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Health care quality is often linked to patient satisfaction. Yet, there is a lack of national studies examining the relationship between patient satisfaction, patient-reported outcomes, and medical expenditure.
The aim of this study is to examine the contribution of physical health, mental health, general health, and total health care expenditures to patient satisfaction using a longitudinal, nationally representative sample.
Using data from the 2010-2011 Medical Expenditure Panel Survey, analyses were conducted to predict patient satisfaction from patient-reported outcomes and total health care expenditures. The study sample consisted of adult participants (N=10,157), with sampling weights representative of 233.26 million people in the United States.
The results indicated that patient-reported outcomes and total health care expenditure were associated with patient satisfaction such that higher physical and mental function, higher general health status, and higher total health care expenditure were associated with higher patient satisfaction.
We found that patient-reported outcomes and total health care expenditure had a significant relationship with patient satisfaction. As more emphasis is placed on health care value and quality, this area of research will become increasingly needed and critical questions should be asked about what we value in health care and whether we can find a balance between patient satisfaction, outcomes, and expenditures. Future research should apply big data analytics to investigate whether there is a differential effect of patient-reported outcomes and medical expenditures on patient satisfaction across different medical specialties.
Value-based health care has become a buzzword for health care reforms across the globe. In the United States, the Affordable Care Act has placed huge emphasis on health care value and quality [
One question is "what constitutes value in health care?" Porter posited that health care value is linked to patient-reported outcomes (PROs) and expenditures [
While PROs have become increasingly important in assessing health care value, the second part of the value equation is cost or expenditures in health care. Prior research has showed mixed findings between expenditures and patient satisfaction [
Research has suggested that a number of sociodemographic characteristics correlate with patient satisfaction, but there is not a strong consensus [
Although sociodemographics and other nonmodifiable characteristics may influence patient satisfaction, they are not very useful for implementing changes in health care. Modifiable characteristics such as patient outcomes and health care expenditures are more useful for attaining actionable changes and reform in health care, yet there is a shortage of research in this area. There is also a lack of longitudinal, national studies examining the relationship between PROs, medical expenditures, and patient satisfaction. This study aims to fill this gap by investigating this relationship using a longitudinal, nationally representative sample, adjusting for various sociodemographics and health-related characteristics.
Data from the longitudinal study of adult respondents to the 2010-2011 longitudinal panel Medical Expenditures Panel Survey (MEPS) [
The MEPS collects data on various categories of medical expenditures such as prescription drugs, emergency patient visits, and inpatient hospital stays. The variable total expenditure is an aggregate of medical expenditures in various categories. In this study, we used total health care expenditures (EXP) from Year 1 (2010) as an independent variable.
The MEPS also contains PROs data. Specifically, it contains data from the SF-12v2, which measures patients’ self-reported functional health status and well-being. The SF-12v2’s physical health component score (PCS), the mental health component score (MCS), and the general health perceptions (GH) score from Year 1 were also included as independent variables in this study. The PCS and MCS possible scores range from 0 (worst health) to 100 (best health). To put GH in the same direction as the PCS and MCS scoring, we reverse coded the original GH such that the possible scores range from 1 (worst health) to 5 (best health).
We used the CAHPS’s single-item global rating of satisfaction in Year 2 (2011) as a dependent variable. This global patient satisfaction item (SAT) reflects patients’ rating of their health care from all physicians and health care providers in the last 12 months when the patients were taking the survey. It is not a recall of a certain visit at a specific time (eg, at 3 month or 5 months ago), rather it represents a patient's average experience of all health care encounters within the last 12 months. The possible scores range from 0 (worst health care possible) to 10 (best health care possible). The survey was administered using computer-assisted personal interviewing technology.
Based on a literature review, we identified a list of potential confounders from the MEPS data to be the covariates and adjusted for them when investigating the contribution of PROs and total health care expenditure to patient satisfaction. The list of potential confounders is included in
The MEPS utilized a multistage, probability clustering sample design that enabled comprehensive examination of the US population. The sampling weight, stratification, clustering, multiple stages of selection, and disproportionate sampling from the MEPS were taken into account in the analyses so that the findings reported represented the entire US population. Sampling weight took into account the differential probability of sample selection and adjusted for nonresponses and missing data. Descriptive statistics were conducted to examine SAT, PCS, MCS, GH scores, EXP, and different service categories of medical expenditures across clinical conditions. We also performed descriptive statistics on all potential confounders and flagged those that had significant associations with patient satisfaction to be included as covariates in subsequent regression analyses.
To investigate the contribution of PCS, MCS, GH, and EXP on patient satisfaction, we conducted logistic regressions with adjustment of covariates and reported the odds ratio with associated 95% CI. We recoded the PROs, satisfaction, and expenditure variables into low/high SAT, PCS, MCS, GH, and EXP prior to running the logistic regressions. All statistical tests were two sided, were set at an alpha level of .05, and were conducted using SAS 9.3. Institutional review board (IRB) and/or ethics committee approval was not required as the MEPS data are freely available to the general public online.
The entire study sample consisted of adult participants (N=10,157), which represented 233.26 million adults (aged ≥ 18 years) in the United States. The mean age was 47 years (SE 0.28, range 18-85). Approximately 52% (121 million/233.26 million) were female and the majority were white (81%, 188 million/233.26 million) and black (12%, 27 million/233.26 million). About 15% (34 million/233.26 million) were Hispanic, approximately 34% (79 million/233.26 million) were unemployed, and nearly 12% (27 million/233.26 million) spoke non-English at home (
Demographics of the sample from MEPS (weighted N=233.26 million; unweighted N=10,157).
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Weighted | Unweighted | |||
Variable | n (millions) | % | n (millions) | % | |
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Male | 113 | 48.3 | 2385 | 72.3 | |
Female | 121 | 51.7 | 916 | 27.7 | |
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White | 188 | 80.7 | 7060 | 69.5 | |
Black | 27 | 11.7 | 1992 | 19.6 | |
American Indian/Alaskan native | 2 | 0.7 | 84 | 0.8 | |
Asian | 12 | 5.1 | 805 | 7.9 | |
Native Hawaiian/Pacific Islander | 2 | 0.7 | 72 | 0.7 | |
Multiple races | 3 | 1.1 | 144 | 1.4 | |
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Hispanic | 34 | 14.5 | 2475 | 24.4 | |
Black (not Hispanic or another race) | 27 | 11.4 | 1950 | 19.2 | |
Asian (not Hispanic or another race) | 12 | 5.0 | 794 | 7.8 | |
Other race (not Hispanic or another race) | 161 | 69.1 | 4938 | 48.6 | |
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Hispanic | 34 | 14.5 | 2475 | 24.4 | |
Not Hispanic | 200 | 85.5 | 7682 | 75.6 | |
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Employed | 154 | 66.2 | 6465 | 63.9 | |
Not employed | 79 | 33.8 | 3653 | 36.1 | |
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Married | 124 | 53.4 | 5172 | 50.9 | |
Widowed | 15 | 6.3 | 612 | 6.0 | |
Divorced | 26 | 11.3 | 1173 | 11.5 | |
Separated | 5 | 2.3 | 312 | 3.1 | |
Never married | 62 | 26.8 | 2888 | 28.4 | |
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Yes | 39 | 18.3 | 1732 | 18.5 | |
No | 176 | 81.7 | 7606 | 81.5 | |
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English | 205 | 88.3 | 8009 | 79.2 | |
Spanish | 19 | 8.1 | 1555 | 15.4 | |
Another language | 8 | 3.6 | 542 | 5.4 | |
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High school or less with no degree | 31 | 13.4 | 1986 | 20.0 | |
High-school graduate or GED | 69 | 29.9 | 3239 | 32.5 | |
Associates degree, beyond college, but no degree | 62 | 26.9 | 2359 | 23.7 | |
Bachelor's degree | 45 | 19.4 | 1596 | 16.0 | |
Master's, PhD, or professional degree | 24 | 10.4 | 777 | 7.8 |
This nationally representative adult population had a number of health conditions. About 32% (74.97 million/233.09 million) had high blood pressure and 5% (12.31 million/233.11 million) had coronary heart disease. Approximately 4% (8.52 million/233.20 million) had previously experienced a stroke. Almost 30% (70.59 million/233.07 million) had high cholesterol and 24% (56.34 million/233.18 million) had arthritis. There were 10% (23.48 million/233.20 million) diagnosed with cancer.
The sample population visited a variety of providers; however, most visited a general/family practice physician (71%, 60.69 million/86.00 million) and internal medicine physicians (20%, 17.10 million/86.00 million) within the last 12 months. About 68% (101.57 million/150.09 million) stated that their providers always showed respect for their treatment, whereas 3% (3.89 million/150.09 million) stated that their providers never showed respect. Almost 96% (161.35 million/168.25 million) indicated that their providers explained treatment options but 4% (6.90 million/168.25 million) did not. Approximately 67% (156.18 million/233.26 million) had private insurance, 18% (41.31 million/233.26 million) had public health insurance, whereas over 15% (35.77 million/233.26 million) were uninsured. A detailed breakdown of the number of responses for each variable is not provided here but can be obtained by contacting the authors.
The 2010 annual total health care expenditure in the US was over US $1.11 trillion and the average total health care expenditure per adult was US $4752.39 (
US medical expenditure by service area, by condition, per adult in 2010 (in US $).
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Median | Mean | SE | Minimum | Maximum |
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High blood pressure | 0 | 38.19 | 3.92 | 0 | 3406 |
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Coronary heart disease | 0 | 60.95 | 11.80 | 0 | 1576 |
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Angina | 0 | 77.89 | 16.52 | 0 | 900 |
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Stroke | 0 | 56.73 | 11.30 | 0 | 1092 |
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Emphysema | 0 | 60.23 | 14.10 | 0 | 701 |
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High cholesterol | 0 | 36.29 | 4.89 | 0 | 4570 |
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Cancer | 0 | 34.52 | 5.72 | 0 | 936 |
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Arthritis | 0 | 42.72 | 5.41 | 0 | 4570 |
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Asthma | 0 | 48.23 | 9.12 | 0 | 4570 |
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Hysterectomy | 0 | 52.49 | 9.38 | 0 | 5424 |
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Heart attack | 0 | 56.23 | 11.52 | 0 | 732 |
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High blood pressure | 0 | 233.94 | 29.24 | 0 | 28,798 |
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Coronary heart disease | 0 | 498.17 | 115.31 | 0 | 17,792 |
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Angina | 0 | 674.49 | 190.66 | 0 | 17,792 |
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Stroke | 0 | 480.89 | 123.35 | 0 | 17,792 |
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Emphysema | 0 | 381.02 | 96.18 | 0 | 5155 |
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High cholesterol | 0 | 228.66 | 27.37 | 0 | 17,792 |
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Cancer | 0 | 229.43 | 41.66 | 0 | 8923 |
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Arthritis | 0 | 227.38 | 27.28 | 0 | 17,792 |
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Asthma | 0 | 183.19 | 33.72 | 0 | 9289 |
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Hysterectomy | 0 | 214.13 | 25.81 | 0 | 7550 |
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Heart attack | 0 | 572.91 | 178.98 | 0 | 17,792 |
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High blood pressure | 0 | 235.39 | 20.11 | 0 | 15,692 |
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Coronary heart disease | 0 | 252.75 | 68.19 | 0 | 10,289 |
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Angina | 0 | 198.33 | 57.69 | 0 | 3514 |
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Stroke | 0 | 180.72 | 38.08 | 0 | 3514 |
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Emphysema | 0 | 250.03 | 75.47 | 0 | 5998 |
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High cholesterol | 0 | 273.77 | 22.51 | 0 | 15,692 |
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Cancer | 0 | 291.01 | 31.46 | 0 | 6192 |
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Arthritis | 0 | 259.89 | 21.74 | 0 | 15,692 |
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Asthma | 0 | 203.07 | 23.56 | 0 | 5998 |
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Hysterectomy | 0 | 266.83 | 23.91 | 0 | 9674 |
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Heart attack | 0 | 188.15 | 58.59 | 0 | 5998 |
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High blood pressure | 711.50 | 1934.43 | 82.76 | 0 | 50,667 |
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Coronary heart disease | 2030.00 | 3209.79 | 246.04 | 0 | 23,355 |
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Angina | 2080.50 | 3269.23 | 329.06 | 0 | 17,576 |
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Stroke | 1459.00 | 2902.59 | 326.25 | 0 | 40,940 |
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Emphysema | 2150.00 | 3292.43 | 334.82 | 0 | 15,071 |
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High cholesterol | 779.00 | 1985.17 | 85.30 | 0 | 40,940 |
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Cancer | 691.00 | 2153.63 | 180.58 | 0 | 37,509 |
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Arthritis | 914.50 | 2178.59 | 100.20 | 0 | 50,667 |
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Asthma | 571.50 | 1863.06 | 130.80 | 0 | 31,763 |
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Hysterectomy | 826.50 | 1826.24 | 104.19 | 0 | 40,940 |
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Heart attack | 1858.00 | 3141.72 | 326.99 | 0 | 23,355 |
Descriptive statistics for medical expenditure, outcomes, and patient satisfaction.
Variables | Mean (SE) | 95% CI |
Physical health component score (PCS) | 49.28 (0.16) | 48.97-49.59 |
Mental health component score (MCS) | 50.98 (0.14) | 50.7-51.25 |
General health (GH) | 3.55 (0.02) | 3.52-3.58 |
Total health care expenditure (EXP), US $ | 4752.39 (145.43) | 4465.61-5039.17 |
Patient satisfaction (SAT) | 8.30 (0.03) | 8.24-8.36 |
The mean score for PCS, MCS, and GH was 49.28 (SE 0.16), 50.98 (SE 0.14), and 3.55 (SE 0.02), respectively. These scores were significantly correlated with each other (PCS with MCS, ρ=.16,
As measured by CAHPS’s global rating, the mean patient satisfaction score across the national adult sample was 8.30 (SE 0.03). The following were some of the variables that were significantly associated with patient satisfaction: age (ρ=.25,
Total health care expenditure was negatively related to PROs; individuals who had lower physical function (ρ=−.43,
Prediction of patient satisfaction.
Independent variables | n | Adjusted odds ratioa | 95% CI |
Physical health component score (PCS) | 4782 | 2.54 | 1.36-4.72 |
Mental health component score (MCS) | 4728 | 1.95 | 1.20-3.15 |
General health (GH) | 3251 | 5.98 | 2.95-12.12 |
Total health care expenditure (EXP) | 5082 | 3.20 | 1.47-6.98 |
aCovariates are listed in
The purpose of this study was to investigate the contribution of physical health, mental health, general health, and total health care expenditures to patient satisfaction. We found that higher scores of physical health, mental health, and general health were related to higher scores of patient satisfaction. Consistent with previous research, we found that some factors such as age and provider interactions could impact patient satisfaction. Additionally, total health care expenditure was associated with patient satisfaction. Taken together, findings from this study can help providers, payers, policy makers, and the general public better understand the relationship between PROs, health care expenditures, and satisfaction.
General health demonstrated the strongest relationship with patient satisfaction. After accounting for demographics and various factors, we see that greater patient satisfaction is directly related to greater physical and mental health, although the relationship is stronger with physical health. The argument that the relation between higher patient satisfaction and better health is an artifact of the tendency of healthy patients to be satisfied is not new [
Mental health often goes unnoticed or it is dismissed as someone having a bad day. Both identifying mental health issues and addressing problems require training that goes beyond that of a general physician. As a result, a patient or a provider often needs to raise a concern about mental health. A stigma still exists about discussing and treating mental health issues, which may lead patients to be less willing to discuss mental health conditions. Seeing a mental health specialist adds an additional cost for patients that they may want to avoid. Finally, patients might not even notice that they have mental health issues. These may be reasons why mental health is a weaker predictor of patient satisfaction than physical health.
A surprising finding was that total health care expenditures also had a significant relation to patient satisfaction. In a recent review of health care quality, only one third of the 61 studies reviewed found a positive association between higher spending and better health care quality [
We were limited by the measurement tools in the MEPS, thus limiting the data we could use to measure physical, mental, and general health. Newer instruments have been developed using advanced techniques and these instruments may have better psychometric properties than instruments included in MEPS. The MEPS was administered using computer-assisted personal interviewing technology and the results obtained from this mode of survey administration may be different from those obtained from other modes. Care needs to be taken when doing cross-comparisons of results from different modes in different studies. Furthermore, the definition of health care expenditures reflects the CMS’s definition but might not represent a complete assessment of medical expenditures. We were not able to look at diseases that were not included in MEPS. Even though we had adjusted for nonresponses and missing data in the sample, such adjustment could only be made to a certain degree. The adjustment might not be adequate in the event that nonrespondents differed from respondents. It is well-known that there could be a larger share of nonrespondents having severe illness, impairment, and dealing with poor social economic conditions on a daily basis. Additionally, the majority of the population is white, but within the past few years, the United States has seen a growth of racial and ethnic diversity. Therefore, our findings might not represent underrepresented minorities at this moment in time.
Finally, the concept of patient satisfaction lacks a clear connotation and definition in the literature. It has sometimes been considered as a component of consumer marketing [
Future research should consider other modifiable characteristics that may influence patient satisfaction so that changes can be enacted. Often, underrepresented minorities have different experiences with health care than European Americans. Therefore, investigating their perspectives may illuminate group differences. Finally, future research should apply business intelligence or big data analytics to investigate whether there is a differential effect of PROs and medical expenditures on patient satisfaction across different medical areas.
We found that PROs and total health care expenditure had a strong relationship with patient satisfaction. As more emphasis is placed on health care value and quality, this area of research will become increasingly needed. Critical questions should be asked about what we value in health care and whether we can find a balance between patient satisfaction, outcomes, and expenditures. These questions need to be asked to policy makers, physicians, patients, insurers, and the general public.
List of all MEPS variables examined in this study.
Association between patient satisfaction and potential confounders. Variables are rank listed with the strongest relations to patient satisfaction at the top and the lowest relations at the bottom.
Consumer Assessment of Healthcare Providers and Systems
Center for Medicare & Medicaid Services
total health care expenditure
general health
mental health component score
Medical Expenditures Panel Survey
physical health component score
patient-reported outcomes
patient satisfaction
None declared.