PRODUCT & SERVICE LINE REPORTS Keith Lohkamp, Senior Director,
Industry Strategy,
Workday: The industry has rightly put signifi- cant emphasis on Interoperability among sys- tems for patient data. But interoperability among systems for master data is also vital to supporting efficient and effective delivery of care. As customers move to our cloud-based sup- ply chain management system, we’ve seen them put an empha- sis on using a system like Workday as the source of truth for their item master. From this item master, Workday then sends that data out to all the other systems that need it, like the sur- gical system and the charge master. Updates, deletions, and adds are all controlled through the item master, meaning that the organization has one process for managing. By centrally managing, the supply chain team is better able to track the supplies and implants being used, encouraging standardization and effective cost management. On the OR side, having up-to-date, accurate product and cost information from the item master helps ensure that item data on procedure cards is current, accurate, and ready to use, enabling quick documentation via barcode scanning or mobile. Post-surgery, downstream processes like billing or payment for consigned items can ow automatically with no or limited intervention leading to faster, more accurate billing and quicker payments to suppliers. By having integrated systems, healthcare providers will be able streamline and automate processes, increase revenue capture, control costs, and improve safety through accurate and up-to-date product and price data. With the increase in supply disruptions and backorders over the past year, it is more critical than ever for health sys- tems to have visibility into planned and anticipated demand. Unfortunately, when it comes to the OR, many supply chain teams don’t get visibility into detailed demand from scheduled cases until orders come in to be picked for the case the next day. But, with the right level of interoperability in a scheduling system, this doesn’t have to be. Since many cases are scheduled 6, 8 , or 12 weeks out, we believe there is great opportunity to leverage advanced planning tools to pull scheduled procedures from the scheduling system along with detailed procedure cards and to forecast out anticipated demand for implants and other items. We’re working with early adopters today to use forecasted demand to compare with expected inventory and anticipate potential shortfalls, allowing plenty of time to adjust ahead of the scheduled case. ZAGONE: Built-in capabilities to interface with other sys-
tems and mobile compatibility are a must, as well as the ability to integrate with ambulatory electronic medical records, lab and radiology as well as physician offices. Surgeon clinic physician office systems are often overlooked when planning for integration of systems.
In order to provide optimal patient care, organizations must aim for ease of communication between caregivers and the different departments required to coordinate care and plan for the care of surgical patients. The scheduling system is the initial point of entry. From there, the other computer platforms need to be able to bring together everything else required for the day of surgery: Lab work, radiology reports, history and physicals, consent forms and anesthesia plan of care. Once the scheduling is completed, it takes a lot of time, and energy is
wasted tracking down these requirements — time and energy better spent caring for patients.
Finally, and often overlooked, is that supply chain needs a line of site for upcoming cases so that supplies and equipment can be made available in a timely, efficient manner. Integrated systems also allow organizations to trace any recalls back to patients more effectively. They also improve accuracy of billing. [Further] mobile device access is important for surgeons and anesthesiologists for accessing patient documentation.
How might artificial intelligence (AI), machine learning (ML) and/or robotic process administration (RPA) play a role in scheduling systems when it comes to workflow, performance improvement and process efficiency, among other factors? RECHIN: Some scheduling systems, like ReadySet, are already using AI to help hospitals with things such as identifying non- compliant inventory requests well in advance of the case. AI also drives the unique advanced scheduling algorithms and predictive alerts ReadySet provides. Through these benefits, RSS has helped hospitals as well as device manufacturers by creating benchmarks for forecasting required inventory levels for orthopedic cases, helping hospitals reduce their opened-but-unused tray volume, and also helping device manufacturers limit or reduce inventory levels that just sits in a rep›s garage.
However, there are other AI applications being used in the OR, such as predicting unused block time in ample time to schedule cases. This feature improves a hospital’s OR utiliza- tion and is a huge driver of surgeon satisfaction. SANYAL: AIRPA in healthcare is in an early stage, but the early results are promising in a few areas: • Prediction of case length and case volumes, cancelations and no-shows: Helping Operating Rooms predict expected case lengths far more accurately than ‘averages’ of past case lengths can help schedule cases better. Also predicting sig- nificant dips or swings in case volumes can both help with over-booking’ and staffing shifts as needed.
• Prediction of the volume and mix of patients and the patient mix in infusion chairs, and clinic exam rooms to be able to optimize patient ow and level load’ the day and staff appropriately.
Image recognition achine-assisted diagnostics that help radiologists and physicians interpret images are showing good results. However, since the biology of each patient is unique, each provider practices medicine in a unique man- ner and each disease progresses in a unique manner. These technologies will likely need time before they can completely take over a meaningful share of the intelligent decisions that need to be made thousands of times each day in any health system.
In the coming years, healthcare will see what we have seen in other industries – for instance, Uber uses AI and machine learning to ensure they have the right number of drivers in the right locations available for any given city or time of day. We should expect to see intelligent assistants that can construct appointment schedules that utilize the existing scarce resources (e.g., providers, ORs, machines, beds, chairs, etc.) much more efficiently while giving the patients more choices in selecting an appointment slot. We should also expect to see vastly improved patient ows throughout the health system, manifested by the emptiness of
44 January 2022 • HEALTHCARE PURCHASING NEWS •
hpnonline.com
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