Healthcare is inherently a data-rich sector, and can, therefore, gain a lot from leveraging data analytics. The shift in the US healthcare industry to value-based care offers the right impetus to adopt data analytics. Healthcare providers and institutions are already using novel technologies that are aiding them to accelerate the operations, make better decisions, and, ultimately, deliver an enhanced experience to patients.
Now predictive analytics tools are used to make decisions that help improve patient outcomes, increase operational efficiency, and reduce spend. All in all, the spectrum of benefits of applying predictive analytics to healthcare covers everyone: Providers, Patients, Payers, and product manufacturers. The ability to save lives leveraging Predictive analytics algorithms is a huge leap forward to mankind
In this article, we delve on a few use cases of predictive analytics leveraged in healthcare
Effective personal medicine
Predictive analytics allows to find cures for particular diseases. This stands true even for diseases unknown at the time. Predictive analytics allows healthcare organizations to introduce precise modeling for mortality rates for individuals. And this is just the tip of the iceberg!
It is known for long that certain medicines work better for some groups of people, but not necessarily others. The reason for that is that human bodies are complex, and still, a lot is unknown about it. It’s not practically feasible, nor possible, for a single, or a few, healthcare providers to analyze all of the detailed data.
But predictive analytics can come in handy. It can discover correlations and patterns when studying huge data sets and then offer insightful predictions. Clearly, such an approach will allow caregivers to come up with the best treatment option for the use case at hand.
Patient Wellness and Prevention of Chronic Diseases
There’s a reason that prediction and prevention go hand in hand; especially in case of healthcare. There is a wealth of data that is generated from devices like Fitbit, Apple Watch, etc. Combining this data with health data of the patient, diagnostics from labs, etc; institutions can use predictive analytics tools to identify individuals who are at higher risks of developing chronic conditions earlier in the disease progression. In doing so, the patients can be kept at a lower risk of developing serious long-term problems.
Accounts Receivable and Denials Management in Revenue Cycle Management
Providers leverage RCM providers for AR and Denials Management and there is a high percentage of cases that still get into the Denials Management queue. Ability to predict these as a Patient schedules an appointment will allow Providers to take additional precautions for some of these cases
Controlling patient deterioration
During their stay at the hospital, patients face various threats, including the development of sepsis, acquisition of infection, or sudden downturn due to the existing clinical conditions.
Equipped with predictive analytics, doctors can figure out possible deterioration based on the changes in a patient’s vitals. Most importantly, they can do that before the symptoms clearly manifest themselves.
Machine learning has proven to be extremely efficient in predicting clinical events at the hospital. For instance, at the University of Pennsylvania, doctors leverage a predictive analytics tool to identify patients who might fall victim to severe sepsis or septic shock 12 hours before the onset of the condition.
Potential in precision medicine
Evidently, healthcare institutions have access to heaps of data that can be used to uncover patients who have had similar responses to specific medications.
Only machine learning-based analytics can help uncover such insights, because the data sets we’re talking about are really huge, and include a lot of cluttered, unstructured data, including age, gender, location, and other relevant healthcare data.
Predictive analytics can, therefore, lead to improved precision medicine outcomes and make it easier for doctors to customize medical treatments, practices, and products to the use case at hand.
Cost reductions from eliminating waste and fraud
According to a NCSL podcast released in Dec 2017, healthcare fraud costs insurers anywhere between $70 billion to $234 billion each year.
By analyzing the heaps of patient data, payers are building or have built predictive models to prevent insurance fraud before payouts. This could include duplicate claims, doctors prescribing high rates of tests, or medically unnecessary treatments.
For the short-term, the increase in number of cases for Covid-19 is another clear use case of the need for prioritization. Organizations have the opportunity to admit patients and offer treatment based on prediction algorithms leveraging symptoms and patient medical conditions.
For the long term, Analytics is a key aspect in most Healthcare systems and an increasing number of organizations are implementing Artificial Intelligence and Machine Learning-based tools to use the data and predict trends.
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