Machine Learning Predictive Analytics in Healthcare

November 5, 2020
Machine Learning Predictive Analytics in Healthcare

Big data and predictive analytics open new horizons. The decoding of the human genome has truly revolutionized healthcare. When it first happened, scientists thought it was a complete victory against diseases of every nature. Or so they thought. With the passage of time, reality revealed that human health is more complex than that. To identify treatment approaches, it is not enough to analyze molecules. To provide personalized and effective care, a large volume of data must be taken into account. Therefore, decision-making is based on a robust data set that undergoes processing.

Predictive analytics applies queries, statistical analysis, and machine learning algorithms to data. The result of this procedure is a predictive model. In other words, artificial intelligence and big data can determine the likelihood of different outcomes.

Image by Proxet - Predictive Analytics Model
Predictive Analytics Model

Predictive analytics involves a chain of processes:

  • Project definition: what are the business objectives, scope, deliverables, etc.
  • Data mining: multiple sources are used to accumulate data sets
  • The analysis: data is inspected and transformed to drive conclusions
  • Statistical analysis: the assumptions and hypotheses are tested and compared to standard statistical models
  • Predictive modeling: the creation of models predicting the future
  • Deployment of predictive models: analytical outcomes are applied to daily decision-making
  • Monitoring of the models: the performance of the model is assessed
“The combination of analytics and human-centered design can ensure that healthcare providers address inefficiencies along the patient journey and tailor services to meet the unique needs of the patient population.”

Lauren Neal, Principal, Booz Allen Hamilton

Predictive analytics for healthcare paves the way for deeply personalized medical care. Nowadays, healthcare providers feel the pressure to enhance coordination of care, outcomes for patients, and ultimately, drive positive business results. Doctors receive alerts on the possibility of future events long before they happen so clinicians can prevent them. Rapid advancements in the field of artificial intelligence, as well as the internet of things, gave birth to algorithms; algorithms are used to process historical information and real-time information to generate meaningful and accurate predictions.

In 2017, the Society of Actuaries carried out research on healthcare trends. It revealed that 47% of healthcare institutions already use predictive analytics, and 93% of respondents reported that predictive analytics is crucially important for their organization. According to Business Insider, the global market for personalized medicine will be $3.92 trillion by 2026. 

“Predictive analytics is not reinventing the wheel. It’s applying what doctors have been doing on a larger scale. What’s changed is our ability to better measure, aggregate, and make sense of previously hard-to-obtain or non-existent behavioral, psychosocial, and biometric data.

Combining these new datasets with the existing sciences of epidemiology and clinical medicine allows us to accelerate progress in understanding the relationships between external factors and human biology—ultimately resulting in enhanced reengineering of clinical pathways and truly personalized care.”

Vinayak Ramesh, Co-founder of Wellframe

Healthcare Predictive Analytics: Common Delusion

As far as healthcare predictive analytics is concerned, we have to debunk the most common myth. Healthcare predictive analytics is not new and it is not defined by software or technology tools. When doctors determine the possible future developments of a patient based on past observation and experience—a cornerstone of conventional medical practice—that is predictive analytics too. Software tools simply expand the data set and include more variety to give the full picture. Software advances predictive analytics, but does not define it.

The Advantages of Predictive Analytics: Why?

Let’s zoom out and look at the benefits of predictive analytics from a business perspective:

  • It is a proven analytical technology
  • Established business value
  • The market constantly and rapidly grows
  • Significant investments in the field

For these reasons, predictive analytics are an established and widespread business practice.

Predictive analytics broadly influences an organization in three ways. First, it helps to better understand the needs of the clients. Second, it manages risk by identifying business opportunities and anticipating competitive challenges. And third, cost-effectiveness: mitigating risk cuts business losses, and analyzing trends determines the optimal approach.

Zooming in on specific areas within a medical company, the benefits of predictive analytics in healthcare include:

  • Smart detection
  • Case management system acquires logic
  • Workloads are prioritized
  • Control and cooperation are enhanced
  • KPIs and overall progress are better monitored
  • Suspicious trends are detected long before losses happen
  • Patterns are defined to become the base for specific action
  • Information is aggregated and correlated.
“The biggest challenge of making the evolution from a knowing culture to a learning culture—from a culture that largely depends on heuristics in decision making to a culture that is much more objective and data-driven and embraces the power of data and technology—is really not the cost. Initially, it largely ends up being imagination and inertia… What I have learned in my last few years is that the power of fear is quite tremendous in evolving oneself to think and act differently today, and to ask questions today that we weren’t asking about our roles before. And it’s that mindset change—from an expert-based mindset to one that is much more dynamic and much more learning-oriented, as opposed to a fixed mindset—that I think is fundamental to the sustainable health of any company, large, small, or medium.”

Murli Buluswar, Chief Science Officer, AIG

Healthcare Predictive Analytics Software

The market is steadily expanding to bring new, innovative solutions to the table. At the moment, we’d like to highlight the following predictive analytics software:

  • IBM SPSS MODELER. This software has proven to be effective in reducing the complexities in data transformation with the help of easy-to-use models. 
  • RAPIDMINER STUDIO. Users are able to generate complicated predictive models with the help of the plain interface. Its ML algorithms library has over 1500 entries to help a user build models according to a specific case. Provides templates and recommendations.
  • SAP. A handy tool for creating, deploying, and maintaining various predictive models. Provides enhanced guidance for decision-making and helps grow the business. Has in-memory tech and ML to detect predictive insights in real-time.
“Our healthcare predictive analytics tools have been effective in managing clinical decision-making. When huge volumes of data can be easily aggregated and analyzed across a medical institution, the patient experience acquires a new value and depth. Leveraging data helps healthcare go further, and become proactive and predictive; healthcare predictive modeling plays a crucial role in this respect.”

Vlad Medvedovsky at Proxet, custom software development solutions company.
Predictive Analytics Healthcare Examples

As far as the practical applications of predictive analytics in hospitals is concerned, we can point out the following use case examples in healthcare:

  • Tackling operating room bottlenecks. Delays are bad for everyone; analytics prevents delays by organizing workflows, generating notifications, and streamlining processes.
  • Quickly diagnosing and providing treatment for aggressive ailments. Some diseases, such as sepsis, are pretty tricky and doctors may miss or ignore the initial symptoms of it; predictive analytics won’t.
  • More accurate diagnostics. Some symptoms, such as chest pain, might signal a variety of diseases. Predictive analytics gives a doctor’s clinical judgment data backing.
  • Predicts insurance and product cost. Healthcare is a huge business, we haven’t forgotten. Predictive analytics is applied to calculate possible medical costs and economize where possible.

Proxet developers and engineers have been successful in defining the specific business needs of a wide range of healthcare providers. The case for and benefits of predictive analytics are clear; our team is always ready to help you grow.

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