Experience adjusted life years and critical medical allocations within the British context: which patient should live?

Towards machine learning for moral choice analysis in health economics: A literature review and research agenda

BACKGROUND

Discrete choice models (DCMs) for moral choice analysis will likely lead to erroneous model outcomes and misguided policy recommendations, as only some characteristics of moral decision-making are considered. Machine learning (ML) is recently gaining interest in the field of discrete choice modelling. This paper explores the potential of combining DCMs and ML to study moral decision-making more accurately and better inform policy decisions in healthcare.

METHODS

An interdisciplinary literature search across four databases - PubMed, Scopus, Web of Science, and Arxiv - was conducted to gather papers. Based on the Preferred Reporting Items for Systematic and Meta-analyses (PRISMA) guideline, studies were screened for eligibility on inclusion criteria and extracted attributes from eligible papers. Of the 6285 articles, we included 277 studies.

RESULTS

DCMs have shortcomings in studying moral decision-making. Whilst the DCMs' mathematical elegance and behavioural appeal hold clear interpretations, the models do not account for the 'moral' cost and benefit in an individual's utility calculation. The literature showed that ML obtains higher predictive power, model flexibility, and ability to handle large and unstructured datasets. Combining the strengths of ML methods with DCMs has the potential for studying moral decision-making.

CONCLUSIONS

By providing a research agenda, this paper highlights that ML has clear potential to i) find and deepen the utility specification of DCMs, and ii) enrich the insights extracted from DCMs by considering the intrapersonal determinants of moral decision-making.

Forecasting of Future Medical Care Expenditure in Japan Using a System Dynamics Model

Background

The aim of this study was to construct a system dynamics (SD) model to estimate the future medical care expenditure and to address the dynamic issues of health care that should be resolved. In particular, the measures for promoting the spread of generic drug (GE drug) usage in Japan and reducing cancer-related medical expenses were investigated regarding their future impact on medical finances.

Methods

The SD model was constructed from FY 2018 to FY 2050. The change in the future GE drug quantity share was analyzed by using a regression equation. The impact of the increase in medical expense for cancer and the change in the future national medical care expenditure were also estimated.

Results

The annual total medical care expenditure in FY 2050 would arrive at 58.9–64.2 trillion JPY (US$ 535.1–584.0 billion) (1.3–1.5 times higher than that in FY 2018) with different trends in age groups. The cumulative total medical care expenditure was expected to decrease by about 787.0–989.4 billion JPY (US$ 7.2–9.0 billion) if the impact of the spread of GE drug usage was considered. On the other hand, due to the continuous increase in the cancer-related medical expense, the cumulative total medical care expenditure was estimated to increase about 7554.3–11715.0 billion JPY (US$ 68.7–106.5 billion).

Conclusions

If the cancer-related medical expense continues to increase in the future, an increase of 686.4–1104.2 billion JPY (US$ 6.2–10.0 billion) in FY 2050 is expected which suggests that this disease field should be prioritized regarding the measures to maintain medical finances.

Friendship as a framework for resolving dilemmas in clinical ethics

Healthcare professionals often need to make clinical decisions that carry profound ethical implications. As such, they require a tool that will make decision-making intuitive. While the discussion about the principles that should guide clinical ethics has been going on for over two thousand years, it does not seem that making such decisions is becoming any more straight forward. With an abundance of competing ethical systems and frameworks for their application in real life, the clinician is still often not sure how to proceed in the face of ethical dilemmas, either due to a lack of background ethical knowledge or experience in applying it. This paper will discuss whether considering what one would expect one's friend to do if one was the patient, or what would one think they would do for a friend if they were the patient, can be a helpful, more intuitive, tool for clinical decision-making that can produce outcomes that are congruent with major ethical systems.

Covid-19: Refracting decision-making through the prism of resource allocation

Medical decision-making has, across the history of the NHS, made a transitional journey from a model characterised by paternalism to one which places emphasis on partnership and patient autonomy. This article assesses the extent to which the circumstances generated by the Covid-19 pandemic affect the mode of critical care decision-making. It observes that clinical judgment influenced by protocols, algorithms and resource constraints do not lend themselves to full identification with either of the two frameworks familiar to the NHS. The unique mode of decision-making engendered can only be understood on its own terms.