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  • Title: Acute Predict: a clinician-led cardiovascular disease quality improvement project (Predict-CVD 12).
    Author: Kerr AJ, Looi JL, Garofalo D, Wells S, McLachlan A.
    Journal: Heart Lung Circ; 2010; 19(5-6):378-83. PubMed ID: 20392667.
    Abstract:
    BACKGROUND: New Zealand data demonstrate major disparities in cardiovascular health, particularly by ethnicity and socioeconomic deprivation. ACUTE PREDICT AIM: Acute Predict, the secondary care arm of primary care based PREDICT, is a multidisciplinary project based in the coronary care unit, and is jointly led by nursing and medical staff. The project aim is to ensure patients with acute coronary syndromes (ACS) receive appropriate evidence-based secondary prevention management short- and long-term, regardless of age, socioeconomic status or ethnicity. METHODS AND RESULTS: Acute Predict utilises an electronic backbone to provide the following (1) guideline-based patient-specific decision support, (2) data collection as part of routine clinical workflow, (3) linkage of patients to cardiac rehabilitation and primary care chronic care management programs, (4) clinical and management data capture, (5) real-time whole group and sub-group Key Performance Indicators reporting with drill-down to individual patient data, and (6) long-term tracking of individual patient outcome via linkage to national databases. Over the four years of the project in-hospital provision of cardiac rehabilitation has improved and appropriate discharge medication is high. There are no differences according to ethnicity. Despite this, Maori patients in the Acute Predict ACS cohort are twice as likely as Europeans to have recurrent events post-discharge, even after adjustment for known risk factors. CONCLUSIONS: The built-in real-time data reporting and outcomes/prescribing linkage facilitate monitoring of the quality of CVD prevention activity across the continuum of care. It allows early identification of treatment gaps and of persistent disparities in outcome in our patients. We are learning how best to use this real-time data collection and reporting to support the design and assessment of targeted interventions to close gaps and reduce disparity.
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