Shifting Culture Toward Age-Friendly Care: Lessons From VHA Early Adopters

Background

The need for a health care workforce with expanded skills in the care of older adults is increasingly evident as the US population ages. The evidence-based Age-Friendly Health Systems (AFHS) framework establishes a structure to reliably assess and deliver effective care of older adults with multiple chronic conditions: what matters, medication, mentation, and mobility (4Ms). Half of veterans receiving Veterans Health Administration (VHA) care are aged ≥ 65 years, driving its transformation into the largest AFHS in the US. In this article, we offer lessons on the challenges to AFHS delivery and suggest opportunities to sustaining age-friendly care.

Observations

Within 3 months of implementation, 85% to 100% of patients received 4M care in all care settings at our VA facilities. Key lessons learned include the importance of identifying, preparing, and supporting a champion to lead this effort; garnering facility and system leadership support at the outset; and integration with the electronic health record (EHR) for reliable and efficient data capture, reporting, and feedback. Although the goal is to establish AFHS in all care settings, we believe that initially including a geriatrics-focused care setting helped early adoption of 4Ms care in the sites described here.

Conclusions

Early adopters at 2 VHA health care systems demonstrated successful AFHS implementation spanning different VHA facilities and care settings. Successful growth and sustainability may require leveraging the EHR to reduce documentation burden, increase standardization in care, and automate feedback, tracking, and reporting. A coordinated effort is underway to integrate AFHS into VHA documentation, performance evaluation, and metrics in both the legacy and new Cerner EHRs.

Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs

Background

We developed an accurate, stakeholder-informed, automated, natural language processing (NLP) system to measure the quality of heart failure (HF) inpatient care, and explored the potential for adoption of this system within an integrated health care system.

Objective

To accurately automate a United States Department of Veterans Affairs (VA) quality measure for inpatients with HF.

Methods

We automated the HF quality measure Congestive Heart Failure Inpatient Measure 19 (CHI19) that identifies whether a given patient has left ventricular ejection fraction (LVEF) <40%, and if so, whether an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker was prescribed at discharge if there were no contraindications. We used documents from 1083 unique inpatients from eight VA medical centers to develop a reference standard (RS) to train (n=314) and test (n=769) the Congestive Heart Failure Information Extraction Framework (CHIEF). We also conducted semi-structured interviews (n=15) for stakeholder feedback on implementation of the CHIEF.

Results

The CHIEF classified each hospitalization in the test set with a sensitivity (SN) of 98.9% and positive predictive value of 98.7%, compared with an RS and SN of 98.5% for available External Peer Review Program assessments. Of the 1083 patients available for the NLP system, the CHIEF evaluated and classified 100% of cases. Stakeholders identified potential implementation facilitators and clinical uses of the CHIEF.

Conclusions

The CHIEF provided complete data for all patients in the cohort and could potentially improve the efficiency, timeliness, and utility of HF quality measurements.

An Evolving Ecosystem for Natural Language Processing in Department of Veterans Affairs

In an ideal clinical Natural Language Processing (NLP) ecosystem, researchers and developers would be able to collaborate with others, undertake validation of NLP systems, components, and related resources, and disseminate them. We captured requirements and formative evaluation data from the Veterans Affairs (VA) Clinical NLP Ecosystem stakeholders using semi-structured interviews and meeting discussions. We developed a coding rubric to code interviews. We assessed inter-coder reliability using percent agreement and the kappa statistic. We undertook 15 interviews and held two workshop discussions. The main areas of requirements related to; design and functionality, resources, and information. Stakeholders also confirmed the vision of the second generation of the Ecosystem and recommendations included; adding mechanisms to better understand terms, measuring collaboration to demonstrate value, and datasets/tools to navigate spelling errors with consumer language, among others. Stakeholders also recommended capability to: communicate with developers working on the next version of the VA electronic health record (VistA Evolution), provide a mechanism to automatically monitor download of tools and to automatically provide a summary of the downloads to Ecosystem contributors and funders. After three rounds of coding and discussion, we determined the percent agreement of two coders to be 97.2% and the kappa to be 0.7851. The vision of the VA Clinical NLP Ecosystem met stakeholder needs. Interviews and discussion provided key requirements that inform the design of the VA Clinical NLP Ecosystem.