Medical disputes in relation to prenatal ultrasound in China

Analysis of the characteristics, efficiency, and influencing factors of third-party mediation mechanisms for resolving medical disputes in public hospitals in China

BACKGROUND

Medical disputes, which are prevalent in China, are a growing global public health problem. The Chinese government has proposed third-party mediation (TPM) to resolve this issue. However, the characteristics, efficiency, and influencing factors of TPM in resolving medical disputes in public hospitals in China have yet to be determined.

METHODS

We conducted a systematic study using TPM records from medical disputes in Gansu Province in China from 2014 to 2019. A χ2 test was used to compare differences between groups, and binary logistic analysis was performed to determine the factors influencing the choice of TPM for resolving medical disputes.

RESULTS

We analyzed 5,948 TPM records of medical disputes in Gansu Province in China. The number of medical disputes and the amount of compensation awarded in public hospitals in the Gansu Province increased annually from 2014 to 2019, with most of the disputes occurring in secondary and tertiary hospitals. Approximately 89.01% of the medical disputes were handled by TPM; the average compensation amount with TPM was Chinese Yuan (CNY) 48,688.73, significantly less than that awarded via court judgment and judicial mediation. TPM was more likely to succeed in settling medical disputes in the < CNY10,000 compensation group than in the no-compensation group (odds ratio [OR] = 3.14, 95% confidence interval [CI] 1.53-6.45). However, as the compensation amount increased, the likelihood of choosing TPM decreased significantly. Moreover, TPM was less likely to be chosen when medical disputes did not involve death (OR = 0.49, 95% CI 0.36-0.45) or when no-fault liability was determined (vs. medical accidents; OR = 0.37, 95% CI 0.20-0.67).

CONCLUSION

Our findings demonstrate that TPM mechanisms play a positive role in efficiently reducing compensation amounts and increasing medical dispute resolution rates which was the main settlement method in resolving medical disputes in public hospitals of Gansu Province in China. TPM could help greatly reduce conflicts between doctors and patients, avoid litigation, and save time and costs for both parties. Moreover, compensation amounts, non-fatal outcomes, and no-fault liability determinations influence the choice of TPM for settling medical disputes.

Comparison between MRI and the Combination of 2D and 3D US in the Prenatal Diagnosis of Closed Spina Bifida

INTRODUCTION

Closed spina bifida (CSB) is a rare condition with a challenging prenatal diagnosis. Herein, we assess the conventional two-dimensional (2D) ultrasound (US) combined with three-dimensional (3D) ultrasound (US) and magnetic resonance imaging (MRI) in the prenatal diagnosis of CSB.

METHODS

In this retrospective study, we included 20 cases of fetal CSB confirmed by postnatal MRI, post-mortem pathological examination, or postpartum surgery. Prenatal 2D US complemented with the 3D US was performed in all fetuses to evaluate the characteristics of the conus, vertebral arch, and scoliosis. Moreover, MRI was performed to establish the split vertebrae, with or without a bulging mass. Thereafter, we compared the performance of the US and MRI.

RESULTS

Diagnosis accuracy of US was comparable with MRI (70% vs. 75%, κ = 0.62); US detected more cases with interpediculate distance ≥95% (55% vs. 35%, κ = 0.22) than MRI. On the other hand, MRI had a superior capacity for identifying vertebral arch fissures (20% vs. 35%, κ = 0.39). MRI and ultrasound had good agreement in the conus medullaris (65% vs. 70%, κ = 0.42) and scoliosis (45% vs. 35%, κ = 0.59). Both US and MRI detected 1 (5.0%) case with "lemon sign" and "banana sign." The missed diagnosis rates of US and MRI were 15% (3/20) and 5% (1/20), respectively. The misdiagnosis rates of US and MRI were 15.0% (3/20) and 20.0% (4/20), respectively.

CONCLUSION

Both MRI and 2D US combined with the 3D US had excellent performance in prenatal diagnosis of CSB.

Prediction of Medical Disputes Between Health Care Workers and Patients in Terms of Hospital Legal Construction Using Machine Learning Techniques: Externally Validated Cross-Sectional Study

BACKGROUND

Medical disputes are a global public health issue that is receiving increasing attention. However, studies investigating the relationship between hospital legal construction and medical disputes are scarce. The development of a multicenter model incorporating machine learning (ML) techniques for the individualized prediction of medical disputes would be beneficial for medical workers.

OBJECTIVE

This study aimed to identify predictors related to medical disputes from the perspective of hospital legal construction and the use of ML techniques to build models for predicting the risk of medical disputes.

METHODS

This study enrolled 38,053 medical workers from 130 tertiary hospitals in Hunan province, China. The participants were randomly divided into a training cohort (34,286/38,053, 90.1%) and an internal validation cohort (3767/38,053, 9.9%). Medical workers from 87 tertiary hospitals in Beijing were included in an external validation cohort (26,285/26,285, 100%). This study used logistic regression and 5 ML techniques: decision tree, random forest, support vector machine, gradient boosting decision tree (GBDT), and deep neural network. In total, 12 metrics, including discrimination and calibration, were used for performance evaluation. A scoring system was developed to select the optimal model. Shapley additive explanations was used to generate the importance coefficients for characteristics. To promote the clinical practice of our proposed optimal model, reclassification of patients was performed, and a web-based app for medical dispute prediction was created, which can be easily accessed by the public.

RESULTS

Medical disputes occurred among 46.06% (17,527/38,053) of the medical workers in Hunan province, China. Among the 26 clinical characteristics, multivariate analysis demonstrated that 18 characteristics were significantly associated with medical disputes, and these characteristics were used for ML model development. Among the ML techniques, GBDT was identified as the optimal model, demonstrating the lowest Brier score (0.205), highest area under the receiver operating characteristic curve (0.738, 95% CI 0.722-0.754), and the largest discrimination slope (0.172) and Youden index (1.355). In addition, it achieved the highest metrics score (63 points), followed by deep neural network (46 points) and random forest (45 points), in the internal validation set. In the external validation set, GBDT still performed comparably, achieving the second highest metrics score (52 points). The high-risk group had more than twice the odds of experiencing medical disputes compared with the low-risk group.

CONCLUSIONS

We established a prediction model to stratify medical workers into different risk groups for encountering medical disputes. Among the 5 ML models, GBDT demonstrated the optimal comprehensive performance and was used to construct the web-based app. Our proposed model can serve as a useful tool for identifying medical workers at high risk of medical disputes. We believe that preventive strategies should be implemented for the high-risk group.

Exploring the Role of Modified Vascular Anatomical Molding (MVAM) in Prenatal Diagnosis Teaching and Prognosis Prediction of Fetal Complex Congenital Heart Disease (CCHD): A Preliminary Study

Objective

The present study aimed to explore the role of modified vascular anatomical molding (MVAM) in prenatal diagnosis teaching and prognosis prediction of fetal complex congenital heart disease (CCHD).

Methods

Step 1, MVAM method was used to cast the micro-blood vessels and trachea of 52 CCHD specimens. Subsequently, 52 MVAMs were analyzed and compared with the prenatal ultrasound to summarize their characteristics, misdiagnosis and MVAM's teaching role. Step 2, the surgical and follow-up data of 206 CCHD cases were retrospectively analyzed. Cases that evolved into critical illnesses or died within 1-3 years after surgery (poor prognosis) were classified into the study group (n = 77) and those with good prognosis into the control group (n = 129), which were split into the training set and the test set in the ratio 7:3 based on the time cut-off. In the training set, the prognosis of CCHD was predicted using the MVAM anatomical soft markers (distortion and narrowing of aorta/pulmonary artery, right ventricular infundibulum, etc.) and the decision curve analysis (DCA) performed. The model was validated using the test set, and a nomogram was finally established.

Results

It was observed that all 52 CCHD cases were confirmed using MVAM. A total of 91 cardiac malformations were recorded, among which 41 malformations were misdiagnosed, and 29 malformations were missed by the prenatal echocardiography. The MVAM method has a good teaching/feedback effect on prenatal diagnosis. The combined model exhibited a higher predictive performance in the training- and test-set. Its high clinical net benefit was proved by DCA. Additionally, the nomogram established using the combined model received a favorable response in clinical practice.

Conclusion

The research results indicated that MVAM improved the prenatal diagnosis teaching and training performance. The combined model established based on MVAM anatomical soft markers can offer a high clinical significance for prognosis prediction of CCHD.

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/.

Measuring Public Reaction to Violence Against Doctors in China: Interrupted Time Series Analysis of Media Reports

Background

Violence against doctors in China is a serious problem that has attracted attention from both domestic and international media.

Objective

This study investigates readers’ responses to media reports on violence against doctors to identify attitudes toward perpetrators and physicians and examine if such trends are influenced by national policies.

Methods

We searched 17 Chinese violence against doctors reports in international media sources from 2011 to 2020. We then tracked back the original reports and web crawled the 19,220 comments in China. To ascertain the possible turning point of public opinion, we searched violence against doctors–related policies from Tsinghua University ipolicy database from 2011 to 2020, and found 19 policies enacted by the Chinese central government aimed at alleviating the intense patient–physician relationship. We then conducted a series of interrupted time series analyses to examine the influence of these policies on public sentiment toward violence against doctors over time.

Results

The interrupted time series analysis (ITSA) showed that the change in public sentiment toward violence against doctors reports was temporally associated with government interventions. The declarations of 10 of the public policies were followed by increases in the proportion of online public opinion in support of doctors (average slope changes of 0.010, P<.05). A decline in the proportion of online public opinion that blamed doctors (average level change of –0.784, P<.05) followed the declaration of 3 policies.

Conclusions

The government’s administrative interventions effectively shaped public opinion but only temporarily. Continued public policy interventions are needed to sustain the reduction of hostility toward medical doctors.