Dataset Information

Comparing the Effectiveness of Dynamic Treatment Strategies Using Electronic Health Records: An Application of the Parametric g-Formula to Anemia Management Strategies.

ABSTRACT:

Objective

To compare the effectiveness of dynamic anemia management strategies by applying the parametric g-formula to electronic health records.

Data source/study setting

Patients with end-stage renal disease from the US Renal Data System who had congestive heart failure or ischemic heart disease and were undergoing hemodialysis in outpatient dialysis facilities between 2006 and 2010.

Study design

We explicitly emulated a target trial of three ?erythropoietin dosing strategies (aimed at achieving a low, middle, or high hematocrit) and estimated the observational analog of the per-protocol effect.

Results

Of 156,945 eligible patients, 41,970 died during the 18-month follow-up. Compared to the low-hematocrit strategy, the estimated risk of death was 4.6 (95% CI 4.4-4.9) percentage points higher under the high-hematocrit strategy and 1.8 (95% CI 1.7-1.9) percentage points higher under the mid-hematocrit strategy. The corresponding risk differences for a composite outcome of death and myocardial infarction were similar.

Conclusion

An explicit emulation of a target trial using electronic health records, combined with the parametric g-formula, allowed comparison of real-world dynamic strategies that have not been compared in randomized trials.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC5980367 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Publications

Comparing the Effectiveness of Dynamic Treatment Strategies Using Electronic Health Records: An Application of the Parametric g-Formula to Anemia Management Strategies.

Zhang Yi Y Young Jessica G JG Thamer Mae M Hernán Miguel A MA

Health services research 20170530 3

<h4>Objective</h4>To compare the effectiveness of dynamic anemia management strategies by applying the parametric g-formula to electronic health records.<h4>Data source/study setting</h4>Patients with end-stage renal disease from the US Renal Data System who had congestive heart failure or ischemic heart disease and were undergoing hemodialysis in outpatient dialysis facilities between 2006 and 2010.<h4>Study design</h4>We explicitly emulated a target trial of three ‎erythropoietin dosing strate ...[more]

PMID: 28560811

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Project description:BackgroundElectronic clinical quality measures (eCQMs) from electronic health records (EHRs) are a key component of quality improvement (QI) initiatives in small-to-medium size primary care practices, but using eCQMs for QI can be challenging. Organizational strategies are needed to effectively operationalize eCQMs for QI in these practice settings.ObjectiveThis study aimed to characterize strategies that seven regional cooperatives participating in the EvidenceNOW initiative developed to generate and report EHR-based eCQMs for QI in small-to-medium size practices.MethodsA qualitative study comprised of 17 interviews with representatives from all seven EvidenceNOW cooperatives was conducted. Interviewees included administrators were with both strategic and cooperative-level operational responsibilities and external practice facilitators were with hands-on experience helping practices use EHRs and eCQMs. A subteam conducted 1-hour semistructured telephone interviews with administrators and practice facilitators, then analyzed interview transcripts using immersion crystallization. The analysis and a conceptual model were vetted and approved by the larger group of coauthors.ResultsCooperative strategies consisted of efforts in four key domains. First, cooperative adaptation shaped overall strategies for calculating eCQMs whether using EHRs, a centralized source, or a "hybrid strategy" of the two. Second, the eCQM generation described how EHR data were extracted, validated, and reported for calculating eCQMs. Third, practice facilitation characterized how facilitators with backgrounds in health information technology (IT) delivered services and solutions for data capture and quality and practice support. Fourth, performance reporting strategies and tools informed QI efforts and how cooperatives could alter their approaches to eCQMs.ConclusionCooperatives ultimately generated and reported eCQMs using hybrid strategies because they determined neither EHRs alone nor centralized sources alone could operationalize eCQMs for QI. This required cooperatives to devise solutions and utilize resources that often are unavailable to typical small-to-medium-sized practices. The experiences from EvidenceNOW cooperatives provide insights into how organizations can plan for challenges and operationalize EHR-based eCQMs.

Project description:IntroductionData extraction from electronic health record (EHR) systems occurs through manual abstraction, automated extraction, or a combination of both. While each method has its strengths and weaknesses, both are necessary for retrospective observational research as well as sudden clinical events, like the COVID-19 pandemic. Assessing the strengths, weaknesses, and potentials of these methods is important to continue to understand optimal approaches to extracting clinical data. We set out to assess automated and manual techniques for collecting medication use data in patients with COVID-19 to inform future observational studies that extract data from the electronic health record (EHR).Materials and methodsFor 4,123 COVID-positive patients hospitalized and/or seen in the emergency department at an academic medical center between 03/03/2020 and 05/15/2020, we compared medication use data of 25 medications or drug classes collected through manual abstraction and automated extraction from the EHR. Quantitatively, we assessed concordance using Cohen's kappa to measure interrater reliability, and qualitatively, we audited observed discrepancies to determine causes of inconsistencies.ResultsFor the 16 inpatient medications, 11 (69%) demonstrated moderate or better agreement; 7 of those demonstrated strong or almost perfect agreement. For 9 outpatient medications, 3 (33%) demonstrated moderate agreement, but none achieved strong or almost perfect agreement. We audited 12% of all discrepancies (716/5,790) and, in those audited, observed three principal categories of error: human error in manual abstraction (26%), errors in the extract-transform-load (ETL) or mapping of the automated extraction (41%), and abstraction-query mismatch (33%).ConclusionOur findings suggest many inpatient medications can be collected reliably through automated extraction, especially when abstraction instructions are designed with data architecture in mind. We discuss quality issues, concerns, and improvements for institutions to consider when crafting an approach. During crises, institutions must decide how to allocate limited resources. We show that automated extraction of medications is feasible and make recommendations on how to improve future iterations.

Dataset Information

Comparing the Effectiveness of Dynamic Treatment Strategies Using Electronic Health Records: An Application of the Parametric g-Formula to Anemia Management Strategies.

Objective

Data source/study setting

Study design

Results

Conclusion

Publications

Comparing the Effectiveness of Dynamic Treatment Strategies Using Electronic Health Records: An Application of the Parametric g-Formula to Anemia Management Strategies.

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