Objective: Recent years have witnessed an increased interest in the use of multicriteria decision analysis (MCDA) to support health technology assessment (HTA) agencies for setting healthcare priorities. However, its implementation to date has been criticized for being "entirely mechanistic," ignoring opportunity costs, and not following best practice guidelines. This article provides guidance on the use of MCDA in this context. Methods:The present study was based on a systematic review and consensus development. We developed a typology of MCDA studies and good implementation practice. We reviewed 36 studies over the period 1990 to 2018 on their compliance with good practice and developed recommendations. We reached consensus among authors over the course of several review rounds. Results:We identified 3 MCDA study types: qualitative MCDA, quantitative MCDA, and MCDA with decision rules. The types perform differently in terms of quality, consistency, and transparency of recommendations on healthcare priorities. We advise HTA agencies to always include a deliberative component. Agencies should, at a minimum, undertake qualitative MCDA. The use of quantitative MCDA has additional benefits but also poses design challenges. MCDA with decision rules, used by HTA agencies in The Netherlands and the United Kingdom and typically referred to as structured deliberation, has the potential to further improve the formulation of recommendations but has not yet been subjected to broad experimentation and evaluation. Conclusion:MCDA holds large potential to support HTA agencies in setting healthcare priorities, but its implementation needs to be improved.

Mortondisease which noone suffers from?). We explore why models might fail such tests (as the models of some existing published studies would do) and offer some suggestions as to how practice should be improved.

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SUMMARYIn this editorial, we consider the vexing issue of 'unrelated future costs' (for example, the costs of caring for people with dementia or kidney failure after preventing their deaths from a heart attack). The National Institute of Health and Care Excellence (NICE) guidance is not to take such costs into account in technology appraisals. However, standard appraisal practice involves modelling the benefits of those unrelated technologies. We argue that there is a sound principled reason for including both the costs and benefits of unrelated care. Changing this practice would have material consequences for decisions about reimbursing particular technologies, and we urge future research to understand this better.

Objectives: This study explores the financial consequences of decreased acute care utilization and expanded community-based care for patients at the end of life in England. Method: A Markov model based on cost and utilization data was used to estimate the costs of care for cancer and organ failure in the last year of life and to simulate reduced acute care utilization. Results: We estimated at £1.8 billion the cost to the taxpayer of care for the 127,000 patients dying from cancer in 2006. The equivalent cost for the 30,000 people dying from organ failure was £553 million. Resources of £16 to £171 million could be released for cancer. Conclusion: People generally prefer to die outside hospital. Our results suggest that reducing reliance on acute care could release resources and better meet peoples’ preferences. Better data on the cost-effectiveness of interventions are required. Similar models would be useful to decision-makers evaluating changes in service provision.