scholarly journals Prospects of Integrating Gig Economy in the Saudi Arabian Health-care System from the Perspectives of Health-care Decision-makers and Practitioners

2021 ◽  
Vol Volume 13 ◽  
pp. 255-265
Author(s):  
Turki Alanzi
Keyword(s):  
Health Care ◽  
Health Care System ◽  
Saudi Arabian ◽  
Decision Makers ◽  
Health Care Decision ◽  
Gig Economy ◽  
Care Decision ◽  
Care System

Ready for Genomic Medicine? Perspectives of Health Care Decision Makers

2005 ◽  
Vol 165 (16) ◽  
pp. 1917 ◽  
Author(s):  
Paul R. Billings ◽  
Rick J. Carlson ◽  
Josh Carlson ◽  
Mary Cain ◽  
Charles Wilson ◽  
...  
Keyword(s):  
Health Care ◽  
Genomic Medicine ◽  
Decision Makers ◽  
Health Care Decision ◽  
Care Decision

Concordance in advance care preferences among high-risk surgical patients and surrogate health care decision makers in the perioperative setting

Surgery ◽  
2020 ◽  
Vol 167 (2) ◽  
pp. 396-403 ◽  
Author(s):  
Brooks V. Udelsman ◽  
Nicolas Govea ◽  
Zara Cooper ◽  
David C. Chang ◽  
Angela Bader ◽  
...  
Keyword(s):  
Health Care ◽  
High Risk ◽  
Decision Makers ◽  
Surgical Patients ◽  
Health Care Decision ◽  
Advance Care ◽  
Care Decision

scholarly journals Comparative Effectiveness Research in Ontario, Canada: Producing Relevant and Timely Information for Health Care Decision Makers

2009 ◽  
Vol 87 (3) ◽  
pp. 585-606 ◽  
Author(s):  
DANIELLE M. WHICHER ◽  
KALIPSO CHALKIDOU ◽  
IRFAN A. DHALLA ◽  
LESLIE LEVIN ◽  
SEAN TUNIS
Keyword(s):  
Health Care ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Decision Makers ◽  
Effectiveness Research ◽  
Health Care Decision ◽  
Care Decision

scholarly journals Reference Case Methods for Expert Elicitation in Health Care Decision Making

2021 ◽  
pp. 0272989X2110282
Author(s):  
Laura Bojke ◽  
Marta O. Soares ◽  
Karl Claxton ◽  
Abigail Colson ◽  
Aimée Fox ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Expert Elicitation ◽  
Decision Makers ◽  
Alternative Methods ◽  
Consensus Methods ◽  
Reference Case ◽  
Case Methods ◽  
Health Care Decision ◽  
Care Decision

Background The evidence used to inform health care decision making (HCDM) is typically uncertain. In these situations, the experience of experts is essential to help decision makers reach a decision. Structured expert elicitation (referred to as elicitation) is a quantitative process to capture experts’ beliefs. There is heterogeneity in the existing elicitation methodology used in HCDM, and it is not clear if existing guidelines are appropriate for use in this context. In this article, we seek to establish reference case methods for elicitation to inform HCDM. Methods We collated the methods available for elicitation using reviews and critique. In addition, we conducted controlled experiments to test the accuracy of alternative methods. We determined the suitability of the methods choices for use in HCDM according to a predefined set of principles for elicitation in HCDM, which we have also generated. We determined reference case methods for elicitation in HCDM for health technology assessment (HTA). Results In almost all methods choices available for elicitation, we found a lack of empirical evidence supporting recommendations. Despite this, it is possible to define reference case methods for HTA. The reference methods include a focus on gathering experts with substantive knowledge of the quantities being elicited as opposed to those trained in probability and statistics, eliciting quantities that the expert might observe directly, and individual elicitation of beliefs, rather than solely consensus methods. It is likely that there are additional considerations for decision makers in health care outside of HTA. Conclusions The reference case developed here allows the use of different methods, depending on the decision-making setting. Further applied examples of elicitation methods would be useful. Experimental evidence comparing methods should be generated.

scholarly journals The World Federation of Societies of Anaesthesiologists Minimum Capnometer Specifications 2021—A Guide for Health Care Decision Makers

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Adrian W. Gelb ◽  
Robert J. McDougall ◽  
Julian Gore-Booth ◽  
Phoebe-Anne Mainland ◽  
Keyword(s):  
Health Care ◽  
Decision Makers ◽  
World Federation ◽  
Health Care Decision ◽  
The World ◽  
Care Decision

Estimating Downstream Budget Impacts in Implementation Research

2020 ◽  
Vol 40 (8) ◽  
pp. 968-977
Author(s):  
Todd H. Wagner ◽  
Alex R. Dopp ◽  
Heather T. Gold
Keyword(s):  
Health Care ◽  
Time Horizon ◽  
Impact Analysis ◽  
Decision Makers ◽  
Care Utilization ◽  
Evidence Based ◽  
Improvement Program ◽  
Cost Estimates ◽  
Health Care Decision ◽  
Care Decision

Health care decision makers often request information showing how a new treatment or intervention will affect their budget (i.e., a budget impact analysis; BIA). In this article, we present key topics for considering how to measure downstream health care costs, a key component of the BIA, when implementing an evidence-based program designed to reduce a quality gap. Tracking health care utilization can be done with administrative or self-reported data, but estimating costs for these utilization data raises 2 issues that are often overlooked in implementation science. The first issue has to do with applicability: are the cost estimates applicable to the health care system that is implementing the quality improvement program? We often use national cost estimates or average payments, without considering whether these cost estimates are appropriate. Second, we need to determine the decision maker’s time horizon to identify the costs that vary in that time horizon. If the BIA takes a short-term time horizon, then we should focus on costs that vary in the short run and exclude costs that are fixed over this time. BIA is an increasingly popular tool for health care decision makers interested in understanding the financial effect of implementing an evidence-based program. Without careful consideration of some key conceptual issues, we run the risk of misleading decision makers when presenting results from implementation studies.

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