Revisiting utilization management: Taking a real-time approach to analytics and artificial intelligence

Unlike most other sectors of the economy, healthcare has been slow to adopt technology.

While online commerce, finance and logistics are routinely supported by the most advanced real-time analytics and predictive models, hospitals often rely on unchanged manual processes, dated communications tools, and siloed data to drive decision-making. There have been good reasons for this: while a financial mishap can impact accounts or markets, an incorrect diagnosis or determination can have profound consequences on a life. Furthermore, strict regulations govern healthcare, which although well-intended, often make positive change more difficult.

However, as technology, like machine learning and predictive analytics, has matured and proven to be both accurate and effective in care delivery, this inertia is a disservice to both patients and caregivers. Though there must be special consideration given to the quality of information and services utilized, data has proven time and again to be a bridge towards more effective and less costly care delivery.

I spoke on a recent webinar with Jason King, PhD, about the changing nature of healthcare analytics and artificial intelligence capabilities used in care determinations and delivery, and where the market will go from here. While there are quite a few companies in the market today taking novel approaches, many still rely on solitary data points to drive decisions and try to conform cases into rigid boxes. Additionally, they aren’t fully automated: unless you rely on real-time analytics, status changes and other threats to revenue often go unaddressed until it is too late. Continuous monitoring of every case is not only possible using the latest data science, it is crucial in assessing the entire clinical picture instead of only moving each case down a narrow, static pathway.

Case management and utilization review is a crucial component of value-based care, though case managers are often equipped with dated technology and burdened by manual processes that don’t utilize their expertise to its full potential. One approach I strongly advocate is precision utilization management. By using the latest in machine learning, case management staff and physician advisors can assess patients in real time and connect with those who need them, at the right time. Machine learning models make connections that might otherwise be overlooked, and allow accuracy and insight beyond what has previously been possible. Additionally, by only having to review cases where determinations might fluctuate, case managers are freed from manual processes that keep them from providing personalized attention and clinical expertise to their patients. Through this approach, providers are the beneficiaries of greater revenue integrity and higher levels of compliance.

Response to technology like artificial intelligence and machine learning has been bolstered by visionary healthcare leaders who see value in positioning their organizations for success. I’m heartened by the changing healthcare landscape and the rapid pace I see for future progress – in the past few years, providers, payers and government leaders have taken large strides in embracing change and adopting technology to the betterment of all. As these changes and technologies continue to prove their value, precision, performance and productivity will increase the quality of care patients receive and pave the way for a data-driven future of healthcare.

About Heather:
In 2013, amidst a healthcare industry characterized by huge revenue losses for hospitals nationwide due to unreimbursed or under-reimbursed services, XSOLIS emerged with the aim of providing a data-driven approach to addressing operational, compliance and regulatory issues. Dr. Bassett has helped build XSOLIS’ real-time predictive analytics capabilities from the ground up, bridging silos in healthcare data and operations. Today, she serves as Chief Medical Officer for XSOLIS, which now provides cognitive computing-driven technology solutions to hospitals and health systems across the nation.

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