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ConsumerMedical Combines AI with Predictive Analytics to Improve Medical Outcomes and Double Savings for Employers, Plan Sponsors

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New Program Identifies People Considering Surgery Early in the Process; Helps to Address $200 Billion Annually(1) Spent on Unnecessary or Harmful Treatments

Could earlier identification of people considering elective surgeries result in better medical decisions and lower costs? The answer is yes. ConsumerMedical’s innovative combination of artificial intelligence and predictive analytics succeeded in reducing surgical expenses by half, while at the same time improving medical outcomes.

“Often patients are already on the pathway to surgeries that may not be necessary, without having evidence-based information on whether this is the best option for them, or knowing about other alternatives that would help them without the risks of surgery,” said Dr. Randy Hawkins, Chief Medical Officer and Executive Vice President of Health Analytics at ConsumerMedical, the medical ally empowering individuals to make informed healthcare decisions.  “We’ve greatly enhanced the predictive modeling tool we released in 2016 into a next-generation offering by using artificial intelligence to identify these individuals when they are likely to be thinking about surgery and before they have made that decision.”

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Results from this new model demonstrated that identifying people before they were in a crisis situation succeeded in doubling the average program engagement rate and in generating significant cost savings, while at the same time achieving and maintaining high satisfaction rates.

A Honeywell study found that 37% of members chose to avoid surgery after going through the ConsumerMedical program. For another employer, ConsumerMedical observed that 27% of the Surgery Decision Support candidates who participated in the program avoided surgery for a savings of $2.3 million and a total ROI of 4.4:1.

Individuals identified in the early stages of the decision-making process were more than twice as likely to engage with the counseling services than those who were identified later – 31.3% engagement versus 14.3% engagement.

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Claims savings for these individuals was more than double the savings from employees who were not identified early – $7.33 per employee per month, versus $3.05 per employee per month.

The new AI-powered model focuses on five types of surgery that are unrelated to mortality or saving the life of the patient and for which clear clinical guidelines do not exist. Consequently, it is up to the patient to decide on what they think is the best choice for them. These procedures include low back surgery, hip and knee replacement, hysterectomies and weight loss surgery.

“Before the decision to move forward, these individuals should be armed with information on the pros and cons of the procedures, the likelihood of potential complications, and other clinical pathways that may be more effective,” noted Hawkins.  “Our role is to present these individuals with research-based findings, help them analyze their situation and options, and assist them in identifying the most experienced care team. Armed with this information, the best decisions can be made.”

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