Data is everywhere and using it for the business advantage is for everyone and not limited to specific industries. Be it an airline, logistics, eCommerce or hospital. Airlines are apparently are of course more operation intensive, asset heavy and arguably, have to comply with more regulations than hospitals. However best operators are managing it exceptionally well by far most hospitals at keeping costs low and making healthy operational margins without losing the focus on delivering customer experience and value for money. Spicejet airlines, for example, has aptly identified and acted upon key operational parameters that pivots the operational performance: Reducing idle time for planes and keeping the seats filled more often than the competitors. Same way some of busiest airports, Fedex and alike are making a positive impact through their service delivery through most feasible and affordable ways. They all operate in asset heavy service industries.
Above examples are simple and have analogical relevance to how a hospital operates.
There are multiple steps, processes, variables, standards and compliances throughout the customer journey. For example in airlines case, operational process entails steps right from booking to checking in to onboarding and then on flight services, compliances and regulations and a set of checking out process. Every of these processes encompases further smalle pieces of operations spanning across a customer’s experience journey. All these operations involve people and not just machines.
Hospitals these days are facing the same pressure on optimising operational efficiencies and asset utilization that probably airlines, retail and transportation industries have faced for long. As Spicejet, Flipkart, FedEx have stayed competitive in asset- intensive services industries by streamlining operations and getting the max out of their available resources. Hospitals cannot have a long term competitive edge if they keep spending and investing more on infrastructures as short terms fixes to challenges. They must rethink how they are utlisting their available assets in the best possible way to gain ROIs.
To do this, hospitals must look at their data with different lenses like airline, transportation players do. Decision making methodologies must be driven by facts backed with statistics and not only based on a limited set of traditionally available information & experience. It is like having an “operational air traffic control system” for hospital – a centralised repository of vital data and systems around it that has capability to integrate process and analyse a vast variety, velocity and amount of data to learn and predict outcomes. Increased awareness of the potential of data and insights are pushing many healthcare organisations to streamlining operations by using data analytics technologies and tools to mine and process large quantities of data to deliver recommendations to administrative and clinical end users.
Business intelligence and Predictive analytics is playing a key role in improving planning and execution decisions for important care delivery processes and resource utilization( Space, Machines, Human) as well as improving scheduling of staff, availability of key equipment & maintenance. These can lead to better care delivery with optimised asset utilisation and lower costs. Few examples:
Operating Room Utilisation
Operating room is one of most revenue generating assets to the tune of more than 55% of revenues for hospitals. Allocation of OR assets has direct impact on care quality, patient’s experience and preparation staff’s bandwidth. However scheduling them in the most efficient way has been bottlenecked by traditional approaches practiced by most hospitals that involved phones and emails. These means of scheduling and rescheduling is tedious when it comes keeping all stakeholders informed. Obviously the scheduling process is tedious, slow and prone to human errors. Coursey to advance data analytics techniques exploiting cloud, mobile and predictive analytical models that help visualise predicted availability and suggesting time slots for better distribution of hospital resources – Human, Machine & Time – leading to take best out of key assets – the OR.
Surgeons can block the time they need with a single click on a mobile app and the connected apps in the hospitals makes it real time communication and confirmation of OR schedules/availability. Concerned staff can be well aware of any changes/cancellation/additional bookings in real time making the entire planning and execution efficient towards delivering better patient care and higher OR utilisations. At UCHealth in Colorado, scheduling apps allow patients to get treated faster (surgeons release their unneeded blocks 10% sooner than with manual techniques), surgeons gain better control and access (the median number of blocks released by surgeon per month has increased by 47%), and overall utilization (and revenue) increases. With these tools, UCHealth increased per-OR revenue by 4%, which translates into an additional $15 million in revenue annually.
Patient wait times
Same way scheduling for infusions in a function of math and timelines. Mathematically, there are enormous permutations and combinations to pick an optimal slot (to avoid staff and material allocation challenges) for a given type of infusion procedure. Not to mention patient wait time is something that hospitals must minimize.
NewYork-Presbyterian Hospital optimised scheduling based through predictive analytics and machine learning that processed multiple data points around the infusion process to identify patterns and suggest optimised scheduling, resulting in a 45% drop in patient wait times. Infusion center could better manage last-minute add-ons, late cancellations, and no-shows as well as optimize nurses’ work hours.
Emergency departments are famous for bottlenecks, whether because patients are waiting for lab results or imaging backed up in queues or because the department is understaffed. Analytics-driven software that 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. Emory University Hospital, for example, used predictive analytics to forecast patient demand for each category of lab test by time of day and day of week. In so doing, the provider reduced average patient wait times from one hour to 15 minutes, which reduced ED bottlenecks proportionally.
Faster Decisions – ED to inpatient-bed transfer
Predictive tools can also 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. Using a centralized, data-driven patient logistics system, Sharp HealthCare in San Diego reduced its admit order-to-occupy time by more than three hours.
Efficient 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. By using such tools, case managers and social workers can create a shortlist of high-priority patients whose discharge planning they can start as soon as the patient is admitted. Using discharge analytics software, MedStar Georgetown University Hospital in Washington, DC, for example, increased its daily discharge volume by 21%, reduced length of stay by half a day, and increased morning discharges to 24% of all daily discharges.
Making excellent operational decisions consistently, hundreds of times per day, demands sophisticated data science. Used correctly, analytics tools can lower health care costs, reduce wait times, increase patient access, and unlock capacity with the infrastructure that’s already in place.