Disruptive technologies in health care disenchanted: a systematic review of concepts and examples
Bibliographic record
Abstract
Abstract Objectives To clarify the concept of disruptive technologies in health care, provide examples and consider implications of potentially disruptive technologies for health technology assessment (HTA). Methods We conducted a systematic review of conceptual and empirical papers on healthcare technologies that are described as “disruptive.” We searched MEDLINE and Embase from 2013 to April 2019 (updated in December 2021). Data extraction was done in duplicate by pairs of reviewers utilizing a data extraction form. A qualitative data analysis was undertaken based on an analytic framework for analysis of the concept and examples. Key arguments and a number of potential predictors of disruptive technologies were derived and implications for HTA organizations were discussed. Results Of 4,107 records, 28 were included in the review. Most of the papers included conceptual discussions and business models for disruptive technologies; only few papers presented empirical evidence. The majority of the evidence is related to the US healthcare system. Key arguments for describing a technology as disruptive include improvement of outcomes for patients, improved access to health care, reduction of costs and better affordability, shift in responsibilities between providers, and change in the organization of health care. A number of possible predictors for disruption were identified to distinguish these from “sustaining” innovations. Conclusions Since truly disruptive technologies could radically change technology uptake and may modify provision of care patterns or treatment paths, they require a thorough evaluation of the consequences of using these technologies, including economic and organizational impact assessment and careful monitoring.
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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 | 0.029 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.033 | 0.032 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".