Patient Experience Day One: Wednesday, 30 September 2020
Wednesday, March 25th, 2020
1:30 PM Case Study: Designing and Developing a selfcare app to help patients manage their own condition
Evidence shows that Type 2 diabetes can be prevented or delayed in up to 58% of cases. However, due to busy lifestyles and ignorance, people often fail to manage their dietary habits and physical activity appropriately. To help diabetic patients better manage their condition, Swinburne University of Technology collaborated with health professionals and patients at Northern Health, Victoria, to create a self-care App designed to be part of the lifestyle of diabetic patients.
Learn how the app is improving the lives of diabetic patients by:
- Capturing key clinical and behavioural data to provide feedback to patients and enabling them to optimise self-management of their diet and activity
- Allowing patients to view details of their blood glucose history over a given period, to plan and find new meals, to view recommended personalized exercises, and to learn more about being a diabetic
- Enabling nurses and nutritionists to provide patients with targeted guidance based on the feedback data and assist patients with medication compliance
2:00 PM INTERACTIVE SESSION: Panel Discussion: Improving Clinical Decision Making by Ensuring the Objectivity and Validity of Patient Data
Data has the potential to provide improved clinical decision making and a superior patient experiences. However, while data seems to promise objectivity, the pursuant analysis is typically replete with subjective interpretation. This panel will discuss the fundamentals of ensuring the validity and objectivity of patient experience data and analysis.
Learn how to:
- Choose sources of data for objectivity – including Patient Reported Outcome Measures (PROMS) and Patient Reported Experience Measures (PREMS) – and how you can build on PROMS and PREMS to contribute valuable and high quality data to clinicians
- Be objective towards the data we already have while recognising bias in data
- Justify adequate funding for specific data sources by eliminating the subjectivity in what sources of data are good sources of data
- Engage patients to define relevant meaningful data and metrics from the patient’s perspective