Electronic Health Record Activity Research Articles

Improving the Effectiveness of Health Information Technology: The Case for Situational Analytics.

Health information technology has contributed to improvements in quality and safety in clinical settings. However, the implementation of new technologies in health care has also been associated with the introduction of new sociotechnical hazards, produced through a range of complex interactions that vary with social, physical, temporal, and technological context. Other industries have been confronted with this problem and have developed advanced analytics to examine context-specific activities of workers and related outcomes. The skills and data exist in health care to develop similar insights through situational analytics, defined as the application of analytic methods to characterize human activity in situations and identify patterns in activity and outcomes that are influenced by contextual factors. This article describes the approach of situational analytics and potentially useful data sources, including trace data from electronic health record activity, reports from users, qualitative field data, and locational data. Key implementation requirements are discussed, including the need for collaboration among qualitative researchers and data scientists, organizational and federal level infrastructure requirements, and the need to implement a parallel research program in ethics to understand how the data are being used by organizations and policy makers.

Tracking resident pre-rounding electronic health record usage

PurposeResidents collect information from the electronic health record (EHR) to present during rounds, but this crucial process is understudied. The purpose of this paper is to examine the feasibility of utilizing an EHR embedded time-tracking software to quantify resident pre-round EHR activity and how patient acuity impacts this activity.Design/methodology/approachThis was a retrospective observational study that quantified resident EHR activities (total time spent, tasks performed and patient encounters accessed) during pre-rounds on their pediatric intensive care unit rotation between May 2016 and December 2016. Patient encounters were reviewed to determine resident ownership and critical care resources provided.FindingsAllo 21 eligible participants were included. In total, 907 patient encounters were included to evaluate patient acuity impact. EHR usage per patient encounter (median in minutes (25th, 75th percentile)) was significantly affected by the critical care resources utilized. Total EHR time: both ventilator and vasoactive support (10.54 (6.68, 17.19)); neither ventilator nor vasoactive support (8.23 (5.07, 12.72)); invasive/noninvasive ventilator support (8.74 (5.69, 13.2)); and vasoactive support (10.37 (7.72, 11.65)), p<0.001. Chart review, order entry and documentation EHR times demonstrated similar trends.Practical implicationsResidents spend more time utilizing the EHR to collect data on patients who require significant critical care resources. This information can be useful to determine optimal resident to patient workload. Future research is required to assess this EHR tool’s ability to contribute to physician workflow study.Originality/valueEHR embedded time-tracking software can offer insights into resident workflow.

Characterizing electronic health record usage patterns of inpatient medicine residents using event log data.

Amid growing rates of burnout, physicians report increasing electronic health record (EHR) usage alongside decreasing clinical facetime with patients. There exists a pressing need to improve physician-computer-patient interactions by streamlining EHR workflow. To identify interventions to improve EHR design and usage, we systematically characterize EHR activity among internal medicine residents at a tertiary academic hospital across various inpatient rotations and roles from June 2013 to November 2016. Logged EHR timestamps were extracted from Stanford Hospital’s EHR system (Epic) and cross-referenced against resident rotation schedules. We tracked the quantity of EHR logs across 24-hour cycles to reveal daily usage patterns. In addition, we decomposed daily EHR time into time spent on specific EHR actions (e.g. chart review, note entry and review, results review).In examining 24-hour usage cycles from general medicine day and night team rotations, we identified a prominent trend in which night team activity promptly ceased at the shift’s end, while day team activity tended to linger post-shift. Across all rotations and roles, residents spent on average 5.38 hours (standard deviation = 2.07) using the EHR. PGY1 (post-graduate year one) interns and PGY2+ residents spent on average 2.4 and 4.1 times the number of EHR hours on information review (chart, note, and results review) as information entry (note and order entry).Analysis of EHR event log data can enable medical educators and programs to develop more targeted interventions to improve physician-computer-patient interactions, centered on specific EHR actions.

Association of Medical Scribes in Primary Care With Physician Workflow and Patient Experience

Widespread adoption of electronic health records (EHRs) in medical care has resulted in increased physician documentation workload and decreased interaction with patients. Despite the increasing use of medical scribes for EHR documentation assistance, few methodologically rigorous studies have examined the use of medical scribes in primary care. To evaluate the association of use of medical scribes with primary care physician (PCP) workflow and patient experience. This 12-month crossover study with 2 sequences and 4 periods was conducted from July 1, 2016, to June 30, 2017, in 2 medical center facilities within an integrated health care system and included 18 of 24 eligible PCPs. The PCPs were randomly assigned to start the first 3-month period with or without scribes and then alternated exposure status every 3 months for 1 year, thereby serving as their own controls. The PCPs completed a 6-question survey at the end of each study period. Patients of participating PCPs were surveyed after scribed clinic visits. PCP-reported perceptions of documentation burden and visit interactions, objective measures of time spent on EHR activity and required for closing encounters, and patient-reported perceptions of visit quality. Of the 18 participating PCPs, 10 were women, 12 were internal medicine physicians, and 6 were family practice physicians. The PCPs graduated from medical school a mean (SD) of 13.7 (6.5) years before the study start date. Compared with nonscribed periods, scribed periods were associated with less self-reported after-hours EHR documentation (<1 hour daily during week: adjusted odds ratio [aOR], 18.0 [95% CI, 4.7-69.0]; <1 hour daily during weekend: aOR, 8.7; 95% CI, 2.7-28.7). Scribed periods were also associated with higher likelihood of PCP-reported spending more than 75% of the visit interacting with the patient (aOR, 295.0; 95% CI, 19.7 to >900) and less than 25% of the visit on a computer (aOR, 31.5; 95% CI, 7.3-136.4). Encounter documentation was more likely to be completed by the end of the next business day during scribed periods (aOR, 2.8; 95% CI, 1.2-7.1). A total of 450 of 735 patients (61.2%) reported that scribes had a positive bearing on their visits; only 2.4% reported a negative bearing. Medical scribes were associated with decreased physician EHR documentation burden, improved work efficiency, and improved visit interactions. Our results support the use of medical scribes as one strategy for improving physician workflow and visit quality in primary care.

Keystrokes, Mouse Clicks, and Gazing at the Computer: How Physician Interaction with the EHR Affects Patient Participation

Evidence is mixed regarding how physicians' use of the electronic health record (EHR) affects communication in medical encounters. To investigate whether the different ways physicians interact with the computer (mouse clicks, key strokes, and gaze) vary in their effects on patient participation in the consultation, physicians' efforts to facilitate patient involvement, and silence. Cross-sectional, observational study of video and event recordings of primary care and specialty consultations. Thirty-two physicians and 217 patients. Predictor variables included measures of physician interaction with the EHR (mouse clicks, key strokes, gaze). Outcome measures included active patient participation (asking questions, stating preferences, expressing concerns), physician facilitation of patient involvement (partnership-building and supportive talk), and silence. Patients were less active participants in consultations in which physicians engaged in more keyboard activity (b = -0.002, SE = 0.001, p = 0.02). More physician gaze at the computer was associated with more silence in the encounter (b = 0.21, SE = 0.09, p = 0.02). Physicians' facilitative communication, which predicted more active patient participation (b = 0.65, SE = 0.14, p < 0.001), was not related to EHR activity measures. Patients may be more reluctant to actively participate in medical encounters when physicians are more physically engaged with the computer (e.g., keyboard activity) than when their behavior is less demonstrative (e.g., gazing at EHR). Using easy to deploy communication tactics (e.g., asking about a patient's thoughts and concerns, social conversation) while working on the computer can help physicians engage patients as well as maintain conversational flow.

Using electronic health records and Internet search information for accurate influenza forecasting

BackgroundAccurate influenza activity forecasting helps public health officials prepare and allocate resources for unusual influenza activity. Traditional flu surveillance systems, such as the Centers for Disease Control and Prevention’s (CDC) influenza-like illnesses reports, lag behind real-time by one to 2 weeks, whereas information contained in cloud-based electronic health records (EHR) and in Internet users’ search activity is typically available in near real-time. We present a method that combines the information from these two data sources with historical flu activity to produce national flu forecasts for the United States up to 4 weeks ahead of the publication of CDC’s flu reports.MethodsWe extend a method originally designed to track flu using Google searches, named ARGO, to combine information from EHR and Internet searches with historical flu activities. Our regularized multivariate regression model dynamically selects the most appropriate variables for flu prediction every week. The model is assessed for the flu seasons within the time period 2013–2016 using multiple metrics including root mean squared error (RMSE).ResultsOur method reduces the RMSE of the publicly available alternative (Healthmap flutrends) method by 33, 20, 17 and 21%, for the four time horizons: real-time, one, two, and 3 weeks ahead, respectively. Such accuracy improvements are statistically significant at the 5% level. Our real-time estimates correctly identified the peak timing and magnitude of the studied flu seasons.ConclusionsOur method significantly reduces the prediction error when compared to historical publicly available Internet-based prediction systems, demonstrating that: (1) the method to combine data sources is as important as data quality; (2) effectively extracting information from a cloud-based EHR and Internet search activity leads to accurate forecast of flu.

Physician activity during outpatient visits and subjective workload

We describe methods for capturing and analyzing EHR use and clinical workflow of physicians during outpatient encounters and relating activity to physicians' self-reported workload. We collected temporally-resolved activity data including audio, video, EHR activity, and eye-gaze along with post-visit assessments of workload. These data are then analyzed through a combination of manual content analysis and computational techniques to temporally align streams, providing a range of process measures of EHR usage, clinical workflow, and physician-patient communication. Data was collected from primary care and specialty clinics at the Veterans Administration San Diego Healthcare System and UCSD Health, who use Electronic Health Record (EHR) platforms, CPRS and Epic, respectively. Grouping visit activity by physician, site, specialty, and patient status enables rank-ordering activity factors by their correlation to physicians' subjective work-load as captured by NASA Task Load Index survey. We developed a coding scheme that enabled us to compare timing studies between CPRS and Epic and extract patient and visit complexity profiles. We identified similar patterns of EHR use and navigation at the 2 sites despite differences in functions, user interfaces and consequent coded representations. Both sites displayed similar proportions of EHR function use and navigation, and distribution of visit length, proportion of time physicians attended to EHRs (gaze), and subjective work-load as measured by the task load survey. We found that visit activity was highly variable across individual physicians, and the observed activity metrics ranged widely as correlates to subjective workload. We discuss implications of our study for methodology, clinical workflow and EHR redesign.

Integration of Cognitive Modeling into the Evaluation of Electronic Health Records

Previous research has consistently cited the importance of a usable interface in the successful adoption of the electronic health record (EHR). Among the most time intensive EHR activities is Computerized Physician Order Entry (CPOE) a method of creating and modifying orders through an electronic interface. The Marshfield Clinic, a non-profit, regional health care organization in Wisconsin, is in the process of implementing a new CPOE system. As a part of early evaluation, cognitive and behavioral modeling was coupled with formative usability testing to better understand the potential use of a new CPOE design. Participants consisted of 11 medical residents. Results suggest that participants were able to complete the majority of task scenarios in less than 20 seconds per task. Additionally, the integration of a cognitive and behavioral tool provided new evaluation information to help aid in future design efforts.

Treatment and Management of Postmenopausal Osteoporosis within an Ambulatory Care Network

Background: Postmenopausal osteoporosis (PMO) is associated with substantial morbidity and mortality. Despite established guidelines for the care of PMO, the treatment and management of this disease remain suboptimal. Objective: To examine the treatment and management of PMO in a community-based ambulatory care setting. Methods: Women with a new diagnosis of PMO (International Classification of Diseases, 9th Revision, code 733.01) were identified through the electronic health records (EHR) of a community-based ambulatory care network in northern California between January 1, 2007, and December 31, 2008. Patients were included if they were 65 years of age or older and had EHR activity 26 months or more after diagnosis. They were excluded if they had a diagnosis of osteoporosis (ICD-9:733.xx) prior to the study period. A retrospective review of the EHR and medical charts was performed to identify patients who were prescribed FDA-approved pharmacotherapy, who received bone mineral density (BMD) testing via dual-energy X-ray absorptiometry (DXA), and who experienced a fracture. Results: One hundred thirty-one PMO patients were identified who met study eligibility criteria. Fifty-nine patients (45%) were prescribed FDA-approved pharmacotherapy at diagnosis. Ninety patients (69%) were prescribed pharmacotherapy at any point during follow-up. Sixty-four patients (49%) had a baseline DXA scan and 95 patients (73%) had 1 or more DXA scans during follow-up. Seventeen of 90 patients (19%) who received pharmacotherapy had 2 DXA scans within 2 years of follow-up. Nineteen fractures were reported among 16 patients during the study period. Conclusions: The proportion of patients with PMO who received pharmacologic treatment at diagnosis and had a baseline DXA scan was relatively low; however, the majority of these patients received treatment and had 1 or more DXA scans at some point during the 26-month follow-up period. Future studies should be conducted to assess the role of early BMD testing and pharmacologic intervention in optimizing outcomes among women with PMO.

Infection preventionists' awareness of and engagement in health information exchange to improve public health surveillance

Advances in electronic health record (EHR) systems and health information exchange (HIE) are shifting efforts in public health toward greater use of information systems to automate notifiable disease surveillance. Little is known about infection preventionists' (IPs) awareness, adoption, and use of these technologies to report information to public health. To measure awareness and engagement in EHR and HIE activities, an online survey of IPs was conducted in states with HIE networks. A total of 63 IPs was invited to participate; 44 IPs (69%) responded. The survey asked about the adoption and use of EHR systems, participation in regional HIE initiatives, and IP needs with respect to EHR systems and public health reporting. Over 70% of responding IPs reported access to an EHR system, but less than 20% of IPs with access to an EHR reported being involved in the design, selection, or implementation of the system. Just 10% of IPs reported that their organizations were formally engaged in HIE activities, and 49% were unaware of organizational involvement in HIE. IPs expressed a desire for better decision support, paperless reporting methods, and situational awareness of community outbreaks. Many IPs lack awareness and engagement in EHR and HIE activities, which may limit IPs ability to influence or utilize key information technologies as they are implemented in health care organizations.

The Progression of Electronic Health Records and Implications for Psychiatry

The Progression of Electronic Health Records and Implications for Psychiatry

Examining the value of electronic health records on labor and delivery

The objective of the study was to evaluate the impact of an electronic health record (EHR) on documentation completeness and patient care in a labor and delivery unit. We conducted a pre- and postintervention study to compare documentation quality and workflow before and after EHR implementation. Documentation was compared using chi(2) and Fisher's exact tests. Objective observers measured workflow activities across all shifts before and after EHR implementation and activities were compared using Kruskal-Wallis tests and analysis of covariance. Paper admission records were significantly more likely to miss key clinical information such as chief complaints (contractions, membrane status, bleeding, fetal movement, 10-64% vs 2-5%; P < .0001) and prenatal laboratory results and history (Varicella, group B Streptococcus, human immunodeficiency virus, 26-66% vs 1-16%, P < .0001). Both direct patient care and computer activities increased after EHR implementation (2 vs 12 and 12 vs 17 activities/shift, respectively, P < .0001). The introduction of an obstetric EHR improved documentation completeness without reducing direct patient care.