Current Limitations of Electronic Health Record Systems in Supporting Pragmatic Clinical Trials: Insights from the eMERGE Consortium

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Wagholikar , Jennifer Allen Pacheco , Adam S. Gordon , Atlas Khan , Bahram Namjou Khales , Barbara Benoit , Benjamin J. Kerman , Chunhua Weng , Casey Ta , Cynthia A. Prows , Robert Johnson , Dan M. Roden , David Crosslin , Elizabeth M. McNally , Elizabeth W. Karlson , Frank Mentch , Gail P. Jarvik , Georgia L. Wiesner , Hakon Hakonarson , James J. Cimino , Jeritt G.Thayer , Jordan W. Smoller , Jodell E. Linder , John Connolly , Josh F. Peterson , Josh Cortopassi , Krzysztof Kiryluk , Marwan Hamed , Mary Maradik , Megan J. Puckelwartz , Mohammadreza Naderian , Nephi Walton , Nita Limdi , Devi Priyanka Maripuri , Theresa Walunas , Vivian Gainer , Yuan Luo , Cong Liu , Eimear E. Kenny , Angelica Espinoza , Robb Rowley , Wei-Qi Wei , Shawn N. Murphy doi: https://doi.org/10.1101/2025.04.01.25325049 Kavishwar B. Wagholikar 1 Massachusetts General Hospital MBBS, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: kwagholikar{at}mgh.harvard.edu Jennifer Allen Pacheco 2 Northwestern University Feinberg School of Medicine MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Adam S. Gordon 3 Center for Genetic Medicine, Northwestern University PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Atlas Khan 4 Vagelos College of Physicians & Surgeons, Columbia University MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bahram Namjou Khales 5 Cincinnati Children’s Hospital Medical Center MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Barbara Benoit 6 Mass General Brigham Inc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Benjamin J. Kerman 7 Brigham and Women’s Hospital and Harvard Medical School MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chunhua Weng 8 Columbia University Irving Medical Center PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Casey Ta 9 Columbia University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cynthia A. Prows 10 APRN, Children’s Hospital Medical Center MSN Find this author on Google Scholar Find this author on PubMed Search for this author on this site Robert Johnson 11 The University of Alabama at Birmingham PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dan M. Roden 12 Vanderbilt University School of Medicine MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site David Crosslin 13 University of Washington PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elizabeth M. McNally 14 Northwestern University MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elizabeth W. Karlson 15 Brigham and Women’s Hospital Department of Medicine MD, MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Frank Mentch 16 The Children’s Hospital of Philadelphia Center for Applied Genomics Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gail P. Jarvik 17 University of Washington Medical Center MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Georgia L. Wiesner 18 Vanderbilt University MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hakon Hakonarson 19 Perelman School of Medicine of the University of Pennsylvania MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site James J. Cimino 20 University of Alabama-Birmingham MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jeritt G.Thayer 21 Children’s Hospital of Philadelphia PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jordan W. Smoller 1 Massachusetts General Hospital MD, ScD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jodell E. Linder 22 Vanderbilt University Medical Center PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site John Connolly 16 The Children’s Hospital of Philadelphia Center for Applied Genomics Find this author on Google Scholar Find this author on PubMed Search for this author on this site Josh F. Peterson 22 Vanderbilt University Medical Center MD, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Josh Cortopassi 23 University of Alabama at Birmingham Health System Authority PharmD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Krzysztof Kiryluk 9 Columbia University MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marwan Hamed 24 Mayo Foundation for Medical Education and Research MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mary Maradik 2 Northwestern University Feinberg School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Megan J. Puckelwartz 14 Northwestern University PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mohammadreza Naderian 25 Mayo Clinic , Rochester, MN MD, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nephi Walton 26 National Institutes of Health MD, MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nita Limdi 11 The University of Alabama at Birmingham PharmD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Devi Priyanka Maripuri 21 Children’s Hospital of Philadelphia MS, CGC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Theresa Walunas 14 Northwestern University PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vivian Gainer 6 Mass General Brigham Inc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuan Luo 2 Northwestern University Feinberg School of Medicine PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cong Liu 27 Boston Children’s Hospital PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eimear E. Kenny 28 Icahn School of Medicine at Mount Sinai PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Angelica Espinoza 2 Northwestern University Feinberg School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Robb Rowley 26 National Institutes of Health MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wei-Qi Wei 22 Vanderbilt University Medical Center MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shawn N. Murphy 6 Mass General Brigham Inc MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Pragmatic clinical trials (PCTs) evaluate interventions in real-world settings, often using electronic health records (EHRs) for efficient data collection. We report on the challenges in performing EHR analysis of health-care provider orders in a PCT within the eMERGE consortium, which investigates the impact of reporting genome-informed risk assessments (GIRA) to over 25,000 patients across 10 academic medical centers. Clinical informaticians conducted a landscape analysis to identify approaches for evaluating the outcomes of GIRA reporting through the EHR. Of 98 identified outcomes, 54 (55.1%) were determined to be difficult to extract because they involved provider orders, which are typically documented in free text or proprietary formats within the EHR and only mapped to standardized codes after the service is completed. These findings highlight a critical barrier in using EHRs to support PCTs. The authors recommend closer collaboration between clinicians and informaticians, improved EHR systems that support standardized order entry, and future use of machine learning to automate analysis of provider behavior in clinical trials. Introduction Pragmatic clinical trials (PCTs) are designed to evaluate the effectiveness of interventions in real-world practice by focusing on diverse populations, flexible protocols, and practical outcomes. [ 1 ] Unlike traditional trials in controlled environments, PCTs often leverage electronic health records (EHR) for cost-efficient enrollment and data collection. Here, we report on the challenges in using health-care provider orders in a PCT within the eMERGE consortium, which investigates the impact of reporting genome-based scores. [ 2 ] Analyzing provider orders is crucial to distinguish whether gaps in clinical care stem from provider inaction, or other barriers that preclude patients from following up on recommended care.[ 3 ] Methods The study includes 25,003 patients recruited from medical practices across 10 academic medical centers. The participants provided electronic consent and age-based assent, completed surveys and submitted biological samples. These data were used to generate a “genome-informed risk assessment” (GIRA) report for 11 common diseases, which was shared with the patients and their healthcare providers. Clinical experts created disease-specific follow-up recommendations in the GIRA report that was returned to high-risk patients. Clinical experts also created a corresponding list of outcome variables for EHR extraction to determine the clinical impact of the GIRA report – whether the proportions of providers recommending risk-reducing interventions and patients undertaking interventions are higher amongst the high-risk patients. Clinical informaticians performed a landscape analysis to identify methods for extracting the outcome variables from the EHR. Results The clinicians identified 98 outcomes for extraction from the EHR. The informaticians considered the use of the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and terminology mappings for extracting EHR data from each site including demographic, visits, diagnoses, procedures, laboratory-test results and medications.[ 4 ] However, the informaticians determined that extracting 54 (55.1%) of the outcomes would be challenging, as they related to provider actions, such as ordering laboratory tests, procedures or referrals (see supplementary file). This difficulty arises because provider orders in EHR systems are recorded in the clinical data model but are not initially coded (see Figure 1 ).[ 5 ] Instead, they are documented using proprietary codes or free text during ordering and are mapped to Current Procedural Terminology (CPT) codes only after the ordered service is completed. Consequently, the group decided to extract the text descriptions associated with the orders and to manually map them to the outcomes. Download figure Open in new tab Figure 1. Provider orders for labs and procedures are recorded in the EHR, but they are not coded i.e., mapped to a standard terminology like CPT until after the procedure is completed or the results are reported. Discussion Our study reveals that EHR systems are not inherently designed to facilitate the analysis of provider ordering, requiring extensive manual effort to determine if the provider adhered to the recommended actions. This finding highlights a major obstacle for pragmatic clinical trials, where it is often essential to decompose the clinical effects at both provider and patient levels– i.e. provider adherence to the study protocol or clinical guideline, and patient adherence to the provider recommendations. Patient behavior can be influenced by various factors, including disagreement with the provider, seeking follow-up care in a different healthcare system, and social determinants of health. Since many orders remain ‘not completed’ due to patient and system-related factors, relying solely on CPT code analysis may underestimate the true clinical impact of the intervention being evaluated in PCTs. To address this issue, we advocate for close collaboration between informaticians and clinicians to decipher the record of provider orders. Additionally, EHR vendors should integrate order entry systems with terminology databases or implement drop-down menus, for recording orders using standardized codes. Until such systems are implemented researchers will need to resort to manual methods to analyze the provider actions. Eventually the use of large language models [ 6 ] and machine learning may be useful to monitor and analyze the provider actions. Supplementary file 1 . The clinical outcomes identified by the clinical experts are associated with concepts that need to be extracted from the EHR to assess the outcomes. Among the 98 distinct outcomes, 54 (55.1%) are considered challenging to extract, as they require extracting data on provider actions, such as ordering laboratory tests, procedures, or referrals (as noted in Column F). Conflict of Interest Theresa Walunas reports research funding from Gilead Sciences. Other authors reported no conflict of interest. Data Availability All data produced in the present work are contained in the manuscript Acknowledgements This phase of the eMERGE Network was initiated and funded by the NHGRI through the following grants: U01HG011172 (Cincinnati Children’s Hospital Medical Center); U01HG011175 (Children’s Hospital of Philadelphia); U01HG008680 (Columbia University); U01HG011176 (Icahn School of Medicine at Mount Sinai); U01HG008685 (Mass General Brigham); U01HG006379 (Mayo Clinic); U01HG011169 (Northwestern University); U01HG011167 (University of Alabama at Birmingham); U01HG008657 (University of Washington); U01HG011181 (Vanderbilt University Medical Center); U01HG011166 (Vanderbilt University Medical Center serving as the Coordinating Center). Dr. Wagholikar’s efforts were partially supported by R01-HL151643. References 1. ↵ Platt R , Bosworth HB , Simon GE . Making Pragmatic Clinical Trials More Pragmatic . JAMA . 2024 Dec 10; 332 ( 22 ): 1875 – 1876 . doi: 10.1001/jama.2024.19528 . 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PMID: 38175665 ; PMCID: PMC10873827 . OpenUrl CrossRef PubMed 5. ↵ Cimino JJ . Putting the “why” in “EHR”: capturing and coding clinical cognition . J Am Med Inform Assoc . 2019 Nov 1; 26 ( 11 ): 1379 – 1384 . doi: 10.1093/jamia/ocz125 . PMID: 31407781 ; PMCID: PMC6798564 . OpenUrl CrossRef PubMed 6. ↵ FHIR-GPT enhances health interoperability with large language models Y Li , H Wang , HZ Yerebakan , Y Shinagawa , Y Luo Nejm Ai 1 (8) View the discussion thread. Back to top Previous Next Posted April 03, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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Cimino , Jeritt G.Thayer , Jordan W. Smoller , Jodell E. Linder , John Connolly , Josh F. Peterson , Josh Cortopassi , Krzysztof Kiryluk , Marwan Hamed , Mary Maradik , Megan J. Puckelwartz , Mohammadreza Naderian , Nephi Walton , Nita Limdi , Devi Priyanka Maripuri , Theresa Walunas , Vivian Gainer , Yuan Luo , Cong Liu , Eimear E. Kenny , Angelica Espinoza , Robb Rowley , Wei-Qi Wei , Shawn N. Murphy medRxiv 2025.04.01.25325049; doi: https://doi.org/10.1101/2025.04.01.25325049 Share This Article: Copy Citation Tools Current Limitations of Electronic Health Record Systems in Supporting Pragmatic Clinical Trials: Insights from the eMERGE Consortium Kavishwar B. Wagholikar , Jennifer Allen Pacheco , Adam S. Gordon , Atlas Khan , Bahram Namjou Khales , Barbara Benoit , Benjamin J. Kerman , Chunhua Weng , Casey Ta , Cynthia A. Prows , Robert Johnson , Dan M. Roden , David Crosslin , Elizabeth M. McNally , Elizabeth W. Karlson , Frank Mentch , Gail P. Jarvik , Georgia L. Wiesner , Hakon Hakonarson , James J. Cimino , Jeritt G.Thayer , Jordan W. Smoller , Jodell E. Linder , John Connolly , Josh F. Peterson , Josh Cortopassi , Krzysztof Kiryluk , Marwan Hamed , Mary Maradik , Megan J. Puckelwartz , Mohammadreza Naderian , Nephi Walton , Nita Limdi , Devi Priyanka Maripuri , Theresa Walunas , Vivian Gainer , Yuan Luo , Cong Liu , Eimear E. Kenny , Angelica Espinoza , Robb Rowley , Wei-Qi Wei , Shawn N. 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