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Collective Planning of Cancer Care

Collective Planning of Cancer Care. by Seza Orcun 09/18/2007 RCHE Fall Conference @ Purdue University. Motivation. Initiated by a clinical problem How can we motivate informed decision making Engage patients and their families in the care decision making. Motivation (con’t). Cancer Care

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Collective Planning of Cancer Care

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  1. Collective Planning of Cancer Care by Seza Orcun 09/18/2007 RCHE Fall Conference @ Purdue University

  2. Motivation • Initiated by a clinical problem • How can we motivate informed decision making • Engage patients and their families in the care decision making

  3. Motivation (con’t) • Cancer Care • Complex • Many options, new information, emerging technologies • Not in everyday language • Uncertainty (variability) • Distressed mode • Time constraint communication

  4. Motivation (con’t) • …almost two-thirds of patients denied being offered treatment options other than the one they chose, despite the documentation of these options in the medical record in all cases... (Sekeres et. al, Leukemia, 18, pp 809-816, 2004)

  5. Motivation (con’t) • In a recent study of 126 patients, 98% said they wanted their oncologists to be realistic, provide an opportunity for them to ask questions, and acknowledge them as an individual when discussing prognosis. (Robin Matsuyama, Sashidhar Reddy, and Thomas J. Smith, JOCO, 24-21, pp 3490-3496, 2006)

  6. Motivation (con’t) • (Hagerty et. al, JOCO, 22-09, pp 1721-1730, 2004)

  7. Motivation (con’t) • Sample Guidelines (www.nccn.org)

  8. What we know elsewhere? • Education/Learning/Teaching • Learning < 33% at a given session/lecture/presentation • Problem solving advances learning • 3-way teaching advances learning • Decision tree • Used to represent investment options/risks

  9. Objective • Tool • improve patient-physician communication • facilitate informed team care planning • Personalize-able

  10. Where we are? (con’t)

  11. Where we are?

  12. Where we are? (con’t) • Duration of treatment (data presented here is for demo purposes and it is not actual patient data)

  13. Where we are? (con’t)

  14. Where we are? (con’t)

  15. Summary • Decision tree formalism • What happens to the patients • Offline Explorer (knowledge at patient’s learning pace and curiosity/comfort level, presentation modalities) • On-Demand data aggregation and analysis • Questions to aid care planning (features that will alter natural course of patient’s living): treatment duration, cost, survival, complications, # of hospitalizations, relapse risks, possible outcomes, etc. • Data capture • My Care

  16. Future Directions • Data • EMR/legacy system integration (practice specific data) • Data consolidation (Regional/National data) • Like me • Focus group study

  17. Aknowledgements • Team: • L. Cripe, H. Kraebber, K. Hincher, T. Robers • Sponsors: • seed funded jointly by Discover Park Centers: RCHE, OSC, e-EC • T. Robers’ internship funded by IUCC

  18. Thank You Q & A

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