Safety in Teletriage by Nurses and Physicians in the United States and Israel: Narrative Review and Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The safety of telemedicine in general and telephone triage (teletriage) safety in particular have been a focus of concern since the 1970s. Today, telehealth, now subsuming teletriage, has a basic structure and process intended to promote safety. However, inadequate telehealth systems may also compromise patient safety. The COVID-19 pandemic accelerated rapid but uneven telehealth growth, both technologically and professionally. Within 5-10 years, the field will likely be more technologically advanced; however, these advances may still outpace professional standards. The need for an evidence-based system is crucial and urgent. OBJECTIVE: Our aim was to explore ways that developed teletriage systems produce safe outcomes by examining key system components and questioning long-held assumptions. METHODS: We examined safety by performing a narrative review of the literature using key terms concerning patient safety in teletriage. In addition, we conducted system analysis of 2 typical formal systems, physician led and nurse led, in Israel and the United States, respectively, and evaluated those systems' respective approaches to safety. Additionally, we conducted in-depth interviews with representative physicians and 1 nurse using a qualitative approach. RESULTS: The review of literature indicated that research on various aspects of telehealth and teletriage safety is still sparse and of variable quality, producing conflicting and inconsistent results. Researchers, possibly unfamiliar with this complicated field, use an array of poorly defined terms and appear to design studies based on unfounded assumptions. The interviews with health care professionals demonstrated several challenges encountered during teletriage, mainly making diagnosis from a distance, treating unfamiliar patients, a stressful atmosphere, working alone, and technological difficulties. However, they reported using several measures that help them make accurate diagnoses and reasonable decisions, thus keeping patient safety, such as using their expertise and intuition, using structured protocols, and considering nonmedical factors and patient preferences (shared decision-making). CONCLUSIONS: Remote encounters about acute, worrisome symptoms are time sensitive, requiring decision-making under conditions of uncertainty and urgency. Patient safety and safe professional practice are extremely important in the field of teletriage, which has a high potential for error. This underregulated subspecialty lacks adequate development and substantive research on system safety. Research may commingle terminology and widely different, ill-defined groups of decision makers with wide variation in decision-making skills, clinical training, experience, and job qualifications, thereby confounding results. The rapid pace of telehealth's technological growth creates urgency in identifying safe systems to guide developers and clinicians about needed improvements.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".