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Did you know that resident-focused analytics provide more opportunities to improve the overall health and happiness of residents, which translates to longer stays? That a resident with closer ties to the community and meaningful connections to people inside and outside the community has a more enjoyable experience, stays in a community longer and is more profitable? That analytics can help determine the factors that are different in a resident’s experience who stays longer within a community? And that analytics can even help predict when a resident is a bad fit for a community?
That’s the power of data analytics. And as a senior living executive, you can have that power at your fingertips today. It’s the reason predictive analytics needs to move sooner rather than later from operational measures—occupancy, profitability and insights—into measures that help improve the resident, caregiver and staff member experience.
Analytics is the new math in senior living
While we tend to think of senior living as being a soft, relationship-based business, it is the perfect foundation for math. Senior living is all about the residents, but what do we really know about them?
With today’s new math, it’s truly possible to understand the factors that impact the quality of life for residents, the quality of life for caregivers, and the quality of life for executive directors. To do that, we have to understand the demographics of each resident. Individual health issues, known and unknown expectations, aspirations, interactions, and even the food eaten impact how a resident feels, how that resident responds to others, and what the resident is willing to do to impact his or her health and happiness.
Analytics starts with the data
What data should you be collecting? The answer is easy: Every piece of data you can. It doesn’t matter that you may not know exactly what you’ll do with that data, it’s just important that you have the data to leverage when you’re ready. Data storage is essentially free, which gives you the ability to collect everything from the number of times your resident moved in their bed overnight to the amount and types of food eaten each day.
Data collection is actually much easier than it used to be. You can collect it manually by capturing it through interviews, notes, surveys, etc. It’s important that this data is consistent so that it’s usable.
Automated data collection is another option. For example, smoke detectors collect data about air quality. Motion sensors detect the number of times a resident gets up in the middle of the night. Uploaded photographs can capture the food eaten and left untouched. Biological and behavioral monitoring can track everything from weight and blood pressure to a resident’s walking gate, balance changes and speed of movement. Remote controls can even capture the number of clicks to get to a desired action.
The reality is that you really want to capture data from both manual and automated sources to help create a more complete picture of a resident’s daily experience.
It’s also important to keep data accurate so that any valuable insight is not tainted. The type of data you collect drives your data integrity processes. You’ll need to consider everything from collection routines and transportation to data backup and data management. You’ll also need expert eyes to evaluate the validity of the data and the insights it’s delivering. Does the information make sense? Are there outliers, and if yes, how do you treat them? When is data too old to be valid anymore?
From data collection to actionable insights
Once you’re comfortable with your data collection and integrity processes, it’s time to think about how to drive actionable insights from the data. What is actionable data? It’s data (or information) that compels you to do something—to take action. It differentiates itself from benign data, or rows and rows of data, by giving you a call to action to which a reasonable person would respond.
There are two types of data analysis that can drive actionable insights—scientific analysis and machine learning / artificial intelligence. Both concepts have a place in senior living.
The scientific method is what you learned in school. It includes creating a hypothesis and then using the raw data to evaluate against it to determine if the impact was true. The problem with this method is that it is super time-consuming, especially when running it against the daily lives the data is tracking in a senior living building. This research doesn’t happen fast, and most communities don’t have a (data) scientist on staff. A shorter alternative that nets similar results is to do research on existing findings and see how your results compare.
New analytics leverage artificial intelligence and its sibling, machine learning, to analyze large data sets that have a combination of significant variables and factors that produce the same outcomes. While there is no hypothesis, there are correlated insights about cause and event. You can work backwards from “an event” and look for microdata points, or a subset of everything collected.
The microdata points are critical because they prevent people from focusing on the end without understanding how the end is created. Microdata points essentially break down every outcome (or sum) into relevant individual pieces so you can understand both the parts and the sum of the parts.
Data helps you balance risk and reward
Some senior living leaders may hesitate to adopt a data-driven approach because of concerns that too much data might put the community at risk if they do not respond to that data. So how do they balance risk and reward?
My answer is simple: We should not be a fear-based industry. We should leverage the tools at hand to create the best experience, and the best business possible. It is no longer a matter of risk; we are morally responsible to use the proven tools at hand to help our residents, and their caregivers have longevity with vitality. It is no longer ok to simply say “I didn’t know.”
We cannot hide from the fact that we might make a mistake. People are overworked, capital is hard to find, we are going to make mistakes. It’s how we handle mistakes that matter. The black eye comes not from making the mistake, it comes from not acknowledging our flaws and using tools that make us better.
In fact, data can support your message that 99 percent of the time, your team is taking the right action and doing right by your residents. Most operators cannot prove this; you can with the right data.
Even our industry language has changed. Data transparency, a phrase that became common, if not expected, more than 10 years ago in healthcare, has taken center stage among the pandemic. While families have long asked for transparency, there is now a strong pull toward it in the industry. The tide is turning; there is no reason to hide from data.
Leveraging data requires team adoption
It takes a team to leverage data appropriately. Raw data cannot be used in a vacuum; it must be used by everyone. To create an analytic mindset, you need to communicate the benefits. That starts with understanding how and why people make decisions, the criteria they use to do the job well, the tools they use to evaluate a job well done, and then tying the benefits to that thinking. It’s also important to communicate and foster the mindset that what’s not measured is not managed.
You don’t need a data analyst on your team; you need to foster a culture of data analytics. What worked well 15 years ago may not work as well today. It’s hard to convince a team of that without data.
Start with a baseline of integration
While it’s possible to build an analytics solution from scratch, it’s much easier to use technology that is optimized for it. Integrated solutions tend to capture and support analytics more than individual solutions cobbled together. Integrated solutions remove the burden of analytics off the shoulders of the community. They highlight the areas that are anomalous or correlated so that you can make a healthcare, operational or engagement decision based on your expertise. Integrated solutions bring each valuable insight to the surface, rather than flooding you with rows and rows of data you might have missed.
It doesn’t take away the need to have people who know what to do with the information once they receive it. An integrated solution suite, with built-in analytics makes your healthcare experts, nurses, caregivers, engagement experts, psychologists smarter…faster by removing the burden of understanding every data point.
It’s time to embrace, not run from, data analytics tools
If you could predict which residents will stay the longest in your community, how would it change your business model? What actions might you and your staff take to extend that same experience to other residents? And how might it change the way you train your staff?
The promise of data is predictive help. The real reason you need data is not to know WHEN something has gone wrong; it’s to know BEFORE something goes wrong. Data analytics is the gateway drug to prevention. It’s the last mile into turning what has frequently been thought of as a real estate investment into a true healthcare organization that manages the continuum of care for your resident from the moment they engage until they are no longer in your community.
Data is not the enemy. It is the missing step our industry needs to move forward.
To find out more about the Sentrics360SM integrated suite, and the thousands of data points it captures, correlates and analyzes every day, click here.