Abstract PO2-10-12: The devil is in the details: Workflow analysis of breast cancer treatment decision making and implications for decision support design

Abstract

Abstract While numerous decision aids have been developed to support treatment decision-making in breast cancer1, widespread implementation remains limited due to challenges integrating these tools in clinical workflows2. Given these persistent issues, we need to better understand the complex workflows and needs of decision aid users - patients, their family caregivers, and breast cancer clinicians. Previous studies have explored various aspects of breast cancer team workflows3,4. Despite this work, it is still unclear how these complex workflows support or hinder decision aid use. In this study, we conducted an in-depth analysis of breast cancer team workflow to elicit design requirements for informatics tools to support patient-centered decision-making. This study was part of a larger project aimed at designing a COMputeried PAtient-centered Collaborative Technology (COMPACT) to support personalized decision-making for breast cancer patients. We conducted observations of clinicians and patients at one breast center, using a tablet computer with smart pen to take notes on all aspects of clinic workflow. We uploaded all observation notes into a qualitative data analysis software, Dedoose. Two researchers coded the observations in an inductive, consensus-based process. We met to review our codes and identify key themes emerging in the data. We observed 95 hours and 127 patient encounters across 20 clinician-centered and 8 patient-centered observations. We identified 10 themes related to the design of decision support, which we grouped into the 4 components of workflow6 (see Table 1). We identified numerous patient-specific factors communicated from the patient to the clinical team that influenced treatment decision-making. These “decision factors” covered the broader scope of the patient’s life that was important in the decision-making process (e.g., upcoming family wedding, work). Patients were frequently “knowledge brokers” and were asked to remember and relay information across members of the care team. We identified 28 different tools and technologies used to support treatment decision-making. Patient family caregivers (e.g., aunt, husband) played an important role in treatment decision-making. Based on these findings, we developed design requirements for informatics tools to better support patients (and clinicians). Our study highlights the complexity and interdependence of breast cancer treatment decision-making. Patients frequently made treatment decisions that influenced subsequent decisions. Decision aids typically focus on one decision or one care specialty and do not support consideration of several treatment options at one time1. Future research is needed to determine if and how decision support tools could incorporate the broader scope and interdependence between decisions to support patient-centered care. We highlight the need for informatics tools to consider the diverse and unique decision factors (e.g., hobbies) that are crucial to patient-centered decision-making. Decision support could help to elicit and incorporate these factors during treatment decision-making. However, the variable nature of these patient-specific factors presents a challenge to decision support design. Further research is needed to determine how to optimally incorporate such factors into decision support tools. We found inadequate system support for clinicians’ tasks including limited feedback loops to notify clinicians of completed tasks (e.g., mammogram, genetic testing). As a result, patients were often responsible for relaying information to and between different members of the clinical team. Given that patients only remember 50% of what clinicians tell them during visits7, a better system is needed to improve the reliability of information transfer. Our design guidelines can be used to improve the design of decision support tools and ultimately improve patient-centered decision-making. Table 1. Workflow components, themes, and associated challenges to decision support design Citation Format: Megan Salwei, Carrie Reale. The devil is in the details: Workflow analysis of breast cancer treatment decision making and implications for decision support design [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-10-12.