Using a Quality Improvement Priority Metric to Promote Uniformly High-Quality Care: An Illustrative Example Using Child HCAHPS Survey Data

Patient experience data are used to set performance targets and monitor effectiveness of quality improvement (QI) activities. However, assessing and determining areas that need improvement can be challenging, especially when there are many measures, making it harder to synthesize and identify clear priorities. We describe a QI priority metric and technique for assessing cross-sectional patient experience data that explicitly examine subgroup performance and priorities. The QI priority metric combines 2 patient experience metrics (case-mix-adjusted mean for a patient experience measure and partial correlation of that measure with an overall rating) into a single priority value, allowing leaders to quickly identify improvement areas across multiple measures and patient groups. We examined the priority metric overall and by patient groups (ie, by race, ethnicity, and language preference). We found the priority metric synthesized and identified 2 priority areas for improvement for the entire patient population and revealed several additional improvement priorities specific to patient groups. This metric has the potential to be an informative and self-educating technique for promoting uniformly high-quality care and enhancing performance.

Authors

Institutions

Publication Details

Journal
Journal of Patient Experience
Published
2026-04-01
DOI
https://doi.org/10.1177/23743735261437482
Primary Topic
Patient Satisfaction in Healthcare
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Using a Quality Improvement Priority Metric to Promote Uniformly High-Quality Care: An Illustrative Example Using Child HCAHPS Survey Data

Denise D. Quigley, Ron D. Hays, Marc N. Elliott, Mary E. Slaughter
Journal of Patient Experience
Patient Satisfaction in Healthcare
article

Using a Quality Improvement Priority Metric to Promote Uniformly High-Quality Care: An Illustrative Example Using Child HCAHPS Survey Data

Denise D. Quigley, Ron D. Hays, Marc N. Elliott, Mary E. Slaughter
article en

Abstract

Patient experience data are used to set performance targets and monitor effectiveness of quality improvement (QI) activities. However, assessing and determining areas that need improvement can be challenging, especially when there are many measures, making it harder to synthesize and identify clear priorities. We describe a QI priority metric and technique for assessing cross-sectional patient experience data that explicitly examine subgroup performance and priorities. The QI priority metric combines 2 patient experience metrics (case-mix-adjusted mean for a patient experience measure and partial correlation of that measure with an overall rating) into a single priority value, allowing leaders to quickly identify improvement areas across multiple measures and patient groups. We examined the priority metric overall and by patient groups (ie, by race, ethnicity, and language preference). We found the priority metric synthesized and identified 2 priority areas for improvement for the entire patient population and revealed several additional improvement priorities specific to patient groups. This metric has the potential to be an informative and self-educating technique for promoting uniformly high-quality care and enhancing performance.

Journal of Patient ExperienceVol. 13
RAND Corporation (US), University of California, Los Angeles (US)
Agency for Healthcare Research and Quality
Quality Education
Openalex Percentile: Top 22%
Patient Satisfaction in Healthcare
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.