Machine learning (ML) is revolutionizing healthcare by supporting smarter decisions and deeper insights, particularly in Health Economics and Outcomes Research (HEOR), where data-led findings impact real-world healthcare policies.[1] However, growing ML adoption is raising concerns about its reliability, transparency, and ethical use. To address these, the PALISADE Checklist provides a well-defined framework for implementing ML responsibly and reliably in HEOR.[2, 3] The framework ensures responsible adoption of ML by assessing its Purpose, Appropriateness, Limitations, Implementation, Sensitivity and Specificity, Algorithm characteristics, Data characteristics, and Explainability; thereby getting its name- the PALISADE checklist.[3]
The PALISADE Checklist is an innovative framework presented by the ISPOR Machine Learning Task Force to channel the responsible and dependable use of ML in HEOR. It takes into consideration five applications of ML methods that are crucial to HEOR; viz. 1) ML-assisted cohort selection, 2) feature selection, 3) predictive analytics, 4) causal inference, and 5) health economic evaluation, and reflection on transparency and explainability.[3] The rapid adoption of ML techniques in healthcare has necessitated a well-defined methodology to ensure transparency, consistency, and ethical foundation of these methods. PALISADE helps address ML-driven challenges by providing an extensive, standardized set of factors for researchers, analysts, and stakeholders to assess ML applications in HEOR contexts.[2, 3]
Fundamentally, the PALISADE checklist offers prompts that ML developers can use to constitute their thinking about how the suitability of ML methods can be conveyed to stakeholders and healthcare decision makers. The checklist supports comprehensibility by urging practitioners to clearly define the objectives for using ML in a given HEOR study; asking whether the ML model serves prediction, categorization, or extrapolation, and whether its objective aligns with the wider goals of the undergoing health research. This clarity of intention is crucial for accountability and also for allowing regulators, payers, and policy makers to elucidate results meaningfully as they appropriately apply them in decision-making.[3]
Another basic element of the framework is the focus on methodological relevance. ML is not a one-size-fits-all approach, and the checklist stipulates a thorough justification for choosing ML over conventional statistical methods. Researchers must weigh apparent advantages of ML pertaining to accuracy, scalability, or insight, along with the suitability of available data. This promotes the selection of ML not just as a trend, but as a carefully chosen instrument for enhancing the quality and applicability of HEOR studies.[3]
The PALISADE checklist also underscores the inherent limitations and risks in ML applications. It heavily focuses on the importance of recognizing model uncertainty, overfitting, bias, and data quality concerns that could impact the end results. The checklist urges users to openly reveal these limitations, thus reinforcing the integrity of the research and also, training stakeholders to interpret results with caution. Such transparency is crucial in HEOR, where policy decisions influence patient care and overall public health.[3]
Executional considerations also play a key role in the PALISADE checklist. It encourages researchers to contemplate how ML models will work in real-world settings, beyond controlled environments or highly curated datasets. This consists of evaluating whether the model’s extrapolations are generalizable across populations and time periods, and whether the working infrastructure is in place to incorporate the model into standard healthcare decision-making. Practical feasibility is equally important as theoretical execution for applying ML in HEOR.[3]
The framework, along with the performance metrics like accuracy or precision, clearly includes comprehensibility as an important element. Healthcare stakeholders, right from clinicians to patients to policymakers, should be capable at understanding the specific prediction made by a particular model. PALISADE supports models that provide comprehensibility outputs, while also warranting additional tools and documentation to convert complex algorithms into coherent logic. This helps lower the risk of misappropriated findings and build trust in ML tools.[3]
The final constituents of the checklist address data and algorithm features. Researchers are encouraged to document the origin, quality, and representativeness of instructions, authentication, and test datasets. Similarly, PALISADE warrants an exhaustive clarification of the algorithm’s structure, parameters, and tuning decisions. These factors are significant for reproducibility, one of the key factors of scientific accuracy, confirming that findings can be corroborated or improved upon by future researchers.[3]
Finally, the PALISADE Checklist is more than a framework as it warrants accountability in the application of ML in HEOR. By integrating ethical principles, scientific robustness, and practical insight into each phase of ML execution, it helps facilitate the powerful utilization of ML both innovatively and responsibly.
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References
- Dasari M, Dasari P, Fossati S, et al. Applications of Artificial Intelligence and Machine Learning in Health Economics and Outcomes Research: A Targeted Literature Review. Value in Health. 2024; 27(12). (ISPOR Europe 2024, Barcelona, Spain).
- ISPOR. Global Expert Panel Identifies 5 Areas Where Machine Learning Could Enhance Health Economics and Outcomes Research – A Good Practices Report of the ISPOR Machine Learning Task Force. July 2022. Available online at: https://www.ispor.org/heor-resources/news-top/news/view/2022/07/05/global-expert-panel-identifies-5-areas-where-machine-learning-could-enhance-health-economics-and-outcomes-research
- Padula WV, Kreif N, Vanness DJ, et al. Machine Learning Methods in Health Economics and Outcomes Research-The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force. Value Health. 2022 Jul;25(7):1063-1080.
Living systematic literature reviews (SLRs) are a type of SLRs that are continually updated by periodically including relevant new evidence as and when it becomes available. SLRs are often considered to occupy the top of the evidence pyramid because they synthesize evidence from different sources and present a summary of the evidence, thus enabling clinical and policy-level decision-making.