Data is readily available, if you know where to gather it. Analysis doesn’t have to be overwhelming, if you know what’s important to look at. And decision-making based on data isn’t daunting, if you know how to measure systems. The human services field faces so many issues and constraints involving budgets, resources, staffing, and more. But data can offer key insights that take the guess work out of identifying opportunities for improvement and developing effective solutions.
In this Capstone, we’re looking at everything data – highlighting why it’s important, detailing who should be involved, listing sources for collecting data, sharing tips for developing a data system, and flagging pitfalls that you may encounter along the way. Elizabeth Sites, CQL’s Director of Organizational Excellence, and Michael Clausen, CQL’s Director of Personal Outcomes, take a deep dive into data and how you can leverage it for your organizational decision-making.
The Importance of Data Collection Analysis
Human service providers tend to “go off of their gut” or past experience. With where the disability services system is headed, that’s not enough. If you’re implementing outcomes-driven services, you need data to show how the services you’re providing are making a difference and how you’re helping people.
Along with that, data is more black and white than just having anecdotal information – it helps you know exactly where you’re at, what your strengths are, and where your opportunities lie. Then you can start to think about how to improve in the areas where you want to improve. That’s why it’s so critical to informed organizational decision-making and strategic planning.
When I talk to organizations about the benefits, impact, and importance of data, a helpful resource I always refer to them is our 12 Reasons Why Data Is Important guide. It’s an outstanding document.
Getting Started On Your Data Journey
The first step of your data journey is to develop a strategy for your data collection and analysis, which could be organized to address the following areas:
- The “Why” – Why is data collection and analysis important to your organization?
- The “Who” – Who should be involved, responsible, and accountable?
- The “What” – What data are you going to collect?
- The “How” – How are you going to measure the data?
This strategy can serve as your roadmap and become a policy or procedure for your quality management initiatives, but your system should be living and breathing. Organizations should recognize that their strategy to monitor and enhance quality is always going to evolve and change.
Bringing The Right People to the (Data) Table
Of course, organizational leaders should be involved in collecting, analyzing, and decision-making when it comes to data. But you need a variety of perspectives at the table. You can include different stakeholders such as direct support professionals (DSPs), people supported, families, and board members.
It’s essential to hear from program leaders because they’re going to know about all of those systems at your organization. It’s vital to have DSPs involved because they can tell you whether or not initiatives associated with quality management are going to work. It’s crucial to have people that you support at the table because they’re most affected by any of the decisions that are made. Having all of those perspectives allows you to be holistic and to have diverse points of view as you look at data.
I think a misconception is that organizations need people who are experts in data or experts in quality. And really, you don’t. It’s nice if you have that, but you don’t necessarily need that to be successful. There’s also a lot of value in bringing in someone who doesn’t know anything about data or anything about quality management. They can ask those questions such as “why should I care about this?” They’ll help everybody else understand what it is that you’re trying to achieve.
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Identifying Meaningful People To Collect
To identify data to collect, you first need to ask yourself, what kind of data is most important? What’s relevant? What’s meaningful? The answers to those questions depends on the organization’s services, size, internal capacity, goals, and data that’s currently available.
There are “typical” types of data that we often see from organizations such as regulatory data, safety data, health data, financial data, human resources data, etc. A lot of organizations are using electronic health records (EHR) platforms, so you can look at what types of data to collect from there. You can also explore data around complaints, satisfaction surveys, or just general feedback from people.
Let’s use rights as an example. If you’re launching a new training initiative around rights you could conduct a pre-test, asking some simple questions about people’s understanding of rights and collect data from that. Then after you launch your initiative, you could send out a post-test with the same questions to determine if what you implemented made any impact. The results of that data collection and review could inform you about changes that need to be made to your rights training.
Using the Basic Assurances® is a nice framework to better understand what categories of data you might want to collect, covering areas like rights, safety, natural supports, health, dignity and respect, employment, and more. That will help get you going and stay organized. Agencies may need to start small and then work up to the other areas that they would like to collect.
We find that many organizations use the Personal Outcome Measures® (POM) for collecting data. The data from those interviews, from those conversations, can be used to learn about the person – what their life is like, what they want to achieve, what supports would help them along the way. You can collect and use that POM data to improve services for people.
And finally, as you identify what data you want to gather, make sure that you don’t collect data just to collect data. Just because you have certain data, it doesn’t mean it’s useful, meaningful, or providing value.
Developing A System For Collection & Analysis
People assume you need technologically advanced systems to develop a system for collection and analysis. But you can start simple. Even something like Excel spreadsheets can be easy and beneficial to enter and track data. I’ve seen organizations use those simple tools very successfully. When they start to mature in how they look at data, then their systems can mature.
I used to work for a provider organization and had to set up the entire management system. When I left 6 years later, I saw how it had matured and recall being almost embarrassed at what it looked like when I first started. But that’s how it should go! It should mature, and it should be reassessed to make sure that it’s still meeting your needs every year.
You can work your way towards a robust platform over time. More and more organizations are going to EHRs, and most of those platforms have capabilities for pulling data and running reports. So when your organization is at that point, I would definitely suggest looking into those options. The CQL PORTAL Data System, powered by MediSked, is excellent for your Personal Outcome Measures® and Basic Assurances® data.
There are some wonderful resources out there that can help you look at data systems in different ways. We offer a great webinar about this topic titled ‘Developing An Integrated Quality Management System.’ For me personally, OPEN MINDS also shares a lot of helpful information.
You can reach out to other organizations too if you want to know how they developed their system. The Facebook E-Community is a great way to network, otherwise we’re always happy to connect organizations too.
Establishing Goals & Monitoring Progress
Your quality management system should inform your goals and your strategic plan for the future. But don’t overthink it. First, organizations should look at their critical systems and explore ways to measure them. Like Elizabeth mentioned, the Basic Assurances® provide a convenient framework. That involves those 9 Basic Assurances® Factors, where you should think about how you might measure each of those factors to understand where there are strengths and opportunities.
You need to develop ways to track progress over time, looking at data quarterly or annually. It’s a good idea to figure out at what intervals you will analyze your data and work towards building the capacity to evaluate trends over time. Typically, I recommend that organizations trend five quarters of data to enable comparison to the same quarter of the previous year.
And remember that the best systems evolve. They’re always going to change. It’s a good idea to look at your system regularly and consider what else you might need or what you can get rid of, on an ongoing basis.
One of the things we hear is that there isn’t enough time. We recognize there’s an investment in time into building a system to monitor quality, but this will ultimately help organizations be more efficient and better direct resources. So while it is an investment upfront, over the long term, it should save you time.
Confronting Common Pitfalls Involving Data
There are some potential missteps that organizations may take in their data journey, but Michael and Elizabeth offer up tips to keep agencies on the right path.
Elizabeth Sites: One pitfall is avoiding data overload. Don’t try to track something just because you want to, but you don’t have a way to track it. Then you’re spending a lot of time trying to compile data that may not give you a good return upon your time investment. You can always put those areas in your “data parking lot” and down the road decide that you’ll track it later on.
Michael Clausen: Organizations should avoid data silos. We often see organizations where the safety committee is the only group that looks at safety data, or the incident review committee is the only group that looks at incidents, and so on. But none of those things happen in a vacuum.
If your medication errors are increasing, what else is going on? Did you have an increase in staff turnover? Do you also see a spike in other kinds of critical incidents? So it’s important that you’re looking at everything at the same time, exploring all of those areas holistically. It all goes together. It’s all interconnected.
Meaningful Contribution From People Receiving Services
Elizabeth Sites: You need to include at least one person receiving services, who is going to be an active, contributing participant of your group – not just a token member. You should find someone who is willing and interested in getting involved with collecting and reviewing data. You want a person who will explore the information, be involved – and like Mike mentioned earlier – ask “why should I care about this?”
Too Many “Cooks In The Kitchen”
Elizabeth Sites: On the flip side of getting a lot of perspectives, it’s equally important to make sure your group doesn’t get too big. You want to have that representation from different services, different positions, etc., but you likely don’t need multiple people of the same role, which could just be represented through one person.
Sharing Just Charts & Graphs
Michael Clausen: The reality is that charts, graphs, and numbers do not always resonate with people. A lot of people draw inspiration from stories of people living meaningful lives, so you need to attach data to stories. Show people how the data impacts them. If you are sharing data around rights, then you should talk about somebody who is able to exercise a right. If you’re talking about data on social roles, you should tell a story about a person who has a meaningful social role. I should include the caveat here too of course, that you should only share stories if you have people’s permission.
Ineffective (Or Non-Existent) Communication
Michael Clausen: Organizations sometimes struggle with communicating about quality. Agencies should have a communications plan that is attached to the strategy for quality management. This is another opportunity to get people receiving supports involved. They can be part of that effort to share stories and communicate quality.
Parting Words of Data Wisdom
Organizations will never be perfect. That’s okay. You should focus on continued growth rather than perfection. You should strive towards constant improvement, and don’t let “perfect” get in the way of “good.”
Also, don’t solely focus on trying to solve problems or overcome deficits. Use your data to recognize the strengths of your organization. Agencies are always forgetting to highlight accomplishments they’ve had along the way. Don’t forget to celebrate!
It’s important to help people understand why this is important, how this affects people, and what the impact is on continuous quality improvement.
It goes back to a quote from Carly Fiorina, a former CEO of Hewlett-Packard:
Everything you input into an EHR, everything you document, everything related to the Personal Outcome Measures®, the Basic Assurances® … That’s data. That’s information. That’s insight.
12 Reasons Why Data Is Important
Data does not have to be complicated. Simply stated, data is useful information that you collect to support organizational decision-making and strategy. ’12 Reasons Why Data Is Important’ shares why data is important, what you can do with it, and how it relates to the human services field.Get The Guide
How To Use Data For Informed Decision-Making