Key factors in the success of an electronic patient referral system in the family physician programme: what can we do for the future?
Bibliographic record
Abstract
Search, G -Funds CollectionBackground. the electronic referral system (e-Referral) is an initial change in the way health care is provided.there are many factors that prevent the spread of such technologies in developing countries.Objectives.Determining the key factors in the success of the electronic referral system in Iran.Material and methods.this qualitative study was conducted in two phases (semi-structured interview and expert panel).the research participants included 42 people for the interview and 6 local experts, who were selected via the purposive sampling method (stratified sampling) and had at least three years of work experience.Data was collected using in-depth semi-structured interviews which were continued until data saturation.next, the content analysis method was used to analyse the data.Validity and reliability of the data were determined based on the Guba and Lincoln including acceptability, transferability, reliability and verifiability.two professors, as qualitative research experts, also verified the credibility of the data through accurate and stepwise control of the research process.Finally, an expert panel meeting was conducted to refine and improve the categorisation of key factors.Results. the analysis of collected data resulted in the extraction of 6 main themes, 18 subthemes and 47 codes.the main themes included resource management, information technology management, rules and regulations, stakeholder satisfaction and advocacy, domestication and payment mode.subthemes included management of financial, human, physical and equipment resources, intelligence, security, information exchange speed, information integrity, data access, judicial and insurance laws, health service guidelines, organisational culture, community culture, performance-based payment, etc. Conclusions.this study offered rich documentation of the implementation of a successful e-Referral system, the availability of which in an information society is essential and will assist managers and policymakers in the successful implementation of the e-Referral system.
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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 | 0.025 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".