- Electronic medical records and electronic health record (community medical cooperation systems)
- Structuring of clinical data and its applications
- Efficient data collection for multicenter studies by ICT
- Knowledge acquisition by advanced analysis of medical databases
- Clinical decision support systems
Establishment of medical databases from electronic medical charts, acquisition of knowledge by data analysis, and construction of a clinical decision support system
Medical informatics is the scientific discipline that utilizes information- and communications technology (ICT)-based approaches for the analysis and management of medically relevant data and information.
The initial goal to replace paper-based maintenance and management of medical data and information with computer-based tools has been largely achieved. This has led to more streamlined medical care, facilitated the sharing of medical information among medical professionals, promoted team-based medicine and improved the overall safety of medical care. At the Osaka University Hospital, all medically relevant data became computerized starting from 2010, resulting in the accumulation of large data sets. The analysis and utilization of these large data sets represents one of the core objectives of our laboratory.
In this context, we are conducting research on a variety of topics including the prediction of prognosis and identification of prognostic factors and evaluation of the effectiveness and safety of applied medical treatments by conducting observational studies base on electronic medical record, application of data mining technologies to cancer research for early detection methods, establishment of novel and objective diagnostic models and approaches, construction of adequate diagnostic support systems, creation of pipelines for precision medicine based on genome information, and utilization of advanced computational tools including deep learning for automatic image diagnosis.
Proper data collection represents one of the core objectives necessary for achieving the above mentioned research goals. This requires the streamlined collection of unified data from multiple sources and facilities, while keeping track of patients over long periods of time. It is furthermore important to develop tools and techniques able to structure and analyze medical records that are often based on simple text. This necessitates the development of integrative databases that link clinical data with imaging data and genome analysis data. Our laboratory focuses on the establishment of such advanced data analysis tools and technologies in collaboration with other scientists and medical researchers.
Effective feedback of the knowledge acquired by data analysis to the clinical setting is another core objective of our laboratory. Because the knowledge form acquired by data analysis is difficult to be memorized by human doctors, the development of clinical decision support systems using these knowledge are required. It will be a major research theme in the future.
The last two decades were characterized by the digitalization of paper-based medical records and data, while the coming decades will be characterized by the generation of big data and use of artificial intelligence to analyze these large data sets for the advancement of medicine and medical care.