Understanding value in a healthcare setting: An application of the business model canvas
Bibliographic record
Abstract
The business model canvas is a popular tool used to develop value-driven business models. Specific emphasis is placed on understanding what customers value and providing users with steps on how to design and deliver value for their customers. In health care, creating and delivering value for patients is an often-discussed topic, with the provision of patient-centered care becoming a standard for many health care organizations. While patients play a key role in determining value, providers are the key to delivering value. Therefore, effective health care management relies on integrating multiple perspectives from key stakeholders. This process requires consideration of the key needs that must be addressed, the resources and capabilities necessary to meet these needs, and the interests and values specific to each set of stakeholders. The business model canvas lends itself well to health care service planning as it incorporates the factors described above into the business model’s conceptualization and subsequent realization. This article outlines how the business model canvas was applied to assess the needs of physician stakeholders to help guide the expansion of a pharmacogenomic-based precision medicine clinic that conducts genetic testing for patients at risk of experiencing adverse drug reactions. The article provides a detailed description of how the business model canvas was used and adapted to understand physician’s responsibilities and challenges related to drug prescription and dosing, and how the clinic could address physician needs and create value by mapping clinic services onto physician needs and wants. Interviews were conducted with physicians and the data were analyzed following the recommendations of the developers of the business model canvas. The article examines the strengths and limitations of the business model canvas and discusses its applicability to a health care setting.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".