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CMSC 734: Information Visualization

DIABETES patient adherence DATA CHALLENGE Dr . Monifa Vaughn-Cooke Assistant Professor of Mechanical Engineering. CMSC 734: Information Visualization. 9/13/13. Patien t Adherence.

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CMSC 734: Information Visualization

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  1. DIABETES patient adherence DATA CHALLENGEDr. Monifa Vaughn-CookeAssistant Professor of Mechanical Engineering CMSC 734: Information Visualization 9/13/13

  2. Patient Adherence • In the context of health care, adherence has been defined as the extent to which a person's behavior in terms of taking medication or executing life-style changes coincides with medical advice (Sackett, 1979) • More recently, a patient-centric paradigm shift occurred in the medical community • Particular impact on chronic disease care

  3. Non-Adherence Outcomes

  4. Diabetes Patient Adherence • 25.8 million diagnosed diabetes cases (CDC, 2011) • Application Context: Self Monitoring of Blood Glucose (SMBG) Adherence Rates (Peyrot et. al., 2005) 39% (type 1) 37% (type 2) 37% (type 1) 35% (type 2) 83% (type 1) 78% (type 2) 70% (type 1) 64% (type 2)

  5. Research Objectives • Identify, evaluate and mitigate SMBG patient adherence risk factors through a structured multidimensional (when, how, why) systems approach • Ultimate goal to inform glucometer design and diabetes treatment decision making

  6. Empirical Pilot Study • Study Site: Penn State Hershey Medical Center Endocrinology Clinic • 10 participating diabetes providers • 107 patients recruited on-site (99 completed the study) • 60 day study duration • Collected Data • Health vitals and diabetes complications (Penn State Diabetes Registry) • Human response dimension surveys (when, how, why) • SMBG adherence data • Intending daily SMBG frequency (provider reported) • Actual daily SMBG frequency (glucometer download, end of study) Empirical Measure of Adherence = Actual/Intended Daily SMBG frequency

  7. The Big Picture WHEN? HOW? WHY? • Available Patient Data • Level of perceived difficulty for each glucometer interaction task • 60-day outcomes • Daily testing frequency • Daily adherence • Daily average blood glucose • Available Patient Data • Type of adherence error performed (intentional, unintentional) • Reason’s Error Classification (Reason, 1990) • Available Patient Data • Social, personal, behavioral, and technological risk factors • Health vitals (lab tests) • Diabetes complications • Other medical records and self-reported data

  8. SHORT-TERM PROJECT: Physiological Changes Using existing software, create a visualization of blood glucose, daily SMBG testing frequency, and daily adherence values over a 60-day period. • Consider the following: • How would this visualization differ for provider-accessible Electronic Health Records (EHR) versus patient-accessible Patient Health Records (PHR) (i.e, mobile or web-based disease management app)? • What critical features of each variable should be highlighted (i.e., days when testing did not occur, exceeding normal blood glucose values), and how would this information help to track changes in disease progression? • What other physiological, behavioral or technological variables would be important to include in this visualization? • How would you propose this visualization to be integrated into the diabetes treatment decision making process?

  9. LONG-TERM PROJECT: Systems-Level Relationships Develop new software to visualize the relationship between the when, how, why systems in a way that would be meaningful to a diabetes provider and may provide information that enhances traditional medical records. • Consider the following: • What system level of abstraction is the most appropriate for certain types of decisions? • How can patient vs. population level data be captured in the visualization? • How can relationships between longitudinal and static data be visualized? • How would you propose this visualization to be integrated into the diabetes treatment process?

  10. MVC@UMD.EDU

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