The insurance value of medical innovation

Lakdawalla, Darius N.; Malani, Anup; Reif, Julian

doi:10.1016/j.jpubeco.2016.11.012

Cited by 68 publications

(75 citation statements)

References 38 publications

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“…These elicitations should be carried out among the patients who are the direct beneficiaries of this intervention as they are diagnosed with the specific clinical condition as well as the healthy individuals who do not have that specific clinical condition but face varying risks of being diagnosed with that condition in a given year. This combination of values from both patients and nonpatients would reflect the true value of a health care intervention because it would incorporate the value of health insurance covering that intervention [38]. On the basis of these elicitations, a WTP value index could be developed relating to each of the dimensions of a health care intervention.…”

Section: Other Value Elicitation Methodsmentioning

confidence: 99%

Approaches to Aggregation and Decision Making—A Health Economics Approach: An ISPOR Special Task Force Report [5]

et al. 2018

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The fifth section of our Special Task Force report identifies and discusses two aggregation issues: 1) aggregation of cost and benefit information across individuals to a population level for benefit plan decision making and 2) combining multiple elements of value into a single value metric for individuals. First, we argue that additional elements could be included in measures of value, but such elements have not generally been included in measures of quality-adjusted life-years. For example, we describe a recently developed extended cost-effectiveness analysis (ECEA) that provides a good example of how to use a broader concept of utility. ECEA adds two features-measures of financial risk protection and income distributional consequences. We then discuss a further option for expanding this approach-augmented CEA, which can introduce many value measures. Neither of these approaches, however, provide a comprehensive measure of value. To resolve this issue, we review a technique called multicriteria decision analysis that can provide a comprehensive measure of value. We then discuss budget-setting and prioritization using multicriteria decision analysis, issues not yet fully resolved. Next, we discuss deliberative processes, which represent another important approach for population- or plan-level decisions used by many health technology assessment bodies. These use quantitative information on CEA and other elements, but the group decisions are reached by a deliberative voting process. Finally, we briefly discuss the use of stated preference methods for developing "hedonic" value frameworks, and conclude with some recommendations in this area.

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Section: Other Value Elicitation Methodsmentioning

confidence: 99%

Approaches to Aggregation and Decision Making—A Health Economics Approach: An ISPOR Special Task Force Report [5]

et al. 2018

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“…For healthy people, access to healthcare/novel treatments reduces the financial risk of falling ill, but for sick people it reduces the physical risks of being ill. The more likely and/or more severe the disease, the more valuable the treatment to healthy people 69 . Fairness or equity concerns arise when members of society do not believe that health benefit is being allocated to persons who most deserve to enjoy benefits.…”

Section: Societal Preferences For the Distribution Of Qalys Among Indmentioning

confidence: 99%

“…In such cases, there is a tradeoff between efficiency and equity, with a willingness to sacrifice some of the former for the latter. While it is often recognized in HTA that equity concerns are important, there has been a lack of uptake of formally accepted methods for addressing equity, which reduces transparency 65,69 .…”

Section: Societal Preferences For the Distribution Of Qalys Among Indmentioning

confidence: 99%

Limitations of traditional health technology assessment methods and implications for the evaluation of novel therapies

Doctor

MacEwan²

2017

Current Medical Research and Opinion

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“…Insurance companies compute the risk [6] [7] for the subjects for formulating the health insurance policies. These days, there is a sincere attempt by both the insurance and medical industry to work together for estimating risk.…”

Section: Introductionmentioning

confidence: 99%

Detection of Heart Diseases using Machine Learning and Data Mining

Kaur¹,

Singh²

2019

IJCA

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In this research work, alternative seven algorithms that have low overhead have been evaluated for detecting and predicting heart failure risk (HFR), Thirteen risk factors have been considered for the automating the prediction process using machine learning algorithm. Correlation and Histogram based analysis shows that at least four to five parameters vary together. Using this criterion the number of minimum risk factors to label a patient as low or high risk. Secondly, criteria to group the patients were with the help of binning the data based on medical normal ranges for a person. It was found that the Logistic Regression and Linear Discriminant algorithm is best suited for classification of heart failure risk as its results are numerically stable, consistent, reliable, and reproducible. This was validated using tenfold validation process.

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The insurance value of medical innovation

Cited by 68 publications

References 38 publications

Approaches to Aggregation and Decision Making—A Health Economics Approach: An ISPOR Special Task Force Report [5]

Approaches to Aggregation and Decision Making—A Health Economics Approach: An ISPOR Special Task Force Report [5]

Limitations of traditional health technology assessment methods and implications for the evaluation of novel therapies

Detection of Heart Diseases using Machine Learning and Data Mining

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