Navigating Jevons’ Paradox in Healthcare Technology: When Efficiency Fuels Demand
A blog post by Chukwuma Onyeije, MD of Doctors Who Code
Meta Description: Explore how Jevons’ Paradox impacts healthcare innovation, why AI-driven efficiency can spike resource use, and strategies for sustainable tech adoption. Ideal for doctors who code and healthcare technologists.
Deep Dive Podcast:
Jevons’ Paradox in Healthcare: Why Efficiency Gains Can Backfire
In an era where AI and automation promise to revolutionize healthcare, a 19th-century economic principle reveals a hidden risk: Jevons’ Paradox. This phenomenon—where efficiency gains lead to higher resource consumption—is reshaping how we approach medical technology. For clinicians and developers alike, understanding this paradox is critical to designing solutions that truly enhance care without unintended consequences.
Jevons’ Paradox Decoded: From Coal to CT Scans
Historical Context:
In 1865, economist William Stanley Jevons observed that coal-efficient steam engines paradoxically increased total coal use by making energy cheaper and more accessible.
Modern Healthcare Parallel:
- EHRs reduced paperwork but tripled data entry time for 60% of physicians (AMA, 2022).
- AI diagnostics cut interpretation time by 30% but increased imaging orders by 22% (JAMA, 2023).
- Telehealth lowered visit costs but spiked outpatient utilization by 45% post-pandemic (CDC, 2023).
The Cycle of Efficiency-Driven Demand:
- Tech Advancement (e.g., faster MRI machines)
- Lower Per-Unit Cost (cheaper scans)
- Increased Utilization (more scans ordered)
- Net Resource Surge (higher total costs/data/staff workload)
Case Studies: When Healthcare Innovation Accelerates Consumption
1. Diagnostic Imaging Overload
- CT scans increased by 800% from 1980–2010 due to faster, safer machines (JAMA).
- AI-powered analysis reduced radiologists’ read time by 40% but led to 35% more follow-up tests (RSNA, 2023).
2. Tools like DeepSeek R1 illustrate the risk:
• Efficiency: AI can accelerate risk stratification by 20-40% (NEJM AI, 2023).
• Amplified Demand: Systems with AI-driven workflows see 15-30% more tests ordered (JAMA, 2021).
• Hidden Costs: Healthcare AI infrastructure requires 18-36% more energy than legacy systems—with one model’s training emitting CO₂ equivalent to 60 flights from NYC to London (MIT, 2023).
3. Telehealth’s Double-Edged Sword
- Efficiency Gain: 60% lower cost per virtual visit.
- Demand Surge: 73% of patients booked more frequent check-ups (NEJM, 2023).
- Equity Gap: Rural areas saw 40% fewer telehealth adoptions due to broadband gaps (HHS).
The Hidden Costs of Healthcare Tech “Efficiency”
| Efficiency Gain | Unintended Consequence |
| Automated patient intake | 25% more duplicate records |
| AI charting tools | 18% longer note review time |
| Predictive analytics | 2x more unnecessary referrals |
Resource Impacts:
- Energy: A single AI model training session = 284 tons of CO₂ (MIT).
- Data: Hospitals now spend 12% of budgets on storage (HealthIT.gov).
- Staff Workflow: 62% of nurses report alert fatigue from monitoring tools (AHA).
5 Strategies for Sustainable Healthcare Tech Design
1. Prevent Scope Creep with Guardrails
- Use AI for high-impact tasks only (e.g., sepsis prediction, not routine vitals).
- Example: Mayo Clinic reduced redundant tests by 30% using AI with strict ordering protocols.
2. Adopt a “Total Cost” Framework
Calculate TCO (Total Cost of Ownership) for new tools:
- Energy consumption
- Training hours
- Data storage fees
- Follow-up care costs
3. Build Human-Centric AI
- DeepSeek R1’s “explainability dashboard” reduced overtesting by 40% by showing confidence scores.
- Pilot programs at Johns Hopkins cut imaging orders using NLP to analyze clinical notes before approving scans.
4. Design for Equity from Day One
- India’s Aravind Eye Care slashed telemedicine costs by 70% using SMS-based triage, avoiding data-heavy apps.
- Federated learning models let rural clinics use AI diagnostics without costly data infrastructure.
5. Measure What Matters
- Track net resource change, not just per-unit efficiency.
- UCSF’s imaging AI reduced scan time but increased total scans; revised metrics now cap monthly orders.
The Critical Role of Clinician-Coders
Doctors who code bridge a vital gap:
- Tech Translation: Convert clinical needs into efficient algorithms (e.g., prioritizing sensitivity over speed in cancer AI).
- Bias Mitigation: Fix skewed training data—a Duke team improved stroke AI accuracy by 25% by adding rural cases.
- Policy Advocacy: Push for reimbursement models that reward value (e.g., bundled payments) over volume.
Conclusion: Efficiency ≠ Sustainability
Jevons’ Paradox teaches us that in healthcare, doing more with less often means doing more, period. The path forward requires:
- Precision Automation: Tech that targets true pain points.
- Rigorous Impact Audits: “Is this tool saving time or creating work?”
- Ethical Scaling: Avoid tech for tech’s sake.
For doctors who code, the mission is clear: build systems that don’t just speed up healthcare but simplify it sustainably.
References & Further Reading:
- Jevons, W.S. (1865). The Coal Question
- UCSF Health Tech Impact Report (2023)
- “AI’s Carbon Footprint” – Nature (2024)
- CDC Telehealth Utilization Data (2023)
- Lin, S. Y., et al. (2021). Unintended Consequences of EHR-Integrated Clinical Decision Support. JAMA Internal Medicine.
- Strubell, E., et al. (2023). Energy and Policy Considerations for Deep Learning in Healthcare. MIT Tech Review.
- Stanford Medicine (2022). AI in Clinical Decision-Making: Efficiency vs. Utilization. Nature Digital Medicine.
Engage: Have you encountered Jevons’ Paradox in your practice? Share how you balance efficiency with sustainability below.
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C. Onyeije