Data analytics can help clinicians respond to changes in the vitals of a patient as rapidly as possible and may be able to detect an upcoming deterioration before symptoms show themselves clearly to the naked eye.
Fremont, CA: As healthcare organizations develop more advanced capabilities for big data analytics, they are beginning to shift from simple descriptive analytics to the predictive insights area. Predictive analytics may only be the second of three phases along the road to the maturity of analytics, but it represents a major leap forward for many companies.
Instead of merely providing a consumer with knowledge about past events, the predictive analysis predicts the probability of a potential outcome based on historical data trends.
Here are three use cases of predictive analytics in healthcare:
Forestalling Appointment No-Shows
In the daily calendar, unforeseen gaps may have financial consequences for the company while throwing off the entire routine of a clinician.
Using predictive analytics to recognize patients who are likely to miss an appointment without advanced notice will increase providers' satisfaction, minimize revenue losses, and give organizations the ability to offer other patients open slots, thus increasing rapid access to treatment.
Risk Scoring for Chronic Diseases, Population Health
Prediction and prevention go hand-in-hand, maybe nowhere closer to population health management than in the world. Organizations that can detect people with increased risks of developing chronic illnesses as early as possible in the course of the illness have the greatest chance of helping patients prevent expensive and difficult to manage long-term health issues.
Based on laboratory tests, biometric data, claims data, patient-generated health data, and social determinants of health, generating risk ratings will give healthcare providers insight into which patients could benefit from better services or wellness activities.
Getting Ahead of Patient Deterioration
Patients face a variety of possible risks to their well-being while still in the hospital, including the initiation of sepsis, the acquisition of a difficult-to-treat infection, or a sudden decline because of their current health conditions.
Data analytics can help clinicians respond to changes in the vitals of a patient as rapidly as possible and may be able to detect an upcoming deterioration before symptoms show themselves clearly to the naked eye.
Machine learning techniques, such as the development of acute kidney injury (AKI) or sepsis, are especially well suited to predicting clinical events in the hospital.
