One of our colleagues, Karen Monsen, PhD, RN, FAAN, Associate Professor, University of Minnesota School of Nursing and Director of the Omaha System Partnership, recently had the opportunity to speak at the Michigan Premier Public Health Conference on achieving health equity.
Her presentation was titled, “Social Behavioral Determinants of Health (SBDH) & the Omaha System: Empowering Local Health Departments to Address Health Inequity.” At a conference themed, “Bridging the Gap to Achieve Health Equity,” Dr. Monsen’s presentation was on point.
She began by describing her first realization of a need for data as a nurse manager in 1997, “My Director calls me in to her office, and she says, ‘Karen, you will computerize your nursing documentation system and give me outcomes,’ and that was the entire conversation.” The room was immediately filled with sympathetic reactions from the audience: “Wow,” “Oh boy!”
Dr. Monsen continued, “I think one of the reasons my director thought it was so important that I should computerize my documentation and give her outcomes was because she knew my programs were at risk. I managed all the MCH programs in our health department. I managed WIC, MCH home visiting, children with special healthcare needs, and abuse and neglect prevention programs. You can see that I had to justify.”
Dr. Monsen then introduced a study done by the University of Minnesota Population Health Informatics students in 2014, “The [study] is going to be about, ‘What is the Omaha System and how does it capture social behavioral determinants of health and why is that important and what does that look like in electronic health records?’ So [that I can] give you a bit of background, so you can understand, what is this thing – the Omaha System? It surprised me so much when I found it in our software!”
Next, Dr. Monsen explained why it was so vital to collect data such as SBDH using a standardized terminology like the Omaha System, “We in public health get data, we get why it’s important, we want to use data to drive our practice… It’s really cool to have existing data to go to and say, ‘OK, what is it in my population, in my county, in my women of childbearing age? What’s going on? How do I need to direct my services so that my population can have better health equity?’”
Reviewing a second study centered on SBDH and health equity for women of childbearing age (conducted by Dr. Monsen and several colleagues), Dr. Monsen displayed several charts created by the data collected during the study. When she asked the room what the charts showed them, the room was quiet. Most people seemed to be absorbing the data.
Digging Into the Data
Dr. Monsen began to break down the data and the conclusions that could be drawn from the data and what that meant for a public health worker addressing a community’s (or a population’s) needs. “Everybody gets better after they receive public health nursing intervention,” Dr. Monsen explained, pointing to the charts, “In all groups we see improvement because public health nurses were there. Right then and there, you have something to say to your commissioners that you didn’t have to say before!”
Dr. Monsen continued, flipping to a new chart, “This one is a little more shocking… or hard on the heart… These lines go down. Why? Because we cannot get to the same level of outcome when we have more and more and more challenges in our lives. This right here shows you health disparities by the social behavioral determinants index. What is this line? That’s the number of interventions it takes to get to the outcome. So, another stunning finding is that we have to work harder to get to poorer outcomes for people who have more social and behavioral determinants of health. So where should we be putting our resources?” The room murmured in assent.
“So, another stunning finding is that we have to work harder to get to poorer outcomes for people who have more social and behavioral determinants of health. So where should we be putting our resources?”
As Dr. Monsen continued flipping through the slides, explaining the charts, eyebrows in the room raised. This was powerful data. But she was quick to encourage the room that this was also data that was attainable for any public health department, “… everything that we did, could be done in Excel, by you in your own health department at your desk. So, let’s be thinking about it in that way. Let’s be thinking about you finding your own data, finding your own patterns.”
Her tone was encouraging but passionate, “[Health equity] is the reason behind the work that we do,” she said intently, “It’s the reason that we need data. To be able to show ourselves. To be able to make data-driven decisions. But also, to show the outcomes of what we do so that we can continue the work we do.”
“[Health equity] is the reason behind the work that we do. It’s the reason that we need data. To be able to show ourselves. To be able to make data-driven decisions. But also, to show the outcomes of what we do so that we can continue the work we do.”
“It’s really cool to have existing data to go to and say, ‘Ok, what is it in my population, in my county, in my women of childbearing age? What’s going on? How do I need to direct my services so that my population can have better health equity?’”
“This,” Dr. Monsen concluded, referring to her presentation, “is looking at our data to think about what’s going on in our population, relative to our nursing interventions. …we would know as directors, administrators, public health nurses, what was going on [in our county]. This is not just for nurses. It’s not just for… anybody. It’s for all of us. We can easily and simply document these things, to have the kind of powerful data that we need to inform our work and change our policies.”
For the full Q&A, watch the presentation video, accessible by clicking the button at the bottom of this article.
- Can I ask you, who pulled out this data for you? Was it the software system? Or was it your statistician?
- Answer: The local public health departments provided the data. They had the ability to do that from their software themselves. They did not have to ask for help.
- What software system were they using?
- Answer: It varied. In this sample, there were three different softwares. Carefacts, PH Doc, and Champ Software’s Nightingale Notes EHR were the three who contributed data to this study.
- In… the population that you measured, what were other disparities of that particular set? Was that population married? Did they live in rural areas?
- Answer: [This population] were from all over Minnesota. A very small number of them were married. 17% of the sample were actually married.
- Have you been able to drill down then and do that mapping with only certain sectors of the population?
- Answer: We could do anything we wanted to do. This is just showing you the possibilities.
- So I could just pull their data, see where they sit on the income level, low income… ?
- Answer: Yes. (Murmurs from the audience of, “Oh boy,” “Ooh,” “Pretty easy,” and “Pretty powerful.”)
- Could you give examples of a health department that has used their data to make a change to how they deliver service or what services that they deliver?
- Answer: Sure. This is really cool… For example, oral health. There was a nurse manager in Dakota County who said, “Oral health is really important for our low income populations that we serve. I want every single one of our clients to be assessed for oral health.” So now we have this huge repository of data for oral health. We can examine the data and say, “Here are the best interventions to use with this type of client for your oral health.” And that has enabled them to change their policy about how they serve these clients in their health department. So it’s kind of a loop: Okay I’ve got the idea, I want to know about oral health, I’m going to put that into my assessment, now I have the data, that changes my intervention strategies (how I’m going to recommend the nurses intervene with people who have the oral health problem).
- They determined what the best oral health intervention would be… from this data?
- Answer: It’s an algorithm that we’ve developed with them. Some of these analyses are very simple: look at the heat map, draw a line graph, got the message, right? You can do a significance test that will tell you if it’s significant or not. Other of these analyses take a little more machine learning techniques or things like that. The example about which interventions to use, that comes from a data mining project.
- I’m a local health department and I want to start using this. What’s the first thing I do?
- Answer: You have lots of ways you could start. You could pick a tiny program and you could do paper documentation on that program and you could gather up your papers and put your answers in a spreadsheet and you could do the analysis. You could also shop for software. You could make sure it had the Omaha System as the base of the software, not an add on, and you could make sure that you could get your data back. For me it was a three month walk between implementation of the software and starting to look at my data.
“You could also shop for software. You could make sure it had the Omaha System as the base of the software, not an add on, and you could make sure that you could get your data back.”
Next Steps: Empowering Your Local Health Department
If you’d like to learn more about how to collect the type of data Dr. Karen Monsen is talking about, learn more about standardized terminology, what it is, why it matters, and how to evaluate a standardized terminology to determine whether it’s good, download the eBook: Standardized Terminologies for EHRs.