July 17, 2012 More on Wrong-Patient CPOE
Last month we discussed entering orders on the wrong patient during CPOE (see our June 26, 2012 Patient Safety Tip of the Week “Using Patient Photos to Reduce CPOE Errors”). That column highlighted an intervention developed by Children’s Hospital of Colorado (Hyman 2012) in which a patient verification prompt accompanied by photos of the patient reduced the frequency of wrong patient order entry errors.
When we were involved in a CPOE implementation in 2008 we speculated that wrong patient errors would be more likely to occur via CPOE than conventional written orders (see our May 20, 2008 Patient Safety Tip of the Week “CPOE Unintended Consequences: Are Wrong Patient Errors More Common?”). We discussed the need to clearly identify patients on all order entry screens and identified 5 common scenarios that can lead to entering orders on the wrong patient via CPOE (see the May 20, 2008 Tip for details):
1. Patient name and other ID items not appearing on every screen
2. The cursor/stylus error
3. The “truncated scroll” syndrome
4. The dual system issue
5. The failure to log off issue
Now a new study (Adelman 2012) actually provides a quantitative estimate of how frequently wrong-patient CPOE may occur. Those authors developed a computer tool that identified instances where orders were entered on a patient, promptly retracted, and then entered on a different patient. Using this tool they estimated over 5000 orders were placed on wrong patients at four hospitals during one year. They then piloted two distinct interventions, an “ID-verify alert” and an “ID-reentry function”. The former reduced the odds of a retract-and-reorder event by 16% and the latter reduced the odds by an even greater magnitude (41%).
A prior study (Koppel 2008) had shown that discontinuation of medication orders within 2 hours of order input was a good predictor of prescribing errors. So Adelman et al. hypothesized that orders entered on a patient, promptly retracted, and then entered on a different patient would be a good indicator of wrong-patient errors. This combination would actually be identifying near-misses in most cases but the concept is important to provide an estimate of how often this happens. So the tool they developed identified instances where there were orders (for medications, bloodwork, imaging, or general care) that were retracted within 10 minutes and then reordered by the same provider on a different patient within 10 minutes. They also did phone interviews with the providers to confirm that these were wrong-patient occurrences and identify factors that facilitated such occurrences. The phone interviews confirmed that the vast majority of such occurrences were, indeed, wrong-patient errors (the positive predictive value of the tool was 76.2%). Using this they estimated that there were 14 wrong-patient CPOE errors daily at their facilities, that one in six providers had at least one wrong-patient error, and that one in 37 patients admitted to the hospital had an order intended for another patient for an overall estimated rate of 58 wrong-patient orders per 100,000 orders.
About 10% of the errors were “juxtaposition errors” where mis-clicking on the wrong patient name in a list led to the error (this is the “cursor/stylus” error in our prior columns). But over 80% of the errors were due to interruptions of various sorts.
The intervention tools they developed were simple yet elegant. The “ID-verify alert” was triggered by opening an order entry screen and prompted the physician with the patient name, gender and age and the physician was required to acknowledge that was the correct patient before being allowed to proceed with order entry. The “ID-reentry function” prevents the provider from accessing the order entry screen until he/she re-enters the patient’s initials, gender and age. These interventions were piloted in a randomized fashion. While the “ID-verify alert” reduced errors by 16%, the “ID-reentry function” reduced them by 41%.
They estimated that the “ID-reentry function” required an extra 6.6 seconds for every order session. Though that sounds like an insignificant extra time, the authors note that could result in a significant amount of time when accumulated over a long period. However, they note that the interventions were well accepted by the providers.
Though the Adelman study just looked at wrong-patient order entry, the same concepts apply to any input into electronic medical records. Both the Adelman study and the Hyman study quote a paper by Wilcox et al. (Wilcox 2011) that found 51 wrong notes written in the electronic chart per 100,000 notes. Wilcox et al. used an alert with patient name and medical record number which the provider had to click “yes” to verify patient ID and they were able to reduce wrong-patient notes by 40%.
Tools like these and the one described in our June 26, 2012 Patient Safety Tip of the Week “Using Patient Photos to Reduce CPOE Errors” developed by Children’s Hospital of Colorado (Hyman 2012) have great potential to reduce wrong-patient CPOE errors and EHR entry errors. The simple tool developed by Adelman et al. should be easily adaptable to most hospital CPOE systems and could at least provide you with good estimates of how often such wrong-patient events or near-misses are occurring.
See some of our other Patient Safety Tip of the Week columns dealing with unintended consequences of technology and other healthcare IT issues:
Hyman D, Laire M, Redmond D, Kaplan DW. The Use of Patient Pictures and Verification Screens to Reduce Computerized Provider Order Entry Errors. Pediatrics 2012; 130: 1-9 Published online June 4, 2012 (10.1542/peds.2011-2984)
Adelman JS, Kalkut GE, Schechter CB, et al. Understanding and preventing wrong-patient electronic orders: a randomized controlled trial. J Am Med Inform Assoc 2012; Published online 29 June 2012
Koppel R, Leonard CE, Localio AR, et al. Case Report: Identifying and Quantifying Medication Errors: Evaluation of Rapidly Discontinued Medication Orders Submitted to a Computerized Physician Order Entry System. J Am Med Inform Assoc 2008; 15: 461-465
Wilcox AB, Chen Y-H, Hripcsak G. Minimizing electronic health record patient-note mismatches. J Am Med Inform Assoc 2011;18:511-514 Published Online First: 12 April 2011