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Record W4411300921 · doi:10.2196/71279

Value-Based Framework for Evaluating Pre-Commercial Procurement: Case Study of Value-Based Key Performance Indicators

2025· article· en· W4411300921 on OpenAlex
Nils Ørjasæter, Kari Jorunn Kværner, Linn Nathalie Støme

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic Procurement and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintValue (mathematics)Key (lock)ProcurementComputer scienceBusinessStatisticsMathematicsMarketingOperating systemWorld Wide Web

Abstract

fetched live from OpenAlex

Background: The demographic shift toward older populations is placing increasing pressure on health care systems, and only 20% of patients with chronic issues in the industrial world's rural areas have guaranteed access to adequate health care services. This stresses the health care systems, emphasizing the need for innovative solutions. The Horizon 2020 Pre-Commercial Procurement (PCP) project, Crane, addresses these needs by facilitating the procurement of a digital self-management system for treating patients with chronic issues at home. Three rural European regions are participating in the project: Västerbotten (Sweden), Extremadura (Spain), and Agder (Norway). Objective: This study aims to explore and identify key design criteria and value-based key performance indicators (VB-KPIs) to support the development and evaluation of digital health care solutions for patients with chronic issues in rural areas within the Crane PCP process. Methods: A 3-iteration process was used to identify and prioritize the VB-KPIs in the Crane project. First, user needs were investigated based on stakeholder analyses in the participating rural regions. The early health technology assessment tool, Step Up, was used in 5 workshops (2 in Agder, 2 in Extremadura, and 1 in Västerbotten). Participants included patients and health care professionals. Second, post workshop, stakeholders were asked to comment on the summarized results, which were accordingly adjusted. Third, following the workshops, VB-KPIs were identified and prioritized, and discussions among representatives from the 3 buyer regions were conducted. Results: Thirty-five VB-KPIs across 5 domains were identified. User-related (9 VB-KPIs), employee-related (9 key performance indicators), clinical (4 VB-KPIs), organizational (6 VB-KPIs), and economic (8 VB-KPIs) outcomes from the workshops and the subsequent discussions emphasized regional differences in terms of user needs and priorities. While Agder (Norway) and Västerbotten (Sweden) emphasized privacy, digital trust, and physical interaction as important, Extremadura (Spain) prioritized negotiation and shared decision-making. Despite differences, shared values were identified, including empowerment, flexibility, preventative care, and improved quality of life. Conclusions: The identified and prioritized VB-KPIs are likely to provide a need-based foundation for the development and subsequent evaluation of the digital PCP, Crane, although regional socioeconomic and cultural differences may necessitate local adaptations.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.103
GPT teacher head0.455
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it