To conduct advanced psychometric analysis of Primary Care Assessment Tool (PCAT) in Tibet and identify avenues for metric performance improvement.

Measuring progress toward high-performing primary health care can contribute to the achievement of sustainable development goals. The adult version of PCAT is an instrument for measuring patient experience, with key elements of primary care. It has been extensively used and validated internationally. However, only little information is available regarding its psychometric properties obtained based on advanced analysis.

We used data collected from 1386 primary care users in two prefectures in Tibet. First, iterative confirmatory factor analysis examined the fit of the primary care construct in the original tool. Then item response theory analysis evaluated how well the questions and individual response options perform at different levels of patient experience. Finally, multiple logistic regression modeling examined the predicative validity of primary care domains against patient satisfaction.

A best final structure for the PCAT-Tibetan includes 7 domains and 27 items. Confirmatory factor analysis suggests good fit for a unidimensional model for items within each domain but doesn’t support a unidimensional model for the entire instrument with all domains. Non-parametric and parametric item response theory analysis models show that for most items, the favorable response option (4 = definitely) is overwhelmingly endorsed, the discriminability parameter is over 1, and the difficulty parameters are all negative, suggesting that the items are most sensitive and specific for patients with poor primary care experience. Ongoing care is the strongest predictor of patient satisfaction. These findings suggest the need for some principles in adapting the tool to different health system contexts, more items measuring excellent primary care experience, and update of the four-point response options.