Physician Communication via Information and Communication Technology: Understanding its Role in Health System Performance
Bibliographic record
Abstract
Information and communication technology (ICT) can enhance communication among health care providers which may lead to various health system improvements, such as gains in efficiency. However, there is inadequate evidence regarding the extent to which ICT improves communication among specific groups of health care professionals. This dissertation incorporates a mix of methodological approaches across three interrelated research studies to address this gap. Study 1 consists of a systematic review which suggested that ICT can play an important role in enhancing health care related communication among physicians, but the extent of that benefit is influenced by: 1) the impact of ICT on existing work practices; 2) the availability of adequate resources for ICT implementation and use; and 3) the nature of institutional elements, such as privacy legislation. Study 2 consists of a document analysis that examined guidelines for health information protection when using ICT from the provincial regulatory colleges for physicians. These documents were notable for the considerable variation in the scope and detail of guidance provided, which may result in unequal and inequitable protection of health information across the provinces. Study 3 is a case study that examined the use of a relatively novel form of ICT, smartphones, for communication among postgraduate medical trainees (medical residents). Efficiency and convenience were identified as the main reasons that medical residents use smartphones to communicate health care related information with colleagues. In addition, by applying a neo-institutional perspective, it became clear that medical residents base their smartphone use primarily on normative elements (professional norms; what peers/staff are doing) and cultural-cognitive elements (beliefs/perceptions regarding facilitation of task completion). Regulative elements (guidelines/policies) around smartphone use play a smaller role in shaping behaviour, particularly when they: 1) lack clarity; 2) are not seen as credible/legitimate; or 3) are viewed as cumbersome and do not align with workflow needs. Taken together, these studies provide timely insights regarding the use of ICT by physicians, which can be drawn upon by a variety of decision-makers as efforts to improve health system performance continue.
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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 | 0.020 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".