Using Proactive and Predictive Analytics in Healthcare

In the shift to value-based care in the healthcare industry, analytics will play an active role as practitioners look to create a proactive care plan rather than the traditional reactive care plan.

According to Deloitte, this shift will more than likely cause demand for “significant analytic infrastructure investments and expansions across all health stakeholders interested in fully realizing and optimizing its value.”

As data continues to stream in from electronic health records, wearables and other sources, analysts can use the data to find patterns and give insights into patient care that are actionable and reliable. This data can be assessed by artificial intelligence as well, enhancing human ability to predict and find alternative routes.

Using Predictive and Proactive Analytics in a Care Plan

A proactive care plan has been an industry goal for a while, according to MedCity News. By combining EHR data with predictive analytics to create a proactive mindset, this can become a reality.

Predictive and proactive analytics have different purposes, according to Zachary Davis, PhD, assistant professor of decision sciences and information management at Jacksonville University.

“To explain the difference between proactive and predictive analytics, you must start with descriptive analytics. In descriptive analytics, we determine what has occurred, according to our data,” Davis said. “Through aggregation and queries of the data, we can begin to see where predictive analytics are useful. Predictive analytics allow us to make predictions based on statistics, machine learning and data mining. Based on previous performance, we can extrapolate and predict what might happen. If we have a good idea of what the future holds already, we can use proactive analytics to take advantage of a trend. Through optimization, we can make a decision or take action based on our findings.”

In fact, these proactive and predictive analytics can be used in many ways, according to Deloitte, including:

  • Help improve costs and quality
  • Identify at-risk populations
  • Connect with consumers
  • Understand performance of interventions on patient outcomes

How Predictive and Proactive Analytics Improve Patient Outcomes

According to Davis, proactive analytics can be used to improve patient outcomes “through the development of predictive models that assist physicians to compare different treatment options.”

Armed with this information, providers can work to determine which treatment could create the most effective outcome with objective knowledge.

“Predictive analytics would be used when we are less sure of the future,” Davis said. “Proactive analytics contribute more toward preventative healthcare. For example, telemedicine monitoring of patients with congestive heart failure – constant streams of data could be sent to an analytics team and when the patient’s health state decreases, their physician would be notified.”

By being proactive, healthcare providers can quickly assess clinical decisions while improving workflow. Both predictive and proactive analytics will be instrumental in the future of healthcare analysis, but each situation will require different approaches and analytics will have diverse applications.

“I liken analytics to having a toolbox. If I wanted to nail a sign to a door, I would use a hammer, not a screwdriver,” Davis said. “Similarly, depending on the nature of the problem I am trying to solve, I would match the kind of analytics used. Being an analyst is more about critical thinking and problem solving than being an expert on one particular kind of analytics. Knowing when to use each kind of analytics and how it is applied is what makes someone a stronger analyst.”

What Are the Challenges Behind Implementing Analytics in Healthcare?

In order to effectively implement analytical processes, some obstacles will need to be overcome by the healthcare industry, including budget issues and data siloes. According to Health IT Analytics, these issues “have contributed to a fragmented environment where predictive analytics and proactive care planning are still often little more than a pipe dream.”

About 80% of hospital executives feel predictive analytics can improve the future of healthcare, but only about one-third of hospitals use it, according to a Health Catalyst study. Many cited budget concerns as well as the lack of staff to properly implement a program.

A study conducted by Silicon Valley Bank of 122 health tech company founders, executives and investors found that 37% believe that consumer, patient and client adoption is the biggest challenge, and 34% believed regulations would be a challenge, as well.

To fight these issues, organizations need to develop a strategy that solves these problems and the goals behind them using predictive and proactive analytics. By finding a specific use case to demonstrate effectiveness, like sepsis reduction or fall rates, and being transparent with data and breaking down siloes, payers and providers can work together to find the best ways to apply analytics information to improving patient outcomes.

The Future of Predictive Analytics

As wearables connected to the internet of things (IoT) continue to transmit real-time data, the healthcare industry will need to find ways not only to store, but also to analyze the data. Machine learning and artificial intelligence (AI) may play a role in this as interest in these continues to grow.

The SVB study revealed that 46% of industry leaders think big data will have a great impact, while 35% felt AI would have the biggest impact.

According to Health IT Analytics, AI can be applied in many situations like chronic disease management, clinical decision support and population health management. AI and machine learning can help providers integrate patient data with other factors like socioeconomics, geographical data and demographics to create better patient outcomes, but also to predict at-risk populations. The process will still need to be fine-tuned, but as innovations continue to be made, analysts can work to create these plans and processes.

The future of big data in healthcare is dependent on qualified professionals being equipped with the necessary training and tools. Jacksonville University offers a Master of Science in Applied Business Analytics, as well as an innovative dual MSN/MBA degree that pairs nursing knowledge with business acumen, for those looking to move into this developing area of healthcare.

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