Exploring the nature, drivers and consequences of electronic medical record workarounds in Tanzanian public primary health care
Joseph Makaranga et al.
What the paper says
Purpose This study aims to explore the nature, drivers and consequences of electronic medical record (EMR) workarounds in Tanzanian public primary health-care facilities. Design/methodology/approach Drawing on workaround theory, the authors conducted 41 interviews with health-care providers in selected public primary health-care facilities. Convenience sampling guided site selection, while purposive sampling identified participants. Thematic analysis was applied to uncover patterns in the data. Findings This analysis discovered six recurrent EMR workaround cases, driven by a combination of technical, organisational and human factors spanning restrictive EMR design and unreliable infrastructure, insufficient technical support and heavy patient loads, as well as varied user digital literacy. This combination of factors compels users to adopt workarounds, which in turn lead to significant consequences: compromised data integrity, disrupted clinical continuity, operational inefficiencies from redundant manual processes, increased financial and regulatory risks and, ultimately, diminished user trust in the EMR system, which reduces morale and digital engagement. Originality/value While workaround behaviours have increasingly been studied, limited research has focused on Tanzania, particularly in primary health care. To the best of the authors’ knowledge, this is the first study to examine EMR workarounds in this setting. The study findings address a critical knowledge gap and provide insights for strengthening EMR design, user training and workflow alignment to improve digital health implementation in Tanzania’s public primary health care.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.