Uploaded on Mar 17, 2022
Predicting when, where, and how care should be administered improves patient care, improves health outcomes, and lowers costs. Predictive analytics can help health care organizations in this way. Simply defined, predictive analytics in healthcare informs caregivers and physicians about the likelihood of certain occurrences and outcomes, allowing them to detect and treat health problems before they occur.
The Future Of Predictive Analytics In Healthcare
The Future Of Predictive
Analytics In Healthcare
What Is Predictive Analytics
• Predictive analytics is a subset of advanced analytics that involves making
predictions about unknown future occurrences or activities in order to make
judgments.
• Predictive analytics employs data and modelling techniques to predict future
performance
• Predictive analytics systems integrate vast amounts of past data points with
future estimates using specifically designed algorithms.
• It is used in industries and professions such as insurance and marketing to
make critical judgments.
Predictive Analytics In Healthcare
• Predicting when, where, and how care should be given improves patient care,
improves health outcomes, and lowers costs.
• Predictive analytics in healthcare identifies the possibility of events and
outcomes, allowing them to detect and treat health problems before they
occur.
• Algorithms are provided with historical and real-time data to create
meaningful predictions in healthcare
• Although predictive analytics is still in its beginnings, it has already had a
significant positive impact on many aspects of healthcare.
Real World Use Cases Of Predictive Analytics In
Healthcare
• Early Detection of Patient Deterioration
• Patient Utilization Patterns Can Be Predicted
• Precision Medicine is being developed
• Management of the Supply Chain
• Efficiency in Scheduling
• Data Protection
• Cost-cutting and profit-boosting
Benefits & Risks Of Predictive In Healthcare
Benefits
• Improving the efficiency of health-care company operations' operational
management
• In personal medicine, the accuracy of diagnosis and treatment is crucial
• Increased understanding to improve cohort therapy
Risks
• Technology's rapid advancement and its impact on decision-making processes
• With the machine, there are moral hazard and human intervention points
(including choice architecture dilemmas)
• Algorithm bias and a lack of regulation
• Pressures on privacy
Thank You
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