Mental health disparities between physicians and nurses: Analyzing the impact of occupational stress and work environment fitness using random forest algorithm.

BACKGROUND: The impact of occupational stress and work environment fitness on mental health disparities between physicians and nurses are not well understood. This study aims to identify and rank key determinants of mental health in physicians and nurses in China and compare the differences in their impact on mental health between physicians and nurses. METHODS: A large cross-sectional survey with multistage cluster sampling was conducted. The survey included the Self-Rating Anxiety Scale (SAS Scale), the Center for Epidemiologic Studies Depression Scale (CES-D Scale), the Maslach Burnout Inventory-General Survey (MBI-GS) and the Person-Environment (PE) Fit. We applied a principled, machine learning-based variable selection algorithm, using random forests, to identify and rank the determinants of the mental health in physicians and nurses. RESULTS: In our study, we analyzed a sample of 9964 healthcare workers, and 2729 (27 %) were physicians. The prevalence of anxiety and depressive disorders among physicians and nurses was 31.0 % and 53.3 %, 30.8 % and 47.9 %, respectively. Among physicians with anxiety disorder, we observed a higher likelihood of cynicism, emotional exhaustion, reduced personal accomplishment, and poor organization fitness, job fitness, group fitness, and supervisor fitness, in order of importance. When comparing the effects on depressive disorder in physicians, group fitness and supervisor fitness did not have significant impacts. For nurses, emotional exhaustion had a more significant effect on depressive disorder compared to cynicism. Supervisor fitness did not have a significant impact on anxiety disorder in nurses. LIMITATIONS: Cross-sectional design, self-reporting screening scales. CONCLUSIONS: Compared to individual and hospital characteristics, the primary factors influencing mental health disorders are occupational burnout and the compatibility of the work environment. Additionally, the key determinants of depressive and anxiety disorders among doctors and nurses exhibit slight variations. Employing machine learning methods proves beneficial for identifying determinants of mental health disorders among physicians and nurses in China. These findings could help improve policymaking aimed at addressing the mental well-being of healthcare professionals.

Health care costs of cardiovascular disease in China: a machine learning-based cross-sectional study.

Background: Cardiovascular disease (CVD) causes substantial financial burden to patients with the condition, their households, and the healthcare system in China. Health care costs for treating patients with CVD vary significantly, but little is known about the factors associated with the cost variation. This study aims to identify and rank key determinants of health care costs in patients with CVD in China and to assess their effects on health care costs. Methods: Data were from a survey of patients with CVD from 14 large tertiary grade-A general hospitals in S City, China, between 2018 and 2020. The survey included information on demographic characteristics, health conditions and comorbidities, medical service utilization, and health care costs. We used re-centered influence function regression to examine health care cost concentration, decomposing and estimating the effects of relevant factors on the distribution of costs. We also applied quantile regression forests-a machine learning approach-to identify the key factors for predicting the 10th (low), 50th (median), and 90th (high) quantiles of health care costs associated with CVD treatment. Results: Our sample included 28,213 patients with CVD. The 10th, 50th and 90th quantiles of health care cost for patients with CVD were 6,103 CNY, 18,105 CNY, and 98,637 CNY, respectively. Patients with high health care costs were more likely to be older, male, and have a longer length of hospital stay, more comorbidities, more complex medical procedures, and emergency admissions. Higher health care costs were also associated with specific CVD types such as cardiomyopathy, heart failure, and stroke. Conclusion: Machine learning methods are useful tools to identify determinants of health care costs for patients with CVD in China. Findings may help improve policymaking to alleviate the financial burden of CVD, particularly among patients with high health care costs.

Measuring and improving performance of clinicians: an application of patient-based records.

BACKGOUND: Efforts to measure performance and identify its driving factors among clinicians are needed for building a high-quality clinician workforce. The availability of data is the most challenging thing. This paper presented a summary performance measure for clinicians and its application on examining factors that influence performance using routine patient-based records. METHODS: Perfomance indicators and difficulty score were extracted from electronic medical records (EMRs). Difficulty adjustment and standardized processing were used to obtain indicators which were comparable between specialties. Principal component analysis (PCA) was used to estimate the summary performance measure. The performance measure was then used to examine the influence of person-job fit and burnout through a mediator effect model and cluster analysis. RESULTS: A valid sample of 404 clinicians were included in this study, and 244 of them had valid response in the questionnaire. PCA explained 79.37% of the total variance presented by the four adjusted performance indicators. Non-performance attributes and performance driving factors help distinguish different clusters of clinicians. Burnout mediates the relationship between person-job fit and performance in a specific group of clinicians (ß = 0.120, p = 0.008). CONCLUSIONS: We demonstrated the analytical steps to estimate clinicians' performance and its practical application using EMRs. Our findings provide insight into personnel classified management. Such practice can be applied in countries where electronic medical record systems are relatively less developed to continuously improve the application of performance management.

Determinants Influencing the Adoption of Internet Health Care Technology Among Chinese Health Care Professionals: Extension of the Value-Based Adoption Model With Burnout Theory.

BACKGROUND: The global COVID-19 pandemic has been widely regarded as a catalyst for adopting internet health care technology (IHT) in China. IHT consists of new health care technologies that are shaping health services and medical consultations. Health care professionals play a substantial role in the adoption of any IHT, but the consequences of doing so can often be challenging, particularly when employee burnout is prevalent. Few studies have explored whether employee burnout influences the adoption intention of IHT in health care professionals. OBJECTIVE: This study aims to explain the determinants influencing the adoption of IHT from the perspective of health care professionals. To do so, the study extends the value-based adoption model (VAM) with consideration for employee burnout as a determining factor. METHODS: A cross-sectional web-based survey using a sample of 12,031 health care professionals selected through multistage cluster sampling from 3 provinces in mainland China was conducted. The hypotheses of our research model were developed based on the VAM and employee burnout theory. Structural equation modeling was then used to test the research hypotheses. RESULTS: The results indicate that perceived usefulness, perceived enjoyment, and perceived complexity positively correlate with perceived value (ß=.131, P=.01; ß=.638, P<.001; ß=.198, P<.001, respectively). Perceived value had a positive direct effect on adoption intention (ß=.725, P<.001), perceived risk negatively correlated with perceived value (ß=-.083, P<.001), and perceived value negatively correlated with employee burnout (ß=-.308, P<.001). In addition, employee burnout was negatively related to adoption intention (ß=-.170, P<.001) and mediated the relationship between perceived value and adoption intention (ß=.052, P<.001). CONCLUSIONS: Perceived value, perceived enjoyment, and employee burnout were the most important determinants of IHT adoption intention by health care professionals. In addition, while employee burnout was negatively related to adoption intention, perceived value inhibited employee burnout. Therefore, this study finds that it is necessary to develop strategies to improve the perceived value and reduce employee burnout, which will benefit the promotion of the adoption intention of IHT in health care professionals. This study supports the use of the VAM and employee burnout in explaining health care professionals' adoption intention regarding IHT.

Correction: The Application of Graph Theoretical Analysis to Complex Networks in Medical Malpractice in China: Qualitative Study.

[This corrects the article DOI: 10.2196/35709.].

The Application of Graph Theoretical Analysis to Complex Networks in Medical Malpractice in China: Qualitative Study.

BACKGROUND: Studies have shown that hospitals or physicians with multiple malpractice claims are more likely to be involved in new claims. This finding indicates that medical malpractice may be clustered by institutions. OBJECTIVE: We aimed to identify the underlying mechanisms of medical malpractice that, in the long term, may contribute to developing interventions to reduce future claims and patient harm. METHODS: This study extracted the semantic network in 6610 medical litigation records (unstructured data) obtained from a public judicial database in China. They represented the most serious cases of malpractice in the country. The medical malpractice network of China was presented as a knowledge graph based on the complex network theory; it uses the International Classification of Patient Safety from the World Health Organization as a reference. RESULTS: We found that the medical malpractice network of China was a scale-free network-the occurrence of medical malpractice in litigation cases was not random, but traceable. The results of the hub nodes revealed that orthopedics, obstetrics and gynecology, and the emergency department were the 3 most frequent specialties that incurred malpractice; inadequate informed consent work constituted the most errors. Nontechnical errors (eg, inadequate informed consent) showed a higher centrality than technical errors. CONCLUSIONS: Hospitals and medical boards could apply our approach to detect hub nodes that are likely to benefit from interventions; doing so could effectively control medical risks.

Development and validity of computerized neuropsychological assessment devices for screening mild cognitive impairment: Ensemble of models with feature space heterogeneity and retrieval practice effect.

OBJECTIVE: This study aimed to develop and validate computerized neuropsychological assessment devices for screening patients with mild cognitive impairment (MCI). METHODS: We conducted this study in three phases. Phase I involved the development of a conceptual framework of Memory Guard (MG) based on the principles of the cognitive design system (CDS). Phase II involved three steps of feature engineering: item development, filter, and wrapper. Based on the initial items, the number of items in each dimension was determined through analytic hierarchy process. We constructed an initial set with a total of 198 items with three levels of difficulty. Next, we performed feature selection through comprehensive reliability and validity tests, which resulted in the best item bank of 38 test items. The features for modeling were obtained from the best item bank (option scores, reading time scores and total time scores), demographic variables and their MoCA groups. Regarding the heterogeneity of the feature space, we combined the AdaBoost with the Naive Bayes classification algorithm as the decision model of MG. For the screening tool to be used repeatedly, the retrieval practice effect was considered in the design. Phase III involved the validation of measuring instruments. The features incorporated into the modeling process were optimized based on the classification accuracy and area under curve. We also verified the classification effect of the other three classification models with MG. RESULTS: After three steps of feature engineering, a total of 6 dimensions of cognitive areas were included in MG: orientation, memory, attention, calculation, recall, and language & executive function. 38 features were included in the model (17 features of option score, 20 features of time score, and 1 demographic feature). A total of 333 individuals from two communities in Shanghai and Henan province were included in the measuring instrument verification process. Women accounted for 68.2% of the sample. The median age was 63. 15.3% of the participants had bachelor's degrees or above and 111 participants lived in urban areas (33.3%). The results showed that MG had an accuracy of 93.75% and AUC of 0.923, with a sensitivity of 91.67% and a specificity of 95.45%. Compared to the other three classification models, MG that combined the AdaBoost with the Naive Bayes classification algorithm was the most accurate classifier. CONCLUSIONS: MG was proved to be reliable and valid in early screening for patients with MCI. MG that integrated heterogeneous features such as demography, option scores, and time scores had a better predictive performance for screening MCI.

Which Contributes to Clinical Performance: Academic Output or Person-Environment Fit?

Background: The measures put in place by health authorities to ensure the professionalism of doctors are important. Hospitals in China have included academic outputs in the promotion criteria to incentive medical clinicians to engage in scientific research so that to improve job performance (JP). However, such practice disproportionally focuses on academic outputs but ignores the force of needs fulfilled brought by intrinsic incentive. This study aims to discuss the realistic problem regarding the promotion mechanism and the potential drivers to clinical JP. Methods: This study was based on multi-source data collection on clinical performance from electric medical record (EMR), person-environment (P-E) fit from the survey, and academic output from personnel files of ward clinicians (n = 244) of general public hospitals who sought for career progression in Shanghai in 2020. Independent-Sample t-test and chi-square test were used for comparison of two sample means or constituent ratio between promoted and not promoted clinicians. Linear multilevel regression was conducted to examine the relationship between clinical performance and academic outputs and P-E fit. Results: Clinicians who were promoted were more productive in producing academic outputs than those who were not (t = -5.075, p < 0.001). However, there was no difference in clinical performance between the two groups (t = -1.728 to 0.167, p > 0.05). The regression showed that academic outputs were not related to clinical performance, while higher P-E fit was associated with the improvement of various clinical performances. Conclusion: This study shows that P-E fit plays a more important role in facilitating clinical performance than academic performance and highlights the importance of intrinsic motivation of clinicians in achieving clinical performance.

Impact of the COVID-19 Pandemic on Chronic Disease Care in India, China, Hong Kong, Korea, and Vietnam.

This study aims to provide evidence on how the COVID-19 pandemic has impacted chronic disease care in diverse settings across Asia. Cross-sectional surveys were conducted to assess the health, social, and economic consequences of the pandemic in India, China, Hong Kong, Korea, and Vietnam using standardized questionnaires. Overall, 5672 participants with chronic conditions were recruited from five countries. The mean age of the participants ranged from 55.9 to 69.3 years. A worsened economic status during the COVID-19 pandemic was reported by 19% to 59% of the study participants. Increased difficulty in accessing care was reported by 8% to 24% of participants, except Vietnam: 1.6%. The worsening of diabetes symptoms was reported by 5.6% to 14.6% of participants, except Vietnam: 3%. In multivariable regression analyses, increasing age, female participants, and worsened economic status were suggestive of increased difficulty in access to care, but these associations mostly did not reach statistical significance. In India and China, rural residence, worsened economic status and self-reported hypertension were statistically significantly associated with increased difficulty in access to care or worsening of diabetes symptoms. These findings suggest that the pandemic disproportionately affected marginalized and rural populations in Asia, negatively affecting population health beyond those directly suffering from COVID-19.

Correction: Rating Hospital Performance in China: Review of Publicly Available Measures and Development of a Ranking System.

[This corrects the article DOI: 10.2196/17095.].

Rating Hospital Performance in China: Review of Publicly Available Measures and Development of a Ranking System.

BACKGROUND: In China, significant emphasis and investment in health care reform since 2009 has brought with it increasing scrutiny of its public hospitals. Calls for greater accountability in the quality of hospital care have led to increasing attention toward performance measurement and the development of hospital ratings. Despite such interest, there has yet to be a comprehensive analysis of what performance information is publicly available to understand the performance of hospitals in China. OBJECTIVE: This study aims to review the publicly available performance information about hospitals in China to assess options for ranking hospital performance. METHODS: A review was undertaken to identify performance measures based on publicly available data. Following several rounds of expert consultation regarding the utility of these measures, we clustered the available options into three key areas: research and development, academic reputation, and quality and safety. Following the identification and clustering of the available performance measures, we set out to translate these into a practical performance ranking system to assess variation in hospital performance. RESULTS: A new hospital ranking system termed the China Hospital Development Index (CHDI) is thus presented. Furthermore, we used CHDI for ranking well-known tertiary hospitals in China. CONCLUSIONS: Despite notable limitations, our assessment of available measures and the development of a new ranking system break new ground in understanding hospital performance in China. In doing so, CHDI has the potential to contribute to wider discussions and debates about assessing hospital performance across global health care systems.

Person-environment fit and medical professionals' job satisfaction, turnover intention, and professional efficacy: A cross-sectional study in Shanghai.

OBJECTIVES: Using the person-environment (PE) fit theory, this study aims to explore factors affecting medical professionals' job satisfaction, turnover intention, and professional efficacy, and to examine individual characters associated with PE fit. DESIGN AND METHODS: This study used data from the sixth National Health Service Survey conducted in 2018, with a focus on job outcomes among medical professionals in Shanghai. The reliability and validity of the tools for measuring PE and job outcomes were calculated. A structural equation model was used to examine the relationship among person-job (PJ) fit and person-group (PG) fit, job satisfaction, turnover intention, and professional efficacy. Finally, a hierarchical regression model was used to analyze the association between demographic variables and the PJ and PG fit. RESULTS: PG fit was directly and positively associated with job satisfaction and professional efficacy. PJ fit had a direct and positive association with job satisfaction but had a direct and negative association with turnover intention. The indirect association of PJ fit with turnover intention was statistically significant. The results from the hierarchical regression analysis showed that younger physicians generally had a lower level of PJ fit and older physicians with higher education tended to have a lower level of PG fit. CONCLUSIONS: Medical professionals with higher PJ or PG fit have higher job satisfaction, and those with higher PG fit have higher professional efficacy. The impact of PJ fit on turnover intention was mediated by job satisfaction. Healthcare managers should take actions to effectively promote medical professionals' PJ and PG fit to improve their retention and efficiency.