Partial Personalization of Medical Treatment Decisions: Adverse Effects and Possible Solutions

2021 ◽  
pp. 0272989X2110137
Author(s):  
Christopher Weyant ◽  
Margaret L. Brandeau
Keyword(s):  
Adverse Effects ◽  
Health Outcomes ◽  
Medical Treatment ◽  
Patient Preferences ◽  
Population Level ◽  
Treatment Decisions ◽  
New Method ◽  
Patient Specific ◽  
Life Years

Background Personalizing medical treatment decisions based on patient-specific risks and/or preferences can improve health outcomes. Decision makers frequently select treatments based on partial personalization (e.g., personalization based on risks but not preferences or vice versa) due to a lack of data about patient-specific risks and preferences. However, partially personalizing treatment decisions based on a subset of patient risks and/or preferences can result in worse population-level health outcomes than no personalization and can increase the variance of population-level health outcomes. Methods We develop a new method for partially personalizing treatment decisions that avoids these problems. Using a case study of antipsychotic treatment for schizophrenia, as well as 4 additional illustrative examples, we demonstrate the adverse effects and our method for avoiding them. Results For the schizophrenia treatment case study, using a previously proposed modeling approach for personalizing treatment decisions and using only a subset of patient preferences regarding treatment efficacy and side effects, mean population-level health outcomes decreased by 0.04 quality-adjusted life-years (QALYs; 95% credible interval [crI]: 0.02–0.06) per patient compared with no personalization. Using our new method and considering the same subset of patient preferences, mean population-level health outcomes increased by 0.01 QALYs (95% crI: 0.00–0.03) per patient as compared with no personalization, and the variance decreased. Limitations We assumed a linear and additive utility function. Conclusions Selecting personalized treatments for patients should be done in a way that does not decrease expected population-level health outcomes and does not increase their variance, thereby resulting in worse risk-adjusted, population-level health outcomes compared with treatment selection with no personalization. Our method can be used to ensure this, thereby helping patients realize the benefits of treatment personalization without the potential harms.

scholarly journals Pharmacist prescribing and care improves cardiovascular risk, but is it cost-effective? A cost-effectiveness analysis of the RxEACH study

2019 ◽  
Vol 152 (4) ◽  
pp. 257-266 ◽  
Author(s):  
Yazid N. Al Hamarneh ◽  
Karissa Johnston ◽  
Carlo A. Marra ◽  
Ross T. Tsuyuki
Keyword(s):  
Cost Effectiveness ◽  
Health Outcomes ◽  
Usual Care ◽  
Population Level ◽  
Cost Effective ◽  
Pharmacist Intervention ◽  
Sensitivity Analyses ◽  
Pharmacist Prescribing ◽  
Canadian Health Care System ◽  
Life Years

Background: The RxEACH randomized trial demonstrated that community pharmacist prescribing and care reduced the risk for cardiovascular (CV) events by 21% compared to usual care. Objective: To evaluate the economic impact of pharmacist prescribing and care for CV risk reduction in a Canadian setting. Methods: A Markov cost-effectiveness model was developed to extrapolate potential differences in long-term CV outcomes, using different risk assessment equations. The mean change in CV risk for the 2 groups of RxEACH was extrapolated over 30 years, with costs and health outcomes discounted at 1.5% per year. The model incorporated health outcomes, costs and quality of life to estimate overall cost-effectiveness. It was assumed that the intervention would be 50% effective after 10 years. Individual-level results were scaled up to population level based on published statistics (29.2% of Canadian adults are at high risk for CV events). Costs considered included direct medical costs as well as the costs associated with implementing the pharmacist intervention. Uncertainty was explored via probabilistic sensitivity analysis. Results: It is estimated that the Canadian health care system would save more than $4.4 billion over 30 years if the pharmacist intervention were delivered to 15% of the eligible population. Pharmacist care would be associated with a gain of 576,689 quality-adjusted life years and avoid more than 8.9 million CV events. The intervention is economically dominant (i.e., it is both more effective and reduces costs when compared to usual care). Conclusion: Across a range of 1-way and probabilistic sensitivity analyses of key parameters and assumptions, pharmacist prescribing and care are both more effective and cost-saving compared to usual care. Canadians need and deserve such care.

scholarly journals Personalizing Medical Treatment Decisions: Integrating Meta-analytic Treatment Comparisons with Patient-Specific Risks and Preferences

2019 ◽  
Vol 39 (8) ◽  
pp. 998-1009
Author(s):  
Christopher Weyant ◽  
Margaret L. Brandeau ◽  
Sanjay Basu
Keyword(s):  
Side Effects ◽  
Treatment Decisions ◽  
Personalized Treatment ◽  
Patient Treatment ◽  
Patient Specific ◽  
Treatment Preferences ◽  
Modeling Framework ◽  
Risk Estimates ◽  
Life Years ◽  
Acceptable Risks

Background. Network meta-analyses (NMAs) that compare treatments for a given condition allow physicians to identify which treatments have higher or lower probabilities of reducing the risks of disease complications or increasing the risks of treatment side effects. Translating these data into personalized treatment plans requires integration of NMA data with patient-specific pretreatment risk estimates and preferences regarding treatment objectives and acceptable risks. Methods. We introduce a modeling framework to integrate data probabilistically from NMAs with data on individualized patient risk estimates for disease outcomes, treatment preferences (such as willingness to incur greater side effects for increased life expectancy), and risk preferences. We illustrate the modeling framework by creating personalized plans for antipsychotic drug treatment and evaluating their effectiveness and cost-effectiveness. Results. Compared with treating all patients with the drug that yields the greatest quality-adjusted life-years (QALYs) on average (amisulpride), personalizing the selection of antipsychotic drugs for schizophrenia patients over the next 5 years would be expected to yield 0.33 QALYs (95% credible interval [crI]: 0.30–0.37) per patient at an incremental cost of $4849/QALY gained (95% crI: dominant–$12,357), versus 0.29 and 0.04 QALYs per patient when accounting for only risks or preferences, respectively, but not both. Limitations. The analysis uses a linear, additive utility function to reflect patient treatment preferences and does not consider potential variations in patient time discounting. Conclusions. Our modeling framework rigorously computes what physicians normally have to do mentally. By integrating 3 key components of personalized medicine—evidence on efficacy, patient risks, and patient preferences—the modeling framework can provide personalized treatment decisions to improve patient health outcomes.

Personalization of Medical Treatment Decisions: Simplifying Complex Models while Maintaining Patient Health Outcomes

2021 ◽  
pp. 0272989X2110379
Author(s):  
Christopher Weyant ◽  
Margaret L. Brandeau
Keyword(s):  
Variable Selection ◽  
Medical Treatment ◽  
Treatment Decisions ◽  
Original Model ◽  
Patient Specific ◽  
Simplified Models ◽  
Ensemble Models ◽  
Computational Performance ◽  
Patient Health ◽  
Complex Models

Background Personalizing medical treatments based on patient-specific risks and preferences can improve patient health. However, models to support personalized treatment decisions are often complex and difficult to interpret, limiting their clinical application. Methods We present a new method, using machine learning to create meta-models, for simplifying complex models for personalizing medical treatment decisions. We consider simple interpretable models, interpretable ensemble models, and noninterpretable ensemble models. We use variable selection with a penalty for patient-specific risks and/or preferences that are difficult, risky, or costly to obtain. We interpret the meta-models to the extent permitted by their model architectures. We illustrate our method by applying it to simplify a previously developed model for personalized selection of antipsychotic drugs for patients with schizophrenia. Results The best simplified interpretable, interpretable ensemble, and noninterpretable ensemble models contained at most half the number of patient-specific risks and preferences compared with the original model. The simplified models achieved 60.5% (95% credible interval [crI]: 55.2–65.4), 60.8% (95% crI: 55.5–65.7), and 83.8% (95% crI: 80.8–86.6), respectively, of the net health benefit of the original model (quality-adjusted life-years gained). Important variables in all models were similar and made intuitive sense. Computation time for the meta-models was orders of magnitude less than for the original model. Limitations The simplified models share the limitations of the original model (e.g., potential biases). Conclusions Our meta-modeling method is disease- and model- agnostic and can be used to simplify complex models for personalization, allowing for variable selection in addition to improved model interpretability and computational performance. Simplified models may be more likely to be adopted in clinical settings and can help improve equity in patient outcomes.

Medical treatment decisions: Individual differences in risk tradeoffs

1998 ◽  
Author(s):  
Andrea L. Washburne ◽  
Sandra L. Schneider ◽  
Teresa Broughton
Keyword(s):  
Individual Differences ◽  
Medical Treatment ◽  
Treatment Decisions ◽  
Risk Tradeoffs

STRATEGI KOMUNIKASI CROWDFUNDING PADA KOLASE.COM

2019 ◽  
Vol 2 (2) ◽  
pp. 177-187
Author(s):  
Venessa Agusta Gogali ◽  
Fajar Muharam ◽  
Syarif Fitri
Keyword(s):  
Social Media ◽  
Communication Strategies ◽  
Communication Strategy ◽  
New Method ◽  
Media Communication ◽  
Social Activists ◽  
Social Media Communication

Crowdfunding is a new method in fundraising activities based online. Moreover, the level of penetration of social media to the community is increasingly high. This makes social activists and academics realize that it is important to study social media communication strategies in crowdfunding activities. There is encouragement to provide an overview of crowdfunding activities. So the author conducted a research on "Crowdfunding Communication Strategy Through Kolase.com Through Case Study on the #BikinNyata Program Through the Kolase.com Website that successfully achieved the target. Keywords: Strategic of Communication, Crowdfunding, Social Media.

Identifying Public Health Problems

2018 ◽  
Author(s):  
Scott Burris ◽  
Micah L. Berman ◽  
Matthew Penn, and ◽  
Tara Ramanathan Holiday
Keyword(s):  
Public Health ◽  
Health Problems ◽  
Health Priorities ◽  
Disability Adjusted Life Years ◽  
Quality Adjusted Life Years ◽  
Disability Adjusted Life ◽  
Life Years ◽  
Study Designs ◽  
Quality Adjusted Life

Chapter 5 discusses the use of epidemiology to identify the source of public health problems and inform policymaking. It uses a case study to illustrate how researchers, policymakers, and practitioners detect diseases, identify their sources, determine the extent of an outbreak, and prevent new infections. The chapter also defines key measures in epidemiology that can indicate public health priorities, including morbidity and mortality, years of potential life lost, and measures of lifetime impacts, including disability-adjusted life years and quality-adjusted life years. Finally, the chapter reviews epidemiological study designs, differentiating between experimental and observational studies, to show how to interpret data and identify limitations.

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