The daily grind of healthcare takes an emotional and physical toll on both staff and patients. Pair that with high-stakes situations and staff shortages choking facilities nationwide, the U.S. healthcare system is facing a crisis. Predictive analytics help leaders make important decisions about business and patient care that help continually improve the value of healthcare.
Data types supporting predictive analytics in healthcare include electronic health records (EHR), diagnostics, patient paperwork, insurance claims, medical imaging, etc. Using these types of data in combination with artificial intelligence (AI), data scientists can create algorithms to analyze data and plan a course of treatment with accuracy, speed, and confidence. Indeed, predictive analytics have the capability to transform healthcare operations and outcomes entirely.
Predictive Analytics Use cases
1. Hospital Stays
Hospital Readmission Prevention
Using data, doctors can identify patients who are at high-risk for readmission. Upon recognizing which patients may be at risk, doctors can use additional resources to personalize any discharge instructions and make the necessary adjustments to the patients’ treatment plans following their hospitalizations.
Depending on a patient’s insurance plan, hospitals can incur high penalties for readmittance. For example, Medicare has a program which subjects hospitals to large penalties to discourage readmissions. By reducing readmissions, hospitals can save money, look to allocate resources more appropriately, and help enhance patient care.
Hospital Overstays
Hospital overstays are unnecessary and cause adverse effects to the healthcare system and patients. For hospitals, overstays can drive-up costs and divert resources that are often limited. For patients, overstays can increase the risk of exposure to secondary infections. The insight gathered from data can help clinicians recognize which patients are more prone to overstays and assist them with patient treatment to help keep their recovery process on schedule.
2. Insurance Issues
Speeding up insurance claims submission
The number of procedures and codes in place for billing can be confusing and cause delays. Predictive analytics plays a significant role in helping to speed up insurance claims while minimizing costly errors. By using analytics, hospitals can identify the correct codes for insurance claims as these codes determine the amount insurance companies have to pay.
With medical billing, two issues that hospitals need to be aware of are deadlines and filing constraints. If deadlines are not met, hospitals stand to lose revenue. The sooner medical claims are submitted, the quicker they are paid. Patients stand to gain significantly from properly submitted claims. They are less likely to be billed incorrectly and, in some cases, lead to less unexpected, out-of-pocket expenses.
Insurance Fraud Detection
According to the National Health Care Anti-Fraud Association (NHCAA), financial losses from healthcare fraud are in the tens of billions of dollars each year. It serves as a great monetary loss, but also devalues the healthcare system and brings shame to the profession. It can also affect innocent patients if the fraud includes identity theft.
Predictive analytics plays an integral role in stopping scammers and preventing further attempts by these criminals. Predictive analytics can detect and even prevent fraud faster, more accurately, and more affordably than humans are capable of.
3. Increasing patient engagement and outreach
As patients look to become stronger participants in their own healthcare decisions, hospitals are increasingly looking for ways to service a population with stronger healthcare demands. Using predictive analytics, doctors can study the behavior of individuals throughout their entire patient journeys. With insights as to what healthcare messages reverberate best with individuals, care providers can strengthen their relationships with engaged patients and disconnected patients alike.
Predictive Analytics for Smarter Healthcare Operations and Outcomes
The Global Healthcare Predictive Analytics Market is forecast to reach $74.62 billion by 2028. Factors contributing to its growth include the need to decrease healthcare costs, developments in evidence-based and personalized medicine, and a further push to increase efficiency. Using data to prognosticate future events in healthcare helps leaders in the industry to make critical business and life-saving decisions when it comes to patient care. From an organizational perspective, predictive analytics can be used to reduce costs, improve healthcare, and to ensure better utilization of resources. Patients benefit from improved diagnoses, better treatment plans, and more knowledge about their prognosis. When applied to a healthcare scenario, predictive analytics have the potential to change the course of a life.
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