A network perspective on patient experiences and health status: the Medical Expenditure Panel Survey 2004 to 2011.

BACKGROUND: There is a growing emphasis on the need to engage patients in order to improve the quality of health care and improve health outcomes. However, we are still lacking a comprehensive understanding on how different measures of patient experiences interact with one another or relate to health status. This study takes a network perspective to 1) study the associations between patient characteristics and patient experience in health care and 2) identify factors that could be prioritized to improve health status.

METHODS: This study uses data from the two-year panels from the Medical Expenditure Panel Survey (MEPS) initiated between 2004 and 2011 in the United States. The 88 variables regarding patient health and experience with health care were identified through the MEPS documentation. Sex, age, race/ethnicity, and years of education were also included for analysis. The bnlearn package within R (v3.20) was used to 1) identify the structure of the network of variables, 2) assess the model fit of candidate algorithms, 3) cross-validate the network, and 4) fit conditional probabilities with the given structure.

RESULTS: There were 51,023 MEPS interviewees aged 18 to 85 years (mean = 44, 95% CI = 43.9 to 44.2), with years of education ranging from 1 to 19 (mean = 7.4, 95% CI = 7.40 to 7.46). Among all, 55% and 74% were female and white, respectively. There were nine networks identified and 17 variables not linked to others, including death in the second years, sex, entry years to the MEPS, and relations of proxies. The health status in the second years was directly linked to that in the first years. The health care ratings were associated with how often professionals listened to them and whether professionals' explanation was understandable.

CONCLUSIONS: It is feasible to construct Bayesian networks with information on patient characteristics and experiences in health care. Network models help to identify significant predictors of health care quality ratings. With temporal relationships established, the structure of the variables can be meaningful for health policy researchers, who search for one or a few key priorities to initiate interventions or health care quality improvement programs.