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Evaluating quality indicators for physical therapy in primary care.

Marijn Scholte1, Catharina W M Neeleman-van der Steen2, Erik J M Hendriks3

  • 1IQ Healthcare, University Medical Centre St. Radboud, PO Box 9101, 114, Nijmegen 6500 HB, The Netherlands IQ Healthcare, Geert Grooteplein 21, Nijmegen 6525 EZ, The Netherlands.

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Summary

This study evaluated quality indicators for physical therapy, finding moderate reproducibility but high ceiling effects. Improving data collection and indicator specificity is recommended to enhance quality of care.

Keywords:
measurement of qualitymeasurement propertiesphysical therapyquality indicators

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Area of Science:

  • Healthcare Quality Measurement
  • Physical Therapy Practice
  • Health Services Research

Background:

  • Publicly available quality indicators are crucial for assessing and improving healthcare services.
  • Physical therapy quality indicators require rigorous evaluation of their measurement properties.
  • Understanding the performance of these indicators is essential for evidence-based practice.

Purpose of the Study:

  • To evaluate the measurement properties of a set of public quality indicators used in physical therapy.
  • To assess content validity, construct validity, reproducibility, floor/ceiling effects, and interpretability.
  • To identify areas for improvement in the existing quality indicators for physical therapy.

Main Methods:

  • Observational study utilizing web-based survey data from 2009-2010.
  • Data collected from 11,274 physical therapists across 3743 Dutch primary care practices.
  • Eight quality indicators were constructed and their measurement properties analyzed using comparative statistics and multilevel modeling.

Main Results:

  • Content validity was found to be acceptable.
  • Construct validity was acceptable for two outcome indicators, with hypotheses on patient characteristics confirmed.
  • Reproducibility was moderately positive (Spearman's ρ 0.3-0.42 at practice level), but high ceiling effects were observed (30%-95%).

Conclusions:

  • The study identified weaknesses in data collection contributing to bias and high ceiling effects.
  • Recommendations include random data extraction from electronic medical records to mitigate bias.
  • Increased specificity of indicators, focusing on prevalent conditions, is suggested to improve usability and quality of care.