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

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.

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

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

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

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

How is this different to other initiatives? Health Care Homes

Gold Coast Integrated Care (GCIC)

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

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

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