This week we bring you a guest blogger! Meet Malcolm Graham, Senior Vice President of Operations at Silversphere. Malcolm will be taking over a technology series for us, discussing how to effectively navigate the pitfalls of changing and adopting new technology.
Every successful manager that I have met is great at listening to the universe around them, identifying problems and controlling change while objectively evaluating the results of change. Sometimes we have to turn the dial back, and sometimes we get it right and get to tighten the control a bit more. The evolutionary change allows for us to evaluate the outcomes of the dials we change in controlled chunks while maintaining a relatively consistent operational environment. As leaders in senior technology and change management, we get the benefit of constant change without the inherent fear of making a wrong decision that we have to unwind in a wholesale manner.
Driving change in a consistent, measured way allows us to demonstrate our effectiveness in regards to delivering outcomes while still giving the appearance of a coherent and operational environment and delivery methodology. At Silversphere we’ve embraced the dynamic nature of the senior living environment and incorporated learning to evolve the way you respond in real time to your data sets, personnel, customers and environment change.
In my experience managing technology adoption, as discussed in part one of this topic, managed change requires a lot of attention to data analysis, understanding of systems and the ability to make changes quickly. Most of us are overwhelmed with the basic routine of our job and continuously evaluating this kind of data just doesn’t fit into our regular work routine.
Using tools like the Stratos Rules Engine by Silversphere allows for machine learning to continuously analyze operational and environmental data, and make small adjustments that align with your predefined outcomes, re-evaluate, and adjust again until you meet your desired outcome. The tool sends you automated notifications, reports, and recommendations all based on your desired results, which in turn allows you to focus on what you do best while still benefiting from state of the art innovations. The best part of this juxtaposition to revolutionary change is that your staff won’t notice the small adjustments that are guiding their behavior, so they too get to focus on what they do best: providing care for your residents.
Let’s look at a real world example of how this can work in your community:
Mrs. Jones moves in your community and her family immediately indicates that she has incontinence issues that can be mitigated if she receives bathroom assistance regularly. In a standard environment, you could elect to help her visit the restroom regularly, leading to more work for your care staff and ultimately an increased risk of falls. Alternatively, you might miss the opportunity to prevent the occurrence resulting in decreased dignity for the resident and extra work for your care and house cleaning staff.
Using our proprietary learning algorithms, we combine the inputs collected from incontinence sensors, toileting history, activity and other environmental sensors to improve the likelihood of assisting the resident before it’s too late while reducing the notifications sent to caregivers. Thus, this data monitoring leads to reducing overall workload and the alarm fatigue. No adjustments to the system required on your part. No pouring over reports to identifying trends. Seamlessly, in the background, theses evaluations and changes are made and continuously reevaluated until your organization meets your goals, or you modify the desired outcome.
In short, we want to free you from the tedious and continuous pressure of managing technology adoption and allow you to focus on what you and your staff do best, caring for people. Let Silversphere’s machine learning platform and rules engine do the micromanaging while you concentrate on the big picture and drive the outcomes that separate you from the competition.