Systematic review of electronic health records to manage chronic conditions among displaced populations
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to assess the impact of electronic health records (EHRs) on health outcomes and care of displaced people with chronic health conditions and determine barriers and facilitators to EHR implementation in displaced populations. DESIGN: A systematic review protocol was developed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Systematic Reviews. DATA SOURCES: MEDLINE, Embase, PsycINFO, CINAHL, Health Technology Assessment, Epub Ahead of Print, In-Process and Other Non-Indexed Citations, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews was searched from inception to 12 April 2021. ELIGIBILITY CRITERIA FOR SELECTED STUDIES: Inclusion criteria were original research articles, case reports and descriptions of EHR implementation in populations of displaced people, refugees or asylum seekers with related chronic diseases. Grey literature, reviews and research articles unrelated to chronic diseases or the care of refugees or asylum populations were excluded. Studies were assessed for risk of bias using a modified Cochrane, Newcastle-Ottawa and Joanna Briggs Institute tools. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently extracted data from each study using Covidence. Due to heterogeneity across study design and specific outcomes, a meta-analysis was not possible. An inductive thematic analysis was conducted using NVivo V.12 (QSR International, Melbourne, Australia). An inductive analysis was used in order to uncover patterns and themes in the experiences, general outcomes and perceptions of EHR implementation. RESULTS: A total of 32 studies across nine countries were included: 14 in refugee camps/settlements and 18 in asylum countries. Our analysis suggested that EHRs improve health outcomes for chronic diseases by increasing provider adherence to guidelines or treatment algorithms, monitoring of disease indicators, patient counselling and patient adherence. In asylum countries, EHRs resource allocation to direct clinical care and public health services, as well as screening efforts. EHR implementation was facilitated by their adaptability and ability to integrate into management systems. However, barriers to EHR development, deployment and data analysis were identified in refugee settings. CONCLUSION: Our results suggest that well-designed and integrated EHRs can be a powerful tool to improve healthcare systems and chronic disease outcomes in refugee settings. However, attention should be paid to the common barriers and facilitating actions that we have identified such as utilising a user-centred design. By implementing adaptable EHR solutions, health systems can be strengthened, providers better supported and the health of refugees improved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".