Health Data Pool (HDP) is the data store of primary health care records and hospital admissions data from secondary care. 

View an end-to-end patient record 

Assist in making informed decisions

Conduct population health analysis 

Develop precision health models 

Undertake  preventative health measure

Benefits of HDP for healthcare providers

To instantiate the use of HDP we developed a predictive model – Hospital Admission Risk Predictor (HARP). 

Predicts the hospital admission risk


Predictive health models can be built leveraging the Health Data Pool.

Precision Health Models


Predicts hospital readmission risk

Future Models

Have an idea for any future models

Project Partners

Summary of Results

To select a set of patients for potential intervention to avoid admission we selected a 50% “cut-off” probability. This is best done in discussion with stakeholders, taken account of the relative costs and benefit associated with true and false positives. 

Assuming, 50% as our cut-off probability, the model returned 27 true positives and 40 false positives. If we plot these set of points: x false positives (1 – Specificity) versus y true positives (sensitivity) for all predictions we can plot a gives the Receiver Operating Characteristic (ROC) curve. The Area Under the Curve (c-statistic) for the model was 0.71.

Logistic regression modelling approach 

We adopted the logistic regression modelling approach for HARP predictions. For testing and verification of HARP data of 50,000 patients have been collected/extracted from Whanganui DHB and Whanganui Regional Health Network (PHO). The project demonstrates key interfaces for data acquisition, prediction model development and distribution of risk information to users as responses to single queries or batches. 

HARP Methodology & Results

© 2018 Farhaan Mirza, Barry Gribben, Mirza Baig et al