1. Practice based Population health management: a new approach to quality and safety in primary care

2. Focussed on whole of population data set
Focussed on what GPs want to see about their populations
Supporting populations within the region
Extraction
Focused on whole practice population data set
Developed automated system to de-identify individual level data at the practice
Then we developed automated system to securely extract data at frequency to be determined with individual practices (including daily)
Coding
Currently all four general practice software systems have different coding systems and practices may implement/use that differently
Our automated system standardises, and systematically applies internationally coding (ICPC2+) to all of the general practice and other data sets allowing us to cross reference and build a complete picture
including free text data (like a rosetta stone to make sense of all the different practice software coding systems)
Analytics
Based on what GPs want to see and find useful, developed analytics and revised business intelligence reporting to support quality and safety in general practice without need for them to invest practice time and resources
Undertook research and case finding to identify quality and safety initiatives that could use the data,
automatic/ overnight data extraction and reporting back to practices enables a live system for interventions
Reporting
Developing information displays that minimise time/cost to practices to implement
Developing an enhanced set of products and services available to practices
the ability to de-identify data, allocate a number to it, and send back for ease of patient identification and intervention  in the practice. Previously a labour intensive activity at the practice end and therefore unlikely to be undertaken.
Legal
Legal advice on Australian privacy principles, Intellectual property and Indemnity
The PMS infrastructure
What the coding would look like
What data base the practice software was installed on
Whether query tools such as SQL management studio were installed
The contents of the tables based the descriptor
The size of queries or time required to run
Whether we could run queries on the live system without causing issues.

3. Construction of reliable coded data from the practice management systems on the entire practice population
Converting data to meaningful, easy interface
Use the data to target application of evidence based safety and quality interventions

4. Number of average diagnosis per patient
Number of patients
Number of average diagnosis per patient
Data from 1 practice
Stratifying the population into Resource utilisation bands
5 highest

5. More diagnosis detail – not just focused on chronic diseases
282 clustered diagnosis –filtered by highest patient count

6. Closer look at mental health: Profile and data quality
Data from 5 practices

7. How is this different to other initiatives?
Health Care Homes

8. Gold Coast Integrated Care (GCIC)

9. Practice Based Population Health Management
Whole practice population
Strong prevention focusing on wellness as well as illness
Group individuals based on standardised, reliable, coded data
Reducing over-treatment (de-prescribing, de-specialising and de-intervening)
Making use of recognised quality and safety standards and indicators
Practice Based Population Health Management

10. Support for practices Case finding of individuals Decision assist
Planning and evaluating services
Data quality
Case finding:
patients eligible for prevention activities e.g. due pneumovax
patients in high risk bands who are not frequently attending
patients new to high risk bands
patients moving into higher risk band
patients at risk of developing certain chronic diseases
patients eligible for health assessment
patients eligible for care plan
patients due a health assessment
patients due a care plan review
decision assist:
patients with specified combinations of diagnoses, pathology results and contraindicated or missing medications
patients in specific age groups with medications at high dose, or that add to the anticholinergic burden
Planning
segmentation of the active practice population into risk bands
segmentation of target groups of interest e.g. patients with metabolic syndrome/pre-diabetes, or highest risk of hospitalisation
Data quality
volume of diagnoses not coded )
volume of diagnoses indicated by medication (no diagnosis code)
number and type of care gaps e.g. incomplete cycles of care
patients with a new diagnosis who meet the criteria for related community/support services available (links to service directory

11. Example – Tier 4 intervention Avoidable admissions for people aged 75+
4 practices, 4 different software packages
Through facilitated co-design process all 4 agreed on:
common template to support 75+ health check
common complex care plan template aligned to MBS items
Through our data platform:
177 high risk patients
400 moderate risk patients
recalls commenced
care plans being completed
new needs being addressed
Recruited 4 practices with 4 different common GP software packages
Practice support staff undertook co-design process, ensuring everything aligned to existing MBS item number requirements and agreed health care check and care plan templates.
Data which is deidentified before leaving the practice brought into the platform (the primary sense tool) to look at key data items already sitting in practice software data which may indicate patients at risk.
Indicated a total of 177 high risk patients, 400 moderate risk patients and this is fed back to practices. Once back in practice at risk patients can be reidentified, by the practice, and this can inform recall of the at risk patients.
Practices are currently recalling patients and at least 16 have occurred at one practice and indications are that it is likely the other practices will have similar numbers.
Early feedback from clinicians is this process is uncovering a range of previously unknown issues for patients including social issues, a major factor in avoidable admissions for this age group which they begin to intervene