Developing indicators and measures of high-quality for Australian general practice.

BACKGROUND: Rising health costs and health inequity are major challenges in Australia, as internationally. Strong primary health care is well evidenced to address these challenges. Primary Health Networks (PHNs) work with general practices to collect data and support quality improvement; however, there is no consensus regarding what defines high quality. This paper describes the development of an evidence-based suite of indicators and measures of high-quality general practice for the Australian context. METHODS: We reviewed the literature to develop a suitable framework and revise quality assurance measures currently in use, then reviewed these in three workshops with general practitioners, practice managers, nurses, consumers and PHN staff in western Sydney. We used a descriptive qualitative research approach to analyse the data. RESULTS: A total of 125 evidence-based indicators were agreed to be relevant, and 80 were deemed both relevant and feasible. These were arranged across a framework based on the Quadruple Aim, and include structure, process and outcome measures. CONCLUSIONS: The agreed suite of indicators and measures will be further validated in collaboration with PHNs across Australia. This work has the potential to inform health systems innovation both nationally and internationally.

Can existing electronic medical records be used to quantify cardiovascular risk at point of care?

BACKGROUND: Using electronic data for cardiovascular risk stratification could help in prioritising healthcare access and optimise cardiovascular prevention. AIMS: To determine whether assessment of absolute cardiovascular risk (Australian absolute cardiovascular disease risk (ACVDR)) and short-term ischaemic risk (History, ECG, Age, Risk factors, and Troponin (HEART) score) is possible from available data in Electronic Medical Record (EMR) and My Health Record (MHR) of patients presenting with acute cardiac symptoms to a Rapid Access Cardiology Clinic (RACC). METHODS: Audit of EMR and MHR on 200 randomly selected adults who presented to RACC between 1 March 2017 and 4 February 2020. The main outcomes were the proportion of patients for which ACVDR score and HEART score could be calculated. RESULTS: Mean age was 55.2 ± 17.8 years and 43% were female. Most (85%) were referred from emergency for chest pain (52%). Forty-six percent had hypertension, 35% obesity, 20% diabetes mellitus, 17% ischaemic heart disease and 18% were current smokers. There was no significant difference in MHR accessibility with age, gender and number of comorbidities. An ACVDR score could be estimated for 17.5% (EMR) and 0% (MHR) of patients. None had complete data to estimate HEART score in either EMR or MHR. Most commonly missing variables for ACVDR score were blood pressure (MHR) and high-density lipoprotein cholesterol (EMR), and for HEART score the missing variables were body mass index and comorbidities (MHR and EMR). CONCLUSIONS: Significant gaps are apparent in electronic medical data capture of key variables to perform cardiovascular risk assessment. Medical data capture should prioritise the collection of clinically important data to help address gaps in cardiovascular management.