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Apathy NC, Biro J, Holmgren AJ. Consistency is key: documentation distribution and efficiency in primary care. J Am Med Inform Assoc 2024; 31:1657-1664. [PMID: 38905016 PMCID: PMC11258406 DOI: 10.1093/jamia/ocae156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/22/2024] [Accepted: 06/11/2024] [Indexed: 06/23/2024] Open
Abstract
OBJECTIVES We analyzed the degree to which daily documentation patterns in primary care varied and whether specific patterns, consistency over time, and deviations from clinicians' usual patterns were associated with note-writing efficiency. MATERIALS AND METHODS We used electronic health record (EHR) active use data from the Oracle Cerner Advance platform capturing hourly active documentation time for 498 physicians and advance practice clinicians (eg, nurse practitioners) for 65 152 clinic days. We used k-means clustering to identify distinct daily patterns of active documentation time and analyzed the relationship between these patterns and active documentation time per note. We determined each primary care clinician's (PCC) modal documentation pattern and analyzed how consistency and deviations were related to documentation efficiency. RESULTS We identified 8 distinct daily documentation patterns; the 3 most common patterns accounted for 80.6% of PCC-days and differed primarily in average volume of documentation time (78.1 minutes per day; 35.4 minutes per day; 144.6 minutes per day); associations with note efficiency were mixed. PCCs with >80% of days attributable to a single pattern demonstrated significantly more efficient documentation than PCCs with lower consistency; for high-consistency PCCs, days that deviated from their usual patterns were associated with less efficient documentation. DISCUSSION We found substantial variation in efficiency across daily documentation patterns, suggesting that PCC-level factors like EHR facility and consistency may be more important than when documentation occurs. There were substantial efficiency returns to consistency, and deviations from consistent patterns were costly. CONCLUSION Organizational leaders aiming to reduce documentation burden should pay specific attention to the ability for PCCs to execute consistent documentation patterns day-to-day.
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Affiliation(s)
- Nate C Apathy
- Health Policy and Management, University of Maryland School of Public Health, College Park, MD 20742, United States
| | - Joshua Biro
- National Center for Human Factors in Healthcare, MedStar Health Research Institute, Hyattsville, MD 20782, United States
| | - A Jay Holmgren
- Division of Clinical Informatics and Digital Transformation, University of California—San Francisco School of Medicine, San Francisco, CA 94117, United States
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2
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Murad MH, Vaa Stelling BE, West CP, Hasan B, Simha S, Saadi S, Firwana M, Viola KE, Prokop LJ, Nayfeh T, Wang Z. Measuring Documentation Burden in Healthcare. J Gen Intern Med 2024:10.1007/s11606-024-08956-8. [PMID: 39073484 DOI: 10.1007/s11606-024-08956-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 07/17/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND The enactment of the Health Information Technology for Economic and Clinical Health Act and the wide adoption of electronic health record (EHR) systems have ushered in increasing documentation burden, frequently cited as a key factor affecting the work experience of healthcare professionals and a contributor to burnout. This systematic review aims to identify and characterize measures of documentation burden. METHODS We integrated discussions with Key Informants and a comprehensive search of the literature, including MEDLINE, Embase, Scopus, and gray literature published between 2010 and 2023. Data were narratively and thematically synthesized. RESULTS We identified 135 articles about measuring documentation burden. We classified measures into 11 categories: overall time spent in EHR, activities related to clinical documentation, inbox management, time spent in clinical review, time spent in orders, work outside work/after hours, administrative tasks (billing and insurance related), fragmentation of workflow, measures of efficiency, EHR activity rate, and usability. The most common source of data for most measures was EHR usage logs. Direct tracking such as through time-motion analysis was fairly uncommon. Measures were developed and applied across various settings and populations, with physicians and nurses in the USA being the most frequently represented healthcare professionals. Evidence of validity of these measures was limited and incomplete. Data on the appropriateness of measures in terms of scalability, feasibility, or equity across various contexts were limited. The physician perspective was the most robustly captured and prominently focused on increased stress and burnout. DISCUSSION Numerous measures for documentation burden are available and have been tested in a variety of settings and contexts. However, most are one-dimensional, do not capture various domains of this construct, and lack robust validity evidence. This report serves as a call to action highlighting an urgent need for measure development that represents diverse clinical contexts and support future interventions.
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Affiliation(s)
- M Hassan Murad
- Mayo Clinic Evidence-Based Practice Center, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA.
| | - Brianna E Vaa Stelling
- Division of Community Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Colin P West
- Division of General Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Bashar Hasan
- Mayo Clinic Evidence-Based Practice Center, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Suvyaktha Simha
- Mayo Clinic Evidence-Based Practice Center, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Samer Saadi
- Mayo Clinic Evidence-Based Practice Center, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Mohammed Firwana
- Mayo Clinic Evidence-Based Practice Center, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Kelly E Viola
- Mayo Clinic Evidence-Based Practice Center, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | | | - Tarek Nayfeh
- Mayo Clinic Evidence-Based Practice Center, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Zhen Wang
- Mayo Clinic Evidence-Based Practice Center, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
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Patterson BW, Hekman DJ, Liao FJ, Hamedani AG, Shah MN, Afshar M. Call me Dr Ishmael: trends in electronic health record notes available at emergency department visits and admissions. JAMIA Open 2024; 7:ooae039. [PMID: 38779571 PMCID: PMC11110617 DOI: 10.1093/jamiaopen/ooae039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/16/2024] [Accepted: 04/23/2024] [Indexed: 05/25/2024] Open
Abstract
Objectives Numerous studies have identified information overload as a key issue for electronic health records (EHRs). This study describes the amount of text data across all notes available to emergency physicians in the EHR, trended over the time since EHR establishment. Materials and Methods We conducted a retrospective analysis of EHR data from a large healthcare system, examining the number of notes and a corresponding number of total words and total tokens across all notes available to physicians during patient encounters in the emergency department (ED). We assessed the change in these metrics over a 17-year period between 2006 and 2023. Results The study cohort included 730 968 ED visits made by 293 559 unique patients and a total note count of 132 574 964. The median note count for all encounters in 2006 was 5 (IQR 1-16), accounting for 1735 (IQR 447-5521) words. By the last full year of the study period, 2022, the median number of notes had grown to 359 (IQR 84-943), representing 359 (IQR 84-943) words. Note and word counts were higher for admitted patients. Discussion The volume of notes available for review by providers has increased by over 30-fold in the 17 years since the implementation of the EHR at a large health system. The task of reviewing these notes has become commensurately more difficult. These data point to the critical need for new strategies and tools for filtering, synthesizing, and summarizing information to achieve the promise of the medical record.
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Affiliation(s)
- Brian W Patterson
- BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53705, United States
- Department of Information Services, UW Health, Madison, WI 53705, United States
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI 53705, United States
| | - Daniel J Hekman
- BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53705, United States
| | - Frank J Liao
- BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53705, United States
- Department of Information Services, UW Health, Madison, WI 53705, United States
| | - Azita G Hamedani
- BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53705, United States
| | - Manish N Shah
- BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53705, United States
- Department of Medicine, University of Wisconsin-Madison, Madison, WI 53705, United States
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI 53705, United States
| | - Majid Afshar
- Department of Medicine, University of Wisconsin-Madison, Madison, WI 53705, United States
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Shafer PR, Palani S, Saeed I, Legler A, Barr K, Carvalho K, Pizer SD. Costs and Productivity Benefits of the Department of Veterans Affairs Maintaining Internal Systems and Strengthening Integrated Outside Networks Act Scribes Trial. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2024; 27:713-720. [PMID: 38462222 DOI: 10.1016/j.jval.2024.02.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 02/18/2024] [Accepted: 02/28/2024] [Indexed: 03/12/2024]
Abstract
OBJECTIVES To improve access, the VA Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act of 2018 mandated a 2-year study of medical scribes in Veterans Health Administration specialty clinics and emergency departments. Medical scribes are employed in clinical settings with the goals of increasing provider productivity and satisfaction by minimizing physicians' documentation burden. Our objective is to quantify the economic outcomes of the MISSION Act scribes trial. METHODS A cluster-randomized trial was designed with 12 Department of Veterans Affairs (VA) medical centers randomized into the intervention. We estimated the total cost of the trial, cost per scribe-year, and projected cost of hiring additional physicians to achieve the observed scribe productivity benefits in relative value units and visits per full-time-equivalent over the 2-year intervention period (June 30, 2020 to July 1, 2022). RESULTS The estimated cost of the trial was $4.6 million, below the Congressional Budget Office estimate of $5 million. A full-time scribe-year cost approximately $74 600 through contracting and $62 900 through VA hiring. Randomization into the trial led to an approximate 30% increase in productivity in cardiology and 20% in orthopedics. The projected incremental cost of using additional physicians instead of scribes to achieve the same productivity benefits was nearly $1.7 million more, or 75% higher, than the observed cost of scribes in cardiology and orthopedics. CONCLUSIONS As the largest randomized trial of scribes to date, the MISSION Act scribes trial provides important evidence on the costs and benefits of scribes. Improving productivity enhances access and scribes may give VA a new tool to improve productivity in specialty care at a lower cost than hiring additional providers.
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Affiliation(s)
- Paul R Shafer
- Partnered Evidence-based Policy Resource Center, VA Boston Healthcare System, Boston, MA, USA; Department of Health Law, Policy, and Management, School of Public Health, Boston University, Boston, MA, USA.
| | - Sivagaminathan Palani
- Partnered Evidence-based Policy Resource Center, VA Boston Healthcare System, Boston, MA, USA; Department of Health Law, Policy, and Management, School of Public Health, Boston University, Boston, MA, USA
| | - Iman Saeed
- Partnered Evidence-based Policy Resource Center, VA Boston Healthcare System, Boston, MA, USA; Department of Health Law, Policy, and Management, School of Public Health, Boston University, Boston, MA, USA
| | - Aaron Legler
- Partnered Evidence-based Policy Resource Center, VA Boston Healthcare System, Boston, MA, USA
| | - Kyle Barr
- Partnered Evidence-based Policy Resource Center, VA Boston Healthcare System, Boston, MA, USA
| | - Kristina Carvalho
- Partnered Evidence-based Policy Resource Center, VA Boston Healthcare System, Boston, MA, USA
| | - Steven D Pizer
- Partnered Evidence-based Policy Resource Center, VA Boston Healthcare System, Boston, MA, USA; Department of Health Law, Policy, and Management, School of Public Health, Boston University, Boston, MA, USA
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Safrai M, Azaria A. Does small talk with a medical provider affect ChatGPT's medical counsel? Performance of ChatGPT on USMLE with and without distractions. PLoS One 2024; 19:e0302217. [PMID: 38687696 PMCID: PMC11060598 DOI: 10.1371/journal.pone.0302217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 03/28/2024] [Indexed: 05/02/2024] Open
Abstract
Efforts are being made to improve the time effectiveness of healthcare providers. Artificial intelligence tools can help transcript and summarize physician-patient encounters and produce medical notes and medical recommendations. However, in addition to medical information, discussion between healthcare and patients includes small talk and other information irrelevant to medical concerns. As Large Language Models (LLMs) are predictive models building their response based on the words in the prompts, there is a risk that small talk and irrelevant information may alter the response and the suggestion given. Therefore, this study aims to investigate the impact of medical data mixed with small talk on the accuracy of medical advice provided by ChatGPT. USMLE step 3 questions were used as a model for relevant medical data. We use both multiple-choice and open-ended questions. First, we gathered small talk sentences from human participants using the Mechanical Turk platform. Second, both sets of USLME questions were arranged in a pattern where each sentence from the original questions was followed by a small talk sentence. ChatGPT 3.5 and 4 were asked to answer both sets of questions with and without the small talk sentences. Finally, a board-certified physician analyzed the answers by ChatGPT and compared them to the formal correct answer. The analysis results demonstrate that the ability of ChatGPT-3.5 to answer correctly was impaired when small talk was added to medical data (66.8% vs. 56.6%; p = 0.025). Specifically, for multiple-choice questions (72.1% vs. 68.9%; p = 0.67) and for the open questions (61.5% vs. 44.3%; p = 0.01), respectively. In contrast, small talk phrases did not impair ChatGPT-4 ability in both types of questions (83.6% and 66.2%, respectively). According to these results, ChatGPT-4 seems more accurate than the earlier 3.5 version, and it appears that small talk does not impair its capability to provide medical recommendations. Our results are an important first step in understanding the potential and limitations of utilizing ChatGPT and other LLMs for physician-patient interactions, which include casual conversations.
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Affiliation(s)
- Myriam Safrai
- Department of Obstetrics and Gynecology, Chaim Sheba Medical Center (Tel Hashomer), Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Obstetrics, Gynecology and Reproductive Sciences, Magee-Womens Research Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America
| | - Amos Azaria
- School of Computer Science, Ariel University, Ari’el, Israel
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Shah M, De Arrigunaga S, Forman LS, West M, Rowe SG, Mishuris RG. Cumulated time to chart closure: a novel electronic health record-derived metric associated with clinician burnout. JAMIA Open 2024; 7:ooae009. [PMID: 38333109 PMCID: PMC10852987 DOI: 10.1093/jamiaopen/ooae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 02/02/2024] [Indexed: 02/10/2024] Open
Abstract
Objective We sought to determine whether average cumulated time to chart closure (CTCC), a novel construct to measure clinician workload burden, and electronic health record (EHR) measures were associated with a validated measure of burnout. Materials and methods Physicians at a large academic institution participated in a well-being survey that was linked to their EHR use data. CTCC was defined as the average time from the start of patient encounters to chart closure over a set of encounters. Established EHR use measures including daily total time in the EHR (EHR-Time8), time in the EHR outside scheduled hours, work outside of work (WOW8), and time spent on inbox (IB-Time8) were calculated. We examined the relationship between CTCC, EHR use metrics, and burnout using descriptive statistics and adjusted logistic regression models. Results We included data from 305 attendings, encompassing 242 432 ambulatory encounters (2021). Among them, 42% (128 physicians) experienced burnout. The median CTCC for all clinicians was 32.5 h. Unadjusted analyses revealed significant associations between CTCC, WOW8, IB-Time8, and burnout. In a final adjusted model, only CTCC remained statistically significant with an odds ratio estimate of 1.42 (95% CI, 1.00-2.01). Discussion These results suggest that CTCC is predictive of burnout and that purely measuring duration of interaction with the EHR itself is not sufficient to capture burnout. Conclusion Workload burden as manifested by average CTCC has the potential to be a practical, quantifiable measure that will allow for identification of clinicians at risk of burnout and to assess the success of interventions designed to address burnout.
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Affiliation(s)
- Madhura Shah
- Boston University Aram V. Chobanian & Edward Avedisian School of Medicine, Boston, MA 02118, United States
| | - Sofia De Arrigunaga
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Medical School, Miami, FL 33136, United States
| | - Leah S Forman
- Biostatistics and Epidemiology Data Analytics Center, Boston University School of Public Health, Boston, MA 02118, United States
| | - Matthew West
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
| | - Susannah G Rowe
- Office of Equity, Vitality and Inclusion, Boston University Medical Group, Boston, MA 02118, United States
- Wellness and Professional Vitality, Boston Medical Center, Boston, MA 02118, United States
- Department of Ophthalmology, Boston University Aram V. Chobanian & Edward Avedisian School of Medicine, Boston, MA 02118, United States
| | - Rebecca G Mishuris
- Digital, Mass General Brigham, Somerville, MA 02145, United States
- Department of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA 02115, United States
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Cross DA, Holmgren AJ, Apathy NC. The role of organizations in shaping physician use of electronic health records. Health Serv Res 2024; 59:e14203. [PMID: 37438938 PMCID: PMC10771898 DOI: 10.1111/1475-6773.14203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2023] Open
Abstract
OBJECTIVE The aim of the study was to (1) characterize organizational differences in primary care physicians' electronic health record (EHR) behavior; (2) assess within-organization consistency in EHR behaviors; and (3) identify whether organizational consistency is associated with physician-level efficiency. DATA SOURCES EHR metadata capturing averaged weekly measures of EHR time and documentation composition from 75,124 US primary care physicians across 299 organizations between September 2020 and May 2021 were taken. EHR time measures include active time in orders, chart review, notes, messaging, time spent outside of scheduled hours, and total EHR time. Documentation composition measures include note length and percentage use of templated text or copy/paste. Efficiency is measured as the percent of visits with same-day note completion. STUDY DESIGN All analyses are cross-sectional. Across-organization differences in EHR use and documentation composition are presented via 90th-to-10th percentile ratios of means and SDs. Multilevel modeling with post-estimation variance partitioning assesses the extent of an organizational signature-the proportion of variation in our measures attributable to organizations (versus specialty and individual behaviors). We measured organizational internal consistency for each measure via organization-level SD, which we grouped into quartiles for regression. Association between internally consistent (i.e., low SD) organizational EHR use and physician-level efficiency was assessed with multi-variable OLS models. DATA COLLECTION Extraction from Epic's Signal platform used for measuring provider EHR efficiency. PRINCIPAL FINDINGS EHR time per visit for physicians at a 90th percentile organization is 1.94 times the average EHR time at a 10th percentile organization. There is little evidence, on average, of an organizational signature. However, physicians in organizations with high internal consistency in EHR use demonstrate increased efficiency. Physicians in organizations with the highest internal consistency (top quartile) have a 3.77 percentage point higher same-day visit closure rates compared with peers in bottom quartile organizations (95% confidence interval: 0.0142-0.0612). CONCLUSIONS Results suggest unrealized opportunities for organizations and policymakers to support consistency in how physicians engage in EHR-supported work.
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Affiliation(s)
- Dori A. Cross
- Division of Health Policy and ManagementUniversity of Minnesota School of Public HealthMinneapolisMinnesotaUSA
| | - A Jay Holmgren
- Department of MedicineUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Nate C. Apathy
- Center for Human Factors in Healthcare, MedStar Health Research InstituteHyattsvilleMarylandUSA
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Arvisais-Anhalt S, Gonias SL, Murray SG. Establishing priorities for implementation of large language models in pathology and laboratory medicine. Acad Pathol 2024; 11:100101. [PMID: 38292297 PMCID: PMC10825232 DOI: 10.1016/j.acpath.2023.100101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 10/02/2023] [Accepted: 10/29/2023] [Indexed: 02/01/2024] Open
Abstract
Artificial intelligence and machine learning have numerous applications in pathology and laboratory medicine. The release of ChatGPT prompted speculation regarding the potentially transformative role of large-language models (LLMs) in academic pathology, laboratory medicine, and pathology education. Because of the potential to improve LLMs over the upcoming years, pathology and laboratory medicine clinicians are encouraged to embrace this technology, identify pathways by which LLMs may support our missions in education, clinical practice, and research, participate in the refinement of AI modalities, and design user-friendly interfaces that integrate these tools into our most important workflows. Challenges regarding the use of LLMs, which have already received considerable attention in a general sense, are also reviewed herein within the context of the pathology field and are important to consider as LLM applications are identified and operationalized.
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Affiliation(s)
- Simone Arvisais-Anhalt
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Steven L. Gonias
- Department of Pathology, University of California San Diego, La Jolla, CA, USA
| | - Sara G. Murray
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
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Lee J, Patel S, Taxter A. How to make the electronic health record your friend. Curr Opin Pediatr 2023; 35:579-584. [PMID: 37233610 DOI: 10.1097/mop.0000000000001261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
PURPOSE OF REVIEW The electronic health record (EHR) has become ubiquitous among healthcare providers. It has revolutionized how we care for patients allowing for instant access to records, improved order entry, and improved patient outcomes. However, it has also been implicated as a source of stress, burnout, and workplace dissatisfaction among its users. The article provides an overview of factors associated with burnout focusing on the pediatrician and pediatric subspecialist workflows and will summarize practical tips based on clinical informatics principles for addressing these factors. RECENT FINDINGS Several metrics related to EHR including training, efficiency and lack of usability have been cited as factors associated with burnout. Organizational, personal, and interpersonal factors as well as work culture are more associated with burnout than EHR use. SUMMARY Organizational strategies to address burnout include first monitoring metrics including physician satisfaction and wellbeing, incorporating mindfulness and teamwork, and decreasing stress from the EHR by providing training, standardized workflows, and efficiency tools. All clinicians should feel empowered to customize workflows and seek organizational help for improving EHR use.
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Affiliation(s)
- Jennifer Lee
- Department of Pediatrics
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus
- Division of Clinical Informatics
- Division of Gastroenterology
| | - Shama Patel
- Department of Pediatrics
- Division of Clinical Informatics
- Division of Neonatology
| | - Alysha Taxter
- Department of Pediatrics
- Division of Clinical Informatics
- Division of Rheumatology, Nationwide Children's Hospital, Columbus, Ohio, USA
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Dye ME, Runyan P, Scott TA, Dietrich MS, Hatch LD, France D, Alrifai MW. Workload In Neonatology (WORKLINE): Validation and feasibility of a system for measuring clinician workload integrated into the electronic health record. J Perinatol 2023; 43:936-942. [PMID: 37131049 DOI: 10.1038/s41372-023-01678-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/05/2023] [Accepted: 04/12/2023] [Indexed: 05/04/2023]
Abstract
OBJECTIVE The purpose of the study was to validate WORKLINE, a NICU specific clinician workload model and to evaluate the feasibility of integrating WORKLINE into our EHR. STUDY DESIGN This was a prospective, observational study of the workload of 42 APPs and physicians in a large academic medical center NICU over a 6-month period. We used regression models with robust clustered standard errors to test associations of WORKLINE values with NASA Task Load Index (NASA-TLX) scores. RESULTS We found significant correlations between WORKLINE and NASA-TLX scores. APP caseload was not significantly associated with WORKLINE scores. We successfully integrated the WORKLINE model into our EHR to automatically generate workload scores. CONCLUSION WORKLINE provides an objective method to quantify the workload of clinicians in the NICU, and for APPs, performed better than caseload numbers to reflect workload. Integrating the WORKLINE model into the EHR was feasible and enabled automated workload scores.
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Affiliation(s)
- M Eva Dye
- Monroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN, USA.
- Department of Pediatrics, Division of Neonatology, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Patti Runyan
- Monroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN, USA
| | - Theresa A Scott
- Department of Pediatrics, Division of Neonatology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mary S Dietrich
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- School of Nursing, Vanderbilt University, Nashville, TN, USA
| | - L Dupree Hatch
- Monroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN, USA
- Department of Pediatrics, Division of Neonatology, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Child Health Policy, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel France
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mhd Wael Alrifai
- Monroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN, USA
- Department of Pediatrics, Division of Neonatology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
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11
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Apathy NC, Rotenstein L, Bates DW, Holmgren AJ. Documentation dynamics: Note composition, burden, and physician efficiency. Health Serv Res 2023; 58:674-685. [PMID: 36342001 PMCID: PMC10154172 DOI: 10.1111/1475-6773.14097] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVE To analyze how physician clinical note length and composition relate to electronic health record (EHR)-based measures of burden and efficiency that have been tied to burnout. DATA SOURCES AND STUDY SETTING Secondary EHR use metadata capturing physician-level measures from 203,728 US-based ambulatory physicians using the Epic Systems EHR between September 2020 and May 2021. STUDY DESIGN In this cross-sectional study, we analyzed physician clinical note length and note composition (e.g., content from manual or templated text). Our primary outcomes were three time-based measures of EHR burden (time writing EHR notes, time in the EHR after-hours, and EHR time on unscheduled days), and one measure of efficiency (percent of visits closed in the same day). We used multivariate regression to estimate the relationship between our outcomes and note length and composition. DATA EXTRACTION Physician-week measures of EHR usage were extracted from Epic's Signal platform used for measuring provider EHR efficiency. We calculated physician-level averages for our measures of interest and assigned physicians to overall note length deciles and note composition deciles from six sources, including templated text, manual text, and copy/paste text. PRINCIPAL FINDINGS Physicians in the top decile of note length demonstrated greater burden and lower efficiency than the median physician, spending 39% more time in the EHR after hours (p < 0.001) and closing 5.6 percentage points fewer visits on the same day (p < 0.001). Copy/paste demonstrated a similar dose/response relationship, with top-decile copy/paste users closing 6.8 percentage points fewer visits on the same day (p < 0.001) and spending more time in the EHR after hours and on days off (both p < 0.001). Templated text (e.g., Epic's SmartTools) demonstrated a non-linear relationship with burden and efficiency, with very low and very high levels of use associated with increased EHR burden and decreased efficiency. CONCLUSIONS "Efficiency tools" like copy/paste and templated text meant to reduce documentation burden and increase provider efficiency may have limited efficacy.
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Affiliation(s)
- Nate C. Apathy
- National Center for Human Factors in HealthcareMedStar Health Research InstituteWashingtonDistrict of ColumbiaUSA
- Center for Biomedical InformaticsRegenstrief InstituteIndianapolisIndianaUSA
| | - Lisa Rotenstein
- Harvard Medical SchoolBostonMassachusettsUSA
- Population Health Brigham & Women's HospitalBostonMassachusettsUSA
| | - David W. Bates
- Harvard Medical SchoolBostonMassachusettsUSA
- Division of General Internal MedicineBrigham & Women's HospitalBostonMassachusettsUSA
- Present address:
Department of Health Policy and ManagementHarvard School of Public HealthBostonMAUSA
| | - A. Jay Holmgren
- Center for Clinical Informatics and Improvement Research, University of California – San Francisco School of MedicineSan FranciscoCaliforniaUSA
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