Patient Safety Tip of the Week

January 19, 2016

Patient Identification in the Spotlight

 

 

In last week’s column (January 12, 2016 Patient Safety Tip of the Week “New Resources on Improving Safety of Healthcare IT”) we again mentioned the problem of entering data or orders on the wrong patient. Our Patient Safety Tips of the Week for May 20, 2008 “CPOE Unintended Consequences – Are Wrong Patient Errors More Common?” and July 17, 2012 “More on Wrong-Patient CPOE” discussed many of the reasons such errors occur and noted some of the tools that have been used to minimize the chances of such occurring.

 

In that 2008 column we noted the following factors that contribute to wrong patient errors:

 

Remote order entry

First and foremost is the fact that with CPOE orders are often being entered remotely, that is not at the patient’s bedside. We previously cited examples of unintended consequences of remote order entry. ISMP had an example of a nonventilated patient inadvertently being given a paralytic agent. This occurred in part because the ordering physician was entering orders from a remote site and accidentally ordered this, not recognizing the patient was not ventilated (ISMP 2007).

 

One might argue that in the old paper-based system we also often enter orders remotely. We often take a chart from the patient chart rack in the nursing station and enter orders there. Certainly one could pick up the wrong chart and begin writing orders there. But there are several factors that probably make it more likely during CPOE and you need to address them during your CPOE implementation to minimize the risk of this unintended consequence. Below are 5 common scenarios that can lead to entering orders on the wrong patient via CPOE:

 

Patient name and other ID items not appearing on every screen

We’ve seen systems where navigation clicks or scrolling remove these critical identifiers from the screen. You need to ensure that the name and other identifiers remain anchored at the top of every screen of your CPOE system. (And remember to make your identifiers consistent with your Joint Commission compatible patient identification policy).

 

The cursor/stylus or juxtaposition error
The same error one sees with selecting a drug from a drop-down list obviously can also occur when selecting a patient from a drop-down list. Both are juxtaposition errors but we often call this a cursor error when it occurs while using a larger data entry device, and a stylus error when using a smartphone or tablet as an entry device. There errors are probably more common with the latter devices. There are no quick fixes for these, though thoughtful screen layouts can minimize the risk of these errors.

 

The “truncated scroll” syndrome and similar names

When searching for a specific patient, the results list may be longer than the current screen. The physician may simply pick the last name on the screen if it looks like the one he/she is looking for, failing to realize that there may be more patients with that name (he/she would have to continue scrolling the list to see them). You need to attempt to prevent your patient searches from “splitting” patients with like names in any screen window (or otherwise alert the user to scroll because there may be more similar names).

 

You would be surprised to see how often patients with the same or very similar names may be hospitalized at the same time. Shojania (Shojania 2003) described a near-miss related to patients having the same last name and noted that a survey on his medical service over a 3-month period showed patients with the same last names on 28% of the days. The problem is even more significant on neonatal units, where multiple births often lead to many patients with the same last name being hospitalized at the same time and medical record numbers being similar except for one digit. Gray et al. (Gray 2006) found multiple patients with the same last names on 34% of all NICU days during a full calendar year, and similar sounding names on 9.7% of days. When similar-appearing medical records numbers were also included, not a single day occurred where there was no risk for patient misidentification. Both these studies were on relatively small services so one can anticipate that the risks of similar names is much higher when the entire hospitalized patient population is in the database.

 

The dual system issue

Some CPOE systems that have limited integration with other systems, such as a radiology PACS system. It is not uncommon for a physician to look at information on that other system while trying to input orders into the CPOE system. Since they are two different systems, it is possible to be looking at two different patients in the two systems. You therefore need to ensure that when the physician moves between these two systems the same patient must be visible on each system. That means you need to develop a way to launch the other application and port the patient identification information to the other application.

 

The failure to log off issue

This occurs when a physician leaves the order entry screen temporarily without logging off and a second physician comes by and leaves orders on a patient (without logging on separately). The first physician then returns to the screen and assumes that he/she is still entering orders on the original patient.

 

 

The wrong patient error has been in the spotlight again recently. Green and Adelman, who have developed some of the excellent tools we’ve mentioned in previous columns, published an excellent example in a recent AHRQ Web M&M (Green 2016). This was an incident where a new receptionist, learning a new EHR, registered a patient who was new to the practice but had the same name and age as an existing patient in the practice (but different birth dates). The error was noted only when lab results were sent to the previously existing patient, who called the practice to note he had not had any labwork done. Green and Adelman note that wrong patient errors are actually more common in outpatients, being almost double the rate seen in the emergency department.

 

Green and Adelman stress the importance of training for anyone performing patient registration and note there are certification programs available.

 

In our July 17, 2012 “More on Wrong-Patient CPOE” discussed many of the reasons such errors occur and noted some of the tools developed by Adelman and colleagues to minimize the chances of such occurring (Adelman 2013). 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%.

 

Patient photographs have been used in attempt to reduce the risk of wrong patient errors. Our June 26, 2012 Patient Safety Tip of the Week “Using Patient Photos to Reduce CPOE Errors”) 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. But photographs are not foolproof and need to be updated regularly. In the hospital setting, in particular, patients may be difficult to identify from prior photographs because of trauma, surgery, etc. We further discussed patient photographs in detail in our April 30, 2013 Patient Safety Tip of the Week “Photographic Identification to Prevent Errors”.

 

Biometrics are likely the next generation of patient identification tools (Davis 2016). These include not only fingerprints but things like retinal scans, iris patterns, palm patterns, vein patterns, etc. Those of you who have struggled unlocking your iPhones with your thumbprint will readily recognize that even these biometric techniques are not infallible. Facial recognition software is also evolving as another potential tool.

 

One added dimension to the problem of correct patient identification has to do with interoperability of IT systems. As patients get services at different hospitals, multiple labs, radiology practices, etc. multiple medical records are established with different identification numbers. As of yet we do not have a universal patient identifier. In lieu of such a universal identifier, various algorithms are used to attempt to match patients at one facility with the same patient at another facility in what is known as a master patient index or MPI. Algorithms may contain bits and pieces of a patient’s first and last names, social security number, date of birth, and other items to come up with an identification code that is very likely to identify the patient correctly at both facilities. However, such algorithms are not failsafe and misidentification does occur.

 

A recent survey of AHIMA health information professionals revealed that more than half of respondents noted they routinely have to try to mitigate against duplicate patient records (Dooling 2016). That same survey showed gaps in the quality improvement processes applying to registration or tracking such patient matching errors routinely. The barriers identified include: registration staff turnover, record matching/patient search terminology and/or algorithms, lack of resources to correct duplicates, inadequate information governance policy support, and lack of executive support. The authors note that reliable and accurate calculation of the duplicate rate is critical to reducing potential patient safety risks.

 

Both the Green and Adelman article and the AHIMA survey point out the importance of including those involved in patient registration in quality improvement and patient safety activities. A recent Canadian study (Campbell 2015) did a qualitative and observational evaluation of patient identification practices in the Pre-Admission Clinic, Admitting Department and the Perioperative Care Center and uncovered confusion, with 90% of patient verification occurrences not matching current policies. The authors conclude these discrepancies identify an opportunity to reassess and standardize workflow, clarify what identification methods are acceptable and determine additional appropriate identification verification practices with ID bracelets and patient charts.

 

Last week we also mentioned the SAFER guides from the Office of the National Coordinator for Health Information Technology (ONC 2014). One of the modules in the SAFER guides is on ensuring accurate patient identification. It includes both a checklist for organizational self-assessment and recommendations for improvement.

 

And, of course, not all solutions are high tech. In our August 2015 What's New in the Patient Safety World column “Newborn Name Confusion” we discussed another study by Adelman and colleagues in which they applied their “retract and reorder” (RAR) tool to assess the impact of a change in naming conventions for newborns (Adelman 2015). Hospitals need to create a name for each newborn promptly on delivery because the families often have not yet decided on a name for their baby. Most hospitals have used the nonspecific convention “Baby Boy” Jones or “Baby Girl” Jones. A suggested alternative uses a more specific naming convention. It uses the first name of the mother. For example, it might be “Wendysgirl Jones”. Montefiore Medical Center switched to this new naming convention in its 2 NICU’s in July 2013 and the RAR tool was used to measure the impact on wrong patient errors. Wrong patient error rates measured in the one year after implementation of the new more specific naming protocol were 36% fewer than in the year prior to implementation.

 

Patient misidentification and wrong patient orders or charting are significant threats to patient safety. We all need to be vigilant and track such errors as part of our regular quality improvement activities. We also need to learn from others and adopt best practices as they are discovered.

 

 

 

See some of our other Patient Safety Tip of the Week columns dealing with unintended consequences of technology and other healthcare IT issues:

 

 

 

References:

 

 

ISMP (Institute for Safe Medication Practices). Remote CPOE error—a situation that’s more than remotely possible. ISMP Medication Safety Alert! Acute Care Edition. May 31, 2007

http://www.ismp.org/Newsletters/acutecare/articles/20070531.asp

 

 

Shojania KG. AHRQ Web M&M Case and Commentary. Patient Mix-Up. February 2003

http://www.webmm.ahrq.gov/case.aspx?caseID=1&searchStr=shojania

 

 

Gray JE, Suresh G, Ursprung R, Edwards WH, et al. Patient Misidentification in the Neonatal Intensive Care Unit: Quantification of Risk. Pediatrics 2006; 117: e43-e47

http://pediatrics.aappublications.org/content/pediatrics/117/1/e43.full.pdf

 

 

Green RA, Adelman J. New Patient Mistakenly Checked in as Another. AHRQ PSNet Web M&M 2016; January 2016

https://psnet.ahrq.gov/webmm/case/366

 

 

Adelman JS, Kalkut GE, Schechter CB, et al. Understanding and preventing wrong-patient electronic orders: a randomized controlled trial. J Am Med Inform Assoc 2013; 20(2): 305-310 Published online 29 June 2012

http://jamia.oxfordjournals.org/content/20/2/305

 

 

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)

http://pediatrics.aappublications.org/content/early/2012/05/29/peds.2011-2984.abstract

 

 

Davis J. Raymond Aller: Biometrics a crucial next step for patient safety. HealthcareITNews January 7, 2016

http://healthcareitnews.com/news/raymond-aller-biometrics-crucial-next-step-patient-safety

 

 

Dooling J, Fernandes L, Kirby A, et al. Survey: Patient Matching Problems Routine in Healthcare. Journal of AHIMA 2016; January 6, 2016

http://journal.ahima.org/2016/01/06/survey-patient-matching-problems-routine-in-healthcare/

 

 

Campbell K, Muniak A, Rothwell S, et al. Improving Quality and Safety through Positive Patient Identification. Healthcare Quarterly 2015; 18(3): 56-60

http://www.longwoods.com/content/24431

 

 

ONC (Office of the National Coordinator for Health Information Technology). SAFER GUIDES—Safety Assurance Factors for EHR Resilience. 2014

https://www.healthit.gov/safer/safer-guides

 

 

Adelman J, Aschner J, Schechter C, et al. Use of Temporary Names for Newborns and Associated Risks. Pediatrics 2015; Published online July 13, 2015

http://pediatrics.aappublications.org/content/early/2015/07/08/peds.2015-0007.full.pdf+html

 

 

 

 

 

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