Addressing the Gaps: Rethinking Responsibility in the Commercial Digital Health Landscape
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By
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Hajira Dambha-Miller
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Lucy Smith
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Lysanne Veerle Michels
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September 17, 2026
Clinical Scorecard: Addressing the Gaps: Rethinking Responsibility in the Commercial Digital Health Landscape
At a Glance
| Category | Detail |
|---|
| Condition | Digital Health Technologies |
| Key Mechanisms | Measurement bias, dataset bias, algorithmic bias, and inequitable procurement practices. |
| Target Population | High-income and middle-income settings. |
| Care Setting | Routine health care delivery. |
Key Highlights
- Digital health technologies are increasingly integrated into health care systems.
- Algorithmic bias can lead to significant health inequities.
- Commercial digital health products often lack transparency in performance across diverse populations.
- Equitable performance should be a prerequisite for the procurement of digital health technologies.
- Existing frameworks focus on algorithm development but overlook commercial accountability.
Guideline-Based Recommendations
Diagnosis
- Evaluate the performance of digital health technologies across diverse populations.
Management
- Implement accountability models that cover the entire commercial digital health life cycle.
Monitoring & Follow-up
- Require ongoing fairness monitoring and evaluation of digital health technologies.
Risks
- Recognize the potential for measurement and dataset biases to exacerbate health inequities.
Patient & Prescribing Data
Diverse patient groups across various socioeconomic and geographic backgrounds.
Digital health technologies must demonstrate equitable performance before implementation.
Clinical Best Practices
- Ensure representative datasets are used in the development of digital health technologies.
- Conduct subgroup performance evaluations to identify potential biases.
- Adopt frameworks that promote equitable outcomes throughout the AI life cycle.
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