The State of Global Healthcare
The global health sector is currently facing a conflux of issues which will ultimately impact the degree of access to quality services.
On a global scale, the emerging impact of climate change and biosecurity threats will impact how funds are allocated across hospital networks. In the short term, the increasing digitization of data and the requirement to ensure trust in data sharing, improve digital and health literacy and advance system interoperability will dominate the policy domain.
Public and private health networks will need to rise to the challenge to facilitate the secure advancement of the digital infrastructure, removing key barriers to allow for more integrated and data-enabled health system.
AI and ML techniques are becoming an essential ingredient for health organisations to ensure they are maximising limited resources in order to benefit patient outcomes. Organisations are deploying these techniques in clinical, research, operational and analytical settings where solutions ranging from simple visualisations through to large-scale image analytics are unlocking new insights, optimising resources, enabling data literacy, and guiding leaders toward better management decision making.
From data linkage and exploratory analysis, through to large-scale ML deployments, AutoStat® allows health organisations to take their data science capabilities to the next level, allowing them to surpass barriers that hinders the journey to enabling the predictive enterprise.
AutoStat® in Health
AutoStat is currently driving the consumption of value based metrics across the largest health network in Australasia. The Ministry of Health NSW selected AutoStat as its application of choice to model, visualize and share value based metrics across the NSW health network. AutoSata can help your organization by:
1. Generating and distributing accurate forecasts of emergency patient numbers.
2. Optimize the scheduling of staff based on demand.
3. Efficiently account for the impact of policy interventions and new treatments on the prevalence and incidence of disease.
4. Model the impact of new hospitals and wards, and changes in healthcare demand.
5. Analyse the relationship between different healthcare streams
6. Model future demand for various health services by changing key variables and undergoing scenario analysis.
7. Upload new datasets automatically and in real-time