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Data Profile: Regional Healthcare Information Platform in Halland, Sweden
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0001-5688-0156
Research and Development, Region Halland, Sweden.
Economic Department, Region Halland, Sweden.
Halland Hospital, Region Halland, Sweden.
Show others and affiliations
2019 (English)In: International Journal of Epidemiology, ISSN 0300-5771, E-ISSN 1464-3685Article in journal (Refereed) Submitted
Abstract [en]

Accurate and comprehensive healthcare data coupled with modern analytical tools can play a vital role in enabling care providers to make better-informed decisions, leading to effective and cost-efficient care delivery. This paper describes a novel strategic healthcare analysis and research platform that encapsulates 360-degree pseudo-anonymized data covering clinical, operational capacity and financial data on over 500,000 patients treated since 2009 across all care delivery units in the county of Halland, Sweden. The over-arching goal is to develop a comprehensive healthcare data infrastructure that captures complete care processes at individual, organizational and population levels. These longitudinal linked healthcare data are a valuable tool for research in a broad range of areas including health economy and process development using real world evidence.

Key messages

Structured and standardized variables have been linked from different regional healthcare sources into a research information platform including all healthcare visits in the county of Halland in Sweden, from 2009 to date.

Since 2015, the regional information platform integrates a cost component to each healthcare visit: thus being able to quantify patient level value, safety and cost efficiency across the continuum of care.

Place, publisher, year, edition, pages
Oxford: Oxford University Press, 2019.
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
URN: urn:nbn:se:hh:diva-39308OAI: oai:DiVA.org:hh-39308DiVA, id: diva2:1313026
Available from: 2019-05-02 Created: 2019-05-02 Last updated: 2019-05-03
In thesis
1. Predicting clinical outcomes via machine learning on electronic health records
Open this publication in new window or tab >>Predicting clinical outcomes via machine learning on electronic health records
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The rising complexity in healthcare, exacerbated by an ageing population, results in ineffective decision-making leading to detrimental effects on care quality and escalates care costs. Consequently, there is a need for smart decision support systems that can empower clinician's to make better informed care decisions. Decisions, which are not only based on general clinical knowledge and personal experience, but also rest on personalised and precise insights about future patient outcomes. A promising approach is to leverage the ongoing digitization of healthcare that generates unprecedented amounts of clinical data stored in Electronic Health Records (EHRs) and couple it with modern Machine Learning (ML) toolset for clinical decision support, and simultaneously, expand the evidence base of medicine. As promising as it sounds, assimilating complete clinical data that provides a rich perspective of the patient's health state comes with a multitude of data-science challenges that impede efficient learning of ML models. This thesis primarily focuses on learning comprehensive patient representations from EHRs. The key challenges of heterogeneity and temporality in EHR data are addressed using human-derived features appended to contextual embeddings of clinical concepts and Long-Short-Term-Memory networks, respectively. The developed models are empirically evaluated in the context of predicting adverse clinical outcomes such as mortality or hospital readmissions. We also present evidence that, surprisingly, different ML models primarily designed for non-EHR analysis (like language processing and time-series prediction) can be combined and adapted into a single framework to efficiently represent EHR data and predict patient outcomes.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2019
Series
Halmstad University Dissertations ; 58
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:hh:diva-39309 (URN)978-91-88749-24-6 (ISBN)978-91-88749-25-3 (ISBN)
Presentation
2019-05-23, R4318, R Building, Halmstad University, Halmstad, Sweden, 13:00 (English)
Opponent
Supervisors
Available from: 2019-05-06 Created: 2019-05-02 Last updated: 2019-05-06Bibliographically approved

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Citation style
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