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The collection and use of large-scale medical information for developing artificial intelligence engines are actively ongoing. In Japan, collection systems have been built to collect data for medical image analysis and disease repositories. In the experimental project for the next generation medical infrastructure law, a centrally integrated basic system was developed, and standardized electronic medical record (EMR) storage data distributed to each hospital were transferred into one data center and imported to a database for secondary use. The law requires a mechanism for maintaining a list of notified or opted-out patients. To operate these systems, safe and efficient secondary use of collected information is essential not just for the law but also for large-scale data collection projects, such as multifacility clinical research. This paper considers whole-somely, requirements for providing medical care information to data collection projects and proposes additional requirements for a gateway system under development.

Health information systems (HIS) and clinical workflows generate medication errors that affect the quality of patient care. The rigorous evaluation of the medication process's error risk, control, and impact on clinical practice enable the understanding of latent and active factors that contribute to HIS-induced errors. This paper reports the preliminary findings of an evaluation case study of a 1000-bed Japanese secondary care teaching hospital using observation, interview, and document analysis methods. Findings were analysed from a process perspective by adopting a recently introduced framework known as Human, Organisation, Process, and Technology-fit. Process factors influencing risk in medication errors include template- and calendar-based systems, intuitive design, barcode check, ease of use, alert, policy, systematic task organisation, and safety culture Approaches for managing medication errors also exert an important role on error reduction and clinical workflow.

To improve the efficiency of clinical research, we developed a system to integrate electronic medical records (EMRs) and the electronic data capture system (EDC). EDC is divided into case report form (CRF) reporter and CDMS with CRF receiver with data communication using the operational data model (ODM). The CRF reporter is incorporated into the EMR to share data witth the EMR. In the data transcription type, doctors enter data using a progress note template, which are transmitted to the reporter template. It then generates the ODM. In the direct record type, reporter templates open from the progress note and generate narrative text to make record in the progress note. The configuration files for a study are delivered from the contents server to minimize the setup. This system has been used for 15 clinical studies including 3 clinical trials. This system can save labor and financial costs in clinical research.

Electronic health record (EHR) systems are necessary for the sharing of medical information between care delivery organizations (CDOs). We developed a document-based EHR system in which all of the PDF documents that are stored in our electronic medical record system can be disclosed to selected target CDOs. An access control list (ACL) file was designed based on the HL7 CDA header to manage the information that is disclosed.

Early diagnosis and treatment of pancreatic cancer is challenging. We attempted to find diagnostic rules for pancreatic cancer from laboratory data in the Osaka University Hospital's data warehouse using Bayesian estimation. We calculated the pretest odds based on the number of laboratory tests and the cutoff value at which the diagnostic accuracy is over 20%. By this method, we identified diagnostic rules of 6 types for one item and 79 types for 2 items. Pancreatic cancer is difficult to detect from only general laboratory tests. However, this method may be promising in early diagnosis.

Issues related to ensuring patient privacy and data ownership in clinical repositories prevent the growth of translational research. Previous studies have used an aggregator agent to obscure clinical repositories from the data user, and to ensure the privacy of output using statistical disclosure control. However, there remain several issues that must be considered. One such issue is that a data breach may occur when multiple nodes conspire. Another is that the agent may eavesdrop on or leak a user's queries and their results. We have implemented a secure computing method so that the data used by each party can be kept confidential even if all of the other parties conspire to crack the data. We deployed our implementation at three geographically distributed nodes connected to a high-speed layer two network. The performance of our method, with respect to processing times, suggests suitability for practical use.

There is a great need to reuse data stored in electronic medical records (EMR) databases for clinical research. We previously reported the development of a system in which progress notes and case report forms (CRFs) were simultaneously recorded using a template in the EMR in order to exclude redundant data entry. To make the data collection process more efficient, we are developing a system in which the data originally stored in the EMR database can be populated within a frame in a template. We developed interface plugin modules that retrieve data from the databases of other EMR applications. A universal keyword written in a template master is converted to a local code using a data conversion table, then the objective data is retrieved from the corresponding database. The template element data, which are entered by a template, are stored in the template element database. To retrieve the data entered by other templates, the objective data is designated by the template element code with the template code, or by the concept code if it is written for the element. When the application systems in the EMR generate documents, they also generate a PDF file and a corresponding document profile XML, which includes important data, and send them to the document archive server and the data sharing saver, respectively. In the data sharing server, the data are represented by an item with an item code with a document class code and its value. By linking a concept code to an item identifier, an objective data can be retrieved by designating a concept code. We employed a flexible strategy in which a unique identifier for a hospital is initially attached to all of the data that the hospital generates. The identifier is secondarily linked with concept codes. The data that are not linked with a concept code can also be retrieved using the unique identifier of the hospital. This strategy makes it possible to reuse any of a hospital's data.

In clinical trials, investigating the ratio of patients with each disease who are treated in a hospital is important for determining the number of patients who are allocated to hospitals. The Japanese health insurance claims data includes standardized disease and medicine data. However, the disease data has some problems in terms of reliability, because the healed diseases are sometimes not deleted or because a disease that a patient does not actually have is registered to claim the cost of the examination. On the other hand, therapeutic medicines are administered to target particular diseases. In this study, we developed a system for estimating the number of patients with each disease using the disease data and the therapeutic medicine data. We converted the ICD-10 code to a 4-grade classification code so that we could predict the diseases in the shallow layer (e.g. gastrointestinal disease) when it was difficult to predict the precise diseases in the deep layer (e.g. gastric ulcers). A table showing the disease code and the corresponding therapeutic medicine code was provided by the Japan Pharmaceutical Information Center (JAPIC). We calculated the disease probability score from the diseases and therapeutic medicines and recorded the predicted disease. For the system evaluation, we used the health insurance claims data from Osaka University Hospital for January 2015. A total of 58,526 diseases were predicted from the health insurance claims data of 18,393 patients. One hundred twenty patients were randomly extracted for use in a chart review that was performed by an expert physician. Two hundred twenty-four of 329 predicted diseases, were correctly predicted; 56 were reasonably predicted, and 49 were incorrectly predicted. The main disease was correctly predicted in 71 patients. In conclusion, we could estimate the number of patients with each disease using the health insurance claims data with a certain degree of accuracy.

Adverse events are detected by monitoring the patient's status, including blood test results. However, it is difficult to identify all adverse events based on recognition by individual doctors. We developed a system that can be used to detect hematotoxicity adverse events according to blood test results recorded in an electronic medical record system. The blood test results were graded based on Common Terminology Criteria for Adverse Events (CTCAE) and changes in the blood test results (Up, Down, Flat) were assessed according to the variation in the grade. The changes in the blood test and injection data were stored in a database. By comparing the date of injection and start and end dates of the change in the blood test results, adverse events related to a designated drug were detected. Using this method, we searched for the occurrence of serious adverse events (CTCAE Grades 3 or 4) concerning WBC, ALT and creatinine related to paclitaxel at Osaka University Hospital. The rate of occurrence of a decreased WBC count, increased ALT level and increased creatinine level was 36.0%, 0.6% and 0.4%, respectively. This method is useful for detecting and estimating the rate of occurrence of hematotoxicity adverse drug events.

In a hospital, doctors and nurses shares roles in treating admitted patients. Communication between them is necessary and communication errors become the problem in medical safety. In Japan, verbal instruction is prohibited and doctors write their instruction on paper instruction slips. However, because it is difficult to ascertain revision history and the active instructions on instruction slips, human errors can occur. We developed template-based computerized instruction entry system to reduce ward workloads and contribute to medical safety. Templates enable us to input the instructions easily and standardize the descriptions of instructions. By standardizing and combine the instruction into one template for one instruction item, the systems could prevent instructions overlap. We created sets of templates (e.g., admission set, preoperative set), so that doctors could enter their instructions easily. Instructions entered via any of the sets can be subdivided into separate items by the system before being submitted, and can also be changed on a per-item basis. The instructions were displayed as calendar form. Calendar form represents the instruction shift and current active instructions. We prepared 382 standardized instruction templates. In our system, 66% of instructions were entered via templates, and 34% were entered as free-text comments. Our system prevents communication errors between medical staff.

Recently one patient received care from several hospitals at around the same time. When the patient visited a new hospital, the new hospital's physician tried to get patient information the previous hospital. Thus, patient information is frequently exchanged between them. Many types of healthcare facilities have implemented an electronic medical record system, but in Japan, healthcare information exchange is often done by paper. In other words, after a clinical doctor prints a referral document and sends it to another hospital's physician, another hospital's doctor receives it and scans to store the EMR in his own hospital's system. It is a wasteful way to exchange healthcare information about a patient. In order to solve this problem, we have developed a cross-institutional document exchange system using clinical document architecture (CDA) with a virtual printing method.

EDC system has been used in the field of clinical research. The current EDC system does not connect with electronic medical record system (EMR), thus a medical staff has to transcribe the data in EMR to EDC system manually. This redundant process causes not only inefficiency but also human error. We developed an EDC system cooperating with EMR, in which the data required for a clinical research form (CRF) is transcribed automatically from EMR to electronic CRF (eCRF) and is sent via network. We call this system as "eCRF reporter". The interface module of eCRF reporter can retrieves the data in EMR database including patient biography data, laboratory test data, prescription data and data entered by template in progress notes. The eCRF reporter also enables users to enter data directly to eCRF. The eCRF reporter generates CDISC ODM file and PDF which is a translated form of Clinical data in ODM. After storing eCRF in EMR, it is transferred via VPN to a clinical data management system (CDMS) which can receive the eCRF files and parse ODM. We started some clinical research by using this system. This system is expected to promote clinical research efficiency and strictness.

We developed a system that transfers images via network and started using them in our hospital's PACS (Picture Archiving and Communication Systems) in 2006. We are pleased to report that the system has been re-developed and has been running so that there will be a regional liaison in the future. It has become possible to automatically transfer images simply by selecting the destination hospital that is registered in advance at the relay server. The gateway of this system can send images to a multi-center, relay management server, which receives the images and resends them. This system has the potential to be useful for image exchange, and to serve as a regional medical liaison.

Standard Japanese electronic medical record (EMR) systems are associated with major shortcomings. For example, they do not assure lifelong readability of records because each document requires its own viewing software program, a system that is difficult to maintain over long periods of time. It can also be difficult for users to comprehend a patient's clinical history because different classes of documents can only be accessed from their own window. To address these problems, we developed a document-based electronic medical record that aggregates all documents for a patient in a PDF or DocuWorks format. We call this system the Document Archiving and Communication System (DACS). There are two types of viewers in the DACS: the Matrix View, which provides a time line of a patient's history, and the Tree View, which stores the documents in hierarchical document classes. We placed 2,734 document classes into 11 categories. A total of 22,3972 documents were entered per month. The frequency of use of the DACS viewer was 268,644 instances per month. The DACS viewer was used to assess a patient's clinical history.

We aim at making a diagnosis support system that can be put to practical use. We proposed a diagnostic process model based on simple knowledge which can be gleaned from textbooks. We defined clinical finding (CF) as a general concept for patient's symptom or findings etc., whose value is expressed by Boolean. We call the combination of several CFs a "CF pattern", and a set of CF patterns with concomitant diseases "case base". We consider diagnosis as a process of searching an instance from the case base whose CF pattern is concomitant with that of a patient. The diseases which have the same CF pattern are candidates for diagnosis. Then we select a CF which is present in part of the candidates and check whether it is present or absent in the patient in order to narrow down the candidates. Because the case base does not exist in reality, the probability of CF pattern is calculated by the product of CF occurrence rate assuming that occurrence of CF is independent. Therefore the knowledge required for diagnosis is frequency of disease under sex and age group and CF-disease relation (CF and its occurrence rate in the disease). By processing these two types of knowledge, diagnosis can be made.