Revisiting Project Definition/Initiation for Telemedicine Services
Bibliographic record
Abstract
Healthcare organizations and stakeholders are profoundly challenged in transiting a telemedicine project into a sustainable telehealth service line. While project management best practices have added values across multiple domains, a knowledge gap exists on informed execution of telehealth best practices. Project definition, or initiation, sets the strategic vision (and plan) for a project. It is the predominant stage in a project. As project initiation hugely defines project success, revisiting this stage for telemedicine may help to inform key actors on ways to achieve an optimal delivery of such services. Indeed, winning telehealth services require well-knitted intra- and inter-organizational collaboration on technology adoption across different organizational arrangements and among key stakeholders. Hence, a model redefining key project initiation components is used to drive our analysis. Drawing from collected data of a multisite telestroke implementation and anchoring on the model's conceptualization, the authors explore in-depth how project initiation can be strategically framed within the telemedicine context. The interpretative findings from the data analysis, with each case surmising a distinct telemedicine business model, provide further insights on the collaborative uptake of telestroke programs. More specifically, the authors extend the analysis through comparative examination of key factors that promote or impede adoption via the lens of five distinct telecare business models: (1) the outsourced model; (2) the alliance model; (3) the not-for-profit private hospital network model; (4) the not-for-profit university sponsored network model; and (5) the for-profit private hospital network model. Together, the insights provided by this contribution will help efforts directed towards contextualizing key elements of project initiation in telemedicine and highlight the alignments of critical factors that can impact future telehealth efforts.
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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 | 0.156 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".