Data source
A cross-sectional inpatient survey was conducted in 47 tertiary public hospitals (32 general hospitals and 15 specialty hospitals) in Shanghai in July and August 2018. Only three tertiary public hospitals in Shanghai (one mental health center, one hospital specializing in infectious disease, and one hospital with no inpatient care) were excluded from the study. Because 90% of all patients receive medical care at public hospitals in China [
29], patient care in public hospitals can generally represent patient care in China.
A random sample of inpatients who had completed their main medical care (e.g., surgeries or therapeutic procedures) was selected from each of the sampled tertiary public hospitals within one workweek. The average number of sampled inpatients per hospital was 55 (52–79). All voluntary investigators, who were mainly senior medical students from major medical colleges in Shanghai, received training regarding the inpatient survey. The survey was conducted via an e-questionnaire administered using iPads. Oral informed consent was obtained before the patients’ participation in the survey.
In the questionnaire survey, data related to inpatient satisfaction, inpatients’ perceived SDM, public hospital type (general vs. specialty), inpatient characteristics [e.g., gender, age, residence (Shanghai vs. non-Shanghai), education, family monthly income (< 5 k, 5 k-, 10 k-, 20 k-, or 50 k yuans)], patients with or without cancer (yes vs. no), having surgery (yes vs. no) and admitting clinical department (e.g., internal medicine, surgery, gynecology, pediatrics, other) were collected.
Measures
SDM scale
Four aspects were used to assess SDM in inpatient care, including “Patients’ information preference”, “Patients’ active involvement in SDM”, “Patients’ perceived encouragement from their physicians to achieve SDM” and “Informed consent”. Of the four aspects, the former two aspects reflected the patients’ desire for autonomy, while the latter two aspects reflected the patients’ perceived autonomy support [
20]. The items in the aspects of “Patients’ information preference”, “Patients’ active involvement in SDM”, and “Patients’ perceived encouragement from their physicians to achieve SDM” were based on the API [
8], PICS [
9], and the SDM-Q-9, SDM-Q-Doc, CollaboRATE and PICS [
9‐
12], respectively. The items in the aspect of “Informed consent” were developed by the authors. Twenty-five experts in medical care quality from Shanghai were consulted regarding all items on the SDM scale. During the consultation, each item related to SDM was rated according to its importance by experts, and a 10-point scale was adopted for the rating (1 for “very unimportant” and 10 for “very important”). If the average score of the importance of an item was equal to or greater than 7, the item was included in the SDM assessment (Additional file
1: Table S1). Finally, 13 items were used in this study to assess the four aspects of SDM in inpatient care (Table
1).
Table 1
PRRs and HPRRs to SDM among inpatients in tertiary hospitals in Shanghai
Patients’ information preference | 97.33 | 90.04 |
I should sufficiently understand the effects of the disease(s) that I have on my health | 96.43 | 88.33 |
The physician should explain to me the purposes of the test(s) and/or examination(s) | 98.07 | 90.95 |
I believe that getting information about the disease(s) is as important as getting information about the treatment | 97.48 | 90.83 |
Patients’ active involvement in SDM | 93.71 | 85.57 |
I asked my physician to explain the treatment alternatives and process in detail | 91.78 | 83.92 |
I asked my physician to provide treatment recommendations to me | 91.10 | 82.29 |
I described my disease symptoms to my physician in detail | 98.26 | 90.50 |
Patients’ perceived encouragement from their physicians to achieve SDM | 94.88 | 87.21 |
My physician provided me with detailed information about the disease(s) that I have | 97.95 | 90.44 |
My physician explained to me the diagnostic and therapeutic decisions that I need to make | 98.24 | 91.42 |
My physician informed me of different treatment alternatives | 88.63 | 80.09 |
My physician asked me which treatment alternative I prefer | 91.50 | 82.86 |
My physician and I reached a consensus on the subsequent treatment process | 98.10 | 91.24 |
Informed consent | 95.69 | 89.68 |
My physician explained the medical expenses of special medical care | 93.78 | 87.81 |
My physician obtained informed consent from me for special medical care | 97.60 | 91.54 |
Overall | 95.30 | 87.86 |
Each item in the SDM assessment was rated using a 5-point Likert scale as follows: 1 for “strongly disagree”, 2 for “disagree”, 3 for “neither agree nor disagree”, 4 for “agree” and 5 for “strongly agree”. The SDM measure used in this study had relatively acceptable construct validity and internal reliability (goodness of fit using a confirmatory factor analysis model: SRMR = 0.09, RMSEA = 0.12, GFI = 0.86, AGFI = 0.79; and overall Cronbach’s α = 0.82).
In this study, the percentage of inpatients who rated an item on the SDM scale as “strongly agree” or “agree” was referred to as the positive response rate (PRR) to this item, and the percentage of inpatients who rated an item on the SDM scale as “strongly agree” was referred to as high positive response rate (HPRR) to this item.
Inpatient satisfaction scale
Based on our previous inpatient satisfaction scale and consultation with experts in medical care quality, four dimensions with 35 items were used to assess inpatient satisfaction (Additional file
1: Table S2). The four dimensions of the inpatient satisfaction scale were “Facilities and equipment”, “Physician services”, “Nonphysician services” and “Medical care process and effectiveness”. To assess the association of inpatients’ perceived SDM with their satisfaction, overall inpatient satisfaction with medical care, the dimension of “Physician services” (hereafter “physician services”) and two items [“Medical expenses are reasonable” and “I was satisfied with medical care outcomes” (hereafter “medical expenses” and “treatment outcomes”, respectively)] were used.
Each item on the inpatient satisfaction scale was scored using a 5-point Likert scale as follows: 1 for “very dissatisfied”, 2 for “dissatisfied”, 3 for “neither satisfied nor dissatisfied”, 4 for “satisfied” and 5 for “very satisfied”. If an item was irrelevant to a surveyed inpatient, the item was treated as a missing value for this patient. In the analyses, a missing value of an item was replaced by the average score of the item. The percentage of inpatients who rated medical care equal to 5 is referred to as the inpatient high satisfaction rate (HSR).
The psychometric analysis indicated that the inpatient satisfaction measure used in this study had relatively good construct validity based on standard tests of goodness of fit using a confirmatory factor analysis model (GFI = 0.85; AGFI = 0.90; SRMR = 0.04; and RMSEA = 0.05) and had high internal reliability (overall Cronbach’s α = 0.95).
Statistical analyses
We computed the average PRR and HPRR of the items related to a given aspect as the PRR and HPRR of each aspect of SDM, respectively. We also calculated the average PRR and HPRR if the 13 items on the SDM scale in the survey as a summary statistic, which we refer to as “overall PRR” and “overall HPRR”, respectively. Overall HPRR was computed as the average of all responses received and then computed separately for general hospitals and specialty hospitals, inpatients with cancer and inpatients without cancer, and inpatients who underwent surgery and inpatients who did not undergo surgery.
We computed the HSRs of overall medical care, physician services, medical expenses and treatment outcomes. The HSRs of physician services and overall inpatient care were the average HSRs of the items related to the “Physician services” dimensions and all items on the inpatient satisfaction scale.
To examine whether the hospital type, admission department, inpatient with cancer, and surgery during admission affected the inpatients’ HPRRs to the four aspects of SDM and overall SDM, we applied t-tests and linear regression models.
To illustrate the differences in the adjusted HPRRs between groups of inpatients, we used the coefficients in linear regression models to calculate the adjusted HPRRs while holding all other variables constant at their means and graphically present the relevant predictions.
To test the differences in the inpatients’ overall HSRs and HSRs of physician services, medical expenses and treatment outcomes between the inpatients with or without high level of overall SDM and the four aspects of SDM, we used two-level regression models that accounted for the nesting of individuals within hospitals. In the models, high level of SDM referred to the average HPRRs of each aspect of SDM or overall SDM that were equal to or greater than 80%, while a non-high level of SDM referred to the average HPRRs of each aspect of SDM or overall SDM that were less than 80%. More specifically, two-level linear regression models were used to analyze overall HSR and the HSR of physician services, and the dependent variables were the average HSRs of the items in the “Physician services” dimension and all items on the inpatient satisfaction scale; two-level logistic models were used for the HSRs of medical expenses and treatment outcomes (1: “very satisfied”, 0: others). In addition, high-level SDM, the inpatients’ characteristics (admitting department, inpatient with cancer, surgery during admission, gender, age, residence, education and family monthly income) and the hospital type were used as fixed effects.
The following equations were applied in the two-level mixed linear regression models:
$$\begin{aligned} {\text{HSR}}_{ij} = & \beta_{0j} + \beta_{1} \;{\text{high-level}}\_{\text{of}}\_{\text{SDM}}_{ij} + \beta_{2} \;{\text{Surgery}}_{ij} + \beta_{3} \;{\text{Obstetrics and gynecology}}_{ij} \\ & \quad + \beta_{4} \;{\text{Pediatrics}}_{ij} + \beta_{5} \;{\text{Other}}\_{\text{departments}}_{ij} + \beta_{6} \;{\text{Cancer}}_{ij} + \beta_{{7}} \;{\text{Surgery}}_{ij} \\ & \quad + \beta_{8} \;{\text{Gender}}_{ij} + \beta_{9} \;{\text{Age}}_{ij} + \beta_{10} \;{\text{Residence}}_{ij} + \beta_{11} \;{\text{Education}}_{ij} + \beta_{12} \;{\text{Income}}_{ij} + e_{0j} \\ \end{aligned}$$
$$\beta_{0j} = \gamma_{00} + \gamma_{01} \;{\text{hospital}}\_{\text{type}}_{1j} + \mu_{0j}$$
$$\begin{aligned} {\text{HSR}}_{ij} = & \gamma_{00} + \gamma_{01} \;{\text{hospital}}\_{\text{type}}_{1j} + \beta_{1} \;{\text{high-level}}\_{\text{of}}\_{\text{SDM}}_{ij} + \beta_{2} \;{\text{Surgery}}_{ij} + \beta_{3} \;{\text{Obstetrics and gynecology}}_{ij} \\ & \quad + \beta_{4} \;{\text{Pediatrics}}_{ij} + \beta_{5} \;{\text{Other}}\_{\text{departments}}_{ij} + \beta_{6} \;{\text{Cancer}}_{ij} + \beta_{{7}} \;{\text{Surgery}}_{ij} + \beta_{8} \;{\text{Gender}}_{ij} \\ & \quad + \beta_{9} \;{\text{Age}}_{ij} + \beta_{10} \;{\text{Residence}}_{ij} + \beta_{11} \;{\text{Education}}_{ij} + \beta_{12} \;{\text{Income}}_{ij} + (\mu_{0j} + e_{0j} ) \\ \end{aligned}$$
The following equations were applied in the two-level logistic regression models:
$$\begin{aligned} {\text{In [p}}_{ij} {/}({1} - {\text{p}}_{ij} ){]} = & \beta_{0j} + \beta_{1} \;{\text{high-level}}\_{\text{of}}\_{\text{SDM}}_{ij} + \beta_{2} \;{\text{Surgery}}_{ij} + \beta_{3} \;{\text{Obstetrics and gynecology}}_{ij} \\ & \quad + \beta_{4} \;{\text{Pediatrics}}_{ij} + \beta_{5} \;{\text{Other}}\_{\text{departments}}_{ij} + \beta_{6} \;{\text{Cancer}}_{ij} + \beta_{{7}} \;{\text{Surgery}}_{ij} \\ & \quad + \beta_{8} \;{\text{Gender}}_{ij} + \beta_{9} \;{\text{Age}}_{ij} + \beta_{10} \;{\text{Residence}}_{ij} + \beta_{11} \;{\text{Education}}_{ij} + \beta_{12} \;{\text{Income}}_{ij} \\ \end{aligned}$$
$$\beta_{0j} = \gamma_{00} + \gamma_{01} \;{\text{hospital}}\_{\text{type}}_{1j} + \mu_{0j}$$
$$\begin{aligned} {\text{In[p}}_{ij} {/}({1} - {\text{p}}_{ij} ){]} = & \gamma_{00} + \gamma_{01} \;{\text{hospital}}\_{\text{type}}_{1j} + \beta_{1} \;{\text{high-level}}\_{\text{of}}\_{\text{SDM}}_{ij} + \beta_{2} \;{\text{Surgery}}_{ij} \\ & \quad + \beta_{3} \;{\text{Obstetrics and gynecology}}_{ij} + \beta_{4} \;{\text{Pediatrics}}_{ij} + \beta_{5} \;{\text{Other}}\_{\text{departments}}_{ij} \\ & \quad + \beta_{6} \;{\text{Cancer}}_{ij} + \beta_{{7}} \;{\text{Surgery}}_{ij} + \beta_{8} \;{\text{Gender}}_{ij} + \beta_{9} \;{\text{Age}}_{ij} + \beta_{10} \;{\text{Residence}}_{ij} \\ & \quad + \beta_{11} \;{\text{Education}}_{ij} + \beta_{12} \;{\text{Income}}_{ij} + \mu_{0j} \\ \end{aligned}$$
In the above equations, i = 1, 2,…, n, j = 1, 2,…, m; n is the number of surveyed inpatients, and m is the number of surveyed hospitals.
To determine the appropriateness of the two-level regression models, we examined the empty models of the inpatients’ overall HSR and HSRs to physician services, medical expenses and treatment outcomes. The results showed significant differences in HSRs among hospitals (P < 0.001), and the intraclass correlation coefficients (ICC) in the empty models of the inpatients’ overall HSR and HSRs to physician services, medical expenses and treatment outcomes were 0.12, 0.08, 0.09 and 0.09, respectively. Additionally, the − 2 log likelihood, AIC, AICC and BIC in the empty models of the inpatients’ overall HSR and HSRs to physician services and treatment outcomes were greater than those in the non-empty models, and those in the empty model of the inpatients’ HSR to medical expenses were close to those in the non-empty model in this study. Therefore, two-level regression models were appropriate for the analysis of the association between SDM and inpatient satisfaction.
In this study, we also analyzed the variances of the HSRs of overall inpatient care across hospitals and individuals, using the method described by Snijders and Bosker.
This study was approved by the Institutional Review Board of the School of Public Health, Fudan University (IRB#2018-05-0683).