Using IT to identify patients at risk for outbreaks of delirium
Hospital uses IT to help identify at-risk patients
In early 2011, a team of clinicians and information technology specialists at Hartford (Conn.) Hospital convened a work group to address one of the most dangerous, costly and often-overlooked complications affecting hospitalized older adults.
Their target: delirium, a sudden, acute change in mental status often characterized by confusion, disorientation, inattentiveness and changes in consciousness. It’s shockingly common, experts say, affecting roughly a quarter of general-medicine geriatric patients and as many as 75% to 80% of patients over age 65 in intensive-care units.
Although it was long assumed to be a hard-to-avoid problem among older patients, particularly in unfamiliar, stressful hospital settings, there is growing awareness that delirium is associated with a host of negative outcomes, including longer lengths of stay, falls, pressure ulcers, malnourishment, decreased function, pneumonia and increased risk of death both during and after hospitalization.
“It’s a huge problem from a number standpoint,” says Dr. James Rudolph, site director of the Geriatric Research, Education and Clinical Center at the Veterans Affairs Boston Healthcare System. “Some studies have shown equivalent chances of dying between delirium and heart attacks.”
And the patients who do survive can suffer long-term—sometimes even permanent— cognitive impairment after a delirium episode, says Christine Waszynski, a geriatric nurse practitioner who was part of 601-bed Hartford Hospital’s delirium team. “Delirium is an acute brain injury, and some people never really recover from it,” she says, noting that for many patients, an episode can mark the beginning of a precipitous decline.
Estimated per-patient costs of delirium range from $16,000 to as high as $64,000, while total annual costs to the healthcare system are more than $100 billion, according to recent figures.
Experts stress the need for early identification of those patients who are at greatest risk for developing delirium, including those who have dementia, certain types of infections, visually impairments, or severe illness and multiple comorbidities.
And many of the strategies recommended for the prevention of delirium are relatively simple. They include making sure patients have their glasses or hearing aids on at all times, get- ting patients up and moving, hanging pictures of family or placing familiar objects nearby, steering clear of certain medications unless necessary, and ensuring patients have their treatment plans explained to them.
But identifying and assessing those high- risk patients often proves challenging, particularly with frequent patient handoffs, Waszynski says. So the team at Hartford Hospital—known as Action for Delirium Assessment, Prevention and Treatment, or ADAPT—came up with a systems-based approach that leverages the hospital’s existing information technology capabilities.
In its analysis, the ADAPT group concluded that Hartford Hospital’s nurses struggled to perform adequate initial assessments of patients’ mental status, which are critical for gauging changes from the baseline. That’s where Julie Michaelson, clinical consulting analyst in Hartford Hospital’s information services department, came in.
“What I do is analyze where people are clicking and pointing, and then I try to put reference data at the point of care,” Michaelson says. “We want technology to meet clinicians’ workflow and not the opposite.”
Like many hospitals, Hartford Hospital uses the Confusion Assessment Method, or CAM, a delirium screening tool developed more than 20 years ago by Dr. Sharon Inouye, a professor of medicine at Harvard University and a longtime leader in the field of delirium research.
The CAM asks users to report changes in patients’ mental status from the baseline, but Hartford Hospitals electronic health record wasn’t able to capture that data, Waszynski says. “We recognized immediately that we needed to change that.”
Determining mental status
The ADAPT team created an initial nursing assessment within the EHR that allows nurses to choose from five simple categories of mental status, including normal, mildly forgetful and significantly forgetful. The goal, Waszynski says, is to determine whether patients have dementia or some other cognitive problem in their everyday lives, which makes it easier to judge whether future episodes of confusion are actually delirium.
“The beauty of this technology is that when a nurse does the CAM on a patient she’s never taken care of before, that patient’s baseline information comes up on the screen,” she explains. “The nurse can see, for example, that this patient is usually normal, and now they can’t put two words together, or this patient usually has mild impairment, and now it’s severe.”
The team put other changes in place, including clinical decision-support tools that help nurses assess whether patients are displaying inattention—another element of the CAM. They also configured the EHR so nurses can send messages to physicians, notifying them of a possible case of delirium. And they created specialized alerts for medications such as Benadryl, Pepcid and Ambien, which are often avoided in patients at higher risk.
“Now when any of these medications are ordered for a high-risk patient, the user gets a warning that the medication could cause delirium,” Waszynski says. Since the drug alerts were put in place a year ago, use of such medications among high-risk patients has dropped 70%.
The full decision-support system for delirium went live Aug. 21, Michaelson says, but in that short time, nurses have embraced the technology.
“They say that this is exactly what they needed,” she says. “They don’t have to do any hunting and pecking.”
Hartford Hospital is not the only site using computerized decision-support solu-
tions to monitor delirium. Donna Fick, professor of nursing at Penn State University, and several of her colleagues, have secured a $2.7 million grant from the National Institute of Nursing Research to test a strategy they crafted for detecting delirium in patients with dementia.
Their intervention—Early Nurse Detection of Delirium Superimposed on Dementia, or END-DSD—includes decision support as well as training for nurses.
“This is a difficult population for nurses to assess,” says Fick, who has spent several decades studying delirium. “These patients are already confused. It takes some work to help nurses understand that delirium is preventable, and if they don’t prevent it, these patients will get worse.”
The END-DSD features a delirium-associated factors screen that pops up in the EHR and pulls data from the patient’s record about potential causes. That screen includes lab values for sodium, blood sugar, potassium and thyroid. It also indicates what types of medications a patient is taking and whether a patient has an infection or is dehydrated.
Another screen, designed for management and prevention, prompts nurses to remove devices such as catheters whenever possible, to engage patients in cognitively stimulating activities, and to monitor patients’ nutrition and hydration closely.
The five-year study has enrolled 225 patients so far, with plans to enlist 165 more, Fick says. More than 1,000 nurses have gone through the training program.
“I think we have a fair amount of evidence on delirium, but the challenge is translating that data into practice,” she says. “Using IT systems, hospitals can think about who is most at risk.”
Such systems can be very helpful, says Rudolph, of the VA Boston Healthcare Center.
Rudolph, a geriatrician at Brigham and Women’s Hospital, Boston, is also the immediate past-president of the American Delirium Society, a group created just a few years ago to raise awareness and boost research efforts.
“At the VA, we see real potential to use the EHR to identify patients who are at highest risk for delirium,” Rudolph says. “If a patient comes in on a dementia drug, for instance, or if they are admitted to the ICU or they come in with a hip fracture, they are at higher risk. It’s not a perfect measure, but it can help us to focus our efforts.”
But he also cautions that clinical decision-support systems can lead to “alert fatigue,” when clinicians, inundated with constant alerts, begin to pay less and less attention to them.
“It depends on the hospital’s records system, but it is something to keep in mind,” Rudolph says.
He is working on a quality-improvement project called the Delirium Toolbox, which provides nurses with reading glasses, hearing amplifiers, stress balls, puzzles, decks of cards, sleeping masks and other tools they can provide to patients to ward off delirium.
“I’m not really sure yet that the stuff in the box makes a difference,” Rudolph says. “What does seem to make a difference is that a nurse recognizes the patient is high risk, goes to the toolbox and says, ‘I’m going to take a little extra time with this patient.’ That’s what’s showing promise so far.”