Table 4 Recommendations regarding the feasibility of data extraction from EMR for secondary uses
Countries | Feasible to extract data from EMR? | Factors influencing recommendation | ||||
|---|---|---|---|---|---|---|
| Â | 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. | Â |
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. |