Assessment of Electronic Health Record Search Patterns and Practices by Practitioners in a Large Integrated Health Care System

Abstract

IMPORTANCE The electronic health record (EHR) is a source of practitioner dissatisfaction in part because of challenges with information retrieval. To improve data accessibility, a better understanding of practitioners' information needs within individual patient records is needed. OBJECTIVE To assess EHR users' searches using data from a large integrated health care system. DESIGN, SETTING, AND PARTICIPANTS This retrospective cross-sectional analysis used EHR search data from Kaiser Permanente Northern California, an integrated health care delivery system with more than 4.4 million members. Users' EHR search activity data were obtained from April 1, 2018, to May 15, 2019. MAIN OUTCOMES AND MEASURES Search term frequency was grouped by user and practitioner types. Network analyses were performed of co-occurring search terms within a single search episode, and centrality measures for search terms (degree and betweenness centrality) were calculated. RESULTS A total of 12 313 047 search activities (including 4 328 330 searches and 7 984 717 result views) conducted by 34 735 unique users within 977 160 unique patient EHRs were identified. In aggregate, users searched for 208 374 unique search terms and conducted a median of 4 searches (interquartile range, 1-28 searches). Of all 97 367 active EHR users, 34 735 (35.7%) conducted at least 1 search. However, of all 12 968 active EHR physician users, 9801 (75.6%) conducted at least 1 search, and of all 1908 active pharmacist users, 1402 (73.5%) conducted at least 1 search. The top 3 most commonly searched terms were statin (75 017 searches [1.7%]), colonoscopy (73 545 [1.7%]), and pft (54 990 [1.3%]). However, wide variation in top searches were noted across practitioner groups. Terms searched most often with another term in a single linked search episode included statin, lisinopril, colonoscopy, gabapentin, and aspirin. CONCLUSIONS AND RELEVANCE Although physicians and pharmacists were the most active users of EHR searches, search volume and frequently searched terms varied considerably by and within user role. Further customization of the EHR interface may help leverage users' search content and patterns to improve targeted information retrieval.

A Simple Electronic Medical Record System Designed for Research

“…The user interfaces of electronic medical record (EMR) systems are a hodgepodge of legacy designs, billing requirements, and local customizations. The resulting misalignment of clinical and electronic workflow adds extraneous work and is a major source of clinician frustration [1]. To remedy the ills of the EMR, greater attention needs placed on understanding EMR use, design consequences [2], and potential improvements.…”

Modeling physician variability to prioritize relevant medical record information

“… 1–4 Current EMR systems do not possess sophisticated search capability or do they prioritize patient information relative to the clinical task at hand. 5 , 6 The inability to identify relevant patient information can lead to poor care and medical errors. 7–9 Further, in complex clinical environments, such as the intensive care unit (ICU), large quantities of data per patient accumulate rapidly, 10 which can exacerbate information retrieval challenges.…”

“…However, information-seeking behavior has been shown to vary across individual physicians as well as across EMR system user types such as physicians, nurses, and pharmacists. 1 , 5 In this study, we use hierarchical models to explicitly model this variability because such models have been shown to be useful when the data are collected from subjects with different behaviors. 17 In particular, we compare the performance of hierarchical logistic regression (HLR) models and standard logistic regression (LR) models in predicting relevant patient information in a LEMR system.…”

“… 18 In particular, variation in individual physician characteristics and practice styles has been recognized as a source of variability in clinical outcomes after adjusting for the health status of patients and the quality of healthcare services. 5 , 19–25 For example, variability in cesarean section rates has been attributed to physician practice style after controlling for patient characteristics and risk factors, the status of the medical facility, and physician years of experience. 25 A study concluded that variability across individual physicians may impact the quality of preference-sensitive critical care delivery.…”

“… 20 A recent study analyzed physician search patterns in the EMR and uncovered considerable variation in information-seeking behavior. 5 In general, hierarchical modeling has been applied in various clinical settings to account for physician-related variability where the data have a hierarchical structure and can be grouped by a variety of factors such as country, state, or hospital site. 26–32 …”