Last month we had yet another example of a failure of health information technology to improve some facets of care (see our March 2015 Whats New in the Patient Safety World column CPOE Fails to Catch Prescribing Errors). We began apologetically noting weve had so many columns outlining some of the untoward consequences and other problems with CPOE and healthcare IT in general even though we remain huge supporters of CPOE and clinical decision support and IT applications in healthcare.
Fortunately this month we have a health IT winner! Intermountain Medical Center has recently published a study on the value of a clinical decision support system in reducing mortality for patients with community-acquired pneumonia (Dean 2015). This is a great example of what weve long visualized for the utilization of computer support to improve patient outcomes.
Most physicians in emergency departments have long used simple decision support tools like the Pneumonia Severity Index (PSI) or the CURB-65 tool. These have been of some help in decisions about whether a patient with community-acquired pneumonia might be treated as an outpatient or admitted to the hospital (and, if admitted, whether an ICU is indicated). Many hospitals have integrated these tools into their electronic medical record systems or at least attached links to these tools. More often emergency physicians simply use these tools on their smartphones. We find these tools useful but they probably have a limited impact on actual patient outcomes in the big picture.
Intermountain Healthcare really took clinical decision support to the next level. The clinical decision support tool developed at Intermountain lets the computer do work that the busy clinician often does not or cannot take the time to do. Their clinical decision support tool has been described previously (Dean 2013). The IT system culls the patients medical record and collects and analyzes data on 40 factors. When it calculates the probability of pneumonia as 40% or higher it notifies the physician. If the physician confirms the diagnosis of pneumonia the CDS tool provides treatment recommendations and recommendations about severity that may help with patient disposition (eg. inpatient, ICU, or outpatient). It makes recommendations about antibiotics as well. For example, the tool looks for risk factors for drug-resistant organisms such as a previous hospital admission or an actual previous culture of a drug-resistant organism. It also looks at where the patient is coming from, such as a long-term care facility which is another risk factor for a drug-resistant organism (the CDS tool apparently even cross references patient addresses with known addresses of long-term care facilities). Thus, the CDS tool may suggest to the clinician that the patient is high risk for a drug-resistant organism in cases where the clinician would not have suspected it. It also provides recommendations about lab tests to identify responsible organisms.
The currently published study (Dean 2015) and the related commentary (Intermountain press release 2015) describe use of the CDS tool and its impact on patient mortality. The study was not a randomized controlled trial. Rather it was a prospective study on almost 5000 pneumonia patients, comparing patient outcomes for 4 emergency departments using the tool vs. 3 emergency departments not using the tool. There was no statistically significant difference for the total pneumonia population in severity-adjusted mortality between intervention and usual care EDs but that included patients with healthcare-acquired pneumonia as well as community-acquired pneumonia. When the healthcare-acquired pneumonia patients were excluded, there was a statistically significant reduction in mortality in the group using the CDS tool (almost 50% lower mortality).
The CDS tool was developed at Intermountain over several years following use of paper-based pneumonia guidelines that had limited impact. The 40 factors utilized include 6 vital sign variables, 6 laboratory values, 25 nursing assessment variables, the patients age, the patient's chief complaint, and findings extracted from the chest imaging report using natural language processing. The team chose a threshold of 40% probability of pneumonia to alert the clinician to balance usefulness against the risk of alert fatigue (Dean 2013). Screening tools that emphasize sensitivity generate lots of alerts, many of which will be false alerts. So they chose to emphasize specificity instead. But the clinicians also have access to the CDS tool via a desktop icon if they wish to use it when the alert had not triggered.
Judging from the comments in the press release the CDS tool is well accepted by the physicians in the emergency departments.
The real value of computers in medicine is that they can grab data from multiple sources and assemble that data in one place where complex calculations and rules can be used to help with diagnosis and management. Few clinicians would be able to spend the time getting all the data from those multiple sources nor be able to remember all the rules and calculations that would be needed. This fine implementation of a clinical decision support system is an outstanding example of the potential value of such systems and its great to see how it translates to better patient outcomes.
You can imagine similar CDS tools to help with other conditions. For example, an antidiarrheal agent ordered by a clinician for an inpatient might flag the system to activate a CDS tool that would consider the possibility of a C. diff infection. The tool could search for evidence of prior C. diff infection on a prior hospital stay or check to see if the patient had been at other healthcare facilities where exposure to C. diff is known to occur. Importantly, it can check multiple sources to see if the patient has received antibiotics, the most common risk factor for C. diff, or other risk factors such as use of proton pump inhibitors. Based on the results, the tool could alert the Infection Control team, suggest contact precautions be implemented, and suggest appropriate diagnostic testing. If the patient does have a C. diff infection the tool could also recommend appropriate therapy, including recommendations for recurrent C. diff infection.
This is really good work by Intermountain and reinvigorates our faith that clinical decision support tools will finally make a significant impact on patient safety and patient outcomes.
See some of our other Patient Safety Tip of the Week columns dealing with unintended consequences of technology and other healthcare IT issues:
Dean NC, Jones BE, Jones JP, et al. Impact of an Electronic Clinical Decision Support Tool for Emergency Department Patients with Pneumonia. Annals of Emergency Medicine 2015; Published online: February 26, 2015
Intermountain Medical Center. "New study finds digital clinical decision support tools save lives of pneumonia patients ." Medical News Today. MediLexicon, Intl., 12 Mar. 2015. Web. 15 Mar. 2015.
Dean NC, Jones BE, Ferraro JP, et al. Performance and Utilization of an Emergency Department Electronic Screening Tool for Pneumonia. JAMA Intern Med 2013; 173(8): 699-701
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