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Home | Blogs | How Predictive Analytics Can Improve Healthcare
Fri Jan 24 06:59:27 UTC 2020

How Predictive Analytics Can Improve Healthcare


Healthcare organizations are creating more advanced Big Data analytics capabilities; they start moving from simple descriptive analytics to the field of quantitative insights.

This helps physicians, financial experts, and administrative staff to provide warnings before they happen about future incidents, and thus make more informed decisions about how to proceed with a decision. In the healthcare sector, predictive analytics will enable the best decisions to be made, allowing for care to be personalized to each individual.

WHAT IS PREDICTIVE ANALYTICS AND HOW DOES IT WORK?

Predictive analytics is the method of extracting data from existing data sets to identify patterns and predict future trends and outcomes. Instead of simply presenting information to a user about past events, predictive analytics estimates the likelihood of a outcome based on historical data patterns.

Below are five ways of how predictive analytics can be utilized in the healthcare industry.

1) IDENTIFYING PATIENTS HEALTH DETORIATION

While still in the hospital, a patient can face many potential threats to their well-being, including sepsis growth, hard-to-treat infection, or a sudden decline due to their established clinical conditions.

Data analytics can help clinicians react to changes in the vitalities of a patient as quickly as possible and can detect an imminent worsening before signs clearly show up to the naked eye. Predictive analytics technology using Machine learning techniques are particularly suitable for predicting hospital clinical events such as acute kidney injury (AKI) or sepsis.

2) AVOIDING APPOINTMENT NO-SHOW

Unexplained delays or skipping appointments of patients can throw-off the entire routine of a clinician and can have financial consequences for the organization. 

Using predictive analytics to recognize patients likely to miss an appointment without advanced notice will enhance physician satisfaction, minimize revenue losses, and provide opportunities for organizations to give more patients open slots, thus improving rapid access to care.

3) IDENTIFYING PATIENT UTILIZATION PATTERNS

Beyond helping organizations get ahead of no-shows, predictive analytics will offer a heads up to clinicians when the clinic is set to get busy.

Using these data service facilities operating without set hours, such as emergency departments and emergency care centers, may change their staffing levels to compensate for patient flow variability. Inpatient wards must have beds available for patients who need treatment, while outpatient clinics and doctor's offices are responsible for keeping patient wait times short. Use analytics to forecast demand trends can help ensure optimal levels of staffing, thus reducing waiting times and increasing patient satisfaction.

4) CREATING DISEASE SPECIFIC THERAPY

When precision medicine and genomics gain momentum, providers and researchers turn to analytics to complement conventional clinical trials and methods for drug discovery.

Predictive analytics and enabling methods for clinical decision making also play key roles in transforming new drugs into precision therapies. CDS systems are beginning to be able to predict the reaction of a patient to a certain course of treatment by comparing genetic information with the outcomes of previous patient trials, enabling clinicians to choose the therapy with the highest probability of success.

5) BETTER PATIENT ENGAGEMENT AND SATISFACTION

Customer experience management has become a vital skill for both insurers and insurance companies looking to promote well-being and minimize long-term costs ? and anticipating customer behavior is a key component in establishing efficient interactions and adherence strategies.    

Both payers and providers are issued with a wealth of information they can use to build models. Healthcare providers can also acquire other sources, such as health social determinants, that will really help their models to be strong and accurate. More positive partnerships between patients and caregivers can enhance long-term commitment and reduce the risks associated with chronic diseases by using predictive analytics to support care management decisions and grow stronger.

 

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