Publications by authors named "Isaac H Goldstein"

18 Publications

  • Page 1 of 1

Clinical Documentation During Scribed and Non-scribed Ophthalmology Office Visits.

Ophthalmol Sci 2021 Dec 6;1(4). Epub 2021 Dec 6.

Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon.

Purpose: Observe the impact of employing scribes on documentation efficiency in ophthalmology clinics.

Design: Single-center retrospective cohort study.

Participants: A total of 29,997 outpatient visits conducted by seven attending ophthalmologists between 1/1/2018 and 12/31/2019 were included in the study; 18,483 with a scribe present during the encounter and 11,514 without a scribe present.

Intervention: Use of a scribe.

Main Outcome Measures: Total physician documentation time, physician documentation time during and after the visit, visit length, time to chart closure, note length, and percent of note text edited by physician.

Results: Total physician documentation time was significantly less when working with a scribe (mean ± SD, 4.7 ± 2.9 vs. 7.6 ± 3.8 minutes/note, <.001), as was documentation time during the visit (2.8 ± 2.2 vs. 5.9 ± 3.1 minutes/note, <.001). Physicians also edited scribed notes less, deleting 1.9 ± 4.4% of scribes' draft note text and adding 14.8 ± 11.4% of the final note text, compared to deleting 6.0 ± 9.1%(<.001) of draft note text and adding 21.2 ± 15.3%(<.001) of final note text when not working with a scribe. However, physician after-visit documentation time was significantly higher with a scribe for 3 of 7 physicians (<.001). Scribe use was also associated with an office visit length increase of 2.9 minutes (<.001) per patient and time to chart closure of 3.0 hours (<.001), according to mixed-effects linear models.

Conclusions: Scribe use was associated with increased documentation efficiency through lower total documentation time and less note editing by physicians. However, the use of a scribe was also associated with longer office visit lengths and time to chart closure. The variability in the impact of scribe use on different measures of documentation efficiency leaves unanswered questions about best practices for the implementation of scribes, and warrants further study of effective scribe use.
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http://dx.doi.org/10.1016/j.xops.2021.100088DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8765735PMC
December 2021

Electronic health record note review in an outpatient specialty clinic: who is looking?

JAMIA Open 2021 Jul 31;4(3):ooab044. Epub 2021 Jul 31.

National Eye Institute, National Institutes of Health, Bethesda, Maryland, USA.

Note entry and review in electronic health records (EHRs) are time-consuming. While some clinics have adopted team-based models of note entry, how these models have impacted note review is unknown in outpatient specialty clinics such as ophthalmology. We hypothesized that ophthalmologists and ancillary staff review very few notes. Using audit log data from 9775 follow-up office visits in an academic ophthalmology clinic, we found ophthalmologists reviewed a median of 1 note per visit (2.6 ± 5.3% of available notes), while ancillary staff reviewed a median of 2 notes per visit (4.1 ± 6.2% of available notes). While prior ophthalmic office visit notes were the most frequently reviewed note type, ophthalmologists and staff reviewed no such notes in 51% and 31% of visits, respectively. These results highlight the collaborative nature of note review and raise concerns about how cumbersome EHR designs affect efficient note review and the utility of prior notes in ophthalmic clinical care.
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http://dx.doi.org/10.1093/jamiaopen/ooab044DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325486PMC
July 2021

Methods for Large-Scale Quantitative Analysis of Scribe Impacts on Clinical Documentation.

AMIA Annu Symp Proc 2020 25;2020:573-582. Epub 2021 Jan 25.

Department of Medical Informatics and Clinical Epidemiology.

Many medical providers employ scribes to manage electronic health record (EHR) documentation. Prior studies have shown the benefits of scribes, but no large-scale study has quantitively assessed scribe impact on documentation workflows. We propose methods that leverage EHR data for identifying scribe presence during an office visit, measuring provider documentation time, and determining how notes are edited and composed. In a case study, we found scribe use was associated with less provider documentation time overall (averaging 2.4 minutes or 39% less time, p < 0.001), fewer note edits by providers (8.4% less added and 4.2% less deleted text, p < 0.001), but significantly more documentation time after the visit for four out of seven providers (p < 0.001) and no change in the amount of copied and imported note text. Our methods could validate prior study results, identify variability for determining best practices, and determine that scribes do not improve all aspects of documentation.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075531PMC
June 2021

Application of Machine Learning to Predict Patient No-Shows in an Academic Pediatric Ophthalmology Clinic.

AMIA Annu Symp Proc 2020 25;2020:293-302. Epub 2021 Jan 25.

Department of Ophthalmology, Casey Eye Institute, and.

Patient "no-shows" are missed appointments resulting in clinical inefficiencies, revenue loss, and discontinuity of care. Using secondary electronic health record (EHR) data, we used machine learning to predict patient no-shows in follow-up and new patient visits in pediatric ophthalmology and to evaluate features for importance. The best model, XGBoost, had an area under the receiver operating characteristics curve (AUC) score of 0.90 for predicting no-shows in follow-up visits. The key findings from this study are: (1) secondary use of EHR data can be used to build datasets for predictive modeling and successfully predict patient no-shows in pediatric ophthalmology, (2) models predicting no-shows for follow-up visits are more accurate than those for new patient visits, and (3) the performance of predictive models is more robust in predicting no-shows compared to individual important features. We hope these models will be used for more effective interventions to mitigate the impact ofpatient no-shows.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075453PMC
June 2021

Clinical Documentation as End-User Programming.

Proc SIGCHI Conf Hum Factor Comput Syst 2020 Apr;2020

Medical Informatics & Clinical Epidemiology, Oregon Health & Science University.

As healthcare providers have transitioned from paper to electronic health records they have gained access to increasingly sophisticated documentation aids such as custom note templates. However, little is known about how providers use these aids. To address this gap, we examine how 48 ophthalmologists and their staff create and use - a customizable and composable form of note template - to document office visits across two years. In this case study, we find 1) content-importing phrases were used to document the vast majority of visits (95%), 2) most content imported by these phrases was structured data imported by data-links rather than boilerplate text, and 3) providers primarily used phrases they had created while staff largely used phrases created by other people. We conclude by discussing how framing clinical documentation as end-user programming can inform the design of electronic health records and other documentation systems mixing data and narrative text.
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http://dx.doi.org/10.1145/3313831.3376205DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901830PMC
April 2020

Predicting Wait Times in Pediatric Ophthalmology Outpatient Clinic Using Machine Learning.

AMIA Annu Symp Proc 2019 4;2019:1121-1128. Epub 2020 Mar 4.

Departments of Medical Informatics and Clinical Epidemiology and.

Patient perceptions of wait time during outpatient office visits can affect patient satisfaction. Providing accurate information about wait times could improve patients' satisfaction by reducing uncertainty. However, these are rarely known about efficient ways to predict wait time in the clinic. Supervised machine learning algorithms is a powerful tool for predictive modeling with large and complicated data sets. In this study, we tested machine learning models to predict wait times based on secondary EHR data in pediatric ophthalmology outpatient clinic. We compared several machine-learning algorithms, including random forest, elastic net, gradient boosting machine, support vector machine, and multiple linear regressions to find the most accurate model for prediction. The importance of the predictors was also identified via machine learning models. In the future, these models have the potential to combine with real-time EHR data to provide real time accurate estimates of patient wait time outpatient clinics.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7153152PMC
August 2020

Aggressive Posterior Retinopathy of Prematurity: Clinical and Quantitative Imaging Features in a Large North American Cohort.

Ophthalmology 2020 08 7;127(8):1105-1112. Epub 2020 Feb 7.

Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon. Electronic address:

Purpose: Aggressive posterior retinopathy of prematurity (AP-ROP) is a vision-threatening disease with a significant rate of progression to retinal detachment. The purpose of this study was to characterize AP-ROP quantitatively by demographics, rate of disease progression, and a deep learning-based vascular severity score.

Design: Retrospective analysis.

Participants: The Imaging and Informatics in ROP cohort from 8 North American centers, consisting of 947 patients and 5945 clinical eye examinations with fundus images, was used. Pretreatment eyes were categorized by disease severity: none, mild, type 2 or pre-plus, treatment-requiring (TR) without AP-ROP, TR with AP-ROP. Analyses compared TR with AP-ROP and TR without AP-ROP to investigate differences between AP-ROP and other TR disease.

Methods: A reference standard diagnosis was generated for each eye examination using previously published methods combining 3 independent image-based gradings and 1 ophthalmoscopic grading. All fundus images were analyzed using a previously published deep learning system and were assigned a score from 1 through 9.

Main Outcome Measures: Birth weight, gestational age, postmenstrual age, and vascular severity score.

Results: Infants who demonstrated AP-ROP were more premature by birth weight (617 g vs. 679 g; P = 0.01) and gestational age (24.3 weeks vs. 25.0 weeks; P < 0.01) and reached peak severity at an earlier postmenstrual age (34.7 weeks vs. 36.9 weeks; P < 0.001) compared with infants with TR without AP-ROP. The mean vascular severity score was greatest in TR with AP-ROP infants compared with TR without AP-ROP infants (8.79 vs. 7.19; P < 0.001). Analyzing the severity score over time, the rate of progression was fastest in infants with AP-ROP (P < 0.002 at 30-32 weeks).

Conclusions: Premature infants in North America with AP-ROP are born younger and demonstrate disease earlier than infants with less severe ROP. Disease severity is quantifiable with a deep learning-based score, which correlates with clinically identified categories of disease, including AP-ROP. The rate of progression to peak disease is greatest in eyes that demonstrate AP-ROP compared with other treatment-requiring eyes. Analysis of quantitative characteristics of AP-ROP may help improve diagnosis and treatment of an aggressive, vision-threatening form of ROP.
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http://dx.doi.org/10.1016/j.ophtha.2020.01.052DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384953PMC
August 2020

Electronic Health Records in Ophthalmology: Source and Method of Documentation.

Am J Ophthalmol 2020 03 5;211:191-199. Epub 2019 Dec 5.

Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA; Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, USA. Electronic address:

Purpose: This study analyzed and quantified the sources of electronic health record (EHR) text documentation in ophthalmology progress notes.

Design: EHR documentation review and analysis.

Methods: Setting: a single academic ophthalmology department.

Study Population: a cohort study conducted between November 1, 2016, and December 31, 2018, using secondary EHR data and a follow-up manual review of a random samples. The cohort study included 123,274 progress notes documented by 42 attending providers. These notes were for patients with the 5 most common primary International Statistical Classification of Diseases and Related Health Problems, version 10, parent codes for each provider. For the manual review, 120 notes from 8 providers were randomly sampled. Main outcome measurements were characters or number of words in each note categorized by attribution source, author type, and time of creation.

Results: Imported text entries made up the majority of text in new and return patients, 2,978 characters (77%) and 3,612 characters (91%). Support staff members authored substantial portions of notes; 3,024 characters (68%) of new patient notes, 3,953 characters (83%) of return patient notes. Finally, providers completed large amounts of documentation after clinical visits: 135 words (35%) of new patient notes, 102 words (27%) of return patient notes.

Conclusions: EHR documentation consists largely of imported text, is often authored by support staff, and is often written after the end of a visit. These findings raise questions about documentation accuracy and utility and may have implications for quality of care and patient-provider relationships.
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http://dx.doi.org/10.1016/j.ajo.2019.11.030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7073273PMC
March 2020

Redundancy of Progress Notes for Serial Office Visits.

Ophthalmology 2020 01 21;127(1):134-135. Epub 2019 Jun 21.

Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon; Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon.

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http://dx.doi.org/10.1016/j.ophtha.2019.06.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6925342PMC
January 2020

Secondary Use of Electronic Health Record Data for Prediction of Outpatient Visit Length in Ophthalmology Clinics.

AMIA Annu Symp Proc 2018 5;2018:1387-1394. Epub 2018 Dec 5.

Departments of Medical Informatics and Clinical Epidemiology, OHSU, Portland, OR.

Electronic health record systems have dramatically transformed the process of medical care, but one challenge has been increased time requirements for physicians. In this study, we address this challenge by developing and validating analytic models for predicting patient encounter length based on secondary EHR data. Key findings from this study are: (1) Secondary use of EHR data may be captured to predict provider interaction time with patients; (2) Modeling results using secondary data may provide more accurate predictions of provider interaction time than an expert provide; (3) These findings suggest that secondary use of EHR data may be used to develop effective customized scheduling methods to improve clinical efficiency. In the future, this has the potential to contribute toward methods for improved clinical scheduling and efficiency.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371379PMC
January 2020

Clinical Documentation in Electronic Health Record Systems: Analysis of Similarity in Progress Notes from Consecutive Outpatient Ophthalmology Encounters.

AMIA Annu Symp Proc 2018 5;2018:1310-1318. Epub 2018 Dec 5.

Medical Informatics & Clinical Epidemiology.

Content importing technology enables duplication of large amounts of clinical text in electronic health record (EHR) progress notes. It can be difficult to find key sections such as Assessment and Plan in the resulting note. To quantify the extent of text length and duplication, we analyzed average ophthalmology note length and calculated novelty of each major note section (Subjective, Objective, Assessment, Plan, Other). We performed a retrospective chart review of consecutive note pairs and found that the average encounter note was 1182 ± 374 words long and less than a quarter of words changed between visits. The Plan note section had the highest percentage of change, and both the Assessment and Plan sections comprised a small fraction of the full note. Analysis of progress notes by section and unique content helps describe physician documentation activity and inform best practices and EHR design recommendations.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371381PMC
January 2020

Clinical Documentation in Electronic Health Record Systems: Analysis of Patient Record Review During Outpatient Ophthalmology Visits.

AMIA Annu Symp Proc 2018 5;2018:584-591. Epub 2018 Dec 5.

Department of Medical Informatics and Clinical Epidemiology.

Busy clinicians struggle with productivity and usability in electronic health record systems (EHRs). While previous studies have investigated documentation practices and strategies in the inpatient setting, outpatient documentation and review practices by clinicians using EHRs are relatively unknown. In this study, we look at clinicians' patterns of note review in the EHR during outpatient follow-up office visits in ophthalmology. Key findings from this study are that the number and percentage of notes reviewed is very low, there is variation between providers, specialties, and users, and staff access more notes than physicians. These findings suggest that the vast majority of content in the EHR is not being used by clinicians; improved EHR designs would better present this data and support the information needs of outpatient clinicians.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371368PMC
October 2019

Analysis of Total Time Requirements of Electronic Health Record Use by Ophthalmologists Using Secondary EHR Data.

AMIA Annu Symp Proc 2018 5;2018:490-497. Epub 2018 Dec 5.

Medical Informatics & Clinical Epidemiology.

Electronic Health Records (EHRs) are widely used in the United States for clinical care and billing activities. Their widespread adoption has raised a variety of concerns about their effects on providers and medical care. As researchers address these concerns, they will need to understand how much time providers actually spend on the EHR. This study develops and validates methods for calculating total time requirements for EHR use by ophthalmologists using secondary EHR data from audit logs. Key findings from this study are that (1) Secondary EHR data can be used to estimate lower bounds on provider EHR use, (2) Providers spend a large amount of time using the EHR, (3) Most time spent on the EHR is spent reviewing information. These findings have important implications for practicing clinicians, and for EHR system design in the future.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371357PMC
September 2019

Changes in Electronic Health Record Use Time and Documentation over the Course of a Decade.

Ophthalmology 2019 06 18;126(6):783-791. Epub 2019 Jan 18.

Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon. Electronic address:

Purpose: With the current wide adoption of electronic health records (EHRs) by ophthalmologists, there are widespread concerns about the amount of time spent using the EHR. The goal of this study was to examine how the amount of time spent using EHRs as well as related documentation behaviors changed 1 decade after EHR adoption.

Design: Single-center cohort study.

Participants: Six hundred eighty-five thousand three hundred sixty-one office visits with 70 ophthalmology providers.

Methods: We calculated time spent using the EHR associated with each individual office visit using EHR audit logs and determined chart closure times and progress note length from secondary EHR data. We tracked and modeled how these metrics changed from 2006 to 2016 with linear mixed models.

Main Outcome Measures: Minutes spent using the EHR associated with an office visit, chart closure time in hours from the office visit check-in time, and progress note length in characters.

Results: Median EHR time per office visit in 2006 was 4.2 minutes (interquartile range [IQR], 3.5 minutes), and increased to 6.4 minutes (IQR, 4.5 minutes) in 2016. Median chart closure time was 2.8 hours (IQR, 21.3 hours) in 2006 and decreased to 2.3 hours (IQR, 18.5 hours) in 2016. In 2006, median note length was 1530 characters (IQR, 1435 characters) and increased to 3838 characters (IQR, 2668.3 characters) in 2016. Linear mixed models found EHR time per office visit was 31.9±0.2% (P < 0.001) greater from 2014 through 2016 than from 2006 through 2010, chart closure time was 6.7±0.3 hours (P < 0.001) shorter from 2014 through 2016 versus 2006 through 2010, and note length was 1807.4±6.5 characters (P < 0.001) longer from 2014 through 2016 versus 2006 through 2010.

Conclusions: After 1 decade of use, providers spend more time using the EHR for an office visit, generate longer notes, and close the chart faster. These changes are likely to represent increased time and documentation pressure for providers. Electronic health record redesign and new documentation regulations may help to address these issues.
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http://dx.doi.org/10.1016/j.ophtha.2019.01.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6534421PMC
June 2019

Data-Driven Scheduling for Improving Patient Efficiency in Ophthalmology Clinics.

Ophthalmology 2019 03 10;126(3):347-354. Epub 2018 Oct 10.

Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon; Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.

Purpose: To improve clinic efficiency through development of an ophthalmology scheduling template developed using simulation models and electronic health record (EHR) data.

Design: We created a computer simulation model of 1 pediatric ophthalmologist's clinic using EHR timestamp data, which was used to develop a scheduling template based on appointment length (short, medium, or long). We assessed its impact on clinic efficiency after implementation in the practices of 5 different pediatric ophthalmologists.

Participants: We observed and timed patient appointments in person (n = 120) and collected EHR timestamps for 2 years of appointments (n = 650). We calculated efficiency measures for 172 clinic sessions before implementation vs. 119 clinic sessions after implementation.

Methods: We validated clinic workflow timings calculated from EHR timestamps and the simulation models based on them with observed timings. From simulation tests, we developed a new scheduling template and evaluated it with efficiency metrics before vs. after implementation.

Main Outcome Measures: Measurements of clinical efficiency (mean clinic volume, patient wait time, examination time, and clinic length).

Results: Mean physician examination time calculated from EHR timestamps was 13.8±8.2 minutes and was not statistically different from mean physician examination time from in-person observation (13.3±7.3 minutes; P = 0.7), suggesting that EHR timestamps are accurate. Mean patient wait time for the simulation model (31.2±10.9 minutes) was not statistically different from the observed mean patient wait times (32.6±25.3 minutes; P = 0.9), suggesting that simulation models are accurate. After implementation of the new scheduling template, all 5 pediatric ophthalmologists showed statistically significant improvements in clinic volume (mean increase of 1-3 patients/session; P ≤ 0.05 for 2 providers; P ≤ 0.008 for 3 providers), whereas 4 of 5 had improvements in mean patient wait time (average improvements of 3-4 minutes/patient; statistically significant for 2 providers, P ≤ 0.008). All of the ophthalmologists' examination times remained the same before and after implementation.

Conclusions: Simulation models based on big data from EHRs can test clinic changes before real-life implementation. A scheduling template using predicted appointment length improves clinic efficiency and may generalize to other clinics. Electronic health records have potential to become tools for supporting clinic operations improvement.
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http://dx.doi.org/10.1016/j.ophtha.2018.10.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6391189PMC
March 2019

Quantifying the Impact of Trainee Providers on Outpatient Clinic Workflow using Secondary EHR Data.

AMIA Annu Symp Proc 2017 16;2017:760-769. Epub 2018 Apr 16.

Medical Informatics & Clinical Epidemiology, Portland, OR.

Providers today face productivity challenges including increased patient loads, increased clerical burdens from new government regulations and workflow impacts of electronic health records (EHR). Given these factors, methods to study and improve clinical workflow continue to grow in importance. Despite the ubiquitous presence of trainees in academic outpatient clinics, little is known about the impact of trainees on academic workflow. The purpose of this study is to demonstrate that secondary EHR data can be used to quantify that impact, with potentially important results for clinic efficiency and provider reimbursement models. Key findings from this study are that (1) Secondary EHR data can be used to reflect in clinic trainee activity, (2) presence of trainees, particularly in high-volume clinic sessions, is associated with longer session lengths, and (3) The timing of trainee appointments within clinic sessions impacts the session length.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977711PMC
March 2019

Association of the Presence of Trainees With Outpatient Appointment Times in an Ophthalmology Clinic.

JAMA Ophthalmol 2018 01;136(1):20-26

Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland.

Importance: Physicians face pressure to improve clinical efficiency, particularly with electronic health record (EHR) adoption and gradual shifts toward value-based reimbursement models. These pressures are especially pronounced in academic medical centers, where delivery of care must be balanced with medical education. However, the association of the presence of trainees with clinical efficiency in outpatient ophthalmology clinics is not known.

Objective: To quantify the association of the presence of trainees (residents and fellows) and efficiency in an outpatient ophthalmology clinic.

Design, Setting, And Participants: This single-center cohort study was conducted from January 1 through December 31, 2014, at an academic department of ophthalmology. Participants included 49 448 patient appointments with 33 attending physicians and 40 trainees.

Exposures: Presence vs absence of trainees in an appointment or clinic session, as determined by review of the EHR audit log.

Main Outcomes And Measures: Patient appointment time, as determined by time stamps in the EHR clinical data warehouse. Linear mixed models were developed to analyze variability among clinicians and patients.

Results: Among the 33 study physicians (13 women [39%] and 20 men [61%]; median age, 44 years [interquartile range, 39-53 years]), appointments with trainees were significantly longer than appointments in clinic sessions without trainees (mean [SD], 105.0 [55.7] vs 80.3 [45.4] minutes; P < .001). The presence of a trainee in a clinic session was associated with longer mean appointment time, even in appointments for which the trainee was not present (mean [SD], 87.2 [49.2] vs 80.3 [45.4] minutes; P < .001). Among 33 study physicians, 3 (9%) had shorter mean appointment times when a trainee was present, 1 (3%) had no change, and 29 (88%) had longer mean appointment times when a trainee was present. Linear mixed models showed the presence of a resident was associated with a lengthening of appointment time of 17.0 minutes (95% CI, 15.6-18.5 minutes; P < .001), and the presence of a fellow was associated with a lengthening of appointment time of 13.5 minutes (95% CI, 12.3-14.8 minutes; P < .001).

Conclusions And Relevance: Presence of trainees was associated with longer appointment times, even for patients not seen by a trainee. Although numerous limitations to this study design might affect the interpretation of the findings, these results highlight a potential challenge of maintaining clinical efficiency in academic medical centers and raise questions about physician reimbursement models.
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http://dx.doi.org/10.1001/jamaophthalmol.2017.4816DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5766373PMC
January 2018

Secondary use of electronic health record data for clinical workflow analysis.

J Am Med Inform Assoc 2018 01;25(1):40-46

Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA.

Objective: Outpatient clinics lack guidance for tackling modern efficiency and productivity demands. Workflow studies require large amounts of timing data that are prohibitively expensive to collect through observation or tracking devices. Electronic health records (EHRs) contain a vast amount of timing data - timestamps collected during regular use - that can be mapped to workflow steps. This study validates using EHR timestamp data to predict outpatient ophthalmology clinic workflow timings at Oregon Health and Science University and demonstrates their usefulness in 3 different studies.

Materials And Methods: Four outpatient ophthalmology clinics were observed to determine their workflows and to time each workflow step. EHR timestamps were mapped to the workflow steps and validated against the observed timings.

Results: The EHR timestamp analysis produced times that were within 3 min of the observed times for >80% of the appointments. EHR use patterns affected the accuracy of using EHR timestamps to predict workflow times.

Discussion: EHR timestamps provided a reasonable approximation of workflow and can be used for workflow studies. They can be used to create simulation models, analyze EHR use, and quantify the impact of trainees on workflow.

Conclusion: The secondary use of EHR timestamp data is a valuable resource for clinical workflow studies. Sample timestamp data files and algorithms for processing them are provided and can be used as a template for more studies in other clinical specialties and settings.
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http://dx.doi.org/10.1093/jamia/ocx098DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6080808PMC
January 2018
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