Primary care providers (PCPs) received an overall mean of 76.9 notifications per day (blue line). Specialists (site A only) received a mean of 29.1 notifications per day (P < .001).
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Murphy DR, Meyer AND, Russo E, Sittig DF, Wei L, Singh H. The Burden of Inbox Notifications in Commercial Electronic Health Records. JAMA Intern Med. 2016;176(4):559–560. doi:10.1001/jamainternmed.2016.0209
Copyright 2016 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.
With wider use of electronic health records (EHRs), physicians increasingly receive notifications via EHR-based inboxes (eg, Epic’s In-Basket and General Electric Centricity’s Documents). Examples of types of notifications include test results, responses to referrals, requests for medication refills, and messages from physicians and other health care professionals.1,2 Previous work within the Department of Veterans Affairs found that health care professionals receive large quantities of EHR-based notifications, making it harder to discern important vs irrelevant information and increasing their risk of overlooking abnormal test results.3-6 Information overload is of emerging concern because new types of notifications and “FYI” (for your information) messages can be easily created in the EHR (vs in a paper-based system). Furthermore, the additional workload to read and process these messages remains uncompensated in an environment of reduced reimbursements for office-based care.1,2,4 Conversely, EHRs make it easier to measure the amount of information received. We quantified the notifications that physicians received via inboxes of commercial EHRs to estimate their burden.
We obtained electronic logs of all notifications received by all physicians from January 1 through June 30, 2015, at 3 large practices in Texas (2 primary care and 1 multispecialty). Data analysis was performed from September 21 to October 29, 2015. We then tabulated notifications that conveyed new information to physicians for the 125 workdays during the study period. Types of notifications were categorized according to whether or not they were related to test results. To account for different times each physician spent in the outpatient clinic, we normalized the number of notifications by percentage of time worked such that the number of notifications would represent the number of notifications received if the physician was full-time. We excluded physicians working less than 20% of a 40-hour week and if they did not work the entire 6 months. We calculated means of normalized notifications each physician would receive each workday and performed univariable analysis of variance comparisons across sites and between primary care physicians (PCPs) (including family medicine, internal medicine, and geriatrics) and specialty physicians at the multispecialty clinic. In addition, we performed a correlation analysis to determine whether an association existed between time worked and normalized number of notifications. The study was approved by the Baylor College of Medicine Institutional Review Board, as well as by review boards at each site participating in the study. A waiver of consent was approved.
Of 125 physicians, 33 met the exclusion criteria, leaving the EHR inboxes of 92 physicians for analysis: 19 PCPs and 46 specialists at site A using Epic, 12 PCPs at site B using General Electric Centricity, and 15 PCPs at site C using Epic. We analyzed 276 207 notifications (146 521 at site A, 51 090 at site B, and 78 596 at site C).
Across the 3 sites, 46 PCPs received a mean (SD) of 76.9 (38.0) total notifications per day (65.2, 113.5, and 62.6 at sites A, B, and C, respectively; P < .001), of which a mean (SD) of 15.5 [6.0] notifications per day (20.2%) were related to test results (15.1, 18.7, and 13.4 at sites A, B, and C, respectively; P = .07) (Figure). In addition to previously identified types of notifications,2 physicians also received messages directly from patients and pharmacies. At site A, we found that specialists received 29.1 total and 10.4 test result notifications per day, significantly fewer than PCPs at the same site (P < .001 and P = .03, respectively). Finally, there were significant negative correlations between time worked and normalized daily total notifications received (r = –0.27; P = .008), suggesting that part-time physicians received more notifications for time spent in the clinic than did full-time physicians.
A large quantity of information is communicated to PCPs each day via commercial EHRs. In prior work within the Department of Veterans Affairs, PCPs spent a mean of 49 minutes processing 56.4 notifications of comparable content each day (ie, a mean of 52 seconds per notification).2 Extrapolating this finding to commercial EHRs suggests that physicians spend an estimated 66.8 minutes per day processing notifications, which likely adds a substantial burden to their workday. Specialists received less than half this amount of notifications, and part-time physicians appeared to receive proportionately more notifications. Because a single notification often contains multiple data points (eg, results of metabolic panels contain 7-14 laboratory values), the actual burden and required cognitive effort required of the physicians is likely greater. Strategies to help filter messages relevant to high-quality care, EHR designs that support team-based care, and staffing models that assist physicians in managing this influx of information are needed.
Corresponding Author: Daniel R. Murphy, MD, MBA, Houston Veterans Affairs Center for Innovations in Quality, Effectiveness, and Safety, Michael E DeBakey Veterans Affairs Medical Center, 2002 Holcombe Blvd, Houston, TX 77030 (firstname.lastname@example.org).
Published Online: March 14, 2016. doi:10.1001/jamainternmed.2016.0209.
Author Contributions: Dr Murphy had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Murphy, Sittig, Singh.
Acquisition, analysis, or interpretation of data: Murphy, Meyer, Russo, Wei, Singh.
Drafting of the manuscript: Murphy.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Murphy, Meyer.
Obtained funding: Singh.
Administrative, technical, or material support: Murphy, Russo.
Study supervision: Murphy, Russo, Sittig, Singh.
Conflict of Interest Disclosures: None reported.
Funding/Support: This study is funded by grant R01HS022087 from the Agency for Healthcare Research and Quality and partially funded by grant CIN 13–413 from the Houston Veterans Affairs Health Services Research and Development ServiceCenter for Innovations in Quality, Effectiveness and Safety. Dr Murphy is additionally supported by grant K08-HS022901 from the Agency for Healthcare Research and Quality Mentored Career Development Award. Dr Singh is additionally supported by grants CRE 12-033 and USA 14-274 (Presidential Early Career Award for Scientists and Engineers) from the Veteran Affairs Health Services Research and Development Service, and by the Veterans Affairs National Center for Patient Safety.
Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Disclaimer: The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the US government.
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