The impact of the use of health information and communication technology on health care delivery in Manitoba, Canada
Bibliographic record
Abstract
Background: Health Information and Communication Technology (HICT) has the potential to reduce patient wait time and improves patient satisfaction. The Long wait times for patients to receive medical services are a big issue in Canada. The Canadian government has invested in Information and Communication Technology (ICT) to shorten patient referral wait times for medical services. Little was known about the association between ICT investments and the quality of health care delivery, and particularly between the use of ICT and referral wait times in the Manitoba Health System (MHS). Methods: The purpose of this quantitative correlational study was to determine if a relationship existed between the use of HICT and the quality of health care delivery in the MHS. The quality of health care delivery was measured in terms of referral wait time, health information sharing effectiveness, physicians’ satisfaction, and patients’ satisfaction. Conclusion: Findings indicated the absence of a significant association between HICT use and referral wait times. Significant correlations were found to exist between (1) HICT use and health information sharing effectiveness, (2) HICT use and physician’s satisfaction, and (3) HICT use and patient’s satisfaction. Four recommendations emerged from this study: First, patient satisfaction should be used as an indicator of the quality of health care delivery. Second, health knowledge repository and expert systems should be integrated into health ICT systems to minimize unnecessary referrals. Third, a mixed health system should be implemented to shorten wait times. Fourth, the portability of the Canadian Medicare should be enhanced to allow Manitobans in particular and Canadians in general to seek medical services abroad. This study was intended to contribute to the existing body of knowledge associated with ICT investments’ outcomes and health care delivery in the MHS.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".