One commonly mentioned unintended consequence of CPOE systems is patient identification errors. However, this is the first study to quantify the frequency and analyze risk factors for this type of error. We successfully utilized email surveys to determine potential risk factors, created and validated a trigger to identify OOMP events, and completed a case control study which identified risk factors.
Several points became evident from the email surveys. The first is that there is little consensus between the CMIO respondents and the CHP physicians. Their views differ on the frequency, contributing factors, and error interception. This could be due to the fact that CMIOs may be less aware of the actual work flow and processes that occur when placing an order. Also, the CMIO respondents may be more focused on the computer and not on human limitations. Becoming aware of physician needs would allow for a computer design that is more helpful and useful to the users.
The surveys also showed that having multiple charts open simultaneously is thought to cause errors. The CMIOs and the CHP physicians both cited this feature as a cause for errors. Email surveys were helpful in establishing potential risk factors, but the wide variety of beliefs made evident the need for a rigorous and quantitative method to identify patient misidentification.
To determine potential cases of patient misidentification, we created a trigger approach. This method was used to avoid contacting and relying on providers to identify their own errors. Self-report would be problematic because of recall bias, unwillingness to admit mistakes, and the burden of contacting the providers. A previously used trigger is the abrupt cancellation of a medication. To apply it to patient identification errors, we extended the trigger to include events in which the cancellation is followed by a quick reorder on a different patient. The case validation shows that our trigger was sufficiently specific in detecting OOMP events to proceed with the case-control study. Our specificity of 61–100% compares favorably with typical trigger specificities.19
The reported incidence at the encounter level for adverse drug reactions for hospitalized children is 0.6% to 16.8%.20
Our incidence rate for OOMP events at the encounter level is within this range. When additional events that are not caught are considered, this indicates that patient identification errors may be as common as adverse drug reactions. Our rate at the order level is comparable to the reported incidence rate of mislabeled laboratory specimens.7
Both lab results and closed loop medication administration are highly automated processes that depend on initially picking the correct patient by the clinician at the bedside.
The most commonly mentioned risk factors in previous qualitative research have been similar name, similar condition, and distraction. In the present study, ten variables were used to measure similar names (both by sound and spelling). Only the spelling of the last name proved significant. It is possible that errors could occur when the provider is selecting a name from a list sorted alphabetically by last name. To measure similar conditions, we created measures for diagnosis overlap and same service. The diagnosis overlap was not significant. The same service measure indicates that Patient A is more likely to be on a different service from Patient B. These results do not support the notion that similar condition increases the chance of an OOMP event.
Consistent with previous research, our proxy measure for distraction was significant in the multivariate analysis. The email survey suggested fatigue as a cause of patient misidentification. In this study, fatigue was measured by number of four hour blocks in the previous 24 hours in which the provider had placed orders. Our results do not demonstrate fatigue contributing to patient misidentification. The other most commonly mentioned risk factor in the email surveys was having two charts open. Data were not available to measure this.
The results of our case-control study indicate additional characteristics that are strongly associated with the case dyads. High risk situations are based on day of the week, time, patient age, and room location. Provider familiarity with the drug and patient affect the likelihood of error.
Overall, this study demonstrates that it is the context of the order entry process, more than the characteristics of the patient names themselves, which are associated with increased risk of patient identification errors.
Certain limitations must be considered in interpreting the results of our study. First, the qualitative analysis is limited by the response rate of the informal email survey; however, it fulfilled its intended purpose of generating suggestions of risk factors. There are several limitations regarding the trigger. The validation was limited to a retrospective analysis of what was documented in the EMR at the time of the event and there were no examples of providers documenting their error. Only four of 644 events had been reported through the hospital reporting system.
Second, the sensitivity of the trigger was not evaluated. The algorithm required the same provider for all order actions. If a different provider or a nurse cancelled or reordered, the trigger did not detect it. If the cancel/reorder sequence was reversed the OOMP would not be detected. Medications that actually reached the wrong patient were not detected. These limitations would suggest that the true incidence of OOMP events is greater than reported here.
Third, the variables created did not always fully measure the suggested risk factor. For example, distraction was measured by quantifying the number of orders placed in the previous four hours. This is an imperfect representation of distraction as a provider could be distracted without having many orders to place. Fatigue was also an imperfect measure because data were not available for the provider’s amount of sleep. The data for similar name are based on the patient’s current name in the EMR. When the error occurred, we do not know if their name was the same as it is now. For example, in hospital information systems newborns are often named “Baby Boy Smith” or “Baby Girl Garcia.” Thus, at the time of the OOMP event the patient names may have been more similar than reported here.
Finally, the data analyzed were only collected from inpatient medication orders from a single institution, so may not be generalizable to other nonpediatric hospitals or CPOE contexts.
Future research will be required to determine if not allowing multiple charts to be open simultaneously will decrease the rate of errors. Research should also be conducted to identify the sensitivity of our patient misidentification trigger. Implementing the trigger in real time would allow for risk factors to be investigated immediately after the patient identification error. Ultimately, to reduce OOMP events and improve safety, future research should focus on creating a pop-up alert at the time of order for Patient A warning the provider of a high risk for error. This study does not confirm that a sound-alike name alert alone would be a fruitful intervention.