新型冠状病毒肺炎临床研究和专家意见数据分析的思考与建议

Thoughts and suggestions on arrangement, analysis and summary of medical data during COVID-19 epidemic

临床科研数据分析利用是推动医学进步的手段,是医学发展的关键步骤。新型冠状病毒肺炎疫情期间,如何有效利用临床研究,用真实的数据合理的分析来进行决策,是备受关注的话题。该文基于疫情期间数据采集提取的低效性、数据分析的不一致性、数据报告的不规范性、数据更新的高时效性等现状,对疫情下数据应用现状背景原因进行简要分析,进而探讨疫情发展下以及更重要的今后的医学临床研究中,临床数据应用的诸多问题及可行的解决方式,提出方法学建议:①逐步完善医疗大数据模式,形成国家医药健康数据中心;②提升医护人员科研素养,普及GCP基本技能和知识;③推广科技化、网络化、共享化的数据采集、管理模式;④运用混合研究方法聚敛式分析,提高临床数据分析利用效率;⑤关注叙事医学情怀,重视医学临床人文数据等建议。有望促进临床科研数据的规范合理使用、专家意见严谨整合、最终促进国家健康医疗大数据发展。

The analysis and utilization of clinical scientific research data is an effective means to promote the progress of diagnosis and treatment, and a key step in the development of medical sciences. During the epidemic of coronavirus disease 2019(COVID-19), how to transform the limited diagnostic data into clinical research resources has attracted much attention. Based on the low efficiency of data collection and extraction, the inconsistency of data analysis, the irregularity of data report and the high timeliness of data update during the epidemic, this paper briefly analyzed the background and reasons of data application under the current situation, and then discusses the problems and feasible solutions of clinical data applications under the epidemic situation and, more importantly, for future medical clinical research methods. We put forward several methodological suggestions: ① gradually improve the medical big data model and establish the national medical health data center;② improve the scientific research literacy of medical staff and popularize the basic skills and knowledge of GCP;③ promote a scientific, networked and shared data collection and management mode;④ use the mixed research method and collective analysis to improve the efficiency of clinical data analysis;⑤ pay attention to narration of the medical feelings and emphasize the humanistic data of clinical medicine. It is expected to promote the standardized and reasonable use of clinical scientific research data, the rigorous integration of expert opinions, and ultimately the development of big data for national health care.