“You Can See the Connections”: Facilitating Visualization of Care Priorities in People Living with Multiple Chronic Health Conditions

Title of the Paper

“You Can See the Connections”: Facilitating Visualization of Care Priorities in People Living with Multiple Chronic Health Conditions

Paper Information

• Research Domain: Human-Computer Interaction, Health Informatics, Patient-Centered Care Prioritization
• Keywords: Multiple Chronic Conditions, Reflection, Sensemaking, Patient-Clinician Communication, Visualization, Patient-Centered Care, Values

Research Background and Problems

• Identified Issues or Challenges:

  • Patients with multiple chronic conditions (MCC) face complex self-management tasks, struggle to clarify personal values and priorities, and healthcare providers often find it difficult to understand patients' value systems.
  • There is a mismatch in care priorities between patients and clinicians, with limited mechanisms for patients to effectively share their healthcare priorities.
  • Most current health information technologies focus on medical perspectives, neglecting the complexities of patients' daily lives and their values.

• Significance:

  • Aligning patients' values with healthcare decisions in chronic disease management may improve health outcomes and quality of life.
  • Understanding the relationship between individual values and condition management is central to advancing patient-centered care.

• Motivation and Related Work:

  • In the field of Human-Computer Interaction (HCI), research on self-care technologies is increasingly prominent, but there is insufficient exploration of how visualization can support patient reflection and clinician communication.
  • Previous studies have shown that externalizing patients' values and tasks can improve priority setting, but there is a lack of tools that support long-term reflection or application in real clinical scenarios.

Solution

• Proposed Method/Solution:

  • Developed an interactive visualization tool called “Conversation Canvas (CC)” to help patients externalize and articulate their prioritized healthcare concerns during consultations through a two-phase intervention.
  • Trained social workers were invited as interpersonal facilitators to assist patients in using the CC tool for value reflection and task organization, enhancing communication with healthcare providers.

• Innovations:

  • Visualizing the relationships between patients' personal values, health management tasks, and health conditions.
  • Utilizing dynamic, interactive methods to reorganize data, allowing patients to iteratively refine their priorities.
  • Testing the tool in real clinical settings, surpassing previous validation conducted in simulated or laboratory environments.

• Implementation Steps and Key Technologies:

  1. Guiding Patient Value Perception: Using questionnaires to guide patients in reflecting on personal values (e.g., emotional relationships, daily activities, health goals) and current health management tasks.
  2. Visualization Tool Design: Developed using D3.js, the tool intuitively presents patients' health management tasks, values, and health conditions, represented as nodes (values/tasks/conditions) and connections indicating relationships.
  3. First Intervention Round: Facilitators assist patients in exploring key issues, helping them identify critical connections (e.g., “foundational issues” and their cascading effects on other concerns).
  4. Second Intervention Round: Updating existing reflection data or initiating new topic discussions, with expanded thoughts summarized in text and shared with clinicians to aid better clinical planning.

Research Outcomes

• Specific Results:

  • The visualization network helped patients define core health issues and their interconnections, forming a clear roadmap for management.
  • Using the CC tool for reflection enhanced patients' confidence and supported more effective, patient-centered consultations with clinicians.
  • In certain patient cases, the tool even prompted behavioral changes in self-management, such as adjusting dietary habits or exercise routines to better align with personal values and health needs.

• Comparison with Existing Solutions and Advantages:

  • Compared to traditional health tools focused on data or condition-centric approaches, the CC tool emphasizes value-driven care, providing better support for the holistic aspects of individual lives.
  • Offers an integrated visual environment that enables patients to progress from “learning how to manage multiple interconnected issues” to higher-level cognition (e.g., exploring causal relationships to determining priority directions).

• Experimental or Evaluation Results:

  • Interviews with 13 MCC participants, 3 family caregivers, and 7 primary care physicians revealed:
    • Participants felt more respected and understood.
    • Patient-clinician communication became more focused on practical issues, fostering more meaningful conversations.
    • Some participants altered daily behaviors or healthcare decisions to better align with their values.
  • The tool proved effective in short-term, two-session interventions, capturing and refining patients' dynamic perspectives.

• Limitations and Future Directions:

  • Limitations:

    • All participants were within the same integrated healthcare system, which may limit generalizability to resource-constrained environments.
    • The CC tool relies on facilitator guidance, and its effectiveness may be reduced for patients without external support.
    • The current design incorporates limited consideration of social health risk factors, such as structural inequalities.

  • Future Directions:

    • Explore tool adaptation in low-resource or cross-cultural contexts.
    • Investigate ways to make the tool more autonomous (reducing reliance on facilitators), integrating AI or adaptive systems.
    • Collect long-term follow-up data to examine the sustained value of patients' self-optimization behaviors.
    • Further integrate social health risk and inequality factors to support diverse groups more equitably.