Development of a General Statistical Analytical System Using Nationally Standardized Medical Information.

In Japan, since the Next Generation Medical Infrastructure Act regarding anonymized medical data contributing to R&D came into force in 2018, it is expected to exploit medical data for R&D. The Millennial Medical Record Project has been collected a large amount of standardized medical data of a number of hospitals stored in a database under the act. In order for users to widely exploit the medical data when carrying out trial-and-error, there is a difficulty of data access because of a highly secured management of non-anonymous medical data. To solve the data access problem, we develop a general statistical analytical system for executing a variety of statistical significance tests with statistical power analysis in an environment of trial-and-error for users' analyses without programming. In the analytical system, the front-end is a registration form as the input and the analysis results as the output on Microsoft Excel, and the back-end is based on Python, R and SQL. Although the fixed registration form covers limited application for the analysis, since the analysis results using the stored Millennial Medical Record data is provided in a short time without collecting the necessary data for the analysis, the exploitation of medical data could widely and rapidly promote by medical experts/researchers in the manner of trial-and-error. The developed system could apply to make protocols for clinical research and clinical trial, and the potential to discover real-world evidence could be increased.

A random forest algorithm-based approach to capture latent decision variables and their cutoff values.

Although reference intervals (RIs) and clinical decision limits (CDLs) are vital laboratory information for supporting the interpretation of numerical clinical pathology results, there is evidence that RIs and CDLs vary in certain contexts as well as other evidence that RIs and CDLs are flawed. We propose a random forest algorithm-based exploration methodology by using phenotype transformation of independent variables in relation to dependent variables to capture latent decision variables and their cutoff values. We denote certain CDLs within the RIs estimated by an indirect method that affect some diagnostics or outcomes in the context of specific patients' conditions as latent CDLs. We then apply the proposed methodology to clinical laboratory data regarding bodily fluids, such as blood, urine at the admission of patients for the exploration of latent CDLs of hospital length of stay (HLOS) for each patients' condition identified by diseases of patients who undergoing surgeries. From the exploration results, we found that free Thyroxine (T4) above five unique cutoff values: 1.16 ng/dL, 1.19 ng/dL, 1.2 ng/dL, 1.23 ng/dL and 1.25 ng/dL for tachyarrhythmia predicted longer HLOS, though these cutoff values fall within the estimated RIs as well as the hospital-determined RIs. In addition to the evidence that higher free Thyroxine (T4) levels within the RIs have an association with the corresponding disease, on the whole, the cutoff values except 1.16 ng/dL tended to affect long HLOS with the significant differences. The cutoff values could be taken up for discussion among clinical experts whether it is meaningful to alert the risk of patients' conditions and the long HLOS at the admission of patients. If clinical experts appreciate its meaningfulness in clinical practice, the alerts could be embedded in electronic medical records for handling those risks at the admission of patients.

Development of an "Alert Framework" Based on the Practices in the Medical Front.

At the University of Miyazaki Hospital (UMH), we have accumulated and semantically structured a vast amount of medical information since the activation of the electronic health record system approximately 10 years ago. With this medical information, we have decided to develop an alert system for aiding in medical treatment. The purpose of this investigation is to not only to integrate an alert framework into the electronic heath record system, but also to formulate a modeling method of this knowledge. A trial alert framework was developed for the staff in various occupational categories at the UMH. Based on findings of subsequent interviews, a more detailed and upgraded alert framework was constructed, resulting in the final model. Based on our current findings, an alert framework was developed with four major items. Based on the analysis of the medical practices from the trial model, it has been concluded that there are four major risk patterns that trigger the alert. Furthermore, the current alert framework contains detailed definitions which are easily substituted into the database, leading to easy implementation of the electronic health records.