How to Make a Medical Error Disclosure to Patients This paper aims to investigate Chinese publicu0027s expectations of medical error disclosure, and to develop guidelines for hospitals. A national questionnaire survey was conducted in 2019, collecting 1,008 valid responses. Respondentsu0027 were asked their views of the severity of error they would like to be disclosed and what, when, where and who they preferred in an error disclosure. Results showed that Chinese public would like to be disclosed any error reached them even no harm. For both moderate and severe outcome errors, they preferred to be disclosed facetoface, all the information as detail as possible, immediately after the error was recognized and in a prepared meeting room. Regarding attendance of patient side, disclosure was expected to be made to the patient and family. For hospital side, the healthcare provider who committed the error, hisher leader, patient safety manager and highpositioned person of the hospital were anticipated to be present. About the person to make the disclosure, respondents preferred the healthcare provider who committed the error in a moderate outcome case while the leader or highpositioned person in a severe case.

A Study on Automatic Generation of Chinese Discharge Summary Discharge summary, which summarizes a patientu0027s health information during hospitalization, is very important for transferring information between the hospitalist and primary care physician. Discharge summary writing is a necessary but timeconsuming job for physicians. Automatically generating discharge summaries using information technology is helpful to physicians, but challenging as discharge summaries are typically long and contain amounts of information to outline patientu0027s reason for admission, labtests, examinations, diagnostic findings, treatments, and medication care plan. In this study, we propose a framework based on deep learning methods for automatic discharge summary generation. In the framework, we split information of discharge summary into two parts: 1) history information such as reason for admission, admission diagnosis; 2) outcome information such as discharge diagnosis and medication care plan, and deploy different neural networks to generate them separately. Hierarchical sequence labeling methods are proposed to select key sentences from existing documents, e.g., admission notes, progress notes, examination reports as the history information, and multitask learning methods to predict the outcomes, e.g., diagnosis and medication care plan. Experiments on a Chinese corpus show that our approach has the ability to generate effective discharge summaries.