Satoshi Koiso https://s-koiso.github.io/posts.html Quarto Academic Website Template adapted by Satoshi Koiso https://quarto.org/quarto.png Satoshi Koiso https://s-koiso.github.io/posts.html quarto-1.5.57 Fri, 19 Jun 2026 00:00:00 GMT TA for Online Certificate Programs at CHOICE Institute https://s-koiso.github.io/ I will be serving as a TA for the online certificate programs at CHOICE Institute, University of Washington. The course will be offered in 2026 academic year! Looking forward to working with the students and instructors! news teaching https://s-koiso.github.io/ Fri, 19 Jun 2026 00:00:00 GMT Japan’s Health Insurance System and Reimbursement for AI Medical Devices Satoshi Koiso https://s-koiso.github.io/posts/JapanInsureAI042026/ On April 29, 2026, I presented this talk on Japan’s health insurance system and reimbursement for AI medical devices at the CHOICE Graduate Seminar, University of Washington.

The talk walks through three parts: an overview of how Japan’s public health insurance system is structured and financed, how medical services, drugs, and devices are approved and priced under the national fee schedule, and how AI medical devices fit or struggle to fit into that existing framework.

Japan provides near-universal health coverage, but an aging population and shrinking workforce are putting growing pressure on its sustainability. AI medical devices are emerging as a potential tool to ease labor shortages in healthcare, but their intangible and evolving nature makes them hard to evaluate under a system built for fixed-price drugs and devices. Drawing on two real use cases, I discussed how regulators and payers are currently improvising solutions, and where the open questions remain.

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]]> presentation https://s-koiso.github.io/posts/JapanInsureAI042026/ Wed, 29 Apr 2026 00:00:00 GMT Pandemic Response in Low- and Middle-Income Countries Satoshi Koiso https://s-koiso.github.io/posts/DisabilityLMICs/

Abstract

The COVID-19 pandemic exposed and exacerbated pre-existing inequities in health systems worldwide, disproportionately affecting people with disabilities. In low- and middle-income countries (LMICs), these challenges were intensified by limited resources, underdeveloped or weakly enforced legal protections, fragile health infrastructure, and pervasive social stigma. As a result, people with disabilities frequently encountered barriers to timely information, preventive measures, healthcare services, and economic stability. These problems were compounded by a lack of political representation, scarcity of disability-disaggregated data, and the competing priorities that characterize policymaking during emergencies. Current advocacy for disability inclusion in pandemics often relies on humanitarian or human-rights rhetoric. While morally compelling, such arguments may have limited influences on policymaking in emergency contexts, where policymakers must prioritize among multiple urgent demands. This entry, therefore, advances pragmatic arguments for disability-inclusive pandemic responses, emphasizing their potential public health advantages, such as reducing severe infections, secondary transmissions, and outbreaks in care facilities, and economic advantages, including lower long-term healthcare costs and improved workforce participation. Organizations of Persons with Disabilities (OPDs) emerge as critical actors in bridging the gap between people with disabilities and policymakers. By gathering first-hand data, articulating their unmet needs, and demonstrating the broader benefits of inclusive strategies, OPDs can help integrate disability considerations into emergency public health planning. Strengthening disability-inclusive approaches is essential not only for safeguarding the rights of people with disabilities but also for enhancing the equity, resilience, and efficiency of health systems in LMICs in the face of future pandemics and public health emergencies.

Citation

BibTeX citation:
@incollection{koiso2026,
  author = {Koiso, Satoshi},
  editor = {Bennett, G. and Goodall, E.},
  publisher = {Palgrave Macmillan},
  title = {Pandemic {Response} in {Low-} and {Middle-Income}
    {Countries}},
  booktitle = {The Palgrave Encyclopedia of Disability},
  date = {2026},
  address = {Cham},
  url = {https://doi.org/10.1007/978-3-031-40858-8_476-1},
  doi = {10.1007/978-3-031-40858-8_476-1},
  isbn = {978-3-031-40858-8},
  langid = {en}
}
For attribution, please cite this work as:
Koiso, Satoshi. 2026. “Pandemic Response in Low- and Middle-Income Countries.” In The Palgrave Encyclopedia of Disability, edited by G. Bennett and E. Goodall. Cham: Palgrave Macmillan. https://doi.org/10.1007/978-3-031-40858-8_476-1.
]]> publication https://s-koiso.github.io/posts/DisabilityLMICs/ Sat, 06 Dec 2025 00:00:00 GMT Decision Science: Policy Design and Clinical Decision-Making Through Health Economics Satoshi Koiso https://s-koiso.github.io/posts/DecisionScience042025/ On April 19, 2025, I presented this talk on Decision Science at the Boston Japanese Researchers Forum.

The talk walks through three parts: what Decision Science is and the eight-step process used to analyze decisions, a hands-on example applying that process to a simple everyday choice, and real-world use cases showing how this approach has shaped national screening guidelines, vaccine recommendations, and drug pricing.

Decision Science integrates economics, statistics, and behavioral science to support choices under uncertainty by explicitly weighing effects, costs, and the risk involved. Rather than relying on intuition alone, it builds simulation models including decision trees, Markov models, and others, which estimate outcomes like QALYs (quality-adjusted life years) and compare options using cost-effectiveness analysis. I worked through a flight-seat-class example step by step, including an audience exercise to elicit utility values, then showed how the same logic underlies decisions made by the USPSTF, the CDC’s ACIP, and Japan’s drug-pricing system. I closed by discussing where this approach runs into real limits when assumptions drive results, when utility is hard to measure, and when economic efficiency is not the right yardstick at all.

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]]> presentation https://s-koiso.github.io/posts/DecisionScience042025/ Sat, 19 Apr 2025 00:00:00 GMT 意思決定を科学する：医療経済学の手法で考える政策立案・治療方針 小磯　聡 https://s-koiso.github.io/posts/DecisionScience042025_ja/ 2025年4月19日、ボストン日本人研究者交流会にて、意思決定科学をテーマに発表を行いました。

発表は三部構成です。１）八段階の分析プロセスに基づく意思決定科学の概論、２）その分析手法を日常のシンプルな選択に当てはめた例、３）国のスクリーニングガイドライン、ワクチン接種スケジュール、薬価設定にこの手法がどう活かされているかを示す事例紹介、という流れです。

意思決定科学とは、経済学・統計学・行動科学を統合し、効果・費用・リスクを明示的に比較することで、不確実性の下での意思決定を支援する分野です。直感だけに頼るのではなく、意思決定木モデル（decision tree）やマルコフモデルといったシミュレーションモデルを構築し、QALY（質調整生存年）などのアウトカムを推計したうえで、費用対効果分析によって選択肢を比較します。発表では、飛行機の座席クラス選びを題材に、聴衆自身に効用値を答えてもらう演習を交えながら段階的に解説し、同じロジックが米国予防医療専門委員会（USPSTF）、米国CDCのACIP（予防接種諮問委員会）、そして日本の薬価制度における意思決定の根底にもあることを示しました。最後に、前提が結果を大きく左右してしまう場合、効用の測定が難しい場合、そしてそもそも経済効率性が適切な尺度ではない場合など、この手法が実際に直面する限界にも触れました。

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]]> presentation 日本語 https://s-koiso.github.io/posts/DecisionScience042025_ja/ Sat, 19 Apr 2025 00:00:00 GMT