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5 months ago
The Power of Prediction in Healthcare Operations

 

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Improving operational efficiency in healthcare through data analytics boils down to applying predictive analytics to improve planning and execution of key care delivery processes, chief among them resource utilization, staff schedules, and patient admittance and discharge. When this is done right, healthcare providers see an increase in patient access (accommodation of more patients, sooner) and revenue, lower cost, increased asset utilization, and an improved patient experience. Here are a few examples:

1. Increasing operating room (OR) utilization. For a resource that brings in more than 60 percent of admissions and 65 percent of revenue at most hospitals, current block scheduling techniques fall far short in optimizing operating room time and in improving patient access, surgeon satisfaction, and care quality. Current techniques of looking for open time — phone calls, faxes and emails — make the process of getting access to the OR cumbersome, error prone and slow even when time is available. Using machine learning, historical booking patterns can be monitored to identify blocks that are likely to be underutilized and sends reminders to block owners to proactively release these blocks.

Browser-based solutions — whether phone, tablet, laptop or desktop — now allow surgeons and their schedulers to request the time (essentially OR “reservations”) they need out of block with one click. Combining machine learning-driven release reminders with a simple, easy-to-use, OpenTable-like time reservation solution allows patients to get treated faster. At UCHealth in Colorado surgeons release their unneeded blocks 10 percent sooner than with manual techniques, gain better control and access (the median number of blocks released by surgeons per month has increased by 47 percent), and overall utilization — and revenue — increases. With these tools, UCHealth increased per-OR revenue by 4 percent, which translates into an additional $10 million in revenue annually.

2.  Slashing infusion center wait times while increasing patient access. Infusion scheduling at cancer centers is an extremely complex mathematical problem. Even for a 30-chair center, avoiding the 10 a.m. to 2 p.m. “rush hour” in a patient-centric way requires picking one of a googol of possible solutions. Faced with this challenge, NewYork-Presbyterian Hospital applied predictive analytics and machine learning to optimize its schedule templates, resulting in a 50 percent drop in patient wait times. In addition to improving longer-term patient scheduling, these technologies help schedulers manage an infusion center’s day-to-day uncertainty — last-minute add-ons, late cancellations, and no-shows — as well as optimize nurses’ workloads so that they no longer miss lunch breaks, a common occurrence in almost every infusion center.

3.  Streamlining emergency department (ED) operations. Emergency departments are famous for bottlenecks, whether due to patients waiting for lab results or imaging being backed up in queues or because the department is understaffed. Analytics-driven software can determine the most efficient order of ED activities, dramatically reducing patient wait times. When a new patient needs an X-ray and a blood draw, knowing the most efficient sequence can save patients time and make smarter use of ED resources. Software can now reveal historic holdups (maybe there’s a repeated Wednesday EKG staffing crunch that needs fixing) and show providers in real time each patient’s journey through the department and wait times. This allows providers to eliminate recurring bottlenecks and call for staff or immediately reroute patient traffic to improve efficiency.

4. ED to inpatient-bed transfer. Predictive tools also can allow providers to forecast the likelihood that a patient will need to be admitted and provide an immediate estimate of which unit or units can accommodate them. With this information, the hospitalist and ED physician can quickly agree on a likely onboarding flow, which can be made visible to everyone across the onboarding chain. This data-driven approach also helps providers prioritize which beds should be cleaned first, which units should accelerate discharge, and which patients should be moved to a discharge lounge.

5.  Accelerated discharge planning. To optimize discharge planning, case managers and social workers need to be able to foresee and prevent discharge delays. Electronic health records or other internal systems often gather data on “avoidable discharge delays” — patients who in the last month, quarter or year were delayed because of insurance verification problems or lack of transportation, destination or post-discharge care. This data is a gold mine for providers; with the proper analytics tools, within an hour of a patient arriving and completing their paperwork, a provider can predict with fairly high accuracy who among its hundreds of patients is most likely to run into trouble during discharge.

Making excellent operational decisions consistently, hundreds of times per day, demands sophisticated data science. Used correctly, Analytics tools can lower healthcare costs, reduce wait times, increase patient access, and unlock capacity with the infrastructure that’s already in place. Healthcare needs to do more with less, and that’s a big reason why data analytics is perhaps the most significant operational innovation healthcare will experience in the coming decade.

About the Author

Sanjeev Agrawal is President and Chief Marketing Officer of LeanTaaS, a Silicon Valley-based innovator of predictive analytics solutions to healthcare’s biggest operational challenges. He works closely with dozens of leading healthcare institutions including Stanford Health Care, UCHealth, NewYork-Presbyterian, Cleveland Clinic, MD Anderson and more. Sanjeev was Google’s first head of product marketing. Since then, he has had leadership roles at three successful startups: CEO of Aloqa, a mobile push platform (acquired by Motorola); VP product and marketing at Tellme Networks (acquired by Microsoft); and as the founding CEO of Collegefeed (acquired by AfterCollege). Sanjeev graduated Phi Beta Kappa with an EECS degree from MIT and along the way spent time at McKinsey & Co. and Cisco Systems. He is an avid squash player and has been named by Becker’s Hospital Review as one of the top entrepreneurs innovating in healthcare. Follow Sanjeev and LeanTaaS at: TwitterFacebookLinkedIn

 

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