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This study aimed to develop a method to enable the financial estimation of each patient's uncertainty without focusing on healthcare technology. We define financial uncertainty (FU) as the difference between an actual amount of claim (AC) and the discounted present value of the AC (DAC). DAC can be calculated based on a discounted present value calculated using a cash flow, a period of investment, and a discount rate. The present study considered these three items as AC, the length of hospital stay, and the predicted mortality rate. The mortality prediction model was built using typical data items in standard level electronic medical records such as sex, age, and disease information. The performance of the prediction model was moderate because an area under curve was approximately 85%. The empirical analysis primarily compares the FU of the top 20 diseases with the actual AC using a retrospective cohort in the University of Miyazaki Hospital. The observational period is 5 years, from April 1, 2013, to March 31, 2018. The analysis demonstrates that the proportion of FU to actual AC is higher than 20% in low-weight children, patients with leukemia, brain tumor, myeloid leukemia, or non-Hodgkin's lymphoma. For these diseases, patients cannot avoid long hospitalization; therefore, the medical fee payment system should be designed based on uncertainty. Our method is both practical and generalizable because it uses a small number of data items that are required in standard electronic medical records. This method contributes to the decision-making processes of health policymakers., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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.

This study aimed to improve generalizability of our previous study that analyzed clinical pathway (CP) completion. Although our previous study demonstrated that CP completion can reduce the length of hospital stay, it is possible for few medical organizations to extract the implementation of treatment registered on CP from typical electronic medical records. Therefore, we have defined a prospective event for event substitution, called meal completion (MC), in which patients can take their meal daily. Data were collected from April 2013 to March 2018 from the electronic medical records of the University of Miyazaki Hospital. We used propensity score matching to extract records from 8033 patients. Patients were further divided into the MC and non-MC groups; 2577 patients in each group were available for data analysis. The numbers of patients with CP completion were 646 (28.1%) in the MC group and 411 (18.2%) in the non-MC group. The P value of the chi-square test was <0.001. According to this result, there was the causation from MC to increase in CP completion. Additionally, it was possible to consider the inclusion relationship in all treatments (universal set), treatments registered on CP (subset of all treatments), and meals (subset of treatments registered on CP). In conclusion, MC can substitute for CP completion because the demonstration is appropriate for the Prentice criterion, which is often used for the evaluation of a surrogate endpoint.

We have previously demonstrated that clinical pathway completion helps reduce hospital stays. However, our previous results showed only a correlation, not causation. Therefore, the current study's aim was to analyze the causation between clinical pathway completion and reduced hospital stays for patients with lung cancer. Data were collected from April 2013 to March 2018 from the electronic medical records of the University of Miyazaki Hospital. We used propensity score matching to extract records from 227 patients. Patients were further divided into a pathway completed group and a pathway not completed group; 74 patients in each group were available for data analysis. Our main analysis involved estimating the discharge curve, which was comprised of the in-hospital rate and hospital stay. Additional analyzes were performed to compare the frequency of medical treatments registered in the clinical pathway but not implemented (termed deviated medical treatments). The occurrence of these treatments meant that the clinical pathway was not completed. The main results indicated a decrease in the in-hospital rate of the completion group, compared with the not completed group. The p value of the log-rank test was <0.001 for total patients and patients who underwent resection, and 0.017 for patients who did not undergo resection. Additional results indicated that a number of intravenous drips were not implemented, despite their registration on clinical pathways. Our results indicate that clinical pathway completion contributes to improved efficiency and safety. This simplified procedure is expected to be applicable to other diseases and clinical indicators.

Medical Markup Language (MML) is a standard format for exchange of healthcare data among healthcare providers. Following the last major update (version 3), we developed new modules and discussed the requirements for the next major updates. Subsequently, in 2016 we released MML version 4 and used it to obtain clinical data from healthcare providers for a nationwide electronic health records (EHR) system. In this article we provide an overview of this major update of MML version 4 and discuss its interoperability for clinical data.

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.

Many studies have analyzed the effects of clinical pathways, but most have considered only single diseases. The purpose of the present study was to exploratively analyze electronic medical records related to the use of clinical pathways, seeking trends that could usefully benefit clinical activity. From the data warehouse of University of Miyazaki Hospital, collected from April 2014 to March 2016, we retrospectively identified 6523 patients for whom a clinical pathway was applied. Other inclusion criteria were single hospitalization, the type of medical fee was comprehensive, and data were available so that all clinical indicators could be calculated. Two types of deviation from the clinical pathway were defined: cancellation (described in the clinical pathway but not implemented) and addition (not described in the clinical pathway but nevertheless implemented). If the code of International Classification of Diseases describing the clinical pathway differed from that describing where the medical resource was mostly spent, we considered this as indicating a complication. We compared principal clinical indicators (length of hospital stay, mortality rate, and comprehensive-volume ratio) by completion rate for the clinical pathway. Regardless of whether patients had complications, completing the clinical pathway was associated with a significant reduction in length of hospital stay. This finding indicated that length of hospital stay could be shortened if all medical treatments described in the clinical pathway were implemented. Our results demonstrated that it is possible to shorten the length of hospital stay by improving clinical pathways to include medical treatment for preventing complications.

An electronic medical record (EMR) system was introduced to the University of Miyazaki Hospital, in Japan, in 2006. This hospital is the only one in Japan to store digital photographs of patients within EMRs. In this paper, we report on the utility of these digital photographs for disease diagnosis. Digital photographs of patients were taken at the time of hospitalization, and have been used for patient identification by medical staff. More than 20,000 digital photographs have been saved, along with examination data and medical history classified by disease, since the introduction of EMR. In the first part of the present study, we analyzed the facial cheek color of patients using photographs taken at the time of hospitalization in relation to diagnoses in six disease categories that were considered to lead to characteristic facial skin characteristics. We verified the presence or absence of a characteristic color for each disease category. Next, we focused on four diseases, Analysis of the facial skin color of 1268 patients found the same patterns of characteristic color. Overall, we found significant differences in complexion according to disease type, based on the analysis of color from digital photos and other EMR information. We propose that color analysis data should become an additional item of information stored in EMRs.

In August 2009, Miyazaki Health and Welfare Network (Haniwa Net, hereafter referred to as "the Net"), centrally led by University of Miyazaki Hospital (UMH), adopted a center hospital-based system offering a unilateral linkage that enables the viewing of UMH's medical records through a web-based browser (electronic medical records (EMR)). By the end of December 2010, the network had developed into a system of 79 collaborating physicians from within the prefecture. Beginning in August 2010, physicians in 12 medical institutions were visited and asked to speak freely on the operational issues concerning the Net. Recordings and written accounts were coded using the text analysis software MAXQDA 10 to understand the actual state of operations. Analysis of calculations of Kendall's rank correlation confirmed that the interdependency between human networks and information networks is significant. At the same time, while the negative opinions concerning the functions of the Net were somewhat conspicuous, the results showed a correlation between requests and proposals for operational improvements of the Net, clearly indicating the need for a more user-friendly system and a better viewer.

Post-relational databases provide high performance and are currently widely used in American hospitals. As few hospital information systems (HIS) in either China or Japan are based on post-relational databases, here we introduce a new-generation electronic medical records (EMR) system called Hygeia, which was developed with the post-relational database Caché and the latest platform Ensemble. Utilizing the benefits of a post-relational database, Hygeia is equipped with an "integration" feature that allows all the system users to access data-with a fast response time-anywhere and at anytime. Performance tests of databases in EMR systems were implemented in both China and Japan. First, a comparison test was conducted between a post-relational database, Caché, and a relational database, Oracle, embedded in the EMR systems of a medium-sized first-class hospital in China. Second, a user terminal test was done on the EMR system Izanami, which is based on the identical database Caché and operates efficiently at the Miyazaki University Hospital in Japan. The results proved that the post-relational database Caché works faster than the relational database Oracle and showed perfect performance in the real-time EMR system.

The movement of create medical information systems that is now taking place involves both progress in EMR (Electronic Medical Records)-computerization of records at hospitals and clinics, and also in EHR (Electronic Health Records) in which information is shared with individual regions. However, the geographical coming and going of people in modern society is extremely active. Naturally the places these people move to are not necessarily within the same region. For this reason, even if the basic unit for the health care supply system is in practical terms limited to the local level, if services are restricted to only one region, many persons may be unable to receive the benefits of health care cooperation. In this study, we constructed a mechanism for a medical cooperation system which links the EHR systems of individual regions and is able to create a one-patient, one-record system on the national level. In this paper, we will provide a report of this mechanism and of the 4-year operational trial.

Clinical Pathways (CP) enhance the quality of patient care, and are thus important in health management. However, there is a need to address the challenge of adaptation of treatment procedures in CP-that is, the treatment schemes must be re-modified once the clinical status and other care conditions of patients in the healthcare setting change, which happen frequently. In addition, the widespread and frequent use of Electronic Medical Records (EMR) implies an increasing need to combine CP with other healthcare information systems, especially EMR, in order to greatly improve healthcare quality and efficiency. This study proposed an ontology-based method to model CP: ontology was used to model CP domain terms; Semantic Web Rule language was used to model domain rules. In this way, the CP could reason over the rules, knowledge, and information collected, and provides automated error checking for the next steps of the treatment in runtime, which is adaptive to treatment procedures. To evaluate our method, we built a Lobectomia Pulmonalis CP and realized it based on an EMR system.

Current EMR system benefits physicians by facilitating order entry and reducing errors. It can improve the safety and effectiveness of medical services, but cannot manage the whole medical process and the quality of medical services. In addition to physicians, EMR should be designed for all medical professionals because medical services cannot be accomplished by physicians alone, but also requires the involvement of other medical professionals. Therefore, we applied PDCA, the famous quality management cycle to design a comprehensive and coherent EMR system which can be used throughout the entire treatment process. EMR with the PDCA Cycle can record every order state and every treatment procedure in order to monitor the whole medical process. This extends the safety from planning the treatment to fulfilling it. By analyzing the records, doctors and hospital managers can perfect the medical process and improve healthcare quality. The EMR we designed with the PDCA Cycle provides a record entry interface for physicians and a worksheet interface for nurses and other professionals. Every treatment procedure and every change of orders or tasks will be fed back to medical professionals. So information generated from the beginning to the end of treatment will link with each other to avoid any information islands. Furthermore, the EMR can display the additional information intuitively and real-timely without increasing the burden of medical professionals' work.

CLinical Accounting InforMation (CLAIM) is a standard for the exchange of data between patient accounting systems and electronic medical record (EMR) systems. It uses eXtensible Markup Language (XML) as a meta-language and was developed in Japan. CLAIM is subordinate to the Medical Markup Language (MML) standard, which allows the exchange of medical data between different medical institutions. It has inherited the basic structure of MML 2.x and the current version, version 2.1, contains two modules and nine data definition tables. In China, no data exchange standard yet exists that links EMR systems to accounting systems. Taking advantage of CLAIM's flexibility, we created a localized Chinese version based on CLAIM 2.1. Since Chinese receipt systems differ from those of Japan, some information such as prescription formats, etc. are also different from those in Japan. Two CLAIM modules were re-engineered and six data definition tables were either added or redefined. The Chinese version of CLAIM takes local needs into account, and consequently it is now possible to transfer data between the patient accounting systems and EMR systems of Chinese medical institutions effectively.

Medical Markup Language (MML) is a standard for the exchange of medical data among different medical institutions. It was developed in Japan in 1995. Since version 2.21, MML has used eXtensible Markup Language (XML) as a meta-language. The latest version, 3.0, conforms to HL7 Clinical Document Architecture (CDA) and contains 14 modules and 36 data definition tables. In China, a standard which structures entire medical records in XML does not yet exist. Taking advantage of MML's flexibility, we created a localized Chinese version based on MML 3.0. Parts of the original specifications have been enhanced; these include a newly developed health insurance information module and 12 additional or redefined data definition tables. The Chinese version takes local needs into account and now makes it possible to exchange medical data among Chinese medical institutions.

As a set of standards, Medical Markup Language (MML) has been developed over the last 8 years to allow the exchange of medical data between different medical information providers MML version 2.21 was characterized by XML as metalanguage and was announced in 1999, at which time full-scale implementation tests were carried out; subsequently, various information and functional inadequacies were discovered in this version. MML was therefore updated to version 2.3 in 2001. At present, MML contains 12 MML modules including the new referral, test result, and report modules. In version 2.3, the group ID element was added; the access right definition and health insurance module were amended.

Medical Markup Language (MML) has been developed over the last 6 years in order to create a set of standards by which medical data, within Japan and hopefully worldwide, can be stored, accessed and exchanged in any number of physical locates. The MML version 2.21 is characterized by XML as meta-language, module structure for each document and enhancement of linking function among documents. Data exchange specification has been also added for query and reply. MML instances are composed of MML header and MML body. The MML header includes information for data transmission, while MML body includes several module items. One module item contains two elements: document information and module content. Nine MML module contents are defined at the present time: patient information, health insurance information, diagnosis information, lifestyle information, basic clinic information, particular information at the time of first visit, progress course information, surgery record information and clinical summary information.