Protecting the privacy of individual general practice patient electronic records for geospatial epidemiology research
| Autor: | Nasser Bagheri, Ian McRae, Michael Hewett, Peter Del Fante, Soumya Mazumdar, Paul Konings |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Clinical audit
Male de‐identified data Patient Identification Systems Diabetes risk Geospatial analysis General Practice Audit Population health computer.software_genre privacy Unit (housing) Public health surveillance Medicine Electronic Health Records Humans Confidentiality geospatial business.industry lcsh:Public aspects of medicine Public Health Environmental and Occupational Health Australia lcsh:RA1-1270 Middle Aged Data science Geographical Information Systems (GIS) Geographic Information Systems Female business general practice (GP) data collection computer |
| Zdroj: | Australian and New Zealand Journal of Public Health, Vol 38, Iss 6, Pp 548-552 (2014) |
| ISSN: | 1326-0200 1753-6405 |
| Popis: | Background: General practitioner (GP) practices in Australia are increasingly storing patient information in electronic databases. These practice databases can be accessed by clinical audit software to generate reports that inform clinical or population health decision making and public health surveillance. Many audit software applications also have the capacity to generate de-identified patient unit record data. However, the de-identified nature of the extracted data means that these records often lack geographic information. Without spatial references, it is impossible to build maps reflecting the spatial distribution of patients with particular conditions and needs. Links to socioeconomic, demographic, environmental or other geographically based information are also not possible. In some cases, relatively coarse geographies such as postcode are available, but these are of limited use and researchers cannot undertake precision spatial analyses such as calculating travel times. Methods: We describe a method that allows researchers to implement meaningful mapping and spatial epidemiological analyses of practice level patient data while preserving privacy. Results: This solution has been piloted in a diabetes risk research project in the patient population of a practice in Adelaide. Conclusions and Implications: The method offers researchers a powerful means of analysing geographic clinic data in a privacy-protected manner. |
| Databáze: | OpenAIRE |
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