Shinsuke Fujita

Automatic DPC code selection from electronic medical records: text mining trial of discharge summary.

OBJECTIVES We extracted index terms related to diseases recorded in hospital discharge summaries and examined the capability of the vector space model to select a suitable diagnosis with these terms. METHODS By morphological analysis, we extracted index terms and constructed an original dictionary for the discharge summary analysis. We chose 125 different DPC (Japanese DRG system) codes for the diseases, each of which had more than 20 cases. We divided them into two groups. One group consisted of 5927 cases from 2004 fiscal year and was used to generate the document vector space according to the DPC. The other group of 3187 cases was collected to verify the automatic DPC selection by using data from 2005 fiscal year. The top 200 extracted index terms for each disease were used to calculate the weight of each disease. RESULTS The DPC code obtained by the calculated similarity was compared with the original codes of patients for 125 DPCs of 3187 cases. Eighty percent of the cases matched the diagnosis of the DPC (first six digits) and 56% of the cases completely matched all 14 digits of the DPC. CONCLUSIONS We demonstrated that we could extract suitable terms for each disease and obtain characteristics, such as the diagnosis, from the calculated vectors. This technique can be used to measure the qualification of discharge summaries and to integrate discharge summaries among different facilities. By the text mining technique, we can characterize the contents of electronic discharge summaries and deduce diagnoses with the data.

Estimation and Evaluation of Future Demand and Supply of Healthcare Services Based on a Patient Access Area Model

Accessibility to healthcare service providers, the quantity, and the quality of them are important for national health. In this study, we focused on geographic accessibility to estimate and evaluate future demand and supply of healthcare services. We constructed a simulation model called the patient access area model (PAAM), which simulates patients’ access time to healthcare service institutions using a geographic information system (GIS). Using this model, to evaluate the balance of future healthcare services demand and supply in small areas, we estimated the number of inpatients every five years in each area and compared it with the number of hospital beds within a one-hour drive from each area. In an experiment with the Tokyo metropolitan area as a target area, when we assumed hospital bed availability to be 80%, it was predicted that over 78,000 inpatients would not receive inpatient care in 2030. However, this number would decrease if we lowered the rate of inpatient care by 10% and the average length of the hospital stay. Using this model, recommendations can be made regarding what action should be undertaken and by when to prevent a dramatic increase in healthcare demand. This method can help plan the geographical resource allocation in healthcare services for healthcare policy.

What we expect for wearable health, from clinical settings

Today so many devices of wearable health are produced and they are very useful in physical training and leisure. Increasing aged population arises the problem of life style related disease, ie, diabetes mellitus, atherosclerosis, hyper lipidemia. Medical practitioners expect so much on wearable health devices in controlling these diseases. But most of them have no chance to mention what they want. We provided a small chance what they want if they can contribute the development of wearable health devices. 10 minutes of brain storming produced so much idea and after that we put them into affinity diagram. We classified them between passive health and active health. Active health is what physicians want. We hope this study will open the gate among physicians, education specialists and IT developers.

Using GIS to Simulate Inpatient's Behavior and Visualize Healthcare Demand

Abstract In this study, we developed the Patient Access Area Model by using a Geographic information system (GIS), and, in order to evaluate the balance of medical supply and demand in the future in small areas, simulated patients’ access to hospitals. We set the accessible area by patients’ transit time for each hospital. The patients living in each 500 meters mesh were allowed to enter hospitals only within the access area. The hospitals have its limit to admit patients based on their actual numbers of beds. We distributed inpatients from each mesh across hospitals. For the evaluation of demand, if patients could not be distributed to the hospitals within the accessible area, we defined the situation as “over-demand.” As a result, although it was expected that over 9000 inpatients will not receive inpatient care in a southwest area region in the studied prefecture, most of the over-demand is in the densely regions along large traffic lines in 2030. Using this model, we can know demand for local health resources more clearly. This method is very useful to plan geographical resource allocation in medical services.