A FRAMEWORK FOR A CLOUD-BASED ELECTRONIC HEALTH RECORDS SYSTEM FOR NIGERIA

  In most countries of the developed world, one of the integral components of Health Information System (HIS) is Electronic Health Records (EHR). With advances in Information and Communications Technology (ICT) and the rise in the adoption of cloud computing approaches in the health sector of these countries by a substantial number of health institutions, cloud servers are now remote repository of EHRs. However, in Nigeria and many other developing countries, health information of patients is still predominantly paper-based medical records. This manual method is not scalable in terms of storage, prone to error, insecure, susceptible to damage and degradation over time, highly unavailable, time consuming in accessing and with no visible audit trail and version history to mention but a few. In this paper, a framework for a cloud-based electronic health records system that is capable of storage, retrieval and updating of patients’ medical records for Nigeria is proposed. The framework provides for various medical stakeholders in a health institution and patients to access the EHR system via a web portal by using a variety of devices in the contextual scenario whereby the health institution is migrating from paper-based patient record documentation to an EHR system

Success and failure in eHealth

Introduction In the field of eHealth, there seems to be a gap between promising research and clinical reality. This master thesis aims to give insight in patterns that can be found regarding the possible outcome in terms of success and/or failure. An in-depth review of workflow will be done, to get an understanding of the implications of eHealth on workflow. Methods Using a systematic article search, papers have been collected regarding the subject of this thesis. Through multiple search strategies, one final search string has been formulated. This final search string led to 903 papers. These papers have been assessed on relevance using qualitative methods. This resulted in 258 papers, which have been categorised by topic, entity and success or failure. After categorisation, the topic of workflow has been selected for an additional in-depth full-text review. Results The categorisation led to 27 categories. The categories are separated among the following entities: patient, health professional, health system and all. The first three have been separated in terms of success and failure as well. This led to a quantitative overview of different categories, for different actors in terms of success and failure. Workflow appeared to be essential for the possible success or failure of eHealth implementations. It is important to include workflow in the design of the tool as well. Conclusion Different categories show a unique combination in success and failure, and to what entity they belong. The category costs appeared to be mostly based on the health system and is attributed to failure. Therefore it is a pre-requisite for the implementation of eHealth. Other categories like quality healthcare and user expectations seem to target on success. The category legal was smaller than anticipated, which could have been caused by categories that are closely linked to each other

Analysis of the process of representing clinical statements for decision-support applications: a comparison of openEHR archetypes and HL7 virtual medical record

[EN] Delivering patient-specific decision-support based on computer-interpretable guidelines (CIGs) requires mapping CIG clinical statements (data items, clinical recommendations) into patients data. This is most effectively done via intermediate data schemas, which enable querying the data according to the semantics of a shared standard intermediate schema. This study aims to evaluate the use of HL7 virtual medical record (vMR) and openEHR archetypes as intermediate schemas for capturing clinical statements from CIGs that are mappable to electronic health records (EHRs) containing patient data and patient-specific recommendations. Using qualitative research methods, we analyzed the encoding of ten representative clinical statements taken from two CIGs used in real decision-support systems into two health information models (openEHR archetypes and HL7 vMR instances) by four experienced informaticians. Discussion among the modelers about each case study example greatly increased our understanding of the capabilities of these standards, which we share in this educational paper. Differing in content and structure, the openEHR archetypes were found to contain a greater level of representational detail and structure while the vMR representations took fewer steps to complete. The use of openEHR in the encoding of CIG clinical statements could potentially facilitate applications other than decision-support, including intelligent data analysis and integration of additional properties of data items from existing EHRs. On the other hand, due to their smaller size and fewer details, the use of vMR potentially supports quicker mapping of EHR data into clinical statements.This study was partially funded by the European Commission 7th Framework Program, grant #287811. 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