Publications by authors named "Michelle R Hribar"

36 Publications

Comparing Scribed and Non-scribed Outpatient Progress Notes.

AMIA Annu Symp Proc 2021 21;2021:1059-1068. Epub 2022 Feb 21.

Oregon Health & Science University, Portland, OR.

Working with scribes can reduce provider documentation time, but few studies have examined how scribes affect clinical notes. In this retrospective cross-sectional study, we examine over 50,000 outpatient progress notes written with and without scribe assistance by 70 providers across 27 specialties in 2017-2018. We find scribed notes were consistently longer than those written without scribe assistance, with most additional text coming from note templates. Scribed notes were also more likely to contain certain templated lists, such as the patient's medications or past medical history. However, there was significant variation in how working with scribes affected a provider's mix of typed, templated, and copied note text, suggesting providers adapt their documentation workflows to varying degrees when working with scribes. These results suggest working with scribes may contribute to note bloat, but that providers' individual documentation workflows, including their note templates, may have a large impact on scribed note contents.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861667PMC
April 2022

Extraction of Active Medications and Adherence Using Natural Language Processing for Glaucoma Patients.

AMIA Annu Symp Proc 2021 21;2021:773-782. Epub 2022 Feb 21.

Medical Informatics & Clinical Epidemiology.

Accuracy of medication data in electronic health records (EHRs) is crucial for patient care and research, but many studies have shown that medication lists frequently contain errors. In contrast, physicians often pay more attention to the clinical notes and record medication information in them. The medication information in notes may be used for medication reconciliation to improve the medication lists' accuracy. However, accurately extracting patient's current medications from free-text narratives is challenging. In this study, we first explored the discrepancies between medication documentation in medication lists and progress notes for glaucoma patients by manually reviewing patients' charts. Next, we developed and validated a named entity recognition model to identify current medication and adherence from progress notes. Lastly, a prototype tool for medication reconciliation using the developed model was demonstrated. In the future, the model has the potential to be incorporated into the EHR system to help with realtime medication reconciliation.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861739PMC
April 2022

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

Frequent but fragmented: use of note templates to document outpatient visits at an academic health center.

J Am Med Inform Assoc 2021 12;29(1):137-141

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

Recent changes to billing policy have reduced documentation requirements for outpatient notes, providing an opportunity to rethink documentation workflows. While many providers use templates to write notes-whether to insert short phrases or draft entire notes-we know surprisingly little about how these templates are used in practice. In this retrospective cross-sectional study, we observed the templates that primary providers and other members of the care team used to write the provider progress note for 2.5 million outpatient visits across 52 specialties at an academic health center between 2018 and 2020. Templates were used to document 89% of visits, with a median of 2 used per visit. Only 17% of the 100 230 unique templates were ever used by more than one person and most providers had their own full-note templates. These findings suggest template use is frequent but fragmented, complicating template revision and maintenance. Reframing template use as a form of computer programming suggests ways to maintain the benefits of personalization while leveraging standardization to reduce documentation burden.
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http://dx.doi.org/10.1093/jamia/ocab230DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714279PMC
December 2021

Pediatric Ophthalmology Provider and Staff Attitudes and Patient Satisfaction in Telehealth Implementation During COVID-19.

Telemed J E Health 2022 May 14;28(5):675-681. Epub 2021 Sep 14.

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

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http://dx.doi.org/10.1089/tmj.2021.0189DOI Listing
May 2022

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

Length and Redundancy of Outpatient Progress Notes Across a Decade at an Academic Medical Center.

JAMA Netw Open 2021 07 1;4(7):e2115334. Epub 2021 Jul 1.

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

Importance: There is widespread concern that clinical notes have grown longer and less informative over the past decade. Addressing these concerns requires a better understanding of the magnitude, scope, and potential causes of increased note length and redundancy.

Objective: To measure changes between 2009 and 2018 in the length and redundancy of outpatient progress notes across multiple medical specialties and investigate how these measures associate with author experience and method of note entry.

Design, Setting, And Participants: This cross-sectional study was conducted at Oregon Health & Science University, a large academic medical center. Participants included clinicians and staff who wrote outpatient progress notes between 2009 and 2018 for a random sample of 200 000 patients. Statistical analysis was performed from March to August 2020.

Exposures: Use of a comprehensive electronic health record to document patient care.

Main Outcomes And Measures: Note length, note redundancy (ie, the proportion of text identical to the patient's last note), and percentage of templated, copied, or directly typed note text.

Results: A total of 2 704 800 notes written by 6228 primary authors across 46 specialties were included in this study. Median note length increased 60.1% (99% CI, 46.7%-75.2%) from a median of 401 words (interquartile range [IQR], 225-660 words) in 2009 to 642 words (IQR, 399-1007 words) in 2018. Median note redundancy increased 10.9 percentage points (99% CI, 7.5-14.3 percentage points) from 47.9% in 2009 to 58.8% in 2018. Notes written in 2018 had a mean value of just 29.4% (99% CI, 28.2%-30.7%) directly typed text with the remaining 70.6% of text being templated or copied. Mixed-effect linear models found that notes with higher proportions of templated or copied text were significantly longer and more redundant (eg, in the 2-year model, each 1% increase in the proportion of copied or templated note text was associated with 1.5% [95% CI, 1.5%-1.5%] and 1.6% [95% CI, 1.6%-1.6%] increases in note length, respectively). Residents and fellows also wrote significantly (26.3% [95% CI, 25.8%-26.7%]) longer notes than more senior authors, as did more recent hires (1.8% for each year later [95% CI, 1.3%-2.4%]).

Conclusions And Relevance: In this study, outpatient progress notes grew longer and more redundant over time, potentially limiting their use in patient care. Interventions aimed at reducing outpatient progress note length and redundancy may need to simultaneously address multiple factors such as note template design and training for both new and established clinicians.
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http://dx.doi.org/10.1001/jamanetworkopen.2021.15334DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290305PMC
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

Measures of electronic health record use in outpatient settings across vendors.

J Am Med Inform Assoc 2021 04;28(5):955-959

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

Electronic health record (EHR) log data capture clinical workflows and are a rich source of information to understand variation in practice patterns. Variation in how EHRs are used to document and support care delivery is associated with clinical and operational outcomes, including measures of provider well-being and burnout. Standardized measures that describe EHR use would facilitate generalizability and cross-institution, cross-vendor research. Here, we describe the current state of outpatient EHR use measures offered by various EHR vendors, guided by our prior conceptual work that proposed seven core measures to describe EHR use. We evaluate these measures and other reporting options provided by vendors for maturity and similarity to previously proposed standardized measures. Working toward improved standardization of EHR use measures can enable and accelerate high-impact research on physician burnout and job satisfaction as well as organizational efficiency and patient health.
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http://dx.doi.org/10.1093/jamia/ocaa266DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8068413PMC
April 2021

Registered Nurse Strain Detection Using Ambient Data: An Exploratory Study of Underutilized Operational Data Streams in the Hospital Workplace.

Appl Clin Inform 2020 08 16;11(4):598-605. Epub 2020 Sep 16.

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

Background: Registered nurses (RNs) regularly adapt their work to ever-changing situations but routine adaptation transforms into RN strain when service demand exceeds staff capacity and patients are at risk of missed or delayed care. Dynamic monitoring of RN strain could identify when intervention is needed, but comprehensive views of RN work demands are not readily available. Electronic care delivery tools such as nurse call systems produce ambient data that illuminate workplace activity, but little is known about the ability of these data to predict RN strain.

Objectives: The purpose of this study was to assess the utility of ambient workplace data, defined as time-stamped transaction records and log file data produced by non-electronic health record care delivery tools (e.g., nurse call systems, communication devices), as an information channel for automated sensing of RN strain.

Methods: In this exploratory retrospective study, ambient data for a 1-year time period were exported from electronic nurse call, medication dispensing, time and attendance, and staff communication systems. Feature sets were derived from these data for supervised machine learning models that classified work shifts by unplanned overtime. Models for three timeframes -8, 10, and 12 hours-were created to assess each model's ability to predict unplanned overtime at various points across the work shift.

Results: Classification accuracy ranged from 57 to 64% across three analysis timeframes. Accuracy was lowest at 10 hours and highest at shift end. Features with the highest importance include minutes spent using a communication device and percent of medications delivered via a syringe.

Conclusion: Ambient data streams can serve as information channels that contain signals related to unplanned overtime as a proxy indicator of RN strain as early as 8 hours into a work shift. This study represents an initial step toward enhanced detection of RN strain and proactive prevention of missed or delayed patient care.
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http://dx.doi.org/10.1055/s-0040-1715829DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7542221PMC
August 2020

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology.

Transl Vis Sci Technol 2020 02 27;9(2):13. Epub 2020 Feb 27.

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

Widespread adoption of electronic health records (EHRs) has resulted in the collection of massive amounts of clinical data. In ophthalmology in particular, the volume range of data captured in EHR systems has been growing rapidly. Yet making effective secondary use of this EHR data for improving patient care and facilitating clinical decision-making has remained challenging due to the complexity and heterogeneity of these data. Artificial intelligence (AI) techniques present a promising way to analyze these multimodal data sets. While AI techniques have been extensively applied to imaging data, there are a limited number of studies employing AI techniques with clinical data from the EHR. The objective of this review is to provide an overview of different AI methods applied to EHR data in the field of ophthalmology. This literature review highlights that the secondary use of EHR data has focused on glaucoma, diabetic retinopathy, age-related macular degeneration, and cataracts with the use of AI techniques. These techniques have been used to improve ocular disease diagnosis, risk assessment, and progression prediction. Techniques such as supervised machine learning, deep learning, and natural language processing were most commonly used in the articles reviewed.
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http://dx.doi.org/10.1167/tvst.9.2.13DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347028PMC
February 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

Promoting Quality Face-to-Face Communication during Ophthalmology Encounters in the Electronic Health Record Era.

Appl Clin Inform 2020 01 19;11(1):130-141. Epub 2020 Feb 19.

Health Sciences Department of Biomedical Informatics, University of California San Diego, La Jolla, California, United States.

Objective: To evaluate informatics-enabled quality improvement (QI) strategies for promoting time spent on face-to-face communication between ophthalmologists and patients.

Methods: This prospective study involved deploying QI strategies during implementation of an enterprise-wide vendor electronic health record (EHR) in an outpatient academic ophthalmology department. Strategies included developing single sign-on capabilities, activating mobile- and tablet-based applications, EHR personalization training, creating novel workflows for team-based orders, and promoting problem-based charting to reduce documentation burden. Timing data were collected during 648 outpatient encounters. Outcomes included total time spent by the attending ophthalmologist on the patient, time spent on documentation, time spent on examination, and time spent talking with the patient. Metrics related to documentation efficiency, use of personalization features, use of team-based orders, and note length were also measured from the EHR efficiency portal and compared with averages for ophthalmologists nationwide using the same EHR.

Results: Time spent on exclusive face-to-face communication with patients initially decreased with EHR implementation (2.9 to 2.3 minutes,  = 0.005) but returned to the paper baseline by 6 months (2.8 minutes,  = 0.99). Observed participants outperformed national averages of ophthalmologists using the same vendor system on documentation time per appointment, number of customized note templates, number of customized order lists, utilization of team-based orders, note length, and time spent after-hours on EHR use.

Conclusion: Informatics-enabled QI interventions can promote patient-centeredness and face-to-face communication in high-volume outpatient ophthalmology encounters. By employing an array of interventions, time spent exclusively talking with the patient returned to levels equivalent to paper charts by 6 months after EHR implementation. This was achieved without requiring EHR redesign, use of scribes, or excessive after-hours work. Documentation efficiency can be achieved using interventions promoting personalization and team-based workflows. Given their efficacy in preserving face-to-face physician-patient interactions, these strategies may help alleviate risk of physician burnout.
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http://dx.doi.org/10.1055/s-0040-1701255DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7030957PMC
January 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

Using electronic health record audit logs to study clinical activity: a systematic review of aims, measures, and methods.

J Am Med Inform Assoc 2020 03;27(3):480-490

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

Objective: To systematically review published literature and identify consistency and variation in the aims, measures, and methods of studies using electronic health record (EHR) audit logs to observe clinical activities.

Materials And Methods: In July 2019, we searched PubMed for articles using EHR audit logs to study clinical activities. We coded and clustered the aims, measures, and methods of each article into recurring categories. We likewise extracted and summarized the methods used to validate measures derived from audit logs and limitations discussed of using audit logs for research.

Results: Eighty-five articles met inclusion criteria. Study aims included examining EHR use, care team dynamics, and clinical workflows. Studies employed 6 key audit log measures: counts of actions captured by audit logs (eg, problem list viewed), counts of higher-level activities imputed by researchers (eg, chart review), activity durations, activity sequences, activity clusters, and EHR user networks. Methods used to preprocess audit logs varied, including how authors filtered extraneous actions, mapped actions to higher-level activities, and interpreted repeated actions or gaps in activity. Nineteen studies validated results (22%), but only 9 (11%) through direct observation, demonstrating varying levels of measure accuracy.

Discussion: While originally designed to aid access control, EHR audit logs have been used to observe diverse clinical activities. However, most studies lack sufficient discussion of measure definition, calculation, and validation to support replication, comparison, and cross-study synthesis.

Conclusion: EHR audit logs have potential to scale observational research but the complexity of audit log measures necessitates greater methodological transparency and validated standards.
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http://dx.doi.org/10.1093/jamia/ocz196DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7025338PMC
March 2020

Subtle cues: Qualitative elicitation of signs of capacity strain in the hospital workplace.

Appl Ergon 2019 Nov 11;81:102893. Epub 2019 Jul 11.

Oregon Health & Science University, Department of Medical Informatics & Clinical Epidemiology, 3181 S.W. Sam Jackson Park Rd, Portland, OR, 97239-3098, USA; Oregon Health & Science University Hospital, 3181 S.W. Sam Jackson Park Rd, Portland, OR, 97239-3098, USA.

Through everyday care experiences, nurses develop expertise in recognition of capacity strain in hospital workplaces. Through qualitative interview, experienced nurses identify common activity changes and adaptive work strategies that may signal an imbalance between patient demand and service supply at the bedside. Activity change examples include nurse helping behaviors across patient assignments, increased volume of nurse calls from patient rooms, and decreased presence of staff at the nurses' station. Adaptive work strategies encompass actions taken to recruit resources, move work in time, reduce work demands, or reduce thoroughness of task performance. Nurses' knowledge of perceptible signs of strain provides a foundation for future exploration and development of real-time indicators of capacity strain in hospital-based work systems.
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http://dx.doi.org/10.1016/j.apergo.2019.102893DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6834115PMC
November 2019

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

Impact of Electronic Health Record Implementation on Ophthalmology Trainee Time Expenditures.

J Acad Ophthalmol 2019 Jul;11(2):e65-e72

UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California.

Objective: Electronic health records (EHRs) are widely adopted, but the time demands of EHR use on ophthalmology trainees are not well understood. This study evaluated ophthalmology trainee time spent on clinical activities in an outpatient clinic undergoing EHR implementation.

Design: Prospective, manual time-motion observations of ophthalmology trainees in 2018.

Participants: Eleven ophthalmology residents and fellows observed during 156 patient encounters.

Methods: Prospective time-motion study of ophthalmology trainees 2 weeks before and 6 weeks after EHR implementation in an academic ophthalmology department. Manual time-motion observations were conducted for 11 ophthalmology trainees in 6 subspecialty clinics during 156 patient encounters. Time spent documenting, examining, and talking with patients were recorded. Factors influencing time requirements were evaluated using linear mixed effects models.

Main Outcome Measures: Total time spent by ophthalmology residents and fellows per patient, time spent on documentation, examination, and talking with patients.

Results: Seven ophthalmology residents and four ophthalmology fellows with mean (standard deviation) postgraduate year of 3.7 (1.2) were observed during 156 patient encounters. Using paper charts, mean total time spent on each patient was 11.6 (6.5) minutes, with 5.4 (3.5) minutes spent documenting (48%). After EHR implementation, mean total time spent on each patient was 11.8 (6.9) minutes, with 6.8 (4.7) minutes spent documenting (57%). Total time expenditure per patient did not significantly change after EHR implementation (+0.17 minutes, 95% confidence interval [CI] for difference in means: -2.78, 2.45; = 0.90). Documentation time did not change significantly after EHR implementation in absolute terms (+1.42 minutes, 95% CI: -3.13, 0.29; = 0.10), but was significantly greater as a proportion of total time (48% on paper to 57% on EHR; +9%, 95% CI: 2.17, 15.83; = 0.011).

Conclusion: Total time spent per patient and absolute time spent on documentation was not significantly different whether ophthalmology trainees used paper charts or the recently implemented EHR. Percentage of total time spent on documentation increased significantly with early EHR use. Evaluating EHR impact on ophthalmology trainees may improve understanding of how trainees learn to use the EHR and may shed light on strategies to address trainee burnout.
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http://dx.doi.org/10.1055/s-0039-3401986DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8095731PMC
July 2019

Time Requirements of Paper-Based Clinical Workflows and After-Hours Documentation in a Multispecialty Academic Ophthalmology Practice.

Am J Ophthalmol 2019 10 22;206:161-167. Epub 2019 Mar 22.

Department of Biomedical Informatics, University of California San Diego, La Jolla, California; Department of Medicine, University of California San Diego, La Jolla, California; Division of Health Services Research and Development, Veterans Administration San Diego Healthcare System, La Jolla, California.

Purpose: To assess time requirements for patient encounters and estimate after-hours demands of paper-based clinical workflows in ophthalmology.

Design: Time-and-motion study with a structured survey.

Methods: This study was conducted in a single academic ophthalmology department. A convenience sample consisted of 7 attending ophthalmologists from 6 subspecialties observed during 414 patient encounters for the time-motion analysis and 12 attending ophthalmologists for the survey. Outcome measurements consisted of total time spent by attending ophthalmologists per patient and time spent on documentation, examination, and talking with patients. The survey assessed time requirements of documentation-related activities performed outside of scheduled clinic hours.

Results: Among the 7 attending ophthalmologists observed (6 men and 1 woman), mean ± SD age 43.9 ± 7.1 years, during encounters with 414 patients (57.8 ± 24.6 years of age), total time spent per patient was 8.1 ± 4.8 minutes, with 2.8 ± 1.4 minutes (38%) for documentation, 1.2 ± 0.9 minutes (17%) for examination, and 3.3 ± 3.1 minutes (37%) for talking with patients. New patient evaluations required significantly more time than routine follow-up visits and postoperative visits. Higher clinical volumes were associated with less time per patient. Survey results indicated that paper-based documentation was associated with minimal after-hours work on weeknights and weekends.

Conclusions: Paper-based documentation takes up a substantial portion of the total time spent for patient care in outpatient ophthalmology clinics but is associated with minimal after-hours work. Understanding paper-based clinical workflows may help inform targeted strategies for improving electronic health record use in ophthalmology.
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http://dx.doi.org/10.1016/j.ajo.2019.03.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755078PMC
October 2019

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

Evaluating and Improving an Outpatient Clinic Scheduling Template Using Secondary Electronic Health Record Data.

AMIA Annu Symp Proc 2017 16;2017:921-929. Epub 2018 Apr 16.

Medical Informatics & Clinical Epidemiology.

Improving the efficiency of outpatient clinics is challenging in the face of increased patient loads, decreased reimbursements and potential negative productivity impacts of using electronic health records (EHR). We modeled outpatient ophthalmology clinic workflow using discrete event simulation for testing new scheduling templates that decrease patient wait time and improve clinic efficiency. Despite challenges in implementing the new scheduling templates in one outpatient clinic, the new templates improved patient wait time and clinic session length when they were followed. Analyzing EHR data about these schedules and their adherence to the template provides insight into new policies that can better balance the competing priorities of filling the schedules, meeting patient demand and minimizing wait time.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977636PMC
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
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