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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.

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OBJECTIVES: Determine if clinical decision support (CDS) malfunctions occur in a commercial electronic health record (EHR) system, characterize their pathways and describe methods of detection. METHODS: We retrospectively examined the firing rate for 226 alert type CDS rules for detection of anomalies using both expert visualization and statistical process control (SPC) methods over a five year period. Candidate anomalies were investigated and validated. RESULTS: Twenty-one candidate CDS anomalies were identified from 8,300 alert-months. Of these candidate anomalies, four were confirmed as CDS malfunctions, eight as false-positives, and nine could not be classified. The four CDS malfunctions were a result of errors in knowledge management: 1) inadvertent addition and removal of a medication code to the electronic formulary list; 2) a seasonal alert which was not activated; 3) a change in the base data structures; and 4) direct editing of an alert related to its medications. 154 CDS rules (68%) were amenable to SPC methods and the test characteristics were calculated as a sensitivity of 95%, positive predictive value of 29% and F-measure 0.44. DISCUSSION: CDS malfunctions were found to occur in our EHR. All of the pathways for these malfunctions can be described as knowledge management errors. Expert visualization is a robust method of detection, but is resource intensive. SPC-based methods, when applicable, perform reasonably well retrospectively. CONCLUSION: CDS anomalies were found to occur in a commercial EHR and visual detection along with SPC analysis represents promising methods of malfunction detection.

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PROBLEM: The U.S. health care system is undergoing a major transformation. Clinical delivery systems are now being paid according to the value of the care they provide, in accordance with the Triple Aim, which incorporates improving the quality and cost of care and the patient experience. Increasingly, financial risk is being transferred from insurers to clinical delivery systems that become responsible for both episode-based clinical care and the longitudinal care of patients. Thus, these delivery systems need to develop strategies to manage the health of populations. Academic medical centers (AMCs) serve a unique role in many markets yet may be ill prepared for this transformation. APPROACH: In 2013, Oregon Health & Science University (OHSU) partnered with a large health insurer and six other hospitals across the state to form Propel Health, a collaborative partnership designed to deliver the tools, methods, and support necessary for population health management. OHSU also developed new internal structures and transformed its business model to embrace this value-based care model. OUTCOMES: Each Propel Health partner included the employees and dependents enrolled in its employee medical plan, for approximately 55,000 covered individuals initially. By 2017, Propel Health is expected to cover 110,000 individuals. Other outcomes to measure in the future include the quality and cost of care provided under this partnership. NEXT STEPS: Anticipated challenges to overcome include insufficient primary care networks, conflicting incentives, local competition, and the magnitude of the transformation. Still, the time is right for AMCs to commit to improving the health of populations.

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Little is known about how existing electronic health records (EHRs) influence the practice of pediatric medicine. A total of 808 pediatricians participated in a survey about workflows using the EHR. The EHR was the most commonly used source of initial patient information. Seventy-two percent reported requiring between 2 and 10 minutes to complete an initial review of the EHR. Several moderately severe information barriers were reported regarding the display of information in the EHR. Pediatricians acquire information about new patients from EHRs more often than any other source. EHRs play a critical role in pediatric care but require improved design and efficiency.

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Since the launch of the clinical informatics subspecialty for physicians in 2013, over 1100 physicians have used the practice and education pathways to become board-certified in clinical informatics. Starting in 2018, only physicians who have completed a 2-year clinical informatics fellowship program accredited by the Accreditation Council on Graduate Medical Education will be eligible to take the board exam. The purpose of this viewpoint piece is to describe the collective experience of the first four programs accredited by the Accreditation Council on Graduate Medical Education and to share lessons learned in developing new fellowship programs in this novel medical subspecialty.

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BACKGROUND: Red blood cell transfusion is the most common procedure in hospitalized patients in the US. Growing evidence suggests that a sizeable percentage of these transfusions are inappropriate, putting patients at significant risk and increasing costs to the health care system. METHODS: We performed a retrospective quasi-experimental study from November 2008 until November 2014 in a 576-bed tertiary care hospital. The intervention consisted of an interruptive clinical decision support alert shown to a provider when a red blood cell transfusion was ordered in a patient whose most recent hematocrit was ≥21%. We used interrupted time series analysis to determine whether our primary outcome of interest, rate of red blood cell transfusion in patients with hematocrit ≥21% per 100 patient (pt) days, was reduced by the implementation of the clinical decision support tool. The rate of platelet transfusions was used as a nonequivalent dependent control variable. RESULTS: A total of 143,000 hospital admissions were included in our analysis. Red blood cell transfusions decreased from 9.4 to 7.8 per 100 pt days after the clinical decision support intervention was implemented. Interrupted time series analysis showed that significant decline of 0.05 (95% confidence interval [CI], 0.03-0.07; P < .001) units of red blood cells transfused per 100 pt days per month was already underway in the preintervention period. This trend accelerated to 0.1 (95% CI, 0.09-0.12; P < .001) units of red blood cells transfused per 100 pt days per month following the implementation of the clinical decision support tool. There was no statistical change in the rate of platelet transfusion resulting from the intervention. CONCLUSIONS: The implementation of an evidence-based clinical decision support tool was associated with a significant decline in the overuse of red blood cell transfusion. We believe this intervention could be easily replicated in other hospitals using commercial electronic health records and a similar reduction in overuse of red blood cell transfusions achieved.

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Clinicians today face increased patient loads, decreased reimbursements and potential negative productivity impacts of using electronic health records (EHR), but have little guidance on how to improve clinic efficiency. Discrete event simulation models are powerful tools for evaluating clinical workflow and improving efficiency, particularly when they are built from secondary EHR timing data. The purpose of this study is to demonstrate that these simulation models can be used for resource allocation decision making as well as for evaluating novel scheduling strategies in outpatient ophthalmology clinics. Key findings from this study are that: 1) secondary use of EHR timestamp data in simulation models represents clinic workflow, 2) simulations provide insight into the best allocation of resources in a clinic, 3) simulations provide critical information for schedule creation and decision making by clinic managers, and 4) simulation models built from EHR data are potentially generalizable.

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Despite federal incentives for adoption of electronic health records (EHRs), surveys have shown that EHR use is less common among specialty physicians than generalists. Concerns have been raised that current-generation EHR systems are inadequate to meet the unique information gathering needs of specialists. This study sought to identify whether information gathering needs and EHR usage patterns are different between specialists and generalists, and if so, to characterize their precise nature. We found that specialists and generalists have significantly different perceptions of which elements of the EHR are most important and how well these systems are suited to displaying clinical information. Resolution of these disparities could have implications for clinical productivity and efficiency, patient and physician satisfaction, and the ability of clinical practices to achieve Meaningful Use incentives.

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PURPOSE: To measure the effect of electronic health record (EHR) implementation on productivity and efficiency in the pediatric ophthalmology division at an academic medical center. METHODS: Four established providers were selected from the pediatric ophthalmology division at the Oregon Health & Science University Casey Eye Institute. Clinical volume was compared before and after EHR implementation for each provider. Time elapsed from chart open to completion (OTC time) and the proportion of charts completed during business hours were monitored for 3 years following implementation. RESULTS: Overall there was an 11% decrease in clinical volume following EHR implementation, which was not statistically significant (P = 0.18). The mean OTC time ranged from 5.5 to 28.3 hours among providers in this study, and trends over time were variable among the four providers. Forty-four percent of all charts were closed outside normal business hours (30% on weekdays, 14% on weekends). CONCLUSIONS: EHR implementation was associated with a negative impact on productivity and efficiency in our pediatric ophthalmology division.

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IMPORTANCE: Although electronic health record (EHR) systems have potential benefits, such as improved safety and quality of care, most ophthalmology practices in the United States have not adopted these systems. Concerns persist regarding potential negative impacts on clinical workflow. In particular, the impact of EHR operating room (OR) management systems on clinical efficiency in the ophthalmic surgery setting is unknown. OBJECTIVE: To determine the impact of an EHR OR management system on intraoperative nursing documentation time, surgical volume, and staffing requirements. DESIGN, SETTING, AND PARTICIPANTS: For documentation time and circulating nurses per procedure, a prospective cohort design was used between January 10, 2012, and January 10, 2013. For surgical volume and overall staffing requirements, a case series design was used between January 29, 2011, and January 28, 2013. This study involved ophthalmic OR nurses (n = 13) and surgeons (n = 25) at an academic medical center. EXPOSURES: Electronic health record OR management system implementation. MAIN OUTCOMES AND MEASURES: (1) Documentation time (percentage of operating time documenting [POTD], absolute documentation time in minutes), (2) surgical volume (procedures/time), and (3) staffing requirements (full-time equivalents, circulating nurses/procedure). Outcomes were measured during a baseline period when paper documentation was used and during the early (first 3 months) and late (4-12 months) periods after EHR implementation. RESULTS: There was a worsening in total POTD in the early EHR period (83%) vs paper baseline (41%) (P < .001). This improved to baseline levels by the late EHR period (46%, P = .28), although POTD in the cataract group remained worse than at baseline (64%, P < .001). There was a worsening in absolute mean documentation time in the early EHR period (16.7 minutes) vs paper baseline (7.5 minutes) (P < .001). This improved in the late EHR period (9.2 minutes) but remained worse than in the paper baseline (P < .001). While cataract procedures required more circulating nurses in the early EHR (mean, 1.9 nurses/procedure) and late EHR (mean, 1.5 nurses/procedure) periods than in the paper baseline (mean, 1.0 nurses/procedure) (P < .001), overall staffing requirements and surgical volume were not significantly different between the periods. CONCLUSIONS AND RELEVANCE: Electronic health record OR management system implementation was associated with worsening of intraoperative nursing documentation time especially in shorter procedures. However, it is possible to implement an EHR OR management system without serious negative impacts on surgical volume and staffing requirements.

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PURPOSE: To evaluate three measures related to electronic health record (EHR) implementation: clinical volume, time requirements, and nature of clinical documentation. Comparison is made to baseline paper documentation. METHODS: An academic ophthalmology department implemented an EHR in 2006. A study population was defined of faculty providers who worked the 5 months before and after implementation. Clinical volumes, as well as time length for each patient encounter, were collected from the EHR reporting system. To directly compare time requirements, two faculty providers who utilized both paper and EHR systems completed time-motion logs to record the number of patients, clinic time, and nonclinic time to complete documentation. Faculty providers and databases were queried to identify patient records containing both paper and EHR notes, from which three cases were identified to illustrate representative documentation differences. RESULTS: Twenty-three faculty providers completed 120,490 clinical encounters during a 3-year study period. Compared to baseline clinical volume from 3 months pre-implementation, the post-implementation volume was 88% in quarter 1, 93% in year 1, 97% in year 2, and 97% in year 3. Among all encounters, 75% were completed within 1.7 days after beginning documentation. The mean total time per patient was 6.8 minutes longer with EHR than paper (P<.01). EHR documentation involved greater reliance on textual interpretation of clinical findings, whereas paper notes used more graphical representations, and EHR notes were longer and included automatically generated text. CONCLUSION: This EHR implementation was associated with increased documentation time, little or no increase in clinical volume, and changes in the nature of ophthalmic documentation.

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OBJECTIVE: To evaluate quantitative and qualitative differences in documentation of the ophthalmic examination between paper and electronic health record (EHR) systems. DESIGN: Comparative case series. PARTICIPANTS: One hundred fifty consecutive pairs of matched paper and EHR notes, documented by 3 attending ophthalmologist providers. METHODS: An academic ophthalmology department implemented an EHR system in 2006. Database queries were performed to identify cases in which the same problems were documented by the same provider on different dates, using paper versus EHR methods. This was done for 50 consecutive pairs of examinations in 3 different diseases: age-related macular degeneration (AMD), glaucoma, and pigmented choroidal lesions (PCLs). Quantitative measures were used to compare completeness of documenting the complete ophthalmologic examination, as well as disease-specific critical findings using paper versus an EHR system. Qualitative differences in paper versus EHR documentation were illustrated by selecting representative paired examples. MAIN OUTCOME MEASURES: (1) Documentation score, defined as the number of examination elements recorded for the slit-lamp examination, fundus examination, and complete ophthalmologic examination and for critical clinical findings for each disease. (2) Paired comparison of qualitative differences in paper versus EHR documentation. RESULTS: For all 3 diseases (AMD, glaucoma, PCL), the number of complete examination findings recorded was significantly lower with paper than the EHR system (P ≤ 0.004). Among the 3 individual examination sections (general, slit lamp, fundus) for the 3 diseases, 5 of the 9 possible combinations had significantly lower mean documentation scores with paper than EHR notes. For 2 of the 3 diseases, the number of critical clinical findings recorded was significantly lower using paper versus EHR notes (P ≤ 0.022). All (150/150) paper notes relied on graphical representations using annotated hand-drawn sketches, whereas no (0/150) EHR notes contained drawings. Instead, the EHR systems documented clinical findings using textual descriptions and interpretations. CONCLUSIONS: There were quantitative and qualitative differences in the nature of paper versus EHR documentation of ophthalmic findings in this study. The EHR notes included more complete documentation of examination elements using structured textual descriptions and interpretations, whereas paper notes used graphical representations of findings. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

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Efficiency and quality of documentation are critical in surgical settings because operating rooms are a major source of revenue, and because adverse events may have enormous consequences. Electronic health records (EHRs) have potential to impact surgical volume, quality, and documentation time. Ophthalmology is an ideal domain to examine these issues because procedures are high-throughput and demand efficient documentation. This time-motion study examines nursing documentation during implementation of an EHR operating room management system in an ophthalmology department. Key findings are: (1) EHR nursing documentation time was significantly worse during early implementation, but improved to a level near but slightly worse than paper baseline, (2) Mean documentation time varied significantly among nurses during early implementation, and (3) There was no decrease in operating room turnover time or surgical volume after implementation. These findings have important implications for ambulatory surgery departments planning EHR implementation, and for research in system design.

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INTRODUCTION: Previous studies within the aeromedical literature have looked at factors associated with fatal outcomes in helicopter medical transport, but no analysis has been conducted on fixed-wing aeromedical flights. The purpose of this study was to look at fatality rates in fixed-wing aeromedical transport and compare them with general aviation and helicopter aeromedical flights. METHODS: This study looked at factors associated with fatal outcomes in fixed-wing aeromedical flights, using the National Transportation Safety Board Aviation Accident Incident Database from 1984 to 2009. RESULTS: Fatal outcomes were significantly higher in medical flights (35.6 vs. 19.7%), with more aircraft fires (20.3 vs. 10.5%) and on-ground collisions (5.1 vs. 2.0%) compared with commercial flights. Aircraft fires occurred in 12 of the 21 fatal crashes (57.1%), compared with only 2 of the 38 nonfatal crashes (5.3%) (P < .001). In the multiple logistic regression model, the only factor with increased odds of a fatal outcome was the presence of a fire (56.89; 95% CI, 4.28-808.23). CONCLUSIONS: Similar to published studies in helicopter medical transport, postcrash fires are the primary factor associated with fatal outcomes in fixed-wing aeromedical flights.

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Test result management is an integral aspect of quality clinical care and a crucial part of the ambulatory medicine workflow. Correct and timely communication of results to a provider is the necessary first step in ambulatory result management and has been identified as a weakness in many paper-based systems. While electronic health records (EHRs) hold promise for improving the reliability of result management, the complexities involved make this a challenging task. Experience with test result management is reported, four new categories of result management errors identified are outlined, and solutions developed during a 2-year deployment of a commercial EHR are described. Recommendations for improving test result management with EHRs are then given.

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OBJECTIVE: Computerized physician documentation (CPD) has been implemented throughout the nation's Veterans Affairs Medical Centers (VAMCs) and is likely to increasingly replace handwritten documentation in other institutions. The use of this technology may affect educational and clinical activities, yet little has been reported in this regard. The authors conducted a qualitative study to determine the perceived impacts of CPD among faculty and housestaff in a VAMC. DESIGN: A cross-sectional study was conducted using semistructured interviews with faculty (n = 10) and a group interview with residents (n = 10) at a VAMC teaching hospital. MEASUREMENTS: Content analysis of field notes and taped transcripts were done by two independent reviewers using a grounded theory approach. Findings were validated using member checking and peer debriefing. RESULTS: Four major themes were identified: (1) improved availability of documentation; (2) changes in work processes and communication; (3) alterations in document structure and content; and (4) mistakes, concerns, and decreased confidence in the data. With a few exceptions, subjects felt documentation was more available, with benefits for education and patient care. Other impacts of CPD were largely seen as detrimental to aspects of clinical practice and education, including documentation quality, workflow, professional communication, and patient care. CONCLUSION: CPD is perceived to have substantial positive and negative impacts on clinical and educational activities and environments. Care should be taken when designing, implementing, and using such systems to avoid or minimize any harmful impacts. More research is needed to assess the extent of the impacts identified and to determine the best strategies to effectively deal with them.

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