Because drug‐induced interstitial pneumonia (DIP) is a serious adverse drug reaction, its quantitative risk with individual medications should be taken into due consideration when selecting a medicine. We developed an algorithm to detect DIP using medical record data accumulated in a hospital. Chest computed tomography (CT) is mainly used for the diagnosis of IP, and chest X‐ray reports, KL‐6, and SP‐D values are used to support the diagnosis. The presence of IP in the reports was assessed by a method using natural language‐processing, in which IP was estimated according to the product of the likelihood ratio of characteristic keywords in each report. The sensitivity and the specificity of the method for chest CT reports were 0.92 and 0.97, while those for chest X‐ray reports were 0.83 and 1, respectively. The occurrence of DIP was estimated by the patterns of presence of IP before, during, and after the administration of the target medicine. The occurrence rate of DIP in cases administered Gefitinib; Methotrexate (MTX); Tegafur, Gimeracil, and Oteracil potassium (TS‐1); and Tegafur and Uracil (UTF) was 6.0%, 2.3%, 1.4%, and 0.7%, respectively. The estimated DIP cases were checked by having the medical records independently reviewed by medical doctors. By chart review, the positive predictive values of DIP against Gefitinib, MTX, TS‐1, and UFT were 69.2%, 44.4%, 58.6%, and 77.8%, respectively. Although the cases extracted by this method included some that did not have DIP, this method can estimate the relative risk of DIP between medicines.

IntroductionFrequent glucose measurements are needed for good blood glucose control in hospitals; however, this requirement means that measurements can be forgotten. We developed a novel glucose management system using an iPod® and electronic health records.MethodsA time schedule system for glucose measurement was developed using point-of-care testing, an iPod®, and electronic health records. The system contains the glucose measurement schedule and an alarm sounds if a measurement is forgotten. The number of times measurements were forgotten was analyzed.ResultsApproximately 7000 glucose measurements were recorded per month. Before implementation of the system, the average number of times measurements were forgotten was 4.8 times per month. This significantly decreased to 2.6 times per month after the system started. We also analyzed the incidence of forgotten glucose measurements as a proportion of the total number of measurements for each period and found a significant difference between the two 9-month periods (43/64,049–24/65,870, P = 0.014, chi-squared test).ConclusionsThis computer-based blood glucose monitoring system is useful for the management of glucose monitoring in hospitals.FundingJohnson & Johnson Japan.

Background The greatest stressor for outpatients is the waiting time before an examination. If the patient is able to use their smartphone to check in with reception, the patient can wait for their examination at any location, and the burden of waiting can be reduced. Objective This study aimed to report the system design and postintroductory outcomes of the Tori RinRin (TR2) system that was developed to reduce outpatient burden imposed by wait times before examination. Methods The TR2 system was introduced at Tottori University Hospital, a large medical facility that accepts a daily average of 1500 outpatients. The system, which links the hospital’s electronic medical record database with patients’ mobile devices, has the following functions: (1) GPS-based examination check-in processing and (2) sending appointment notification messages via a cloud notification service. In order to evaluate the usefulness of the TR2 system, we surveyed the utilization rate of the TR2 system among outpatients, implemented a user questionnaire, and polled the average time required for patients to respond to call notifications about their turn. Results The 3-month average of TR2 users 9 months after the TR 2 system introduction was 17.9% (14,536/81,066). In an investigation of 363 subjects, the mean examination call message response time using the TR2 system was 31 seconds (median 14 seconds). Among 166 subjects who responded to a user survey, 86.7% (144/166) said that the system helped reduce the burden of waiting time. Conclusions The app allowed 17.9% of outpatients at a large medical facility to check in remotely and wait for examinations anywhere. Hence, it is effective in preventing the spread of infection, especially during pandemics such as that of coronavirus disease. The app reported in this study is beneficial for large medical facilities striving to reduce outpatient burden imposed by wait times.

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