Collective Planning of Cancer CareCollective Planning of Cancer Care

by Seza Orcun09/18/2007

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– Complex

• Many options, new information, emerging technologies

• Not in everyday language

• Uncertainty (variability)

– Distressed mode– Time constraint communication

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)

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)

Motivation (con’t)

• (Hagerty et. al, JOCO, 22-09, pp 1721-1730, 2004)

Motivation (con’t)

• Sample Guidelines (www.nccn.org)

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

Objective

• Tool – improve patient-physician communication – facilitate informed team care planning – Personalize-able

Where we are? (con’t)

Where we are?

Where we are? (con’t)

• Duration of treatment (data presented here is for demo purposes and it is not actual patient data)

Average 10.71429Standard Deviiation 22.36012Number of Patients 38Min 0Max 70

Where we are? (con’t)

Where we are? (con’t)

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

Future Directions• Data

– EMR/legacy system integration (practice specific data)

– Data consolidation (Regional/National data)

• Like me

• Focus group study

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

Thank You

Q & A