Changing viewpoints of lab turnaround times:Preanalytical factors take precedence in ED LOS of chest-pain patients

April 1, 2011

You are the lab director of a small community hospital of less than 200 beds, meeting with your supervising associate administrator and going through the monthly laboratory statistics. You show him the quality assurance indicators that you track each month. The associate administrator comments that the indicator on turnaround times (TAT) for the emergency department (ED) shows that although over 90% of the time the lab meets expectations for troponin TAT, the ED director is complaining. His complaint is that the lab is not meeting his TAT expectations for care of chest-pain patients, and this is extending their length of stay (LOS). How can this be addressed? Is greater than 90% possible given the limited resources available? What is the problem?

ED physicians dissatisfied: Hospital laboratory testing TAT for ED requests have been studied by the College of American Pathologists (CAP). Comments on these studies were published in 2001 in an article by Steindel, et al.1 In the article, a literature review finding was that laboratory-test ED TAT had remained at or greater than one hour since at least 1965. The study also found that ED physicians were not satisfied with laboratory TATs because they believed that increased lab TAT caused delayed treatment and increased ED LOS more than 50% of the time.

Acute myocardial infarction (AMI) diagnosis and treatment is constantly in the public eye, with interventions showing success in limiting heart damage when implemented in a timely fashion. The presentation to the ED of a patient with chest pain challenges the ED physician. Literature from Berwanger, et al, found 2% to 6% of patients with acute myocardial infarction had been sent home; and of those that were admitted for further evaluation, half were determined not to have acute coronary syndromes including AMI.2 Managing these patients in the ED over an extended time can cause workflow problems in an ED and, therefore, any improvement in reducing the time to reach a decision to admit or discharge the patient can improve the capacity and workflow of the ED setting.3

Labs focus on TAT consistency:

In the lab, the focus to improve the workflow of ED chest-pain patients has been in troponin TATs.5 With chest-pain patients, troponin testing has the longest run time of the usual tests requested by the ED from the main laboratory setting. Comparing the TAT of troponin to the ED LOS of chest-pain patients seems to be a simple way to investigate the viewpoints of the laboratory and physicians. Such studies have been reported from large university-hospital systems. A study done in 2006 by Holland, et al, reached a conclusion that the elimination of batch processing by the use of automated continuous-flow equipment which centrifuged, sorted, processed, and stored samples eliminated the laboratory as a key variable in determining ED LOS.4 The key to eliminating the laboratory as a significant variable in the ED LOS was the consistent handling of the samples reducing laboratory outlier percentages (percentage of tests not meeting agreed-upon TAT) for ED specimens.

Table 1. Mean and standard deviations of
study variables

Applying this study in the view of a community-hospital setting of less than 200 beds where total laboratory automation is not financially practical is worth discussion. Testing volumes from the EDs in smaller hospitals are much lower than larger university settings, but TATs are typically monitored in the same way. The smaller community-hospital laboratories strive to meet ED TAT goals consistently, just as larger settings. Does consistent performance by a community hospital also demonstrate the elimination of the laboratory as a significant variable in the ED LOS for chest-pain patients? What other variables may be related to ED LOS?

Conflicting viewpoints:

Laboratories typically study specimen-receipt to test-report times over which it has direct control. ED physicians typically want a study from test order to report time. This conflict in views can certainly lead to the dissatisfaction of ED physicians as documented by Steindel, et al, if not discussed and understood by both departments. Both parties tend to ignore the preanalytical variables leading up to their areas of interest — which are often not under their direct control but may have a great impact on the total process. In order to examine both points of view, a study was designed with a goal of simplifying the 2006 Holland study to the TAT-outlier percentage of just one test, troponin (I), and the ED LOS of chest-pain patients using retrospective data from six months.


The hospital selected was one with less than 200 beds with only the main laboratory analyzer as a source of troponin testing. Data from the hospital admission-information system on ED admission, discharge, transfer times, and disposition descriptions, for patients with chest pain as evidenced by a troponin order was obtained. Lab TAT for troponin tests were obtained inclusive of the computer time ordered, time of collection, time of receipt in lab, and time of completion.


The source for the benchmark of 60 minutes for the troponin (I) TAT as measured from receipt to report time to determine the outlier percentage was the same as in the Holland, et al, study of 11 hospitals described earlier. The hospital studied also used the same method for troponin analysis as in the Holland study, the Siemens Dimension Troponin (I) methodology (Siemens Healthcare Diagnostics, Newark, DE). The studied hospital's own benchmark for the receipt to report TAT for troponin was also set at 60 minutes. The ED-physician viewpoint of order to report time with a benchmark goal of 60 minutes, as expressed by ED physicians in the commentary of Steindel, et al, was also evaluated in this study.

The information was downloaded into Microsoft Excel spreadsheets from the laboratory information system (LIS) (Cerner Millenium Pathnet Powervision version 5.2.3790, Cerner, Kansas City, MO) and the hospital information system (HIS) (Siemens AS400 version 28.10, Malvern, PA). Six months of data from September 2008 through February 2009 was gathered. A total of 1,789 ED registrations with troponin testing ordered were received for inclusion in the study. The spreadsheets were opened for review and calculations applied prior to being downloaded into SPSS (SPSS, Chicago, IL) for data analysis.

Reducing the influence of confounding data:

Review of the information to reduce confounding data was performed. The criteria used for exclusion included patients with a category of disposition that would extend or prematurely shorten the ED LOS. A total of 1,789 cases were reviewed and exclusionary criteria applied resulting in a final total of 1,239 cases to be used in the study analysis.

Month-to-month comparisons of the data using descriptive statistics including total times, mean times, and standard deviation were performed to help determine if any major changes occurred between the months of data gathered. Such influences would have been instrument down time, department renovations, staffing changes, or other unanticipated changes. No major fluctuations were seen that influenced the variables of troponin turnaround time and length of stay.

Viewpoint statistics are different:

The first result to be discussed involves the differing viewpoints that are commonly taken by laboratory staff and ED physicians concerning laboratory services using simple frequency statistics. The frequency analysis showed results from the lab viewpoint as having an average monthly outlier percentage of 10% or that the troponin TAT was met 90% of the time. The analysis using the viewpoint of the ED physician of order to report time showed that the average monthly outlier percentage was 69% or troponin TAT was met only 31% of the time. Such a difference in views can lead to misunderstandings over the efficiency of the laboratory service.

What viewpoint is significant?

Linear-regression analysis of the lab viewpoint (receipt to report time outlier % vs. ED LOS) yielded an F ratio value of significance of 0.222, which was greater than the significant level of 0.05 indicating no relationship. The viewpoint of the physician (order to report time outlier % vs. ED LOS) yielded an F ratio value of significance of 0.837 which showed even less significance. With both viewpoints showing no significance for ED LOS, it seemed that conflicts arising from such discussions were not warranted. Other statistics from variables collected in the study were studied to see what would be worthy of consideration for improvement of ED LOS.

The findings of the next linear-regression variable were more significant. The linear relationship between the variable admit to order time and ED LOS had an F ratio value of significance of 0.07. This finding although greater than the significance level 0.05, is dramatically stronger than the two variables of outlier percentages for troponin from order to receipt and receipt to order. This meant that 93% of the time the ED LOS for patients with troponin ordered (probable chest pain presentation) may be predicted using a linear-regression formula applied to the difference between the admit time to the ED and the time of the ED order for the laboratory test troponin. In this study, the formula was ED LOS = 138.022 + 1.249 x ED admit to order time (in minutes) as expressed by linear- regression analysis. This finding pointed toward the preanalytical-phase admission to order time as a key predictor of ED LOS.

Lab consistent:

From the data obtained in this study, frequency analysis demonstrated that 90% of the time the troponin results were reported at 60 minutes or less from the time of receipt. The descriptive statistics further demonstrated the consistent performance of the laboratory. The laboratory's mean value of receipt to report time being 44.55 minutes with a standard deviation of 12.8 minutes. Therefore, in this study, the laboratory maintained consistent processing (see Table 1) and showed that in a small hospital, ED samples can be processed efficiently. Since this study was conducted an even faster troponin I test was implemented from the vendor which would shorten the TAT of the analysis.

Further review of the descriptive statistics in this study (see Table 1) showed that the mean time of ED admit to order was 42 minutes with a standard deviation of 33 minutes. The ED admit-to-order variation may be a key influence for ED LOS for chest-pain patients if one considers the impact that improving consistency of laboratory TATs had in eliminating laboratory TAT outlier percentage as a factor for ED LOS in the Holland, et al, study. Further studies of the source of the ED admit-to-order-time variation may improve ED LOS.

The study showed that with consistent TAT performance of the troponin testing, in a small community hospital setting, other factors had greater significance on the ED LOS for chest pain patients and should be studied to seek improvement. This finding agreed with the follow-up study by Holland et al, after their implementation of a continuous flow total automation system. It appears that study of preanalytical variables will need to take precedence for the ED and lab to improve ED LOS. Such studies may include variables such as Admissions staffing, Admission procedures, ED volume, use of ED protocols, ED staffing, ED acuity levels, lab phlebotomy staffing, and delays from interdepartmental activities limiting phlebotomy access to the patient. Administrative and interdepartmental efforts should foster these studies to improve the preanalytical phases of diagnostic testing and patient outcomes.

Clayton Johnson, MHS MT(ASCP)SH, BSMT graduate of Minot (ND) State University, attained a Master of Health Sciences degree in Management from Western Carolina University. Johnson has served laboratory medicine for 30 years; he currently works at Blue Ridge Healthcare in Morganton, NC. Ted R. Chiappelli, DrPH, MHA, MSSM. CAPT-Ret, USPHS, a retired commissioned officer from the U.S. Public Health Services serving as a behavioral scientist, earned his Doctor of Public Health degree from The Johns Hopkins University and has Master's degrees in Health Administration and Systems Management.


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