Application of machine learning in the preoperative radiomic diagnosis of ameloblastoma and odontogenic keratocyst based on cone-beam CT.

OBJECTIVES: Preoperative diagnosis of oral ameloblastoma (AME) and odontogenic keratocyst (OKC) has been a challenge in dentistry. This study uses radiomics approaches and machine learning (ML) algorithms to characterize cone beam computed tomography (CBCT) image features for the preoperative differential diagnosis of AME and OKC and compares ML algorithms to expert radiologists to validate performance. METHODS: We retrospectively collected the data of 326 patients with AME and OKC, where all diagnoses were confirmed by histopathologic tests. A total of 348 features were selected to train six ML models for differential diagnosis by a five-fold cross-validation. We then compared the performance of ML-based diagnoses to those of radiologists. RESULTS: Among the six ML models, XGBoost was effective in distinguishing AME and OKC in CBCT images, with its classification performance outperforming the other models. The mean precision, recall, accuracy, F1-score, and area under the curve (AUC) were 0.900, 0.807, 0.843, 0.841, and 0.872, respectively. Compared to the diagnostics by radiologists, ML-based radiomic diagnostics performed better. CONCLUSIONS: Radiomic-based ML algorithms allow CBCT images of AME and OKC to be distinguished accurately, facilitating the preoperative differential diagnosis of AME and OKC. ADVANCES IN KNOWLEDGE: ML and radiomic approaches with high-resolution CBCT images provide new insights into the differential diagnosis of AME and OKC.

A Scale for Measuring Electronic Patient Engagement Behaviors: Development and Validation.

Purpose: Advancements in electronic health (eHealth) technology have profoundly impacted patient engagement. This study aimed to develop and validate the Electronic Patient Engagement Behavior (EPEB) scale to measure the conceptual and underlying framework of patient engagement behaviors in an eHealth context. Patients and Methods: Initial measurement items were generated based on a literature review and qualitative research. Two rounds of surveys, a pilot survey and validation survey, were conducted to evaluate the psychometric properties of the scale. Results: The EPEB scale consists of 15 items in four dimensions: disease information search, physician-patient interaction, social interaction between patients, and disease self-monitoring. In the pilot survey, the exploratory factor analysis revealed a four-factor model, explaining 69.411% of variance. In the validation survey, the Cronbach's α coefficient of each sub-scale was 0.865, 0.904, 0.904, and 0.900 respectively. The Spearman-Brown split coefficient of the scale was 0.963. The results of the cross-sex measurement equivalence test indicate that all fit indices met the measurement criteria. The confirmatory factor analysis indicated second-order 4-factor model fit the data well. The EPEB has a good reliability and validity. Conclusion: The EPEB scale provides a reliable tool for measuring patient engagement behaviors in the eHealth context. The utilization of this scale may yield valuable insights into strategies for enhancing patient engagement and optimizing health outcomes.

Estimating the economic burden of stroke in China: a cost-of-illness study.

OBJECTIVES: Stroke imposes a heavy economic burden and loss of productivity on individuals and society. This study assessed a range of crucial factors, including direct costs and indirect costs, to gauge the economic implications of stroke in China. These outcomes were evaluated with specific reference to the year 2018, using the Chinese yuan (¥) as the unit of measurement and providing the corresponding purchasing power parity dollar ($PPP) currency value. METHODS: A cost-of-illness methodology was used to ascertain the economic implications of stroke in 2018. Within the constraints of this approach, economic costs were defined as 'direct costs' or 'indirect costs'. We estimated direct costs from sample data, the National Health Service Survey and the National Health Account and Health Statistical Yearbook. A human capital method was used to conservatively estimate indirect costs. RESULTS: In 2018, of the economic burden of stroke in China, the direct costs were ¥247.8 billion ($PPP 58.6 billion) and indirect costs were ¥704.4 billion ($PPP 166.5 billion). The curative care expenditure for stroke was ¥193.1 billion ($PPP 45.7 billion), consuming nearly 5.5% of curative expenditure. The cost of stroke treatment relied heavily on public financing, with 58% from social health insurance and 14% from government sources. CONCLUSIONS: A significant economic burden is imposed by stroke on China's economy, and there is a risk of underestimating this burden if indirect costs are not comprehensively considered. The importance of implementing effective preventive measures and screening strategies for stroke, with a particular focus on high-risk populations, is underscored by this study's findings. Such investments in public health have the potential to yield substantial benefits.

Linking Frontline Employee Self-Efficacy to Customers Service Performance in Healthcare Industry: A Dynamic Capability Perspective.

Purpose: In the complex and rapidly changing healthcare environment, the dynamic capabilities of frontline employees (FLEs) to integrate resources and adapt to environmental changes are crucial. This study aims to investigate the relationship between FLEs' self-efficacy, dynamic capabilities (including sensing capability and reconfiguring capability), and their impact on service performance. Methods: Data were collected from a matched sample of 123 doctors and 762 corresponding consumers from two medical aesthetic hospitals in China. SPSS and SmartPLS are used to test the proposed model. Results: The findings indicate that FLEs' self-efficacy positively influences their service performance through the mediation of dynamic capabilities. Moreover, while the direct impact of FLEs' sensing capabilities on service performance was found to be insignificant, it was observed that these capabilities indirectly affect service performance through reconfiguring capabilities. Conclusion: This study presents theories and arguments on the role of self-efficacy and dynamic capabilities in improving service performance. These findings contribute to a deeper understanding of how FLEs cultivate the dynamic capability of resource integration, offering valuable insights for the attainment of sustainable competitive advantages.

Attitudes and associated factors of patients' adoption of patient accessible electronic health records in China - A mixed methods study.

Background: Although patient accessible electronic health records (PAEHRs) offer great potential in enhancing the provision of patient-centered care and improving satisfaction, the adoption rate is still low. Currently, few studies are there for researchers and health organization leaders to understand patients' thoughts and related factors of PAEHRs adoption in developing countries. China adopted more limited practices of PAEHRs, among which we selected Yuebei People's Hospital as an example. Objective: The study aimed to research patient attitudes toward PAEHRs use and the associated factors of patients' adoption of PAEHRs in China, which is achieved by both qualitative and quantitative studies. Methods: This study employed sequential mixed-methods. The DeLone & McLean information systems (D&M IS) success model, Unified Theory of Acceptance and Use of Technology (UTAUT) and task-technology fit (TTF) model were used to guide the research. Finally, we collected 28 valid in-depth interview responses, 51 valid semi-structured interview responses and 235 valid questionnaire responses. The research model was tested and validated using data collected. Results: The findings of the qualitative study reveal that patients' rate perceived task productivity and customer satisfaction as benefits, and poor-quality information as flaws. Results of the quantitative study show that the drivers of behavioral intention are performance expectance, effort expectancy and social influence; the predictors of use behavior are TTF and behavioral intention. Conclusion: It is necessary to consider PAEHRs' task-tool role in patients' adoption behavior. Hospitalized patients value PAEHRs' practical attributes and attach much importance to the information content and application design.

Tribological Behavior of 3D-Printed Nanometer SiC and SiO2 Particle-Reinforced Polyamide 12 Composites by Selective Laser Sintering under Seawater Lubrication Condition.

Polymeric matrix composites have been widely used in the marine field. In this study, the tribological behavior under seawater-lubricated conditions of pure Polyamide 12 (PA12), micron-SiC and nanometer SiC and SiO2 particle-reinforced PA12 composites, which are prepared by selective laser sintering (SLS), were studied. The seawater absorption, hardness, contact angle and tribology performance were investigated. The results show that the addition of micron- and nano-SiC particles and nano-SiO2 particles could decrease the seawater adsorption and contact angle, and increase the hardness. Under seawater conditions, the addition of micro SiC particles can reduce the friction coefficient and wear loss, whereas the addition of nano-SiC and -SiO2 particles increases the corresponding values. The specimen printed with recycled powder has a higher friction coefficient, while having a better wear resistance. However, it increases the width and depth of the wear track in some locations. The wear mechanisms of the composite specimens are also analyzed. This was the result of the combined effects of fatigue wear and abrasive wear under seawater conditions. The latter plays a dominant role under seawater conditions. This study may provide a valuable reference for the further research and application of polymeric matrix composites in marine engineering equipment.

Effectiveness and Safety of Vortioxetine for Major Depressive Disorder in Real-World Clinical Practice: Results from the Single-Arm RELIEVE China Study.

Background: Major depressive disorder (MDD) affects >163 million people worldwide and is a leading cause of disability in China. Functional impairment occurs alongside cognitive symptoms, anxiety, and depression, reducing quality of life and productivity in patients with MDD. Purpose: The multimodal antidepressant vortioxetine has demonstrated efficacy in relieving depressive and functional symptoms of MDD in randomized controlled trials (RCTs). The RELIEVE China study aimed to investigate the real-world effectiveness of vortioxetine in China. Patients and Methods: This was an observational, prospective cohort study in patients with MDD initiating treatment with vortioxetine at physician's discretion in China. Participants were followed up for 24 weeks and assessed at 3 time points: baseline, week 8, and week 24. The primary objective was to assess the change from baseline to weeks 8 and 24 in functional impairment as measured by Sheehan Disability Scale (SDS) total score. Additional assessments included SDS subdomains, measures of depression severity, anxiety, and cognition. The safety and tolerability of vortioxetine were also examined. Results: In total, 859 patients were included in the analysis. A consistent and significant improvement in functional impairment was observed during the study, with baseline mean SDS total score (16.7 points) decreasing by 5.42 (SE, 0.22) and 8.71 (SE, 0.226) points at week 8 and week 24, respectively (P<0.0001). Improvements in other functioning, cognitive, and anxiety assessments were also observed (all P<0.0001). A total of 74.7% of patients had responded, and 63.9% had reached remission at week 24. The tolerability profile of vortioxetine in this real-world population was consistent with the established tolerability profile for this drug. Conclusion: This study demonstrated the short- and long-term effectiveness and tolerability of vortioxetine for patients with MDD in a real-world setting in China. These findings are consistent with the efficacy and safety profile observed during RCTs.

Mechanical and Tribological Properties of 3D Printed Polyamide 12 and SiC/PA12 Composite by Selective Laser Sintering.

Polymeric matrix composites are important to the advancement of industries such as the automobile and medicine industries. In this study, the silicon carbide (SiC) particle-reinforced polyamide12 (PA12) matrix composites were fabricated by selective laser sintering system as well as the pure PA12. The surface topographies, mechanical, and tribological properties were further examined. The results indicated that the friction and wear resistance of the composite were improved compared with the PA12 matrix. The compressive strength increased about 8.5%, shore D hardness increased about 6%. The friction coefficient decreased about 10%, the specific wear rate decreased 20% after adding silicon carbide 10% weight to PA12. The wear mechanisms were also discussed. The deformed asperities on the worn surface can withstand more tangential load, and therefore resulted in lower specific wear rate. It was found that the content of SiC particles on the surface were reduced after friction tests. According to the analysis of SEM, EDS, and FTIR results, the wear mechanisms were considered to be the abrasive and fatigue mode. This type of PA12 matrix composite might be a promising potential in marine and energy applications.

Laparoscopic versus open pancreatoduodenectomy for pancreatic or periampullary tumours: a multicentre, open-label, randomised controlled trial.

BACKGROUND: The benefit and safety of laparoscopic pancreatoduodenectomy (LPD) for the treatment of pancreatic or periampullary tumours remain controversial. Studies have shown that the learning curve plays an important role in LPD, yet there are no randomised studies on LPD after the surgeons have surmounted the learning curve. The aim of this trial was to compare the outcomes of open pancreatoduodenectomy (OPD) with those of LPD, when performed by experienced surgeons. METHODS: In this multicentre, open-label, randomised controlled trial done in 14 Chinese medical centres, we recruited patients aged 18-75 years with a benign, premalignant, or malignant indication for pancreatoduodenectomy. Eligible patients were randomly assigned (1:1) to undergo either LPD or OPD. Randomisation was centralised via a computer-generated system that used a block size of four. The patients and surgeons were unmasked to study group, whereas the data collectors, outcome assessors, and data analysts were masked. LPD and OPD were performed by experienced surgeons who had already done at least 104 LPD operations. The primary outcome was the postoperative length of stay. The criteria for discharge were based on functional recovery, and analyses were done on a modified intention-to-treat basis (ie, including patients who had a pancreatoduodenectomy regardless of whether the operation was the one they were assigned to). This trial is registered with Clinicaltrials.gov, number NCT03138213. FINDINGS: Between May 18, 2018, and Dec 19, 2019, we assessed 762 patients for eligibility, of whom 656 were randomly assigned to either the LPD group (n=328) or the OPD group (n=328). 31 patients in each group were excluded and 80 patients crossed over (33 from LPD to OPD, 47 from OPD to LPD). In the modified intention-to-treat analysis (297 patients in the LPD group and 297 patients in the OPD group), the postoperative length of stay was significantly shorter for patients in the LPD group than for patients in the OPD group (median 15·0 days [95% CI 14·0-16·0] vs 16·0 days [15·0-17·0]; p=0·02). 90-day mortality was similar in both groups (five [2%] of 297 patients in the LPD group vs six [2%] of 297 in the OPD group, risk ratio [RR] 0·83 [95% CI 0·26-2·70]; p=0·76). The incidence rate of serious postoperative morbidities (Clavien-Dindo grade of at least 3) was not significantly different in the two groups (85 [29%] of 297 patients in the LPD group vs 69 [23%] of 297 patients in OPD group, RR 1·23 [95% CI 0·94-1·62]; p=0·13). The comprehensive complication index score was not significantly different between the two groups (median score 8·7 [IQR 0·0-26·2] vs 0·0 [0·0-20·9]; p=0·06). INTERPRETATION: In highly experienced hands, LPD is a safe and feasible procedure. It was associated with a shorter length of stay and similar short-term morbidity and mortality rates to OPD. Nonetheless, the clinical benefit of LPD compared with OPD was marginal despite extensive procedural expertise. Future research should focus on identifying the populations that will benefit from LPD. FUNDING: National Natural Science Foundation of China and Tongji Hospital, Huazhong University of Science and Technology, China.

Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics.

OBJECTIVE: To investigate the application of machine learning-based ultrasound radiomics in preoperative classification of primary and metastatic liver cancer. METHODS: Data of 114 consecutive histopathologically confirmed patients with liver cancer from January 2018 to November 2019 were retrospectively analyzed. All patients underwent liver ultrasonography within 1 week before hepatectomy or fine-needle biopsy. The liver lesions were manually segmented by two experts using ITK-SNAP software. Seven categories of radiomics features, including first-order, two-dimensional shape, gray-level co-occurrence matrices, gray-level run-length matrix, gray-level size-zone matrix, neighboring gray tone difference matrix, and gray-level dependence matrix, were extracted on the Pyradiomics platform. Fourteen filters were applied to the original images, and derived images were obtained. Then, the dimensions of radiomics features were reduced by least absolute shrinkage and selection operator (Lasso) method. Finally, k-nearest neighbor (KNN), logistic regression (LR), multilayer perceptron (MLP), random forest (RF), and support vector machine (SVM) were employed to distinguish primary liver cancer from metastatic liver cancer by a fivefold cross-validation strategy. The performance of the established model was mainly evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and accuracy. RESULTS: One thousand four hundred nine radiomics features were extracted from the original images and/or derived images for each patient. The mentioned five machine learning classifiers were able to differentiate primary liver cancer from metastatic liver cancer. LR outperformed other classifiers, with the accuracy of 0.843 ± 0.078 (AUC, 0.816 ± 0.088; sensitivity, 0.768 ± 0.232; specificity, 0.880 ± 0.117). CONCLUSIONS: Machine learning-based ultrasound radiomics features are able to non-invasively distinguish primary liver tumors from metastatic liver tumors. KEY POINTS: • Ultrasound-based radiomics was initially used for preoperative classification of primary versus metastatic liver cancer. • Multiple machine learning-based algorithms with cross-validation strategy were applied to extract machine learning-based ultrasound radiomics features. • Distinction between primary and metastatic tumors was obtained with a sensitivity of 0.768 and a specificity of 0.880.

Corticosteroids treatment in severe patients with COVID-19: a propensity score matching study.

OBJECTIVES: To explore the efficacy of corticosteroid treatment in patients with severe COVID-19 pneumonia and the association between corticosteroid use and patient mortality. METHODS: A retrospective investigation was made on the medical records of the patients with severe and critical patients with COVID-19 pneumonia from January to February 2020. First, the patients who received corticosteroid treatment were compared with patients without given corticosteroid treatment. Then, a propensity score matching method was used to control confounding factors. Cox survival regression analysis was used to evaluate the effect of corticosteroid therapy on the mortality of severe and critical patients with COVID-19. RESULTS: A total of 371 severe and critical patients were included in our analyses. Two hundred and enine patients were treated with corticosteroid therapy. Most of them were treated with methylprednisolone (197[94.3%]). The median corticosteroid therapy was applied 3 (IQR 2-6) days after admission, 13 (IQR 10-17) days after symptoms appeared. Temperature on admission (OR = 1.255, [95%CI 1.021-1.547], p = 0.032), ventilation (OR = 1.926, [95%CI 1.148-3.269], p = 0.014) and ICU admission (OR = 3.713, [95%CI 1.776-8.277], p < 0.001) were significantly associated with corticosteroids use. After PS matching, the cox regression survival analysis showed that corticosteroid use was significantly associated with a lower mortality rate (HR = 0.592, [95%CI 0.406-0.862], p = 0.006). CONCLUSION: Corticosteroid therapy use in severe and critical patients with COVID-19 pneumonia leads to lower mortality but may cause other side effects. Corticosteroid therapy should be used carefully.

Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics.

OBJECTIVE: To investigate the efficacy of contrast-enhanced computed tomography (CECT)-based radiomics signatures for preoperative prediction of pathological grades of hepatocellular carcinoma (HCC) via machine learning. METHODS: In this single-center retrospective study, data collected from 297 consecutive subjects with HCC were allocated to training dataset (n = 237) and test dataset (n = 60). Manual segmentation of lesion sites was performed with ITK-SNAP, the radiomics features were extracted by the Pyradiomics, and radiomics signatures were synthesized using recursive feature elimination (RFE) method. The prediction models for pathological grading of HCC were established by using eXtreme Gradient Boosting (XGBoost). The performance of the models was evaluated using the AUC along with 95% confidence intervals (CIs) and standard deviation, sensitivity, specificity, and accuracy. RESULTS: The radiomics signatures were found highly efficient for machine learning to differentiate high-grade HCC from low-grade HCC. For the clinical factors, when they were merely applied to train a machine learning model, the model achieved an AUC of 0.6698, along with 95% CI and standard deviation of 0.5307-0.8089 and 0.0710, respectively (sensitivity, 0.6522; specificity, 0.4595; accuracy, 0.5333). Meanwhile, when the radiomics signatures were applied in association with clinical factors to train a machine learning model, the performance of the model remarkably increased with AUC of 0.8014, along with 95% CI and standard deviation of 0.6899-0.9129 and 0.0569, respectively (sensitivity, 0.6522; specificity, 0.7297; accuracy, 0.7000). CONCLUSIONS: The radiomics signatures could non-invasively explore the underlying association between CECT images and pathological grades of HCC. KEY POINTS: • The radiomics signatures may non-invasively explore the underlying association between CECT images and pathological grades of HCC via machine learning. • The radiomics signatures of CECT images may enhance the prediction performance of pathological grading of HCC, and further validation is required. • The features extracted from arterial phase CECT images may be more reliable than venous phase CECT images for predicting pathological grades of HCC.

Factors Influencing the Adoption of Health Information Standards in Health Care Organizations: A Systematic Review Based on Best Fit Framework Synthesis.

BACKGROUND: Since the early 1970s, health care provision has experienced rapid growth in the investment and adoption of health information technologies (HITs). However, the development and deployment of HITs has often been conducted in silos, at different organizational levels, within different regions, and in various health care settings; this has resulted in their infrastructures often being difficult to manage or integrate. Health information standards (ie, the set norms and requirements that underpin the deployment of HITs in health care settings) are expected to address these issues, yet their adoption remains to be frustratingly low among health care information technology vendors. OBJECTIVE: This study aimed to synthesize a comprehensive framework of factors that affect the adoption and deployment of health information standards by health care organizations. METHODS: First, electronic databases, including Web of Science, Scopus, and PubMed, were searched for relevant articles, with the results being exported to the EndNote reference management software. Second, study selection was conducted according to pre-established inclusion and exclusion criteria. Finally, a synthesized best fit framework was created, which integrated a thematic analysis of the included articles. RESULTS: In total, 35 records were incorporated into the synthesized framework, with 4 dimensions being identified: technology, organization, environment, and interorganizational relationships. The technology dimension included relative advantage, complexity, compatibility, trialability, observability, switching cost, standards uncertainty, and shared business process attributes. The organization dimension included organizational scale, organizational culture, staff resistance to change, staff training, top management support, and organizational readiness. The environment dimension included external pressure, external support, network externality, installed base, and information communication. Finally, the interorganizational relationships dimension included partner trust, partner dependence, relationship commitment, and partner power. CONCLUSIONS: The synthesized framework presented in this paper extends the current understanding of the factors that influence the adoption of health information standards in health care organizations. It provides policy and decision makers with a greater awareness of factors that hinder or facilitate their adoption, enabling better judgement and development of adoption intervention strategies. Furthermore, suggestions for future research are provided.

What Do Patients Complain About Online: A Systematic Review and Taxonomy Framework Based on Patient Centeredness.

BACKGROUND: Complaints made online by patients about their health care experiences are becoming prevalent because of widespread worldwide internet connectivity. An a priori framework, based on patient centeredness, may be useful in identifying the types of issues patients complain about online across multiple settings. It may also assist in examining whether the determinants of patient-centered care (PCC) mirror the determinants of patient experiences. OBJECTIVE: The objective of our study was to develop a taxonomy framework for patient complaints online based on patient centeredness and to examine whether the determinants of PCC mirror the determinants of patient experiences. METHODS: First, the best fit framework synthesis technique was applied to develop the proposed a priori framework. Second, electronic databases, including Web of Science, Scopus, and PubMed, were searched for articles published between 2000 and June 2018. Studies were only included if they collected primary quantitative data on patients' online complaints. Third, a deductive and inductive thematic analysis approach was adopted to code the themes of recognized complaints into the framework. RESULTS: In total, 17 studies from 5 countries were included in this study. Patient complaint online taxonomies and theme terms varied. According to our framework, patients expressed most dissatisfaction with patient-centered processes (101,586/204,363, 49.71%), followed by prerequisites (appropriate skills and knowledge of physicians; 50,563, 24.74%) and the care environment (48,563/204,363, 23.76%). The least dissatisfied theme was expected outcomes (3651/204,363, 1.79%). People expressed little dissatisfaction with expanded PCC dimensions, such as involvement of family and friends (591/204,363, 0.29%). Variation in the concerns across different countries' patients were also observed. CONCLUSIONS: Online complaints made by patients are of major value to health care providers, regulatory bodies, and patients themselves. Our PCC framework can be applied to analyze them under a wide range of conditions, treatments, and countries. This review has shown significant heterogeneity of patients' online complaints across different countries.

A machine learning based approach to identify protected health information in Chinese clinical text.

BACKGROUND: With the increasing application of electronic health records (EHRs) in the world, protecting private information in clinical text has drawn extensive attention from healthcare providers to researchers. De-identification, the process of identifying and removing protected health information (PHI) from clinical text, has been central to the discourse on medical privacy since 2006. While de-identification is becoming the global norm for handling medical records, there is a paucity of studies on its application on Chinese clinical text. Without efficient and effective privacy protection algorithms in place, the use of indispensable clinical information would be confined. OBJECTIVES: We aimed to (i) describe the current process for PHI in China, (ii) propose a machine learning based approach to identify PHI in Chinese clinical text, and (iii) validate the effectiveness of the machine learning algorithm for de-identification in Chinese clinical text. METHODS: Based on 14,719 discharge summaries from regional health centers in Ya'an City, Sichuan province, China, we built a conditional random fields (CRF) model to identify PHI in clinical text, and then used the regular expressions to optimize the recognition results of the PHI categories with fewer samples. RESULTS: We constructed a Chinese clinical text corpus with PHI tags through substantial manual annotation, wherein the descriptive statistics of PHI manifested its wide range and diverse categories. The evaluation showed with a high F-measure of 0.9878 that our CRF-based model had a good performance for identifying PHI in Chinese clinical text. CONCLUSION: The rapid adoption of EHR in the health sector has created an urgent need for tools that can parse patient specific information from Chinese clinical text. Our application of CRF algorithms for de-identification has shown the potential to meet this need by offering a highly accurate and flexible solution to analyzing Chinese clinical text.

Unhappy Patients Are Not Alike: Content Analysis of the Negative Comments from China's Good Doctor Website.

BACKGROUND: With the rise in popularity of Web 2.0 technologies, the sharing of patient experiences about physicians on online forums and medical websites has become a common practice. However, negative comments posted by patients are considered to be more influential by other patients and physicians than those that are satisfactory. OBJECTIVE: The aim of this study was to analyze negative comments posted online about physicians and to identify possible solutions to improve patient satisfaction, as well as their relationship with physicians. METHODS: A Java-based program was developed to collect patient comments on the Good Doctor website, one of the most popular online health communities in China. A total of 3012 negative comments concerning 1029 physicians (mean 2.93 [SD 4.14]) from 5 highly ranked hospitals in Beijing were extracted for content analysis. An initial coding framework was constructed with 2 research assistants involved in the codification. RESULTS: Analysis, based on the collected 3012 negative comments, revealed that unhappy patients are not alike and that their complaints cover a wide range of issues experienced throughout the whole process of medical consultation. Among them, physicians in Obstetrics and Gynecology (606/3012, 20.12%; P=.001) and Internal Medicine (487/3012, 16.17%; P=.80) received the most negative comments. For negative comments per physician, Dermatology and Sexually Transmitted Diseases (mean 5.72, P<.001) and Andrology (mean 5, P=.02) ranked the highest. Complaints relating to insufficient medical consultation duration (577/3012, 19.16%), physician impatience (527/3012, 17.50%), and perceived poor therapeutic effect (370/3012, 12.28%) received the highest number of negative comments. Specific groups of people, such as those accompanying older patients or children, traveling patients, or very important person registrants, were shown to demonstrate little tolerance for poor medical service. CONCLUSIONS: Analysis of online patient complaints provides an innovative approach to understand factors associated with patient dissatisfaction. The outcomes of this study could be of benefit to hospitals or physicians seeking to improve their delivery of patient-centered services. Patients are expected to be more understanding of overloaded physicians' workloads, which are impacted by China's stretched medical resources, as efforts are made to build more harmonious physician-patient relationships.

New cooperative medical scheme decreased financial burden but expanded the gap of income-related inequity: evidence from three provinces in rural China.

BACKGROUND: Subsidizing healthcare costs through insurance schemes is crucial to overcome financial barriers to health care and to avoid high medical expenditures for patients in China. The health insurance could decrease financial risk by less out-of-pocket (OOP) payment, but not promise the protection equity. With the growth of New Cooperative Medical Scheme (NCMS) financing and coverage since 2008, the protection effectiveness and equity of the modified NCMS policies on financial burden should be further evaluated. METHODS: A cross-sectional household survey was conducted in Zhejiang, Hubei, and Chongqing provinces by multi-stage stratified random sampling in 2011. A total of 1,525 households covered by the NCMS were analyzed. The protection effectiveness and protection equity of NCMS was analyzed by comparing the changes in health care utilization and medical expenditures, and the changes in the prevalence of catastrophic health expenditure (CHE) and its concentration indices (CIs) between pre- and post-NCMS reimbursement, respectively. RESULTS: The medical financial burden was still remarkably high for the low income rural residents in China due to high OOP payment, even after NCMS reimbursement. In Hubei province, the OOP payment of the poorest quintile was almost same as their households' annual expenditures. Even it was higher than their annual expenditures in Chongqing municipality. Effective reimbursement ratio of both outpatient and inpatient services were far lower than nominal reimbursement ratio originally designed by NCMS plans. After NCMS reimbursement, the prevalence of CHE was considerably high in all three provinces, and the absolute values of CIs were even higher than those before reimbursement, indicating the inequity exaggerated. CONCLUSION: Policymakers should further modify NCMS policy in rural China. The high OOP payment could be decreased by expanding the drug list and check directory for benefit package of NCMS to minimize the gap between nominal reimbursement ratio and effective reimbursement ratio. And the increase in medical expenditures should be controlled by monitoring excess demand from both medical service providers and patients, and changing fee-for-service payment for providers to a prospective payment system. Service accessibility and affordability for vulnerable rural residents should be protected by modifying regressive financing in NCMS, and by providing extra financial aid and reimbursement from government.

Disease development trends for maternal and child health care in Shenzhen, China.

Shenzhen in China has undergone rapid economic development over the years. The purpose of this study was to investigate disease development trends among women and children inpatients in Shenzhen. A maternal and child health care hospital in Futian District was chosen for this retrospective study. The data were collected from 55 246 inpatients' information from 2003 to 2012 and were classified into gynecological and pediatric diseases. Descriptive analysis was conducted with Microsoft Excel 2007. The results showed that the number of inpatients had increased year by year, and the constituents of disease had been substantially changed in the study period. More interventions and large-scale studies are needed to improve maternal and child health care in Shenzhen.