Prescriptive Analytics:
Analytics recommends the most effective treatment plans and health interventions based on individual patient needs and characteristics. This approach allows for personalized medicine, where treatments are tailored to the specific medical history, genetic profile, and other relevant factors.
Diagnostic Analytics:
Identifying the underlying causes of specific outcomes, such as higher readmission rates or longer lengths of stay, to help healthcare providers develop strategies for improvement. This can involve analysing data from various sources, including electronic health records, clinical trials, and patient feedback.
Improved Efficiency and Resource Allocation:
Data analytics can help healthcare organizations optimize workflows, reduce costs, and improve resource allocation by identifying inefficiencies and areas for improvement. For example, predictive analytics can help hospitals forecast patient admissions, enabling them to allocate beds and staff more effectively
Pandemic Readiness:
Data analytics can be used to track disease outbreaks, identify risk factors, and develop interventions to improve public health outcomes. For example, analytics can help identify areas with high rates of specific diseases and develop targeted public health campaign Effective Handling
Forecasting Models:
Analysing Forecasts of future health trends and identification of patients at high risk of developing specific conditions, allowing for proactive interventions and preventative measures. For example, predictive models can identify patients at risk of readmission after discharge, enabling targeted outreach and care coordination.
Financial Impact Assessment:
By integrating financial data with clinical and operational metrics, the platform performs comprehensive ROI and cost-benefit analyses. For e.g.: Analysis of telehealth implementation costs versus reductions in hospital readmissions and travel expenses helps justify investments and scale-up strategies.