Table 4.

Recommendations regarding the feasibility of data extraction from EMR for secondary uses

Countries	Feasible to extract data from EMR?	Factors influencing recommendation
		EMR adoption	Quality of data	Implementation trends and incentives	Information governance procedures	Other
Italy	More feasible, optimal regions might include Abruzzo, Piemonte, Lazio, Lombardia and Trento.	High adoption, particularly in general physician clinics.	High fill rates. Already good linkage between EMR systems in general physician practices and hospitals.	Funding incentives.	Clear process. Could take a long time.	Existing research using EMR extracted data.
Saudi Arabia	More feasible, data from public sector.	High adoption in governmental facilities.	High fill rates. Comprehensive data available.	Increasing implementation. Future plans for unified EMR.	Clear process for public sector, but not for private sector. Could take a long time.	Health research oriented facilities exist.
Korea, Rep.	More feasible.	High adoption, particularly in general physician clinics and tertiary hospitals. Low fragmentation of providers in clinics, higher in hospitals.	High fill rates. Comprehensive data available. Consistency of EMR data.	Increasing implementation. Funding incentives.	Clear process. Moderately quick.	Existing research using EMR extracted data including diabetes research.
Taiwan	More feasible, optimal setting may be larger cities or institutions.	High adoption nationwide.	High fill rates. Comprehensive data available.	Increasing implementation. Funding incentives.	Clear process. Variable time.	Existing research using EMR extracted data.
UAE	More feasible, optimal setting in might include Health authority Abu Dhabi (HAAD) affiliated healthcare facilities (SEHA).	High adoption in general physician clinics and hospitals.	High fill rates. Comprehensive data available.	Increasing implementation. Different incentives in the public sector.	Clear process in SEHA facility. Moderately quick.
Brazil	Less feasible	Overall low adoption, centered in a few hospitals and clinics. High fragmentation of providers.	Inconsistency of EMR data between sites.	Slowly increasing implementation. Government initiatives are poor and just beginning.	Clear process. Could take a long time.	Public systems are very difficult to access for research; clinic by clinic basis in the private sector.
South Africa	Less feasible, but when done an optimal setting may be major tertiary institutions in the Western Cape region or directly with the Ministry of Health.	Overall low adoption, higher adoption in private general physician clinics.	Available data are likely to be of modest quality and quantity.	Rapid increase. Attempts for interoperability.	No clear process. Takes a long time.	The use of EMR extracted data is very difficult.