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An intervention to decrease patient identification band errors in a children's hospital
  1. P D Hain,
  2. B Joers,
  3. M Rush,
  4. J Slayton,
  5. P Throop,
  6. S Hoagg,
  7. L Allen,
  8. J Grantham,
  9. J K Deshpande
  1. Monroe Carell Jr Children's Hospital at Vanderbilt, Nashville, Tennessee, USA
  1. Correspondence to Dr Paul D Hain, Monroe Carell Jr Children's Hospital at Vanderbilt, Suite 2516, 2200 Children's Way, Nashville, TN 37232-9750, USA; paul.hain{at}vanderbilt.edu

Abstract

Context Patient misidentification continues to be a quality and safety issue. There is a paucity of US data describing interventions to reduce identification band error rates.

Setting Monroe Carell Jr Children's Hospital at Vanderbilt.

Key measures Percentage of patients with defective identification bands.

Strategies for change Web-based surveys were sent, asking hospital personnel to anonymously identify perceived barriers to reaching zero defects with identification bands. Corrective action plans were created and implemented with ideas from leadership, front-line staff and the online survey. Data from unannounced audits of patient identification bands were plotted on statistical process control charts and shared monthly with staff. All hospital personnel were expected to “stop the line” if there were any patient identification questions.

Effects of change The first audit showed a defect rate of 20.4%. The original mean defect rate was 6.5%. After interventions and education, the new mean defect rate was 2.6%.

Lessons learnt (a) The initial rate of patient identification band errors in the hospital was higher than expected. (b) The action resulting in most significant improvement was staff awareness of the problem, with clear expectations to immediately stop the line if a patient identification error was present. (c) Staff surveys are an excellent source of suggestions for combating patient identification issues. (d) Continued audit and data collection is necessary for sustainable staff focus and continued improvement. (e) Statistical process control charts are both an effective method to track results and an easily understood tool for sharing data with staff.

  • Patient identification systems
  • safety
  • quality assurance
  • statistical process control
  • patient safety
  • healthcare quality improvement
  • continuous quality improvement
  • control charts

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Patient misidentification has been recognised as a contributor to errors in healthcare. The Joint Commission International listed patient identification as the second of nine life-saving patient-safety solutions.1 Between February 2006 and January 2007, the United Kingdom National Patient Safety Agency documented 24 382 reports of patients being mismatched to their care. More than 10% of those reports were related to identification wristbands and their use.2 The Veterans Affairs National Center for Patient Safety reported that from January 2000 to March 2003, >100 individual root cause analyses, relating to patient misidentification, were performed.3 In 2002, the College of American Pathologists Q-Tracks study reported that the wristband error rate decreased markedly when the rate was monitored continuously.4 The College of American Pathologists Q-Tracks study reported error rates as high as 18.8% in adult institutions.5

Problem

To our knowledge, there have been no reports of identification band error rates in paediatric hospitals. We did not know our hospital's rate of identification band errors, nor had we implemented programmes to reduce it. We hypothesised that our rate was greater than zero and that we could reduce that rate by collecting data, implementing educational programmes and feeding the collected data back to the staff.

Setting

The Monroe Carell Jr Children's Hospital at Vanderbilt's quality improvement initiatives are driven by a small group known as Performance Management and Improvement (PM&I). This group is led by its medical director and the hospital's chief operational officer. PM&I includes two registered nurses that function as quality consultants for clinical effectiveness and patient safety, two data analysts and two project specialists that focus on organisational effectiveness. Various other members of the hospital administration are involved with the group on an advisory basis.

Intervention

The PM&I group began unannounced audits of all available patients in the hospital (including the emergency department) in November 2007 to check for identification band errors. After the first audit, baseline data were provided to executive leadership, including physician and nursing leadership, all patient care staff and the board of directors. While audits took place, web-based surveys were sent to all hospital personnel who work with patients, asking them to anonymously identify perceived barriers to reaching full compliance with identification bands. The survey was sent to 1639 people, with 501 responding (response rate 30.6%). A detailed demographic breakdown of the survey was not available. The results of the survey are listed in table 1. One specific action that came from the survey was to get “luggage tag”-style identification bands for the neonatal intensive care unit to improve the identification band fits in that unit. Unit-specific corrective action plans were created with the effort led by the nursing manager of that unit. Input for each action plan was provided by the survey results for that unit, representatives from nursing, respiratory therapy, intravenous therapy, care partners and the medical director of the unit. An example of an action plan is listed in figure 1. These action plans were rapidly presented to and implemented by staff across inpatient units as well as the emergency department.

Table 1

Results of the web-based survey to staff asking what barriers exist to compliance with correct identification bands

Figure 1

Example of a unit corrective action plan.

In addition, members of the PM&I group provided education on identification band checks to multiple ancillary providers within the hospital, including transport, child-life specialists, dietary, and radiology technicians. All educational sessions included a discussion of the dangers of allowing a procedure, medication administration, transport or any other interaction with a patient if the patient was not clearly identified. “Stopping the line”, a term taken from the automotive industry, was used to empower personnel to halt any proceeding if they felt that there were any questions about a patient identification band.

Measures

The first patient audit was unannounced and performed to obtain baseline data for identification band error rates in the hospital. An audit consisted of a team of people checking every available patient's identification band for placement and accuracy. Subsequent measurements were made at least four times monthly, covering days, nights, weekends and holidays to continue monitoring identification band error rates. Identification band errors were defined as missing bands, inappropriately placed bands, illegible bands or inaccurate data on the bands. An identification band was considered defective if it had any one or more of the above errors. The PM&I group performed the first two audits, and then the nursing managers of each unit were enlisted to perform audits as well. Eventually, the PM&I group and the nursing units each made two audits per month.

Analytical methods

Identification bands were considered either correct or incorrect. The number of defects per identification band was not considered. The overall percentage of defective identification bands (number defective/number identification bands checked) per audit was plotted on a p-chart using standard statistical process control methods.6

Situational analysis

The initial audit showed an error rate of 20.4%. Because these data had never been gathered before at The Monroe Carell Jr Children's Hospital at Vanderbilt, they were shared in many forums to help build momentum for interventions. The data were presented at the hospital's medical directors' meeting, the PM&I council, the operations board, the departmental administrative board and to the board of directors of the hospital. Universally, the reaction was shock at the magnitude of the rate, and they expressed a desire to help reduce it to zero.

Outcomes

The full data set is shown in table 2. Figure 2 shows the p-chart plotting the percentage of defective identification band rate.

Table 2

Data tables for populating the run chart in figure 2

Figure 2

Statistical process control chart identification bands, 7 November to 8 May.

It is clear that there was a dramatic decrease in the rate of defective identification bands just by the nurses having the knowledge that the PM&I group was checking identification bands, as demonstrated by the rapid decrease in rate over the first four audits. Interestingly, from January onward, the PM&I group had a statistically significant higher rate of errors found (3.4% vs 2.1%, two-sided test of proportions, p<0.01) than did nursing. While this did not affect the overall results of the study, it is worthy to note. The mean error rate before other interventions was 6.5%.

The timing of the multiple interventions is shown in figure 2. Starting with audit number 20, there were more than eight consecutive points below the mean, implying that the identification band error rate changed as a result of an assignable cause, presumably the interventions described above. Additionally, there were three audits below the original lower control limit of 1.5%, also implying an assignable cause. Continued auditing confirmed that the mean had dropped; the new mean defect rate was 2.6%, which is a drop of 60% from the original mean.

Lessons learnt

Patient misidentification is recognised as a serious issue in healthcare. Despite multiple agencies and reports amplifying its importance, there are few publications in the literature showing quality improvement programmes that result in decreasing rates of identification band errors. We implemented a programme that resulted in a decrease from a beginning error rate of 20.4% to a lowest audit rate of 0%. The preintervention mean was 6.5%, whereas the postintervention mean was 2.6%.

Our experience specifically taught us several things about decreasing identification band error rates:

  1. The initial rate of patient identification band errors in the hospital was higher than expected. The finding of a rate higher than previously reported in the literature, and certainly higher than anyone on the PM&I team would have predicted, emphasised the importance of hospitals auditing their own identification error band rates. It is clearly not sufficient to rely on the anecdotal opinion that “the problem isn't that bad”.

  2. The action resulting in most significant improvement was staff awareness of the problem, with clear expectations to stop the line immediately if patient identification error present. In looking at figure 2, it is clear that the precipitous drop in the identification error band rate occurred after nursing units saw the first audit being performed by the PM&I group. Staff awareness caused a drop from the original baseline to a mean of 6.5%. Even more important to the continued success of the quality initiative is the empowerment of all front-line staff. The identification band error rate continued to drop as data were shared with more groups. Moreover, all staff, from nursing to transport to child-life specialists, were encouraged to check patient identification bands for errors with every encounter.

  3. Staff surveys are an excellent source of suggestions for identifying barriers to correct patient identification. Results from the staff surveys (table 2) guided the development of the corrective action plans.

  4. Continued audit and data collection is necessary for sustainable staff focus and continued improvement. Monthly sharing of data with the leaders of the patient care units continued the leaders' focus on identification band checks. Additionally, the visibility to staff of the identification band audits encouraged continued focus on identification band checks.

  5. Statistical process control charts are an effective tool to track results and show that change is occurring. Data were shared using a p-chart, which enabled staff to see the latest error rates as well as long-term trends and the goals of crossing the lower control limit. For the PM&I team, the knowledge that there was good statistical power behind the graphs made for a success when eight consecutive points below the mean implied that the process had been changed.

References

Footnotes

  • Competing interests None.

  • Ethics approval Approved by the Vanderbilt University Institutional Review Board.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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