Qing Zeng-Treitler*, University of Utah, Salt Lake City, United States Joshua Proulx, University of Utah, Salt Lake City, United States Brent Hill, University of Utah, Salt Lake City, United States Track: Practice Presentation Topic: Consumer empowerment, patient-physician relationship, and sociotechnical issues Presentation Type: Oral presentation Submission Type: Single Presentation Building: Mermaid Room: Room 4 - Queenshithe Date: 2013-09-23 11:45 AM – 01:00 PM Last modified: 2013-09-25

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Background: Millions of patients research their treatment options online. Increasingly, many of the online patients are looking for information from patients like themselves. Self-reported outcomes, however, have significant limitations. For instance, patients may not report their own conditions and outcomes accurately or completely. Patients who suffered from life threatening outcomes are often not able to report them online. Sample size on different treatments and outcomes may differ dramatically. Large clinical data repositories are rich resources of objective and accurate information that are currently unavailable to patients. We plan to provide this information to patients and allow them to learn from others’ treatment experience.

Objective: To develop a novel information tool called Hearts Like Mine (HLM). HLM will identify patients similar to a user in a large clinical repository and provide synthetic stories of the treatment experiences of the patients. This information is intended to provide personalized decision support to patients and facilitate shared decision making.

Methods: We designed a prototype HLM tool. HLM utilizes patient demographics,
disease, and treatment option criteria to search for similar patients in a clinical
record repository. It then calculates summary statistics of the incident rates of different outcomes of interest to patients. In order to visualize the statistics, HLM uses a popular pictograph display that portrays 100 patients. Color-coding is used to reflect both positive and negative outcomes. A particular novelty of HLM is that each patient icon will be linked to a synthetic patient story that describes the outcomes.

Results: To guide the HLM development, we conducted an expert consultation with an MD, a RN, and an experienced patient. They reviewed the prototype interface and provided feedback, which we used to improve the interface. We also sought feedback from five patients for synthetic story templates. Currently we are preparing to measure the effectiveness of HLM, especially the effect of the synthetic stories. We are planning a small randomized controlled trial for which we will recruit 28 subjects and conduct scenario-based testing. Two treatment decisions for coronary artery disease and atrial fibrillation will be presented to the subjects who will then simulate the decision making processes when presented with one of two scenarios: outcome statistics or outcome statistics along with patient stories.