Data analytics enables you to make informed decisions by providing actionable insight. I am going to discuss three types of analytics.

While historically analytics was concentrated in descriptive analytics, recently and currently the focus is on predictive and prescriptive analytics. Do not be fooled by a vendor or carrier that claims they perform data analytics. A lot of times, that analytics is limited to very basic descriptive analytics. There is nothing wrong with this, as basic descriptive analytics can be helpful by allowing us to learn from past behaviors.

Here’s a more detailed look at the three types of analytics:

Descriptive Analytics

• Uses data aggregation and data mining to provide insight into the past.
• Reviews key performance indicators (KPIs).
• In healthcare, can come in the form of carrier reports or utilization reports.

Predictive Analytics

• Tells us what is likely to happen next.
• Uses statistical models and forecast techniques to understand the future.
• In healthcare, predicts who will be high cost claimants, have readmissions, etc.

Prescriptive Analytics

• Identifies what should we do about those predictions.
• Uses optimization and simulation algorithms to advise on possible outcomes.
• Uses predictive analytics, with the additional features of actionable data and a feedback system that tracks the outcome.
• In healthcare, recommends disease management or other intervention.

One of the chief hinderances in performing predictive or prescriptive analytics is that most of healthcare data is in silos and worse yet, contained in hard copies or old systems. Advances in technology have afforded us the opportunity to more effectively collect and analyze different types of data to generate actionable insight. We have the ability to de-identify the data and combine those previously siloed data sources, providing powerful databases to perform predictive and prescriptive analytics on our own data sets, but only if we integrate them to give us the full picture.

Uses for Prescriptive Analytics

Predictive analytics helps with the identification of populations of patients in various disease categories and disease states. Populations with a specific chronic disease, such as diabetes or heart disease, can be identified and monitored to prevent the development of other medical conditions associated with the disease. Then prescriptive analytics can determine the best course of action, and measure the effectiveness of each intervention.

With metabolic syndrome, analytics can identify individuals with risk factors or individuals on the precipice of developing metabolic syndrome. The prescriptive part will assist in behavior modification to improve healthier lifestyle choices.

Advances in technology have afforded us the opportunity to more effectively collect and analyze different types of data to generate actionable insight.

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Analytics can help determine the patient and clinical information needed to better promote wellness or manage diseases, and aggregate the information required to demonstrate better outcomes. Seton Healthcare in central Texas identified which patients with congestive heart failure were most likely to be readmitted to the hospital. These patients were provided with proactive disease management to reduce cost and mortality rates resulting in improved quality of life.

We might use prescriptive analytics to look at our quality risk—things such as identifying variation in practice. What are best practices for specific interventions, such as total hip replacement surgery?

When patients aren’t compliant with medication regimens, they are more likely to have complications, particularly for chronic conditions. We might want to know who among a population is most likely to be non-compliant? Who should we monitor closely to make sure they are being compliant? We can use predictive analytics to create that focus, and prescriptive analytics to determine the most effective approach for ensuring adherence.

If a healthcare provider is experiencing an extremely high number of infections, a prescriptive analytics program would not just flag the anomaly and highlight which patients in the ICU may be next on the list due to certain factors, but would also automatically identify the particular nurse involved in the care of all these patients who may be spreading the infection and might need to be retrained about hand hygiene. It may also help the hospital develop more comprehensive communications around hygiene to help prevent similar outbreaks in the future.

Analytics can help determine the patient and clinical information needed to better promote wellness or manage diseases, and aggregate the information required to demonstrate better outcomes.

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The data is there. It’s time to start using it to understand what’s happening in your population so you can take steps to better manage costs and member health.

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