Geospatial data in health and welfare research -seminar in Helsinki 23rd October 2018.

1. 25.10.2018 1
Using electronic health records
in studying spatial disparities and quality of care
among chronic patients in Siun Sote region
Maija Toivakka​, maija.toivakka@uef.fi
Teppo Repo, teppo.repo@uef.fi
Department of Geographical and Historical Studies,
University of Eastern Finland
Seminar on geospatial data in health and welfare research 23.10.2018 1

2. Content
1. Electronic health records in health research
• General information
2. Electronic health records in geospatial health research
• Background
• Pros and cons & experiences from Siun Sote
• Screening of type 2 diabetes has improved in the region
3. Implementing research experiences in operational use
• Interface between research and operational use
• Possibilities and challenges
4. Conlcusions
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3. 1. Electronic health records in health research
General information
• The use of electronic health records (EHRs) have increased in the past decade.
• EHR: a software platform that contains individual-level patient-provider data captured
during health care encounters.
• Partly patient transactions are routinely collected to health records and partly health
professionals are responsible for recording them.
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4. 2. Electronic health records in geospatial health research
Background
• Siun Sote utilizes regional electronic patient register including primary health care and
specialized care – the only regional jointly used register in Finland at the moment.
• In Finland, Current Care Guidelines form the basis of the treatment and management of
diseases and risk factors in health care.
→ The information on treatment outcomes and equity in service use is scarce.
→ It is poorly known how well the clinical guidelines are implemented in practice in different
geographical contexts and what are the real outcomes of care.
• Patient data from regional electronic patient database
- Age, gender, date of birth, address information, laboratory and measurement results, visits in
primary care and special care, diagnosis of diseases, prescriptions
- Geocoded locations of the patients → EHR data becomes spatial data
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5. 2. Electronic health records in geospatial health research
Pros and cons & experiences from Siun Sote
• Pros
- Cost-effective data acquisition
- Large “real-world” datasets
- Data is available in near-real time
- Can be easily linked to other data when geocoded
- Comparability to international studies (ICD-10
codes)
• Cons
- Data is not designed for research purposes
- Data quality issues
- Privacy protection
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• Large comprehensive patient groups but includes only
patients who have been diagnosed with the disease or have
been treated in the primary/specialized care.
• EHR data becomes spatial data
- Information on the place of domicile.
- Information on current place of domicile only.
- Demographic differences in the region makes
interpretation difficult.
• Enables to use lot of data, but not everything is possible
- EHRs have no personal information on diet or physical activity
- Duration of the disease not available
• Missing data
• Quality of records
• What is a relevant scale for analysing and reporting sensitive EHR data?
• Strict regulations in using and combining other sensitive data (e.g. Statistics
Finland).

6. 2. Electronic health records in geospatial health research
Screening of type 2 diabetes has improved in the region.
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7. 3. Implementing research experiences in operational use
Interface between research and operational use
1. Useful spatial information about the current situation of health outcomes and disease
burden comes as a "side product" of academic research.
2. Manually going through patient register data for reports is labor intensive and lacks
continuity.
- Some of the information could be refined automatically and integrated in electronic
heath records (EHR) reporting systems and visualized in a web map services or equivalent.
3. Research should focus also in issues related to operational use and distribution of
geospatial health information.
What further research is needed?
- What kind of (spatial) analysis of (chronic diseases) is meaningful in operational
perspective?
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8. 3. Implementing research experiences in operational use
Possibilities and challenges
1. Choosing the health information to be presented in geospatial/map format
- What subjects and spatial scale are relevant for each stakeholder? (decision makers/health
professionals/patients)
2. How personal devices, mobile applications and GEO tracker data could be used, for
example, in T2D/CHD prevention programs and screening?
- New ways to fill the gaps of health-related behavior information in EHR?
3. Is advanced spatial analysis needed in health care planning?
- Location-cost links and spatial econometrics?
- GIS tools developed for other disciplines (e.g. in forestry) could be applied in health care analytics.
• What kind of GIS system or geospatial data is needed in social and welfare sector in
Finland?
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9. 4. Conclusions
• Spoiled for choice
- EHRs provide countless opportunities for spatial health research
- Can be easily linked to other geospatial data
• Quality of the recordings is improving
- Less missing data, better comparability locally, regionally, nationally and globally
• Spatial visualizations and spatial analysis could probably be utilized more in
operational use
- Geospatial features should be integrated in EHR reporting systems and data processing
automated
• New ways to fill the gaps of EHR data should be tested
- Supplementary data sources: MyData, individuals privately collected data about health behavior
and locations from mobile apps and (health monitoring) devices, customer satisfaction data
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10. Thank you!
Comments, questions?
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11. References
• Casey, J. A., B. S. Schwartz, W. E. Stewart & N. E. Adler (2016). Using electronic
health records for population health research: A review of methods and applications.
Annual Review of Public Health 37, 61–81.
• Farmer, R., R. Mathur, K. Bhaskaran, S.V. Eastwood, N. Chaturvedi & L. Smeeth
(2018). Promises and pitfalls of electronic health record analysis. Diabetologia 61: 6,
1241–1248.
• Anselin L. Thirty years of spatial econometrics. Papers in Regional Science.
2010;89(1):3-25. doi:10.1111/j.1435-5957.2010.00279.x.
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