Every department in a hospital has its own complexities. Have you considered how orchestration intelligence tools can help your hospital? Read on to learn more.
Patients Make Hospital Workflows Time and Emotionally Sensitive
Most hospital workflows involve patients which make them a lot more time and emotion sensitive.
Whereas delays in a manufacturing facility would mean a slowdown or a temporary stop in productivity with typically low impact on human emotions, in a hospital setting delays can affect matters of life and death.
As nurses, physicians and hospital staff navigate an increasingly complex world each day, many of their decisions are made based on intuition and their deep knowledge.
An experienced charge nurse in ED knows that during February they can expect a lot of flu cases and automatically adjusts the waiting areas to accommodate the specific flow needed to treat the flu patients. However, they make sure that route is clear for more complicated ESI (Emergency Service Indicator) cases such as Stroke or STEMI patients at the same time.
The ER charge nurse also knows that to meet the recommended average length of stay in the ER they will need to clear some of the less complicated ESI cases as soon as safely possible. Those can be used to average out the longer stays needed for serious patients that the staff does not really have any control over how long it would take to stabilize a patient.
In the OR, the charge nurse would have an intimate knowledge of how a particular surgeon works, their preferences, their times for closing and getting to the next surgery and any special needs for the types of surgeries the surgeon normally performs.
The OR charge nurse would use this knowledge to make decisions that affect the flow of the OR throughout the day.
Hospital Orchestration Intelligence Gleans from Past Performance & Behavior to Apply to Future Operations, Performance & Behavior
The expectation with Hospital Orchestration Intelligence Systems is that some of this knowledge can be gleaned from past performance and behavior, and then can be applied to future operations, performance and behavior.
This is different from the traditional analytics and process improvement methods. Traditional process improvement teams interview hospital staff and takes measures from recorded data. These are used as guide posts for future operations. The process repeats after a few years.
Using Orchestration Intelligence tools differs in that these tools can work with large amounts of data, in real-time, using modern architecture to process data faster.
Once data is continuously being fed into the Orchestration Intelligence tool it can help with various estimates and predictions. One example is case length for procedures in OR can now be calculated much more accurately rather than using a traditional moving average.
Similar in the ER, the length of stay especially for the simpler ESI patients, can be modeled much better using broader set of features than a moving average.
The ability to feed multiple features to derive recommendations in real-time makes the Orchestration Intelligence tools very useful as they are continuously updating the recommendations.
As the system collects data, there could be even more features collected that relate to the efficiency and behavior of certain departments. This data could be used to correct behavior. For example: if left without been seen (LWBS) is more prevalent during certain times of the day, nurses could be notified to watch out for these.
The advent of multiple artificial intelligence and machine learning algorithms and the fact that they are getting widely used and fine-tuned in various industries is making it easier to take the same algorithms and carefully apply them to the healthcare operations.
Partnering with Hospitals to Use Orchestration Intelligence Tools
TAGNOS is partnering with hospitals to use these Orchestration Intelligence tools to start gathering data that can be used to predict case length time in OR and length of stay in ER.
>> Read about Client Success Stories
The accuracy of these predictions would depend on consistent real time data.
- The use of RTLS (real-time location systems) provides a reliable ways to track equipment, patient and staff in hospitals. The collection and use of these data points, over time, are beneficial inputs into the prediction engines.
- The use of voice and video technologies will add to the richness of the data that is available. Alexa is one example where operating room staff can mark completion of milestones during surgery handsfree.
The proliferation of data in hospitals using RTLS and other technologies can feed Orchestration Intelligence tools making them a powerful addition to a hospitals toolset.
TAGNOS is the future of clinical automation software solutions with Artificial Intelligence. It is the only platform offering predictive analytics utilizing machine learning and RTLS. This groundbreaking platform leverages historical patient data continuously and adjusts operational intelligence to provide sustainable improvement to both the patient experience and metrics.
TAGNOS provides clinical systems integration, customizable reporting, dashboards, alerts, critical communication with staff and family to improve turnaround times, supporting patient flow, workflow orchestration, and asset management.
In the course of 13 months, hospitals see a 12.7% reduction in its overall cycle time - saving an average of 40 minutes from each case and over $1.6M per year - more than 11x the typical investment.
Note: We originally published this article on August 1, 2018 and have updated it.