Improve Operational Efficiency

Operational efficiency determines profitability for healthcare organizations (HCOs), and it’s quickly becoming the determinant of survivability as well. Today’s HCOs operate on razor-thin margins and face a breadth of challenges that affect their viability. To survive and thrive, they must address the underlying problem: matching volatile demand with constrained and disorganized supply.

Size of the Problem

• $200,000 is the potential annual revenue from improving a single OR’s efficiency 2–3% (1).
• 600,000 people is the estimated shortage of physicians and nurses in the U.S. by 2032 (4,5).
• 30% of patients report leaving a clinic before seeing a physician due to long wait times (6).
• Twice the number of patients have left the ED without being seen in recent years (7).

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Operational efficiency determines profitability for healthcare organizations (HCOs), and it’s quickly becoming the determinant of survivability as well. Today’s HCOs operate on razor-thin margins and face a breadth of challenges that affect their viability, including shrinking reimbursements, physician and nurse shortages, transitioning to value-based care, and caring for an aging population afflicted with chronic diseases. If they are to survive and thrive, they must address the underlying problem: matching unpredictable and volatile demand with constrained and disorganized supply

.Improving operational efficiency at scale is extremely challenging. Patients don’t get sick at scheduled times and the availability of rooms, physicians, nurses, and equipment is always finite. However, even the smallest improvements in efficiency and utilization present immense financial payoffs. Operating rooms—the economic backbone of a hospital—often generate more than 50% of all HCO revenue, and a single block can generate between $50,000 to $100,000 a day (1). A 2–3% improvement in prime-time operating room (OR) utilization can be worth $200,000 per OR every year. For larger HCOs with hundreds of ORs, this represents tens of millions of dollars in annual revenue. Similarly, each inpatient bed is an asset representing $2,000 in potential revenue daily, and optimizing their use is critical to improving financial outcomes (1).

Effectively matching supply and demand requires addressing the interlinkage of all departments, processes, and services across the care continuum. Assessing inefficiencies in the emergency department (ED) exemplifies this. Over half of all hospital admissions come through the ED, and the average patient waits more than 90 minutes before being taken to a bed (2,3). Often, these delays are caused by a lack of supply elsewhere (e.g., inpatient beds, vital testing equipment, and personnel availability). It can take more than 30 calls between the ED and inpatient units before an available inpatient bed is identified, and it can still take hours for the transfer to actually occur (1). This leads to prolonged ED stays, patients leaving without being seen, increased burden on ED teams, and potential underutilization in other departments.

Technologies capable of sophisticated mathematics are essential to solve the underlying supply-demand challenge, and while electronic health records (EHRs) are vital to help streamline operations, they can’t perform the necessary predictive analytics. They aren’t designed to address complex, HCO-specific optimization problems. Instead, healthcare must implement new technologies. Despite its status as a digital-laggard and its unique challenges, operationally, it is very similar to other industries that have overcome similar logistical difficulties.

HCOs can employ AI-based models to optimize operations, following in the footsteps of airline and shipping leaders, such as Delta, Fedex, and UPS, that have definitively proven the effectiveness of such technologies. Predictive analytics can enable HCOs to refine utilization and scheduling, improve the patient experience, maximize the value of their assets, and do more with less. Ultimately, they can leverage AI to systematically streamline care, benefiting patients, providers, and the bottom line.

• Operations & Services: Data from health plans, clinics and service providers that capture details about service interactions, billing and payment, technical issues, and complaints.
• Social Needs Assessments: Self-reported data that identify an individual's specific needs and the acute social and economic challenges they are experiencing.
• Medical Claims: Data extracted from health insurance medical claims with details about dates and place of service, diagnosis codes, key procedures, use of medical equipment, and provider specialties.