New technologies that use predictive analytics in conjunction with evidence-based criteria are transforming utilization management and improving clinical and financial outcomes.
During a July webinar hosted by Becker's Hospital Review and sponsored by Change Healthcare, Laura Coughlin, vice president of clinical innovation and development at Change Healthcare, discussed how AI when paired with clinical evidence can help hospitals modernize utilization management.
Four key takeaways:
- Modernizing inefficient and costly utilization management leads to better clinical and financial outcomes. Current UM processes are inefficient and reactive. Clinical information is available but is difficult to find and use when decisions need to be made at the time of admission. These inadequacies result in costly decisions when claims are denied, or discharge delayed, and inefficient use of staff tune and expertise manually completing medical necessity reviews. Modernizing UM involves foundational components of clinical expertise and transparent evidence based criteria combined with technologies that optimize data access to drive what Ms. Coughlin calls a "clinancial" outcome — one which combines what is clinically right for the patient with improved financial outcomes by treating patients in the right setting at the right time, efficiently.
- AI can process large amounts of clinical information and provide insights but has its limits. Applying AI within UM can automate processes, identify patterns and trends to provide proactive insights and assist with patient prioritization. However, predictive AI is only as accurate as the data available. Patients are complex and AI alone is not enough to defend decision-making because it needs evidence to paint a full picture. "The benefits of AI must be complemented by providers having access to clinical data and evidence to provide transparent defense for reimbursement," Ms. Coughlin said.
- AI plus evidence with InterQual® AutoReview improves the efficiency and effectiveness of UM. Utilization managers are involved with patient management across the continuum of care and need new technologies that leverage EHR data to increase efficiency and support clinical decision-making throughout the patient’s stay. " Our InterQual AutoReview solution has been enhanced with predictive analytics,” Ms. Coughlin said, "it uses extracted EHR data to automatically populate the InterQual medical necessity review, as well as to provide patient-specific predictions."
Incorporating InterQual AutoReview into the UM workflow reduces the administrative burden with automated medical reviews and enables proactive management with the following predictive analytics:- Level of care. Provides prediction for whether to keep a patient in observation or admit them as an inpatient, which affects reimbursement.
- Length of stay. Provides prediction for patient's length of stay and dynamically updates as more data becomes available. This helps case managers adjust care in a timely manner, reduce readmissions, and decrease avoidable days.
- Discharge destination. Provides prediction for whether a patient will needed services upon discharge. This allows discharge and transition planning to truly begin upon admission.
InterQual AutoReview also supports clinical decision making with criteria based on the latest evidence and research, something AI alone can’t provide. The combination of evidence-based reviews complete with clinical data from the EHR increases trust and transparency with payers.
4. AI-driven predictive analytics assist in transforming UM, but the human component is irreplaceable.
Using three case examples, Ms. Coughlin demonstrated how the blend of AI plus evidence and clinical expertise leads to improved patient management and reduced risk of denials. "Proactive, efficient, evidence-based UM is the future," Ms. Coughlin said. "With AI, let the easy cases be easy, so we can focus our time managing complex patients through the continuum of care"
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To learn more about InterQual AutoReview with predictive analytics, click here.