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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

Pay-for-performance policy and data-driven decision making within nursing homes: a qualitative study.

Kathleen Abrahamson¹, Edward Miech², Heather Wood Davila³

¹School of Nursing, Purdue University, West Lafayette, Indiana, USA.

BMJ Quality & Safety

|March 10, 2015

Summary

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Nursing homes participating in pay-for-performance (P4P) programs increasingly use data for quality improvement. Leadership roles in data utilization are key for effective data collection and application in decision-making.

Area of Science:

Health Services Research
Nursing Home Quality Improvement
Health Policy Analysis

Background:

Pay-for-performance (P4P) programs are implemented globally and in the USA to enhance nursing home quality.
Effective P4P administration hinges on accurate, timely, and clinically relevant data for decision-making.
Understanding how nursing home staff collect, perceive, and utilize data is crucial for P4P success.

Purpose of the Study:

To explore the collection, perception, and utilization of data within nursing homes participating in P4P-sponsored quality improvement (QI) projects.
To identify themes related to data use for QI decision-making in the context of P4P initiatives.
To examine how policy changes influence data use for quality improvement in nursing homes.

Main Methods:

Keywords:

Nursing homes Pay for performance Quality improvement

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Semistructured interviews were conducted with 232 employees across 70 nursing homes involved in P4P-QI projects.
Qualitative analysis of interview data identified key themes concerning data practices.
The Promoting Action on Research Implementation in Health Services (PARIHS) framework was used to contextualize findings on policy impact.

Main Results:

The term 'data' was frequently and spontaneously mentioned by nursing home staff (228/247 instances).
Leadership personnel were more inclined to discuss data utilization than direct care staff.
Key themes included using data for problem identification, novel local data collection methods, and outcome measurement driven by P4P participation.

Conclusions:

Nursing home staff actively engaged with data, particularly for identifying quality improvement issues and measuring outcomes.
Leadership plays a significant role in driving data use for quality improvement initiatives.
Reimbursement structures that support the collection and application of local evidence are vital for successful P4P policy adaptation.