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.
Learn how AI can help HCOs refine resource utilization, improve scheduling and patient experiences, and maximize asset value. Discover how AI can help to account for the interlinking of services to accurately predict appointment delays, streamline operating room scheduling, and evaluate and optimize staff workloads and healthcare resource allocation.
Data from health plans, clinics and service providers that capture details about service interactions, billing and payment, technical issues, and complaints.
Self-reported data that identify an individual's specific needs and the acute social and economic challenges they are experiencing.
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.
ClosedLoop generates explainable predictions using
thousands of auto-generated, clinically relevant contributing factors
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.
1. Agrawal Sanjeev, Giridharadas Mohan. Better Healthcare through Math: Bending the Access and Cost Curves. Charleston, SC: ForbesBooks; 2020.
2. Moore, Brian J., et al. “Trends in Emergency Department Visits 2006–2014.” Healthcare Cost and Utilization Project 227, Sep. 2017, https://www.hcup-us.ahrq.gov/reports/statbriefs/sb227-Emergency-Department-Visit-Trends.pdf.
3. Dyrda, Laura. “25 facts and statistics on emergency departments in the US.” BeckersHospitalReview.com, 7 Oct. 2016, https://www.beckershospitalreview.com/hospital-management-administration/25-facts-and-statistics-on-emergency-departments-in-the-us.html.
4. Heiser, Stuart. “New Findings Confirm Predictions on Physician Shortage.” Association of American Medical Colleges, 23 Apr. 2019, https://www.aamc.org/news-insights/press-releases/new-findings-confirm-predictions-physician-shortage.
5. Alemian, David. “The Nurse and Physician Shortage.” MD Magazine, 9 Aug. 2016, https://www.hcplive.com/view/the-nurse-and-physician-shortage.
6. Heath, Sara. “Long Appointment Wait Time a Detriment to Patient Satisfaction.” Patient Engagement Hit, 23 Mar. 2018, https://patientengagementhit.com/news/long-appointment-wait-time-a-detriment-to-high-patient-satisfaction.
7. “Timely and Effective Care – Hospital.” Data.Medicare.Gov, accessed July 2020, https://data.medicare.gov/Hospital-Compare/Timely-and-Effective-Care-Hospital/yv7e-xc69.
ClosedLoop is an exciting and important partner in our strategy to develop timely predictive insights through operationalized AI solutions that will drive member engagement and better health outcomes.
Our partnership with ClosedLoop adds the depth of AI to more accurately identify those patients who are most likely to benefit from our primary care interventions as well to evaluate these interventions and make more relevant and timely modifications to improve their effectiveness.
We are extremely impressed with the predictive modeling capabilities the ClosedLoop platform has delivered. The ClosedLoop team has exceeded all defined goals and benchmarks set to date and we anticipate a substantial return on our investment as these predictions are operationally deployed.
We’re able to store and operationalize analytics directly from ClosedLoop. That’s driving real value—it’s accelerated the implementation of key insights into clinical workflows and it allows us to more easily account for all of the different factors that influence intervention decisions.
At SWHR, our patient-centered network is committed to innovative care models that are value-based, high-quality, and data-driven. Because of our partnership with ClosedLoop, we’re better able to identify and act on the most impactful opportunities for our physician partners to improve patient results and reduce unnecessary costs.
We compared the ClosedLoop predicted spend to the spend predicted by a leading rules-based risk prediction engine and found that 75% of the time ClosedLoop was closer, and in most cases significantly closer, to the actual spend.
Using predictive analytics to supplement clinician-driven referrals has helped me identify more patients more quickly for complex case management. I have greater assurance knowing this tool is helping me find patients most in need of my care.
ClosedLoop stood out from other AI firms in that they offered not only a usable, flexible analytics platform but extensive healthcare expertise.