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1 Health informatics working for PHNs and their communities: Mapping and tracking diabetes and related outcomes for local population health planning Natalie Rinehart & Adam McLeod: Outcome Health
2 Population Health Planning Population Health Planning Population health (PH) planning is integrated and collaborative planning that demands that health and non-health sectors, government departments, and service delivery agencies work together to address the issues faced by their communities and populations. It focuses on achieving real and sustainable health improvements and is committed to reducing health and social inequities. This offers opportunities for innovation by seeking and applying evidence about new and changing needs of populations, and how these are influenced by the determinants of health. Victorian Healthcare Association, 2010
3 Population Health Planning and PHNs Data and consultations inform the PH planning and Needs Assessments. These are a central facet to inform PHN commissioning cycles and outcome tracking.
4 Population Health Planning and PHNs PH Needs Assessments should include: Health needs analysis Service needs analysis Consultations with communities Consultations with health professionals, providers, funders and other stakeholders
5 Population Health Planning and PHNs Information is assessed and triangulated to determine regional health and service system priorities Activities/interventions are identified to bring about change Services are commissioned Activities and services tracked and evaluated over time
6 Outcome Health & POLAR In 2011, Outcome Health (formerly Melbourne East GP Network) created a data warehouse with a vision that anyone could easily search for GP and population health data in a range of useful, easy to find, meaningful ways. Since that time, the warehouse has continued to grow in scope and size and is now known as POLAR (POpulation Level Analysis & Reporting). POLAR Explorer for PHNs contains unit record level data and Qliksense analysis dashboards for: De-identified GP data- clinical and billing systems Victorian Emergency Minimum Dataset (VEMD) Victorian Admitted Episodes Dataset (VAED) Victorian Alcohol and Other Drug Treatment data Ambulance Victoria Low Acuity (Code 3) Callouts and Referrals
7 GP Data on POLAR GP data is extracted and used by practices in POLAR GP tool for practice auditing. Another extract is de-identified and securely sent to the POLAR Data Warehouse. It is aggregated for PHNs to conduct population health planning on the OUR GPs tool. POLAR has mapped a range of GP data types including: GP coded and free-text diagnoses to SNOMED-CT-AU Medications to World Health Organisation s Anatomical Therapeutic Chemical (ATC) classification system. GP relevant pathology groups mapped to LOINC codes.
8 Tracking Diabetes on POLAR PHNs can track patients with diabetes on OUR GPs to show: Different types of diabetes Patients that are on diabetic medications but with no diagnosis How diagnoses may be growing over time, age groups and other variables
9 Tracking Diabetes on POLAR Once PHNs have tracked their highest areas of diabetes via the maps, they can start to build a picture of how the services in those regions are conducting patient Diabetes Cycle of Care
10 Tracking Diabetes on POLAR OUR GPs shows: which elements of the Diabetes Cycle of Care have been conducted in the last 12 months across the catchment. What proportion of diabetic patients have undergone a full Diabetes Cycle of Care annually.
11 Tracking Diabetes on POLAR PHNs can also select any combination of their filters regarding diabetes and understand which of their practices see patients with those parameters. From here PHNs can identify which practices to work with on quality improvement activities, research or evaluation.
12 Tracking Diabetes on POLAR Through agreement with the State, POLAR also contains the Victorian Admitted Hospital dataset (VAED) for PHNs to analyse. POLAR has mapped Potentially Preventable Hospitalisations (PPH) as set out in national standards. PHNs can filter by the PPH diabetes complications to determine who in their region is being admitted for diabetes related conditions.
13 Tracking Diabetes on POLAR This allows PHNs to track key diagnoses have the most separations and also length of stay- in other words, which diagnoses use the most resources.
14 Tracking Diabetes on POLAR PHNs can also track whether separations and rates in the population are: Increasing over time Different across age groups More predominant in different parts of their catchments or hospitals
15 What Health Informatics can bring to PH Planning Accessible- PHNs don t have to be a SPSS or SAS expert PHNs can: Cut the information the way they want based on unit record level data View from a service system, population needs or financial perspective Combine a full service system understanding- primary care to tertiary and back again to target best opportunities for interventions Inform evaluation of interventions over time Check back on consultation findings in hard data Bring evidence to the table to work with partners on combined projects
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