Elizabeth Maitland

Background: Obesity is a unique chronic disease, with China having the largest number of people living with overweight and obesity in the world. There has been little research from the demand perspective for online medical consultation (OMC) by individuals living with obesity. With the growing demand for obesity OMC, especially due to the emergence of new pharmacotherapies, such as glucagon-like peptide-1 receptor agonists, individuals living with obesity are seeking both advice on obesity management and the prescription of obesity drugs. Therefore, our demand scenarios defined 2 OMC motivations to manage obesity: “For-Drugs” use and “For-Advice” use. Objective: This study aims to assess and compare the preferences for For-Drugs and For-Advice OMC among individuals living with obesity in China. Methods: Following the International Society for Pharmacoeconomics and Outcomes Research’s checklist and comprising 400 participants assigned to the For-Drugs scenario and 400 to the For-Advice scenario, the For-Drugs and For-Advice preferences were estimated through discrete choice experiments. The groups in the 2 scenarios followed a similar distribution, and the 2 different demand scenarios shared the same discrete choice experiment design, comprising 16 choice sets with 6 representative attributes. Mixed logit modeling was used to estimate the willingness to pay and relative importance scores. Results: Doctors with well-known and general expert titles, versus ordinary doctors; doctors from high-level, provincial, tertiary, and municipal hospitals, versus lower-level county hospitals; less waiting time; and lower OMC fees were preferred in both the For-Drugs and For-Advice scenarios. The differences between the 2 scenarios lay in the consultation format, consultation duration, and the relative importance of consultation duration versus waiting time. The For-Advice group preferred telephone consultations, while the For-Drugs group did not; the For-Drugs group preferred longer consultation duration (β=.029), while the For-Advice group preferred shorter consultation duration (β=–.030); and the For-Drugs group rated consultation duration higher than waiting time, while the For-Advice group rated the waiting time as more important than consultation duration. Combined with our qualitative research, the differences can be explained by the different consultation needs in the 2 scenarios, where longer patient consultations were preferred by the For-Drugs patients who sought detailed advice on drug side effects, while quick and direct responses were preferred by the For-Advice participants. Conclusions: By revealing user preferences on costs, doctors’ titles and hospital level, wait time, and consultation duration and format, our research informs OMC platforms, OMC regulators, and doctors on market segmentation and service differentiation strategies.

Long-term care insurance (LTCI) is an essential system in the context of fast-growing population aging, yet it lacks evidence on how to improve the acceptance and coverage of non-compulsory LTCI in China. Using a survey experiment, we randomly assigned 1025 older adults to control group, disability information group and dementia information group, and explored whether information about the adverse health outcomes would change their willingness to LTCI insure. We found that disability and dementia information significantly changed respondents’ willingness to LTCI insure, and health status had negative moderating effects on the relationship between information interventions and the change of LTCI intentions. Also, we found respondents with lower education and living in non-pilot cities were more sensitive to information interventions. We recommend LTCI information campaigns differentiating information by dementia-related and disability-related risks; by LTCI pilot and non-pilot cities; by education levels and by physical and mental health status.

Aim. This study aims to investigate the level of organizational learning within urban Chinese Community Health Centres and reveal its potential association with primary care nurses’ work performance and well‐being. Background. Globally, there is a push to establish learning healthcare systems for complex health reform challenges. Existing studies on organizational learning mainly focus on North American and European hospital settings, offering limited insights into primary care environments, particularly in developing countries. Design. Cross‐sectional study. Methods. We recruited 175 nurses from 38 community health centres in four Chinese cities (Shanghai, Shenzhen, Tianjin, and Jinan) using convenience sampling. Trained research assistants conducted face‐to‐face surveys, measuring organizational learning with the Learning Orientation Scale. Nurse‐level outcomes included self‐directed learning, quality of care, organizational commitment, and work stress. Data analysis employed multilevel linear modelling. Results. The 38 community health centres displayed a relatively high level of organizational learning, and there was a positive and significant association between organizational learning within community health centres and nurses’ self‐directed learning as well as the quality of care. However, there was no significant association between organizational learning and nurses’ organizational commitment or work stress. Conclusion. This study demonstrates a high‐level organizational learning capacity in urban community health centres in China. It provides a new perspective on the potential relationship between CHCs’ organizational learning and primary care nurses’ well‐being and work performance. Further research is needed to clarify unexpected findings and identify factors promoting organizational learning in primary care settings. Implications for Nursing Management. In China’s evolving primary care system, nurses play a vital role amidst physician shortages. Policy should prioritize internal management reform alongside structural changes. This study highlights the importance of fostering organizational learning in primary care settings. Strategic interventions should promote a learning culture in CHCs, which may enhance nurses’ self‐directed learning and improve the quality of care.

BACKGROUND Obesity is a unique chronic disease, with China having the largest number of people living with overweight and obesity in the world. There has been little research from the demand perspective for online medical consultation (OMC) by individuals living with obesity. With the growing demand for obesity OMC, especially due to the emergence of new pharmacotherapies, such as glucagon-like peptide-1 receptor agonists, individuals living with obesity are seeking both advice on obesity management and the prescription of obesity drugs. Therefore, our demand scenarios defined 2 OMC motivations to manage obesity: “For-Drugs” use and “For-Advice” use. OBJECTIVE This study aims to assess and compare the preferences for For-Drugs and For-Advice OMC among individuals living with obesity in China. METHODS Following the International Society for Pharmacoeconomics and Outcomes Research’s checklist and comprising 400 participants assigned to the For-Drugs scenario and 400 to the For-Advice scenario, the For-Drugs and For-Advice preferences were estimated through discrete choice experiments. The groups in the 2 scenarios followed a similar distribution, and the 2 different demand scenarios shared the same discrete choice experiment design, comprising 16 choice sets with 6 representative attributes. Mixed logit modeling was used to estimate the willingness to pay and relative importance scores. RESULTS Doctors with well-known and general expert titles, versus ordinary doctors; doctors from high-level, provincial, tertiary, and municipal hospitals, versus lower-level county hospitals; less waiting time; and lower OMC fees were preferred in both the For-Drugs and For-Advice scenarios. The differences between the 2 scenarios lay in the consultation format, consultation duration, and the relative importance of consultation duration versus waiting time. The For-Advice group preferred telephone consultations, while the For-Drugs group did not; the For-Drugs group preferred longer consultation duration (β=.029), while the For-Advice group preferred shorter consultation duration (β=–.030); and the For-Drugs group rated consultation duration higher than waiting time, while the For-Advice group rated the waiting time as more important than consultation duration. Combined with our qualitative research, the differences can be explained by the different consultation needs in the 2 scenarios, where longer patient consultations were preferred by the For-Drugs patients who sought detailed advice on drug side effects, while quick and direct responses were preferred by the For-Advice participants. CONCLUSIONS By revealing user preferences on costs, doctors’ titles and hospital level, wait time, and consultation duration and format, our research informs OMC platforms, OMC regulators, and doctors on market segmentation and service differentiation strategies.

Objective: In China, rural residents experience poorer health conditions and a higher disease burden compared to urban residents but have lower healthcare services utilization. Rather than an insurance focus on enhanced healthcare services utilization, we aim to examine that whether an income shock, in the form of China’s New Rural Pension Scheme (NRPS), will affect outpatient, inpatient and discretionary over-the-counter drug utilization by over 60-year-old rural NRPS residents. Methods: Providing a monthly pension of around RMB88 (USD12.97), NRPS covered all rural residents over 60 years old. Fuzzy regression discontinuity design (FRDD) was employed to explore the NRPS causal effect on healthcare services utilization, measured by outpatient and inpatient visits and discretionary over-the-counter drug purchases. The nationwide China Health and Retirement Longitudinal Study (CHARLS) 2018 provided the data. Results: Without significant changes in health status and medication needs, 60-plus-year-old NRPS recipients significantly increased the probability of discretionary OTC drug purchases by 33 percentage points. NRPS had no significant effect on the utilization of outpatient and inpatient utilization. The increase in the probability of discretionary OTC drug purchases from the NRPS income shock was concentrated in healthier and low-income rural residents. Robustness tests confirmed that FRDD was a robust estimation method and our results are robust. Conclusion: NRPS was an exogenous income shock that significantly increased the probability of discretionary over-the-counter drug purchases among over 60-year-old rural residents, but not the utilization of inpatient or outpatient healthcare services. Income remains an important constraint for rural residents to improve their health. We recommend policymakers consider including commonly used over-the-counter drugs in basic health insurance reimbursements for rural residents; provide health advice for rural residents to make discretionary over-the-counter drug purchases; and to mount an information campaign on over-the-counter drug purchasing in order to increase the health awareness of rural residents.

Background: Total healthcare expenditures are concentrated among a small number of patients. To date, studies on the concentration of health care expenditures in developing countries are limited, mainly focusing on concentration measures and the demographic, clinical and socioeconomic characteristics of high-cost users (HCU). The drivers of the skewed overall distribution of health care expenditures are opaque. Using inpatient administrative claims data, this study provides new evidence on the concentration of healthcare expenditures in China; the demographic and clinical characteristics of high-cost users; and the drivers of the overall distribution of healthcare expenditures. Methods: Utilizing administrative claims data for hospitalization in a prefecture-level city in China, we investigated the concentration of healthcare expenditure. We used recentered influence function (RIF) regression to examine the drivers of healthcare expenditure concentration, decomposing and estimating the effects of demographic and disease characteristics on the overall distribution of health care expenditures. Results: Using a sample of 87,841 adults, we found extreme skewness in the distribution of inpatient medical expenditures in China, with approximately 49% of annual medical expenditures generated by the top 10% of inpatient groups. HCUs tend to be elderly and male, with high-frequency hospitalizations and long lengths of stay. In addition, healthcare expenditure concentration was related to diseases of the circulatory system, malignant neoplasms, diseases of the musculoskeletal system and connective tissue, diseases of the digestive system, injury and poisoning, and diseases of the respiratory system. Malignant and major diseases reinforced the concentration of healthcare spending, and a 10% increase in the prevalence of malignancy would result in a predicted Gini coefficient increase of 7.2%, heart disease of 0.92% and cerebrovascular disease of 1.5%. The above significant positive effects were not observed for hypertension and diabetes mellitus. Conclusions: Our study provides new insights into the concentration of inpatient medical expenditures in China, including the precise picture of HCU expenditure concentration, the drivers of HCU expenditure concentration and the magnitude of their impact. With the aging of China's population and the profound shift in the disease spectrum, policymakers need to strengthen the early detection and intervention management of specific chronic diseases and high-risk populations, especially the early diagnosis and treatment of key cancers.

Background: China’s social medical insurance system faces challenges in financing, product coverage, patient health responsibility sharing and data security, which commercial health insurance companies can help address. Confronting accelerated population aging, the rapid increase of patients with chronic diseases and the maternal and child healthcare needs created by the three-child policy, the Chinese government has encouraged the development of commercial health insurance. But China's commercial health insurance companies face financial sustainability problems, limited product ranges and high operating costs. At the same time, the informatization level of China's healthcare industry, and the value of healthcare big data, is increasing. We analyze and describe the potential application of healthcare big data in the life cycle of China's commercial health insurance system and provide specific action plans for Chinese commercial health insurance companies; identify the challenges to commercial health insurers; and make recommendations for the application of big health data by commercial health insurers. Our recommendations inform healthcare policy makers on the development of commercial health insurance and the improvement of the healthcare financing system. We not only verify the value of healthcare big data, but also identify specific ways that healthcare big data plays in the development of commercial health insurance. Based on the research results, we recommend new policies for government and new uses of healthcare big data for commercial health insurance institutions. The benign development of commercial health insurance will improve the level of health services in China. Methods: By interviewing health insurance managers (including actuaries, product managers, business executives, information technology medical workers, and commercial health insurance personnel) and by accessing research papers, industry reports, news reports and public information disclosure documents about commercial health insurance, we describe the impact of healthcare big data on the life cycle of commercial health insurance products and processes. Results: We identify the issues and challenges of commercial health insurers in the use of healthcare big data, and advance specific strategies to expand the use of healthcare big data. In the life cycle of commercial health insurance products, healthcare big data can improve premium income, control medical costs and increase operational efficiency. First, healthcare big data can increase premiums, products and services by attenuating moral hazard and adverse selection problems, where high quality clients over-pay and high-risk clients underpay for health insurance. Second, healthcare big data can reduce medical expenses compensation pay-outs by promoting the establishment of a management medical system. Finally, the use of healthcare big data improves operational efficiency by increasing payment speeds, identifying fraud and increasing claim verification processes through automating payments and reducing offline processes. We discuss the obstacles to obtain healthcare big data confronting commercial health insurance companies. The sharing and data mining of healthcare big data brings privacy risks to the insured and there are significant differences in data standards and quality of healthcare big data that limit the application of healthcare big data in commercial health insurance. We recommend that national, regional and local government departments coordinate policies to facilitate the cooperation between commercial health insurance companies and regional healthcare big data platforms. In terms of technology, we recommend the establishment of data sharing platforms and data exchange mechanism across institutions and regions according to nation-wide standards and specifications. Government management departments should establish healthcare big data standards and specification system, promote the construction of healthcare big data and ensure the integrity, authenticity and reliability of health data. We recommend data quality continuous improvement and management mechanisms that combine technology and management. Government regulation should oversee commercial health insurance institutions and establish data security management systems to monitor and supervise the privacy of personal data. Conclusions: Healthcare big data can play an important role in the development of China's commercial health insurance industry. Healthcare big data can increase commercial health insurers’ financial viability while providing improved, and cost-effective, products and services. By providing more and better information to insurers, healthcare big data attenuates the asymmetric information problem, addressing moral hazard and adverse selection problems. By combining hospital and medical organization management information systems with insurers’ data management, healthcare big data can help insurers set sustainable premiums, control medical costs and promote operational efficiency. At present, the informatization degree of China's healthcare industry remains limited. To improve the performances, products and services of commercial health insurers, we recommend government reforms in healthcare big data, such as expanding medical industry cooperation; further developing the processes of applying healthcare big data; augmenting data sharing; addressing privacy risks; setting data standards; and improving data quality.