective tissue disorder referrals were significantly more common in older men, hypermobility referrals in younger women; older patients in general were more likely to be referred for single-organ-system dysfunction, while younger patients were more likely to be referred for disease affecting multiple organs. These differences likely reflect a mix of biology (age-dependent penetrance, sex-specific expression), referral heuristics, and specialty referral practices. Patient age also influenced testing behavior: the likelihood of genetic testing being ordered increased with patient age. This trend may reflect a higher referral threshold in older adults, such that testing is pursued only when suspicion for a genetic condition is already strong, or referrals for familial cascade testing in older patients, where older relatives are evaluated after a variant is identified in a younger family member; in both cases a high rate of genetic testing might be expected. Similarly, indication-specific effects on likelihood of testing were also apparent. For example, patients referred for intellectual disability or developmental delay were both very likely to undergo testing (80.9%) and very likely to obtain a positive result (52.3%), underscoring that many such patients likely remain under-referred and undiagnosed in the community, and that targeted efforts to increase referral for patients with this indication might be warranted. Together, these age- and indication-based patterns highlight practical opportunities to address referral biases – for example, clarifying appropriate referral triggers for multisystem disease in older adults or for high-yield indications such as ID/DD to ensure these patients are not being missed. These patterns also suggest that adult genetics clinics might benefit from a dual-track approach to patient evaluation, balancing resource-intensive evaluations for high-probability index cases, such as ID/DD or multisystem disease referrals, with streamlined pathways for high-yield but lower-complexity indications such as familial cascade testing. Our experience with telehealth also provided important lessons. Utilization rose sharply during the COVID-19 pandemic and then stabilized in the post-pandemic era, particularly for follow-up visits. In this setting, virtual encounters proved well suited for counseling, result disclosure, and coordinating cascade testing across geographically dispersed relatives, and provided a more efficient means of following patients longitudinally, without the need for repeated in-person evaluation, after a diagnosis had been made. Combined with genetic counselor-led triage, hybrid virtual/in-person pathways could reduce wait times while preserving clinic capacity for more complex in-person evaluations. Realizing this potential, however, will require policy reforms, such as improved reimbursement for counselor-led telehealth and cascade testing, as well as licensure frameworks that permit cross-state genetics care. These changes could broaden the pool of providers able to deliver genetics services, empower genetic counselors to independently manage appropriate cases, and mitigate the current urban concentration of adult genetics services that limits access for patients in rural areas. Perhaps not surprisingly, operational and policy factors were powerful determinants of what genetic testing was ordered, and at what lab. Inflection points in laboratory choice tracked closely with payer and testing lab policies (e.g., capped costs, in-network changes), EMR integration, and reflex testing availability. These shifts illustrate how reimbursement and ordering infrastructure can steer utilization and highlight how dependent clinicians are on system-level policies to deliver consistent and appropriate care. The best path forward may be to align policies with clinical value: streamline access to exome/genome in indications with high, stable yield; deploy capped-cost or sponsored programs strategically for cascade testing and other high-impact scenarios; and use EHR order sets and integrations to standardize laboratory choices for specific clinical indications where costs can be guaranteed to empower non-genetics providers to order appropriate testing. The latter is currently being tested at UPHS to ensure equitable testing practices for all patients who meet criteria for genetic testing. 22 Several limitations of our study warrant consideration. This was a single-center study at a large academic medical center and may not reflect community practice. Additionally, our health system routes certain neurogenetic, cardiac genetic, and many routine hereditary cancer cases to other specialty clinics outside of the general genetics clinic, such that the case-mix and yields we report reflect a general adult genetics clinic practice rather than the yield of genetic testing across the entire health system. Similarly, this study focused on patients seen for evaluation by genetics trained physicians or genetic counselors within our clinic; certain follow up patients, particularly those with genetic cancer syndromes, are seen by nurse practitioners within our division for continuity care and these visits are not reflected in our data. Importantly, regarding probability of undergoing genetic testing, our data only capture whether testing occurred, not why; we cannot distinguish patient preference, insurer denial, prior external testing, or logistical barriers. Lastly, prior genetic testing outside of our clinic is likely incompletely ascertained, potentially biasing some of our results. Overall, while these constraints may limit generalizability, they do not substantially diminish the practice-level signals we report. Altogether, our findings suggest several actionable steps to improve the equitable and appropriate access to genetics evaluation and testing for adult patients. First, implement indication- and age-aware triage and order pathways that focus resources where yield and actionability are highest, minimize the ordering of testing likely to return only inconclusive results where feasible and standardize exome/genome-first approaches in adults when benefit is clear. Second, embed decision support in the EHR, incorporating indication-specific yields, preferred labs, and payer rules, to reduce unwarranted variation in ordering practice and to support non-geneticist clinicians in pretest counseling and test selection. Third, use empiric yield and utilization data to engage payers on coverage, capped-cost programs, and reflex workflows. Finally, target referring clinician education to the indications and patient groups most likely to benefit, concentrating efforts where practice change is most attainable. In sum, adult patients present with broad indications for genetics evaluation and testing and represent a heterogeneous population in which both utilization and diagnostic outcomes are shaped by indication, age, and policy/operational context. Clinically meaningful diagnoses are common across the adult lifespan, with exome/genome sequencing showing sustained utility from young adulthood into later life. By linking who is seen to how testing is used and what is found, this study fills a critical gap in the adult genetics evidence base and offers concrete guidance for triage design, staffing, payer engagement, and the development of adult-focused practice guidelines, key steps toward making precision and genomic medicine routine in adult patient care. Declaration of Interests The authors declare no competing interests. Data Availability The summary data supporting the findings of this study are available upon reasonable request. Individual-level data, aside from what has been included in the manuscript and supplemental materials, cannot be shared due to patient privacy concerns. https://rpubs.com/tdrivas/MedGenOutcomes Author Contributions J.I.G. and T.G.D. conceived and planned the study and analysis plan. J.I.G., C.K., E.T. and T.G.D. carried out the analyses. J.I.G., Y.E., S.A., A.R., C.C., Z.B., I.E., L.H., E.K., T.C., S.C, B.N.G, S.M.G., A.S., K.L.N., M.R., S.K. and T.G.D. contributed to data preparation and analysis. J.I.G., K.L.N., M.R., S.K., and T.G.D. contributed to the interpretation of the results. T.G.D. supervised the project. J.I.G. and T.G.D. took the lead in writing the manuscript. All authors provided critical feedback and helped shape the research, analysis and manuscript. Data and Code Availability The summary data supporting the findings of this study are available upon reasonable request. Individual-level data, aside from what has been included in the manuscript and supplemental materials, cannot be shared due to patient privacy concerns. Acknowledgments This study itself did not receive external funding. T.G.D. is supported in by the Burroughs Wellcome Fund. K.L.N is supported in part by the Breast Cancer Research Foundation, Basser Center for BRCA, the Gray Foundation, and the National Institutes of Health - R01 CA176785 and R01 CA192393. Footnotes Additional patient visits (those seen only by a nurse practitioner or genetic counselor) were added to our dataset for a more complete analysis of our clinical practice. References 1. ↵ Ethical Considerations in Precision Medicine and Genetic Testing in Internal Medicine Practice: A Position Paper From the American College of Physicians - PubMed https://pubmed.ncbi.nlm.nih.gov/35878403/ . 2. ↵ Bastarache , L. , Tinker , R.J. , Schuler , B.A. , Richter , L. , Phillips , J.A. , Stead , W.W. , Hooker , G.W. , Peterson , J.F. , and Ruderfer , D.M . ( 2025 ). Characterizing trends in clinical genetic testing: A single-center analysis of EHR data from 1.8 million patients over two decades . Am. J. Hum. Genet . 112 , 1029 – 1038 . doi: 10.1016/j.ajhg.2025.03.009 . 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