Mislabeled laboratory specimens are a common source of harm to patients, such as repeat phlebotomy; repeat diagnostic procedure, including tissue biopsy; delay in a necessary surgical procedure; and the execution of an unnecessary surgical procedure. Mislabeling has been estimated to occur at a rate of 0.1% of all laboratory and anatomic pathology specimens submitted.
To identify system vulnerabilities in specimen collection, processing, analysis, and reporting associated with patient misidentification involving the clinical laboratory, anatomic pathology, and blood transfusion services.
A qualitative analysis was performed on 227 root cause analysis reports from the Veterans Health Administration. Content analysis of case reports from March 9, 2000, to March 1, 2008, was facilitated by a Natural Language Processing program. Data were categorized by the 3 stages of the laboratory test cycle.
Patient misidentification accounted for 182 of 253 adverse events, which occurred in all 3 stages of the test cycle. Of 132 misidentification events occurring in the preanalytic phase, events included wrist bands labeled for the wrong patient were applied on admission (n = 8), and laboratory tests were ordered for the wrong patient by selecting the wrong electronic medical record from a menu of similar names and Social Security numbers (n = 31). Specimen mislabeling during collection was associated with “batching” of specimens and printed labels (n = 35), misinformation from manual entry on laboratory forms (n = 14), failure of 2-source patient identification for clinical laboratory specimens (n = 24), and failure of 2-person verification of patient identity for blood bank specimens (n = 20). Of 37 events in the analytic phase, relabeling all specimens with accession numbers was associated with mislabeled specimen containers, tissue cassettes, and microscopic slides (n = 27). Misidentified microscopic slides were associated with a failure of 2-pathologist verification for cancer diagnosis (n = 4), and wrong patient transfusions were associated with mislabeled blood products (n = 3) and a failure of 2-person verification for blood products before release by the blood bank (n = 3). There were 13 events in the postanalytic phase in which results were reported into the wrong patient medical record (n = 8), and incompatible blood transfusions were associated with failed 2-person verification of blood products (n = 5).
Patient misidentification in the clinical laboratory, anatomic pathology, and blood transfusion processes were due to a limited set of causal factors in all 3 phases of the test cycle. A focus on these factors will inform systemic mitigation and prevention strategies.
Specimen misidentification in laboratory medicine can have significant consequences for patients. Mislabeled tissue specimens will often result in harm to patients, such as undergoing unnecessary surgical or diagnostic procedures. In other cases, patients will be subjected to unnecessary diagnostic studies or experience significant delays in the treatment of medical conditions they never knew they had. Mislabeling specimens can lead to unnecessary hospitalizations or failure to treat unreported conditions. If a patient suffers no physical harm from specimen misidentification, the prospect of being subjected to additional procedures is not without risk. Such additional procedures, such as prostate biopsy, colonoscopy, fine-needle aspiration of the lung, phlebotomy, or other diagnostic testing, are not without risks that have associated opportunity costs to patients, families, the health care system, and society.
We set 3 objectives for this study. The first was to develop an algorithm for the categorization of adverse events in laboratory medicine mapped to the 3 phases of the testing cycle. The second was to better understand how and why patient specimens can be misidentified in the laboratory. In the third objective, we considered potential solutions to reduce the probability of future recurrence of these adverse events.
Q-PROBES was established in 1989 as the first major interinstitutional quality improvement program sponsored by the College of American Pathologists; it has yielded a multitude of studies evaluating indicators of laboratory quality and led to Q-TRACKS in 1998, which has developed measures for tracking laboratory quality. In a Q-PROBES study of 1 004 114 cases from 417 health care institutions, Nakhleh and Zarbo1 identified laboratory specimen accessioning and identification deficiencies in 60 042 cases (6%). The most common deficiency was “no clinical history or diagnosis present on the requisition slip.” 1 Valenstein et al2 surveyed 127 clinical laboratories for information about specimen misidentification and found 345 adverse events reported during a 5-week period, or a rate of 55 events per 1 million billable tests. Most of the events caused material inconvenience to patients without causing harm. Extrapolating these data to all clinical laboratories in the United States, the authors estimated that more than 160 000 adverse events per year result from patient misidentification of patients' laboratory specimens.
Wagar et al3 reported a longitudinal study of laboratory specimen misidentification. Of 4.29 million specimens and 2.3 million phlebotomy requests during a 26-week period, 16 632 misidentified specimens were found, for a rate of approximately 1 in 1000 opportunities. The 3 most common categories of misidentification events were mislabeled specimens (1%), requisition/specimen mismatches (6.3%), and unlabeled specimens (4.6%). The authors implemented 3 patient safety projects: (1) expanding phlebotomy service to 24/7; (2) introduction of an electronic event-reporting system; and (3) activation of an automated processing system. Trend analysis showed a decrease in all misidentification events during a 26-month period.
Valenstein and Sirota4 concluded that specimen misidentification occurs most frequently in the preanalytic phase of the laboratory test cycle, with specimen collection, labeling, and transport to the laboratory. They estimated the misidentification rate in transfusion medicine with blood products to occur in 0.05% of specimens, with a much higher rate of approximately 1% of specimens for the clinical laboratory. However, they acknowledged that the multiple specimen transfers and handoffs with anatomic pathology specimens may confer the highest misidentification rate of all laboratory specimens.4 Zarbo and D'Angelo5 decried the risks for misidentification in surgical pathology as directly related to “transfer points in sequential processes with identification defects occurring in mostly manual work processes.” They found that 78% of “defects” were related to the slide-labeling processes, citing “pencil writing on slides, affixing labels to slides after staining, and mislabeled cassettes” as contributing factors.5
In June 2008, Paxton6 described the College of American Pathologists' 2007 Q-Probes report from the College of American Pathologists Quality Practices Committee, titled “Specimen mislabeling errors.” In this study, Wagar et al reviewed 3.4 million clinical laboratory specimens collected at 147 institutions and identified 3043 mislabeled or unlabeled specimens. They calculated a median specimen misidentification rate for US laboratories to be 1.31 per 1000 specimens. The chief recommendations from this study were to institute ongoing quality monitors for specimen identification and to have 24/7 phlebotomy services for centralized specimen collection for inpatients.6
With regard to transfusion medicine, in a Japanese study based on a survey of 777 hospitals during a 4-year period, 115 hospitals (20%) had experienced ABO blood type–mismatched transfusion reactions. These cases were due to misidentification of blood bags (43%), incorrect blood typing (15%), and failure to identify the patient (11.5%).7
In a prospective cohort study of surgical specimens, Makary et al8 found 91 surgical specimen misidentification cases from a total of 21 351 specimens, for a rate of 4.3 per 1000. These misidentification events were due to 18 unlabeled specimens, 16 empty containers, 16 laterality incorrect, 14 incorrect tissue site, 11 incorrect patient, 9 no patient name, and 7 no tissue site. These events were more prevalent when specimens were collected from outpatient clinics (53 of 10 354; 0.512%) than from an operating room (38 of 10 997; 0.346%). The authors estimated from their data an annualized rate of surgical specimen misidentification in their health system of 182 mislabeled specimens per year.8
The Joint Commission for the Accreditation of Healthcare Organizations has issued national patient safety goals that apply to laboratory medicine services, including surgical pathology. The first 2008 Laboratory Services National Patient Safety Goal is: “Improve the accuracy of patient identification.” 9 The College of American Pathologists has issued its own national goals for patient safety in the laboratory, the first of which is to “[i]mprove patient and sample identification.” 10
Improvement Efforts for Specimen Identification
To improve the accuracy of patient identification in the laboratory, the underlying contributing factors that have led to misidentification must be understood. Nakhleh11 identified many of the important contributing factors leading to patient adverse events in surgical pathology: variable input, complexity, human intervention, time constraints, handoffs, and inflexible hierarchic culture. Compared with the clinical laboratory and transfusion service, surgical pathology was acknowledged as more vulnerable to specimen misidentification because of the number of handoffs with tissue specimens: collection, labeling, transport, accessioning, dissection, transfer to cassette, transfer to block, transfer to slide, transcription of findings, and reporting.
There has been considerable interest in the laboratory medicine community for improving specimen identification. Most agree that automation of the specimen processes will enhance patient safety, citing the aphorism that “humans do poorly at routine repetitive tasks…[m]achines on the other hand are best for these tasks.” 11 Many of the processes in the clinical laboratory and some in surgical pathology have been successfully automated. Wagar et al3 demonstrated a significant decrease in mislabeled specimens during a 26-month period after implementing 24/7 laboratory medicine phlebotomy service, electronic event reporting, and an automated processing system.
Manual labeling of tissue containers, requisition slips, and microscopic slides remains a common practice today. The “batching” of specimens and printed labels for multiple patients is also a prevalent practice. One solution to address this problem that has been applied in several health systems is to use wireless bar code technology to match the identification of patient and specimen at the episode of specimen collection and affix the specimen label generated at the point of care.3 Hayden et al12 reported their experience in using bar code identifiers and handheld personal digital assistants supporting real-time order verification. They estimated a decreased rate of misidentification during the 3-year course of their study, and they estimated that 62 mislabeling events were prevented in the first year of operation.12 Davies et al13 demonstrated that a bar code patient identification system with handheld computers applied in cardiac surgery improved blood sample collection, the collection of blood from blood refrigerators, and the documentation of transfusion.
MATERIALS AND METHODS
A qualitative data analysis was conducted on a data set of 227 root cause analysis (RCA) reports extracted from the database of the Veterans Affairs (VA) National Center for Patient Safety (NCPS). These data were self-reported narratives of RCA cases that were submitted by VA medical centers to the NCPS from March 9, 2000, to March 1, 2008. All RCA case reports were organized in a format that followed a specific protocol as outlined in the Veterans Health Administration (VHA) National Patient Safety Handbook, available on the NCPS Web site.14 From a total of 14 858 individual and aggregate RCA case reports in our database, we built a data set of 227 RCA reports (Table 1) of 253 adverse events in laboratory medicine through an iterative process of data mining and data cleaning. We identified 26 RCA reports with multiple adverse events. Of those reports, 23 cases included 2 events, and 3 cases had 3 events. We identified 182 RCA reports (72%) involving patient misidentification in laboratory medicine (Table 2).
Our search of the RCA database was an iterative process that generated 2 summary reports15,16 in 2006 and culminated in a comprehensive search employing a Natural Language Processing (NLP) program, PolyAnalyst, a data and text mining software program (Megaputer Intelligence Inc, Bloomington, Indiana). Primary and secondary search terms that were employed are listed in Table 3. These search terms were used for text mining of the narrative data using the NLP program. A combination of data-driven discovery and automated categorization of reports in the RCA database facilitated significant improvements in the quality and speed of analyses compared with traditional key word searching and manual analysis of text. The NLP program facilitated our detection of common patterns and emerging trends of causal contributing factors specific to close calls and adverse events in laboratory medicine.
The unit of analysis for this study was the VA medical center providing laboratory medicine services for veterans. Each case report represented an individual event or, in only 4 instances, aggregated events, that involved laboratory medicine services occurring in VA medical centers. During the 8-year timeframe for this study, the VHA consolidated from more than 170 health systems into 153 VA health systems providing these services for inpatient and ambulatory care by medical centers and affiliated community-based outpatient clinics. The community-based outpatient clinics have typically collected patient specimens from their patients and forwarded them to their affiliated medical centers for laboratory processing, analysis, and reporting of the results. The RCA case reports for this study involved veterans receiving care in these health systems.
Data Mining and Search Progression in NLP
Our data search was augmented by the NLP software program. This application facilitated the creation of text fields associated with RCA case reports relevant to patient identification in laboratory medicine. This program enabled data “cleansing” through text analysis. After the RCA database was loaded into the program (“Dataset Link” icon; Figure 1), general search terms with embedded event categories were selected for the initial search (“Dimension Matrix” icon; Figure 1). Primary search terms, such as “laboratory, misidentification, delay in diagnosis/treatment, and correct surgery,” were used, and these terms were expanded to include “specimen and identification.” Text fields incorporating the primary and secondary search terms listed in Table 1 were created and used to refine the search. Next, specific cases were identified and added to the data set, and other cases were removed in the process of building a relevant data set within the boundaries of the study (“Create Text Field” and “Filter Column” icons; Figure 1). Data mining was further refined as to location of event and major event categories, or root nodes (Figure 2). Finally, the identified RCA case reports were compiled for export into a data file suitable for individual case reviews.
As of this writing, the NCPS RCA database included 14 858 RCA reports, of which 10 381 were individual cases and 4477 were aggregate reviews involving multiple similar cases from the same medical center (NCPS RCA database, accessed June 16, 2008). Of the 227 RCA reports identified in our data set, 223 were individual reports and 4 were aggregate reviews, which involved 126 individual events. In every case report, at least 1 patient was harmed at some level. The level of patient harm ranged from the inconvenience of a second phlebotomy to the risk of undergoing an unnecessary medical diagnostic procedure or unnecessary surgical procedure, or significant delays in medical treatment. Therefore, the minimum number of patients harmed in our data was 223 + 126 = 349. In each of the anatomic pathology cases, at least 2 patients were harmed—one who had undergone an unnecessary diagnostic or therapeutic procedure and another who experienced delay in diagnosis or definitive treatment for a medical condition.
Forty-two variables were coded for each RCA case report. Of these, 8 were selected for review. These included case identification, event date, station number, case description or final understanding (a summary of the event), root cause contributing factors (why the event happened), action (recommendations for reducing the probability of future recurrence), and the short phrase categories event (“selected event 1”) and subcategory event (“selected event 2”).
Each RCA report was reviewed and coded by the authors independently. A unique coding algorithm was developed during the process of case reviews (Table 4). During the course of our case reviews, we built our coding algorithm through a process of iterative improvements by refining our categorization of the RCA events. As a result of this process, we established major categories and subcategories for coding our data. We also added a variable for “patient harm,” coded as 0 or 1.
After independent case reviews and coding, the authors reconciled their differences in coding by a second collaborative review of every case report. We found minor differences in our coding in 10% of cases. Initial coding was done according to major laboratory test cycle categories as described in the Department of Health and Human Services' Clinical Laboratory Improvement Amendments of 1988.17 This law was the basis for federal regulation of all clinical laboratories testing specimens from humans. The major test cycle categories included preanalytic (from specimen collection until laboratory transport), analytic (from specimen accession into the laboratory until completion of laboratory analysis with result), and postanalytic (from determination of laboratory result until reporting of result or, in the case of the transfusion service, distribution of a blood product). The laboratory result could be from the hematology, chemistry, or microbiology laboratory, from anatomic pathology, or from the transfusion service testing for future blood product administration.
Each RCA case report was coded for major laboratory category and subcategory. In each case, the root cause contributing factors were identified, and action step recommendations to address those factors were noted. In addition, representative case examples were selected and summarized for each laboratory category and subcategory to enhance understanding. The focus of our analysis was on patient misidentification in laboratory medicine, which accounted for 88% of preanalytic cases, 47% of analytic cases, and 37% of postanalytic cases. Our goal in this study was to increase understanding of how and why these events occurred through qualitative analysis of emerging themes in the data. We were not attempting to estimate the prevalence of these events.
All 227 RCA case reports were examined by year for trends in reporting (Figure 3). Each RCA reported at least 1 adverse event related to laboratory medicine; 23 RCAs included 2 events, and 3 RCAs included 3 events. A total of 253 events were reported in 227 RCAs. There was a general increase in the number of laboratory medicine RCA reports from 2000 to 2008. The data were segregated according to phase in the laboratory test cycle (Figure 4). From a total of 253 laboratory medicine adverse events, 150 occurred in the preanalytic phase, 68 in the analytic phase, and 35 in the postanalytic phase. Patient misidentification events accounted for 73% of the 253 adverse events. The breakdown of the misidentification events included 73% preanalytic, 20% analytic, and 7% postanalytic phases. Seven of the RCA cases in our data set were coded as overlapping 2 phases in the laboratory test cycle.
The RCA case reports were divided according to phase in the laboratory test cycle, event category, and subcategory. These events were examined by year for trends in reporting (Figure 5). Since 2001, the number of preanalytic laboratory medicine events has increased steadily, with an accelerated rate of increase observed in 2007. A similar accelerated rate of increase was also observed in analytic and postanalytic case reports for 2007.
One potential rationale for the steady increase in the rate of reported events could be an enhanced awareness of patient misidentification related to laboratory medicine through patient safety programs in VA facilities. In addition, development of the VA Bar Code Expansion project started in 2006. This project has focused on specimen labeling with application of wireless bar code technology at the episode of specimen collection. The Bar Code Expansion project has brought national attention to the risks of patient misidentification through mislabeled clinical laboratory and anatomic pathology specimens.
The RCA case reports were segmented by laboratory phases in the test cycle, as well as categories and subcategories of laboratory medicine events according to our algorithm (Table 4). Each reported event was coded for what happened. We did not code events for why the event happened (causal contributing factors) or for recommendations to reduce the probability of the event recurrence in the future.
In our data set, patient misidentification was associated with 88% of preanalytic events, 47% of analytic events, and 37% of postanalytic events. The RCA case examples in abbreviated form appear in Table 5. These cases are organized by phase in laboratory test cycle, a brief description of what happened, causal factors attributed by the RCA teams, and associated patient harm.
Since 2002, there has been a trend of increasing overall RCA case reports (Table 6). Quarterly reports of aggregate reviews for multiple similar events from the same facility became required during the 2004–2006 period, which may explain the high rate of reporting during that time period. The reporting requirement of aggregate reviews was reduced to biannual frequency in 2007, which may explain the relative decrease in reporting these events in 2007. The frequency of RCA events in our database does not represent their prevalence, because we cannot assume 100% reporting of all events. However, the VHA has developed a much more reporting culture since the inception of the patient safety program in 1999; the larger number of RCA case reports that have accrued over this time period compared with the private sector is evidence of that fact. Therefore, the trends in reported laboratory medicine events are relevant to comparisons between VA medical centers within the context of the VA health system.
The rate of increasing annual laboratory medicine–related RCA reports followed the same trend of overall RCA case reporting. This trend was true for laboratory medicine–reported events in all 3 phases of the test cycle, although case reports from the preanalytic phase had a much sharper increase (Figure 5). However, an accelerated rate of case reporting was observed for laboratory medicine events in 2007 that was also consistent with the sharp increase in individual RCA case reports (Figure 6). One explanation for accelerated reporting in 2007 could be the new minimum requirement, which began in 2007, for VA medical centers to submit 4 RCA reports per year. An alternate explanation could be an increased awareness of patient safety in the organizational culture of VA facilities. Finally, patient misidentification events related to laboratory medicine have had a higher profile because of the attention brought to VA facilities by the national program development of the Bar Code Expansion project, which began in 2006.
Of 227 RCA case reports, 72% were associated with patient misidentification from mislabeled specimens; failure to use 2 sources of patient identification, or failure of 2-person patient identification when drawing blood for a type and crossmatch; misinformation on laboratory forms; mislabeling specimens, tissue cassettes, and microscopic slides with the wrong accession number; failure of 2-pathologist reviews of microscopic sides with a cancer diagnosis; and wrong blood product transfusion associated with failure of 2-person identification prior to initiating transfusion (Figure 7). The major finding from our study was the considerable risk for patient harm due to laboratory specimen mislabeling.
Several common themes were identified by RCA teams in our analysis of the narrative data. Patient misidentification in the preanalytic phase of the test cycle was largely due to mislabeling during the process of specimen collection. Several contributing factors identified by RCA teams were the batching of unlabeled specimens and the presence of printed labels from multiple patients in common areas of the emergency department, operating room suites, and nursing units. These labels were often collocated in proximity to a common printer in a clinical unit.
Another important factor was the collocation of patients in the same clinical units, such as the operating room, clinic, med-surg unit, or emergency department, with similar names, Social Security numbers, and birthdays. Surgical specimens from different patients were submitted in the same container to the laboratory. Laboratory tests were ordered for the wrong patient because of selection from a menu of patients with similar names and last 4 digits of their Social Security number from Vista, the VA electronic medical information system. Patients had the wrong patient wrist band affixed on admission, which was due to the selection from a menu of patients with similar names, last 4 Social Security number digits, and birthdays. Manual entry of identifying information on outdated and user-unfriendly laboratory forms, such as the SF 515 or SF 518, contributed to patient misidentification. Several RCA teams cited these forms as user friendly, with detailed analysis from a usability perspective. In addition, the elimination of the embossed addressograph patient identification cards requiring manual entry of identifying patient information was attributed by RCA teams as a vulnerability for patient misidentification on these forms.
In the analytic stage of the test cycle, manual entry of accession numbers on laboratory specimen labels after they had been received into the laboratory contributed to specimen misidentification. Mislabeling of microscopic slides was also associated with manual entry and limiting the patient identifier to accession number only. Batching multiple slides from different patients in the same folder for microscopic review was associated with pathologists reporting findings into the wrong patient's medical record. The postanalytic stage comprised reporting laboratory results into the wrong medical record, delays in reporting critical results, and reporting inaccurate results.
Blood transfusion events were attributable to blood drawn from the wrong patient for type and crossmatch because of failure of confirming 2-source patient identifiers, misidentified blood products from discrepant attached forms, and failure of 2-person identification of blood product and patient prior to initiating the transfusion. Each RCA case was evaluated for “harm to the patient,” which was described in the analysis. Each event subcategory was analyzed from the perspective of why and how these events occurred. The authors reviewed recommendations offered by RCA teams to prevent recurrence of these events in the RCA reports.
Patient harm was identified in virtually all adverse events and close calls from our study sample. Each episode of patient misidentification was found to affect more than 1 patient. Mislabeled biopsy specimens were associated with wrong patient surgical procedures (eg, hysterectomy, radical prostatectomy, pulmonary lobectomy), delay in necessary surgical procedures, delay in diagnosis and necessary medical treatment, blood specimen redraw, and repeat tissue biopsy, as well as wrong patient diagnosis, reporting, and treatment (eg, medications and blood products).
Usability of Laboratory Forms
There were 14 reported cases involving errant manual entry of patient identifying data into standard forms that accompanied laboratory specimens and blood products. Misinformation on the SF 515 form for clinical laboratory and anatomic pathology and the SF 518 for blood bank specimens and products contributed to patient misidentification. Although these forms have been used successfully in the VA and the Department of Defense for decades, RCA teams cited similar usability challenges in both forms, such as small font size, limited white space for manual data entry, and spaces for extraneous data elements. These forms had been originally designed for addressograph stamping of embossed patient identification cards. In 2003, because of privacy concerns, the embossed cards were replaced with the Veterans Identification Card, which contains the patient identifying data in a magnetic strip. However, the laboratory and blood bank forms had not been redesigned since that change occurred. These problems will be addressed with the implementation of the VA Laboratory Reengineering Project. This program will automate the SF 515 and SF 518 forms with digital templates, redundant patient identification screens, and electronic signatures to improve the accuracy of information transfer.
Root cause analysis case reporting is required for all VA medical centers. Nevertheless, we cannot assume 100% reporting of all adverse events in the VA. As source data in this study, RCA reports do not represent the prevalence of laboratory medicine adverse events and close calls. Rather than focus on the prevalence of events, we were interested in how and why these events occurred and what could be done to reduce their probability of future recurrence. With that purpose in mind, we observed emerging themes from text analysis of these narrative reports.
It is important to understand the limitations of RCA case reports. Although these reports are replete with rich narrative data, they do not include all of the patient clinical information that can be found in a medical record. In conducting an RCA, team members have access to the patient's medical record. Technical details, such as laboratory values, operative notes, and radiograph reports, cannot be found in an RCA report. However, the medical record is not part of an RCA narrative report.
Another limitation of this study is linked to limitations inherent in RCA data. The strength of an RCA is directly related to rigor of inquiry by an interdisciplinary team. It is entirely possible that RCA teams in our data set missed important causal contributing factors associated with the events they were examining. Similarly, the recommendations of these teams are dependent on the rigor of their analysis and limited by hindsight bias, as suggested by Hofer and Hayward.18 However, the authors admitted their case study was largely one of clinical judgment and technical competence rather than one of systems issues, which an RCA is designed to address. Nevertheless, the value of RCA is its ability to explain behavior rather than make judgments of “what should have been done.” 19
Many of the important RCA team recommendations from this study are displayed in Table 7. The focus of RCA team recommendations in the VHA is to reduce the likelihood of future adverse events in their respective medical centers. Some of the strongest recommendations included the following:
Application of wireless bar code technology at the bedside to confirm patient identity and affix a bar code label to a specimen immediately after collection.
Application of bar code technology to the blood transfusion process.
Use of unique patient identifier for selecting a patient medical record and for labels on all specimens and blood products (full Social Security number is currently the unique patient identifier in the VA).
Automate laboratory forms limited to electronic data entry and eliminate all manual entry for specimen labeling.
Eliminate relabeling clinical laboratory and anatomic pathology specimens with accession numbers after they are received in the laboratory, and move the accession numbering process forward to the original label generated immediately after specimen collection.
Continuously available centralized phlebotomy service for hospital inpatients.
Eliminate all paper labels in the operating room with all room turnovers before admitting the next patient to the room.
Forcing function for 2-pathologist review as required documentation in final pathology report of all anatomic pathology slides with a cancer diagnosis.
There is evidence that automating laboratory processes through all 3 phases of the laboratory test cycle has been demonstrated to reduce patient misidentification, increase efficiency, and increase the productivity of operations. When these improved processes were linked to an updated laboratory information system, the turnaround time for reporting laboratory results to clinical decision makers was reduced, and the quality of patient care was enhanced.20
The goal of this study was to enhance our understanding of system vulnerabilities that lead to adverse patient events related to laboratory medicine. Our focus was on patient misidentification from mislabeled laboratory specimens, failure of 2-source patient identification during specimen collection, and failure of 2-person verification of patient identity before collecting blood specimens for type and crossmatch or prior to blood product transfusions. We wanted to understand why and how these events occurred. Our purpose was also to stimulate ideas of what could be done to prevent future recurrence of these events. Coding of laboratory medicine events by 3 phases in the laboratory testing cycle, with event categories and subcategories, facilitated our data analysis and identification of emerging themes. Subsequent review of RCA team recommendations stimulated our thinking about further recommendations for consideration by health systems. If any of these interventions are implemented, future studies will be necessary to evaluate their effectiveness in reducing the likelihood of patient misidentification in laboratory medicine.
The authors have no relevant financial interest in the products or companies described in this article.
We wish to acknowledge the efforts of Dea Mannos Hughes (New York Harbor VA Health System, New York, New York) and Carol Samples (VA National Center for Patient Safety, Ann Arbor, Michigan) for their work in data collection and summary reporting in the early phases of this study. We also wish to extend our appreciation to Scott McKnight, Aartee Ignaczak, and Jim Turner (VA National Center for Patient Safety) for their assistance in recent data collection and application of the NLP software program to facilitate our analysis. We thank the VA Ann Arbor Healthcare System Department of Pathology and Laboratory Medicine: Stephen Chensue, MD, service chief; Hedwig Murphy, MD, staff pathologist; and Aron Pollock, Lyn St. Dennis (surgical pathology), Joseph J. Mraz (blood bank), Bruce Haugen (hematology), Parul Shah (microbiology), and Cheryl Rollins (blood bank) for providing their insight into laboratory processes. Finally, we wish to thank Michael Brophy, Quynh Vantu, and Valerie Miller from the VA Central Office Laboratory Medicine, Washington, DC, for their insightful comments, which informed this study.
From the Lexington VA Medical Center, and the Department of Surgery, University of Kentucky, Lexington (Dr Dunn); and the Veterans Health Administration, Division of Ambulatory Care and Integrative Medicine, VA Ann Arbor Healthcare System, Ann Arbor, Michigan (Dr Moga).