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 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 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.

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 Impact of CancelRx on Discontinuation of Controlled Substance Prescriptions

2021 ◽  
Author(s):  
Taylor L. Watterson ◽  
Jamie A Stone ◽  
Aaron Gilson ◽  
Roger Brown ◽  
Ka Z Xiong ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health System ◽  
Secondary Data ◽  
Interrupted Time Series ◽  
Health Record ◽  
Interrupted Time Series Analysis ◽  
Pharmacy Records ◽  
Controlled Substance ◽  
Electronic Health ◽  
Over Time

ObjectiveTo assess how controlled substance medication discontinuations were communicated over timeData SourcesSecondary data from a midwestern academic health system electronic health record and pharmacy platform were collected 12-months prior to CancelRx implementation and for 12-months post implementation.Study DesignThe study utilized an interrupted time series analysis (ITSA) to capture the proportion of controlled substance medications that were cancelled in the clinic’s electronic health record and also cancelled in the pharmacy’s dispensing software. The ITSA plotted the proportion of successful cancellation messages over time, particularly after the health system’s implementation of CancelRx, a novel technology.Data Collection/ExtractionData were extracted from the EHR and pharmacy records for patients aged 18+ who had a controlled substance discontinued by a health system provider. Information collected included patient demographics, drug information (name, dose), and dates discontinued in the clinic and pharmacy records.Principal FindingsAfter CancelRx implementation there was a significant increase in the proportion of discontinued controlled substance medications that were communicated to the pharmacy.ConclusionsThis study demonstrates the role that technology can play in promoting controlled substance policy and medication safety.

Defining the hepatitis C cure cascade in an Urban health system using the electronic health record

2019 ◽  
Vol 27 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Lesley S. Miller ◽  
Alexander J. Millman ◽  
Jennifer Lom ◽  
Ademola Osinubi ◽  
Farah Ahmed ◽  
...  
Keyword(s):  
Hepatitis C ◽  
Electronic Health Record ◽  
Health System ◽  
Urban Health ◽  
Health Record ◽  
Electronic Health

scholarly journals Generic Medication Prescription Rates After Health System–Wide Redesign of Default Options Within the Electronic Health Record

2016 ◽  
Vol 176 (6) ◽  
pp. 847 ◽  
Author(s):  
Mitesh S. Patel ◽  
Susan C. Day ◽  
Scott D. Halpern ◽  
C. William Hanson ◽  
Joseph R. Martinez ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health System ◽  
Health Record ◽  
Generic Medication ◽  
Medication Prescription ◽  
Default Options ◽  
Prescription Rates ◽  
Electronic Health

scholarly journals Using Electronic Health Record Data to Support Research and Quality Improvement: Practical Guidance from a Qualitative Investigation

ACI Open ◽  
2020 ◽  
Vol 04 (01) ◽  
pp. e91-e101
Author(s):  
Richard C. Wasserman ◽  
Daria F. Ferro
Keyword(s):  
Quality Improvement ◽  
Electronic Health Record ◽  
Health System ◽  
System Level ◽  
Health Record ◽  
Learning Health System ◽  
Level Ii ◽  
Electronic Health ◽  
Level I ◽  
Support Research

Abstract Objective The aim of the study is to identify how academic health centers (AHCs) have established infrastructures to leverage electronic health record (EHR) data to support research and quality improvement (QI). Methods Phone interviews of 18 clinical informaticians with expertise gained over three decades at 24 AHCs were transcribed for qualitative analysis on three levels. In Level I, investigators independently used NVivo software to code and identify themes expressed in the transcripts. In Level II, investigators reexamined coded transcripts and notes and contextualized themes in the learning health system paradigm. In Level III, an informant subsample validated and supplemented findings. Results Level I analysis yielded six key “determinants”—Institutional Relationships, Resource Availability, Data Strategy, Response to Change, Leadership Support, and Degree of Mission Alignment—which, according to local context, affect use of EHR data for research and QI. Level II analysis contextualized these determinants in a practical frame of reference, yielding a model of learning health system maturation, over-arching key concepts, and self-assessment questions to guide AHC progress toward becoming a learning health system. Level III informants validated and supplemented findings. Discussion Drawn from the collective knowledge of experienced informatics professionals, the findings and tools described offer practical support to help clinical informaticians leverage EHR data for research and QI in AHCs. Conclusion The learning health system model builds on the tripartite AHC mission of research, education, and patient care. AHCs must deliberately transform into learning health systems to capitalize fully on EHR data as a staple of health learning.

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