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Blood pressure control and the patient-centered medical home — what can we learn from patients?

Gray B, Weng W. — American Board of Internal Medicine

Presented: AcademyHealth Annual Research Meeting, June 2009

Purpose: We examined the relationship between the risk of high blood pressure (BP<40/90) (HBP) and both an infrastructure measure and a patient-based measure of the "Patient-Centered Medical Home" (PCMH).

Background: The PCMH model of care encourages physicians to operate in an environment supporting systematic care management enabled by use of health informatics tools, coordination of care among different providers, and fostering a partnership between patient and physician to produce the best health outcomes. Numerous efforts are underway to demonstrate the effectiveness of PCMH to enhance quality-of-care. Notably, NCQA’s PCMH recognition is based on measures of infrastructure related to patient-centered care (PCC) rather than measures of the quality of patient experiences with their provider.

Methodology: The ABIM PIM Practice Improvement Module® was used physicians to complete their requirements for Maintenance of Certification (MOC). We used data from the 2008 Hypertension and Diabetes PIMs, which include chart audits (7,043), patient surveys (6,793), and a practice-system-survey (592). Applying these data, we constructed a system-level measure of PCC-MH (PPC-index-proxy) that closely mirrored an instrument commonly used to qualify practices as medical homes (NCQA’s PPC®-index). We also applied a measure of patient perception of physician quality (PPPQ) and a patient-based measure of patient centered care (PPCC) (i.e., measures of communication, access to care, care coordination, quality of staff interaction) drawn from patient surveys. We modeled HBP as a function of the PPC-index-proxy, patients' overall assessment of quality-of-care, and our PPCC measures (controlling for patient and physician demographics). To account for the dichotomous nature of our dependent measure and the hierarchal nature of our data, we modeled these relationships using a random-effects logistic model.

Results: Our investigation indicated that all three of our quality-of-care measures have a clinically and statistically significant association with HBP. For example: a 50% increase in the overall share of patients who believe their care is of high quality was associated with a 12% reduction in HBP (p-stat<5%), doubling the share of a physician practice whose patients meet all our PPCC standards (from a mean of 31% to 61%) was associated with a 6% reduction in the relative risk of high blood pressure (p-stat <10%) and a 50% increase in the PPC-index was associated with a 7% reduction in HBP (p-stat<5%).

Conclusions: Overall, our finding suggests that both infrastructure-based PCMH measures and patient-based measures are important predictors of health outcomes.

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