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Medical Big Data for Research Use: Current Status and Related Issues.
1Head, Economics Section, Division of Surveillance, Center for Cancer Control and Information Services, National Cancer Center, Tokyo, Japan ( kishikaw@ncc.go.jp ).
Healthcare big data, including diagnosis procedure combination (DPC) data, offers significant research potential. Analysis of anonymized DPC data enables insights into clinical practice and community medical care, with future applications in long-term outcomes research.
Area of Science:
- Health Informatics
- Medical Big Data Analytics
- Epidemiology
Background:
- Technological advancements facilitate the management of large datasets, driving the growth of big data applications in healthcare.
- National initiatives are developing large-scale medical databases, such as the national receipt database (NDB) and diagnosis procedure combination (DPC) data.
- Secondary use of publicly collected health data is gaining attention for research purposes.
Purpose of the Study:
- To focus on the utilization of diagnosis procedure combination (DPC) data for research.
- To outline the current scale of DPC data available for research.
- To discuss the potential applications, limitations, and issues associated with medical big data.
Main Methods:
- Collection and analysis of anonymized diagnosis procedure combination (DPC) data from cooperating institutions.
- Adherence to ethics guidelines for epidemiologic studies.
Main Results:
- DPC data enables microscopic analysis of clinical practice and macroscopic analysis of community medical care provision.
- The scale of currently available DPC data for research use is outlined.
Conclusions:
- Diagnosis procedure combination (DPC) data represents a valuable resource for healthcare research.
- Further research can extend to long-term outcomes studies, though limitations and challenges in medical big data utilization must be addressed.
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