Predictive Analytics

Predictive Healthcare Analytics: Improving Care for High-Risk Patients

By Sriram Parthasarathy
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Machine learning, artificial intelligence, and predictive analytics are all becoming an integral part of the healthcare industry. Predictive analytics can provide real-time insights and recommended actions to vastly improve patient care. Predictive analytics allows medical professionals to gain a 360-degree view around critical aspects of healthcare.

One example of predictive analytics in healthcare is improving care for high-risk patients. A whopping fifty percent of healthcare spending can be attributed to just five percent of patients. Predictive health analytics can help doctors identify factors that can lead to chronic conditions down the road, ultimately improving patient outcomes and reducing long-term healthcare costs.

How Predictive Health Analytics Helps High-Risk Patients

A predictive analytics application can analyze data from a wide variety of patients—including healthy individuals, those with chronic illnesses, and many people in between. These factors may include regular checkup schedules, smoking, family medical history, occupational hazards, health insurance status, age, and location. It can also consider the efficacy of various preventive measures for those patients.

Next, the predictive analytics solution identifies common factors among high-risk patients. In particular, it can look for early signs that the patient is becoming high risk. It can also identify which treatments are most effective and what the best timing is to introduce those treatments.

Recommended Actions to Take

Early identification of patients who are likely to develop high-risk conditions can help medical practitioners prevent conditions from getting worse in the first place.

Predictive modeling in healthcare may recommend some actions, including:

  • Encouraging the patient to adopt some new healthy habits
  • Suggesting medication that may alleviate the condition if taken early enough
  • Offering after-hours appointments for patients who can’t make it to the office during the day
  • Identify unnecessary tests and procedures to eliminate in the course of the patient’s care
  • Coordinating with other providers and healthcare facilities to provide focused treatment paths for specific chronic illnesses
  • Adding healthcare navigators or other people who can help the patient get the services he or she needs

These actions, driven by data insights, can lead to better patient outcomes and improved care overall. The information can also be used to target groups that are at risk of developing chronic conditions, allowing physicians to come up with early intervention treatment plans.

Predictive health analytics should be viewed as a tool for informing decisions, not a quick fix that can immediately resolve problems. Continuously feeding new information into your analytics solution can improve predictions and allow your organization to identify trends in the data.

Originally published July 18, 2019; updated on July 31st, 2020

About the Author

Sriram Parthasarathy is the Senior Director of Predictive Analytics at Logi Analytics. Prior to working at Logi, Sriram was a practicing data scientist, implementing and advising companies in healthcare and financial services for their use of Predictive Analytics. Prior to that, Sriram was with MicroStrategy for over a decade, where he led and launched several product modules/offerings to the market.