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.

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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 common factors that contribute to developing interventions to reduce future claims and patient harm. METHODS This study implemented a null hypothesis whereby malpractice claims are random events—attributable to bad luck with random frequency. As medical malpractice is a complex issue, thus, this study applied the complex network theory, which provided the methodological support for understanding interactive behavior in medical malpractice. Specifically, 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 (MMNC) was presented as a knowledge graph; it employs the International Classification of Patient Safety from the World Health Organization as a reference. RESULTS We found that the MMNC 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 emergency department were the three most frequent specialties that incurred malpractice; inadequate informed consent work constituted the most errors. Non-technical errors (e.g. 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. CLINICALTRIAL Not applicable