Course:CPSC522/Patient Centric Decision Support The Utility Assessment Method

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Patient-Centric Decision Support: The Utility Assessment Method

This article champions prioritizing patients in decision-support systems, aiming to empower individuals to make well-informed choices tailored to their unique needs and preferences in the realm of medicine.

Principal Author: Amirhossein Abaskohi

Collaborators:

Abstract

Decision-support systems in medicine are designed to assist healthcare professionals and patients in making informed decisions. However, the challenge lies in accurately capturing the patient's preferences and goals. This article advocates for the use of a utility assessment approach to ascertain the patient's preferences. This method involves directly asking the patient about their preferences and assigning a utility value to each possible outcome. This approach is patient-centered, transparent, and can be integrated into the decision-making toolkit. It also addresses the issue of bias in AI-based decision-support systems by focusing on individual patient preferences rather than relying on historical data.

Builds on

Building upon the foundation of developing a decision-support system, the method introduced in this article rests on the pivotal concepts of utility and utility assessment. In this context, utility assessment becomes integral, engaging in the quantitative measurement of patients' sentiments towards various health outcomes. This approach not only aligns with our patient-centric[1] philosophy but also extends the discourse established in the article, emphasizing the imperative of accurately capturing and incorporating patient preferences within decision-support systems in the field of medicine.

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Content

Introduction

In the realm of healthcare decision-making, the intricate challenge lies in capturing the nuanced preferences and distinct goals of individual patients within the framework of decision-support systems. This article delves into the crucial role of patient preferences in designing and implementing such systems, introducing a patient-centered[1] and transparent utility assessment approach[2]. By actively engaging patients and assigning utility values to outcomes, this method not only caters to individual preferences but also acts as a shield against biases inherent in AI-based systems. Consequently, it offers a more personalized and equitable approach to medical decision support.

Crafted to aid healthcare professionals and patients in informed decision-making, decision-support systems in medicine grapple with the task of accurately incorporating patient preferences and goals. Recognizing the uniqueness of each patient— with their specific values, medical history, and preferences—is crucial. A decision-support system must navigate these distinctions to provide optimal advice tailored to the individual patient.

The VAS-Based Utility Assessment Approach

This article advocates for the use of a Visual Analog Scale (VAS)-based utility assessment approach, enhancing the utility assessment method introduced earlier. The VAS adds a dynamic visual element to preference elicitation, allowing patients to express the intensity of their preferences on a continuous scale. By directly engaging patients and employing the VAS to rank different treatment options, this method assigns utility values to guide the decision-making process[3].

Crucially, the VAS-based approach remains patient-centered, focusing on individual patient preferences without reliance on historical data or general assumptions. Its transparency is underscored by the direct involvement of patients in the decision-making process. Moreover, the VAS-based utility assessment approach seamlessly integrates into the decision-making toolkit, providing a structured and systematic means of incorporating patient preferences into medical decisions.

The VAS-Based Utility Assessment Approach: A Strategic Implementation Guide

  1. Initiation: Commence the VAS-based utility assessment process proactively during routine healthcare visits to cultivate a comprehensive understanding of patient preferences over time.
  2. Education: Educate patients about the VAS-based utility assessment approach during healthcare visits, emphasizing the pivotal role of their preferences in shaping decisions. Provide insights into potential benefits and risks associated with various treatment options.
  3. Preemptive Preference Elicitation: Conduct initial preference elicitation during routine health check-ups or designated wellness visits, establishing a baseline understanding of patient values and priorities. This step mirrors preemptive will writing during times of good health.
  4. Ongoing Elicitation: Periodically continue preference elicitation during routine healthcare interactions, adapting to changes in the patient's health status, lifestyle, or priorities.
  5. Decision-Specific Elicitation: Conduct targeted preference elicitation when a specific healthcare decision arises, gathering information directly relevant to the decision at hand. This ensures that preferences are current and specific.
  6. Preference Refinement: Refine and clarify preferences during decision-specific elicitation based on the unique circumstances of the healthcare decision, adding granularity to utility values.
  7. Identifying Key Outcomes: Collaboratively focus on identifying key and relevant outcomes for the specific healthcare decision. Involve healthcare professionals, patients, and relevant stakeholders in creating a comprehensive list of potential outcomes.
  8. Utility Value Assignment: Collaboratively assign utility values to identified key outcomes using the VAS, quantifying preferences for an objective integration into the decision-making process.
  9. Documentation: Document patient preferences and assigned utility values using the VAS in the patient's health record or a dedicated decision-support system for accessible reference in future healthcare decisions.

Considerations for Implementation:

  • Workflow Integration: Evaluate and seamlessly integrate the VAS-based utility assessment approach into existing healthcare workflows for routine adoption.
  • Training and Resources: Provide healthcare professionals with training on implementing the VAS-based utility assessment approach, emphasizing effective communication strategies for preference elicitation.
  • Technology Integration: Explore the integration of technology, such as decision support tools or electronic health records, to streamline the VAS-based utility assessment process and facilitate documentation.
  • Patient Engagement: Implement strategies for actively engaging patients in the VAS-based utility assessment process, promoting awareness and understanding of the role of preferences in their healthcare decisions

This comprehensive approach remains patient-centered, directly involving individual patient preferences while avoiding reliance on historical data or assumptions. Its transparent nature ensures active patient participation in the decision-making process, and its seamless integration into the decision-making toolkit provides a structured and systematic approach to incorporating patient preferences into medical decisions[1].

Example: Applying the VAS-based Utility Assessment Approach in Cancer Treatment Decision-Making

In the realm of cancer treatment decision-making, the VAS-based utility assessment approach emerges as a pivotal tool for tailoring decisions to individual patient preferences. Early in the process, the approach is introduced to underscore the significance of personalized treatment choices, marking the initiation of a patient-centered journey[4].

Education becomes a pivotal step, with healthcare providers offering detailed insights into various treatment options, elucidating potential benefits, associated risks, and expected outcomes. This equips the patient with a comprehensive understanding, laying the groundwork for informed decision-making.

A proactive approach is adopted during routine check-ups for preemptive preference elicitation using the VAS. This early gathering of patient values and preferences establishes a baseline understanding, akin to anticipating needs before decisions become imminent. As the treatment journey progresses, ongoing preference elicitation becomes a dynamic process, adapting to the patient's evolving health status.

Decision-specific elicitation becomes crucial when specific treatment decisions arise. Tailoring the preference elicitation to the decision at hand allows for a focused exploration of outcomes directly relevant to the patient's situation. The patient, supported by the healthcare team, refines their preferences based on the nuances of the decision context, adding a layer of depth to the process.

Collaboration between the patient and healthcare team is paramount in identifying key outcomes using the VAS. Factors such as the likelihood of cancer recurrence and the impact on overall well-being are identified collaboratively, ensuring a comprehensive consideration of relevant aspects.

Active patient involvement is emphasized in assigning utility values to outcomes using the VAS. This collaborative quantification provides a structured basis for prioritizing preferences and informs the eventual treatment decision. The assigned values and documented preferences are then stored for easy reference in the patient's record, ensuring continuity in patient-centered care.

In terms of implementation, seamless integration into routine healthcare practices is prioritized. Training healthcare professionals in effective communication during the VAS-based utility assessment process ensures a smooth and patient-friendly experience. Technological integration streamlines the process, making it efficient and accessible. Patient engagement remains a cornerstone, actively involving them in their care journey and fostering a sense of control and satisfaction with their personalized treatment plan.

This condensed example illustrates the practical application of the VAS-based utility assessment approach in cancer treatment decision-making, highlighting its adaptability and patient-centered focus within routine healthcare practices.

Addressing Bias in AI-Based Decision-Support Systems

The inherent bias in AI-based decision-support systems, often stemming from historical data, raises concerns about equitable healthcare recommendations[5]. While these systems aim for objectivity, they may inadvertently perpetuate existing biases. The utility assessment approach, utilizing methods like VAS, addresses this by prioritizing individual patient preferences. This personalized approach shifts the focus from relying solely on historical data patterns to centering on the unique values and priorities of each patient. Continuous preference elicitation ensures real-time updates, minimizing reliance on potentially outdated societal norms and providing transparency in decision-making.

Utility assessments play a pivotal role in mitigating bias by involving patients directly in expressing their preferences. Methods like VAS quantify these preferences, offering transparency in the decision-making process. This individualized and transparent approach not only minimizes the impact of historical biases but also fosters trust in AI-based decision-support systems.

Addressing Black Box in AI-Based Decision-Support Systems

In addition, the utility assessment approach can also help to mitigate the "black box" problem often associated with AI-based decision-support systems. This problem refers to the lack of transparency in how these systems make decisions. By incorporating the utility assessment approach, healthcare professionals and patients can understand how the system arrived at a particular recommendation, thereby increasing trust in the system[6].

Incorporating Patient Preferences in Decision-Support Systems

Incorporating patient preferences into decision-support systems is not only ethically sound but also has practical benefits[7]. Research has shown that when patients are involved in the decision-making process, they are more likely to adhere to the treatment plan, leading to better health outcomes. Furthermore, patient involvement can also lead to greater patient satisfaction and improved quality of life.

However, incorporating patient preferences into decision-support systems is not without challenges. It requires a shift from a paternalistic model of healthcare, where the doctor makes decisions on behalf of the patient, to a shared decision-making model, where the patient and healthcare professional make decisions together. This shift requires changes in healthcare professional training and practice, as well as changes in the design and implementation of decision-support systems.

Conclusion

In conclusion, the utility assessment approach provides a patient-centered and transparent method for capturing patient preferences in decision-support systems. It addresses the issue of bias in AI-based decision-support systems and can be integrated into the decision-making toolkit. Therefore, resources should be allocated towards implementing this approach in decision-support systems in medicine.

Annotated Bibliography

  1. ↑ 1.0 1.1 1.2 Neumann PJ, Weinstein MC. Legislating against use of cost-effectiveness information. N Engl J Med. 2010 Oct 14;363(16):1495-7.
  2. ↑ Torrance GW. Measurement of health state utilities for economic appraisal: a review. Journal of health economics. 1986 Mar 1;5(1):1-30.
  3. ↑ Parkin D, Devlin N. Is there a case for using visual analogue scale valuations in cost‐utility analysis?. Health economics. 2006 Jul;15(7):653-64.
  4. ↑ Pickard AS, Neary MP, Cella D. Estimation of minimally important differences in EQ-5D utility and VAS scores in cancer. Health and quality of life outcomes. 2007 Dec;5:1-8.
  5. ↑ Gupta M, Parra CM, Dennehy D. Questioning racial and gender bias in AI-based recommendations: Do espoused national cultural values matter?. Information Systems Frontiers. 2021 Jun 20:1-7.
  6. ↑ Rudin C. Why black box machine learning should be avoided for high-stakes decisions, in brief. Nature Reviews Methods Primers. 2022 Oct 27;2(1):81.
  7. ↑ Nuwagaba J, Olum R, Bananyiza A, Wekha G, Rutayisire M, Agaba KK, Chekwech G, Nabukalu J, Nanyonjo GG, Namagembe R, Nantongo S. Patients’ involvement in decision-making during healthcare in a developing country: a cross-sectional study. Patient preference and adherence. 2021 May 26:1133-40.

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