Organizing Telemonitoring—Decision-Making Between Centralized and Distributed Models in the Netherlands, Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) Framework: Case Study
Bibliographic record
Abstract
Background: Telemonitoring can be implemented using either centralized or distributed organizational models. However, few published studies explore which conditions make one model preferable over the other, or how to choose between these two. Objective: This study aimed to investigate the decision-making factors across several domains (eg, technological, personal, and organizational) when selecting the telemonitoring model. Methods: We conducted a multiple case study across 4 purposively sampled hospitals to gain a range of perspectives on organizational models for telemonitoring. Selection criteria included: (1) type of organizational model, (2) type of collaborating partners, (3) task division of handling notifications, and (4) it had to be implemented at scale, rather than being in an exploratory phase. Data was collected in a document study, 13 semistructured interviews, and a focus group. The topic list was based on the domains of the NASSS (non-adoption, abandonment, scale-up, spread, and sustainability) framework. Interviewees (n=13) were 5 project leaders, 2 tele-nurses, 4 health care professionals, and 2 clinical informaticians. Data analysis was performed iteratively and included reflective thematic analysis. A member-checking focus group was organized to verify and reflect on the findings. Results: Various preferential factors based on the seven domains of the NASSS framework were explored for both centralized and distributed telemonitoring models: (1) Condition: the choice of objective, usually based on organizational strategy, determines whether telemonitoring will be centralized or distributed. (2) Technology: the preference for a model is determined by the anticipated number of notifications the application generates for a specific patient group. (3) Value proposition: the perceived cost-effectiveness and overall value to the patient shape the value proposition for each model. (4) Adopters: the new role of tele-nurse emerged in centralized monitoring centers (CMCs), necessitating new competencies, task redistribution, and shifts in responsibility. The importance of trust among staff became evident in the context of task redistribution. (5) Organization: CMCs are typically organized regionally, in partnerships or network arrangements, which can be time-consuming yet offer significant potential for impact. (6) Wider system: The existing Dutch reimbursement system does not incentivize CMCs because the payment structure is still based on a per-treatment model. (7) Adaptation over time: with advancements in technology, including artificial intelligence, organizing telemonitoring through CMCs is likely to gain popularity. Conclusions: Our study highlights that when decision makers are choosing which telemonitoring model-centralized or distributed-to implement in their organization, deciding on the suitability of the model depends on multiple contextual factors. Our findings illustrate that decisions made for patient group selection, technology design, and value proposition significantly influence each other. It is therefore crucial for decision makers to understand these interactions and corresponding dynamics to better align their strategies with the operational realities of their organization.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".