Balanced Opioid Prescribing via a Clinical Trade-Off: Pain Relief vs. Adverse Effects of Discomfort, Dependence, and Tolerance/Hypersensitivity

In the backdrop of the opioid epidemic, opioid prescribing has distinct medical and social challenges. Overprescribing contributes to the ongoing opioid epidemic, whereas underprescribing yields inadequate pain relief. Moreover, opioids have serious adverse effects including tolerance and increased sensitivity to pain, paradoxically inducing more pain. Prescribing trade-offs are recognized but not modeled in the literature. We study the prescribing decisions for chronic, acute, and persistent pain types to minimize the cumulative pain that incorporates opioid adverse effects (discomfort and dependence) and the risk of tolerance or hypersensitivity (THS) developed with opioid use. After finding closed-form solutions for each pain type, we analytically investigate the sensitivity of acute pain prescriptions and examine policies on incorporation of THS, patient handover, and adaptive treatments. Our analyses show that the role of adverse effects in prescribing decisions is as critical as that of the pain level. Interestingly, we find that the optimal prescription duration is not necessarily increasing with the recovery time. We show that not incorporating THS or information curtailment at patient handovers leads to overprescribing that can be mitigated by adaptive treatments. Last, using real-life pain and opioid use data from two sources, we estimate THS parameters and discuss the proximity of our model to clinical practice. This paper has a pain management framework that leads to tractable models. These models can potentially support balanced opioid prescribing after their validation in a clinical setting. Then, they can be helpful to policy makers in assessment of prescription policies and of the controversy around over- and underprescribing.

Rehabilitation settings after joint replacement: an application of multiattribute preference elicitation

While advances in medical treatment and technologies have the potential to improve the delivery of health care, their use typically involves making multiple, complex decisions. Patients and their medical providers may share in the decision-making processes and balance a variety of criteria and/or attributes in the pursuit of improved health. This necessitates a stronger understanding of the role of human behavior in health care processes and presents a timely opportunity to use decision analysis tools to contribute to this important aspect of health care operations. This article reports on the application of multiattribute preference elicitation to identify postsurgical rehabilitation setting options for elective hip and knee replacement patients and their discharge planning team prior to placement in these settings. These preferences are analyzed to identify trends in emphases across patients and the discharge planning team, including a comparison with actual outcomes to determine the extent of congruence with each other, an important component of patient-centered care. Variances are identified in what patients and the discharge planning team expected and what actually happened. Reasons for these variances are discussed.

A linear index for predicting joint health-states utilities from single health-states utilities

Direct elicitation of utilities for joint health (JS) states may pose substantial interview burden, while traditional models to predict these utilities from utilities of component single states (SS) are inconsistent with the data. Using individual-level data on utilities for health states associated with prostate cancer, we report the performance of a new model that encompasses three traditional models - additive, multiplicative, and minimum - previously used for predicting utilities for joint health states. Describing utilities in terms of utility losses l(.) relative to prefect health, our final estimated linear index for predicting joint health-state utilities is El(JS)=0.05+0.72 x max l(SS1),l(SS2)+0.33.min x l(SS1),l(SS2)-0.18 x l(SS1) x l(SS2). Based on out-of-sample predictions, this model produces up to 50% reduction in mean-square error compared with traditional models and consistent prediction across different ranges of joint-state utilities, which the traditional models do not. Parameter estimates of the new model proposed here provide direct evidence on the inconsistencies of the traditional models, are grounded in psychological theory by emphasizing the more severe component of a joint health state, and provide a simple linear index to generate consistent predictions of utilities for joint health states. Further validation of this function for joint health states in other clinical scenarios is warranted.