8 research outputs found

    Reality check - reliable national data from general practice electronic health records

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    Since 1998, data about general practice activity in Australia has been collected, analysed and disseminated through the Bettering the Evaluation and Care of Health (BEACH) program. BEACH has provided valuable information about how general practice has changed over time, the impact of policy on practice and general practitioner (GP) professional development, and is the most reliable national source of data on GP activity. However, its cross-sectional design precludes comparison of outcomes of different approaches to care. It is estimated that 96% of GPs currently use computers for clinical purposes. However, some GPs only use Electronic Health Records (EHR) for part of their clinical work, such as prescribing or ordering pathology tests. Others are paperless and only use EHRs, but even in these circumstances the EHRs themselves lack the structure to reliably link management actions to a patient problem. There are at least eight EHRs used in general practice, each developed independently and structured differently. In short, there are no nationally agreed and implemented standards for EHRs in Australia, in three areas: EHR structure (including linkages) data element names and definitions use of clinical terminology and classifications. Therefore it is not possible to reliably export standardised data from general practice EHRs of a sufficient quality to be used for clinical and research purposes. With current policy focuses on data linkage, integration of care, improved use of the My Health Record (formerly the PCEHR) and attempts to use EHR data for research, the need for a reliable source of data from general practice EHRs has never been higher. Unfortunately there is no ‘quick fix’ solution, but the issues can be addressed with a targeted work program to address the three underlying problem areas. This Issues Brief describes four steps required to produce high quality data from general practice EHRs: A defined EHR data model that links related data elements Consistent data element labels and definitions across EHRs Use of standardised clinical terminology and classifications Accreditation of GP electronic health records. This recommended program of work requires a national, cohesive approach, involving stakeholders from government, professional organisations, the EHR software industry and organisations that use data from general practice

    Extensions of SNOMED taxonomy abstraction networks supporting auditing and complexity analysis

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    The Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) has been widely used as a standard terminology in various biomedical domains. The enhancement of the quality of SNOMED contributes to the improvement of the medical systems that it supports. In previous work, the Structural Analysis of Biomedical Ontologies Center (SABOC) team has defined the partial-area taxonomy, a hierarchical abstraction network consisting of units called partial-areas. Each partial-area comprises a set of SNOMED concepts exhibiting a particular relationship structure and being distinguished by a unique root concept. In this dissertation, some extensions and applications of the taxonomy framework are considered. Some concepts appearing in multiple partial-areas have been designated as complex due to the fact that they constitute a tangled portion of a hierarchy and can be obstacles to users trying to gain an understanding of the hierarchy’s content. A methodology for partitioning the entire collection of these so-called overlapping complex concepts into singly-rooted groups was presented. A novel auditing methodology based on an enhanced abstraction network is described. In addition, the existing abstraction network relies heavily on the structure of the outgoing relationships of the concepts. But some of SNOMED hierarchies (or subhierarchies) serve only as targets of relationships, with few or no outgoing relationships of their own. This situation impedes the applicability of the abstraction network. To deal with this problem, a variation of the above abstraction network, called the converse abstraction network (CAN) is defined and derived automatically from a given SNOMED hierarchy. An auditing methodology based on the CAN is formulated. Furthermore, a preliminary study of the complementary use of the abstraction network in description logic (DL) for quality assurance purposes pertaining to SNOMED is presented. Two complexity measures, a structural complexity measure and a hierarchical complexity measure, based on the abstraction network are introduced to quantify the complexity of a SNOMED hierarchy. An extension of the two measures is also utilized specifically to track the complexity of the versions of the SNOMED hierarchies before and after a sequence of auditing processes

    Using structural and semantic methodologies to enhance biomedical terminologies

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    Biomedical terminologies and ontologies underlie various Health Information Systems (HISs), Electronic Health Record (EHR) Systems, Health Information Exchanges (HIEs) and health administrative systems. Moreover, the proliferation of interdisciplinary research efforts in the biomedical field is fueling the need to overcome terminological barriers when integrating knowledge from different fields into a unified research project. Therefore well-developed and well-maintained terminologies are in high demand. Most of the biomedical terminologies are large and complex, which makes it impossible for human experts to manually detect and correct all errors and inconsistencies. Automated and semi-automated Quality Assurance methodologies that focus on areas that are more likely to contain errors and inconsistencies are therefore important. In this dissertation, structural and semantic methodologies are used to enhance biomedical terminologies. The dissertation work is divided into three major parts. The first part consists of structural auditing techniques for the Semantic Network of the Unified Medical Language System (UMLS), which serves as a vocabulary knowledge base for biomedical research in various applications. Research techniques are presented on how to automatically identify and prevent erroneous semantic type assignments to concepts. The Web-based adviseEditor system is introduced to help UMLS editors to make correct multiple semantic type assignments to concepts. It is made available to the National Library of Medicine for future use in maintaining the UMLS. The second part of this dissertation is on how to enhance the conceptual content of SNOMED CT by methods of semantic harmonization. By 2015, SNOMED will become the standard terminology for EH R encoding of diagnoses and problem lists. In order to enrich the semantics and coverage of SNOMED CT for clinical and research applications, the problem of semantic harmonization between SNOMED CT and six reference terminologies is approached by 1) comparing the vertical density of SNOM ED CT with the reference terminologies to find potential concepts for export and import; and 2) categorizing the relationships between structurally congruent concepts from pairs of terminologies, with SNOMED CT being one terminology in the pair. Six kinds of configurations are observed, e.g., alternative classifications, and suggested synonyms. For each configuration, a corresponding solution is presented for enhancing one or both of the terminologies. The third part applies Quality Assurance techniques based on “Abstraction Networks” to biomedical ontologies in BioPortal. The National Center for Biomedical Ontology provides B ioPortal as a repository of over 350 biomedical ontologies covering a wide range of domains. It is extremely difficult to design a new Quality Assurance methodology for each ontology in BioPortal. Fortunately, groups of ontologies in BioPortal share common structural features. Thus, they can be grouped into families based on combinations of these features. A uniform Quality Assurance methodology design for each family will achieve improved efficiency, which is critical with the limited Quality Assurance resources available to most ontology curators. In this dissertation, a family-based framework covering 186 BioPortal ontologies and accompanying Quality Assurance methods based on abstraction networks are presented to tackle this problem

    Regional Integration: Physician Perceptions on Electronic Medical Record Use and Impact in South West Ontario

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    Regional initiatives in the health care context in Canada are typically organized and administered along geographic boundaries or operational units. Regional integration of Electronic Medical Records (EMR) has been continuing across Canadian provinces in recent years, yet the use and impact of regionally integrated EMRs are not routinely assessed and questions remain about their impact on and use in physicians’ practices. Are stated goals of simplifying connections and sharing of electronic health information collected and managed by many health services providers being met? What are physicians’ perspectives on the use and impact of regionally integrated EMR? In this thesis, I examined how primary health care and family physicians use electronic medical records and associated electronic health information resources in South West Ontario, the challenges they face in doing so, as well as the impact of an integrated EMR. A mixed methods-grounded theory research approach was employed to explore physician EMR use, and data acquired using participant consultation, observership and shadowing, semi-structured interviews, and a self-administered questionnaire. The study revealed that there are clear and present challenges to regional integration of EMR. Although regional integration initiatives such as implementation of ClinicalConnect, a regional EMR clinical viewer, continue to expand, physicians face challenges related to implementation, support and advanced use of electronic records. Not every patient has data access, patient portals are often not fully integrated, and the impact of EMR transitioning can reshape a primary care physician practice. A comprehensive model of physician integrated EMR use and a six-stage maturity model were developed from this study: The comprehensive model conceptualizes how the experience of EMR transitioning, managing patient expectation, meeting information needs, engaging regional entities, support and practice context, influence physician perception of EMR integration, and often resulted in practice changing moments. It further describes influences on physician perception of EMR use by EMR offering, EMR content, integration tools, information attributes, practice type, and patient and physician characteristics. The six-stage maturity model provides a framework that describes key elements of operative EMR use within the context of regional integration of electronic health information resources. It enhances understanding of EMR maturity by shifting orientation from theoretical evolutionary improvement path, which characterized prior maturity models, to assessment of EMR maturity based on how practicing physicians actually use EMR in primary health care. Insights from this study will advance understanding of regional integration of electronic medical records and serve as additional resource for individuals interested in assessment of the use and impact of electronic health information resources in primary health care
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