> Okay, Great. Hi Everyone. My Name's Allison Murphy. I'm a Phd Candidate from Penn

The Identification and Management of Information Problems by Patient-Care Teams in Hospitals
Presenter: Alison Murphy, Penn State University
Recorded on: April 22, 2015

It's ready.

> Okay, great. Hi everyone. My name's Allison Murphy. I'm a PhD candidate from Penn State University. I work with Doctor Madhu Reddy, who's also part of the College of Information Sciences and Technology here at Penn State. This research study also include Doctor Jennifer Kraschnewski, who's from Penn State Hershey Medical Center.

So I'm excited to talk to you today about our research study, which is currently ongoing. And it looks at the identification and management of patient information problems during morning rounds. So in order to introduce this topic and provide some context, I'd like to just talk about hospital work in general.

So those of us that work in hospitals or do research in hospitals understand that hospital work is highly collaborative. So, individual outpatient care teams are very much dependent on each other to provide information and to make sure that their patient care tasks get done, because they're all dependent on one another.

And this includes individuals like physicians, nurses, care coordinators, social workers, therapists, pharmacists, and many other clinical and non-clinical individuals. So, because patient care teams are so highly dependent on each other to provide accurate and updated information. So that they can make these informed decisions about patient care and to make sure that their patient care tasks can be completed effectively.

However, there still are problems that occur with patient information, and these just exist in the hospitals. They can also have a negative effect on the patient care experience. However, as we know, they can also just be an inherent part of hospital work. And when I talk about patient information problems, or what I'll refer to as PIP's in this presentation.

Is what we define as any issues related to clinical patient information that impact the patient cares team ability to perform work. So this may include a physician who goes into an electronic health record system in order to look at a patient's medication list, for their active medications. And it may be outdated or there may be wrong medications listed in there.

So this would be an example of an information problem. Another information problem may be a nurse who's tasked with giving a patient a blood transfusion and perhaps the blood type is not listed. It's missing from the patient's record in the EHR system. And so therefore that would be missing patient information that can cause her additional work and impact her patient care task.

So since patient care, I'm sorry, patient information problems are such an important part of hospital work, they just exist and they can have a drastic impact on the patient care teams themselves and the patient. We wanted to look in the background to see what;s currently being studied about patient information problems.

And we specifically looked in the domains of medical informatics, which is more practitioner-based, looking at the hospital work itself. As well as Human-computer interaction, which looks at the technical side of designing systems that help users more effectively conduct their work. What we saw in the literature was that there's a lot of studies around the causes of PIP's, which is very important.

Because if we can understand what's causing them, we can hopefully help prevent them, or at least mitigate some of the impacts that occur. And since we're looking in a more technical domain, there were really two main causes that were talked about by prior research. The first one was electronic health records system design.

So this is simply just that way the systems are designed can sometimes lead to information problems. One example of this would be the fact that sometimes in drop-down values, one example that I saw in my own study, was when physicians are trying to select medication for a patient.

And sometime medication names are very long or they include additional information such as the form, whether it's a tablet or a capsule or a liquid. And the string of information is so long that it sometimes gets truncated by the screen. And so the physician selecting that medication can't always see all of the information.

So this would then provide for them an information problem and therefore they may actually select the wrong information because of this. The second cause that we saw researchers talk about was the EHR users themselves, which is simply the fact that sometimes people enter the wrong information. Either they enter incorrect information or sometimes they use a copy and paste function when they're trying to make their work more efficient.

And they'll copy something from one patient's record Into another patient's record. And then try and change some of the things that are applicable for that record, but not everything is always changed. And then what this does is it creates a persistent problem that gets carried through other patient records.

Another example given by the literature with EHR users is that at times just simply due to the sheer amount of work that needs to be done by clinicians, that they're very busy and they don't always have time to immediately update the formal record which in most cases would be the electronic health record.

And so it leaves the system outdated for a period of time which impacts anybody else that's relying on that particular information to do their work. The second area that we saw a lot of researchers talk about were the impacts of patient information problems. And these, obviously, when they encounter a problem, it requires a lot of additional work for them to fix it, or at least to figure out if it is in fact wrong information or outdated information.

Other times, it can actually lead to confusion about what treatments or procedures were done to patients. And throughout the process of additional work and confusion about treatments, this then leads to our third point, which is delayed patient care decisions. The researchers spoke about how having to work through all these patient information problems, delayed their ability to make decisions about treatment, about diagnosis, or even about discharge.

And then finally, if these patient information problems are not even identified. If there's wrong or out-dated information and the person using that information does not realize it's wrong or out-dated. Decisions can be made that can then lead to medical errors that can harm patients. So based on understanding what current research is going on in the field of patient information problems, we have certain research motivations.

Certain aspects that really motivate our own research in this project. The first is that we noticed current research focuses on the causes and impacts of PIP's. But there's really no general taxonomy that helps us classify the different types of PIP's. And what we mean by this is, a lot of times they would mention that there was wrong information or inaccurate or something wasn't the same.

But we really don't know the entire scope of what are all the different types of information problems occurring and how do we kind of define and neutrally balance those different types. And what this can do is it can help better inform design of systems if we're aware of all of the different types.

As well as just make the patient care teams more aware of the types of issues that can occur with their information. And so this leads to our first research objective, which is to create a PIP taxonomy. The second aspect that motivates our research is we saw there was a limited understanding of how PIP's are actually identified and managed.

We talked a lot about how they were actually caused, which is important. But the fact is they still exist. There's still going to be a kind if wrong information or outdated or missing information. So we wanted to know how the patient care, who's so highly collaborative and very mobile and tasked with doing a lot of different things at once.

When they identify that there actually is an information problem, how do they know that it's an information problem? And then what do they do about it afterwards? So how do they go about managing it. So our second research objective is to describe how PIP's are actually identified and managed so we can better understand that process.

And then finally, as we know, due to High Tech Act, especially here in the United States, there's been a rapid implementation of electronic health record systems. So this transition from paper-based patient records to EHR electronic records. And this does, as studies show, change how patient care teams interview and share information.

And so we also want to understand the role that technology plays in this. So, how does including a system change the way in which information problems are identified and managed? And can we possibly improve the design of these systems, which is our third objective, to provide improvements for the EHR design.

To better help facilitate this collaborative patient care team's ability to identify and manage these information problems. So now that I've talked a little bit about the motivation of our study and our objective, I'll walk through our methodology, which is how we conducted our study. So, our setting for the study was I conducted a qualitative research in an inpatient area of a large teaching hospital here in Pennsylvania, and I shadowed five internal medicine teams during their morning rounds.

So here's a list of the 116 individuals that I observed, and I primarily followed around these internal medical teams which had one attending physician and it had a number of medical residents and medical residents as a part of that team. And then throughout the process of morning rounds, their interaction with a lot of other members of the Patient care team including nurses can positions such as individual cardiology or neurology that has the consult on the patient as well as therapists, occupational therapists and physical therapists, care coordinators, social workers and we also have some pharmacists that rounded with our team.

So the way in which I collected data as I mentioned it was a qualitative study we employed qualitative methods of shadowing and observation of 29 morning rounds. And it resulted in 155 hours of observations over the course of three months. And just to be specific about the activities that I observed, I went in very early with the residents to watch the hand-off discussions that occurred between the night flow and the day shift.

And then, I walked with the residents and interns during their pre-rounds where they visit all of their patients that they're responsible for, to get preliminary information as those patients woke up and their vitals from the nurses. And then we met back In a resident workroom with the attending and then we went our a full round with the entire team, which typically included visiting anywhere between 12 to 16 patients throughout various units within the hospital.

I then follow some back to the resident workroom and watch their EHR documentation process. And at times we also went to the emergency department when there were new inpatient admissions to watch that transfer process from the ED to the inpatient area. And then future data collections will also include some semi-structured interviews with participants so we can better understand the patient care team's perception of patient information problems as well.

In terms of data analysis, the field study resulted in 280 pages of transcribed field notes and I analyzed this using the six phase thematic analysis by Braun & Clarke. If individuals aren't familiar with this, this is just a very rigorous process of becoming first familiar with your data by reading through it and transcribing it, and then I identified individual codes within our data.

So here's some segments that relate to missing information. And then as I developed all these individual codes, I strip them into preliminary themes. So in this case, the preliminary themes ended up being the different communication modes that missing information ended up occurring in. And then as all of these preliminary themes start to evolve, we group them into final themes, which are defined and bounded.

And one of our final themes, of course, was missing information which is a type of patient information problems. Now I'd like to talk a little bit about our results and then go into a discussion about how this can impact those in industry as well as research. So the first objective we had was to develop a PIP taxonomy.

So we're currently still going through the analysis of the data but here's our taxonomy for now. It was 120 discrete PIPs were identified throughout all of the study data and they were classified into six different types. So here are the types. The first is wrong information, which is simply when information is not accurate and is identified as being not accurate.

Second type is missing information. And this was information that the team clearly stated that they expected it to be there, but it was not there. The third type is incomplete where only part of the information is provided. And this frequently occurred when they had consulting physicians that came in to look at a patient for a particular reason.

And at times their progress notes only included certain information, but the patient care team was missing other aspects of that information such as do recommended treatment, or diagnosis, or different tests that needed to be run and therefore, they had to follow up with the team. The fourth type is outdated information.

And this was classified as information that frequently changes and is no longer valid and has not been updated. This also frequently occurred with nurses. So nurses are typically tasked with, throughout the day gathering vitals of the patient which includes input and output, the blood pressure, the glucose monitoring and any other vitals that need to be monitored for that patient.

And since this is done so frequently they typically monitor this and they'll post it in the room using other forms of documentation. But, they don't always update it in the EHR system immediately. And so, sometimes, this is outdated information. But then the clinical team needs to follow up with the nurse on.

The fifth type is unclear information, which is what we categorized as there being an uncertainty about the accuracy or meaning of information. So it wasn't that the information was immediately identified as being wrong or incomplete, it just wasn't quite clear what was being said. And then the final type is segregated information.

This is where multiple pieces of related information are stored in different locations and are not easily viewed together, and this typically happened in the EHR system, which we also saw in prior literature where if individuals need it for instance they saw that the medication was ordered, but they didn't know who ordered it or why they ordered it.

And so, they then had to go into the pharmacy system to look at who ordered it, when it was ordered, and sometimes even have to scan pages and pages of progress notes to understand why that medication was ordered. And just to give you a bit of the data from the field study, I'll give one example of wrong information directly from our field study.

So here's a time when our team and our nurse exited a patient's room, and we had a pharmacist rounding with the team. And the pharmacist told the team that the patient was on the medication, Penicillamine, and she questioned whether or not this was correct or whether it was supposed to be Penicillin VK.

So the team then gathered around the EHR and looked at the patient's medication list and patient history, and they realized that it was the wrong medication. The patient should've been on Penicillin VK. But this one medication had been carried over in the system from the patient's ED admission, and so they hadn't caught it.

So, this was an example of when they identified wrong information. As part of the PIP taxonomy, we also looked at what mode of communication was the information problem identified during. So in this case you can see 69% of the information problems were identified when they were looking at the EHR system itself.

Which makes sense since this particular hospital has had an information EHR system implemented for over ten years, and it's their primary source of patient information. However, it's also interesting to point out that a quarter, 25% of the PIPs were identified during verbal communication which was typically face-to-face with individuals or over the phone.

And then 6% were also identified when they were looking at their hand-written notes that were on paper-based documentation. So what we can see from this the EHRsystem is where the majority of the PIP's are identified, which further motivates our study to take a look at the design of these EHR system to see if we can better design it to help with the identification and management of PIP.