scholarly journals A new method for making treatment decisions for incapacitated patients: what do patients think about the use of a patient preference predictor?

2016 ◽  
Vol 42 (4) ◽  
pp. 235-241 ◽  
Author(s):  
David Wendler ◽  
Bob Wesley ◽  
Mark Pavlick ◽  
Annette Rid
Keyword(s):  
Patient Preference ◽  
Treatment Decisions ◽  
New Method ◽  
Incapacitated Patients

scholarly journals Autonomy-based criticisms of the patient preference predictor

2021 ◽  
pp. medethics-2021-107629
Author(s):  
EJ Jardas ◽  
David Wasserman ◽  
David Wendler
Keyword(s):  
Decision Making ◽  
Patient Preference ◽  
Patient Autonomy ◽  
Treatment Decisions ◽  
Treatment Preferences ◽  
Decision Making Process ◽  
Number Of Factors ◽  
Computer Based ◽  
Incapacitated Patients ◽  
Substituted Judgement

The patient preference predictor (PPP) is a proposed computer-based algorithm that would predict the treatment preferences of decisionally incapacitated patients. Incorporation of a PPP into the decision-making process has the potential to improve implementation of the substituted judgement standard by providing more accurate predictions of patients’ treatment preferences than reliance on surrogates alone. Yet, critics argue that methods for making treatment decisions for incapacitated patients should be judged on a number of factors beyond simply providing them with the treatments they would have chosen for themselves. These factors include the extent to which the decision-making process recognises patients’ freedom to choose and relies on evidence the patient themselves would take into account when making treatment decisions. These critics conclude that use of a PPP should be rejected on the grounds that it is inconsistent with these factors, especially as they relate to proper respect for patient autonomy. In this paper, we review and evaluate these criticisms. We argue that they do not provide reason to reject use of a PPP, thus supporting efforts to develop a full-scale PPP and to evaluate it in practice.

scholarly journals Patient preference predictors and the problem of naked statistical evidence

2018 ◽  
Vol 44 (12) ◽  
pp. 857-862 ◽  
Author(s):  
Nathaniel Paul Sharadin
Keyword(s):  
Patient Preference ◽  
Treatment Decisions ◽  
Statistical Evidence ◽  
The Other ◽  
Medical Professionals ◽  
Legal Scholarship ◽  
Normative Problem ◽  
Incapacitated Patients ◽  
Legal Domain ◽  
The One

Patient preference predictors (PPPs) promise to provide medical professionals with a new solution to the problem of making treatment decisions on behalf of incapacitated patients. I show that the use of PPPs faces a version of a normative problem familiar from legal scholarship: the problem of naked statistical evidence. I sketch two sorts of possible reply, vindicating and debunking, and suggest that our reply to the problem in the one domain ought to mirror our reply in the other. The conclusion is thus conditional: if we think the problem of naked statistical evidence is a serious problem in the legal domain, then we should be concerned about the symmetrical problem for PPPs.

Integrating Patient Preference into Treatment Decisions for Men with Prostate Cancer at the Point of Care

2016 ◽  
Vol 196 (6) ◽  
pp. 1640-1644 ◽  
Author(s):  
David C. Johnson ◽  
Dana E. Mueller ◽  
Allison M. Deal ◽  
Mary W. Dunn ◽  
Angela B. Smith ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Patient Preference ◽  
Point Of Care ◽  
Treatment Decisions

Patient Confidentiality and the Surrogate's Right to Know

2000 ◽  
Vol 28 (2) ◽  
pp. 137-143 ◽  
Author(s):  
Lynn A. Jansen ◽  
Lainie Friedman Ross
Keyword(s):  
Treatment Decisions ◽  
Decision Makers ◽  
Best Interests ◽  
Right To Know ◽  
Patient Confidentiality ◽  
Surrogate Decision Makers ◽  
Incapacitated Patients ◽  
Surrogate Decision

Physicians treating newly incapacitated patients often must navigate surrogate decision-makers through a difficult course of treatment decisions. Such a process can be complex. Physicians must not only explain the medical facts and prognosis to the surrogate, but also attempt to ensure that the surrogate arrives at decisions that are consistent with the patient's own values and wishes. Where these values and wishes are unknown, physicians must help surrogates make decisions that reflect the patient's best interests.

scholarly journals How Should Treatment Decisions Be Made for Incapacitated Patients, and Why?

PLoS Medicine ◽  
2007 ◽  
Vol 4 (3) ◽  
pp. e35 ◽  
Author(s):  
David I Shalowitz ◽  
Elizabeth Garrett-Mayer ◽  
David Wendler
Keyword(s):  
Treatment Decisions ◽  
Incapacitated Patients

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.

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