scholarly journals Model-Based Algorithms for Detecting Peripheral Artery Disease Using Administrative Data From an Electronic Health Record Data System: Algorithm Development Study

10.2196/18542 ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. e18542 ◽  
Author(s):  
Elizabeth Hope Weissler ◽  
Steven J Lippmann ◽  
Michelle M Smerek ◽  
Rachael A Ward ◽  
Aman Kansal ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health System ◽  
Administrative Data ◽  
Peripheral Artery Disease ◽  
Health Record ◽  
Peripheral Artery ◽  
Model Based ◽  
Diagnosis Codes ◽  
Electronic Health ◽  
Artery Disease

Background Peripheral artery disease (PAD) affects 8 to 10 million Americans, who face significantly elevated risks of both mortality and major limb events such as amputation. Unfortunately, PAD is relatively underdiagnosed, undertreated, and underresearched, leading to wide variations in treatment patterns and outcomes. Efforts to improve PAD care and outcomes have been hampered by persistent difficulties identifying patients with PAD for clinical and investigatory purposes. Objective The aim of this study is to develop and validate a model-based algorithm to detect patients with peripheral artery disease (PAD) using data from an electronic health record (EHR) system. Methods An initial query of the EHR in a large health system identified all patients with PAD-related diagnosis codes for any encounter during the study period. Clinical adjudication of PAD diagnosis was performed by chart review on a random subgroup. A binary logistic regression to predict PAD was built and validated using a least absolute shrinkage and selection operator (LASSO) approach in the adjudicated patients. The algorithm was then applied to the nonsampled records to further evaluate its performance. Results The initial EHR data query using 406 diagnostic codes yielded 15,406 patients. Overall, 2500 patients were randomly selected for ground truth PAD status adjudication. In the end, 108 code flags remained after removing rarely- and never-used codes. We entered these code flags plus administrative encounter, imaging, procedure, and specialist flags into a LASSO model. The area under the curve for this model was 0.862. Conclusions The algorithm we constructed has two main advantages over other approaches to the identification of patients with PAD. First, it was derived from a broad population of patients with many different PAD manifestations and treatment pathways across a large health system. Second, our model does not rely on clinical notes and can be applied in situations in which only administrative billing data (eg, large administrative data sets) are available. A combination of diagnosis codes and administrative flags can accurately identify patients with PAD in large cohorts.

scholarly journals Model-Based Algorithms for Detecting Peripheral Artery Disease Using Administrative Data From an Electronic Health Record Data System: Algorithm Development Study (Preprint)

2020 ◽  
Author(s):  
Elizabeth Hope Weissler ◽  
Steven J Lippmann ◽  
Michelle M Smerek ◽  
Rachael A Ward ◽  
Aman Kansal ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health System ◽  
Administrative Data ◽  
Peripheral Artery Disease ◽  
Health Record ◽  
Peripheral Artery ◽  
Model Based ◽  
Diagnosis Codes ◽  
Electronic Health ◽  
Artery Disease

BACKGROUND Peripheral artery disease (PAD) affects 8 to 10 million Americans, who face significantly elevated risks of both mortality and major limb events such as amputation. Unfortunately, PAD is relatively underdiagnosed, undertreated, and underresearched, leading to wide variations in treatment patterns and outcomes. Efforts to improve PAD care and outcomes have been hampered by persistent difficulties identifying patients with PAD for clinical and investigatory purposes. OBJECTIVE The aim of this study is to develop and validate a model-based algorithm to detect patients with peripheral artery disease (PAD) using data from an electronic health record (EHR) system. METHODS An initial query of the EHR in a large health system identified all patients with PAD-related diagnosis codes for any encounter during the study period. Clinical adjudication of PAD diagnosis was performed by chart review on a random subgroup. A binary logistic regression to predict PAD was built and validated using a least absolute shrinkage and selection operator (LASSO) approach in the adjudicated patients. The algorithm was then applied to the nonsampled records to further evaluate its performance. RESULTS The initial EHR data query using 406 diagnostic codes yielded 15,406 patients. Overall, 2500 patients were randomly selected for ground truth PAD status adjudication. In the end, 108 code flags remained after removing rarely- and never-used codes. We entered these code flags plus administrative encounter, imaging, procedure, and specialist flags into a LASSO model. The area under the curve for this model was 0.862. CONCLUSIONS The algorithm we constructed has two main advantages over other approaches to the identification of patients with PAD. First, it was derived from a broad population of patients with many different PAD manifestations and treatment pathways across a large health system. Second, our model does not rely on clinical notes and can be applied in situations in which only administrative billing data (eg, large administrative data sets) are available. A combination of diagnosis codes and administrative flags can accurately identify patients with PAD in large cohorts.

scholarly journals Health System Implementation of a Tobacco Quitline eReferral

2019 ◽  
Vol 10 (04) ◽  
pp. 735-742 ◽  
Author(s):  
Eve Angeline Hood-Medland ◽  
Susan L. Stewart ◽  
Hien Nguyen ◽  
Mark Avdalovic ◽  
Scott MacDonald ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health System ◽  
Best Practice ◽  
Health Record ◽  
System Implementation ◽  
Electronic Health Record Data ◽  
Outpatient Group ◽  
Primary Care Specialty ◽  
Electronic Health ◽  
Order Set

Abstract Background Proactive referrals through electronic orders (eReferrals) can increase patient connection with tobacco quitlines. More information is needed on “real-world” implementation of electronic health record tools to promote tobacco cessation while minimizing provider burden. Objectives This paper examines the health system implementation of an eReferral to a tobacco quitline without best practice alerts in primary care, specialty, and hospital settings in an academic health system. Methods This is a prospective implementation study of a health system tobacco eReferral to a state quitline that was completed with an approach to minimize provider cognitive burden. Data are drawn from electronic health record data at University of California, Davis Health Systems (March 2013–February 2016). Results Over 3 years, 16,083 encounters with smokers resulted in 1,137 eReferral orders (7.1%). Treatment reach was 1.6% for quitline services and 2.3% for outpatient group classes. While the group classes were offered to outpatient smokers, the eReferral order was included in an outpatient order set and eventually an automated inpatient discharge order set; no provider alerts were implemented. Referrals were sustained and doubled after inpatient order set implementation. Among all first time eReferral patients, 12.2% had a 6 to 12 month follow-up visit at which they were documented as nonsmoking. Conclusion This study demonstrates a quitline eReferral order can be successfully implemented and sustained with minimal promotion, without provider alerts and in conjunction with group classes. Reach and effectiveness were similar to previously described literature.

scholarly journals Rethinking the electronic health record through the quadruple aim: time to align its value with the health system

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Hassane Alami ◽  
Pascale Lehoux ◽  
Marie-Pierre Gagnon ◽  
Jean-Paul Fortin ◽  
Richard Fleet ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health System ◽  
Health Record ◽  
Quadruple Aim ◽  
Electronic Health

scholarly journals Validation of diagnosis codes to identify side of colon in an electronic health record registry

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Patricia Luhn ◽  
Deborah Kuk ◽  
Gillis Carrigan ◽  
Nathan Nussbaum ◽  
Rachael Sorg ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Diagnosis Codes ◽  
Electronic Health

scholarly journals The complex case of EHRs: examining the factors impacting the EHR user experience

2019 ◽  
Vol 26 (7) ◽  
pp. 673-677 ◽  
Author(s):  
Michael A Tutty ◽  
Lindsey E Carlasare ◽  
Stacy Lloyd ◽  
Christine A Sinsky
Keyword(s):  
Electronic Health Record ◽  
Health System ◽  
User Experience ◽  
End Users ◽  
Complex Case ◽  
Health Record ◽  
System Leadership ◽  
Electronic Health ◽  
Face Time ◽  
Administrative Tasks

Abstract Physicians can spend more time completing administrative tasks in their electronic health record (EHR) than engaging in direct face time with patients. Increasing rates of burnout associated with EHR use necessitate improvements in how EHRs are developed and used. Although EHR design often bears the brunt of the blame for frustrations expressed by physicians, the EHR user experience is influenced by a variety of factors, including decisions made by entities other than the developers and end users, such as regulators, policymakers, and administrators. Identifying these key influences can help create a deeper understanding of the challenges in developing a better EHR user experience. There are multiple opportunities for regulators, policymakers, EHR developers, payers, health system leadership, and users each to make changes to collectively improve the use and efficacy of EHRs.

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