Is Artificial Intelligence (AI) Friend or Foe to Patients in Healthcare?
Bibliographic record
Abstract
Failure to appropriately measure Value is one of the reasons for slow reform in health. Value brings together quality and cost, both defined around the patient. With technology we can measure value in the new ways: commercially developed algorithms are capable of mining large, connected data sets to present accurate information for patients and providers. But how do we align these new capabilities with clinical and operational realities, and further with individual privacy? The right amount of information, shared at the right time, can improve practitioners' ability to choose treatments, and patients' motivation to provide consent and follow the treatment. Dynamic Consent, where IT is used to determine just what patients are consenting to share, can address the inherent conflict between the demand from AI for access to data and patients' privacy principles. This chapter describes a pragmatic Commercial Development framework for building digital health tool. It overlays Value Model for healthcare IT investments with Patient Activation Measures and innovation management techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.012 |
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; both teacher heads agree on what is shown here.
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".