scholarly journals Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps

2020 ◽  
Vol 7 (1) ◽  
pp. 35-42
Author(s):  
Yixi Chen ◽  
Viktor V Chirikov ◽  
Xiaocong L Marston ◽  
Jingang Yang ◽  
Haibo Qiu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Economics ◽  
Outcomes Research ◽  
Economic Value ◽  
Care Utilization ◽  
Value Assessment ◽  
Clinical Value ◽  
Treatment Regimens ◽  
Precision Health

Precision health economics and outcomes research (P-HEOR) integrates economic and clinical value assessment by explicitly discovering distinct clinical and health care utilization phenotypes among patients. Through a conceptualized example, the objective of this review is to highlight the capabilities and limitations of machine learning (ML) applications to P-HEOR and to contextualize the potential opportunities and challenges for the wide adoption of ML for health economics. We outline a P-HEOR conceptual framework extending the ML methodology to comparatively assess the economic value of treatment regimens. Latest methodology developments on bias and confounding control in ML applications to precision medicine are also summarized.

The power of health economics and outcomes research (HEOR) in making decisions that matter

2021 ◽  
Vol 1 (1) ◽  
pp. 23-32
Author(s):  
Oscar Herrera Restrepo
Keyword(s):  
Health Economics ◽  
Outcomes Research ◽  
Market Access ◽  
Clinical Performance ◽  
Economic Value ◽  
Research Problem ◽  
Pharmaceutical Products ◽  
Medical Technologies ◽  
Mandatory Requirement ◽  
Specific Country

Deciding on approving and granting market access to new medical technologies such as pharmaceutical products, vaccines, or medical devices is a multifactorial research problem. Balancing out clinical performance, epidemiological implications, burden of disease, economic value, and patient preferences, among other factors, is in itself a challenging endeavor. However, this should be a mandatory requirement when making approval and market access decisions that might affect millions of people in a specific country setting. The aim of this reflection research article is twofold; first, it provides context on the important role that health economics and outcomes research (HEOR) plays in informing decision making for market access and reimbursement of new medical technologies. Second, it outlines the power of HEOR studies in guiding discussions when assessing the value of new medical technologies. Overall, this article aims at highlighting key HEOR considerations for healthcare professionals, students, and institutions interested in building analytical capabilities around this exciting and uninterruptedly growing field of knowledge.

scholarly journals The Role of Health Economics and Outcomes Research in Health Care Reform in China

2014 ◽  
Vol 32 (3) ◽  
pp. 231-234 ◽  
Author(s):  
Wannian Liang ◽  
Jipan Xie ◽  
Hongpeng Fu ◽  
Eric Q. Wu
Keyword(s):  
Health Care ◽  
Health Economics ◽  
Health Care Reform ◽  
Outcomes Research

scholarly journals Artificial Intelligent in Healthcare

2019 ◽  
Vol 11 (2) ◽  
pp. 125-35
Author(s):  
Anna Meiliana ◽  
Nurrani Mustika Dewi ◽  
Andi Wijaya
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Big Data ◽  
Complex Disease ◽  
Disease Diagnosis ◽  
Large Set ◽  
Health Records ◽  
Treatment Data ◽  
Precision Health

BACKGROUND: Giant transformations are going on currently in health care, and the greatest force behind this phenomenon is data.CONTENT: Big data has arrived into medicine field, lead to potential enhancement in accountability, quality, efficiency, and innovation. Most updated, artificial intelligence (AI) and machine-learning (ML) techniques rapidly developed, bring forth the big data analysis into more useful applications, from resource allocation to complex disease diagnosis. To realize this, a very large set of health-care data is needed for algorithms training and evaluation, including patients’ treatment data, patients respond to treatment, and personal patient information, such as genetic data, family history, health behavior, and vital signs.SUMMARY: Precision Health involving preventive, predictive, personalized and precise. The arrival of AI and ML will enhance and facilitates the improvement of this relationship through better accuracy, productivity, and workflow, thus develop a health system that will go beyond just curing disease, but further into wellness that preventing disease before it strikes, thus the patient–doctor bond is expected to be reformed and not be eroded.KEYWORDS: artificial intelligence, machine learning, deep learning, electronic health records, big data

Machine Learning for Predicting Health Care Utilization in COPD using Quantitative CT Imaging

2020 ◽  
Author(s):  
Amir Moslemi ◽  
Wan C. Tan ◽  
Jean Bourbeau ◽  
James C. Hogg ◽  
Harvey O. Coxson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Care Utilization ◽  
Ct Imaging ◽  
Care Utilization ◽  
Quantitative Ct

Treatment Regimens and Health Care Utilization in Children with Persistent Asthma Symptoms

2006 ◽  
Vol 43 (5) ◽  
pp. 385-391 ◽  
Author(s):  
H. Lorrie Yoos ◽  
Harriet Kitzman ◽  
Jill S. Halterman ◽  
Charles Henderson ◽  
Kimberly Sidora-Arcoleo ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Utilization ◽  
Persistent Asthma ◽  
Care Utilization ◽  
Treatment Regimens ◽  
Asthma Symptoms

scholarly journals Precision Health Economics and Outcomes Research to Support Precision Medicine: Big Data Meets Patient Heterogeneity on the Road to Value

2016 ◽  
Vol 6 (4) ◽  
pp. 20 ◽  
Author(s):  
Yixi Chen ◽  
Gregory Guzauskas ◽  
Chengming Gu ◽  
Bruce Wang ◽  
Wesley Furnback ◽  
...  
Keyword(s):  
Big Data ◽  
Health Economics ◽  
Precision Medicine ◽  
Outcomes Research ◽  
The Road ◽  
Patient Heterogeneity ◽  
On The Road ◽  
Precision Health

scholarly journals John Nyman and the Economics of Health Care Moral Hazard

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Sander Kelman ◽  
Albert Woodward
Keyword(s):  
Health Care ◽  
Health Insurance ◽  
Health Economics ◽  
Moral Hazard ◽  
Care Utilization ◽  
Risk Avoidance ◽  
Income Transfer ◽  
Welfare Maximization ◽  
Economics Of Health ◽  
Demand For Health

In 2003, John Nyman published The Theory of Demand for Health Insurance. His principal contributions are (1) to replace the previously unexamined axiom of risk avoidance with the axiom of welfare maximization; (2) to uncover a misinterpretation in the literature on moral hazard, namely, the insurance payoff as a price reduction, rather than as an income transfer. The immediate consequence of these reformulations is to recognize insurance-induced health care utilization as resulting in an increase in social welfare. Despite its evident validity and enormous implications, Nyman’s work has received very little attention or recognition in the health economics literature.

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