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Leveraging large clinic data to provide personalized decision support
Upper Lobby
Leveraging large clinic data to provide personalized decision support
Biomedical Informatics/Nursing Informatics PhD Candidate, University of Utah
Background Shared decision making implies that both patient and healthcare provider are well informed about treatment options. Many patients are not adequately informed. It is also difficult for a patient... Read more

Description

Background

Shared decision making implies that both patient and healthcare provider are well informed about treatment options. Many patients are not adequately informed. It is also difficult for a patient to match their clinical picture with literature or education materials. Leveraging a large clinical data repository to match patient’s clinical demographics with similar patients allows for a new strategy to inform patients: exploring similar patients’ experience with treatment options and outcomes. The goal of this study was to conduct usability testing, using expert consultants, to evaluate the user interface and preference for visual representation of treatment option statistics.

Methods

Our team designed the Hearts Like Mine (HLM) application and created a mock-up interface. HLM uses a large clinical data repository to match cases of patients similar to the user and represents treatment options with a popular100 patient pictograph. Statistics in the repository are used to color the pictograph of 100 patients, visually representing different outcomes. Synthesized patient stories, from an outline populated with mock statistics of treatment option outcomes, completes a vignette that appears with cursor hover over each patient. Three experts, an RN, MD and an expert patient, were recorded evaluating their experience using HLM. Two authors independently extracted comments from one transcribed consultation. Comments were compared for agreement. Among the twenty items each extracted; one disagreement occurred. A third author created a categorization scheme for comments. The authors reviewed coded excerpts for revision and refinement until consensus was achieved.

Results

Category (Count)

Examples

Audience (1)

- This seems designed for the patient.

Communication (6)

- I want to be able to share my decision with my doctor, but control what information she receives.

Comprehension (6)

- Use plain or consumer language in the buttons in addition to the clinical terminology.

Display (30)

- It would be good if you could compare treatments side by side. I could see the number of people with complications for two treatments side by side.

Education (2)

- Keep ‘Things I Can Change’ simple. Provide a link for more information.

Setting (4)

- I want to do this at home. Doctors try to sell you what they want to do and don’t explain any other options and really don’t even give me much information about his preferred treatment option.

Usability (15)

- It’s easy to use, even for someone like me.

Usefulness (9)

- It helps me to personalize the information; even giving them names is great, poor Joe!

Other (7)

- Can I change behavioral risk factors to see how my risk will change?

Conclusions

While each expert provided different feedback, central themes emerged: 1. Patients should be able to use HLM on their own to provide privacy; 2. The outcomes should include behavior change/risk factor modification; 3. Utility of the option to share levels of information from HLM with a healthcare provider. We plan to revise HLM based on the feedback.

 

 

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