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The value of the Skåne Health-care Register: Prospectively collected individual-level data for population-based studies.

Sofia LöfvendahlMaria E C SchelinAnna Jöud
Published in: Scandinavian journal of public health (2019)
Aims: This study aimed to examine the population-based Skåne Health-care Register (SHR) regarding feasibility for scientific research and also strengths and weaknesses. Methods: To analyse the feasibility of the SHR, we performed a bibliographic search for peer-reviewed articles based on SHR data from 2000 to 2018. To analyse strengths and weaknesses, we used original SHR data about coverage and validity. Results: We identified 58 articles based on SHR data, covering different study designs and disorders. Most studies focused on musculoskeletal disorders with a cohort design. The majority of all consultations recorded in the SHR have an assigned diagnosis. However, this differs between the levels of care and between types of consultation. For inpatient care, the proportion of consultations with an assigned diagnosis was close to 100% between 1998 and 2017. The proportion of consultations with an assigned diagnosis was lowest within primary care, although the proportion markedly increased in 2004 when the prerequisite for consultation reimbursement was linked to the requirement for an assigned diagnosis. Limitations are that the SHR does not cover health-care provided within nursing homes and equivalent facilities or treatments received by the population of Skåne outside the region. Conclusions: The SHR may be used as a reliable data source for analyses of clinical changes and improvements. Extended use of the SHR in a research context may highlight important shortcomings within the register and thus serve as a way of indirect quality control. To enhance the use of the SHR further, better harmonisation between registers, within and outside of the region and internationally, is of crucial importance.
Keyphrases
  • healthcare
  • palliative care
  • electronic health record
  • primary care
  • big data
  • affordable care act
  • quality control
  • general practice
  • machine learning
  • data analysis
  • social media
  • chronic pain
  • health information
  • acute care