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

Engaging patients is an important element of healthcare, as it improves patient experience and thus leads to improved health outcomes [1]. For example, experiences in the timeliness and perceived quality of health care and communication with physicians are measured with the Consumer Assessment of Health Plans Study (CAHPS) questionnaire [2]. The perceived quality of primary care has been linked to outcomes, such as emergency room visits [3] and other health care use [4]. The recent focus on patient experience and patient-oriented practice, especially engagement, aims to improve health care quality and patient health [5, 6]. However, there are several problems in studying the relationship between patient experience and health outcomes. One of the issue is that patient experiences may be simplified into a single dimension [7]. This may oversimplify the complexity of patient experiences in health care and overlook the opportunities that we can take advantage of to improve healthcare quality and patient health [7]. On the contrary, there is no common measure or universally agreed - upon definition of patient engagement [8] or patient satisfaction, each an important aspect of patient experience [7]. In fact, there are various experience measures and definitions proposed to identify important components or priorities that we can adapt to improve patient centeredness or engage patient effectively [8]. Only a few of them show promising results in validity or reliability, including the CAHPS [9]. Furthermore, the input of all types of health professionals may not be properly measured in questionnaires. For example, the communication with providers other than doctors may not be considered while assessing patient experience [7]. This can underestimate the input and effectiveness of patient-provider communication to improve patient health [7].

Occasionally, the objective of improving patient experience and subsequently health outcomes may be in conflict [7]. One reason is that the priorities identified via various methods may not be compatible with each other [10]. There isn’t enough evidence to help us understand whether there are pathways or networks to link individual characteristics or experiences to better outcomes in health care. Whether multiplicative benefits through changes in upstream policies or interventions can be achieved remains unclear [11, 12]. This problem is aggravated by the fact that there are few studies with sufficient samples to systematically identify the key factors for patient experience improvement [8].

To address these problems, we adopt a network approach to examine all possible inter-dependence of these measures of patient experience and health outcomes in a large population, while also taking into account individual characteristics. This can help us to identify potential intervention priorities as well as avoid incurring undesirable or adverse interactions between them. This study aims to 1) construct a Bayesian network model with individual characteristics and commonly used measures of patient experience, especially quality of care in the CAHPS, and 2) illustrate the relationships between patient experience and health outcomes through graphics and probability distributions.

This study uses data from the Medical Expenditure Panel Survey (MEPS) that was implemented with a focus on both self-perceived health status and patient experience with health care [13]. The MEPS staff interviewed non-institutionalized civilians in the United States since 1996 [14]. The MEPS provided a nationally representative sample with an oversample of blacks and Hispanics [14]. The interviewees were followed up for 2 years during each MEPS panels [14].

The questions about patient-perceived health and experience in health care were asked once per year or twice in the two-year panels [14]. The questionnaires were modified and new variables were added over time. To ensure the consistency of the variables in the MEPS panels, only data from two-year panels initiated between 2004 and 2011 were used. Because of the lack of adequate tools to adjust for complex survey design under graphic models, all statistics in this study were unweighted and not nationally representative. The flowchart of data processing and analysis is in Fig. 1.

The demographic characteristics of the MEPS participants are listed in Table 2. Between 2004 and 2011, there were 51,023 MEPS participants aged 18 to 85 years (mean = 44.1, 95% CI = 43.9 to 44.2). The years of education ranged from 1 to 18 years (7.4, 95% CI = 7.40 to 7.46). The proportion of female participants was 55% and did not change significantly across MEPS panels. The majority of those sampled were white, at 76%, with the largest sample, 38%, being from the South.

This study shows that network modeling is both feasible and useful for further policy or academic research. The measures of patient experience and physical or mental health are interconnected across time. The results not only show the complexity in patients’ interactions with healthcare systems, but also point to possible approaches to navigating the intricacies of these interactions. The first important finding is that measures of patient experiences and health status are interconnected, but only to a certain degree. The largest network consists of 42 variables that are predominantly dimensions of mental and physical health measured by SF12v2, non-specific psychological distress, and Kessler scale, along with three measures of patient experience and two general health questions. In this network, self-rated health status in the first and second years is linked. The temporal associations of health status across different time points is verified by previous studies [26].

Second, 13 patient experience variables measured by the CAHPS are included in two separate networks, while seven others are included in two other networks that include measures of health status and SF12v2 functional status. The association between health care rating and understandable explanation by providers [7] can be found in the first or second years. The networks in this study show that age, education or income may not have extensive connection with patient experience in health care, if conditional dependencies taken into account. This is different from previous studies that use regression models and show the associations between patient experience and individual characteristics especially age and sex [7, 27]. Other researchers find that the degree to which patients engage in their care could be explained predominantly by income, with race/ethnicity playing a lesser role [28]. The differences in the results from various methods are expected and the network perspective shows that the inter-dependencies between patient experience measures may need to be considered in regression models as well.

Third, there seem to be key arcs that link health status and patient engagement across time. The first- and second-year variables of health status, how often providers show respect to what patients have to say, whether pain limits normal work, and attitudes about insurance in the first and second year, are connected. The other measures are not well connected across time.

In addition, this study highlights some of the undervalued associations and opportunities to improve both patient experience and health status. For example, there are extensive interactions between functional status and psychological stress. The importance of mental status has been well demonstrated, [29] and the findings suggest that some measures of the psychological stress may be more important than others. For example, feeling everything is an effort is directly linked to self-reported health status.

Bayesian network modeling is feasible with health experience data. A network perspective highlights the interactions between the measures of patient experience in health care. This can be used to identify potential priorities for interventions that aim to improve health status or experience in health care. Researchers evaluate potential interventions by using this model to identify immediate parent variables or distinct upstream-factors. The effectiveness of these two dissimilar concepts will require further testing.