About

Nate Apathy is an Assistant Professor in the Department of Health Policy and Management at the University of Maryland School of Public Health. His research sits at the intersection of health policy, health services research, and health informatics. He studies the role of health information technology in support of delivery and payment reform efforts, focusing on the impact of regulations on health IT innovation, adoption, and use. A key area of his work involves the use of system-generated log data to understand health IT's impact on care quality, with current research emphasizing sources of IT-based burden and organizational strategies to reduce that burden. Dr. Apathy's academic background includes a PhD in Health Policy & Management from Indiana University Richard M. Fairbanks School of Public Health and a BSBA in Economics from Creighton University. His research has been published in peer-reviewed journals such as Health Affairs, Health Services Research, JAMA Internal Medicine, and others. His ongoing projects include evaluating the feasibility of EHR audit log data for estimating standardized time inputs and face-to-face time, as well as integrating outside data into clinical workflows to improve care. He has received a postdoctoral fellowship in health services research from the University of Pennsylvania and was awarded the UMD Research Excellence Award in 2025.

Research topics

• Political Science
• Computer Science
• Medicine
• Data science
• Medical emergency
• Business
• Family medicine
• Environmental health
• Nursing

Selected publications

• Learning from Misses: Evaluating a Clinical Decision Support for Chronic Pain Management in Primary Care

  Applied Clinical Informatics · 2025-10-01

  article

  Abstract This study aimed to assess the effect of a passive, opt-in electronic health record (EHR)-based clinical decision support (CDS), the Chronic Pain OneSheet, on guideline-recommended chronic pain management in primary care. A pragmatic randomized controlled trial with a parallel group design was conducted between October 2020 and May 2022. Participants were 137 primary care clinicians (PCCs) treating qualifying patients with chronic pain at 25 primary care clinics within two academic health systems in the United States. PCCs were randomized in the EHR to have access to OneSheet or usual care. OneSheet aggregates guideline-relevant information in a single view and provides shortcuts to guideline-recommended actions (e.g., ordering urine drug screening [UDS] for patients prescribed opioids). We constructed five visit-level binary outcomes: (1) documenting pain-related goals; (2) documenting pain and function via Pain, Enjoyment of Life and General Activity (PEG) scale; (3) reviewing prescription drug monitoring programs (PDMPs); (4) ordering UDS; and (5) ordering naloxone. Analysis used generalized linear mixed models for each outcome. OneSheet access minimally increased rates of pain-related goal documentation (0.2 percentage point increase, p = 0.013), PEG scale documentation (0.7 percentage point increase, p < 0.001), and UDS orders (2.2 percentage point increase, p = 0.006). OneSheet access decreased the rate of ordering naloxone (0.5 percentage point decrease, p < 0.001). OneSheet access did not affect PDMP review rates (0.5 percentage point decrease, p = 0.382). OneSheet access did not result in clinically significant improvements in guideline-recommended management of chronic pain in primary care despite a robust user-centered design incorporating clinician input and EHR integration. Several factors likely limited OneSheet effectiveness, including limited ability to target certain patient visits, workflow limits on data collection and ordering, and evolving COVID-19 and opioid epidemic-related policies and procedures. These findings highlight specific limitations of OneSheet and the broader challenges of implementing effective EHR-based CDS in complex health care environments.

  PublisherDOI

• Emerging Domains for Measuring Health Care Delivery With Electronic Health Record Metadata

  Journal of Medical Internet Research · 2025-02-11 · 7 citations

  reviewOpen accessSenior author

  This article aims to introduce emerging measurement domains made feasible through the electronic health record (EHR) use metadata, to inform the changing landscape of health care delivery. We reviewed emerging domains in which EHR metadata may be used to measure health care delivery, outlining a framework for evaluating measures based on desirability, feasibility, and viability. We argue that EHR use metadata may be leveraged to develop and operationalize novel measures in the domains of team structure and dynamics, workflows, and cognitive environment to provide a clearer understanding of modern health care delivery. Examples of measures feasible using metadata include quantification of teamwork and collaboration, patient continuity measures, workflow conformity measures, and attention switching. By enabling measures that can be used to inform the next generation of health care delivery, EHR metadata may be used to improve the quality of patient care and support clinician well-being. Careful attention is needed to ensure that these measures are desirable, feasible, and viable.

  PublisherDOI

• The Impact of Team‐Based Ordering Workflows on Ambulatory Physician <scp>EHR</scp> Time, Order Volume, and Visit Volume

  Health Services Research · 2025-09-06

  articleOpen access1st authorCorresponding

  OBJECTIVE: To analyze national rates of team-based ordering and evaluate changes in key outcomes following adoption. STUDY SETTING AND DESIGN: We conducted an observational pre-post intervention-comparison study of 249,463 ambulatory physicians across 401 organizations using the Epic EHR. Our intervention was the adoption of team-based ordering, measured as the proportion of orders involving team support. Outcomes include active ordering time, overall EHR time, order volume, and visit volume among adopter physicians. DATA SOURCES AND ANALYTIC SAMPLE: We analyzed the distribution and trends in team-based ordering rates from Epic Signal (September 2019-March 2022). We used multi-variable regression in a difference-in-differences framework to evaluate changes in our outcomes among 115 adopters of team-based ordering and 3115 non-adopters. We defined adopters as physicians who demonstrated a one-time shift from 0% of orders to a consistent non-zero share of orders, and non-adopters as those who demonstrated constant 0% teamwork for at least 18 months. PRINCIPAL FINDINGS: Across our study period, 26.2% of orders involved team support, with surgical specialists averaging greater team-based ordering (43.1%) than primary care (22.2%) and medical specialists (23.0%). There was no association between team-based ordering adoption and time spent ordering (-0.13 min/visit, 95% CI: [-0.48 to 0.22]) or total EHR time (-1.42 min/visit, [-3.79 to 0.95]). Adoption was associated with a 26.8% relative increase in order volume (0.47 orders/visit, [0.14-0.80]) and a 22.3% relative increase in visit volume (6.50 visits/week [2.81-10.19]). CONCLUSIONS: Team-based ordering rates are relatively low, and new adoption of team-based ordering was not associated with physicians' time spent ordering or in the EHR overall. Teamwork may facilitate substantial increases in both order and visit volume, but a greater level of team-based ordering may be required to realize EHR time savings.

  PublisherOA PDFDOI

• Adoption of Health Information Technologies by Area Socioeconomic Deprivation Among US Hospitals

  JAMA Health Forum · 2025-09-05 · 4 citations

  articleOpen access

  Importance: Access to and quality of care vary substantially by area socioeconomic status. Expanding hospital health information technology (HIT) adoption may help reduce these disparities, given hospitals' central role in serving underserved populations. Objective: To examine variations in US hospital adoption of telehealth and health information exchange (HIE) functionalities by hospital service area (HSA) socioeconomic deprivation. Design, Setting, and Participants: This cross-sectional study links data from the 2018-2023 American Hospital Association Annual Survey and Information Technology Survey with HSA-level area deprivation index. Nonfederal acute care hospitals with complete data on HIT outcomes, comprising 16 646 observations for the telehealth outcomes and 9218 observations for the HIE outcomes across 6 years, were included. Data were analyzed from February 2024 to February 2025. Exposures: HSA-level area deprivation index in quartiles. Main Outcomes and Measures: Hospital adoption of treatment-stage telehealth and postdischarge telehealth services and HIE infrastructure supporting electronic data query and availability. Descriptive, regression, and Blinder-Oaxaca decomposition analyses and visualized time trends in hospital HIT adoption were used in analyses. Results: This study included 16 646 hospital-level observations and 9218 observations for health information exchange functionalities. Hospitals in the most socioeconomically deprived HSAs were significantly less likely to adopt HIT compared with those in the least deprived areas (treatment-stage telehealth: marginal effect [ME], -0.03; 95% CI, -0.06 to -0.01; postdischarge telehealth: ME, -0.03; 95% CI, -0.07 to 0.01; electronic data query capability: ME, -0.03; 95% CI, -0.06 to -0.01; electronic data availability: ME, -0.06; 95% CI, -0.11 to -0.01). Year fixed effects indicated significant increases in HIT adoption from 2018 to 2023, regardless of HSA deprivation level. Decomposition analyses showed that differences in hospital bed size, urban/rural location, and accountable care organization participation explained a substantial portion of the disparities by HSA deprivation. Conclusions and Relevance: In this study, hospitals in more socioeconomically disadvantaged HSAs remained likely to adopt telehealth and HIE functionalities. Nevertheless, HIT adoption has grown steadily over time. Accountable care organization participation may support HIT infrastructure and help reduce geographic disparities in adoption and access to care.

  PublisherOA PDFDOI

• Imputation of missing aggregate EHR audit log data across individual and multiple organizations

  Journal of Biomedical Informatics · 2025-02-18

  article
  PublisherDOI

• National trends in oncology specialists’ EHR inbox work, 2019-2022

  JNCI Journal of the National Cancer Institute · 2025-02-27 · 5 citations

  articleOpen access

  BACKGROUND: Electronic health record (EHR) burden is an important driver of the ongoing physician burnout crisis. In particular, EHR-based messaging (also known as "inbox")-including messages from patients-is associated with burnout and decreased well-being. Little is known about EHR messaging burden for oncologists. To address this gap, we assessed trends in oncologist EHR messaging volume and EHR time from 2019 to 2022 across oncology subspecialties. METHODS: This study used EHR metadata for all US oncology physicians (including medical oncologist/hematologists, radiation oncologists, pediatric oncologists, gynecologic oncologists, and surgical oncologists) providing ambulatory care using an Epic EHR system to measure inbox volume and EHR time from July 2019 through April 2022. Descriptive statistics and multivariable regression were used to evaluate differences over time and across subspecialties. RESULTS: This sample of 15 653 oncology physicians across 43 228 633 ambulatory visits found that message volume for oncologists increased 19.0% from 2019 to 2022, and patient-initiated messages increased 34.0%. The EHR time increased 16.2% from 2019 to 2022, whereas EHR "work outside of work" time increased 12.1%. Medical oncologist/hematologists had the highest inbox volume, patient-message volume, and EHR time of oncology subspecialists. CONCLUSION: Rising levels of EHR work and message volume among oncology physicians are concerning given the role of EHR burden in physician burnout. Oncologists have seen increased EHR time and message volume, especially patient-initiated messages, following the onset of the COVID-19 pandemic. Health system leaders and policymakers should invest in efforts to reduce EHR and inbox burden for all oncologists, with a focus on physicians with the greatest burden.

  PublisherOA PDFDOI

• Using Electronic Health Record Metadata to Understand Clinician Work and Behavior

  Cognitive informatics in biomedicine and healthcare · 2025-01-01 · 1 citations

  book-chapterSenior author
  PublisherDOI

• Electronic health record market consolidation and implications for cybersecurity

  Health Affairs Scholar · 2025-08-01

  articleOpen access

  Over the past decade, the electronic health record (EHR) market has become increasingly consolidated, with the majority of care delivery organizations now using 1 of 2 vendors -Epic and Oracle Health. This consolidation creates a "single-point-of-failure" tail risk for cybersecurity: 1 successful attack could expose millions of patients' private data and could potentially impact documentation, billing, and clinical care across thousands of sites. Moreover, dependence on other technology vendors, such as shared cloud hosts, broadens the potential attack surface beyond vendors' core firewalls. Given that reversing consolidation is unlikely due to high EHR switching costs, it is critical that policymakers establish safeguards that ensure robust protections for patients' sensitive data. The Assistant Secretary for Technology Policy plays a critical role in mandating certain security features through the Certified Electronic Health Record Technology Program, and this role should be expanded to provide additional oversight, given the risks presented by the current market structure. Sustained investment in regulatory oversight and continued partnerships between policymakers, care delivery organizations, and EHR vendors are essential to contain the catastrophic risk involved from this ongoing market consolidation.

  PublisherOA PDFDOI

• Current Use And Evaluation Of Artificial Intelligence And Predictive Models In US Hospitals

  Health Affairs · 2025-01-01 · 65 citations

  article

  Effective evaluation and governance of predictive models used in health care, particularly those driven by artificial intelligence (AI) and machine learning, are needed to ensure that models are fair, appropriate, valid, effective, and safe, or FAVES. We analyzed data from the 2023 American Hospital Association Annual Survey Information Technology Supplement to identify how AI and predictive models are used and evaluated for accuracy and bias in hospitals. Hospitals use AI and predictive models to predict health trajectories or risks for inpatients, identify high-risk outpatients to inform follow-up care, monitor health, recommend treatments, simplify or automate billing procedures, and facilitate scheduling. We found that 65 percent of US hospitals used predictive models, and 79 percent of those used models from their electronic health record developer. Sixty-one percent of hospitals that used models evaluated them for accuracy using data from their health system (local evaluation), but only 44 percent reported local evaluation for bias. Hospitals that developed their own predictive models, had high operating margins, and were health system members were more likely to report local evaluation. Policy and programs that provide technical support, tools to assess FAVES principles, and educational resources would help ensure that all hospitals can use predictive models safely and prevent a new organizational digital divide in AI.

  PublisherDOI

• Trends in Physician Electronic Health Record Time and Message Volume

  JAMA Internal Medicine · 2025-02-24 · 15 citations

  letterOpen access

  This cohort study uses national electronic health record data to assess patient medical advice request message volume and the time physicians spend in the records.

  PublisherOA PDFDOI

Frequent coauthors

• Christopher A. Harle

  Indiana University Indianapolis

  31 shared

• Burke W. Mamlin

  Regenstrief Institute

  29 shared

• Joshua R. Vest

  Indiana University – Purdue University Indianapolis

  26 shared

• Lisa S. Rotenstein

  Harvard University

  23 shared

• A Jay Holmgren

  21 shared

• David W. Bates

  Brigham and Women's Hospital

  17 shared

• Bruce E. Landon

  Beth Israel Deaconess Medical Center

  15 shared

• Randall W. Grout

  Indiana University – Purdue University Indianapolis

  15 shared

Awards & honors

• Postdoctoral Fellowship, Health Services Research, Universit…
• UMD Research Excellence Award (2025)