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This study explores the impact of objective medication-taking data on doctor-patient communication. Presented to doctors, the data influenced treatment decisions and patient interactions positively. Objective sensor data can enhance patient care by focusing on behaviors, building trust, and optimizing contact levels. Stakeholder perspectives and data timeliness are critical in designing effective data interventions for medical practice.
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Supporting Grounding with Objective Medication-taking Data Matthew L. Lee Philips Research Anind K. Dey Carnegie Mellon University
How would having objective account of medication-taking affect communication?
closure switches wireless radio accelerometer microprocessor
14 older adults 10 months
Presented data to six doctors Interviewed them about how these data would affect their practice Prompt: “If you the patient were sitting here right now, what would you say?”
Age 66, F Diabetes Cancer Bipolar disease Participant E.S. “I see that her meds are pretty good, so I would talk to her more about her diet and sleep hygiene instead.” - PCP
Age 55, F COPD Depression Participant S.K. “I was worried about her more psychologically than physically, but this shows me that she is paying attention and this matches what she tells me. ” - Geriatrician
Age 70, M Cancer (brain) High cholesterol Participant E.N. “If I had this, I would have had my nurse give him a call and schedule an appointment to find out what’s going on.” – PCP “If I had the time to look at this, I would have asked him about it when he was in my office in December.” - Oncologist
Objective sensor data can… • focus attention on the right behaviors • build trust (or distrust) in the patient’s self-reports • act as a trigger for the optimal level of contact • make the office encounter more collaborative
Considerations for designing “data interventions” • Different stakeholders need different views of data • Timeliness of data is critical • Show data that is interpretable & actionable
Supporting Grounding with Objective Medication-taking Data Matthew L. Lee Philips Research Anind K. Dey Carnegie Mellon University