Exploration of Ambulatory Care Physician Phenotypes for Electronic Health Record Use (Preprint)

2021 ◽  
Author(s):  
Allan Fong ◽  
Mark Iscoe ◽  
Christine A Sinsky ◽  
Adrian Haimovich ◽  
Brian Williams ◽  
...  
Keyword(s):  
Ambulatory Care ◽  
Primary Care Physicians ◽  
Ad Hoc ◽  
Care Physician ◽  
Machine Learning Technique ◽  
Health Records ◽  
Physician Practices ◽  
Learning Technique ◽  
Average Work ◽  
Electronic Health

BACKGROUND Electronic health records (EHRs) have become ubiquitous in United States office-based physician practices. However, the different ways users engage with EHRs remains poorly characterized. OBJECTIVE The objective of this paper is to explore EHR usage phenotypes amongst ambulatory care physicians. METHODS We applied affinity propagation, an unsupervised clustering machine learning technique, to identify EHR user types amongst primary care physicians. RESULTS We identified four distinct clusters generalized across internal medicine, family medicine, and pediatric specialties. Two groups, or phenotype clusters, of physicians with higher-than-average work outside of scheduled hours ratios had varied EHR usage suggesting one group may have worked from home out of necessity while the other preferred ad hoc work hours. From the two remaining groups, one group represented physicians with lower-than-average EHR time. The last group represented physicians who spend the largest proportion of their EHR time documenting notes. CONCLUSIONS These findings demonstrate the utility of cluster analysis for exploring EHR phenotypes and may offer opportunities for interventions to improve EHR design and use to better support EHR users’ needs.

scholarly journals Factors Associated With Electronic Health Record Usage Among Primary Care Physicians After Hours: Retrospective Cohort Study

10.2196/13779 ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. e13779 ◽  
Author(s):  
Selasi Attipoe ◽  
Yungui Huang ◽  
Sharon Schweikhart ◽  
Steve Rust ◽  
Jeffrey Hoffman ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Records ◽  
Primary Care Physicians ◽  
Care Physician ◽  
Published Data ◽  
Health Records ◽  
Dominant Source ◽  
Factors Associated ◽  
After Hours ◽  
Electronic Health

Background There is limited published data on variation in physician usage of electronic health records (EHRs), particularly after hours. Research in this area could provide insight into the effects of EHR-related workload on physicians. Objective This study sought to examine factors associated with after-hours EHR usage among primary care physicians. Methods Electronic health records usage information was collected from primary care pediatricians in a large United States hospital. Inclusion criteria consisted solely of being a primary care physician who started employment with the hospital before the study period, so all eligible primary care physicians were included without sampling. Mixed effects statistical modeling was used to investigate the effects of age, gender, workload, normal-hour usage, week to week variation, and provider-to-provider variation on the after-hour usage of EHRs. Results There were a total of 3498 weekly records obtained on 50 physicians, of whom 22% were male and 78% were female. Overall, more EHR usage during normal work hours was associated with decreased usage after hours. The more work relative value units generated by physicians, the more time they spent interacting with EHRs after hours (β=.04, P<.001) and overall (ie, during normal hours and after hours) (β=.24, P<.001). Gender was associated with total usage time, with females spending more time than males (P=.03). However, this association was not observed with after-hours EHR usage. provider-to-provider variation was the largest and most dominant source of variation in after-hour EHR usage, which accounted for 52% of variance of total EHR usage. Conclusion The present study found that there is a considerable amount of variability in EHR use among primary care physicians, which suggested that many factors influence after-hours EHR usage by physicians. However, provider-to-provider variation was the largest and most dominant source of variation in after-hours EHR usage. While the results are intuitive, future studies should consider the effect of EHR use variations on workload efficiency.

scholarly journals Factors Associated With Electronic Health Record Usage Among Primary Care Physicians After Hours: Retrospective Cohort Study (Preprint)

2019 ◽  
Author(s):  
Selasi Attipoe ◽  
Yungui Huang ◽  
Sharon Schweikhart ◽  
Steve Rust ◽  
Jeffrey Hoffman ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Records ◽  
Primary Care Physicians ◽  
Care Physician ◽  
Published Data ◽  
Health Records ◽  
Dominant Source ◽  
Factors Associated ◽  
After Hours ◽  
Electronic Health

BACKGROUND There is limited published data on variation in physician usage of electronic health records (EHRs), particularly after hours. Research in this area could provide insight into the effects of EHR-related workload on physicians. OBJECTIVE This study sought to examine factors associated with after-hours EHR usage among primary care physicians. METHODS Electronic health records usage information was collected from primary care pediatricians in a large United States hospital. Inclusion criteria consisted solely of being a primary care physician who started employment with the hospital before the study period, so all eligible primary care physicians were included without sampling. Mixed effects statistical modeling was used to investigate the effects of age, gender, workload, normal-hour usage, week to week variation, and provider-to-provider variation on the after-hour usage of EHRs. RESULTS There were a total of 3498 weekly records obtained on 50 physicians, of whom 22% were male and 78% were female. Overall, more EHR usage during normal work hours was associated with decreased usage after hours. The more work relative value units generated by physicians, the more time they spent interacting with EHRs after hours (β=.04, <italic>P</italic>&lt;.001) and overall (ie, during normal hours and after hours) (β=.24, <italic>P</italic>&lt;.001). Gender was associated with total usage time, with females spending more time than males (<italic>P</italic>=.03). However, this association was not observed with after-hours EHR usage. provider-to-provider variation was the largest and most dominant source of variation in after-hour EHR usage, which accounted for 52% of variance of total EHR usage. CONCLUSION The present study found that there is a considerable amount of variability in EHR use among primary care physicians, which suggested that many factors influence after-hours EHR usage by physicians. However, provider-to-provider variation was the largest and most dominant source of variation in after-hours EHR usage. While the results are intuitive, future studies should consider the effect of EHR use variations on workload efficiency.

Health Literacy in Primary Care: Reflections and Suggestions for Physicians, Researchers, and Administrators

2021 ◽  
pp. 155982762110412
Author(s):  
Anne Sprogell ◽  
Allison R. Casola ◽  
Amy Cunningham
Keyword(s):  
Primary Care ◽  
Health Literacy ◽  
Primary Care Physician ◽  
Healthcare System ◽  
Primary Care Physicians ◽  
Care Physician ◽  
Health Records ◽  
The Past ◽  
Personal Bias ◽  
Concept Of Health

As the healthcare system evolves, it is becoming more complicated for physicians and patients. Patients might have had one doctor in the past, but now are likely to regularly see several specialists along with their primary care physician. Patients can access their health records online, which increases transparency and accountability, but adds more information they have to interpret. This is the concept of health literacy—the ability to obtain, process, and act upon information regarding one’s health. This article will characterize health literacy in primary care and provide three areas that primary care physicians and researchers can direct their focus in order to increase health literacy among patients: community engagement, trainee education, and examination of personal bias.

scholarly journals Initiation of treatment for incident diabetes: Evidence from the electronic health records in an ambulatory care setting

2015 ◽  
Vol 9 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Sukyung Chung ◽  
Beinan Zhao ◽  
Diane Lauderdale ◽  
Randolph Linde ◽  
Randall Stafford ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Ambulatory Care ◽  
Care Setting ◽  
Incident Diabetes ◽  
Ambulatory Care Setting ◽  
Health Records ◽  
Electronic Health

Use of instrumental variables in electronic health record-driven models

2016 ◽  
Vol 27 (2) ◽  
pp. 608-621 ◽  
Author(s):  
Luca Salmasi ◽  
Enrico Capobianco
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Instrumental Variables ◽  
Treatment Effects ◽  
Ad Hoc ◽  
Health Record ◽  
Health Records ◽  
Negative Effect ◽  
Electronic Health ◽  
High Degree

Precision medicine presents various methodological challenges whose assessment requires the consideration of multiple factors. In particular, the data multitude in the Electronic Health Records poses interoperability issues and requires novel inference strategies. A problem, though apparently a paradox, is that highly specific treatments and a variety of outcomes may hardly match with consistent observations (i.e., large samples). Why is it the case? Owing to the heterogeneity of Electronic Health Records, models for the evaluation of treatment effects need to be selected, and in some cases, the use of instrumental variables might be necessary. We studied the recently defined person-centered treatment effects in cancer and C-section contexts from Electronic Health Record sources and identified as an instrument the distance of patients from hospitals. We present first the rationale for using such instrument and then its model implementation. While for cancer patients consideration of distance turns out to be a penalty, implying a negative effect on the probability of receiving surgery, a positive effect is instead found in C-section due to higher propensity of scheduling delivery. Overall, the estimated person-centered treatment effects reveal a high degree of heterogeneity, whose interpretation remains context-dependent. With regard to the use of instruments in light of our two case studies, our suggestion is that this process requires ad hoc variable selection for both covariates and instruments and additional testing to ensure validity.

scholarly journals The impact of interoperability of electronic health records on ambulatory physician practices: a discrete-event simulation study

2014 ◽  
Vol 21 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Yuan Zhou ◽  
Jessica S Ancker ◽  
Mandar Upahdye ◽  
Nicolette M McGeorge ◽  
Theresa K Guarrera ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Discrete Event Simulation ◽  
Simulation Study ◽  
Discrete Event ◽  
Health Records ◽  
Physician Practices ◽  
Event Simulation ◽  
Electronic Health ◽  
The Impact

Concept Attribute Labeling and Context-Aware Named Entity Recognition in Electronic Health Records

2018 ◽  
Vol 7 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Alexandra Pomares-Quimbaya ◽  
Rafael A. Gonzalez ◽  
Oscar Mauricio Muñoz Velandia ◽  
Angel Alberto Garcia Peña ◽  
Julián Camilo Daza Rodríguez ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Ad Hoc ◽  
Named Entity Recognition ◽  
Ensemble Classification ◽  
Entity Recognition ◽  
Classification Model ◽  
Health Records ◽  
Named Entity ◽  
Electronic Health ◽  
Concept Attribute

Extracting valuable knowledge from Electronic Health Records (EHR) represents a challenging task due to the presence of both structured and unstructured data, including codified fields, images and test results. Narrative text in particular contains a variety of notes which are diverse in language and detail, as well as being full of ad hoc terminology, including acronyms and jargon, which is especially challenging in non-English EHR, where there is a dearth of annotated corpora or trained case sets. This paper proposes an approach for NER and concept attribute labeling for EHR that takes into consideration the contextual words around the entity of interest to determine its sense. The approach proposes a composition method of three different NER methods, together with the analysis of the context (neighboring words) using an ensemble classification model. This contributes to disambiguate NER, as well as labeling the concept as confirmed, negated, speculative, pending or antecedent. Results show an improvement of the recall and a limited impact on precision for the NER process.

scholarly journals Satisfaction with electronic health records is associated with job satisfaction among primary care physicians

2013 ◽  
Vol 21 (1) ◽  
pp. 18 ◽  
Author(s):  
Christine D Jones ◽  
George M Holmes ◽  
Sarah E Lewis ◽  
Kristie W Thompson ◽  
Samuel Cykert ◽  
...  
Keyword(s):  
Primary Care ◽  
Job Satisfaction ◽  
Electronic Health Records ◽  
Primary Care Physicians ◽  
Health Records ◽  
Electronic Health
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