Lessons from the National institutes of health innovation corps program: defining barriers to developing and commercializing novel solutions for persons with opioid use disorder
Bibliographic record
Abstract
BACKGROUND: Translating innovative research advancements into commercially viable medical interventions presents well-known challenges. However, there is limited understanding of how specific patient, clinical, social, and legal complexities have further complicated and delayed the development of new and effective interventions for Opioid Use Disorder (OUD). We present the following case studies to provide introductory clinical, social, and business insights for researchers, medical professionals, and entrepreneurs who are considering or are currently developing medical. METHODS: Four small business recipients of National Institute on Drug Abuse (NIDA) small business grant funding collected a total of 416 customer discovery interviews during the 2021 National Institutes of Health (NIH) Innovation-Corps (I-Corps) program. Each business received funding to advance an OUD-specific innovation: therapeutics (2 companies), medical device (1 company), and Software as a Medical Device (SaMD) (1 company). Interview participants included stakeholders from a variety of disciplines of Substance Use Disorders (SUD) healthcare including clinicians, first responders, policymakers, relevant manufacturers, business partners, advocacy groups, regulatory agencies, and insurance companies. RESULTS: Agnostic to the type of product (therapeutic, device, or SaMD), several shared barriers were identified: (1) There is a lack of standardization across medical providers for managing patients with OUD, resulting in diverse implementation practices due to a fragmented healthcare policy; (2) Underlying Social Determinants of Health (SDOH) present unique challenges to medical care and contribute to poor outcomes in OUD; (3) Stigma thwarts adoption, implementation, and the development of innovative solutions; (4) Constantly evolving public health trends and legal policies impact development and access to OUD interventions. CONCLUSION: It is critical for innovators to have early interactions with the full range of OUD stakeholders to identify and quantify true unmet needs and to properly position development programs for commercial success. The NIH I-Corps program provides a framework to educate researchers to support their product design and development plans to increase the probability of a commercially successful outcome to address the ongoing opioid epidemic.
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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 | 0.070 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".