A Framework for Evaluating Vendor Procurement in a Digital Health Project
Bibliographic record
Abstract
The eHealth Centre of Excellence, a Waterloo, Ontario-based organization that advances and promotes digital health initiatives in clinical care, developed and assessed an innovative evaluation procurement framework. The purpose of the framework was to assess and support long-term vendor-organization procurement partnerships to develop, improve and expand electronic referral (eReferral) solutions. The framework focused on six criteria: the quality of the eReferral solution, its implementation, the service provided, the extent of training and knowledge transfer, the quality of the vendor's team and the vendor's project experience. These domains were further defined by components and key performance indicators unique to the eReferral solution to accommodate the stakeholders' specified needs as well as change management challenges to create value for users and organizations in long-term relationships. The evaluation used both qualitative and quantitative methodologies. The framework used data from three sources: (1) the System Coordinated Access program and vendor team experience surveys that focused on the six criteria mentioned earlier; (2) key stakeholder interviews that focused on system quality, user satisfaction and perception of net benefits; and (3) a vendor scorecard that focused on deliverables and efficiencies. Vendor procurement should be viewed not as a process that ends when a vendor is selected but rather as a continuing and evolving relationship. Evaluation should assess the ability and willingness of vendors to support stakeholders and meet their needs, stimulate new ideas and adapt to changing environments and expanding systems. The model enabled recording of factors necessary for successful outcomes and provided a strategy to help select vendors for successful long-term partnerships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.196 | 0.121 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.006 | 0.004 |
| 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".