Abstract
We sought to evaluate the context of potential implementation of an automated quality measurement system for inpatients with heart failure in the U.S. Department of Veterans Affairs (VA). The research methodology was guided by the Promoting Action on Research Implementation in Health Sciences (PARIHS) framework and the sociotechnical model of health information technology. Data sources comprised semi-structured interviews ( n = 15), archival review of internal VA documents, and literature review. The interviewees consisted of four VA key informants and 11 subject matter experts (SMEs). Interviewees were VA quality management (QM) staff, clinicians, data analysts, and quality measurement experts, among others. Our interviews identified themes, which confirmed that the automated system is aligned with current internal organizational features, hardware and software infrastructure, and workflow and communication needs. We also identified facilitators and barriers to adoption of the automated system. The themes found will be used to inform future implementation of the system.
Highlights
IntroductionWe designed a new tool to automate hospital congestive heart failure (CHF) quality measurement, using natural language processing (NLP) (Meystre et al, 2016)
Heart failure (HF) is associated with a high rate of morbidity and mortality in the U.S Department of Veterans Affairs (VA) and the Unites States (Benjamin et al, 2018, 2019; Yoon et al, 2016)
Prior research on informatics generally and VA shows that health information technology (HIT) implementation has the potential to facilitate evidence-based care and increase quality, reduce costs, and increase patient and clinician satisfaction (Goldstein, 2008; Stabile & Cooper, 2012), but it is often accompanied by numerous implementation barriers
Summary
We designed a new tool to automate hospital congestive heart failure (CHF) quality measurement, using natural language processing (NLP) (Meystre et al, 2016). The NLP pipeline that we designed extracts data for CHF quality measurement, including assessment of left ventricular systolic function, LVEF mentions and values, ACEI or ARB therapy, if applicable, and contraindications exempting guideline-concordant treatment (Garvin et al, 2018). Prior research on informatics generally and VA shows that health information technology (HIT) implementation has the potential to facilitate evidence-based care and increase quality, reduce costs, and increase patient and clinician satisfaction (Goldstein, 2008; Stabile & Cooper, 2012), but it is often accompanied by numerous implementation barriers.
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