ELIMINATION OF HUMAN ERRORS IN THE LABELING OF URINE ANALYSIS SAMPLES IE 548

1. ELIMINATION OF HUMAN ERRORS IN THE LABELING OF URINE ANALYSIS SAMPLESIE 548 Antar Gutierrez Paul Stelson Mylie Tong Diane Van Scoter

2. Outline Labeling Errors – Background Magnitude of the Problem Process Background Urine Analysis Testing UA Sample Labeling UA Sample Labeling for Lab Literature Review Model Development Example Process HMSEM/FMEA Workbook Summary & Recommendations

3. Labeling Errors - Background • ED identified the labeling error problem • If it is not fixed, it could jeopardize the ED’s ability to perform these tests • If tests are not performed in ED, they must be sent to the hospital laboratory, causing: • Delayed Results • Longer Stay in ED for Patients • Higher Cost for ED Patients

4. Magnitude of the Problem 11,559 6,159 923 831 90 • Emergency Room Dip test per year • Number of containers sent to central lab • From 10 to 15 % have issues Issues • 9 out of 10 have 2 labels from different patients • 1 out of 10 had no label on the specimen container

5. Process Background • Walk-in Patients to ED • Check in at front desk where their information is put into the EPIC system • Receive a wrist band to identify them • Are seen by a Triage nurse to get their general information and have it entered into the EPIC system • Patients are assessed based on the ESI 5 level system to determine the order of being seen by a physician • Patients are taken into the ED for examination • Generic labels are printed at the front desk Some times 4 hours of waiting time

6. Process Background (Continued) • Ambulance Patients to ED • Generic information is radioed in while the ambulance is en-route • Patient goes directly into ED trauma room • Patient receives wristband in trauma room • Patient data is entered into EPIC from bedside • Generic labels printed at Unit Secretary’s desk

7. Urine Analysis Testing • Testing Decision: Made by Nurse or Doctor • Triage Nurse decides to do Urine Analysis test if patient has one of the threshold conditions: • Age over 60 years • Pregnant • Abdominal pain • Fever • Altered Mental Status • Psychiatric Patient • Doctor makes decision based on examination in ED or based on results of Nurse authorized urine dip test. A bar-coded label is created for the specific urine test. These samples get sent to the laboratory for full analysis. Not consistent within the nurses

8. UA Sample Labeling Specimen container must have a patient label affixed, it can be a generic or bar-coded label Generic labels must be brought to the patients room for walk-in patients and from the Unit Nurse’s desk for the ambulance patients All bar coded, Dr. requisition labels come from the Unit Nurse desk No documented procedure for movement of labels from the printer to the patient’s room

9. UA Sample Labeling for Lab Laboratory will accept UA specimens if: Generic label is on the specimen jar and bar-coded label is on the double bagging Generic label is on the specimen jar and copy of doctors order is placed in the double bags Bar-coded label is placed on the specimen jar and it is double bagged

10. Literature Review • Battles, 1999 • Identified 6 sources (root causes) of errors for ED blood transfusions: • Patient Assessment • Care Planning • Laboratory Procedures • Staff Related Factors • Equipment Related Factors • Information Related Factors

11. Literature Review (Continued) • Croskerry and Sinclair (2001) • Identified unique characteristics of ED leading to errors: • High levels of diagnostic uncertainty • High decision density • High cognitive load • High levels of activity • Inexperience of some physicians and nurses • Interruptions and distractions • Uneven and abbreviated care • Narrow time windows • Shift work / Shift Changes • Compromised teamwork • Poor feedback

12. Literature Review (Continued) • Welch (2006) • Identified 4 types of cognitive sources leading to errors: • Availability Heuristic • <the more easily the heuristic can be brought to mind, the more available and the higher the probability associated with a particular event> (Wickens and Hollands, 2000) • Confirmation Bias • <the tendency for individuals to seek information that supports their conclusion and not seek information that conflicts with it> (Wickens and Hollands, 2000) • Coning of Attention • <stress effect reduces the breadth of focus, which can result in a flawed decision making process> (Wickens and Hollands, 2000) • Reversion • <due to stress, participant keeps trying same response even though it has been unsuccessful> (Wickens and Hollands, 2000)

13. Model Development • IDEF0 • Modeling software that focuses on information and resource flow and requirements. • User friendly design facilitates model development and relationship awareness. • Our model highlights aspects of the process relevant to the problem, mislabeled UA specimens. • Model progression was an iterative process • Our SME Carol Bonnono was crucial to understanding and modeling the process

14. Model Development • Model Iterations • Weekly inquiries to Carol • More information  more model detail  more information required • Through many inquiries we found that labeling process had very little standardization or individual task ownership. • Too many different paths/options, none of which have a standard operating routine.

15. Example Process Model Decomposition A-0

16. Example Process Model Decomposition A0: Diagnose patient

17. Example Process Model Decomposition A1: Admit Patient

18. Example Process Model Decomposition A15: Deliver label to patients room or chart Notes: Destination Options Personnel Options

19. HMSEM/FMEA Workbook • Human-Machine Systems Engineering Methodology Workbook • Tool that helps categorize and define individual tasks for analysis. • Integrates Human Fallibilities Identification and Remediation Data Base to expose possible modes of failure for important tasks. • Failure Modes & Effects Analysis summarizes all the possible human fallibilities relating to cognition for a specific task. User then extrapolates how the fallibility could manifest for the specific task.

20. HMSEM/FMEA Workbook • Human-Machine Systems Engineering Methodology Workbook • The results from the workbook analysis highlights potential failure modes. • With the potential errors we can postulate recommendations to limit the manifestations of human errors.

21. Human Error Human Fallibilities Recommendations Summary & Discussion

22. Human Error Potential Errors Found in UA analysis project • Failure to notice label is needed • Failure to collect generic label in chart or patient’s room • Forget to attach label • Test specimen may be incorrectly labeled • Deposit specimen in test room even when it’s not labeled • Failure to collect specific label • Generic label does not match specific label

23. Human Fallibility Human Fallibilities found from HFIRDB and FMEA process • Workload effect • Working memory capacity and duration • Stress performance influence • Bottleneck effect • Intramodality performance decrement • Retroactive memory interference • Stage-defined resource effect • Implementation cost bias

24. Recommendations • Establish a threshold for the patients who need the UA • Generic labels are to be accordion folded and lopped through the wristband and stapled through the margins • Define the responsibilities of delivering the label and collecting the label • Generic labels should be printed on a paper with color • After patient comes from the restroom with the sample then the nurse can get one of the labels from the wristband and label the container

25. Recommendations • Test Room • Big sign in patient’s room which says: IF TEST IS POSITIVE TAKE IT BACK TO PATIENT’S ROOM, IF NEGATIVE DUMP IT! • Shelf with post-it note where the attending can write down patient’s information as a place holder for label • At the Unit’s secretary Desk • Print a blank bar-coded label in between different patient’s labels