1
Introduction
Medical registries are systematic collections of health-related data for specific patient populations, procedures, diseases or healthcare resources. Despite their critical importance in healthcare and research, the utility of these registries is often limited by poor clinician engagement; defined as the investment of clinicians in a registry’s mission, manifesting as planning, implementation, and continual input and evaluation. We systematically review the literature to identify factors affecting clinician engagement in medical registries.
2
Results
45 articles were identified for inclusion ( Supplementary Files 1 and 2 ). Consistent with the study’s focus, poor clinician engagement emerged as a significant barrier to optimal registry use across the reviewed literature. Among the included studies, a range of key contributory factors were identified ( Table 1 ). These include managerial, data management, technological, legal/regulatory, disease and patient factors.
| Category | Factor | n | Studies |
|---|---|---|---|
| Managerial | Insufficient funding | 25 | Behera, Johnson, Mandavia, Pop, Tyson, St-Louis, Bommakanti, Stey, Kates, O’Reilly, Nwomeh, Rosenkrantz, Sawe, Surodina, Stanimirovic, Mandavia, Gao, Rakhost, Ozoilo, Zullig, Morsy, Bentley, Helfrich, Schwamm, Cameron |
| Infrastructure | 8 | Johnson, Pop, Tyson, O’Reilly, Nwomeh, Surodina, Ozoilo, Schwamm | |
| Inadequate staff training programs | 14 | Berea, Pop, St-Louis, Bommakanti, Kates, Nwomeh, Sawe, Azadmanjir, Surodina, Egholm, Tilney, Zullig, Helfrich, Cameron | |
| Inadequate time for training | 7 | Johnson, Tilney, Mandavia, Eldh, Bentley, Helfrich, Cameron | |
| Lack of clinical workspace | 1 | Bentley | |
| Lack of managerial interest/ motivation | 12 | Mandavia, Rosenkrantz, Sawe, Surodina, Egholm, Korngut, Chin, Grove Krause, Morsy, Bentley, Sehgal, Gabbay | |
| Limited participation of key stakeholders | 8 | Mandavia, Pop, Rosenkrantz, Surodina, Stanimirovic, Gullig, Korngut, Grove Krause | |
| Data Management | Incomplete data sets | 26 | Behera, Mandavia, Tyson, St-Louis, Bommakanti, Kates, O’Reilly, Viviani, Zehtabchi, Rosenkrantz, Azadmanjir, Surodina, Mowafi, Egholm, Mandavia, Gao, Ozoilo, Gullig, Hume, Chin, Bernal-Gonzalez, Luong, Schwamm, Gabbay, Cameron, Roder |
| Poorly defined data collection methods | 21 | Johnson, Mandavia, St-Louis, Stay, Viviani, Zehtabchi, Nwomeh, Rosenkrantz, Azadmanjir, Surodina, Mowafi, Egholm, Ozoilo, Eldh, Zullig, Hume, Luong, Schwamm, Sehgal, Gabbay, Cameron | |
| Unstandardised data | 16 | Pop, St-Louis, O’Reilly, Viviani, Zehtabchi, Nwomeh, Egholm, Stanimirovic, Mandavia, Rakhorst, Bernal-Gonzalez, Grove Krause, Schwamm, Sehgal, Cameron, Roder | |
| Technological | Poor interoperability with existing IT systems | 11 | Behera, Lu, Nwomeh, Sawe, Surodina, Egholm, Stanimirovic, Gao, Chin, Grove Krause, Cameron |
| Inappropriate software | 7 | Lu, Surodina, Egholm, Korngut, Chin, Morsy, Luong | |
| Updates / maintenance | 8 | Johnson, Pop, Tyson, O’Reilly, Nwomeh, Surodina, Ozoilo, Schwamm | |
| Legal/ Regulatory | Confidentiality | 14 | Pop, St-Louis, Viviani, Lu, Surodina, Stanimirovic, Mandavia, Rakhorst, Andrew, Korngut, Chin, Luong, Bentley, Cameron |
| Lack of operational guidelines | 14 | Pop, Tyson, St-Louis, Bommakanti, Kates, O’Reilly, Lu, Nwomeh, Surodina, Mowafi, Stanimirovic, McBride, Morsy, Schwamm | |
| Disease | Diversity of disease course | 3 | Nwomeh, Andrew, Bernal-Gonzalez |
| Limited access to diagnostic tools | 3 | Bommakanti, Kates, Luong | |
| Patient | Administration of enrolment | 9 | Tilney, Korngut, Bentley, Nwomeh, Zullig, Johnson, Lu, Mowafi, Kates |
| Burden of follow up | 1 | Korngut |
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