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  • 1. Developing future-proof open source information systems for healthcare Abstract. Healthcare information is full of context. The current design approach to healthcare information systems (HIS) doesn't provide a facility to transfer that context when the data is exchanged with other systems. This paper aims to review the scientific literature regarding success and failures in HIS implementation and integration projects and to present the fundamentals of the development of a future-proof HIS, the Open Source Health Information Platform (OSHIP). 1. Introduction It has long been recognized that there is an inherent need for full semantic interoperability between information systems that are used to manage healthcare information [Lopez and Blobel 2009], [Hovenga 2008], [Ma et al. 2007], [Engel et al. 2006], [Nykanen and Karimaa 2006], [Rossi Mori and Consorti 1998]. Thus, in the broadest sense, a Healthcare Information System (HIS) is any information system that maintains and manages information affecting the health of a person or population [Wikimedia Foundation, Inc. 2009]. Healthcare information has many complex temporal and spatial components and then mix in the fact that medical and healthcare knowledge changes rapidly. It is widely accepted that not only the medical condition of a person is significant to their health but a host of social, economic and environmental conditions place direct and indirect effects on personal and population health [Ghosh 2007], [Norgall et al. 2006], [Ruland and Bakken 2001], [Starr and Starr 1995]. This is the crux of why HIS re so complex and typically 'purpose-built' without any real interoperability built into them. While the science of information design encompasses much more than computerized information systems, the aim of this paper is discussing the computerization of healthcare information. Expressed in its simplest terms, the current approach to developing an information system follows these steps: (a) requirements gathering, using one or more of the many standard methodologies; (b) systems analysis, in order to determine from the requirements the systems involved to meet the needs of the requirements; (c) data modeling, which means building a model of the data elements available from the information sources (systems) to meet the needs of the requirements; (d) implementation of the data model in some persistent storage facility; (e) implementation of the software required to add, edit and manage the storage and use of the data elements defined in the model; and (f) maintenance of the system then entails modifying the data model and the software as the requirements change. This process has proven robust and reliable over many years and millions of software applications being deployed when sound software engineering principles are adhered to in the entire process [Covitz et al. 2003], [Xu et al. 2001], [Kanoui et al. 2000]. Looking into how typical applications handle the information management aspect a bit more, we see how an application like this actually manages information. We see that the data elements are discretely persisted and the software and/or data management layer manages the relationships between these data elements.
  • 2. Data elements are simply data elements. It is their relationship to each other and their relationship to the user, via input and output design in each application, which gives them their semantic context in order to create information from their existence [Schuurman and Leszczynski 2008], [Rector et al. 2002], [Degoulet et al. 1998]. As an example of the data versus information conundrum, let us say that we have a data element of the integer 102. That integer can represent a systolic blood pressure measurement, a heart rate, a body temperature, and so on. The validity, reliability and clinical significance of this data element necessarily depends upon the context (that includes, e.g., units of measurement, patient position, point of measurement, device used, demographic data from the patient, etc.). There are certain elements of a database management system that lend to this context, but this feature is a part of the software layer, not the actual persistence of the data. While it is often conceived that table names and column names in, for example, SQL databases, provide this functionality, it is important to consider situations when the data needs to be exchanged with an application that uses a hierarchical or object or XML database. In this case, there is no standard way to do this translation. In fact, even between applications based on SQL databases, the migration of data must be performed on a case-by-case basis where the table and column names are not the same and a custom translation must be built for every case [Huang et al. 2005], [Stitt 1995]. Certainly the development of standardized HL7 messages was a big leap forward in information exchange in healthcare [Tracy and Dougherty 2002], [Hettinger and Brazile 1994]. Two big drawbacks to this approach are that the message formats had to be made very generic in order to accommodate systems that may or may not have all of the data elements in a specific message and this exchange process requires a manual mapping on both the sending and receiving ends for each message to their local database. An entire industry developed around providing these mapping engines and the expertise to setup the complex mapping routines. Even if the development of the message formats were defined by domain experts, the mapping is typically performed by software experts, not clinicians [Hiroi et al. 2007], [Lin et al. 2006], [Ma et al. 2005], [McDonald et al. 2003], [Kinsey et al. 2000], [Ma 1995]. It is important to highlight that information should be universally available inside the healthcare sector, from professionals at the point of care up to epidemiologists, policy makers and economists [Hartz and John 2009], [Iglehart 2004]. Nevertheless, the immense amount of standardized data sets built into applications published on literature are almost entirely made based on the interests of specific research projects [Elisa and Heimar 2006], [Tierney et al. 2006], [Innes et al. 2001], [Studnicki et al. 2001]. It is not possible for every healthcare application to incorporate all the “minimum data sets” being requested by these competing interests. Even if it happened, the real semantic context of collected data that results in information is in the software of the original application, not in the data itself [Mead, 2006]. Completely controlled environments like the U.S. Veterans Administration and Kaiser-Permanente have demonstrated that a centrally controlled approach to HIS development and management using current approaches does work and does reduce adverse events as well as improve access to patient information at the point of care [Protti and Groen 2008], [Raymond 2005]. The evidence exists that interoperable HIS are a benefit to those organizations as well as to their patient population [Brennan et al. 2008]. However, even in countries with nationalized healthcare services it is still a challenge to mandate a common data set for all HIS that meets the needs of all
  • 3. healthcare information users [Halamka et al. 2005], [Suselj and Cuber 1998]. So, we believe that what is needed is a new way of thinking about complex information systems. The objective of this paper is to present an implementation of an information model that addresses those challenges in healthcare information. 2. Method As an analogy, the idea of Lego® blocks is suggested. A box of these blocks contains various sizes and shapes of components designed to work together to form objects. The kit comes with instructions so that a certain sub-group of the blocks can be used to build a specific object (e.g., a truck). Some of those same blocks can be used in combination with additional ones, to build another specific object (e.g., a helicopter) according to a different set of instructions. There are other instructions to build various other objects all based on the same set of common blocks, all according to the restrictions described in the instructions. In healthcare information, we define a Common Reference Model (CRM) as the common set of healthcare information blocks implemented in software that represent clinical concepts as an information instance, and the instructions about the representation of each clinical concept, as Clinical Knowledge Model (CKM). If the CRM is abstract enough then it could be implemented in any object- oriented language on any hardware platform. It therefore provides the freedom of use and creativity to meet the needs and desires of application developers. The CKM units (instructions) can be described by the domain experts for each concept because they exist as constraint descriptions of the very broad based CRM, not existing in software themselves, but simply informing the software about what they are representing. In fact, the CKM units describe themselves completely with their entire semantic context. Since they exist as data instances and not lines of software code, they can be exchanged with other applications based on the CRM without any loss of semantics, and be persisted in any type of data management system. CKM units can also be queried with a standardized query language so that even the queries only have to be built one time, since the data structure is standardized. Additionally, for every change either of the healthcare science or management, a new version of a specific CKM unit can be adapted from the existing version, with no need to change the software nor is there any loss to the semantic integrity of the existing data. The openEHR specifications are designed following those principles, and it has been proven in multiple programming languages using multiple persistence layers and hardware platforms, and also provides definitions for service and support layers in order to allow the development of real healthcare applications [Beale and Heard 2007]. The Open Source Health Information Platform (OSHIP) is the reference implementation of the openEHR specifications release 1.0.2 in Python language, heavily dependent on the Zope Component Architecture (ZCA) version 3.4.x. It is a library combined with other open source components designed to facilitate the creation of interoperable healthcare applications. The persistence (storage) layer is completely independent of the reference model and component libraries. Any type of database can be used, such as object database or any SQL databases, but a native Python object database such as the Zope Object Database (ZODB) provides transparent object persistence, which eliminates the issues of mapping objects to SQL tables. OSHIP itself
  • 4. uses the ZODB for the Archetype Server and Terminology Server. The current status of development of the project can be found on http://launchpad.net/oship. 3. Conclusions The approach for designing HIS here presented requires the adoption of a new way of thinking, but not letting go of sound software engineering principles. In fact, this approach enforces the sound principles of separation of software and data. Of course high quality, rapidly delivered information is needed by healthcare decision makers; but the mainstream approach on this matter leads to wasted time and effort and even poor quality results due to a lack of interoperability across applications causing misinterpretation of existing data. The context currently lies in the software, where it can’t be exchanged. There is a need to return it to the data, where it belongs. References Beale T, Heard S (eds.). openEHR Architecture Overview Revision 1.1. London, 2007; openEHR Foundation. Brennan DM, Holtz BE, Chumbler NR, Kobb R, Rabinowitz T. Visioning technology for the future of telehealth. Telemed J E Health 2008; 14: 982-5. Covitz PA, Hartel F, Schaefer C, De Coronado S, Fragoso G, Sahni H, Gustafson S, Buetow KH. caCORE: a common infrastructure for cancer informatics. Bioinformatics 2003; 19: 2404-12. Degoulet P, Sauquet D, Jaulent MC, Zapletal E, Lavril M. Rationale and design considerations for a semantic mediator in health information systems. Methods Inf Med 1998; 37: 518-26. Elisa R, Heimar M. Nursing minimum data set: A literature review. Stud Health Technol Inform 2006; 122: 734-7. Engel K, Blobel B, Pharow P. Standards for enabling health informatics interoperability. Stud Health Technol Inform 2006; 124: 145-50. Ghosh T. Structuration and sensemaking: frameworks for understanding the management of health information systems in the ICU. Stud Health Technol Inform 2007; 130: 45-57. Halamka J, Aranow M, Ascenzo C, Bates D, Debor G, Glaser J, Goroll A, Stowe J, Tripathi M, Vineyard G. Health care IT collaboration in Massachusetts: the experience of creating regional connectivity. J Am Med Inform Assoc 2005; 12: 596-601. Hartz S, John J. Public health policy decisions on medical innovations: What role can early economic evaluation play? Health Policy 2009; 89: 184-92. Hettinger BJ, Brazile RP. Health Level Seven (HL7): standard for healthcare electronic data transmissions. Comput Nurs 1994; 12: 13-6. Hiroi K, Ido K, Yang W, Nakaya J. Interface analysis between GSVML and HL7 version 3. J Biomed Inform 2007; 40: 527-38. Hovenga EJS. Importance of achieving semantic interoperability for national health information systems. Texto & contexto enferm 2008; 17: 158-167.
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