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

Abstract

BackgroundThere 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.MethodsThis 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.ResultsThere 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.ConclusionsIt 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-017-2496-5) contains supplementary material, which is available to authorized users.

Patient Experience in Primary Care: A Systematic Review of CG-CAHPS Surveys

“…Study sample sizes were large and varied from 347 patients to 1 322 290 patients in respective studies. Researchers in most studies reported (8,18 –33) statistically significant results in at least one of the study’s patient experience outcome measures. Fourteen of the study’s (20,22 –29,31 –35) authors used comparison groups as a methodological design to measure patient experience from multiple health-care perspectives.…”

“…Fourteen of the study’s (20,22 –29,31 –35) authors used comparison groups as a methodological design to measure patient experience from multiple health-care perspectives. Several teams of researchers (21,32,35) used data from large Medicare databases to conduct their studies. Researchers used the subjective data from CG-CAHPS surveys to analyze the association of patients’ characteristics and patients’ reported experience in health care (36), health-care utilization (35), and a comprehensive primary care initiative (32).…”

A network perspective of engaging patients in specialist and chronic illness care: The 2014 International Health Policy Survey

“…If two variables were related only because they had the same parent variable, they were interpreted as conditionally independent in Bayesian network modeling. [ 13 , 19 , 21 ] Bayesian network modeling could be applied to identify the parent variables and the strengths of the linkages to parent variables in terms of conditional probabilities. [ 13 , 19 , 21 ]…”

“…[ 22 ] Sometimes causal interpretation can be inferred from the arcs in the networks. [ 20 ] The Bayesian network had been used in many disciplines, ranging from health systems research,[ 21 ] medicine,[ 23 , 24 ] biology,[ 25 ] and sociology[ 26 ] to study the conditional dependencies between variables. The modeling process was shown in Fig 1 .…”

“…The Bayesian network models were built and reviewed in a way similar to the development process previously proposed. [ 21 , 29 ] After data cleaning and missing data assessment,[ 29 ] the redundant variables or administrative variables were searched and not included for analysis (see Reasons for exclusion column in S1 Appendix ). The variables that were derived by the Commonwealth Fund and were not labeled with clear definitions or not matched to the questions in the questionnaire were excluded from analysis.…”