The high cost of maintenance and limited availability of Emergency Rooms (ER) facilities are under intense scrutiny by payers, government, providers, and employers. According to the CDC, Americans made 136 million ER visits in 2014 which is likely to increase further. Yet a study in the American Journal of Managed Care, cites more than 30% of ER visits could have been avoided. Avoidable ER visits stem from a lack of coordinated care that drive higher costs of care, longer wait times and worse outcomes. Redirecting only 20% of ER visits to lower-cost alternatives such as urgent care or Primary Care Physicians (PCP) could save $4.4 billion according to HealthAffairs.org. A multi-billion dollar healthcare payer wanted to identify members likely to make avoidable ER visits, and steer them to more cost effective alternatives.
Members may be visiting an ER unnecessarily for convenience, desire for a more effective PCP, from insufficient co-pay funds, or an unmanaged condition. Using clinical rules, we first identified low intensity conditions where an ER visit could have been avoided and offered more than 40 hypotheses for factors which could be predictive of avoidable ER visits.
To test these hypotheses, we identified different structured and unstructured data sources such as call center notes, geographic details for members and providers, and the availability of providers. For unstructured data, we applied multiple feature selection algorithms such as InfoGain1 and BNS2. For structured data, we tested hypothesis such as distance of the Primary Care Physician or urgent care facilities, ease of access to an ER, and difficulty finding quality providers. An ensemble of classifier models was developed to predict the likelihood of visiting an ER for low intensity conditions using advanced analytics such as machine-learning, text mining and traditional modeling techniques.
The solution identified 65% of all avoidable visits among 30% of the population – yielding an opportunity to save more than $10M annually by targeting a small group of Members for alternative care management and provider interventions.