Predictive Analytics in Healthcare

Healthcare organizations continually collect massive volumes of data. These stores of information can help physicians and patients understand risks, focus on prevention rather than reacting to acute conditions, and avoiding undesirable outcomes. Predictive analytics in healthcare applications can turn data into valuable insights. 
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The concept of using data to determine probable outcomes is not new to healthcare — big data has been a buzzword in the field for years, and physicians have always studied the progression of diseases, patients’ responses to medication and treatments, and warning signs of impending critical conditions. Predictive analytics augments physicians’ knowledge built over lifetimes of practice with instant access to data insights and alerts of potential crises so healthcare practitioners can intervene. 

How Predictive Analytics Technology Works

Predictive analytics solutions are designed to address clearly defined questions, for example, “What is the likelihood that a person will develop type 2 diabetes?”

The first step is to import historical data such as symptoms, laboratory results, and patient lifestyle information. The data must be cleaned, removing anomalies and identifying missing data to create a single dataset. Then, developers employ machine learning to build and train a model that can predict the likelihood that a patient will develop the condition. Before using the solution, it’s vital to test it for accurate performance.
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It’s important to use the right application of data when approaching analytics technology. Integrating a MedTech system with an “analytics solution,” for example, may not provide healthcare providers with insights they need. Some solutions deliver descriptive analytics, which use historical data to show what has occurred. Insights from descriptive analytics can be used to fuel more in-depth analysis, such as predictive analytics that can use data from numerous sources to forecast likely outcomes. 

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Applications for Predictive Analytics in Healthcare

In addition to ranking patients’ risk for common conditions, there are a variety of other use cases in which predictive analytics can be used in healthcare: 

• Emergency and intensive care: Predictive analytics can provide alerts from real-time data analysis, saving time — and lives. 
• Predicting a downturn in a patient’s condition: University of Pennsylvania Health System researchers developed a system that uses electronic health record (EHR) data to predict the risk of severe sepsis. The tool can identify patients 12 hours before the onset. 
• Reducing hospital readmissions: Predictive analytics can enable caregivers to proactively address risk factors to ensure patients who are discharged will not have to be readmitted. This use of predictive analytics not only has benefits for the patient, but also for the hospital, which may lose reimbursement under Medicare’s Hospital Readmission Reduction Program (HRRP).
• Suicide prevention: REACH VET uses predictive analytics to identify veterans at a high risk of suicide so practitioners can intervene and provide appropriate care. 
• Developing precision therapies: Big data and patient data can enable predictive analytics solutions to forecast how effective cancer therapies will be for a specific patient. 

​Notably, predictive analytics can also provide valuable insights to healthcare organization management, for example: 

• Predicting peak utilization and under-utilization: Predictive analytics can give healthcare facility management insights into patient flow, helping them plan for peak times — and to keep revenue coming in when fewer patients are seeking treatment. 
• Supply chain management: Navigant research found that hospitals could decrease supply chain costs by 17.8 percent with insights into pricing variation, use of supplies, and outcomes resulting from using specific products. 
• Improving patient satisfaction: Real-time patient data analyzed with a predictive model can result in timely response to their needs and positively influence the patient’s perception of the quality of care they receive. 

Use With Caution

Predictive analytics in healthcare offers great promise, but as some experts are pointing out, replacing a skilled physician’s thought process with predictive analytics currently is not regulated or controlled by standards. Algorithms used to create predictive analytics models can be biased or may lack proper context. 

The industry is also concerned about the security and integrity of centralized data necessary to fuel predictive analytics solutions. Data loss or tampering could have life-threatening consequences — and at the very least, create privacy issues. There are also fears that if a predictive analytics system is a part of care, that human practitioners will pay less attention, putting their faith in the technology that is supposed to have certain aspects of care covered. 

Carefully weigh potential negative implications of integrating predictive analytics solutions with the Medtech systems you provide and ensure your clients will get the benefits they intend — without the downside — from their investment in this technology. 

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About the Author

Carevoyance contributor Bernadette Wilson of B Wilson Marketing Communications is an experienced journalist, writer, editor, and B2B marketer, specializing in content for technology companies.