# Healthcare providers and public reporting of Community Living Center (CLC) quality: Investigating responses and opportunities for intervention through the PROACTIVE mixed-methods study

> **NIH VA I21** · EDITH NOURSE  ROGERS MEMORIAL VETERANS HOSPITAL · 2021 · —

## Abstract

Background: In June, 2018, VHA began public reporting of its 135 Community Living Centers’
(CLCs’) overall quality using a five-star rating system based on data from the national quality
measures captured in CLC Compare. In light of the private sector’s positive experience with
report cards, this is a seminal moment for stimulating measurable quality improvements in
CLCs. Yet public reporting of CLC Compare data raises substantial and immediate implications
for CLCs. The report cards, for example, facilitate comparisons between CLCs and community
nursing homes in which CLCs generally fare worse. This may lead to staff anxiety and potential
unintended consequences (e.g., selective patient admissions—“cream skimming”). In addition,
CLC Compare is designed to spur improvement, yet the motivating aspects of the report cards
are unknown. Understanding staff attitudes and early responses is a critical first step in building
the capacity for public reporting to spur quality.
Specific Aims: We thus propose to adapt an existing community nursing home public reporting
survey to reveal important leverage points to support CLCs’ quality improvement efforts. Our
work will be grounded in a conceptual framework of strategic orientation and conducted in
partnership with the VA Office of Geriatrics and Extended Care (GEC). We have 2 aims.
1. Qualitatively examine a sample of CLC staff reactions to CLC Compare.
2. Adapt and expand upon an extant community nursing home survey to capture a broad
 range of responses, then pilot the adapted survey in CLCs.
Methods: Aim 1: We will conduct interviews with staff at 3 CLCs (one 1-star, one 3-star, and
one 5-star) to identify (1) specific staff actions taken in response to their CLC’s public data, (2)
staff commitment to/difficulties with using CLC Compare for quality improvement, and (3) factors
that motivate staff to improve CLC quality. Aim 2: We will integrate these findings with our
conceptual framework to adapt and expand a community nursing home survey to the current
CLC environment. We will conduct cognitive interviews with staff in 1 CLC to refine survey
items. We will then pilot the survey in 6 CLCs (two 1-star, two 3-star, and two 5-star) to assess
survey feasibility, acceptability, and preliminary psychometric properties.
Expected Results and Next Steps: We expect to develop a brief survey to be used in a future
national administration to (1) identify system-wide responses to CLC Compare; (2) evaluate the
impact of CLC Compare on Veterans’ clinical outcomes and satisfaction; and (3) develop, test,
and disseminate interventions to support meaningful use of CLC Compare for quality
improvement. Knowledge gained from this pilot and from future work will help GEC refine how
CLC Compare is used, ensure that CLC staff understand and are motivated to use its quality
data, and implement concrete actions to improve clinical quality. Products from this pilot will also
facilitate studies of the effects of public reporting ...

## Key facts

- **NIH application ID:** 10186470
- **Project number:** 5I21HX002765-02
- **Recipient organization:** EDITH NOURSE  ROGERS MEMORIAL VETERANS HOSPITAL
- **Principal Investigator:** Camilla Benedicto Pimentel
- **Activity code:** I21 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2021
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-06-01 → 2020-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10186470

## Citation

> US National Institutes of Health, RePORTER application 10186470, Healthcare providers and public reporting of Community Living Center (CLC) quality: Investigating responses and opportunities for intervention through the PROACTIVE mixed-methods study (5I21HX002765-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10186470. Licensed CC0.

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