With the new Pandemic, that the healthcare industry is in crisis is beyond dispute. According to the American Hospital Association, health systems are losing 51% of their revenues, mostly due to the cancellation of elective surgeries1. While the influx of COVID-19 patients has provided some revenue, it has not nearly closed the gap.
Two initiatives health systems are employing to address this crisis are rapidly moving health system management information into the cloud and maximizing the utilization of critical diagnostic machines such as X-ray, MRI, and CT scanning equipment.
In this article, Glassbeam’s Co-founder and CEO Puneet Pandit, and Satrajit Misra, Executive Vice President of Marketing and Strategic Development for Canon Medical Systems discuss these trends and strategies to address them.
C-SUITE WILL FOCUS MORE ON IMPROVING MACHINE UPTIME
Misra: CT, MRI, and other expensive machines are very capital-intensive equipment with high running and maintenance costs. With the COVID-19 crisis, there is enormous pressure on management and operators to optimize the usage of these modalities.
Predictive and prescriptive analytics play an important role in optimization. These analytics can inform operators that parts are failing, enabling them to conduct necessary maintenance before a machine goes down. They help management locate machines at the right hospitals, especially those that serve as clinical hubs versus front-end triage. IT teams use analytics data to study how facilities conduct triage, interventions, and other workflows.
Pandit: Advanced analytics are already here to help in streamlining machine maintenance. Log data will inform management teams when a component on a machine is starting to fail, enabling them to order and install new parts, and avoid downtime. The flip side of this is technicians don’t need to replace parts on a fixed schedule, requiring them to install new parts where the existing ones are still functional – which also saves budget. Uptime for the typical X-ray, MRI, CT, and similar machines is about 96-97%, with advanced analytics it can improve to 99%+. Even a 1% increase in machine uptime can provide an additional 30 hours of productive time per year for that machine, leading to about $462,000 of potential incremental revenues per year for an average facility2.
SHIFTING WORKLOADS WILL FORCE LOAD BALANCING ACROSS ENTIRE FLEET TO IMPROVE UTILIZATION
Misra: Patients’ needs and demographics have evolved dramatically. Prior to the pandemic, the majority of patients were in the hospital for elective and non-elective surgeries and needed to be in ICU post-surgery, for example. Today many, especially in older age groups, are recovering from coronavirus and need ventilators. While procedure volumes will start recovering in Q1 or Q2 of 2021, if there is a new surge in COVID-19, that timetable would certainly change. Administrators will be struggling to determine the new normal and will be analyzing which modalities are experiencing greater demand and those that have less. Such insights will allow them to load balance utilization across different machines and facilities.
Pandit: It is unclear when the pandemic will subside and if it will return this fall. Health system management teams have an opportunity to prepare for the worst by integrating advanced analytics into their fleet of diagnostic equipment assets to maximize utilization. Such analytics can integrate a wide range of structured and unstructured data to give management teams the ability to understand, for example, how many X-rays a machine completed in a given timeframe, which operators completed the scans, how many scans was each operator able to complete, and similar metrics. With this information, for example, managers can retrain operators who tend to be slower to improve utilization. The capability to move and utilize large amounts of machine data, or DICOM and HL7 data, into the cloud securely, will allow administrators to become more empowered to make better scheduling decisions and deliver better patient care at the end of the day.
LOOKING INTO THE FUTURE: WHAT'S NEXT?
Misra: Many technologies have done a good job of analyzing machine data, looking for patterns, and making information available to the end-user. They have built good AI recognition algorithms and natural language processing. Where this needs to go beyond just analyzing log and utilization data is toward the next level of clinical innovation – how do you look for clinical patterns, how do you show the optimal usage criteria for assets – these are tools that are missing now.
Part of the new normal will be deploying assets where they are needed, larger healthcare delivery networks realize not all areas will recover from COVID-19 at the same rate.
Mobility will be an asset within facilities as well. Operators are more likely to take an X-ray machine into an ICU than to wheel a patient to radiology. There will be a need for mobility and the pandemic continues for load balancing; e.g., a large hospital in a city shuts down, patients are diverted to the smaller facility and it may be necessary to move assets around. One issue with mobility is the disinfection of assets. For some smaller footprint modalities, such as ultrasound and X-ray machines, it’s relatively easy to clean them with bleach, solvents or other disinfection techniques, or quarantine the whole modality in a disinfection tent. For other equipment, such as CT scanners, that may be more difficult.
Pandit: For at least the next several months, hospitals and health systems will walk an even more perilous tightrope than normal. With significantly decreased revenues, maximizing the utilization of X-ray, CT and other modalities will be critical. Advanced analytics combined with access to large amounts of data in the cloud will help administrators navigate this difficult period and provide valuable lessons for the future.
2 For an average facility with 5 MRI and 5 CT Scanners, operating for 10 hours per day, 6 days a week, with a typical MRI at $2,000 per exam and CT at $1,200 per exam, and facility operating at 70% utilization.