Predictors of patient satisfaction and outpatient health services in China: evidence from the WHO SAGE survey

Patient satisfaction with primary health care services and primary health care providers

AIM

To evaluate patients' satisfaction levels with primary healthcare services and providers in Jordan and assess differences in patients' satisfaction in relation to sociodemographic factors and accessibility to primary healthcare.

DESIGN

A descriptive cross-sectional design was used in this study.

SAMPLING

A convenient sampling technique was utilized.

MEASURES

A 34-item survey instrument was adopted and distributed to patients in nine primary healthcare centers in Amman in the period between October and December 2022.

RESULTS

A total of 225 patients completed the survey. The mean total score for patient satisfaction with primary healthcare services was 25.22 (SD = 4.13). There were significant differences in satisfaction with services in terms of educational level, visitation reason, mode of transportation, availability of parking, and suitably designed for patients with disabilities. Furthermore, the mean total score for patient satisfaction with primary healthcare providers was 22.85 (SD = 5.86). There were significant differences in relation to visitation reason, mode of transportation, and parking space availability.

CONCLUSION

It is important to improve patient satisfaction in primary healthcare facilities, and the Ministry of Health should implement policies for improving the quality of services provided by primary healthcare.

Associations Among Social Jet Lag, Sleep-Related Characteristics, and Burnout of Nurses in Tertiary Hospitals

To investigate the status of social jet lag (SJL) through sociodemographic factors among clinical nurses and examine the correlation with burnout. There has been relatively little research on the possible factors resulting in SJL among nurses in China and its role in burnout. A multicenter cross-sectional study recruited 596 nurses from 7 Chinese hospitals. Online questionnaires were delivered to assess sociodemographics, shift work, SJL, chronotypes, and the burnout of nurses. Nurses had severe levels of SJL. The number of children, forms of employment, specialty area, length of professional service, and chronotypes were the main predictors of SJL. Moreover, SJL affected burnout (emotional exhaustion and deindividuation), and reducing the nurses' SJL could relieve their burnout. Additional evidence-based interventions indicate that reducing the SJL is essential as the nurses are suffering severe job burnout.

Construction of a predictive model for blood transfusion in patients undergoing total hip arthroplasty and identification of clinical heterogeneity

A precise forecast of the need for blood transfusions (BT) in patients undergoing total hip arthroplasty (THA) is a crucial step toward the implementation of precision medicine. To achieve this goal, we utilized supervised machine learning (SML) techniques to establish a predictive model for BT requirements in THA patients. Additionally, we employed unsupervised machine learning (UML) approaches to identify clinical heterogeneity among these patients. In this study, we recruited 224 patients undergoing THA. To identify factors predictive of BT during the perioperative period of THA, we employed LASSO regression and the random forest (RF) algorithm as part of supervised machine learning (SML). Using logistic regression, we developed a predictive model for BT in THA patients. Furthermore, we utilized unsupervised machine learning (UML) techniques to cluster THA patients who required BT based on similar clinical features. The resulting clusters were subsequently visualized and validated. We constructed a predictive model for THA patients who required BT based on six predictive factors: Age, Body Mass Index (BMI), Hemoglobin (HGB), Platelet (PLT), Bleeding Volume, and Urine Volume. Before surgery, 1 h after surgery, 1 day after surgery, and 1 week after surgery, significant differences were observed in HGB and PLT levels between patients who received BT and those who did not. The predictive model achieved an AUC of 0.899. Employing UML, we identified two distinct clusters with significantly heterogeneous clinical characteristics. Age, BMI, PLT, HGB, bleeding volume, and urine volume were found to be independent predictors of BT requirement in THA patients. The predictive model incorporating these six predictors demonstrated excellent predictive performance. Furthermore, employing UML enabled us to classify a heterogeneous cohort of THA patients who received BT in a meaningful and interpretable manner.

Comparison of Health Care Utilization in Different Usual Sources of Care Among Older People With Cardiovascular Disease in China: Evidence From the Study on Global Ageing and Adult Health

Objectives: To compare the health care utilization in different usual sources of care (USCs) among the elderly population with cardiovascular disease in China. Methods: Cross-sectional data for 3,340 participants aged ≥50 years with cardiovascular disease from Global AGEing and Adult Health (2010)-China were used. Using the inverse probability of treatment weighting on the propensity score with survey weighting, combined with negative binomial regression and logistic regression models, the correlation between USCs and health care utilization was assessed. Results: Patients using primary care facilities as their USC had fewer hospital admissions (IRR = 0.507, 95% CI = 0.413, 0.623) but more unmet health needs (OR = 1.657, 95% CI = 1.108, 2.478) than those using public hospitals. Patients using public clinics as their USC had higher outpatient visits (IRR = 2.188, 95% CI = 1.630, 2.939) than the private clinics' group. Conclusion: The difference in inpatient care utilization and unmet health care needs between public hospitals and primary care facilities, and the difference in outpatient care utilization between public and private clinics were significant. Using primary care facilities as USCs, particularly public ones, appeared to increase care accessibility, but it still should be strengthened to better address patients' health care needs.

Exploring Predictors of Patient Satisfaction in Dental Services: A Secondary Analysis Study

Purpose

Understanding the factors that influence the level of patient satisfaction with dental services and identifying the strengths and weaknesses in dental clinics will subsequently increase patient satisfaction and contribute to improving dental care quality. This study aims to evaluate the variables that impact patients' satisfaction with dental services received in specialized dental care centers of the Ministry of Health in Saudi Arabia.

Patients and Methods

Secondary data at the national level from a patient experience program were used in this study. Completed Press Ganey® surveys submitted by patients during the first half of 2022 were included. The effect of the different domains (access to dental clinic, moving through dental visit and dentist) on the overall assessment rating of patient satisfaction was assessed using Pearson's correlation coefficient (r) and multiple linear regression models.

Results

A total of 964 surveys were completed and subsequently analyzed. The overall assessment rate of patient satisfaction was 73.4%. All items of the domains showed highly significant correlation levels (P < 0.001). However, the Dentist domain exhibited the highest correlation with the overall assessment rate of ‎patient satisfaction.

Conclusion

The dentist acts as the most significant predictor of patient satisfaction.

MMP9 and STAT1 are biomarkers of the change in immune infiltration after anti-tuberculosis therapy, and the immune status can identify patients with spinal tuberculosis

BACKGROUND

Due to a lack of studies on immune-related pathogenesis and a clinical diagnostic model, the diagnosis of Spinal Tuberculosis (STB) remains uncertain. Our study aimed to investigate the possible pathogenesis of STB and to develop a clinical diagnostic model for STB based on immune cell infiltration.

METHODS

Label-free quantification protein analysis of five pairs of specimens was used to determine the protein expression of the intervertebral disc in STB and non-STB. GO enrichment analysis, and KEGG pathway analysis were used to investigate the pathogenesis of STB. The Hub proteins were then eliminated. Four datasets were downloaded from the GEO database to analyze immune cell infiltration, and the results were validated using blood routine test data from 8535TB and 7337 non-TB patients. Following that, clinical data from 164 STB and 162 non-STB patients were collected. The Random-Forest algorithm was used to screen out clinical predictors of STB and build a diagnostic model. The differential expression of MMP9 and STAT1 in STB and controls was confirmed using immunohistochemistry.

RESULTS

MMP9 and STAT1 were STB Hub proteins that were linked to disc destruction in STB. MMP9 and STAT1 were found to be associated with Monocytes, Neutrophils, and Lymphocytes in immune cell infiltration studies. Data from 15,872 blood routine tests revealed that the Monocytes ratio and Neutrophils ratio was significantly higher in TB patients than in non-TB patients (p < 0.001), while the Lymphocytes ratio was significantly lower in TB patients than in non-TB patients (p < 0.001). MMP9 and STAT1 expression were downregulated following the anti-TB therapy. For STB, a clinical diagnostic model was built using six clinical predictors: MR, NR, LR, ESR, BMI, and PLT. The model was evaluated using a ROC curve, which yielded an AUC of 0.816.

CONCLUSIONS

MMP9 and STAT1, immune-related hub proteins, were correlated with immune cell infiltration in STB patients. MR, NR, LR ESR, BMI, and PLT were clinical predictors of STB. Thus, the immune cell Infiltration-related clinical diagnostic model can predict STB effectively.

Patient satisfaction impact indicators from a psychosocial perspective

Background

Patient satisfaction plays an important role in improving patient behavior from care, reducing healthcare costs, and improving outcomes. However, since patient satisfaction is a multidimensional concept, it remains unclear which factors are the key indicators of patient satisfaction. The purpose of this study was to verify whether and how patients' psychosocial perceptions of physicians influenced patient satisfaction.

Method

In China, 2,256 patients were surveyed on stereotypes of physicians, institutional trust, humanized perception, and communication skills, as well as patient expectations and patient satisfaction. The data were analyzed using structural equation modeling.

Results

Stereotypes, institutional trust, and humanized perception have an indirect effect on patient satisfaction through communication, and patient expectations have a direct effect on patient satisfaction.

Conclusions

"Patient-centered" communication is the key to improving patient satisfaction, while positive stereotypes at the societal level, standardization of organizational institutions, expression of the doctor's view of humanity in the doctor-patient interaction, and reasonable guidance of patient expectations are important for improving patient satisfaction.

Unmet health care needs: factors predicting satisfaction with health care services among community-dwelling Canadians living with neurological conditions

Background

Neurological conditions (NCs) can lead to long-term challenges including functional impairments and limitations to activities of daily living. People with neurological conditions often report unmet health care needs and experience barriers to care. This study aimed to (1) explore the factors predicting patient satisfaction with general health care, hospital, and physician services among Canadians with NCs, (2) examine the association between unmet health care needs and satisfaction with health care services among neurological patients in Canada, and (3) contrast patient satisfaction between physician care and hospital care among Canadians with NCs.

Methods

We conducted a secondary analysis on a subsample of the 2010 Canadian Community Health Survey - Annual Component data (N = 6335) of respondents with neurological conditions, who received general health care services, hospital services, and physician services within twelve months. Multivariate logistic regression fitted the models and odds ratios and 95% confidence intervals were reported using STATA version 14.

Results

Excellent quality care predicts higher odds of patient satisfaction with general health care services (OR, 95%CI–237.6, 70.4–801.5), hospital services (OR, 95%CI–166.9, 67.9–410.6), and physician services (OR, 95%CI–176.5, 63.89–487.3). In contrast, self-perceived unmet health care needs negatively predict patient satisfaction across all health care services: general health care services (OR, 95%CI–0.59, 0.37–0.93), hospital services (OR, 95%CI–0.41, 0.21–0.77), and physician services (OR, 95%CI–0.29, 0.13–0.69). Other negative predictors of patient satisfaction include some post-secondary education (OR, 95%CI–0.36, 0.18–0.72) for general health services and (OR, 95%CI–0.26, 0.09–0.80) for physician services. Those with secondary (OR, 95% CI–0.32, 0.13–0.76) and post-secondary graduation (OR, 95%CI– 0.28, 0.11–0.67) negatively predicted patient satisfaction among users of physician services while being an emergency room patient most recently (OR, 95%CI– 0.39, 0.20–0.77) was also negatively associated with patients satisfaction among hospital services users.

Conclusion

This study found self-perceived unmet health care needs as a significant negative predictor of neurological patients’ satisfaction across health care services and emphasizes the importance of ensuring coordinated efforts to provide appropriate and accessible care of the highest quality for Canadians with neurological conditions.

Association between hospital legal constructions and medical disputes: A multi-center analysis of 130 tertiary hospitals in Hunan Province, China

Background

Medical disputes are common in hospitals and a major challenge for the operations of medical institutions. However, few studies have looked into the association between medical disputes and hospital legal constructions. The purpose of the study was to investigate the relationship between hospital legal constructions and medical disputes, and it also aimed to develop a nomogram to estimate the likelihood of medical disputes.

Methods

Between July and September 2021, 2,716 administrators from 130 hospitals were enrolled for analysis. The study collected seventeen variables for examination. To establish a nomogram, administrators were randomly split into a training group (n = 1,358) and a validation group (n = 1,358) with a 50:50 ratio. The nomogram was developed using data from participants in the training group, and it was validated in the validation group. The nomogram contained significant variables that were linked to medical disputes and were identified by multivariate analysis. The nomogram's predictive performance was assessed utilizing discriminative and calibrating ability. A web calculator was developed to be conducive to model utility.

Results

Medical disputes were observed in 41.53% (1,128/2,716) of participants. Five characteristics, including male gender, higher professional ranks, longer length of service, worse understanding of the hospital charters, and worse construction status of hospital rule of law, were significantly associated with more medical disputes based on the multivariate analysis. As a result, these variables were included in the nomogram development. The AUROC was 0.67 [95% confident interval (CI): 0.64-0.70] in the training group and 0.68 (95% CI: 0.66-0.71) in the validation group. The corresponding calibration slopes were 1.00 and 1.05, respectively, and intercepts were 0.00 and -0.06, respectively. Three risk groups were created among the participants: Those in the high-risk group experienced medical disputes 2.83 times more frequently than those in the low-risk group (P < 0.001).

Conclusion

Medical dispute is prevailing among hospital administrators, and it can be reduced by the effective constructions of hospital rule of law. This study proposes a novel nomogram to estimate the likelihood of medical disputes specifically among administrators in tertiary hospitals, and a web calculator can be available at https://ymgarden.shinyapps.io/Predictionofmedicaldisputes/.

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis

Introduction

Ankylosing spondylitis (AS) is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS mainly affects the axial bone, sacroiliac joint, hip joint, spinal facet, and adjacent ligaments. We used machine learning (ML) methods to construct diagnostic models based on blood routine examination, liver function test, and kidney function test of patients with AS. This method will help clinicians enhance diagnostic efficiency and allow patients to receive systematic treatment as soon as possible.

Methods

We consecutively screened 348 patients with AS through complete blood routine examination, liver function test, and kidney function test at the First Affiliated Hospital of Guangxi Medical University according to the modified New York criteria (diagnostic criteria for AS). By using random sampling, the patients were randomly divided into training and validation cohorts. The training cohort included 258 patients with AS and 247 patients without AS, and the validation cohort included 90 patients with AS and 113 patients without AS. We used three ML methods (LASSO, random forest, and support vector machine recursive feature elimination) to screen feature variables and then took the intersection to obtain the prediction model. In addition, we used the prediction model on the validation cohort.

Results

Seven factors—erythrocyte sedimentation rate (ESR), red blood cell count (RBC), mean platelet volume (MPV), albumin (ALB), aspartate aminotransferase (AST), and creatinine (Cr)—were selected to construct a nomogram diagnostic model through ML. In the training cohort, the C value and area under the curve (AUC) value of this nomogram was 0.878 and 0.8779462, respectively. The C value and AUC value of the nomogram in the validation cohort was 0.823 and 0.8232055, respectively. Calibration curves in the training and validation cohorts showed satisfactory agreement between nomogram predictions and actual probabilities. The decision curve analysis showed that the nonadherence nomogram was clinically useful when intervention was decided at the nonadherence possibility threshold of 1%.

Conclusion

Our ML model can satisfactorily predict patients with AS. This nomogram can help orthopedic surgeons devise more personalized and rational clinical strategies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40744-022-00481-6.

AS is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS starts gradually, and its early symptoms are mild. Some hospitals lack HLA-B27 and related imaging instruments to assist in the diagnosis of AS. There are relatively few studies on liver function and kidney function of patients with AS. We used ML methods to construct diagnostic models. Our model can satisfactorily predict patients with AS. This diagnostic model can help orthopedic surgeons devise more personalized and rational clinical strategies.

Drug-related problems and satisfaction among patients receiving pharmacist-led consultations at the initiation of cardiovascular drugs

BACKGROUND

Drug-related problems (DRPs) lead to substantial morbidity and mortality and increase healthcare costs. Several interventions have been developed to reduce DRPs and improve the outcome of drug therapy.

OBJECTIVE

To investigate DRPs identified through a pharmacist-led intervention and to assess patient satisfaction with the intervention.

METHODS

Patients received two pharmacist consultations 1-2 weeks and 3-5 weeks after collecting a new cardiovascular medicine. Information about patient characteristics, beliefs about medicines (BMQ), DRPs, and patient evaluations were collected using questionnaires.

RESULTS

Pharmacists identified DRPs among 52.4% and 43.1% of the 633 patients at consultation 1 and 2, respectively. Of the DRPs reported in consultation 1, 43.7% were solved at consultation 2. Among patients with side effects, patients who received advice on managing these in consultation 1 where more likely to have solved problems at consultation 2 (61.2% vs. 42.6%, p = 0.008). Female gender, high BMQ concern and the number of new medicines were associated with DRPs. Patients were highly satisfied with the intervention. Predictors of satisfaction were female gender, older age, higher BMQ necessity, face-to-face consultations, longer duration of consultation 1, and solved problems in consultation 2.

CONCLUSIONS

The results indicate that the pharmacist-led follow-up intervention can aid early identification and solving of DRPs in patients prescribed new cardiovascular drugs. Knowledge of factors associated with DRPs and patients' satisfaction may allow further improvement of the intervention.

Effective Analysis of Inpatient Satisfaction: The Random Forest Algorithm

PURPOSE

To identify the factors influencing inpatient satisfaction by fitting the optimal discriminant model.

PATIENTS AND METHODS

A cross-sectional survey of inpatient satisfaction was conducted with 3888 patients in 16 large public hospitals in Zhejiang Province. Independent variables were screened by single-factor analysis, and the importance of all variables was comprehensively evaluated. The relationship between patients' overall satisfaction and influencing factors was established, the relative risk was evaluated by marginal benefit, and the optimal model was fitted using the receiver operating characteristic curve.

RESULTS

Patients' overall satisfaction was 79.73%. The five most influential factors on inpatient satisfaction, in this order, were: patients' right to know, timely nursing response, satisfaction with medical staff service, integrity of medical staff, and accuracy of diagnosis. The prediction accuracy of the random forest model was higher than that of the multiple logistic regression and naive Bayesian models.

CONCLUSION

Inpatient satisfaction is related to healthcare quality, diagnosis, and treatment process. Rapid identification and active improvement of the factors affecting patient satisfaction can reduce public hospital operating costs and improve patient experiences and the efficiency of health resource allocation. Public hospitals should strengthen the exchange of medical information between doctors and patients, shorten waiting time, and improve the level of medical technology, service attitude, and transparency of information disclosure.