Time Requirements for Electronic Health Record Use in an Academic Ophthalmology Center

Importance Electronic health record (EHR) systems have transformed the practice of medicine. However, physicians have raised concerns that EHR time requirements have negatively affected their productivity. Meanwhile, evolving approaches toward physician reimbursement will require additional documentation to measure quality and cost of care. To date, little quantitative analysis has rigorously studied these topics. Objective To examine ophthalmologist time requirements for EHR use. Design, Setting, and Participants A single-center cohort study was conducted between September 1, 2013, and December 31, 2016, among 27 stable departmental ophthalmologists (defined as attending ophthalmologists who worked at the study institution for ≥6 months before and after the study period). Ophthalmologists who did not have a standard clinical practice or who did not use the EHR were excluded. Exposures Time stamps from the medical record and EHR audit log were analyzed to measure the length of time required by ophthalmologists for EHR use. Ophthalmologists underwent manual time-motion observation to measure the length of time spent directly with patients on the following 3 activities: EHR use, conversation, and examination. Main Outcomes and Measures The study outcomes were time spent by ophthalmologists directly with patients on EHR use, conversation, and examination as well as total time required by ophthalmologists for EHR use. Results Among the 27 ophthalmologists in this study (10 women and 17 men; mean [SD] age, 47.3 [10.7] years [median, 44; range, 34-73 years]) the mean (SD) total ophthalmologist examination time was 11.2 (6.3) minutes per patient, of which 3.0 (1.8) minutes (27% of the examination time) were spent on EHR use, 4.7 (4.2) minutes (42%) on conversation, and 3.5 (2.3) minutes (31%) on examination. Mean (SD) total ophthalmologist time spent using the EHR was 10.8 (5.0) minutes per encounter (range, 5.8-28.6 minutes). The typical ophthalmologist spent 3.7 hours using the EHR for a full day of clinic: 2.1 hours during examinations and 1.6 hours outside the clinic session. Linear mixed effects models showed a positive association between EHR use and billing level and a negative association between EHR use per encounter and clinic volume. Each additional encounter per clinic was associated with a decrease of 1.7 minutes (95% CI, -4.3 to 1.0) of EHR use time per encounter for ophthalmologists with high mean billing levels (adjusted R2 = 0.42; P = .01). Conclusions and Relevance Ophthalmologists have limited time with patients during office visits, and EHR use requires a substantial portion of that time. There is variability in EHR use patterns among ophthalmologists.

[1]  Jan Horsky,et al.  Development of a cognitive framework of patient record summary review in the formative phase of user-centered design , 2016, J. Biomed. Informatics.

[2]  Charlotte A. Weaver,et al.  Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[3]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[4]  Michael F Chiang,et al.  Accuracy and speed of electronic health record versus paper-based ophthalmic documentation strategies. , 2013, American journal of ophthalmology.

[5]  J. Schold,et al.  Prevalence of Copied Information by Attendings and Residents in Critical Care Progress Notes* , 2013, Critical care medicine.

[6]  D. Blumenthal,et al.  Health care coverage under the Affordable Care Act--a progress report. , 2014, The New England journal of medicine.

[7]  Kai Zheng,et al.  Quantifying the impact of health IT implementations on clinical workflow: a new methodological perspective , 2010, J. Am. Medical Informatics Assoc..

[8]  Lipika Samal,et al.  Use and satisfaction with key functions of a common commercial electronic health record: a survey of primary care providers , 2013, BMC Medical Informatics and Decision Making.

[9]  John M. Green,et al.  Impact of Implementing an Electronic Health Record on Surgical Resident Work Flow, Duty Hours, and Operative Experience , 2015, The American surgeon.

[10]  Elizabeth T. Toll,et al.  A piece of my mind. The cost of technology. , 2012, JAMA.

[11]  J. Stockman Working Conditions in Primary Care: Physician Reactions and Care Quality , 2011 .

[12]  Maureen Bisognano,et al.  More patients, less payment: increasing hospital efficiency in the aftermath of health reform. , 2011, Health affairs.

[13]  R. Hoover Benefits of using an electronic health record. , 2016, Nursing.

[14]  Vimla L. Patel,et al.  Review: A Primer on Aspects of Cognition for Medical Informatics , 2001, J. Am. Medical Informatics Assoc..

[15]  Mark D Schwartz,et al.  Working conditions in primary care: physician reactions and care quality. , 2009, Annals of internal medicine.

[16]  T. Hothorn,et al.  Simultaneous Inference in General Parametric Models , 2008, Biometrical journal. Biometrische Zeitschrift.

[17]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[18]  E. B. Steen,et al.  The Computer-Based Patient Record: An Essential Technology for Health Care , 1992, Annals of Internal Medicine.

[19]  S. Davis,et al.  The duration of office visits in the United States, 1993 to 2010. , 2014, The American journal of managed care.

[20]  E. Montague,et al.  More screen time, less face time - implications for EHR design. , 2014, Journal of evaluation in clinical practice.

[21]  Peter Hoonakker,et al.  Impact of electronic health record technology on the work and workflow of physicians in the intensive care unit , 2015, Int. J. Medical Informatics.

[22]  E. Hess,et al.  Electronic medical records and physician stress in primary care: results from the MEMO Study. , 2014, Journal of the American Medical Informatics Association : JAMIA.

[23]  Michelle R. Hribar,et al.  Secondary Use of EHR Timestamp data: Validation and Application for Workflow Optimization , 2015, AMIA.

[24]  Christine A. Sinsky,et al.  Relationship Between Clerical Burden and Characteristics of the Electronic Environment With Physician Burnout and Professional Satisfaction. , 2016, Mayo Clinic proceedings.

[25]  David Blumenthal,et al.  Implementation of the federal health information technology initiative. , 2011, The New England journal of medicine.

[26]  J. Henry,et al.  Adoption of Electronic Health Record Systems among U . S . Non-Federal Acute Care Hospitals : 2008-2015 , 2013 .

[27]  L Culpepper,et al.  The duration of ambulatory visits to physicians. , 1999, The Journal of family practice.

[28]  L. Jeannotte,et al.  The State of aging and health in America 2007 , 2007 .

[29]  Michael V. Boland,et al.  The impact of an electronic health record transition on a glaucoma subspecialty practice. , 2013, Ophthalmology.

[30]  Stephen A Martin,et al.  The map is not the territory: medical records and 21st century practice , 2016, The Lancet.

[31]  Robyn Tamblyn,et al.  Review Paper: The Impact of Electronic Health Records on Time Efficiency of Physicians and Nurses: A Systematic Review , 2005, J. Am. Medical Informatics Assoc..

[32]  Jing Zhang,et al.  Strategizing EHR use to achieve patient-centered care in exam rooms: a qualitative study on primary care providers , 2016, J. Am. Medical Informatics Assoc..

[33]  R. Chang,et al.  Factors Associated with Patient Press Ganey Satisfaction Scores for Ophthalmology Patients. , 2016, Ophthalmology.

[34]  Sara Poplau,et al.  Worklife and Wellness in Academic General Internal Medicine: Results from a National Survey , 2016, Journal of General Internal Medicine.

[35]  Christine A. Sinsky,et al.  Changes in Burnout and Satisfaction With Work-Life Balance in Physicians and the General US Working Population Between 2011 and 2014. , 2015, Mayo Clinic proceedings.

[36]  S. de Lusignan,et al.  Using the computer in the clinical consultation; setting the stage, reviewing, recording, and taking actions: multi-channel video study. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[37]  Wei Wang,et al.  Electronic Health Record Logs Indicate That Physicians Split Time Evenly Between Seeing Patients And Desktop Medicine. , 2017, Health affairs.

[38]  Abraham Verghese,et al.  Culture shock--patient as icon, icon as patient. , 2008, The New England journal of medicine.

[39]  Nick Sevdalis,et al.  The influence of time pressure on adherence to guidelines in primary care: an experimental study , 2013, BMJ Open.

[40]  David H. Goldstein,et al.  Using a personal digital assistant enhances gathering of patient data on an acute pain management service: a pilot study , 2003, Canadian journal of anaesthesia = Journal canadien d'anesthesie.

[41]  E. Hing,et al.  Adoption of Certified Electronic Health Record Systems and Electronic Information Sharing in Physician Offices: United States, 2013 and 2014. , 2016, NCHS data brief.

[42]  David W. Bates,et al.  Research Paper: Electronic Health Records in Specialty Care: A Time-Motion Study , 2007, J. Am. Medical Informatics Assoc..

[43]  Viju Raghupathi,et al.  Big data analytics in healthcare: promise and potential , 2014, Health Information Science and Systems.

[44]  Christoph U. Lehmann,et al.  Implications of an emerging EHR monoculture for hospitals and healthcare systems , 2015, J. Am. Medical Informatics Assoc..

[45]  Vineet M. Arora,et al.  Impact of Electronic Medical Record Use on the Patient–Doctor Relationship and Communication: A Systematic Review , 2016, Journal of General Internal Medicine.

[46]  Abraham Verghese,et al.  Evolutionary Pressures on the Electronic Health Record: Caring for Complexity. , 2016, JAMA.

[47]  Sarah Read-Brown,et al.  Impact of an electronic health record operating room management system in ophthalmology on documentation time, surgical volume, and staffing. , 2014, JAMA ophthalmology.

[48]  David B. Kaber,et al.  Usability and Safety in Electronic Medical Records Interface Design , 2015, Hum. Factors.