U.S. flag

An official website of the Department of Health & Human Services

  • Search All AHRQ Sites
  • Email Updates

Patient Safety Network

1. Use quotes to search for an exact match of a phrase.

2. Put a minus sign just before words you don't want.

3. Enter any important keywords in any order to find entries where all these terms appear.

  • The PSNet Collection
  • All Content
  • Perspectives
  • Current Weekly Issue
  • Past Weekly Issues
  • Curated Libraries
  • Clinical Areas
  • Patient Safety 101
  • The Fundamentals
  • Training and Education
  • Continuing Education
  • WebM&M: Case Studies
  • Training Catalog
  • Improvement Resources
  • Innovations
  • Submit an Innovation
  • About PSNet
  • Editorial Team
  • Technical Expert Panel

Pre-analytical pitfalls: Missing and mislabeled specimens

A 56-year-old man was admitted to the same-day surgery center for a planned biopsy procedure. Microbiological specimens were collected for culture and first transported to the central laboratory for processing at 1142. The samples were dropped off at the central laboratory receiving window where the time/date of receipt was recorded into a specimen tracking log and a temporary tracking barcode was issued at 1151.

At this institution, culture specimens are ultimately tested at the microbiology laboratory located 10 minutes away by courier (hourly pick up) at a satellite facility. Upon arrival at the satellite facility, samples are logged and accessioned for testing at their respective laboratory ( e.g., microbiology). In this case, receipt of the culture specimen was confirmed by the central laboratory, however, the specimen never arrived at the microbiology laboratory. Both the central laboratory and satellite facility were not aware that the sample was missing until the ordering provider queried the laboratory about the result five days later. The ordering physician was notified of the missing sample. Unfortunately, the specimen was never found. Incident review did not identify any adverse events associated with the missing specimen. The patient did not manifest any signs or symptoms of infection one week and up to one month following the procedure.

A 59-year-old man was treated for a suspected myocardial infarction due to erroneous cardiac troponin results. The patient presented to the Emergency Department (ED) with chest pain, shortness of breath, and a history of chronic obstructive pulmonary disease. Initial cardiac troponin I concentrations were 4400 ng/L (99 th percentile of the upper reference limit was 40 ng/L) with a B-type natriuretic peptide value of >5000 pg/mL. Aspirin, ticagrelor, and heparin were administered, and the patient was taken to the Cardiac Catheterization Lab. While undergoing catherization, it was revealed that the patient did not have any obstructed blood vessels. Chest, abdominal, and pelvis computerized tomography scans were also negative for pulmonary embolism and dissection. A repeat cardiac troponin I specimen was drawn, and the result was <10 ng/L. The Emergency Medicine physician contacted the Laboratory to determine the cause of such a large shift in results and the negative findings by the Cardiac Catherization Laboratory. As per routine procedure, the Clinical Laboratory immediately sequestered all samples related to the patient. Cardiac troponin I measurements were re-run and reported the same discrepancy. Blood typing of the two troponin specimens indicated they were not from the same patient. Follow-up investigation by the ED ultimately revealed the initial sample with the high cardiac troponin was from another patient presenting with septic shock and renal failure.

The Commentary

by Nam K Tran, PhD, HCLD (ABB), FAACC and Ying Liu, MD

It has been suggested that up to 70% of all medical decisions are based on some kind of pathology and/or laboratory result. 1   Medical testing consists of three phases: (a) pre-analytical, (b) analytical, and (c) post-analytical 2-4 Up to 75% of all medical testing errors occur during the pre-analytical phase with the majority happening before any specimen arrives at laboratory. 3,4 These include errors such as mislabeling of specimens, delayed transportation, collection into the wrong specimen container, inadequate specimen collection. In contrast, during the analytic phase, error rates are far lower. Modern laboratory testing incorporates numerous safeguards such as external and internal quality controls, highly regulated documentation of operator competency, and informatic tools – resulting in an analytic error rate of <13%.   Examples of errors encountered during the analytic phase typically resides with improper instrument operation, faulty reagents, and sensor degradation. Sources of testing error occur during the post-analytic phase include transcription errors, delayed reporting of results, and applying incorrect correction factors ( e.g., dilution correction).

Lost specimens between testing facilities is a type of pre-analytic error. 2 In a typical hospital laboratory, tens of thousands of samples may be processed and tested each day. Some facilities may incorporate robotics ( i.e., automation) to aid in the testing process to maximize speed while minimizing error, however, the transportation of specimens to and from the laboratory remains manual in nature. Larger facilities, such as the one described here, utilize internal or contracted courier services to transport samples from patient care facilities to multiple laboratories. Given that space is at a premium in many hospital facilities, clinical laboratories may de-centralize testing services across multiple buildings and placing high priority “STAT” tests in central laboratories located near emergency departments, operating rooms, and intensive care units, while slower or more esoteric tests may be based in more distant satellite facilities. Microbiology is a common specialty that may have facilities away from the main laboratory due to the historically slow nature of culture results – thus relying on couriers to retrieve samples from the patient care sites. 5 For this patient case, the microbiology laboratory was located 10 minutes away from the initial receiving laboratory.

Mislabeled specimens are also a common pre-analytic error. Unfortunately, it is believed mislabeling errors are not always obvious and therefore under-reported. 6-8 The busy nature of the ED environment increases the likelihood for mislabel events to happen and is further compounded when multiple healthcare personnel participate in patient care. Studies conducted by the College of American Pathologists (CAP) observed a rate of  of 0.92/1,000 mislabel events across 120 institutions. Even more sobering, other CAP studies evaluating blood bank mislabels have reported error rates approaching 1.12%. 6-8

For lost specimens, due to the nature of large, complex health systems, both medical care and laboratory facilities may be spread across a wide geographic area – creating a condition where samples exchange hands several times before arriving at their final destination. Large health networks may have multiple clinics strewn across a large geographical area and rely on couriers to transport specimens to the central laboratory. In some cases, samples may be sent to large referral laboratories in other states and require both ground and air couriers for transportation. Thus, specimens could be misplaced, accidentally discarded, or possibly intermixed with other specimen shipments at multiple points during this process. The frequency of lost samples reflects the challenges faced by hospital laboratories. A study by the University of Minnesota Medical Center (UMMC) laboratory found their facility could not account for about 6 to 7 specimens per week. 9 This facility is relatively large and consists of 8 hospitals and 86 clinics. Review of contributing factors to UMMC’s operation found courier workspace, staffing, lack of interfaced specimen tracking systems including barcode and/or radio frequency identification (RFID) systems, and workflow to be potential areas for improvement. In another study, Steelman et al . described 684 adverse events and near misses involving surgical specimens. 10 The data was derived from a database representing 50 health care facilities from 2011 to 2013. Common events included improper specimen labeling, collection/preservation, and transport. Of these 684 events, 8% resulted in either the need for additional treatment, or temporary or permanent harm to the patient. The most common causes of errors were hand-off communication problems, staff inattention, knowledge deficit, and environmental issues.

In Case #1, the root cause analysis identified several areas for improvement for this near-miss event including adoption of not only tracking logs, but staff sign-off of specimen shipment contents, and confirmation by the receiving satellite facility. This ensured central laboratory staff confirmed contents before departure, and the satellite facility confirming contents at time of receipt. Discordant shipment content lists are then immediately investigated, and the frequency of these events tracked

For mislabeled specimens, studies show the failure points occur at the time of collection where patients are misidentified, the use of handwritten labels at any point, mix-ups occurring before or after collection, mislabels at the laboratory during accessioning/aliquoting/centrifugation, or when relabeling specimens. 6-8 Other contributing factors include the tendency to obtain “rainbow draws” (drawing tubes of every possible color to allow for additional testing at a later time). Such rainbow draws are controversial, and no data exists to support the benefit of this practice. In fact, it is more likely to waste blood and create perfect conditions for mislabeled specimens. In one study, the practice of collecting rainbow draws was attributed to 275 L of blood wasted per year. 11  The root cause analysis from Case #2 revealed at least three nurses were managing the patient. The incorrect patient label was placed on the cardiac troponin I sample, which resulted in the report being attributed for the wrong patient. Outside of having an unnecessary invasive procedure, the patient in this case did not experience any other adverse events, but the outcome could certainly have been different.

Errors stemming from missing or mislabeled specimens are costly to institutions . In one study, the average cost due to a single irretrievable lost specimen was $548, and cumulative errors over a three-month period increased this value to $20,430. 12 In contrast, a retrievable lost specimen incurred a cost of $401.25 per event, with a three-month cumulative value of $14,836. In Case #1, if the microbiology sample were to have been positive, resulting delays in the treatment of infection could be substantial. Studies have highlighted that every hour delay in treatment of severe infections, such as sepsis, exponentially increases the odds of death. 13 Costs associated with iatrogenic injuries has been suggested to be about $3,961 and result in an increased length of stay of 0.77 days in the intensive care unit setting 14 with pre-analytic error costs representing 0.23 to 1.2% of a total hospital operating cost 15 .  For mislabeling errors, CAP estimates the cost to be about $712 per specimen. Based on CAP data, multiplying this cost with the number of mislabeled specimens, it is believed hospitals lose $280,000 per million specimens – amounting to equal or greater than $1 million for large high-volume hospitals. 16 These costs are attributed to re-drawing specimens, as well as healthcare provider costs, and prolonging hospital lengths of stay.

In addition to increased financial costs to the healthcare system, the costs from missed, delayed, or wrong diagnoses due to lost or mislabeled specimens can be devastating or catastrophic to individual patients. For example, an incorrect labeled fine needle aspirate sample can lead to inappropriate treatment for the wrong patient. 17 In one reported case, such an error resulted in the wrong patient receiving a pulmonary resection, and the other having delayed disease diagnosis. In another example, post-analytical reporting of results into the wrong patient electronic chart has caused patients to receive inappropriate treatment. 17 In the end, the price for testing errors have both financial and human costs.  

Best practices implemented in these cases highlight the multifactorial nature addressing these common sources of medical error. 9 A system of checks and balances can reduce errors, including organic elements such as laboratory personnel and electronic safeguards via barcode scanners, preventing the ordering of “rainbow draws,” personal barcode printers for nursing staff, reduce errors. 9 , 18 Combining these measures with efficient workflows and workspace facilities provides means to further reduce the frequency of lost and mislabeled specimens in the laboratory. Future directions may include the use of advance informatic tools and RFID could significantly reduce the prevalence of lost specimens. 9 , 19 Radio frequency identification has gained significant interest in laboratory medicine. Briefly, RFID systems rely on tags containing small radio transponders that can be used to track the movement of specimens over a defined space. These systems have been used in the commercial industry and has been adopted in the clinical laboratory for tracking reagent supply utilization. 20   RFIDs were recommended in a study by Norgan et al . that found a 75% (6 vs. 24 events) decrease in lost specimens over a 6-month period after adopting this technology. 19 However, adoption barriers do exist with the primary challenge being the cost of labeling each specimen with an RFID tag. Nonetheless, like with many technologies, the cost continues to decrease as seen with RFID adoption in laboratory reagent supplies and the retail industry.

Take-Home Points

  • The total testing process consists of the pre-analytic, analytic, and post-analytic phases. Medical testing error occurs most frequently during the pre-analytic phase.
  • Specimen loss is a common problem encountered by laboratories and the causes are generally multifactorial.
  • Mislabeled specimens occur frequently in healthcare and result in significant cost to the institution.
  • Some specimen loss or mislabeling events can lead to catastrophic outcomes such as consequential delays in cancer diagnoses, or unnecessary major surgical procedures.
  • Adoption of best practices such as specimen inventorying, elimination of “rainbow blood draws”, providing personal barcode printers to nursing staff can reduce error rates.
  • The use of RFID technology may further reduce specimen loss rates by as much as 75%.

Nam Tran, PhD Associate Clinical Professor Department of Pathology and Laboratory Medicine UC Davis Health

Ying Liu, MD, PhD Resident Department of Pathology and Laboratory Medicine UC Davis Health

Acknowledgments

We thank the UC Davis Department of Pathology and Laboratory Medicine Quality Team for their support in evaluating this case.

  • Datta P. Resolving discordant specimens.  ADVANCE for Administrators of the Laboratory . July 2005:60.
  • Hammerling JA. A review of medical errors in laboratory diagnostics and where we are today. Lab Med 2012;43:41-44.
  • Commission on Office Laboratory Accreditation Available at:  http://www.cola.org/ . Accessed January 25, 2020.
  • Lippi G, Guidi GC. Risk management in the preanalytical phase of laboratory testing. Clin Chem Lab Med . 2007;45(6):720-7.
  • Robinson A, Marcon M, Mortensen JE, et al. Controversies affecting the future practice of clinical microbiology. J Clin Microbiol . 1999 Apr;37(4):883-9.
  • Valenstein PN, Raab SS, Walsh MK. Identification errors involving clinical laboratories: A College of American Pathologists Q-Probes study of patient and specimen identification errors at 120 institutions. Arch Pathol Lab Med 2006;130:1106–13.
  • Wagar EA, Stankovic AK, Raab S, et al. Specimen labeling errors: A Q-Probes analysis of 147 clinical laboratories. Arch Pathol Lab Med 2008;132:1617–22.
  • Grimm E, Friedberg RC, Wilkinson DS, et al. Blood bank safety practices: Mislabeled samples and wrong blood in tube – a Q-Probes analysis of 122 clinical laboratories. Arch Pathol Lab Med 2010;134:1108–15.
  • Medical Laboratory Management website: https://www.medlabmag.com/article/1591 , Accessed on January 24, 2020.
  • Steelman VM, Williams TL, Szekendi MK, et al. Surgical specimen management a descriptive study of 648 adverse events and near misses. Arch Pathol Lab Med 2016;140:1390-1396.
  • Snozek CL, Hernandez JS, Traub SJ. “Rainbow draws” in the emergency department: clinical utility and staff perceptions. J App Lab Med 2019;4:229-234.
  • Medscape: https://www.medscape.com/viewarticle/868957 , Accessed on January 24, 2020.
  • Kumar A, Roberts D, Wood KE, et al.  Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock .  Crit Care Med . 2006;34:1589-1596.
  • Kaushal R, Bates DW, Franz C, et al. Cost of adverse events in intensive care units. Crit Care Med 2007;35:2479-2483.
  • Green SF. The cost of poor blood specimen quality and errors in preanalytical processes.  Clin Biochem . 2013;46(13):1175-1179.
  • Kahn S, Jarosz C, Webster K. Improving Process Quality and Reducing Total Expense Associated with Specimen Labeling in an Academic Medical Center. Poster. 2005 Institute for Quality in Laboratory Medicine Conference: Excellence in Practice
  • Dunn EJ, Moga PJ. Patient misidentification in laboratory medicine: a qualitative analysis of 227 root cause analysis reports in the Veteran Affairs Health Administration. Arch Pathol Lab Med 2010;134:244-255.
  • Nakhleh RE. Lost, mislabeled, and unsuitable surgical pathology specimens. Pathology Case Reviews 2003;8:98-102.
  • Norgan AP, Simon KE, Feehan BA, et al. Radio-frequency identification specimen tracking improve quality in anatomic pathology. Arch Pathol Lab Med 2019;143 [epub ahead of print].
  • Fisher JA, Monahan T. Tracking the social dimensions of RFID systems in hospitals. Int J Med Inform 2008;77:176-183.

This project was funded under contract number 75Q80119C00004 from the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services. The authors are solely responsible for this report’s contents, findings, and conclusions, which do not necessarily represent the views of AHRQ. Readers should not interpret any statement in this report as an official position of AHRQ or of the U.S. Department of Health and Human Services. None of the authors has any affiliation or financial involvement that conflicts with the material presented in this report. View AHRQ Disclaimers

WebM&M Cases

Occupational Health and Organizational Culture within a Healthcare Setting: Challenges, Complexities, and Dynamics. January 17, 2024

Medical Office Survey on Patient Safety Culture: 2014 User Comparative Database Report. June 25, 2014

Hospital Survey on Patient Safety Culture: 2016 User Comparative Database Report. May 11, 2016

AHRQ Nursing Home Survey on Patient Safety Culture: 2014 User Comparative Database Report. November 19, 2014

Hospital Survey on Patient Safety Culture: 2014 User Comparative Database Report. April 23, 2014

2012 User Comparative Database Report: Medical Office Survey on Patient Safety Culture. June 27, 2012

Community Pharmacy Survey on Patient Safety Culture 2015 User Comparative Database Report. July 22, 2015

Ensuring medication reconciliation. December 19, 2007

Sustaining Improvement. July 20, 2016

Characteristics of Weekday and Weekend Hospital Admissions, 2007. March 17, 2010

Guide for Developing a Community-Based Patient Safety Advisory Council. October 3, 2007

Prevention of perioperative medication errors. March 17, 2023

The star of the diagnostic journey: assessing patient perspectives. November 28, 2018

Latex: a lingering and lurking safety risk. April 4, 2018

Surviving a bad diagnosis. September 28, 2016

Speaking up for safety—it’s not simple. October 3, 2018

Why are so many women being misdiagnosed? August 30, 2017

Mother says ER misdiagnosis leads to son's death. December 4, 2013

Hospital design plays important role in patient outcomes. April 27, 2005

Is the future of medical diagnosis in computer algorithms? May 29, 2019

Healthcare 411: medication safety toolkit. March 18, 2009

Whistle-blowing nurse is acquitted in Texas. February 24, 2010

Risky business: James Bagian—NASA astronaut turned patient safety expert—on being wrong. July 14, 2010

The hidden dangers of outsourcing radiology. November 30, 2011

For second opinion, consult a computer? December 12, 2012

Drug shortages persist in US, harming care. November 28, 2012

Getting Your Best Health Care: Real-World Stories for Patient Empowerment. April 27, 2011

Hospitals may be the worst place to stay when you're sick. March 14, 2012

Kaiser learns from tragic medical errors. June 4, 2008

Doctors say 'I'm sorry' before 'See you in court.' May 28, 2008

Your hospital's deadly secret. March 12, 2008

What pilots can teach hospitals about patient safety. November 8, 2006

Hospitals put emphasis on collection of medication data. August 30, 2006

Do no harm: promoting patient safety. September 28, 2005

Perspective

Medication errors. October 11, 2006

Impact of a statewide reporting system on medication error reduction. November 1, 2006

Heartbroken. December 12, 2018

How one health system overcame resistance to a surgical checklist. May 29, 2019

Getting Ahead of Harm Before It Happens: A Guide About Proactive Analysis for Improving Surgical Care Safety. October 11, 2017

Dangerous doses. February 24, 2016

Building a Culture of Patient Safety Through Simulation: An Interprofessional Learning Model. June 10, 2015

Improving Patient Safety Through Teamwork and Team Training. January 29, 2014

Medical Error Reporting System Could Boost Patient Safety. September 21, 2005

A pinpoint beam strays invisibly, harming instead of healing. January 12, 2011

How could this happen? November 4, 2009

To Err Is Human — To Delay Is Deadly. June 3, 2009

Culture of resistance. May 27, 2009

The First Annual HealthGrades Pediatric Patient Safety in American Hospitals Study. August 25, 2010

Common cause analysis. June 16, 2010

Consumers' Priorities for Hospital Quality Improvement and Implications for Public Reporting. May 18, 2011

HealthGrades Eighth Annual Patient Safety in American Hospitals Study. March 23, 2011

An interdisciplinary approach to safer blood transfusion. April 2, 2008

Rx for errors: speed, high volume can trigger mistakes. February 27, 2008

USP initiatives for the safe use of medical gases. December 14, 2005

Simulation in health care: setting realistic expectations. September 19, 2007

Patient Safety: Research into Practice. September 13, 2006

Integrating patient safety into curriculum. April 18, 2007

Implementing a Program of Patient Safety in Small Rural Hospitals. January 16, 2008

Prescribing for the elderly. Part I: Sensitivity of the elderly to adverse drug reactions. March 27, 2005

Understanding Patient Safety, Third Edition. May 23, 2012

Supplement on Deepening our Understanding of Quality in Australia (DUQuA). March 11, 2020

Annual Perspective

Evidence Brief: Implementation of High Reliability Organization Principles. July 24, 2019

Influencing the Quality, Risk and Safety Movement in Healthcare: In Conversation with International Leaders. November 4, 2015

Achieving the Promise of Health Information Technology: Improving Care Through Patient Access to Their Records. October 7, 2015

Do you hear what I hear? Communication practices about medications between physicians and clients with chronic illness in Canada. April 2, 2014

Internal Bleeding: The Truth Behind America's Terrifying Epidemic of Medical Mistakes. Updated edition. March 27, 2005

Current State of Diagnostic Safety: Implications for Research, Practice, and Policy. February 7, 2024

Implementing a safer and more reliable system to monitor test results at a teaching university-affiliated facility in a family medicine group: a quality improvement process report. November 1, 2023

The nature, causes, and clinical impact of errors in the clinical laboratory testing process leading to diagnostic error: a voluntary incident report analysis. October 25, 2023

The delivery of safe and effective test result communication, management and follow-up. September 27, 2023

Patient Safety Innovations

Remote Response Team and Customized Alert Settings Help Improve Management of Sepsis

Journal Article

Catching those who fall through the cracks: integrating a follow-up process for emergency department patients with incidental radiologic findings.

Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count results. June 29, 2022

Adherence to national guidelines for timeliness of test results communication to patients in the Veterans Affairs health care system. May 4, 2022

Technology-based closed-loop tracking for improving communication and follow-up of pathology results. February 2, 2022

Emergency departments are higher-risk locations for wrong blood in tube errors. September 29, 2021

What are the implications for patient safety and experience of a major healthcare IT breakdown? A qualitative study. May 26, 2021

Application of human factors methods to understand missed follow-up of abnormal test results. November 11, 2020

Error Reduction and Prevention in Surgical Pathology, Second Edition. August 28, 2019

Building an ambulatory safety program at an academic health system. May 15, 2019

Measuring the rate of manual transcription error in outpatient point-of-care testing. March 13, 2019

Patient groups, clinicians and healthcare professionals agree—all test results need to be seen, understood and followed up. December 19, 2018

Diagnostic stewardship—leveraging the laboratory to improve antimicrobial use. August 16, 2017

Reduction in hospital-wide clinical laboratory specimen identification errors following process interventions: a 10-year retrospective observational study. October 26, 2016

Patient Safety Network

Connect With Us

LinkedIn

Sign up for Email Updates

To sign up for updates or to access your subscriber preferences, please enter your email address below.

Agency for Healthcare Research and Quality

5600 Fishers Lane Rockville, MD 20857 Telephone: (301) 427-1364

  • Accessibility
  • Disclaimers
  • Electronic Policies
  • HHS Digital Strategy
  • HHS Nondiscrimination Notice
  • Inspector General
  • Plain Writing Act
  • Privacy Policy
  • Viewers & Players
  • U.S. Department of Health & Human Services
  • The White House
  • Don't have an account? Sign up to PSNet

Submit Your Innovations

Please select your preferred way to submit an innovation.

Continue as a Guest

Track and save your innovation

in My Innovations

Edit your innovation as a draft

Continue Logged In

Please select your preferred way to submit an innovation. Note that even if you have an account, you can still choose to submit an innovation as a guest.

Continue logged in

New users to the psnet site.

Access to quizzes and start earning

CME, CEU, or Trainee Certification.

Get email alerts when new content

matching your topics of interest

in My Innovations.

Issue Cover

  • Previous Article
  • Next Article

MATERIALS AND METHODS

Mislabeling of cases, specimens, blocks, and slides: a college of american pathologists study of 136 institutions.

  • Split-Screen
  • Article contents
  • Figures & tables
  • Supplementary Data
  • Peer Review
  • Open the PDF for in another window
  • Guest Access
  • Get Permissions
  • Cite Icon Cite
  • Search Site

Raouf E Nakhleh , Michael O Idowu , Rhona J Souers , Frederick A Meier , Leonas G Bekeris; Mislabeling of Cases, Specimens, Blocks, and Slides: A College of American Pathologists Study of 136 Institutions. Arch Pathol Lab Med 1 August 2011; 135 (8): 969–974. doi: https://doi.org/10.5858/2010-0726-CPR

Download citation file:

  • Ris (Zotero)
  • Reference Manager

Context .—Accurate specimen labeling is a major patient-safety initiative by the Joint Commission and the College of American Pathologists. Inadequate specimen labels have led to patient injury from wrong patient diagnosis, wrong side treatment, and delay in diagnosis.

Objectives .—To quantify the rates of mislabeled cases, specimens, blocks, and slides and to identify the sources of error and the ways in which errors are detected.

Design .—In this voluntary-subscription Q-Probes study, participants prospectively reviewed surgical pathology cases for 8 weeks or until 30 errors (mislabeled cases, specimens, blocks, and slides) were identified. Information collected on each labeling error included the work location where the defect occurred, what was mislabeled, the number of items affected, the point of detection, and the consequences of the mislabeling error, along with institutional demographics and practice. The rates of mislabeled cases, specimens, blocks, and slides were tested for association with institutional demographics and practice variables.

Results .—Of the 136 institutions providing information on a total of 1811 mislabeling occurrences, the overall mislabeling rates per 1000 were 1.1 cases, 1.0 specimen, 1.7 blocks, and 1.1 slides. Of all mislabeling events, 27.1% were cases, 19.8% specimens, 25.5% blocks, and 27.7% slides. The work locations at which the errors occurred were 20.9% before accessioning, 12.4% at accessioning, 21.7% at block labeling, 10.2% during gross pathology, and 30.4% at tissue cutting. Errors were typically detected in the first or second steps immediately following the error. Lower mislabeled slide rates were associated with continuous individual case accessioning and use of formal checks at accessioning. Routinely including a statement in the gross description that the specimen is labeled with the patient's name and is properly identified was also associated with lower rates of specimen mislabeling. The errors were corrected before reports were issued 96.7% of the time; for 3.2% of errors, a corrected report was issued. In 1.3% of error occurrences, participants gauged that patient care was affected.

Conclusions .—This study quantified mislabeling rates across 136 institutions of cases (0.11%), specimens (0.1%), blocks (0.17%), and slides (0.11%). Errors in labeling appear nearly equally throughout the system of accessioning, gross pathology processing, and tissue cutting. Errors are typically detected in the immediate steps after the errors occurred, reinforcing the need for quality checks throughout the system.

Patient and specimen identification are critical elements to patient safety in surgical pathology. Goal 1 of the Joint Commission's patient safety goals (National Patient Safety Goal 01.01.01) emphasizes improved patient identification. 1 This encourages proper identification of specimens by instructing laboratories to “[u]se at least two patient identifiers when providing laboratory services.” 2(p2) Despite the care and effort devoted to specimen management in surgical pathology, many opportunities for mishaps remain. Recognizing multiple levels of tissue handling during which errors can enter the testing process, the Laboratory Accreditation Program of the College of American Pathologists emphasizes the importance of specimen identification with 5 checklist items (Anatomic Pathology [ANP].11800, ANP.21050, ANP.21100, and ANP.21150 [September 27, 2007] and Laboratory General [GEN].40491 [October 31, 2006]). 3 , 4 These checklist questions focus on specimen, block, and slide labeling.

In the preanalytic phase of surgical pathology, as tissue specimens are collected, transported, accessioned, dissected, embedded, cut, and placed on slides, there are multiple points where patients' tissues are transferred or handed to another person. Specimen identification must be maintained across these hand offs. This study documents the rates of misidentification of cases, specimens, blocks, and slides. It also records how identification errors are detected and identifies practices that are associated with better performance.

The basic mechanism of this Q-Probes study has been previously described. 5 Briefly, Q-Probes studies are subscription-based quality assurance studies. They are conducted for a defined period by many laboratories using the same protocol. This design captures the advantages of peer group comparison for determining quantitative levels of performance.

In this particular Q-Probes study, subscribers prospectively reviewed surgical pathology cases in the fall of 2009 until an 8-week period had elapsed or until 30 error incidents related to mislabeled cases, specimens, blocks, or slides had been identified. In categorizing the defects, participants used the following definitions:

Mislabeled Case.— Wrong accession number was applied to the entire case (eg, S07-0001 versus S08-0001; S08-00101 versus S08-00110).

Mislabeled Specimen.— Wrong specimen labeling was due to mix-up of specimens within a case (eg, right versus left specimens in a bilateral biopsy from the same patient).

Mislabeled Block. —Histologic block was labeled with the wrong patient/case identification or wrong sequence number or letter. This could be due to the wrong label being applied to the block or to tissue being placed in the wrong block. Undetected mislabeling of cases, mixed-up cases, or mislabeled specimens also lead to mislabeled blocks.

Mislabeled Slide.— Histologic slide was labeled with the wrong specimen/patient identification, sequence number, or letter. This may be due to an error in labeling at the time of slide preparation or to wrong sections being placed on the slide. This error may also be caused by undetected mislabeling of blocks, specimens, or cases.

To produce denominators for rates of labeling errors during the study, participants counted the total numbers of cases, specimens, blocks, and slides reviewed. When participants discovered instances of mislabeling, they recorded (1) the station in the surgical pathology process at which the error occurred (eg, before or at accessioning, block labeling), (2) which elements in the process were mislabeled (ie, case, specimens, blocks, slides), (3) the number of specimens, blocks, and slides affected, (4) the point in the surgical pathology process when the errors were detected, and (5) the outcome (consequence) of the error (ie, mislabeling corrected, corrected report issued, and/or patient care affected).

Excluded from the study were surgical pathology cases that were processed before the study period but were subsequently identified as mislabeled during the study period. Also excluded were prepared slides received from another institution and blocks or slides cut in other laboratories. Finally, participants also completed a questionnaire that summarized institutional and laboratory demographic and practice variables that could be compared with the mislabeling rates in the surgical pathology process.

Statistical Analyses

The case, specimen, block, and slide mislabeling rates per 1000 were calculated. All four rates were skewed so a log transformation for regression-based analyses was used.

Individual associations between the rates and the demographic and practice variables were investigated using Kruskal-Wallis tests for discrete-valued independent variables and regression analyses for the continuous independent variables. Variables with significant associations ( P < .10) were introduced into a multivariate regression model. All variables remaining were significantly associated at the P < .05 significance level.

One hundred thirty-six laboratories collected and submitted data. The institutional demographic characteristics are shown in Table 1 . Most of participating institutions (93.4%; n  =  127) were located in the United States, with the remaining located in Saudi Arabia (4.4%; n  =  6), Canada (1.5%; n  =  2), and Lebanon (0.7%; n  =  1).

Institution Demographics

Institution Demographics

Of the participating institutions (n  =  136), 40.7% (n  =  55) were teaching hospitals, and 24.4% (n  =  33) had a pathology residency program. Within the past 2 years, the Laboratory Accreditation Program had inspected 89.3% (n  =  121) of the laboratories. Laboratory inspections were also conducted by the Joint Commission at 28.2% (n  =  38) of institutions within the past 2 years. Table 1 displays additional practice characteristics of participating institutions.

Mislabeled Cases, Specimens, Blocks, and Slides

From a total of 427 255 reviewed cases, 1811 cases (0.4%) had some type of mislabeling. In 490 instances (27%; 1.1 cases per 1000), the entire case was mislabeled. In 358 cases (20%), 796 specimens (1 per 1000) of a total 774 373 specimens were mislabeled. In 461 cases (25%), 2172 blocks (1.7 per 1000) of a total of 1 304 650 blocks were mislabeled. In 502 cases (28%), 2509 slides (1.1 per 1000) were mislabeled from a total of 2 261 811.

Table 2 shows the distribution of errors types and the location in the process at which those errors occurred. Misidentification errors of cases and specimens occurred most often before accessioning (at collection) or at accessioning. Block-labeling errors occurred most frequently during block labeling. Slide-labeling errors occurred most often when tissues were cut and when slides were mounted. Table 3 shows where errors occurred in the process and at what location in the process those errors were later detected. Errors were typically detected in the 1 or 2 steps after the point at which the errors occurred. In 96.7% of cases (n  =  1751), the mislabeling was corrected without any additional consequences. In 3.25% of cases (n  =  59), a corrected report was issued, and in 1.3% of cases (n  =  24), participants assessed that patient care was affected in some way.

Error Incidents by the Location at Which the Error Occurred

Error Incidents by the Location at Which the Error Occurred

Frequency of Error Incidents and Detection by Location a

Frequency of Error Incidents and Detection by Locationa

Practice Variables

Table 4 demonstrates the distribution of surgical pathology cases and personnel at participating laboratories. The median participant laboratory had accessioned approximately 13 500 cases in 2008, had 5 pathologists, 1 pathologist assistant, 5 histology technicians, 2 clerks, and 2 “other” workers. Table 5 lists a number of laboratory practice characteristics related to accessioning and processing in the laboratory. Gross examination and section preparation of surgical pathology cases were reported to have been performed 48.1% of the time by a pathologist assistant, 24% by the pathologist, 13.4% by the histology technician, 8.5% by a resident, and 6.1% by another worker.

Distributions of Surgical Pathology Laboratory Characteristics

Distributions of Surgical Pathology Laboratory Characteristics

Participant Characteristics Related to Accessioning and Laboratory Processing

Participant Characteristics Related to Accessioning and Laboratory Processing

Table 6 contains practice characteristics related to block labeling. Table 7 contains practice characteristics related to slide labeling. Table 8 demonstrates other laboratory practice characteristics.

Practice Characteristics Related to Block Labeling

Practice Characteristics Related to Block Labeling

Practice Characteristics Related to Slide Labeling

Practice Characteristics Related to Slide Labeling

Other Practice Characteristics

Other Practice Characteristics

Table 9 lists statistically significant correlations between mislabeling rates and various practice variables. The median rate at which slides were mislabeled was lower in laboratories with continuous individual-case (one by one) accessioning. Slide mislabeling rates were also lower in laboratories that had a formal, documented quality check at accessioning. Finally, the median rate of specimen mislabeling was lower at laboratories in which the gross description report specifically included a statement indicating that each specimen was labeled with the patient's name and properly identified. The remaining practice characteristics listed in Tables 5 through 8 and other demographic data do not correlate at significant levels with the rates of mislabeled cases, specimens, blocks, or slides.

Statistically Significant Correlations Between Mislabeling Rates and Various Practice Variables

Statistically Significant Correlations Between Mislabeling Rates and Various Practice Variables

In this study, we met our primary objective of establishing a 0.11% multi-institutional rate for misidentification or mislabeling, with a rate of 0.1% for specimens, 0.17% for blocks, and 0.11% for slides. Errors were detected relatively evenly throughout the entire tissue processing system. One concern that the study addressed was whether a cascade of errors would follow from early case or specimen identification errors, which would be amplified in blocks and slides. Although this occurred for some cases, the cascade effect was dampened because early case and specimen identification errors were usually corrected in the immediate, subsequent steps. Thus, quick recognition led to few cases in which the misidentification error continued throughout the entire process. This demonstrates that those staff members in closest proximity to the specimen are usually able to identify upstream errors. Rather than cascading, identification errors occurred at 3 major, roughly equally, transition points: (1) before and at accessioning, (2) at block labeling and gross processing, and (3) at tissue cutting and slide mounting. Quality-assurance checks were effective when they focused on mislabeling immediately after each of these events, leading to detection of most errors.

The quality checks in place varied among study participants: 68% of laboratories (n  =  92) had a documented quality check at accessioning, but only 27% (n  =  37) have a quality check at transcription. Most laboratories maintained checkpoints at accessioning, gross examining, embedding, slide cutting, and slide labeling, and at case examination ( Table 8 ). Decreased error rates, however, were associated significantly with only one of these checkpoints: accessioning.

Error prevention is, of course, preferable to error detection. The introduction of technology and the adoption of lean-process methods may improve laboratory efficiency and reduce errors. 6 , 7 However, in this study, we failed to demonstrate a detectable benefit from computer technology (eg, barcodes) or from the introduction of lean processes in decreasing identification errors. The lack of evidence for the prevention of identification errors from computer technology and lean technologies may actually be due to the varied nature of the introduction of technical devices and lean method as well as to the inconsistency of their application.

Specifically inquiring about the use of block and slide labelers, the study documented a varied picture: 57% of laboratories (n  =  74) use automatic block labelers, and 13.5% of these labelers (n  =  10) incorporated barcodes in the labels. Among these labelers, for nearly 36% of the devices (n  =  26), the identification information was generated from the accessioning software. Automatic slide labelers were used by 28.5% of laboratories (n  =  37). Slides with labels automatically produced and attached by a label maker were used in about 10% of processing systems (n  =  12). Barcodes were incorporated in 27% of laboratories (n  =  12) that used automatic labelers of either type (n  =  49). Using labelers that lack integration with accessioning systems can be problematic. Although the automatic labelers reduce the risk of handwriting errors, the lack of their integration with accessioning means that identifying information must be reentered into the automatic labeler. This rework introduces another point at which errors can be introduced.

The pattern of errors in the participant laboratories suggests that there are 3 points in this process that must be tightly controlled: (1) accessioning, (2) transferring tissue into blocks, and (3) tissue cutting and slide mounting. An additional desirable feature that is essential for safety is the ability to track specimens, blocks, and slides from start to finish. To our knowledge, tracking systems that are integrated with all other available technologies, such as the accessioning system, block and slide labelers, barcode readers, and transcription systems, are quite rare but are critically needed to dramatically reduce errors in specimen processing.

Other problems that have been shown to lead to errors include batch work and the ability of laboratories to sufficiently segregate cases, specimen, blocks, and slides, so they are not mixed up at the points of tissue transfer. A substantial percentage of laboratories in this study still process cases for accession (43.4%; n  =  56), block (54.1%; n  =  40), and slide (66.4%; n  =  85) labels as batches. A pull system, as incorporated in lean production methods that process specimens and blocks one-by-one, prompts for identification by scanning barcodes at the gross pathology station for only one specimen at a time. In the same way, slides should also be labeled one-by-one at the cutting station, from a barcode on the block from which the tissue is derived.

This study did confirm the value of lean processing techniques in one specific setting. The mislabeling slide rate was lower in institutions that had continuous (one-by-one), individual-case accessioning and in laboratories that emphasized a formal, documented check that probes for accessioning/labeling errors at the accessioning station. Readers should note that these 2 practices during accessioning were associated with fewer slide-labeling errors; the association speaks to a more-disciplined approach to processing at all stages and to avoidance of batch work.

In summary, this study documents that across 136 institutions mislabeling rates of cases (0.11%; 490 of 427 255), specimens (0.1%; 796 of 774 373), blocks (0.17%; 2172 of 1 304 650), and slides (0.11%; 2509 of 2 261 811) were similar. Errors in labeling appeared equally at each of the stages in the system of accessioning, gross examining, and tissue cutting. Errors were typically detected immediately after the errors occurred. The study reinforced the value of quality checks throughout the system.

Author notes

From the Department of Pathology, Mayo Clinic Florida, Jacksonville, Florida (Dr Nakhleh); the Department of Pathology, Virginia Commonwealth University Health System, Richmond, Virginia (Dr Idowu); the Department of Biostatistics, College of American Pathologists, Northfield, Illinois (Ms Souers); the Department of Pathology, Henry Ford Health System, Detroit, Michigan (Dr Meier); and the Department of Pathology, Phoenixville Hospital, Phoenixville, Pennsylvania (Dr Bekeris).

The authors have no relevant financial interest in the products or companies described in this article.

Recipient(s) will receive an email with a link to 'Mislabeling of Cases, Specimens, Blocks, and Slides: A College of American Pathologists Study of 136 Institutions' and will not need an account to access the content.

Subject: Mislabeling of Cases, Specimens, Blocks, and Slides: A College of American Pathologists Study of 136 Institutions

(Optional message may have a maximum of 1000 characters.)

Citing articles via

Get email alerts.

  • eISSN 1543-2165
  • ISSN 0003-9985
  • Privacy Policy
  • Get Adobe Acrobat Reader

This Feature Is Available To Subscribers Only

Sign In or Create an Account

Log in using your username and password

  • Search More Search for this keyword Advanced search
  • Latest content
  • BMJ Journals More You are viewing from: Google Indexer

You are here

  • Volume 5, Issue 1
  • Reducing the occurrence of errors in a laboratory's specimen receiving and processing department
  • Article Text
  • Article info
  • Citation Tools
  • Rapid Responses
  • Article metrics

Download PDF

  • Nouf Al Saleem ,
  • Khaled Al-Surimi
  • King Saud bin Abdulaziz University for Health Sciences /King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
  • Correspondence to Nouf Al Saleem alsaleem.nouf{at}gmail.com

Frequent, preventable medical errors can have an adverse effect on patient safety and quality as well as leading to wasted resources. In the laboratory, errors can occur at any stage of sample processing; pre-analytical, analytical, and post analytical stages. However evidence shows most of the laboratory errors occur during the pre-analytical stage. The receipt and processing of specimens is one of the main steps in the pre-analytical stage. Errors in this stage could be due to mislabeling, incorrect test entry and entering the wrong location, among other reasons. Most of these errors are preventable. At the Riyadh Regional Laboratory of the Ministry of Health, we found that there was an average of 2.31 errors per 1000 processed samples; these errors had occurred during the pre-analytical stage. These samples were returned back from other laboratory departments, such as Chemistry, Hematology and Microbiology, to the receiving and processing department. We decided to carry out an improvement project where we applied a systematic approach to identify and analyse the root causes of the problem using quality tools such as a process flowchart and a fish-bone diagram. The Model for Improvement was used and several PDSA (Plan, Do, Study, Act) cycles were run to test interventions which aimed to prevent laboratory processing errors and mistakes. The project results showed a 25% reduction in errors during the pre-analytical stage.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See:

http://creativecommons.org/licenses/by-nc/2.0/

http://creativecommons.org/licenses/by-nc/2.0/legalcode

https://doi.org/10.1136/bmjquality.u211474.w4624

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

The specimen receiving and processing department is the key entry point at the Riyadh Regional Laboratory (RRL) of the Ministry of Health. This regional laboratory is one of the largest reference laboratories for the Ministry of Health. RRL consists of around 14 departments. The specimen receiving and processing department is the largest department with around 35 employees. 1 The RRL receives around 2000 samples a day from all of the Kingdom regions. All samples are processed by the specimen receiving and processing department before they are sent to the corresponding laboratory sections for the required tests. Errors in specimen processing can lead to adverse events which include; delays in patient diagnosis and management. Based on a retrospective analysis of data, we noticed that at RRL there were a number of dispatched specimens which were returned back to the receiving and processing department on a daily basis. We analysed a random sample of historical data and found that there were 2.31 errors per 1000 dispatched samples. These samples were sent back due to processing errors and mistakes which included; mislabelling, wrong test entry, wrong location, unjustified delay, and also processing STAT specimens as routine samples. Thus, we decided to undertake a quality improvement project which aimed to reduce the occurrence of defects in the specimen processing department by 50% over a three months period. This was a step towards aiming for zero error.

Since 2000, medical errors have been given significant attention around the world to assure the safety of healthcare. In the book ‘To Err Is Human’, it is stated that medical errors are responsible for around 58% of adverse events and that sadly, most of these events are preventable. 2 The American Center for Disease Control and Prevention (CDC) has documented that laboratory results affect around 70% of the decision making for patients' management. 3 A number of previous studies showed that most laboratory errors occur in the pre-analytical phase of patient investigations, 4 and one of the most common errors is when the patient is not identified correctly. Laboratory error is also an issue of patient safety; the Joint Commission have the identification of patients as their first international patient safety goal (IPSG). 5

A study by Cavenaugh found that laboratory error can result in high costs to organisations. Thus, reducing laboratory errors can increase the efficiency of the laboratory. 6

Baseline measurement

We collected data retrospectively for 12 working days from the department's logbook. The rate of error in the sample was calculated to be 2.31 per 1000. We divided the number of errors (60 errors) by the number of total samples received (60/ 25,942). Missing data from the logbook were excluded. Figure 1 shows the data analysis and the number of processing errors. ⇓

  • Download figure
  • Open in new tab
  • Download powerpoint

Flow chart of specimen processing

The project team included the project leader, the head of department, the laboratory supervisor and two of the senior technicians in the department. The team was led by a quality management specialist.

The team undertook a root cause analysis using the fish-bone diagram and drawing out the department's work flow. The fish-bone diagram showed different areas of error related to people, methods, processes, and culture. The main reasons for error were related to staff training, adherence to policy, work schedule, supervision, and absence of disciplinary action.

Based on these processing errors, several solutions were proposed for testing using PDSA cycles (Plan, Do, Study, Act). The proposed intervention included the following steps: providing a list of tests for each laboratory section, assigning a senior staff member to each unskilled staff member for each entering section in the receiving department and sharing the number of errors with the employees to support them in learning from their mistakes.

The main outcome measure was the number of returned samples from the laboratory sections to the receiving and processing department. The balancing measures were staff satisfaction around sharing and learning from mistakes and with their new work flow.

PDSA (1) aimed to improve accuracy of entering the sample process correctly by providing a list of the tests' names and codes for all entering sections in the department. We also assigned a second staff member to perform a quality control check before sending specimens to the lab sections. Our prediction was that the proposed intervention would help staff to be aware of the codes of tests to prevent incorrect entry and mislabeled samples. The study stage analysis showed that entering errors were still increasing. Although staff were provided with the list and were informed of the importance of checking before sending for processing, staff were not complying, which did not meet our prediction. These results led us to conclude that informing and training staff was not enough, other ideas needed to be tested.

PDSA (2): Based on the results of PDSA (1), we decided to test the idea of sharing with staff, the number and type of errors they had made. We started by testing the idea on two staff members, informing them about the number and type of errors they had made over the last month. We expected that after having been told about the errors that staff would be monitored and further action taken if appropriate. The study stage showed that staff were willing to improve. Our results showed a decrease in error occurrence, which supported the idea of having a monitoring and evaluation mechanism in place for work performance. As a result, we adapted the intervention of reporting, sharing, and learning from mistakes and decided in the next PDSA to roll this out for all staff.

PDSA (3) tested the idea of sharing errors with all staff members by informing them about the number and type of mistakes they had made in the last month and after the project started. We expected that all staff would show a willingness to learn from their mistakes. The staff immediately reacted positively, showing their willingness to improve and to learn from their mistakes. Later, the results showed a noticeable decrease in the number of errors and mistakes revealing the importance of having a monitoring and evaluation system in place as well as the benefits of sharing and learning from mistakes.

The project results, as presented in the control chart, showed a trend of reduction in the number of errors which continued six points after the intervention. The overall impact of the project was to reduce the frequency of errors by 25% in the first 12 days of the post implementation period in comparison to the baseline measurement period. On average during the baseline period there were 5 errors (0.2%) compared with only 3 errors (0.15%) in the post intervention period. This improvement showed a steady trend decrease in error occurrence until reaching zero error in day 23 of the post intervention period. However, on day 24 there was another error occurrence which was still below the average and within the control process limits, suggesting that there was a common cause of variation which needed to be analysed and corrected. We believe that the results of this project will help to ensure patient safety and also avoid wasting valuable resources in RRL at two levels: direct costs from labour and materials (through avoiding extra work for employees), and indirect costs from misdiagnosis and mismanagement of patients. ⇓ ⇓

Project Data

Lessons and limitations

We have learnt an important lesson through this project and that is that giving training, whilst necessary, it is not sufficient to improve staff performance. It is vital to hold people accountable for their performance, and to put effective monitoring and evaluation systems in place. Sharing and learning from mistakes can help to improve culture as teams become aware of the consequences of errors on patient care and safety.

The project has been undertaken over a short period of time, however the tested interventions appear to have given promising results. The results are generalisable and could be replicated in other similar departments. To try and ensure sustainability, we presented and discussed the project results with the higher management team of RRL. The management team showed interest in the project's results and showed a willingness to adapt the interventions as part of routine daily practice in the department. These simple change ideas could be replicated and tested further in other laboratories in the country.

We involved staff in the project by showing them their mistakes and allowing them to be part of the solution. This allowed us to achieve our improvement aim. The project results emphasise the importance of workflow monitoring and evaluation to increase staff compliance with quality assurance measures. As well as improving the quality of laboratory services, we hope that patient safety and efficiency will also improve.

Acknowledgments

I would like to thank the Head of Specimens and Processing Department in Riyadh Regional Laboratory, Mr. Saad Al Suraya, for his great support to improve the department's performance and for his valuable suggestions of the possible solutions.

Thanks go to Dr. Ahmed Al Amry, the Head of Quality Management Department in King Abdullah Medical City, National Guard for his valuable information and knowledge he shared regarding Quality Improvement Model and his successful experience in his organization.

Finally, a special thanks for Dr. Khaled Al-Surimi for his support and guidance during the whole project.

  • Al Saleem N ,
  • El Metwally A ,
  • Kohn L.T. ,
  • Corrigan J.M. , and
  • Donaldson M.S.
  • Moiduddin P.D.a.A.
  • Bonini P. , et al
  • ↵ JCAOH and Joint Commission Resources , Meeting the Joint Commission's 2007 National Patient Safety Goals . 2006 : Joint Commission Resources .
  • Cavenaugh , Edward Lee

Declaration of interests None declared.

Ethical approval The problem was chosen based on the importance and the frequency of the mistakes. This problem is related to the core mission and duties of the specimen receiving and processing department, and fits with the Riyadh Regional Laboratory vision and mission to provide a high quality of patient services. This meets the JCI international standards and CBAHI national standard as well. Thus the head of the department approved the project and participated in it as well. All information relating to the staff who were involved in performing the process were kept private and confidential.

Read the full text or download the PDF:

Book cover

Modern Clinical Molecular Techniques pp 3–9 Cite as

Specimen Collection, Handling, and Processing

  • Lindsy Hengesbach M.S., MB(ASCP)CM 4 &
  • John A. Gerlach Ph.D., D(ABHI) 4  
  • First Online: 01 January 2012

2099 Accesses

Specimen collection handling and processing are key to the pre-analytic phase of clinical laboratory testing. In computer jargon, there is an acronym “GIGO” that stands for garbage in equals garbage out. It is also applicable to clinical laboratory testing. If the proper sample is not collected from the individual to be tested and if the sample is not handled and transported to the laboratory in an efficient and efficacious manner, there will not be usable diagnostic information provided. This chapter deals with the pre-analytic process of sample collection, handling, and processing for diagnostic molecular testing.

  • Collection • Contamination • Identification • Inhibiting substance • Nucleic acid • ­Pre-analytic • Safety • Sample • Sample integrity • Sample shipping

This is a preview of subscription content, log in via an institution .

Buying options

  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
  • Available as EPUB and PDF
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
  • Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Baca A, Haber RJ, Sujishi K, Frost PH, Ng VL (2004) EDTA is a Better Anticoagulant that Heparin or Citrate for Delayed Blood Processing for Plasma DNA Analysis. Clinical Chemistry 50(1); 265–257.

Article   Google Scholar  

Banerjee S, Makdisi A, Weston S, et al. (1995) Microwave based DNA extraction form Paraffin Embedded Tissue for PCZR Amplification. BioTechniques 18;772–3.

Google Scholar  

Bessetti J (2007) An Introduction to PCR Inhibitors. Profiles in DNA 10(1): 9–10.

Bowen RAR, Hortin GL, Csako, G, Otañez OH, Remaley AT (2010) Impact of Blood Collection Devices on Clinical Chemistry Assays. Clinical Biochemistry 43: 4–25.

Article   PubMed   CAS   Google Scholar  

Centers for Disease Control and Prevention, US Department of Health and Human Services: Biosafety in Microbiological and Biomedical Laboratories, 5th Ed., 2009.

Centers for Disease Control and Prevention, US Department of Health and Human Services: Perspectives in Disease Prevention and Health Promotion Update: Universal Precautions for Prevention of Transmission of Human Immunodeficiency Virus, Hepatitis B Virus and Other Bloodborne Pathogens in Health Care Settings (1998) MMWR, 37(24): 377–388.

Clinical Laboratory Standards Institute (CLSI) Collection, Transport, Preparation and Storage of Specimens for Molecular Methods: Approved Guidelines, Wayne, 2005, MM13-A.

Dickover RE, Herman ST, Saddiq H, Wafer D, Dillon M, Bryson Y (1998) Optimization of Specimen-Handling Procedures for Accurate Quantitation of Levels of Human Immunodeficiency Virus RNA in Plasma by Reverse Transcriptase PCR. Journal of Clinical Microbiology 36(4): 1070–1073.

PubMed   CAS   Google Scholar  

Faulkner S and Leigh D (1998) Universal Amplification of DNA Isolated from Small Regions of Paraffin-Embedded Formalin-Fixed Tissue. BioTechniques 24:47–50.

Fleischhacker M, Schmidt B (2008) Cell-free DNA Resuscitated for Tumor Testing. Nature Medicine 14 (9): 914–91.

Gallagher M, Sturchio ZC, Smith A, et al (2011) Evaluation of Mailed Pediatric Buccal Cytobrushes for Use in a Case-Control Study of Birth Defects. Birth Deects Research (part A) 91; 642–8.

Article   CAS   Google Scholar  

Health Care Financing Administration, Department of Health and Human Services: Clinical Laboratory Improvement Amendments of 1988, Federal Register, 55 (9576), 1990 (CLIA’88; Final Rule. 42 Cfr. Subpark K, 493.1201).

Health Insurance Portability and Accountability Act of 1996. Washington, D.C.: U.S. G.P.O, 1996. Print.

Henson B and Wong D (2010) Collection, Storage and Processing of Saliva Samples for Downstream Molecular Applications. Oral Biology, Methods in Molecular Biologsy D01 10.1007/978–1–60761–820–1_2.

Isaksson HS, Nilsson TK (2006) Pre-analytical aspects of quantitative TaqMan Real-time RT-PCR: Applications for TF and VEGR mRNA Quantification. Clinical Biochemistry 39: 373–377.

Ivarsson M and ZCarlson J (2011) Methods in Biobanking, Methods in Molecular Biology. DOI 10.1007/978-1-59745-423-0_14 .

Koni A, Scott R, Wang G, et al (2011) DNA yield and quality of saliva samples and suitability for large-scale epidemiological studies in children. International Journal of Obesity 35;S113–8.

Kwok S, Higuchi R (1989) Avoiding false positives with PCR. Nature 339;237–8.

Leelawiwat W, Youn N, Chaowanachan T, et al. (2009) Dried blood spots for the diagnosis and quantitation of HIV-1: Stability studies and evaluation of sensitivity and specificity for the diagnosis of infant HIV-1 infection in Thailand. Journal of Virological Methods. 155;109–17.

Lehmann A, Hass, D, McCormick C, et al. (2011) Collection of human Genomic DNA from neonates: a comparison between umbilical cord blood and buccal swabs. American Journal of Obstetrics & Gynecology 204;362.e1–6.

Linnarsson S (2010) Recent Advances in DNA Sequencing Methods-General Principles of Sample Preparation. Experimental Cell Research 316: 1339–1343.

Lippi G, Guidi GC, Mattiuzzi C, Plebani M (2006) Preanalytical Variability: The Dark Side of the Moon in Laboratory Testing. Clin. Chem. Lab Med. 44(4): 358–365.

Muniz J, McCauley L, Pak V, et al. (2011) Effects of sample collection and storage conditions on DNA damage in buccal cells from agricultural workers. Mutation Research 720; 8–13.

McGuire AM, Beskow LM (2010) Informed Consent in Genomics and Genetic Research. Annu. Rev. Genomics Hum. Genet. 11: 361–81.

Pipeline and Hazardous Materials Safety Administration, Department of Transportation, Federal Register, 71(32244), 2005.

Pitt J (2010) Newborn Screening. Clin Biochem Rev 31;57–68.

PubMed   Google Scholar  

Occupational Safety and Health Administration, US Department of Labor: Occupational Exposure to Bloodborne Pathogens: Final Rule, Federal Register 76 (33608), 2011 (29 CFR 1910.1030).

Ratcliff RM, Chang G, Kok T, Sloots TP (2007) Molecular Diagnosis of Medical Viruses. Curr. Issues Mol. Biol. 9: 87–10.

Rincon G, Tengvall K Belanger J, et al. (2011) Comparison of buccal and blood-derived canine DNA, either native or whole genome amplified, for array-based genome-wide association studies. BMC Research Notes 4;226–32.

Tuaillon E, Mondain A, Meroueh F et al. (2010) Dried Blood Spot for Hepatitis ZC Virus Serology and Molecular Testing. Hepatology 51;752–8.

Virkler K and Lednev I (2009) Analysis of body fluids for forensic purposes: From laboratory testing to ­non-destructive rapid confirmatory identification at a crime scene 188;1–17.

CAS   Google Scholar  

Download references

Author information

Authors and affiliations.

Biomedical Laboratory Diagnostics Program, Michigan State University, N321A Kedzie, East Lansing, MI, 48824, USA

Lindsy Hengesbach M.S., MB(ASCP)CM & John A. Gerlach Ph.D., D(ABHI)

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to John A. Gerlach Ph.D., D(ABHI) .

Editor information

Editors and affiliations.

, Molecular Genetic Technology Program, M.D. Anderson Cancer Center, 1515 Holcombe Blvd. Unit 2, Houston, 77030, Texas, USA

, Department of Human Genetics, Emory University School of Medicine, 615 Michael St. NE, Suite 315, Atlanta, 30322, Georgia, USA

Madhuri Hegde

, Molecular Genetic Technology Program, M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Unit 2, Houston, 77030, Texas, USA

Patrick Alan Lennon

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Cite this chapter.

Hengesbach, L., Gerlach, J.A. (2012). Specimen Collection, Handling, and Processing. In: Hu, P., Hegde, M., Lennon, P. (eds) Modern Clinical Molecular Techniques. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-2170-2_1

Download citation

DOI : https://doi.org/10.1007/978-1-4614-2170-2_1

Published : 06 March 2012

Publisher Name : Springer, New York, NY

Print ISBN : 978-1-4614-2169-6

Online ISBN : 978-1-4614-2170-2

eBook Packages : Medicine Medicine (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Publish with us

Policies and ethics

  • Find a journal
  • Track your research

Knowledge-Pathway-Hero-image

An Introduction to Specimen Processing

case study 12 3 specimen processing issues

Microscopic analysis of cells and tissues requires the preparation of very thin, high-quality sections (slices) mounted on  glass slides  and appropriately stained to demonstrate normal and abnormal structures.

Most fresh tissue is very delicate and easily distorted and damaged, and it is thus impossible to prepare thin sections from it unless it is chemically preserved or “fixed” and supported in some way whilst it is being cut. Broadly, there are two strategies that can be employed to provide this support:

  • We can freeze the tissue and keep it frozen while we cut our sections. These sections are called  “frozen sections” .
  • Alternatively, we can infiltrate our tissue specimen with a liquid agent that can subsequently be converted into a solid that has appropriate physical properties, which will allow thin sections to be cut from it. Paraffin wax is such an agent. This produces so-called “paraffin sections”.

This article describes the method for processing tissue to create paraffin-embedded specimens ready for sectioning.

Introduction

“ Tissue processing ” describes the steps required to take an animal or human tissue from fixation to the state where it is completely infiltrated with a suitable histological wax and can be embedded ready for section cutting on the microtome.

Tissue processing can be performed manually (hand processing), but where multiple specimens must be dealt with, it is more convenient and much more efficient to use an automated tissue processing machine (a “tissue processor”). These devices have been available since the 1940’s 1  and have slowly evolved to be safer in use, handle larger specimen numbers, process more quickly, and to produce better quality outcomes. There are two main types of processors: the tissue-transfer (or “dip and dunk”) machines where specimens are transferred from container to container to be processed, and the fluid-transfer (or “enclosed”) types where specimens are held in a single process chamber or retort and fluids are pumped in and out as required. Most modern fluid-transfer processors employ raised temperatures, effective fluid circulation and incorporate vacuum/pressure cycles to enhance processing and reduce processing times.

Modern enclosed tissue processor

Modern enclosed tissue processor

“Xylene-free” processing

Although xylene is used widely as a clearing agent for tissue, processing it is a toxic reagent. Some laboratories prefer to use less-toxic alternatives such as isopropanol or other xylene substitutes. For this method to be successful, higher wax temperatures are required so that isopropanol can be eliminated from specimens during infiltration.

Pathologist examining slide to illustrate how high-quality tissue processing is critical for accurate diagnosis

The combined effects of fixation and processing are to harden the tissue, and it is inevitable that shrinkage will also occur. It has been estimated that tissues shrink as much as 20% or more by the time they are infiltrated with wax 4 . Notwithstanding these effects,  sections prepared  from optimally processed tissues will consistently show excellent morphological detail, which allows comparisons to be made between specimens and accurate histopathological diagnoses to be determined.

In theory and practice, the paraffin blocks that will be easiest to section contain relatively homogenous tissue of uniform soft consistency (such as kidney), which, when infiltrated with wax, have a consistency similar to that of solidified wax alone (not containing tissue). Tissues of a dense or fibrous nature or a specimen where both hard and soft tissue are present in discrete layers can pose more of a challenge because parts of them are not so well supported by the solidified wax. Differential shrinkage of the various elements in these blocks during fixation and processing contributes to the problems that might be experienced when they are being sectioned.

Steps to Better Processing and Embedding

From patient to pathologist, preparing tissue specimens for histological examination requires care, skill and sound procedures. This guide provides practical advice on best-practice techniques and simple ways to avoid common errors.

Tips for better tissue processing and embedding are highlighted in this guide. We hope each step provides a valuable reminder of good histology practice and helps with troubleshooting when unacceptable results do occur.

Want to see all 101 Steps to Better Histology?

Download 101 Steps to Better Histology now!

Use an Appropriate Schedule

An appropriate schedule is chosen for the tissue type and size.

An inappropriate schedule is chosen. For example, a very long schedule for a small endoscopic biopsy or a very short schedule for a large, fatty breast specimen.

Use an Appropriate Schedule

Stay updated, free articles. Join our Telegram channel

Comments are closed for this page.

case study 12 3 specimen processing issues

Full access? Get Clinical Tree

case study 12 3 specimen processing issues

VIDEO

  1. CASE STUDY PHY210 VIDEO PRESENTATION

  2. Financial Accounting Specimen Exam Debrief Q4

  3. HSH CASE STUDY 12 JAN 24 Dr ANU KAUSHAL @hsh_homeopathy @HOMEOPATHY_KI_PATHSHALA

  4. How to Study 12 Hours In a Day? Toppers Timetable ICAI CA Foundation Exams l Dec 2023 June 2024

  5. How to *effectively* Study 12 Hours a Day?

  6. Difference between Experiment and Survey

COMMENTS

  1. Solved CASE STUDY 12-3: SPECIMEN PROCESSING ISSUESA

    Question: CASE STUDY 12-3: SPECIMEN PROCESSING ISSUESA phlebotomist has been newly trained to do specimen processing in the hospital laboratory. The phlebotomist is normally expected to work alongside an experienced processor except when that person is on break or at lunch. ... CASE STUDY 1 2-3: SPECIMEN PROCESSING ISSUES. A phlebotomist has ...

  2. Chapter-12 Answers

    CASE STUDY 12-3: SPECIMEN PROCESSING ISSUES 1. No. There was nothing to prevent Janine from accepting the specimens for processing. However, she may find problems with the unspun SSTs after they have been centrifuged. 2. SSTs with the gel at a slant were probably centrifuged in a fixed angle centrifuge.

  3. Pre-analytical pitfalls: Missing and mislabeled specimens

    Case #1: A 56-year-old man was admitted to the same-day surgery center for a planned biopsy procedure. Microbiological specimens were collected for culture and first transported to the central laboratory for processing at 1142.

  4. Quality Measures in Pre-Analytical Phase of Tissue Processing

    The Policy of the laboratory was not to reject any sample, but in case of any discrepancy, documentation and rectification of the issue to be done before the specimen is submitted for further processing in histopathology laboratory. Specimen not sent in fixative: Fixative routinely used was 10% formalin. Out of 18 specimens that were not sent ...

  5. Specimen collection and handling best practices and pitfalls

    Location of urine specimen collection presents challenges as well. A study of urine samples for suspicion of urinary tract infection found samples produced at patients' homes rarely met recommendations required for further diagnostic analysis. Out of the 22 midstream specimen urine samples taken, only 13.3% met urine analysis recommendations. 14

  6. Analysis of errors in histology by root cause analysis: a pilot study

    Errors were detected in each phase: accessioning (6.5%), gross dissecting (28%), processing (1.5%), embedding (4.5%), tissue cutting and slide mounting (23%), coloring, (1.5%), labeling and releasing (35%). Discussion. Root cause analysis is effective and easy to use in clinical risk management.

  7. PDF Biological sample collection, processing, storage

    Examples/case studies Prior to initiating a study that involves specimen collection, several key ... specimen collection and processing to maintain the stability of the resulting sample aliquots, which are expected to number approximately 15 000 000 (5). Among the issues outlined above, cost is a major consideration, especially when designing a ...

  8. Are You Safe? Is my specimen handling process reliable?

    A lack of reliable systems for specimen handling can lead to missed opportunities for earlier treatment. Safer Care: Maintain a chain of custody to track specimens from collection to final disposition. Implement a quality monitoring system (e.g., specimen log).

  9. Mislabeling of Cases, Specimens, Blocks, and Slides: A College of

    Patient and specimen identification are critical elements to patient safety in surgical pathology. Goal 1 of the Joint Commission's patient safety goals (National Patient Safety Goal 01.01.01) emphasizes improved patient identification.1 This encourages proper identification of specimens by instructing laboratories to "[u]se at least two patient identifiers when providing laboratory services ...

  10. Reducing the occurrence of errors in a laboratory's specimen receiving

    Frequent, preventable medical errors can have an adverse effect on patient safety and quality as well as leading to wasted resources. In the laboratory, errors can occur at any stage of sample processing; pre-analytical, analytical, and post analytical stages. However evidence shows most of the laboratory errors occur during the pre-analytical stage. The receipt and processing of specimens is ...

  11. Chapter 3

    These errors most commonly occur in the pre-analytical phase. Among these, problems with patient identification or specimen handling are most frequent. Laboratories must establish strict guidelines for appropriate specimen transport, processing, and in-lab storage for each analyte, based upon their stability at various times and temperatures.

  12. PDF 12 Computers and Specimen NOT FOR SALE OR DISTRIBUTION Handling and

    and processing. 3. Describe the steps involved in processing the different types of specimens, time constraints, and exceptions for delivery, and list the criteria for specimen rejection. 4. Identify OSHA-required protective equipment worn when processing specimens. Study the information in the TEXTBOOK that corresponds to each objective to prepare

  13. PDF Specimen Collection, Handling, Transport and Processing

    Part 1: Specimen Collection Handling and Transport Specimen Quality is Important The results of tests, as they affect patient diagnosis and treatment, are directly related to the quality of the specimen collected and delivered to the laboratory. http://www.aphl.org/aphlprograms/infectious/tuberculosis/Pages/tbtool.aspx

  14. Specimen Collection, Handling, and Processing

    Specimen collection handling and processing are key to the pre-analytic phase of clinical laboratory testing. In computer jargon there is an acronym "GIGO" that stands for garbage in equals garbage out. It is also applicable to clinical laboratory testing. If the proper sample is not collected from the individual to be tested and if the ...

  15. An Introduction to Specimen Processing

    Introduction " Tissue processing " describes the steps required to take an animal or human tissue from fixation to the state where it is completely infiltrated with a suitable histological wax and can be embedded ready for section cutting on the microtome.

  16. III. Specimen Processing

    12. Bourbeau P, Riley J, Heiter BJ, Master R, Young C, Pierson C. 1998. Use of the BacT/Alert blood culture system for culture of sterile body fluids other than blood. J Clin Microbiol 36:3273-3277. Google Scholar; 13. Hughes JG, Vetter EA, Patel R, Schleck CD, Harmsen S, Turgeant LT, Cockerill FR. 2001.

  17. case study 12.docx

    Jamie Sanguins Phlebotomy Case Study's Chapter 12 Case Study 12-1: Specimen Handling and Collection Verification 1. Because the sample wasn't properly verified and remained unverified while on the rack and unlableled so there is a possibility that the sample isn't the patients. 2.

  18. Module 12 COMPUTERS AND SPECIMEN HANDLING AND PROCESSING

    protected from direct sun exposure. All blood specimen transported by a courier must be. centrifuge. A ________________ machine spins blood and other types of specimens at a high number of revolutions per minute. Fluid resistant shoe covers. All of the following are required PPE when processing specimens EXCEPT. -Fluid resistant lab coat.

  19. Chapter 12 Computers and Specimen Handling and Processing Study and

    Chapter 12 Computers and Specimen Handling and Processing Study and Review Questions. ... After obtaining a specimen for a cold agglutinin test, the blood must be ... All of the following are required PPE when processing specimens EXCEPT a. Chin-length face shields b. Disposable gloves c. Fluid-resistant lab coats d. Fluid-resistant shoe covers ...

  20. Specimen Collection and Processing

    Types of biological specimens that are analyzed in clinical laboratories include (1) whole blood; (2) serum; (3) plasma; (4) urine; (5) feces; (6) saliva; (7) spinal, synovial, amniotic, pleural, pericardial, and ascitic fluids; and (8) various types of solid tissue.

  21. Critical Thinking Questions Ch 12 Flashcards

    1 / 9 Flashcards Learn Test Match Q-Chat Created by fireflight321 Computers and Specimen, Handling and Processing Students also viewed Phlebotomy final exam 111 terms shakeria_richardson Preview Chapter 12 30 terms spydirwebb Preview walking away 5 terms incredibleprithika Preview HPSS Third Hundred Words 3I Teacher 10 terms Paula_Minifie Preview