NOTE: This article is the tenth in a series of 10 articles and is part of our Economic Evaluation in Healthcare 101 course. You can find a course overview and links to all 10 course modules here:

Future Trends in Economic Evaluation in Healthcare

As healthcare evolves with rapid technological advancement, economic evaluation must adapt to new types of interventions, complex data streams, and shifting paradigms in clinical decision-making. Traditional methods—while foundational—are increasingly supplemented by innovative tools and frameworks that account for personalized care, real-time data, and emerging digital health solutions. This essay discusses three key trends shaping the future of economic evaluation in healthcare: the integration of big data and machine learning, the rise of personalized medicine, and the economic assessment of digital health technologies.

1. Big Data and Machine Learning in Economic Evaluation

The advent of big data in healthcare—defined by volume, variety, and velocity—has created unprecedented opportunities to enhance the precision, relevance, and scope of economic evaluations. Data from electronic health records (EHRs), wearable devices, genomic databases, insurance claims, and social media are increasingly accessible for analysis.

Machine learning (ML) techniques can mine these data to:

  1. Improve risk prediction models, which are essential for identifying patient subgroups likely to benefit from interventions.
  2. Enhance parameter estimation in decision models, reducing reliance on expert assumptions.
  3. Automate aspects of cost-effectiveness modeling, including dynamic disease progression and treatment pathways.

For example, ML can be used to simulate individual-level microsimulation models that adapt in real time to patient-level characteristics, enabling more personalized economic evaluations.

However, challenges remain. These include:

  1. Data quality and interoperability across systems and providers.
  2. Transparency and interpretability of complex ML algorithms, which may not align with the clear logic structures required in decision modeling.
  3. Ethical and privacy concerns around the use of sensitive patient data.

Nevertheless, the fusion of big data and ML holds significant potential to transform economic evaluation from a static exercise into a learning system that evolves alongside clinical innovation.

References:

  • Obermeyer Z, Emanuel EJ. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216–1219.
  • Willan AR, Briggs AH. (2006). Statistical analysis of cost-effectiveness data: Current state and future directions. Health Economics, 15(1), 1–5.

2. Personalized Medicine and Economic Evaluation

Personalized medicine (also referred to as precision medicine) tailors treatment to individual patient characteristics, often using genomic, proteomic, or biomarker data. This approach challenges conventional “average-effect” models of economic evaluation by highlighting substantial heterogeneity in treatment response.

Implications for economic evaluation include:

  • Subgroup-specific ICERs: Evaluations must assess cost-effectiveness for different genetic or phenotypic profiles rather than population averages.
  • Companion diagnostics: The cost and value of diagnostic tests that guide personalized treatment decisions must be integrated into the analysis.
  • Smaller evidence bases: Personalized therapies often rely on limited trial populations, increasing uncertainty and the need for adaptive modeling and real-world evidence integration.

For example, economic evaluations of oncology drugs that target specific mutations (e.g., EGFR inhibitors in lung cancer) must consider not only drug efficacy but also the cost-effectiveness of testing and the prevalence of targetable mutations.

While personalized medicine promises to increase health outcomes for individuals, it often raises per-patient costs and complicates equity considerations, as access to testing and advanced therapies may be unevenly distributed.

References:

  • Phillips KA, Sakowski JA, Trosman JR, et al. (2014). The economic value of personalized medicine tests. American Journal of Managed Care, 20(6), e223–e232.
  • Faulkner E, Annemans L, Garrison L, et al. (2012). Challenges in the development and reimbursement of personalized medicine—payer and manufacturer perspectives. Value in Health, 15(8), 1162–1171.

3. Digital Health Technologies and Economic Assessment

Digital health technologies—including mobile health apps, telemedicine, remote monitoring devices, and AI-enabled decision support systems—are reshaping healthcare delivery. These tools promise to improve access, adherence, and patient engagement, but their economic value must be rigorously assessed.

Key considerations for economic evaluation of digital health include:

  • Rapid innovation cycles: Digital tools often evolve faster than traditional interventions, necessitating agile evaluation frameworks.
  • User behavior and adherence: Effectiveness depends heavily on patient engagement, which is variable and difficult to model.
  • Blended outcomes: Benefits may include reduced hospital visits, improved self-management, or quality of life enhancements—not always captured in QALYs or traditional metrics.

For example, evaluations of telehealth programs during the COVID-19 pandemic have demonstrated both cost savings and improved access, but the long-term cost-effectiveness depends on sustained use, integration into care pathways, and reimbursement models.

Furthermore, regulatory and methodological standards for economic evaluation of digital health remain underdeveloped. Agencies such as NICE have begun publishing digital health technology evaluation frameworks, incorporating aspects like usability, scalability, and cybersecurity.

References:

  • Neumann PJ, Cohen JT, Kim DD, Ollendorf DA. (2021). Consideration of value-based pricing for digital health products. Health Affairs, 40(9), 1456–1462.
  • NICE. (2019). Evidence Standards Framework for Digital Health Technologies. National Institute for Health and Care Excellence.

Conclusion

The future of economic evaluation is being shaped by powerful trends in data science, personalization, and digital innovation. Big data and machine learning promise greater precision and adaptability in modeling. Personalized medicine challenges the traditional one-size-fits-all approach, requiring nuanced assessments that account for individual variation. Meanwhile, digital health technologies call for more flexible, real-time evaluation frameworks that capture their unique modes of delivery and user interaction. To remain relevant and impactful, economic evaluation must evolve to meet the demands of 21st-century healthcare—balancing methodological rigor with innovation, and efficiency with equity.

References

  • Obermeyer Z, Emanuel EJ. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216–1219.
  • Willan AR, Briggs AH. (2006). Statistical analysis of cost-effectiveness data. Health Economics, 15(1), 1–5.
  • Phillips KA, Sakowski JA, Trosman JR, et al. (2014). The economic value of personalized medicine tests. AJMC, 20(6), e223–e232.
  • Faulkner E, Annemans L, Garrison L, et al. (2012). Challenges in the development and reimbursement of personalized medicine. Value in Health, 15(8), 1162–1171.
  • Neumann PJ, Cohen JT, Kim DD, Ollendorf DA. (2021). Value-based pricing for digital health products. Health Affairs, 40(9), 1456–1462.
  • NICE. (2019). Evidence Standards Framework for Digital Health Technologies.
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