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

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Presentation on theme: "Collective Planning of Cancer Care by Seza Orcun 09/18/2007 RCHE Fall Purdue University."— Presentation transcript:

1 Collective Planning of Cancer Care by Seza Orcun 09/18/2007 RCHE Fall 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 (cont) Cancer Care –Complex Many options, new information, emerging technologies Not in everyday language Uncertainty (variability) –Distressed mode –Time constraint communication

4 Motivation (cont) …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 , 2004)

5 Motivation (cont) 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 , 2006)

6 Motivation (cont) (Hagerty et. al, JOCO, 22-09, pp , 2004)

7 Motivation (cont) 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? (cont)

11 Where we are?

12 Where we are? (cont) Duration of treatment (data presented here is for demo purposes and it is not actual patient data) Average Standard Deviiation Number of Patients38 Min0 Max70

13 Where we are? (cont)

14

15 Summary Decision tree formalism –What happens to the patients –Offline Explorer (knowledge at patients 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 patients 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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