As it shows that healthcare predictive analytics has massive scope in the future, let’s look at some of the most prominent use cases that determine the importance of predictive analytics in the healthcare industry.
1. Restraining Self-Harm
Healthcare predictive analytics can help patients in deterring severe events. With the ability to analyze patterns and flagging the events, healthcare providers can restrict a patient from self-harming.
In a study conducted by Kaiser Permanente and the Mental Health Research Network in 2018, the researchers integrated electronic health records or EHRs and a questionnaire with predictive analytics tools.
With the help of this integration, the study accurately identified patients who were willing or were going to harm them.
2. Limiting Hospital Readmissions
Healthcare predictive analytics allow the hospitals and other care providers to identify the patients that require admissions to the hospitals, and in some cases where the patient is likely to get readmission, the care providers can manage the facilities to accommodate them.
There are cases where a patient wants to get admitted to the hospital irrespective of their condition. In such scenarios, predictive analytics can determine such individuals and restrict their admissions to hospitals.
A real-time use case of this scenario was witnessed by the Texas Hospital, where the hospital integrated HER with real-time predictive analytics and reduced the readmission of the patients by 5%.
Dr. Ethan Halm, MD, MPH, Professor of Internal Medicine and Clinical Sciences and Chief of the Division of General Internal Medicine at UT Southwestern, states that the data automatically help the hospitals to identify the high-risk patients. They require readmission as early as their first admission to the hospital.
3. Precision Medicine and Personalized Care
Since the former U.S. President Barak Obama addressed the State of the Union, precision medicine has become a colossal entrant in the healthcare sector. Researchers across the globe have devoted their careers to conducting genomic research and developing personalized treatment for genetic disorders.
With the help of healthcare data, predicting the disease’ course related to genetic markup has become quite a possibility. Predictive analytics has also been instrumental in fighting chronic diseases such as diabetes, cancer, and conditions such as food poisoning using genetic research data.
Food and Drug Administration (FDA) Commissioner Scott Gottlieb, MD, highlighted the use of predictive modeling by the FDA’s Center for Drug Evaluation and Research (CDER) for predicting clinical outcomes in the clinical trials of precision medicines.
The research team of CDER used predictive modeling to inform clinical trials designs, dose optimization, and predicting product safety.