Developing an Electronic Health Record for ...

User Centred Networked Health Care A. Moen et al. (Eds.) IOS Press, 2011 © 2011 European Federation for Medical Informatics. All rights reserved. doi:10.3233/978-1-60750-806-9-255

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Developing an Electronic Health Record for Intractable Diseases in Japan Eizen KIMURAa,1, Shinji KOBAYASHI b, Yasuhiro KANATANIc, Ken ISHIHARAa, Tsuneyo MIMORId, Ryousuke TAKAHASHIe, Tsutomu CHIBAf, Hiroyuki YOSHIHARAg a Department Medical Informatics of Ehime University Hospital, b Department of Bioregulatory Medicine, Ehime University Graduate School of Medicine, c National Institute of Public Health, d Department of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, e Kyoto University Hospital Neurology, f Department of Gastroenterology and Hepatology Kyoto University, g Dept. Medical Informatics of Kyoto University Hospital, Japan

Abstract. Because intractable diseases result from unidentifiable causes and are very difficult to treat, they require a lifelong epidemiology database. Japan does not use global unique identifiers, such as social security numbers, so we conducted a feasibility study regarding an electronic health record (EHR). An EHR can be used as a lifelong database and reduce conventional administrative work. However, it will be necessary to develop additional tools to overcome issues specific to Japan before an EHR can be implemented. Keywords. Archetype, EHR, intractable disease, ISO/CEN 13606, openEHR

1. Introduction The Japanese Ministry of Health, Labor, and Welfare (MHLW) defined specified intractable diseases as ‘Tokutei Shikkan’ and established ‘The Specified Disease Treatment Research Program’ in 1972. ‘Tokutei Shikkan’ refers to intractable diseases that result from unidentifiable causes and are very difficult to treat; no treatment procedure has been established [1]. Typically, these diseases require long-term care and medicine, which involves great financial and mental stress for the patient. This project selected 56 diseases from specified intractable diseases as part of a research program about publicly funded assistance for medical expenses. The selected diseases were so rare that it was necessary to conduct a nationwide investigation. In Japan, when a physician examines a patient, s/he fills out a clinical research form and gives it to the patient with an application for a subsidy. Patients take these documents to a public health center to request a subsidy. The form contains demographic and insurance information for administrative processing and some 1

Corresponding Author: Associate Prof. Eizen Kimura, BM, PhD, Medical School of Ehime University, Situkawa Toon City Ehime Japan TEL.: +081 089 960 5695; E-mail: [email protected].

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treatment information for clinical research. Public health centers gather these applications and submit them to the governor of the prefecture. The governor decides which patients are eligible for subsidy and sends them claimant certifications. This research group collected forms from the governors of the prefecture to conduct this epidemiological study. In 1999, the MHLW decided to enter the forms in an Intractable Disease Database, but, to date, the form has not been digitized, so data is entered manually. This has resulted in low-quality data, and clinical form digitization is urgently required. Theories about intractable diseases may be modified by long-term research, so we need to build EHR that includes clinical information of intractable diseases and retains the data over a lifetime. The EHR must also be designed to match the standard clinical information model to meet the needs of administrative procedure and clinical research. In 2009, we implemented the clinical research form using the standard clinical model of ISO/CEN13606 [2]. The Japanese government has presented a plan for social system reformation to establish a social security number (SSN), but there has been no progress in review session. We had to overcome constraints related to non-applicable global unique identifiers of patients and collecting personal data in a central repository given the limited administrative procedure and legal requirements. This paper describes the challenges related to building an EHR in Japan under these limitations.

2. Methods 2.1. EHR Standard Adaption Intractable diseases are difficult to distinguish from diseases with a similar clinical profile and are prone to conceptual transition because of developments in medical research. Intractable diseases may also alter in clinical patterns over the long term, so it is important to accumulate life-long health records and standardize clinical information models to enable cross-sectional study between diseases. We needed to achieve these goals within a short time, so we defined selection criteria to standardize the process. We ensured that: 1) specification was open and the license had few restrictions, 2) clinical models were registered to centralize repository, ensuring sustainability for standardization and maximizing reusability by restricting derivation of models, and 3) the standard had a long history and had been used sufficiently to be stable and should not require major modifications in the future. We used “openEHR; [3]” the reference implementation of ISO/CEN 13606 has been used for more than 10 years in Europe. 2.2. Template of the Clinical Research Form Beginning in 2009, we organized items from the conventional clinical research form using mind mapping and grouped relevant disease-specific themes into clusters [4]. Although many physicians participate and develop openEHR archetypes, the number of archetypes is not sufficient to compose templates for intractable disease. We developed intractable disease-specific archetypes from previously described deliverables and composed a form template in combination with archetypes registered at the Clinical Knowledge Manager (CKM) [5]. We developed templates for six diseases: ulcerative colitis, Crohn’s disease, fulminant hepatitis, primary biliary cirrhosis, severe acute pancreatitis, and myasthenia gravis, because these diseases have a large number of


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cases and many pathognomonic symptoms. The standard domain-specific language used in openEHR is Archetype Definition Language (ADL), but we converted template ADL files to XML files (described with XML Schema) to maximize the reusability. 2.3. Workflow We spent extra time ensuring that computerizing the clinical research form would not affect current administrative procedures and that it would enhance convenience at healthcare and public health centers and enable a harmonious introduction of the EHR (see Fig. 1). We set up the EHR at the data center contracted by the National Institute of Public Health (Fig. 1-a). The EHR has two interfaces: one is used by healthcare workers to submit the form to the EHR; the other is used by the governor of the prefecture to retrieve the form. We distributed the clinical research form entry system to participating hospitals (Fig. 1-b). For hospitals that had already introduced an electronic medical record (EMR), we supplied a tool to capture essential patient information, insurance, and disease name from the EMR and to auto-fill this data into the form. The forms entered by physicians were transferred to EHR via the Internet through a secure messaging system (Fig. 1-c). We allocated a unique document identifier (UDID) to each form. This UDID was generated by combining the unique healthcare facility ID and document ID. The Social Insurance Agency assigns an individual facility ID to each healthcare. We removed personal information from the form before transferring it via the Internet. The form was also printed on a conventional paper form with a Quick Response (QR) code, which includes the patient’s demographic information, insurance, and healthcare information. We intentionally implemented the new clinical research form in the format of the conventional paper form so that public health centers without our system could accept the form and process it conventionally. Patients bring the paper form to the public health center in their region to apply for subsidy (Fig. 1-d). The forms are collected at the public health centers and then sent to the governor of the prefecture. The person in charge at the prefecture downloads the content of the clinical research form from EHR and merges it with the data in QR code to generate a complete form for subsidy application (Fig. 1-e) and registers the patient in the intractable disease database on the Wide-area Information-exchange System for Health and Welfare Administration (WISH) network (Fig. 1-f). The governor of the prefecture judges whether the application satisfies accreditation criteria for subsidy, implemented by the MHLW, and for qualifying patients issues a claimant certification (Fig. 1-g). We assumed the subsequent workflow until the subsidy was paid would remain the same. 2.4. Clinical Research Form Entry System The clinical research form entry system was built on an HTML5-capable web browser to support drag-drop data files exported from the EMR. We used the template engine to map the information entered in the form on a web browser into the XML template file of the clinical research form to compile the submission to the EHR. The system also generates a QR code, which embeds the patient’s name, gender, address, place of birth, and UDID. That is, sensitive information is only stored into QR code and no personal information is submitted to the EHR. Finally, it uploads the form data to the EHR through a secure messaging system.

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2.5. Registration Tool Currently, the people in charge at the prefecture manually enter clinical research forms into the intractable disease database at the WISH network. To automate this, we developed a registration tool. The registration tool extracts UDID from QR code and retrieves the corresponding form from the EHR using web service (Fig. 1-e). Because the intractable disease database only has a legacy CSV import interface, we converted XML-formatted form to a CSV file using XSLT. The tool also extracts some patient demographic data and insurance information from QR code and merges them with the CSV file. The governor exports CSV files to encrypted memory and uses this to register patients in the intractable disease database (Fig. 1-f).

Figure 1. Research process related to establishing an EHR in Japan, (2009).

3. Results and Discussion We had to model 49 archetypes (new 15 archetypes and specialized 34 archetypes from CKM) to define the templates for clinical research forms. Because we converted private information to QR code, the EHR database does not hold any private information. However, public health centers and the governor can acquire a complete clinical research form by uniting the data from the EHR and QR code. Embedding the QR code in a traditional clinical research form avoids large-scale administrative reorganization, and even improves operating effectiveness and the quality of data. This study demonstrated the feasibility of building an EHR without fundamental changes to administrative procedures and health regulations.


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Japan does not use a global personal identifier system, such as social security numbers, because it has not reached a consensus that global personal identifiers are constitutional. Thus, patient information may overlap in each prefecture. We assigned a UDID to every clinical research form so that we may implement computer-assisted name identification and minimize overlaps in the future. Currently, the major issue in data quality is caused by human error, such as input errors at registration. After solving this issue, we will investigate the magnitude of statistical influence in epidemiological research caused by patient information overlap. Converting ADL to XML enables us to search EHR semantically by combining an archetype ID and a node ID using general technology, such as XQuery. However, it is difficult to foresee the structure of the XML file converted from ADL. Implementing data mapping to legacy applications requires a trial-and-error approach and increases the complexity of the overall development process. We will need to develop a tool that enables efficient mapping from XML-converted ADL to the formats of legacy applications. The template editor can generate an input form from a template automatically, but we did not use this feature. We achieved localization through wordto-word translation, but we must redesign the form layout by moving field controls, based on grammatical context, and we must develop additional implementation for entry constraints. Japanese has multiple ways to describe words: Kanji (Chinese character), Hiragana (Japanese phonetic syllabary), Katakana (also phonetic syllabary, used mainly for foreign words), and the alphabet. We will need to solve the Japanese language-specific issues to allow smoother induction of ISO/CEN 13606 in Japan.

4. Conclusions The clinical research form has two functions: it is used in subsidy applications and it supports data for epidemiological research. The current version of the form is primarily designed for subsidy application; as a clinical research form it contains a log of vague information and is difficult to use for research. Moreover, Japan’s health system has a decentralized design; it has complex administrative procedures and is unlikely to undergo fundamental reform. This study demonstrated that our approach can be used to build an EHR that keeps personal information private, even in these difficult conditions. Acknowledgement. This work was supported by the Research on Measures for Intractable Diseases Project of MHLW. (H20-IntractableDisease-Generic-039).

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Nakatani H, Kondo T. Characteristics of a medical care program for specific diseases in Japan in an era of changing cost-sharing. Health Policy. 2003 Jun;64(3):377-89. ISO, Committee CTHIT. ISO/CEN13606. Health informatics – Electronic healthcare record communication – Parts 1-5. 2008. openEHR Foundation. openEHR. Accessed Jan 2009; Available from: http://www.openehr.org. Kimura E, Kobayashi S, Kuroda T, et al. Lessons learned from modeling archetypes for intractable disease surveys. Japan journal of medical informatics. 2011;In Publishing. Ocean Informatics. The openEHR Clinical Knowledge Manager. Accessed 2010/April; Available from: http://www.openehr.org/knowledge/.