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Predictive Analytics in Healthcare: High- Value use Cases

Predictive Analytics in Healthcare: High- Value use Cases
September 09, 2021
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predictive analytics in healthcare high value use cases

In recent years, health care providers have been developing more sophisticated big data analytics capabilities. There has been a shift from basic- descriptive analytics towards the world of predictive analytics in healthcare. 

Predictive analytics in healthcare may be new, but it actually represents a huge step towards the future for many organizations. 

Instead of simply presenting the same information about past events to a user, the use of predictive analytics in healthcare estimates the likelihood of a future outcome based on patterns in the historical data.

Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.

Though predictive analytics has been around for decades, it’s a technology whose time has come. More and more organizations are turning to predictive analytics to increase their bottom line and competitive advantage. Why now?

  • Growing volumes and types of data, and more interest in using data to produce valuable insights.
  • Faster, cheaper computers.
  • Easier-to-use software.
  • Tougher economic conditions and a need for competitive differentiation.

With interactive and easy-to-use software becoming more prevalent, predictive analytics is no longer just the domain of mathematicians and statisticians. Business analysts and line-of-business experts are using these technologies as well.

Mapsted’s predictive analytics allows clinicians, financial experts, and administrative staff to receive alerts about potential events before they happen, and therefore make more informed choices about how to proceed with a decision. 

The importance of always staying one step ahead of events is most clearly seen in the realms of intensive care, surgery, or emergency care, where a patient’s life might depend on a quick reaction time and a finely-tuned sense of when something is going wrong. 

But high-value use cases for predictive analytics exist throughout the healthcare ecosystem, and may not always involve real-time alerts that require a team to immediately spring into action.

Provider and payer organizations can apply predictive analytics tools to their financial, administrative, and data security challenges, as well, and see significant gains in efficiency and consumer satisfaction.

How are healthcare organizations deploying predictive capabilities across the enterprise to extract actionable, forward-looking insights from their growing data assets?

Anticipating Appointment No-shows

Unexpected gaps in the daily calendar can have financial ramifications for the organization while throwing off a clinician’s entire workflow. 

Using Mapsted’s predictive modeling in healthcare to identify patients likely to skip an appointment without advanced notice can improve provider satisfaction, cut down on revenue losses, and give organizations the opportunity to offer open slots to other patients, thereby increasing speedy access to care.

Data can reveal individuals who are most likely to no-show, according to a study from Duke University.  A team found that predictive models using clinic-level data could capture an additional 4800 patient no-shows per year with higher accuracy than previous attempts to forecast patient patterns.

Providers may be able to use this data to send additional reminders via push notifications to patients at risk of failing to show up, offer transportation or other services to enable individuals to make their appointments, or suggest alternative settings and times that may better suit their needs.

Predicting Patient Utilization Patterns

In addition to helping organizations get ahead of no-shows, predictive analytics can give providers a heads up when the clinic is about to get busy.

Care sites that operate without fixed schedules, such as emergency departments and urgent care centers, must vary their staffing levels to account for fluctuations in patient flow.  Inpatient wards must have beds available for patients who need to be admitted, while outpatient clinics and physician offices are responsible for keeping wait times low for patients.

Using Mapsted’s predictive analytics in healthcare to predict patterns in utilization can help to ensure optimal staffing levels while reducing wait times and raising patient satisfaction.

Visualization and predictive modeling in healthcare strategies can model patient flow patterns and highlight opportunities to make workflow adjustments or scheduling changes.

At Wake Forest Baptist Health in North Carolina, analytics tools helped the oncology infusion center anticipate peak utilization times and adjust its scheduling practices accordingly, said Karen Craver, Clinical Practice Administrator.

By analyzing typical utilization rates, the infusion center found that popular mid-day appointment times were creating unsustainable spikes in capacity, while early morning and late afternoon spots went unfilled.

Altering certain scheduling procedures helped to “flatten out the bell curve” and create a more even distribution that reduced burdens on nurses and improved patient satisfaction, she said.

“In the morning, we have greater utilization than we did before, but then we can maintain our appointment rate throughout the day, which is a much easier way to work than the steep incline and decline we used to deal with,” Craver remarked.

Managing the Supply Chain

The supply chain is one of a provider’s largest cost centers, and represents one of the most significant opportunities for healthcare organizations to trim unnecessary spending and improve efficiency.

Tools for predictive analytics in healthcare are in high demand among hospital executives looking to reduce variation and gain more actionable insights into ordering patterns and supply utilization.

Only 17 percent of hospitals currently use automated or data-driven solutions to manage their supply chains, said Cardinal Health in 2017. 

In the same year, Global Healthcare Exchange ranked predictive analytics for supply chain management as the number one item on the executive wish list – a follow-up survey in 2018 found that adopting data analytics tools remained a top priority.

Using Mapsted’s predictive analytics tools to monitor the supply chain and make proactive, data-driven decisions about spending could save hospitals almost $10 million per year.  Both descriptive and predictive analytics can support decisions to negotiate pricing, reduce the variation in supplies, and optimize the ordering process.

Patient Engagement And Satisfaction

In addition to managing the supply chain and reducing no-shows, predictive analytics in healthcare can keep patients engaged in other aspects of their care, as well.

Consumer relationship management has become a vital skill for both providers and insurance companies looking to promote wellness and reduce long-term spending – and predicting patient behaviors is a key component of developing effective communications and adherence techniques.

“We need to know what works and what doesn’t in our engagement programs, and how to anticipate and predict the best outcomes given very complex characteristics of our membership sub-populations, which span every single segment of the US population,” said Patrick McIntyre, Senior Vice President of Health Care Analytics at Anthem.

“Our goal is to grow out our consumer engagement skills, because we are shifting into much more of a service-oriented, consumer-oriented industry.”

Anthem is using its data analytics tools to create consumer profiles that allow the payer to send tailored messaging, improve customer retention, and discover what strategies are most likely to be impactful for each individual.

Providers, too, are using behavioral patterns to create meaningful care plans and keep patients engaged with their financial and clinical responsibilities.

“Both payers and providers have a wealth of information that they can use to build models. Healthcare providers can also acquire some other sources, like the social determinants of health, for example, that will really help the strength and accuracy of their models,” said Lillian Dittrick, Fellow of the Society of Actuaries

“When we use predictive models to look at all the variables, it helps us prioritize those patients who are really going to be receptive to changing something in their lifestyle, such as nutrition or exercise.”

Using predictive analytics to inform care management decisions and develop stronger, more motivational relationships between patients and providers can improve long-term engagement and reduce the risks associated with chronic diseases.

“We’re seeing more and more that automation and machine learning tools really help with sorting through and processing these very large amounts of data,” said Dittrick.  “There is some kind of predictive modeling that could help improve processes in just about any facet of healthcare.”

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