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Project Description

The purpose of this project is to review the process and to implement barcode scanning technology into the workflow process to achieve the goals of 2024 national patient safety goals of utilizing two patient identifiers, decreasing improper specimen handling errors, increasing patient safety, and decreasing costs due to mislabeling errors.

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Barcodes are synonymous with specimen labeling accuracy. Barcoding systems are four times less likely to produce errors than manual entry methods. 1 Because of this, barcodes are more common in laboratories now than ever before. Specialized barcode systems help improve every aspect of the laboratory workflow.

Whether you're looking to improve your laboratory management system or implement something new, there is a barcode system for every lab. Follow a sample from collection to storage to see how the latest advancements in barcode technology can improve accuracy in your lab.

Sample Collection

Barcode specimen collection systems have three parts: computer, barcode scanner and label printer.

Whether it's at point-of-care or in the field, collecting samples is an essential but sometimes tumultuous process. In the hospital, nurses and phlebotomists must work expediently to fulfill STAT orders without delay. On the other side of the spectrum, field technicians engaged in ecological research must cope with uncertain environmental conditions. In these conditions, it's unsurprising that handwritten labels are at a high risk of being illegible or incomplete.

With a barcode system, sample data is entered directly into the Laboratory Information System (LIS) database. Samples labeled with a barcode link directly to the LIS data, which makes them much easier to process once they reach the lab.

Hospital

In the hospital, barcodes are already a standard tool for patient identification. When integrated with an LMS database, barcodes help point-of-care staff communicate with doctors and laboratory technicians to ensure samples are collected and labeled accurately.
A barcode specimen collection system has three parts: computer, barcode scanner and label printer. A staff member qualified to collect samples typically follows these steps:

1. Scans their ID badge to unlock their computer and identify themselves as the sample collector.

2. Identifies the patient by scanning their wristband.

3. Prints collection label.

4. Draws the sample and labels the tube.

5. Scans labeled tubes again for verification. 2

The system automatically applies a timestamp to the collection information, as well as the collector ID. At the lab, the staff member only needs to scan the sample's barcode to document its receipt. With all necessary collection data already uploaded to the LIS, testing can begin right away. 2

Fieldwork

In some areas of scientific study, gathering samples in less-than-ideal conditions is the norm. Fieldwork presents unique challenges to sample collection, but barcodes are an excellent solution.

Despite advances in barcode technology, it is still common for field technicians to rely on handwritten notes to help identify samples. In some cases, handwritten notes are not entered into the laboratory database until months after collection, increasing the risk of data loss. With the use of a barcode system, samples are with accuracy during collection.

One laboratory implemented the following solution:

1. Before fieldwork begins, barcode labels are printed in bulk and applied to empty sample containers. Each barcode contains a unique identifier that corresponds to a storage location in the lab.

2. After collecting a sample, the field technician scans the barcode on the container, which opens data entry fields on a handheld computer.

3. The field technician enters the collection data on the handheld computer.

4. Collection data is uploaded to the Laboratory database to await sample arrival. 3

In this solution, sample collection data may be updated at any time by scanning the barcode with the handheld device and updating the relevant data entry fields. From field to lab, the barcode ensures that each sample is traceable and easy to locate.

In The Lab

Barcode labels are printed on the BMP21 and applied to microscope slides in the lab.

After collection, a sample is sent to the laboratory for analysis. Here, barcode technology helps laboratory technicians spend more time analyzing samples and less time agonizing over paperwork.

Audit Trail

An audit trail means everything in laboratory testing. Whether it's for the sake of research reproducibility or patient safety, an audit trail ensures that a sample has passed through the correct protocols during analysis. A LIS database can easily track the audit trail of hundreds of samples, and with an integrated barcode scanner, it can do it quickly and accurately. In barcode-optimized labs, chain-of-custody information -including technician ID, timestamp, and location- is updated automatically with the scan of a sample's barcode.

Verification

Preventing sample destruction and data loss is the purpose of verification. With barcodes, verification is efficient and accurate. Some laboratories implement barcode scanners as another level of verification before using an automated analyzer. Scanning a barcode at an analyzer pulls up the tests ordered for the associated sample. This way, the technician is alerted if there is a mismatch.

Comparators

Sometimes, a large sample is divided into several containers for testing. These "child" samples are linked to the "parent" sample and are labeled using the same unique identifier. For an extra level of verification, a barcode comparator can check the barcodes against one another and ensure that they match.

Sample Storage

Locating samples in storage is easy with barcodes. They may be used in different ways, depending on your system.

A simple method requires only the use of a barcode scanner and a digital spreadsheet. First, a freezer plate is labeled corresponding with the spreadsheet cels (a1, b2, etc.). When samples are placed in the freezer plate, they are scanned into the corresponding cel on the spreadsheet. This creates a map of all the samples in that freezer plate. The process may be repeated by using a new spreadsheet for each new plate.

Another method of sample storage involves embedding the storage location in the sample's unique identifier. This process usually involves custom programming, but it is well worth it for a streamlined workflow.

Bottom Line

Barcodes are an affordable and flexible way to manage samples in your laboratory. When used in combination with a LIS, barcodes are a powerful tool, proven to reduce errors in data entry and processing. From collection to sample storage, barcodes prevent costly mistakes and keep your laboratory operating efficiently.

Want to learn more? Have a chat with our label experts.

Sources:
1 Snyder, Susan R. et al. "Effectiveness of Barcoding for Reducing Patient Specimen and Laboratory Testing Identification Errors: A Laboratory Medicine Best Practices Systematic Review and Meta-Analysis." Clinical Biochemistry, vol. 45, no. 13-14, 2012, https://www.sciencedirect.com/science/article/pii/S0009912012003232?via%3Dihub. Accessed 4 March 2020.

2Trask, Linda. "Barcode Specimen Collection." Medical Laboratory Observer, 14 Dec. 2014, https://www.mlo-online.com/home/article/13007805/barcode-specimen-collection. Accessed 18 March 2020.

3Copp, Adam J. et al. "Barcodes Are A Useful Tool For Labeling And Tracking Ecological Samples." Bulletin of the Ecological Society of America, vol. 95, no. 3, 2014, https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/0012-9623-95.3.293. Accessed 18 March 2020.

4Hazen, Amanda. "Tips from the Repository Trenches: Using Barcodes to Track Samples." Addgene Blog, 6 Oct. 2015, https://blog.addgene.org/tips-from-the-repository-trenches-using-barcodes-to-track-samples. Accessed 18 March 2020.

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Peak Technologies provides labeling solutions for retailers, hospitals, and more. Our experts can lead you to the labeling system right for your business. Buy labels and label makers online or contact us today.

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Discuss how the use of workflow redesign can improve common barriers in healthcare.

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Discuss how the use of workflow redesign can improve common barriers in healthcare.

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Process

The focus of the project is to redesign the blood collection workflow—the redesign of the workflow process by replacing manual specimen labeling with barcode technology.

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Objective

The objective for this redesign workflow process is to be measured with three metrics: mislabeled specimens, unlabeled specimens, and incompletely labeled specimens. The current monthly report contained ten mislabeled specimens, four unlabeled specimens, and seventeen incomplete labeled specimens. The goal of implementing bar code scanning is to decrease error rates by 30-50%.

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Support of the project would be required from administration, laboratory leadership and quality.

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Support of the project would be required from administration, laboratory leadership and quality.

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Current workflow includes labeling of specimens with handwritten labels. This is time consuming and gives way to a large error margin.

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Current workflow includes labeling of specimens with handwritten labels. This is time consuming and gives way to a large error margin.

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Future workflow will include bar code scanning for patient identification and specimen tracking.

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Future workflow will include bar code scanning for patient identification and specimen tracking.

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With the implementation of bar code scanning patient safety is improved and error rates decrease. Patient safety and outcomes are most important and bar code scanning  can be an asset to that. 

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With the implementation of bar code scanning patient safety is improved and error rates decrease. Patient safety and outcomes are most important and bar code scanning  can be an asset to that. 

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· Author: Third Wave Analytics

Mastering the Art and Science of Modern Sample Management

In the lab, efficient and accurate management of samples is crucial. From sample accession to test results delivery, every step of sample processing demands precision, clarity, and high-quality standards.

Without accurate and thorough sample and barcode management, a lab may have suboptimal operations that can get in the way of accurate, timely results. Frustrating delays and misunderstandings between lab personnel and customers can also become common.

Consistent and unique sample identification and intelligent barcoding maintain order in a labyrinth of hundreds or thousands of samples.

This guide will show you how to manage these elements for better lab operations and happier customers.

Why Intelligent Barcodes Make a Lab Run Well

Along with an efficient sample ID system, new forms of barcode technology significantly improve the accuracy and speed with which samples are tracked and identified, reducing manual data entry errors and improving overall efficiency.

Today, the different barcode types used in a lab extend beyond traditional linear barcodes. Modern QR (Quick Response) codes offer advanced data-holding capabilities catering to unique lab requirements. Most labs now require a LIMS to handle modern QR barcodes to maintain accuracy and risk losing customers to more equipped competitors if they lack this capability.

A Closer Look at the Power of Barcodes: Reduced Errors, Improved Operations

Traditionally, manual data entry has been prone to human errors, sometimes resulting in unintended yet drastic consequences. Barcodes mitigate this risk by automating data entry, significantly reducing errors, and accelerating data processing times. With barcodes, laboratory workflow becomes streamlined.

One of the most critical functions of a LIMS is to help lab workers differentiate easily between samples, reagents, and plates. The best and most logical way to separate everything in the lab is for each item to have a unique barcode.

It sounds simple, but barcodes can be complicated; you'll need to ask the right questions to determine what your lab needs from its barcode system. Please see our Lockbox LIMS barcoding functionalities video to learn more about specific barcode considerations.
Where and When to Use Barcodes
First, determine which physical lab items must be tracked with a unique identifier (the barcode). Generally, you'll want to use a barcode to track anything easily confused or swapped with another physical item.
Why Not Just Use Handwritten Labels Instead of Barcodes?
We strongly recommend against relying on handwritten labels for three reasons:

1. Handwriting is often hard to read.

2. You can't always count on team members to write an accurate or reliable description of the sample, reagent, or plate.

3. Some sample tubes are too small to hold more than a few characters, making fitting an accurate and complete handwritten description nearly impossible.

A unique barcode is the solution that replaces handwritten notes and other antiquated lab labeling methods.

Your lab should also use unique barcodes to track samples as you'll likely have hundreds to tens of thousands of pieces in your lab daily. Without barcodes, there's no way to keep track of each sample. Barcodes can track 96- or 384-well plates you keep, which is especially helpful when plates look identical in the freezer or fridge.

Finally, it's also wise to create barcodes for your reagents. You may have dozens of reagent lots in use anytime, making tracking difficult if you don't have barcodes.

Other items you can track with a unique barcode include:

Storage boxes or racks

Storage equipment (e.g., freezers or fridges)

High-complexity testing equipment (e.g., sequencer)

General equipment (e.g., pipette, centrifuge)

Computers, laptops, or computer tablets

When to Use Auto-generated Barcodes

Lockbox LIMS can automatically generate a barcode for any sample, plate, and reagent aliquot. By setting your lab up to automatically generate barcodes, you can ensure that a barcode always exists - and remove the worry of someone forgetting to take this step.

With automatic barcodes, you can reliably trust that the number generated will be unique within your LIMS. This is the most critical requirement for your lab's barcoding because redundant barcodes lead to sample misassociation and incorrect data and results. If you encounter redundant barcodes within your lab, you'll waste valuable time trying to untangle one mixed-up sample from another.

This scenario can be prevented when each barcode in your LIMS system is unique; in other words, when your LIMS automatically generates a barcode for you.

Can A Sample Have Multiple Barcodes or Identifiers?

It is common for some items in a lab (including samples) to require multiple barcodes or identifiers. This scenario is acceptable if your LIMS is organized to keep track of multiple barcodes or identifiers for a single sample.

Applying multiple barcodes to items in your lab can even be beneficial. For example, a clinical testing lab may assign an internal barcode to every sample it receives alongside the external sample barcode that an ordering physician associated with it. With the internal barcode, the lab can be confident that each sample has a unique identifier. At the same time, the lab keeps the external barcode associated with the sample so it can communicate the result appropriately to the correct ordering physician.

Should Barcodes Contain Human-Readable Information?

It's also common and perfectly acceptable for certain portions of a barcode to contain human-readable information. For instance, a barcode including SAL-COV-000188 could indicate the sample is a saliva sample type ("SAL"), with a COVID-19 test order ("COV"), and is the 188th sample ("000188") entered into the lab system.

If a lab usually has various sample types and associated tests, human-readable portions of a barcode are helpful. Lab personnel can immediately know a fair amount of information about a sample by checking its barcode. However, embedding data into a barcode can increase the complexity and take up space on the barcode label - and some are small and only have room for a handful of characters, as noted above.

There is no one best practice for embedding information into a barcode. If each sample has a unique barcode, a lab can add all sorts of helpful information to that barcode if it makes sense for the lab.

When to Generate Barcodes Using Complex Logic

There is nothing inherently wrong with using complicated logic to generate a barcode. Again, the only rule is that each barcode is unique - and if that's the case, you can otherwise make your barcodes as complicated as you'd like.

Highly complicated logic can transform a barcode into more than just a simple identifier. You can encode so much information into a barcode that your lab personnel can immediately know much about the sample from the barcode number.

For example, let's refer to this complex barcode: SAL-COV-OP011-2020.10.10-0001

This sample:

is a saliva sample type ("SAL")

has a COVID-19 test ordered ("COV")

had an Ordering Physician send it with the tracking number "011" in your system ("OP011")

was received on October 10, 2020 ("2020.10.10")

was the first sample you received that day ("0001")

That's a lot of information to be gleaned from the barcode without even looking up the sample in your LIMS. However, it also means your LIMS must process considerable logic to generate a barcode for each sample.

If you want to encode more complex information in your barcodes, check that your LIMS can accurately generate unique barcodes with the required logic. Lockbox LIMS has these capabilities, but not all solutions are up to the task. Using the above example, some LIMS may be unable to easily pull information on the sample type, test ordered, or ordering physician. Some LIMS may also have difficulty resetting the incrementing sample number every day.

Can you Lab Print Barcode Labels From your LIMS?

With a modern LIMS like Lockbox LIMS, you can print barcode labels directly from the system. This ensures the label's accuracy. Unfortunately, most LIMS don't work effectively with a designated barcode or label printer. Consider this when assessing and deciding on a LIMS for your lab. If this function isn't available from day one, it's almost impossible to add it later.

Lockbox LIMS has unique barcode functionality that supports generating and printing barcodes directly from a record in the LIMS , significantly improving efficiency.

If you decide you want to print barcode labels, here are a few important considerations:

1. Will your labels incorporate human-readable characters? Most labs consider it essential that every label has human-readable characters. Otherwise, scanning hundreds of samples to find one specific tube takes a lot of work.

2. Should you have scannable barcodes? Scannable barcodes are handy if you need to find samples within your system quickly. However, not all labs need this functionality; find out if your lab does.

3. If you need scannable barcodes, should they be 1D or 2D? The range of barcodes available today includes traditional linear and two-dimensional (2D) barcodes, including QR (Quick Response) codes. Each barcode type carries varying data capabilities, providing laboratories with flexible options catering to individual needs.

1D barcodes are ideal for encoding information. These barcodes only include numbers and letters - no special characters allowed. The longer the text string encoded, the longer the barcode.

2D barcodes (QR codes) are the new standard for encoding information. These are highly flexible and encode numbers, letters, and many unique characters. 2D barcodes take up much less space, with substantial text strings easily encoded within a tiny square.

Whichever barcode type you choose, ensure your lab's handheld scanner can scan and process the information on the barcodes. Just about all scanners can understand 1D barcodes, but not all can read a 2D barcode. When you're ready to buy scanners, consider what you plan to scan!

Before you add barcodes to your lab, here's a summary of the most critical questions to discuss with your lab team:

Which physical items in your lab require tracking with a barcode?

Should your LIMS automatically generate barcodes for you?

Will anything in your lab (including samples) have multiple barcodes associated with them?

Will you have or need human-readable information contained in your barcodes?

What type of logic is needed to generate your lab's barcodes?

Will you need to print barcode labels directly from the LIMS?

The Big Questions to Consider Regarding Barcodes in Your Lab
Modern Sample Management for Precision Results

Managing samples efficiently and accurately is the most crucial role in lab operations. From the moment a sample enters the lab to the results reported, an unbroken thread of fidelity and care must guide the entire process. The efficiency of the sample management system often defines lab success, dictating the accuracy of results, operational speed, and the level of customer satisfaction.

A logical sample ID system ensures the creation of unique, easily identifiable samples that can be quickly found, even in a sea of samples. Sample IDs are necessary, but there's an art to creating them.

A two-field approach is the best way to start with advanced sample management in the lab.

The Dual Approach to Creating Sample IDs

A two-field sample ID approach is recommended for labs that must balance unique identifiers for proper tracking and client familiarity (so the lab its customers can easily recognize sample data in their respective operating systems). This approach includes a Customer Sample ID and an Internal LIMS Sample ID .

The Customer Sample ID includes any ID a customer assigns to their samples, ensuring a sense of familiarity and ownership for their daily business operations.

The Internal LIMS Sample ID is created within a lab management system as an internal ID generated to meet lab tracking and identification requirements, often more stringent than customer requirements.

Separating these two fields allows a lab to maintain a customer-focused outlook while ensuring smooth internal operations. Customers likely want to see or hear their original sample IDs when a lab delivers the final sample report, and this approach satisfies both parties.

Here's an example: Suppose a customer submits a sample to a lab with the primary ID 'Sample 1.' Chances are a different customer will do the same. If the receiving lab only uses the sample IDs provided by their customers, the lab will quickly accumulate many duplicate sample IDs, leading to confusion and constant errors.

First, store the customer ID (customer 'Sample 1' in this case) in the first of the two data fields in Lockbox LIMS in the section marked 'Customer Sample ID.'

Then, to create the data for the second field, Lockbox LIMS will automatically copy the customer sample ID and amend it so it's globally unique within your Lockbox LIMS platform. Lockbox LIMS completes this action by adding the unique identifier to the end of the non-unique ('Sample 1') customer sample ID.
How to Create Two Sample ID Fields

Working with Complex Sample IDs: Aliquots, Derived Samples, and Child Records. Maintaining identification consistency is even more critical when working with aliquots, derived samples, or child records from a parent sample. Sample IDs should ensure all related or derived samples are easily identifiable, flawlessly trackable, and unambiguously linkable to their source.

A lab must decide on a process to ID and name child records, aliquots, and derived samples. This includes whether to retain the customer's original sample ID in the name of the aliquot or derived sample.

Handling Derivations for Child Records. Labs must also traverse another layer of nuance when using an ID for child sample records. For example, a lab might order these to indicate the sequence in which the child samples were created; a third derived sample could include a "3" within the record ID. Ultimately, this sample management approach is up to the lab to decide upon. If the lab wants to add a sequence counter or unique sample name identifier, Lockbox LIMS also makes this easy to do. Within Lockbox LIMS, specific settings can be turned on or off with a simple point-and-click configuration.

The New Lab Efficiency

With a meticulous sample ID protocol and innovative barcode technology, your lab can expect a significant enhancement to lab management. The two-phased approach also paves the way for transparent, traceable, and customer-centric operations.

As we move toward increased technology integration, labs that embrace advanced practices for sample management will be the chosen environments to deliver precision, speed, and customer confidence.

more insights about laboratory management and discover how a comprehensive LIMS can modernize your lab.

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The Power of Barcodes in LIMS and LIS Systems

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Understand the significance of barcode integration in LIMS and LIS, enhancing sample tracking, data accuracy, and lab efficiency.

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Background: Anatomic pathology laboratory workflow consists of 3 major specimen handling processes. Among the workflow are preanalytic, analytic, and postanalytic phases that contain multistep subprocesses with great impact on patient care. A worldwide representation of experts came together to create a system of metrics, as a basis for laboratories worldwide, to help them evaluate and improve specimen handling to reduce patient safety risk.

Method: Members of the Initiative for Anatomic Pathology Laboratory Patient Safety (IAPLPS) pooled their extensive expertise to generate a list of metrics highlighting processes with high and low risk for adverse patient outcomes.

Results: Our group developed a universal, comprehensive list of 47 metrics for patient specimen handling in the anatomic pathology laboratory. Steps within the specimen workflow sequence are categorized as high or low risk. In general, steps associated with the potential for specimen misidentification correspond to the high-risk grouping and merit greater focus within quality management systems. Primarily workflow measures related to operational efficiency can be considered low risk.

Conclusion: Our group intends to advance the widespread use of these metrics in anatomic pathology laboratories to reduce patient safety risk and improve patient care with development of best practices and interlaboratory error reporting programs.

Patient safety issues within the United States health care system were brought to wide public attention by a report from the Institute of Medicine (IOM) published in the year 2000. The report focused on adverse outcomes from surgical procedures or medication prescription errors and revealed an overwhelming number of undetected diagnostic errors, specifically in the outpatient setting. The IOM report also called for the formation of a federal agency to lead a unified effort in reducing errors that affect patient health. It also highlighted a need for anonymous and standardized reporting, to avoid frivolous malpractice lawsuits., One of the focus areas of the outpatient setting, detailed in the report, was the anatomic pathology (AP) laboratory.

Self-monitoring and self-awareness among individual AP labs resulted in several publications from independent sites on diagnostic errors.,, In response to these initial autonomous efforts, the College of American Pathologists (CAP) established the Q-probes survey and unified the approach to collect AP laboratory workflow errors. A Q-probes survey conducted in the outpatient setting detected an average error incidence rate of 4.8% among a total of 660 institutions. Although accumulative, the Q-probes survey data continue to reflect errors associated with practices restricted within the United States and Clinical Laboratory Improvement Amendments (CLIA)-certified labs. Q-tracks is a voluntary program launched by CAP to capture individual laboratory performance for laboratory services, to track errors and variations in quality. The community based reporting allows individual laboratories to conduct comparisons and collectively improve best practices.

Beyond the CAP-officiated Q-probes survey and Q-tracks program, there were no collective data sets which reflect worldwide practices within the AP-laboratory workflow. Recognizing the value of community input, a few laboratories developed a shared error-reporting database. The Patient Safety Database was created at the Center of Excellence of Pathology Quality and Health Care Research, within the University of Pittsburgh, in Pennsylvania, to collect error data with 3 partner institutions (ie, Western Pennsylvania hospitals, the University of Iowa, and Henry Ford Medical Center). The error database was an attempt to provide measurable data to support resource allocation and decision making, as well as to provide robust evidence for implementation of best practices. However, no comprehensive report was derived from data mining, which likely happened due to the large collection of unstructured data and varied deficiencies in the AP laboratory workflow. The literature suggests that limitations in error reporting are fundamental to the current culture of health care institutions in which reluctance to release unfavorable diagnostics data is ingrained., To date, these combined challenges have hindered development of a comprehensive list on AP workflow process deficiencies for collection of error data.

After the IOM report was released, best estimates suggested that increased surveillance and accountability reduced the rate of misdiagnosis; however, issues remain with AP laboratory processes. The AP laboratory setting continues to be fundamentally challenged by a shrinking workforce. Essentially, there are fewer histotechnology training program graduates, and simultaneously, there has been an increase in retirement of experienced technologists. The strain on resources is offset somewhat as automation replaces a few manual specimen handling processes. However, to truly overcome potential challenges, AP laboratories have proactively adopted the root-cause analysis (RCA) system to identify the causes of common error, thus providing better training. However, data were needed to identify those processes most vulnerable to error that impact patient care, to improve training of the next generation of histotechnologists. Again, the need arises for a list of critical metrics for AP laboratory workflow, in the interest of improving patient care.

Previously, studies concluded that quality issues in the AP laboratory occurred due in part to the inherent diversity of specimens and procurement methods., For example, delivery or procurement of patient specimens into the AP laboratory occurs from various sites and often with no universally shared operating procedures for labeling or fixation. This and other challenges highlight the need to develop and implement new universal best practices. However, once more, there is a need to gather reliable data before implementation of new processes.

In this study, we identified 47 vulnerable workflow steps that can cause diagnostic errors within the AP laboratory. We report these results as a global initiative aimed at identifying the many potential deficiencies within the AP laboratory workflow, in the interest of improving worldwide public health.

Materials and Methods

In 2014, a group of AP laboratory management experts convened as an advisory board for industry. The group realized the expansive potential to improve AP laboratory management practices and rapidly assumed a new independent identity from that of an advocacy group; this new group called itself the Initiative for Anatomic Pathology Laboratory Patient Safety (IAPLPS). The group consisted of highly involved internationally recognized experts in AP laboratory organization, including pathologists, a laboratory administrator, and a pathology assistant. Their first position statement was published in March 2014, calling for an international effort to secure safe, reliable processing of all patient specimens for diagnosis.

Members of this group pooled the experiences of their respective institutions to identify and rank a comprehensive list of steps that they perceived to represent patient risk within the AP laboratory workflow. All 47 metrics were subject to discussion before members reached a consensus on relative risk. Individual members were electronically surveyed with regard to grading each metric as too high, intermediate, or low risk for likelihood of adverse patient outcome. High and low risk groupings were separable, with significant agreement among the membership. However, a group of intermediate-risk measures was not distinguishable, thus leading to a final separation consisting of only high and low risk categories.

Globally harmonized practices and processes within the AP laboratory helped to generate this set of metrics. This list is different from Q-probes criteria or other checklists because it consists of workflow activities that influence risk to patient care and not only standard quality and efficiency. The list specifically focuses on safety threats in the AP laboratory and does not include potential patient safety threats from pathologist interpretative errors because professional training and remediation better address these risks. The scope also excludes preanalytic processes not directly controlled within the AP laboratory, such as specimen handling in the surgical or operating suite. Pathologist interpretation and AP laboratory specimen sourcing remain important issues for future investigation but are not within the scope of this research project.

Results

Exhaustive discussion of this grading led us to realize that in extraordinarily rare circumstances, almost any step in the AP laboratory could lead to an adverse patient event. For example, although typographical discrepancies in the final report usually only pose a minor threat to misdiagnosis, the remote possibility exists that mislabeling can result in a serious medical misdiagnosis. However, although some metrics raise potential severe patient safety concerns, in most instances, even in riskier steps, laboratory professionals avoided actual adverse events by means of early detection.

Figure 1 indicates that we identified high and low risk processes in all 3 phases. Based on this list, the preanalytic phase had 9 possible deficiencies for safety in workflow, of which 4 were designated as high risk. Three metrics listed in the accessioning process were classified as high risk, and 3 others were considered to present low risk to patient care. An example of a high risk scenario is when an inadequate amount of fixative jeopardizes the specimen integrity and results in the need for another biopsy. Some low risk process deficiencies listed have mild consequences, requiring only remedial efforts for resolution, such as correct pathologist case assignment. Other deficiencies, such as mislabeling errors, may be resolved immediately further down the stepwise sequence with limited consequences. However, most of the metrics listed in this article are capable of generating an adverse patient outcome if not properly addressed.

The histotechnology committee affiliated with CAP and the National Society for Histotechnology (NSH) has recently updated a guidance document called the Practical Guide to Specimen Handling in Surgical Pathology. This committee reports to the Council on Scientific Affairs and currently contains 14 members, of which 6 are CAP fellows and 8 are certified histotechnologists and American Society for Clinical Pathology (ASCP) certified. The purpose of the committee is to provide technical information for the improvement of laboratory processes. The specimen handling guideline publication not only identifies process deficiencies but also provides literature to support the entire workflow. The publication also discusses patient safety issues, with the intention to bring about awareness of the Practical Guide to Specimen Handling. It is an extensive report of the types of errors in workflow that can occur in the AP laboratory, which could compromise the quality of the specimen. The report is full of details associated with various processes. Strict criteria for specimen procurement transfer are described in the guide, stating that all containers for specimen material should be rigid, impermeable, unbreakable, and nonreactive to fixative solutions. Also reported are the fixative programs for tissue processing, with references to peer-reviewed manuscripts and to proper waste-management documents. This report provides detailed insights toward optimal performance. However, its content is extensive and is not limited to processes with direct impact on patient care, unlike our metrics list, which focuses on high-risk issues.

Figure 1 suggests that the lengthy analytic phase contains multiple processes associated with the potential for high risk to patients. Features such as processing, staining, and coverslipping specifically contain high risk metrics. Similar to those in the preanalytic phase, most metrics in the analytic phase have the potential to lead to misdiagnosis, if not carefully designed and monitored. Possible solutions to reduce error include advanced automation using individual slide-staining technology and coverslipping, to avoid any cross-contamination among specimens. The potential for error associated with the processing subgroup can be readily addressed with implementation of best practices. Specifically, adhering to proper sequence timing (ie, incorporating tissue type specifications) and performing maintenance of fresh stock reagents will greatly minimize risk.

As highlighted in the final phase of the AP laboratory workflow, the postanalytic phase contains 2 distinct subprocesses. Within these, 6 of the 9 metrics are high risk, indicating that some are closely associated with patient safety. The first process was the pathologist sign-out and the second was grouping of intraoperative procedures associated with frozen sections. Similar to the previous phases, some metrics listed under pathologist sign-out can be corrected with use of automated equipment. For example, the metric of dictation case mismatch can be resolved if the pathologist report is electronically integrated with audio-file compatibility, thus synchronizing the report with the dictation. Some metrics listed in Figure 1 may have established, relatively simple solutions, but others continue to present challenges, which calls for a worldwide effort to compile error data before construction of best practices.

Conclusions

Management of the AP laboratory requires a dedicated effort from health care staff and pathologists, to avoid diagnostic error and patient mismanagement. The challenge is to obtain measureable quality improvements in specimen workflow, reducing risk of adverse outcomes in patient care. The metrics list generated in this study is a comprehensive tool for standardizing collection of quality metrics, derived from pooled experience and expertise. The metrics list, in combination with collection of error data, allows a comprehensive approach to initiate an engaged collaborative dialogue and to reduce risk of adverse outcomes from the AP laboratory workflow. Ultimately, doing this will require support and participation from patient care areas outside the laboratory, particularly those submitting tissue specimens. Surgical suites and other sites, such as gastrointestinal endoscopic and gynecologic clinics, would need to be involved. Implicit in such a scenario is the direct support of health care facility officers, patient safety officers, and other stakeholders, possibly even including patients.

The key objective of the IAPLPS in publishing this list of 47 metrics is to increase data collection regarding workflow deficiencies. In the past few decades since the release of the IOM Report, several individual laboratories in the academic and private sectors have published their collection of error data., However, to our knowledge, only our report contains a list of error metrics composed by a team of global experts from 6 different countries. The metrics listed herein are universal and more comprehensive than those represented in the Q-probes survey, as well as being inclusive of intraoperative-procedure components.

This list of metrics also requires a proactive style of management for improvement of the AP laboratory workflow systems. Because specimen procurement is the beginning of the workflow, it is essential for harmonious cooperation to take place during that step among nonlaboratory health care professionals and institutional administrators. Also, the metrics include risks with pathologist interpretation that continue to remind pathologists to integrate with the AP laboratory workflow before receipt of diagnostics slides. In the past, individual laboratories have attempted to borrow management practices from other fields, such as the Lean principles., Some have utilized the concept of Lean production methods to streamline and quantitate error data into measurable metrics within the AP laboratory with positive results.

However, not all laboratories generate the same volume of specimens. Also, not all laboratories have the resources to approach all 47 metrics at once; more data would be beneficial in selecting solutions that are high in priority but low in cost. Grading of the metrics into higher and lower risk allows prioritization for lower initial cost expenditures. The set of metrics that we present herein will provide a framework from which management can collect data and justify the cost of implementing quality improvement systems.

Many of the metrics listed herein are more readily resolved with the installation of advanced technology, such as barcode readers, specimen-tracking instruments, computerized organizational systems, or advanced automated staining systems. However, in some instances, simple solutions are just as effective. For example in a recent report from the Department of Dermatology at Duke University Medical Center, misplacement of patient biopsy specimens was identified as a major problem within the workflow. Requiring attachment of a sticker to each specimen container, along with the initials of the person who had performed the specimen-retrieval procedure, reduced the error rate from 5.79 to 3.53 per 1000 cases. Thus, by increasing traceable individual accountability, the laboratory achieved dramatic improvement in an area of critical risk to patient safety.

The IAPLPS group hopes that the list of metrics provided in this article will streamline attempts to create effective interinstitutional best practices for AP laboratories. Our goal is to stimulate interest in widespread use of these metrics to encourage standardized, revolutionary safety-management approaches in the laboratory.

We thank Denise Smith and Sherry Dean for their assistance in coordinating the survey and data collection. We also thank Laya Bhavaraju, PhD, for review of the manuscript and assistance with final manuscript preparation.

Personal and Professional Conflicts of Interest

The initial collaboration of authors began as a corporate advisory board, in which Ventana Medical Systems Inc, a member of the Roche Group, compensated the members for their attendance. Subsequently, the authors formed the Initiative for Anatomic Pathology Laboratory Patient (IAPLPS) and creation of these metrics, and any other continued autonomous effort is voluntary and uncompensated. Authors PB and ED are currently employed by Ventana Medical Systems. All other authors have disclosed all employment and affiliations.

References

Abbreviations

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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A Proposed Set of Metrics to Reduce Patient Safety Risk From Within the Anatomic Pathology Laboratory

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Background: Anatomic pathology laboratory workflow consists of 3 major specimen handling processes. Among the workflow are preanalytic, analytic, and postanalyt

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1. Introduction

Patient safety and the medical diagnosis of patients are mainly influenced by laboratory results. Literature studies have revealed that 60% to 70% of medical diagnostic decisions are made based on accurate laboratory tests.[] Identifying errors and failures in the system and the causes of these is a dynamic way of ensuring patient safety. Substantial error rates consistently occur in clinical laboratories, even with advanced automation in diagnostic laboratories.[] Understanding and awareness of the sources of errors are crucial for resolving unexpected laboratory results that are not correlated with clinical information.[]

A distinct and intricate process within laboratory medicine includes laboratory procedures, instruments, technology, and human skills intended to warrant accurate, precise, and appropriate diagnosis and treatment decisions. This is known as the total testing process.[] Laboratory errors are not limited to the analytical phase but can arise in any of the 3 distinct phases: preanalytical phase, analytical phase, and postanalytical phase. Thus, identifying and reducing errors and the risk of errors in laboratory medicine is challenging. Furthermore, assessing the impact of laboratory errors is difficult and often leads to inaccurate clinical decisions, delayed diagnoses, prolonged hospitalization, and increased demand for resources.[] The major source of errors in laboratories occurs in the preanalytical phase, with over 46% to 68%.[]

The participation of various professionals, such as nursing, physicians, laboratory scientist, laboratory technicians, and phlebotomists, makes the preanalytical phase the most essential and challenging to regulate and monitor.[] Underreported and undervalued influence of preanalytical is due to inadequate attention on what occurs to samples before reaching the laboratory.[] Hence, the present study aimed to evaluate the errors in the preanalytical phase of testing in a clinical chemistry diagnostic laboratory in Saudi Arabia. This study underscores the need for quality controls and quality assurance in the preanalytical phase to monitor existing errors to improve patient safety and laboratory diagnosis.

2. Methods

2.1. Study design and setting

The study was conducted at the Clinical Chemistry Laboratory of a hospital in Saudi Arabia, a 200-bed capacity with inpatient, outpatient, and emergency services. The department of Clinical Laboratory provides clinical chemistry services throughout the hospital apart from the routine and special laboratory tests from other sections of the clinical laboratory. The data in this study cover the period from January 2019 to December 2020. Specimen collections are done by nonlaboratory personnel in all the hospital departments or wards.

2.2. Data collection procedure

Using the laboratory information system, the data of all unacceptable, rejected specimens and canceled tests in the clinical chemistry laboratory was retrieved and analyzed for preanalytical errors following the approval of the study protocol by the Research Ethics Committee and permission from laboratory director. Preanalytical errors were carefully determined and recorded. Laboratory requests and patient samples from the emergency, outpatient, and inpatient departments that were received from the 2-year retrospective analysis period were included in the study.

2.3. Statistical analysis

The frequency of preanalytical errors was determined and the rate of errors was calculated and expressed as frequencies and percentages as compared to the total samples received. Assessment of data and all statistical analyses were done using SPSS version 21. The difference between relative frequencies of errors observed in the different departments was tested using the chi-square test. A P value of ≤.05 was considered statistically significant.

2.4. Ethical statement

Approval of the study protocol was obtained from the University of Hail, Research Ethics Committee (H-2021-247). Written permission was also granted by the hospital laboratory director of the Clinical Laboratory. The data were utilized only for research purposes.

3. Results

A total of 55,345 laboratory requests and samples from different departments and wards were evaluated for preanalytical errors. An overall rate of 12.1% (6705) was determined as preanalytical errors. The occurrence of these errors was found to be highest in the ED (21%) as compared to the inpatient department (IPD; 13.4%) and the outpatient department (7%). The overall leading preanalytical errors were nonreceived sample (3.7%) and hemolysis (3.5%). Notably, in the OPD, the single most common preanalytical error was hemolysis (4.2%), whereas 6.1% of the preanalytical errors in the ED were due to nonreceived samples and 5.7% of the preanalytical errors were due to "unauthorized order." For the IPD, 4.8% of the preanalytical errors were nonreceived samples and 3.3% of the preanalytical were hemolyzed samples. The least among the preanalytical errors from all the departments was "specimen contamination" (0.01%) that occurred in the IPD only ( Table 1).

Of the 6705 preanalytical errors found in the study, 72.6% (4870) were mainly from the IPD, whereas 16% (1071) were from the OPD and 11.4% (672) from ED. Non-received samples account for 30.7% of the preanalytical errors, whereas 29.2% were hemolyzed samples ( Table 2). Analysis of the annual preanalytical errors revealed an increasing rate in the OPD and IPD, whereas a decreasing rate was observed in the ED ( Table 3). An increased rate of errors was also noted for the 2-year study period from 11.3% to 12.9%. Statistically, no significant difference exists between the different departments ( P = .128).

4. Discussion

Laboratory errors can negatively impact the quality of laboratory results and have adverse consequences on diagnosis and patient care. Recognition and documentation of these errors are crucial phases in enhancing the quality of laboratory medicine.[] The rates of preanalytical error in the present study revealed that ED exceeded those from IPD and OPD. A similar finding was reported in the study by Zaini et al.[] Emergency and inpatient departments represent a large number of samples received, high workload pressures, more challenging group, and more difficulty in collecting blood samples.

Interestingly, unauthorized order was remarkably high in ED. This type of error refers to a laboratory request entered electronically by staff that is otherwise not allowed or has limited access to test requisitions. This error was almost exclusively seen in ED with a 5.07% occurrence rate. The high rate of this error implies that ED clinical staff were not fully oriented to this criteria or policy. However, analysis showed a significant decline in this error over the 2 years from 40.1% to 17.2%. There was no focus on this error or data, but the decline can be credited to appropriate communication with the laboratory department, increase experience of staff, and proper orientation of test requisition system to new hospital staff. Furthermore, a decline was also observed over the 2 years in the following: nonreceived samples, insufficient sample quantity, specimen broken, leaked, compromised, etc, duplicated test requests, labeling errors, and incomplete test request data. On the contrary, an increase was noted in hemolysis, transport specimen errors, wrong collection procedure, and wrong barcode placement ( Figure 1). These errors are associated with each other, such that wrong collection procedures and incorrect sample transport can potentially lead to sample hemolysis. Moreover, it was noted that transportations of laboratory specimen were done by untrained hospital staff. Inexperienced phlebotomist/staff, insufficient training and education on sample collection and quality, and heavy workload are other factors that could contribute to these errors. An observational study conducted in 12 European countries by the European Federation of Clinical Chemistry and Laboratory Medicine reported unacceptably low compliance of phlebotomy procedures following the Clinical and Laboratory Standards Institute H3-A6 guidelines.[]

The large ratio of laboratory errors in the preanalytical phase stems from the spectrum of variables, including patient variables, specimen collection variables, and specimen handling variables. Some are predictable, whereas others are beyond control and must be understood in order to resolve and interpret appropriately (eg, cold agglutinins in winter seasons).[,] The foremost step in enhancing the quality of the preanalytical phase is to define potential errors and evaluate which errors pose a danger for the outcome of the patient. The increasing rate of preanalytical errors in this study and more specifically in OPD and IPD suggests the need to review the existing preanalytical procedures and explore if procedures should be changed to reduce the risk of errors. Moreover, continual monitoring and analysis of the frequency of laboratory errors should be regularly done to determine improvements and patterns that may exist.

The findings of the present study provide a critical and valuable source of information that could systematically support quality management of the laboratory testing process to promote diagnostic excellence in the laboratory and patient care settings. This will encourage managers, quality officers, and administrative leaders to participate or engage in quality monitoring systems and become more open to scrutiny and challenge. Managers may be driven toward the adoption of internal or external quality monitoring systems. Further, this may result in a focus on audit trails that provide documentary evidence before making decisions and policies, and implementing or improving strategies or practices. Our findings are timely and may stimulate leaders to emulate or conduct collaboratively with all other laboratories in the region to achieve uniformity and a standardized recording and monitoring system. While the study has presented facts or data that can potentially direct and support the quality teams in this institution, this provides an opportunity to further refine and validate our analysis and conceptualize a new approach to monitoring and evaluating laboratory quality performance.

5. Conclusion

Preanalytical errors have a significant impact on the quality of laboratory results and patient safety. Errors in this phase pose serious consequences and potentially compromise the correct diagnosis and management of patients. The rate of error in the study was high and the leading causes were nonreceived samples and hemolysis. An increased occurrence of hemolyzed samples in the OPD was noted. These errors are associated with sample collection and handling; thus, suggesting that enhanced and continuing educational efforts emphasizing specimen quality issues and training in sample collection among hospital staff must be carried out. Likewise, data from this study can serve as guides in defining new approaches and strategies in decreasing the errors in the preanalytical phase. This study can direct and support the quality teams in this institution to establish quality improvement plans, incorporate corrective measures, and develop a systematic quality assessment tool to monitor the preanalytical phase and evaluate the laboratory or hospital performance. The authors have communicated with the laboratory, and quality measures and interventions were initiated.

Acknowledgments

The authors are grateful to Munif Alshammary for the cooperation and support in this project.

Author contributions

Conceptualization: Jerold C. Alcantara, Khalid Alshaghdali, Bandar Alharbi.

Data curation: Jerold C. Alcantara, Bandar Alharbi, Khalid Alshaghdali, Yasser Almotairi.

Formal analysis: Jerold C. Alcantara, Khalid Alshaghdali, Bandar Alharbi, Abdel Rahim Mahmoud Muddathir, Mohammad Jahoor Alam, Yasser Almotairi.

Investigation: Jerold C. Alcantara, Khalid Alshaghdali, Bandar Alharbi, Yasser Almotairi, Abdel Rahim Mahmoud Muddathir, Mohammad Jahoor Alam.

Methodology: Jerold C. Alcantara, Khalid Alshaghdali, Bandar Alharbi, Yasser Almotairi, Abdel Rahim Mahmoud Muddathir, Mohammad Jahoor Alam.

Project administration: Jerold C. Alcantara, Khalid Alshaghdali.

Resources: Jerold C. Alcantara, Khalid Alshaghdali, Bandar Alharbi, Yasser Almotairi, Abdel Rahim Mahmoud Muddathir, Mohammad Jahoor Alam.

Software: Jerold C. Alcantara, Yasser Almotairi, Mohammad Jahoor Alam.

Supervision: Jerold C. Alcantara, Khalid Alshaghdali, Bandar Alharbi.

Validation: Jerold C. Alcantara, Khalid Alshaghdali, Bandar Alharbi, Yasser Almotairi, Abdel Rahim Mahmoud Muddathir, Mohammad Jahoor Alam.

Visualization: Jerold C. Alcantara, Khalid Alshaghdali, Bandar Alharbi, Yasser Almotairi, Abdel Rahim Mahmoud Muddathir, Mohammad Jahoor Alam.

Writing original draft: Jerold C. Alcantara, Khalid Alshaghdali, Bandar Alharbi, Mohammad Jahoor Alam.

Writing review & editing: Jerold C. Alcantara, Khalid Alshaghdali, Bandar Alharbi, Yasser Almotairi, Abdel Rahim Mahmoud Muddathir, Mohammad Jahoor Alam.

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Analysis of preanalytical errors in a clinical chemistry... : Medicine

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Patient safety and medical diagnosis of patients are mainly influenced by laboratory results. The present study aimed to evaluate the errors in the preanalytical phase of testing in a Clinical Chemistry diagnostic laboratory.

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Process Scope

Multiple steps will be required to redesign the specimen handling workflow process successfully. The scope of this project will start with identifying gaps in the specimen handling process of manual labeling of specimens, and the last step is the successful implementation of bar code scanning in the specimen handling process.

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What is a Project Scope? Effectively Planning a Project

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Enhancing specimen handling processes through barcode labeling is vital for healthcare quality, ensuring compliance with standards and regulations, while also preventing patient safety risks and optimizing operational efficiency.

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Clin Biochem. Author manuscript; available in PMC 2015 Jul 29.

Published in final edited form as:

Susan R. Snyder, ¹ Alessandra M. Favoretto, ¹ James H. Derzon, ¹ Robert Christenson, ³ Stephen Kahn, ⁸ Colleen Shaw, ² Rich Ann Baetz, ¹ Diana Mass, ⁴ Corrine Fantz, ⁵ Stephen Raab, ⁶ Milenko Tanasijevic, ⁷ and Edward B. Liebow ¹

Abstract

Objectives

This is the first systematic review of the effectiveness of barcoding practices for reducing patient specimen and laboratory testing identification errors.

Design and Methods

The CDC-funded Laboratory Medicine Best Practices Initiative systematic review methods for quality improvement practices were used.

Results

A total of 17 observational studies reporting on barcoding systems are included in the body of evidence; 10 for patient specimens and 7 for point-of-care testing. All 17 studies favored barcoding, with meta-analysis mean odds ratios for barcoding systems of 4.39 (95% CI: 3.05 - 6.32) and for point-of-care testing of 5.93 (95% CI: 5.28 - 6.67).

Conclusions

Barcoding is effective for reducing patient specimen and laboratory testing identification errors in diverse hospital settings and is recommended as an evidence-based "best practice." The overall strength of evidence rating is high and the effect size rating is substantial. Unpublished studies made an important contribution comprising almost half of the body of evidence.

Keywords: Barcoding, Comparative Effectiveness Research, Healthcare Quality Improvement, Laboratory Medicine, Laboratory Testing Errors, Patient Identification Systems, Patient Safety, Patient Specimen Identification, Specimen Labeling Errors

1.0 Introduction

Reduction of medical errors has been a major national priority since the publication of the Institute of Medicine report To Err is Human [ 1]. Patient specimen and laboratory testing identification errors have been reported as the leading cause of laboratory errors [ 2]. Identification (ID) errors may result in patient harm and are completely preventable. Identifying effective strategies for reducing these errors has been identified as a research priority[ 3], but there are no systematic reviews available providing evidence of effectiveness for quality improvement practices. The purpose of this article is to provide a systematic review that evaluates whether barcoding practices are effective at reducing patient specimen and laboratory testing identification errors. The answer is provided by applying the CDC Laboratory Medicine Best Practices Initiative's (LMBP) systematic review methods for quality improvement practices and translating the results into evidence-based guidance [ 4].

Accurate identification of patients, their specimens and laboratory test results linked to them is essential in all healthcare settings for providing effective, safe, timely, efficient, equitable and patient-centered healthcare. Systems to monitor errors in patient specimen and laboratory testing identification are federally regulated [ 5], and accurate identification is a nationally recognized patient safety priority [ 6- 11]. Although government, accreditation, patient safety, professional and industry organizations require laboratories to establish and follow policies and procedures to ensure accurate identification from specimen collection to result reporting, the guidance provided is largely based on expert opinion. Typical hospital clinical laboratories are responsible for thousands of tests daily, yet there is considerable uncertainty about how to reduce identification errors, and what quality improvement practices are effective [ 12]. ID error consequences include incorrect, delayed and/or lack of treatment which may cause injury, disability, death, longer lengths of stay, and higher healthcare costs, as well as other patient harm and diverted resources [ 13- 15]. Accurate identification is particularly essential to the safe transfusion of blood products since ID errors put patients at risk for adverse outcomes from blood incompatibility [ 14].

Quality Gap: Patient Specimen and Laboratory Testing Identification Errors

ID errors involve incorrect matching of patient, specimen and/or test information, all of which should be unequivocally linked to a correct patient identify throughout the entire testing process [ 7, 13]. There are many causes of ID errors, most of which are associated with human error and under the control of the laboratory [ 13]. ID errors lack a standardized definitiona and systems for detecting, reporting, measuring and categorizing them and their consequences among laboratories and health care organizations. They are generally considered underreported as the true frequency includes undetected errors [ 8, 11]. As a consequence, reported ID error results can vary among organizations due to differences in measurement methods and how effective laboratory and clinical staff are in identifying errors [ 8, 9], which makes it difficult to arrive at conclusions about the true size and variability of the ID error quality gap.

Reported ID error rates of 1% and less are common [ 3, 10, 16- 20], yet are still considered a serious problem since any error has the potential for serious adverse patient consequences. The lowest rates are associated with transfusion medicine and are usually less than 0.1%, followed by the general pathology laboratory at closer to 1%, but as high as 10% [ 21- 26], with even higher rates found in surgical pathology [ 8, 13, 27]. While errors rates at or very close to 0% have been documented, the upper end of the range could be as high as 50%. The highest rates[ 23, 27] have been measured by a prospective, direct observation method using surgical specimen requisitions and container labeling with an extensive list of variables included in the ID error definition. Most detected errors do not harm patients since their detection results in the associated erroneous test reports typically not being released by the laboratory [ 8, 9, 28].

2.0 Methods

This evidence review followed the "A-6 Cycle" systematic review methods for evaluating quality improvement practices funded by the CDC's Laboratory Medicine Best Practices Initiative (LMBP) and reported in detail elsewhere [ 4]. This approach is derived from previously validated methods, and designed to transparently evaluate the results of studies of practice effectiveness to support evidence-based best practice recommendations. A review team conducts the systematic review and includes a review coordinator and staff trained to apply the LMBP methods. Guidance is provided by a multi-disciplinary expert panel including at least one LMBP Workgroup member and individuals selected for their diverse perspectives as well as relevant expertise in the topic area, laboratory management, and evidence review methods.b The results are translated into an evidence-based best practice recommendation by the expert panel for approval by the LMBP Workgroup. These methods as applied in this evidence review of barcoding practices are presented below.

2.1 ASK: Review question and analytic framework

The LMBP methods begin with the ASK step which frames at least one review question supported by an analytic framework and PICO elements (population, intervention/practice, comparator, outcome). The question answered by this evidence review is:

Are barcoding practices effective at reducing patient specimen and laboratory testing identification errors?

This review question is addressed in the context of an analytic framework for the quality issue of patient specimen and laboratory testing identification errors ( Figure 1 ). The relevant PICO elements are:

P opulation: all patients in healthcare settings using laboratory or point-of-care testing and their specimens requiring accurate identification for use in a healthcare context

I ntervention: barcoding practices defined as laboratory test barcoding systems using barcoded patient identification linked to specimen labels or point-of-care testing

C omparison practice/intervention: non-barcoded identification systems for patients, specimens and laboratory tests

O utcome: specimen and/or laboratory testing identification error rates are the primary and most direct outcome of interest.

The two barcoding practices being evaluated in this review are described below.

Barcoding Systems

Electronic barcoding for identification of patients, specimens and laboratory testing is used to positively establish identification and link specimens and tests to a patient throughout the entire testing process including test ordering, specimen collection, analysis and test result reporting[ 13]. Barcode scanners are used to confirm patient identity. Other options include barcoded patient wristbands, portable printers to generate labels at the bedside, and use of an interface with a computerized physician order entry (CPOE) system.

Point-of-Care Test Barcoding Systems

Automated patient specimen and laboratory testing identification system use bar-coded patient identification and bar code scanners with a testing device at or close to the patient. Testing devices can interface with laboratory information systems to receive and transmit patient identification and test result information. This practice may include barcoded patient wristbands.

2.2 ACQUIRE: Search for practice effectiveness evidence

The search for studies of barcoding practice effectiveness to reduce patient specimen and laboratory testing ID errors included a systematic search of multiple electronic databases, hand searching of bibliographies from relevant information sources and their bibliographies, provision of references by as well as consultation with experts in the field including members of the expert panel ( Appendix A). Additional evidence was obtained by solicitation of unpublished quality improvement studies resulting in submissions to the Laboratory Medicine Best Practices Initiative.c The literature search strategy and terms were developed with the assistance of a research librarian and included a systematic search in August 2011 of three electronic databases (PubMed, Embase and CINAHL) for English language articles from 1995 to 2012 about human subjects. The search contained the following Medical Subject Headings: automatic data processing, blood transfusion, hospitals, laboratories, methods, patient identification systems, patients, and specimen handling as well as these keywords: barcode/bar-code/bar code, labeling errors, laboratory/ies, methods/strategy(ies) reduce patient specimen handling practice/identification errors, patient identification systems errors, pharmaceutical, specimen, and transfusion.

2.3 APPRAISE: Screen and evaluate evidence

The ACQUIRE step search results are reviewed by an initial screening of titles and abstracts using pre-specified inclusion criteria consistent with the ASK step, followed by a full-text review of all eligible effectiveness studies, involving abstracting, standardizing and evaluating study quality using the LMBP methods. Included studies are considered to provide valid and useful information addressing the review question [ 29] with barcoding effectiveness findings that include at least one ID error outcome measure. To reduce subjectivity and the potential for bias, all screening, abstraction and evaluation is conducted by at least two independent reviewers, and all reviewer discrepancies are resolved through consensus. The effect size for each study was standardized using its reported data and results to calculate an odds ratio (OR)d since the outcome of interest is dichotomous (i.e., correctly identified versus misidentified) and the findings for these practices are typically expressed in terms of rates or percentages. The OR compares the barcoding practice to a non-barcoding practice in terms of the relative odds of a successful outcome (i.e., the patient's specimen and/or test is correctly identified versus incorrectly identified). Each study is assigned one of three quality ratings (Good, Fair, Poor) and one of three effect size ratings (Substantial, Moderate or Minimal/none).e

2.4 ANALYZE: Evidence review synthesis and results

The individual effectiveness study results from the APPRAISE step are aggregated into two practice-specific bodies of evidence (barcoding systems and POCT barcoding) and then analyzed to produce the systematic review practice effectiveness results for translation into evidence-based recommendations (Recommend, No recommendation for or against, Recommend against). Both qualitative and quantitative analyses are used to assess effect size consistency and patterns of results across studies [ 30]. Qualitative analysis is used to rate the overall strength of the body of evidence for practice effectiveness (High, Moderate, Suggestive, or Insufficient) Criteria for these ratings are described in detail elsewhere [ 4, 31]. The qualitative analysis synthesizes the individual studies to convey key study characteristics, results and evaluation findings summarized in a body of evidence table. A quantitative analysis is provided using meta-analysis of the results from similar individual studies to estimate a weighted average effect size and confidence interval using a random-effects modelf with the results presented in a forest plot.

3.0 Evidence review synthesis and results

The ACQUIRE step procedures identified 1,307 separate bibliographic records that were screened for eligibility to contribute evidence of the relation of barcoding with ID error outcomes. The APPRAISE step screening resulted in 1,211 of these records being excluded as off-topic, and 73 being excluded for not meeting effectiveness study inclusion criteria (i.e., not a study, no barcoding practice, no ID error outcome measure) for a total of 23 full-text studies meeting the review inclusion criteria. A systematic review flow diagram in Figure 2 provides a breakdown of the search results. Abstracted and standardized information as well as study quality ratings for the 23 eligible studies are provided in Appendix B containing evidence summary tables preceded by a Body of Evidence table for each practice. Bibliographic reference information for these studies is provided in Appendix C.

The full-text review and evaluation of the 23 eligible studies resulted in the exclusion of 6 studies due to "poor" study quality ratings which did not meet the minimum required LMBP study quality inclusion criteria (4 barcoding systems studies: 3 published and 1 unpublished; 2 point-of-care test barcoding studies: 1 published and 1unpublished). A total of 17 studies are included in this review as evidence of practice effectiveness (8 of which are unpublished submissions): 10 studies for barcoding systems (3 unpublished) and 7 studies for point-of-care test barcoding (5 unpublished). All included studies used observational before-after study designs.

3.1 Barcoding systems practice effectiveness evidence

Of the 10 studies included in the barcoding systems practice body of evidence, 7 were published and 3 were unpublished, and 6 were rated "Good" study quality and 4 were rated "Fair" with summarized information provided in Table 1 . The earliest study time period was 1999-2000, and the starting date for 6 of the studies was 2005 or later, with 3 published in 2010. All study sample sizes were very large while the number of identification errors was very small. In all studies both the barcoding and the non-barcoding comparison groups were considerably in excess of 1,000 specimens. All but 2 studies exceeded 10,000 specimens for both groups, and 3 studies exceeded 100,000 specimens. All 10 included studies involved laboratory testing with identification of patient specimens using labels in U.S. hospital settings; 8 studies from clinical pathology laboratories and 2 from surgical/anatomic pathology laboratories (Zarbo 2009 and University of Washington 2009). There was geographic and patient diversity across study settings which included inpatient, outpatient, emergency department and pediatric settings. All hospitals were relatively large, with the smallest exceeding 200 beds. Four studies relied exclusively on inpatient blood specimens and used bedside label printing (Brown 2010, Morrison 2010, LBJ 2009, Unpub A 2009), and two studies relied only on emergency department specimens (Hill 2010, Killeen 2005).

3.1.1 Body of evidence qualitative analysis

As summarized in Table 1 , the evidence of practice effectiveness for reducing ID errors indicates consistent improvement associated with barcoding systems compared to non-barcoding practices with a high strength of evidence in hospital settings. The odds ratio for 9 of the 10 barcoding system studies exceeded 2.0 (favoring barcoding), and the 95% confidence interval lower limit exceeded 1.0 for 8 of the 10 studies. The 3 study exceptions (Bologna 2002, Morrison 2010, and LBJ 2009) had the smallest numbers of ID errors in the barcoding and non-barcoding study groups ranging from 0 to 12 which corresponded to very large sample sizes; the smallest being about 25,000 and the rest substantially larger. The odds ratio estimates for all 10 included studies ranged from 1.7 to 147. The unpublished studies' odds ratios were consistent with those of the published studies with the exception of the uppermost unpublished study odds ratio of 147.

3.1.2 Meta-analysis

The forest plot in Figure 3 presents the meta-analysis effect size results for barcoding systems. The odds ratio for each of the 9 included studies favors the barcoding system practice over the non-barcoding practice for improving identification error rates indicating a consistent and statistically significant effect. The overall summary effect mean odds ratio was 4.39 (95% CI: 3.05 - 6.32). The higher rated "Good" study quality subgroup summary effect size exceeded that of the lower rated "Fair" quality subgroup with an odds ratio of 5.14 versus 2.43. The lower limit of the confidence interval for both subgroups exceeded 1.0 at 3.41 for the 6 "Good" quality studies but only 1.1 for the 3 "Fair" studies. One of the ten barcoding system practice effectiveness studies was excluded from the meta-analysis (University of Washington 2009) as its ID error rate outcome measure (processed specimen cassettes/blocks rather than specimens in their original containers with labels) and results (OR = 147; 95% CI: 55 - 391) were considered too heterogeneous relative to the other nine included studies.

Meta-analysis results for barcoding systems show significant statistical heterogeneity which is typical of random effects results. The I ² statistic ranges from 0-100% and estimates the percent of variability in estimates attributable to between study differences. Studies rated "Good" showed somewhat less between-study variation (10.5%) relative to "Fair" studies (15.9%) which had larger estimated odds ratios. This modest attenuation of effect size from "Fair" studies contributes to modest between-study variation (24.8%) in the overall estimate.

3.2. Point-of-care test barcoding practice effectiveness evidence

Of the 7 studies included in the point-of-care barcoding practice effectiveness body of evidence, 2 were published and 5 were unpublished, and 5 were rated "Good" study quality and 2 were rated "Fair" with summarized information provided in Table 2 . All of the included studies relied on U.S. hospital inpatient point-of-care glucose tests, with at least 4 studies also including emergency department patient tests. The earliest study time period began at the end of 2002, while the remaining studies began in 2006 or later, with 4 ending in 2011. Like the barcoding systems' practice effectiveness studies, the point-of-care test barcoding study sample sizes were typically very large with all barcoding and non-barcoding groups exceeding 10,000 tests with one exception (Rao 2005). Several studies had barcoding and/or comparison groups with substantially more than 100,000 tests. Four of the unpublished studies came from separate hospitals within one hospital system (Catholic Health System: Kenmore Mercy Hospital, Mercy Hospital of Buffalo, Sisters of Charity Hospital Buffalo and Sisters of Charity Hospital St. Joseph Campus). As a result, the body of evidence is not as geographically diverse as for barcoding systems, but the study settings may be reasonably representative of diverse hospitalized patient populations.

3.2.1 Body of evidence qualitative analysis

As summarized in Table 2 , the evidence of practice effectiveness for reducing identification errors indicates consistent and substantial improvement for point-of-care test barcoding compared to non-barcoding (manual entry) practices with a high strength of evidence for point-of-care glucose testing in hospital settings. The point-of-care barcoding practice odds ratio for all 7 of the included studies exceeded 2.0 (favoring the barcoding practice over non-barcoding practices), and the lower limit of the odds ratios' 95% confidence interval exceeded 1.0 for 6 of the 7 studies. The one study exception (Rao 2005) had the smallest numbers of errors (4 without barcoding; 0 with barcoding) and the smallest total sample size (462) of the included studies. The odds ratio estimates for all the included studies ranged from 3.76 to 14.72.

3.1.2 Meta-analysis

The forest plot in Figure 4 presents the meta-analysis results for the point-of-care test barcoding practice. The overall summary effect mean odds ratio was 5.93 (95% CI: 5.28 - 6.67). The 5 higher rated "Good" quality studies' subgroup summary effect size was similar: mean odds ratio of 5.83 (95% CI: 3.86 - 8.82). Of the 7 included studies, only one had an odds ratio 95% confidence interval lower limit less than 1.0 (Rao 2005), reflecting the very small number of identification errors in the study (4) as well as a relatively small sample size (462). These meta-analysis results show significant statistical heterogeneity as is typical of random effects results. Most of the point-of-care test barcoding meta-analysis results' statistical heterogeneity is attributable to within-study variance. The higher rated "Good" study quality subgroup showed modest between-study variation (I ² = 27.8%) while all between-study variation in the fair and overall results can be attributed to chance. At the aggregate level, results for "Fair" and "Good" studies are essentially indistinguishable and can be considered well represented by the overall mean estimate.

4.0 Discussion

4.1 Additional considerations

4.1.1 Applicability

Barcoding practices demonstrated effectiveness at reducing ID errors for patient specimen and laboratory testing identification is generalizable to most common hospital settings and patient populations, to clinical pathology laboratory testing, and potentially to surgical/anatomic pathology laboratory testing for which there was more limited evidence [ 28, 32, 33]. Although barcoding effectiveness studies for non-hospital settings and point-of-care testing other than glucose were not included in the body of evidence reviewed, barcoding may be similarly effective at reducing ID errors but no evidence was available to test this hypothesis. Cost, technological requirements and training may be barriers to adoption of barcoding in some non-hospital settings, as well as hospital settings, however this is not clearly supported. In relatively low volume testing settings, limited economies of scale for barcoding technology may present a cost barrier. Non-hospital settings generally do not rely on patient identification armbands typically used in hospital inpatient and emergency departments, but patients may have specimen container or test requisition form labels with identification information that can be barcoded.

4.1.2 Harms

Barcoding technology is not error free. ID errors associated with barcoding practices include those created by the patient identification barcodes themselves [ 34]. Barcode scanners may misread patient identification barcodes due to incompatibility between the barcode print area size or symbology on patient ID bands or specimen labels with scanner settings. In one study a small number of scanner misreads occurred due to the narrow wrist band curvature of pediatric patients [ 35]. Other sources of barcoding ID errors included labels being unreadable by a scanner due to label print quality problems, which may indicate a need for label printer maintenance, and degradation of the barcode on the patient ID band from being worn or written on. Studies and articles have also reported scanner failure attributable to low batteries [ 36]. Even when the scanner works properly, incorrect information such as the wrong patient's barcode, incorrect barcode information from a patient's ID band, or non-patient identification barcodes (e.g., medication) can cause ID errors [ 37, 38]. A specific type of ID error is from scanning incorrect barcode information from a patient ID band with a barcode related to a previous hospitalization or a hospital transfer. Such an episode could include more than one armband and/or multiple patient accounts [ 37, 39]. Although many potential sources of ID errors associated with barcoding have been identified, these errors appear relatively rare, generally preventable, and likely have only a negligible impact on ID error rates.

4.1.3 Additional benefits

The studies reviewed report other beneficial outcomes associated with barcoding including an observed reduction in misidentified patients [ 40], unnecessary phlebotomy [ 40], labor time savings and reduced workflow process time in surgical pathology [ 32, 33]. Implementing barcoding has been credited with improving identification of those responsible for making ID errors, thus enabling targeted measures to improve performance [ 37, 40]. Cost savings noted from fewer ID errors associated with barcoding include reductions in specimen recollections, labor to investigate and correct ID errors, length of patient stays and legal issues [ 41]. Additional benefits to patients from fewer ID errors include avoiding unnecessary discomfort, inconvenience, and treatment delays from recollecting and retesting specimens [ 40, 42].

4.1.4 Economic evaluation

No patient specimen barcoding practice economic evaluations (cost, cost-effectiveness, or cost-benefit analyses) were found in the search results described in section 2.2.g Completing a resource-related inventory for barcoding practices is beyond the scope of this study but it should include the costs associated with implementing and sustaining the practice (e.g., hardware, software, equipment, supplies and labor requirements as well as resources associated with training, testing, monitoring, and maintenance) and all downstream costs and savings that occur because the intervention was performed [ 43].

4.1.5 Feasibility of implementation

The evidence reviewed clearly demonstrates the feasibility of adopting barcoding practices in a variety of hospital settings. Nevertheless, each environment is distinctive and implementation requires adequate process development and modification, training, education and testing to achieve full effectiveness. Barcoding process design issues appear more complex for surgical pathology [ 32, 44] which typically involves more workflow process steps than patient specimens for routine laboratory or point-of-care testing. Many studies on surgical pathology describe the approach used for barcoding-related process changes in detail, along with the accompanying challenges and solutions [ 32, 36- 38, 40, 42, 45]. Key implementation components for making barcoding technology work as intended include adequate training and education, well-designed patient ID bands, and adequate supplies and equipment maintained in good working order (e.g., label printers, computers, batteries, wireless networks) [ 13]. Shortages and performance issues were noted as problems frustrating staff that can result in using error-prone work around processes [ 42]. Support and involvement from all relevant departments and leaders including nursing, laboratory and information systems, were identified as critical success factors since no one department typically has full ownership of implementing and using barcoding technology.

4.2 Future research needs

Standardized outcome measures and measurement methods that consistently and reliably detect ID errors are needed for robust evaluation and comparison of QI practices. For more complete and useful assessment of barcoding practices, studies are needed to address its applicability and effectiveness in ambulatory and non-hospital settings, as well as more research evaluating barcoding in surgical pathology and settings known to have relatively higher ID error rates (e.g., emergency departments). Cost-effectiveness studies evaluating investments in potentially expensive ID error reduction technologies such as barcoding are needed. There should be a focus on settings with greater potential ID error impact due to higher rates and/or more serious consequences. Addressing this requires well-constructed data collection and analysis efforts identifying and measuring resources needed for implementation and maintenance of barcoding along with outcomes of interest. Future effectiveness research can be more informative if expected barcoding effectiveness moderators including implementation variables or practice components (e.g., electronic order system interface, bedside labeling, different barcode formats) can be evaluated for their contribution to overall effectiveness. Other benefits and harms of barcoding for patient specimen identification have not been well studied or reported and may be unknown. More information on other potential practice effects is needed to evaluate the full range of consequences and to allow for a comprehensive assessment of its net benefit. In addition, more information is needed about how to maintain and enhance the effectiveness of barcoding over time.

4.3 Limitations

The LMBP systematic review methods are consistent with practice standards for systematic reviews[ 30], but all such methods are imperfect and include subjective assessments at multiple points that may produce bias. Like most systematic reviews, this one may be subject to publication bias, although this review includes unpublished studies which may mitigate that bias. The restriction to English language studies to satisfy the requirement of multiple reviewers for each study may also introduce bias if barcoding practices differ substantially in international settings. Quality improvement efforts typically differ from research, and are commonly observational studies that rely on natural experiments in realistic practice settings. The major drawback of these uncontrolled designs is that it is not possible to know if measured or unmeasured factors affect the outcomes of interest. Regardless of study design, by gathering evidence from multiple clinical and organizational settings, systematic reviews provide more useful assessments of the totality of evidence for a given QI practice than individual studies [ 46].

Barcoding and other technology or practice changes may be easier to measure than individual step process changes that may contribute to observed results. Also, these processes are rarely uniform, and are clearly very different for clinical versus surgical pathology specimens, and for point-of-care testing. While these factors may moderate study findings and the observed heterogeneity suggests they are not insignificant, it can be observed that all studies reported support for barcoding.

Some studies comprising the barcoding body of evidence involved less than full implementation for all or a portion of the post-implementation period which would have an expected tendency to understate the impact of barcoding on the reduction in ID error rates. In particular, some studies indicated barcoding "scan rates" of substantially less than 100 percent during the post-implementation period such that the effect of a non-barcoding practice (i.e., manual entry of patient identification information) is reflected in a portion of the post-implementation data. This was noted when provided, however as it was not always clearly or consistently reported it could not be used to adjust effect size estimates. As studies were done within a single institution, there may be many site-specific differences that impact their study results. Many studies were missing information including actual study sample sizes, dates for relevant time periods, and practice implementation and setting characteristics. Another perceived limitation is the inclusion of unpublished studies.

Designing and publishing controlled studies are typically not among the primary objectives of individuals collecting and analyzing quality improvement data relevant to laboratory medicine. In the barcoding body of evidence, both the published and unpublished studies had similar limitations. The LMBP experience to date in reviewing and rating study quality for both published and unpublished studies indicates that peer-reviewed journals do not provide assurance of high study quality.

5.0 Conclusion and Recommendation

On the basis of a high overall strength of evidence of effectiveness, barcoding systems for specimen labeling and point-of-care test barcoding are recommended as best practices to reduce identification errors and improve the accuracy of patient specimen and laboratory testing identification in hospital settings. The high overall strength of evidence is due to sufficient evidence of practice effectiveness from individual studies demonstrating consistent and substantial reduction in patient specimen and laboratory testing-related identification error rates in hospital settings. The findings of barcoding effectiveness are based on 10 studies of specimen barcoding systems and 7 studies of point-of-care test barcoding assessing impact on identification errors. In every study barcoding is associated with a reduction in the identification error rate. The meta-analysis overall summary effect mean odds ratio favoring barcoding is 4.39 (95% confidence interval: 3.05 - 6.32) for barcoding systems and 5.93 (95% confidence interval: 5.28 - 6.67) for point-of-care test barcoding. There was limited evidence of additional benefits and potential harms associated with the use of barcoding for specimen and laboratory testing identification, and any effect of potential harms appears to be very small relative to its overall benefits. All included studies were conducted in hospital settings. No evidence was available for assessing the effectiveness and applicability of barcoding in other laboratory testing settings.

Acknowledgments

Melissa Gustafson of Battelle, Devery Howerton, Malaika Washington, and Barbara Zehnbauer of the Centers for Disease Control and Prevention, the LMBP Patient Specimen Identification Expert Panel and LMBP Workgroup members, and Submitters of unpublished studies

ABBREVIATIONS

CDC

U.S. Centers for Disease Control and Prevention

CI

Confidence interval

ID

Identification

IOM

Institute of Medicine

LMBP

Laboratory Medicine Best Practices Initiative

QI

Quality improvement

GLOSSARY

Bias

systematic error; threats to validity; tendency to produce results that depart systematically from the 'true' results. Unbiased results are internally valid. Four types of bias are selection/allocation, performance, measurement/detection and attrition/exclusion.

Consistency

The degree to which estimates of effect for specific outcomes are similar across included studies.

External validity

G eneralizability, applicability - extent to which the effects observed in the study are applicable outside of the study to other populations and settings.

Effect size

A value which reflects the magnitude of the difference in a study's outcome measure between the group with the intervention/practice being evaluated and its control or comparison group.

ID errors

Misidentification in matching a patient or a specimen with a laboratory test. ID errors may include specimen/test requisition mismatches (e.g., specimen labeled with another patient's name, wrong type of specimen, duplicate orders or specimens), mislabeled specimens (sometimes referred to as "wrong blood in tube"), specimen label with partial, missing or incorrect information (e.g., one of two patient identifiers, missing or wrong patient gender, date of birth or middle initial), and unlabeled specimens.[ 9, 12] Different institutions may use different denominators when expressing ID errors as a rate (e.g., number of specimens, phlebotomies, requisitions, accessions). Some ID error types are more likely to be detected by the laboratory than others (e.g., mismatch versus wrong blood in tube), with those detected typically preventing the release of a test result.[ 8] Most ID errors are the result of human error, and causes include but are not limited to: laboratory tests ordered on the wrong patient, incorrect or incomplete entry of patient data in the laboratory information system, collection of specimens from the wrong patient, inappropriate labeling of specimens, multiple users of the same label printer, lost identification label on specimens, incorrect identification information on specimen labels, pre-printed labels from different patients, handwritten labels on specimen containers, tissue cassettes and slides, and incorrect entry of patient results in the laboratory information system.[ 13]

Internal validity

extent to which the design and conduct of the study are likely to prevent systematic error. Internal validity is a prerequisite for external validity.

Meta-analysis

The process of using statistical methods to combine quantitatively the results of similar studies in an attempt to allow inferences to be made from the sample of studies and be applied to the population of interest.

Odds ratio

The ratio of two odds of an event from two groups - a treatment or intervention group ( a/c) versus a control group ( b/d) where a and c represent the number of times the event occurs for the intervention and control group, respectively, using the formula below and the barcoding and comparison practice example table. An OR =1 means the two practices are equally successful (no difference in reducing risk with respect to the outcome evaluated); OR >1 means the barcoding practice is more successful; and OR < 1 means the barcoding practice is less successful.O R=a d b c=p a p d p b p c=p a/p b p c/p d=p a(1-p c)p c(1-p a);

Where pa = a/(a + b), pc = c/(c + d) and a, b, c, and d are proportions in the table below.

Frequencies

Proportions

Success

Failure

Success

Failure

Barcoding Practice
a b pa = a/(a + b) pb = b/(a + b)

Comparison Practice
c d pc = c/(c + d) pd = d/(c + d)

Systematic review

A scientific investigation that focuses on a specific question and that uses explicit, planned scientific methods to identify, select, assess, and summarize the findings of similar but separate studies. It may or may not include a quantitative synthesis of the results from separate studies (meta-analysis).

Transparency

Methods are explicitly defined, consistently applied, and available for public review so that observers can readily link judgments, decisions, or actions to the data on which they are based. Allows users to assess the strengths and weaknesses of the systematic review and associated guidance and recommendations.

Appendix A. LMBP Patient Specimen Identification Expert Panel Members

Corrine Fantz, PhD (Emory University School of Medicine)

Denise Geiger, PhD (John T. Mather Hospital)

David Hopkins, MD, MPH (Centers for Disease Control and Prevention)

Julie Gayken, MT(ASCP)* (Regions Hospital)

Steven Kahn, PhD (Loyola Medical Center of Chicago)

James Nichols, PhD*(Bay State, Tufts University)

Stephen Raab, MD, PhD* (University of Washington)

Ronald Schifman, MD, MPH (Tucson Veterans Administration Health Center)

Milenko Tanasijevic, MD, MBA* (Harvard Medical School, Brigham and Women's Hospital, Dana Farber Cancer Institute)

Paul Valenstein, MD, FCAP (St. Joseph Mercy Hospital)

APPENDIX B. Laboratory Medicine Best Practices Body of Evidence Table

Practice: Bar
Coding Systems

Study Quality Rating

Effect Size Rating

Overall
Consistency

Overall Strength of
Body of Evidence

Study

Practice

Measures

Results

Total

Rating

Published

Unpublished

*not in meta- analysis
Yes High

Bibliographic Information
- Author (s)
- Yr Published/Submitted
- Publication
- Author Affiliations
- Funding

Study
- Design
- Facility/Setting
- Time Period
- Population/Sample
- Comparator
- Study bias

Practice
- Description
- Duration
- Training
- Staff/Other Resources
- Cost

Outcome Measures
- Description (s)
- Recording method

Results/Findings
- Type of Findings
- Findings/Effect Size
- Stat. Significance/Test(s)
- Results/Conclusion Bias

Quality Rating: 7 (Fair) (10 point maximum)Study (3 pts maximum): 3 Practice (2 pts maximum): 2
Effect Size Magnitude Rating: Moderate (Relevance: Direct)
Outcome measures (2 pts. maximum): 1 ; Recording method: Not reported for pre period

Results/findings (3 pts maximum): 1 ; Appropriateness of statistical analysis: Pre and Post estimates appear to rely on different recording methods

- Brown JE [1], Smith N [1], Sherfy B. [2]
- 2010
- Journal of Nursing Care Quality
[1] Howard County General Hospital, member of Johns Hopkins Medicine
[2] University of Maryland Medical Center
- Funding: Self-financed

*Additional study sample size information not in publication. Source: E-mail communication 7/12/2011 from Susan Neal-Lyman, Johns Hopkins Dept. of Pathology at Howard County General Hospital)

- Description: Specimen positive patient identification system integrated with hospital, lab and physician order entry information systems for printing barcoded specimen labels at the bedside using a portable label printer generated from laboratory orders entered from an order management system
- Duration: 12 mos. - Implemented 11/2006 in limited number of units; subsequently expanded hospital-wide over 3 years; ongoing
- Training: 1 hour staff training, one week of 24-hour support during implementation, and "extensive" training/educational program
- Staff/Other Resources
Staff: Patient care technicians (PCTs) and registered nurses Other resources: portable label printers, portable handheld or beside computer including a barcode scanner, patients' barcoded Identification band
- Cost: Not reported

- Description: Specimen labeling errors/rate (# and %): Wrong patient name or specimens with multiple patient names in one specimen bag
Additional qualitative benefits (no data):
- Avoiding unnecessary patient discomfort and inconvenience for redrawing specimens
- Preventing treatment delays
- Eliminating nursing and laboratory staff rework
- Recording method: Adverse event reports for specimen labeling errors entered by nursing and lab personnel; paper event- reporting system 2005- 3/2007; afterward web- based reporting

- Pretest-Posttest
- Findings/Effect Size
specimen labeling errors per 10,0000 specimens
Pre: 2.26 (103/456,069 = 0.0226%)
Post: 0.17 (8/458,461 = 0.0017%)

Absolute decrease: 0.02%
Relative decrease: 92.5%
➢ OR = 12.95 (CI: 6.31 - 26.58)

- Stat. Significance/Test(s): None provided in published article for error rates as calculated above using total specimens; Mann-Whitney U Test showed a significant difference in the mean number of errors for the 2 periods ( p <.001)
- Results/Conclusion Bias: None

Quality Rating: 5 (Poor * ) (10 point maximum)
Effect Size Magnitude Rating: (Relevance: Direct)
*0 Outcome Measure rating

Study (3 pts maximum): 2
- Study Setting: Sufficiently distinctive that results may not be generalizable to other settings/specimens - Surg. path. specimen cassettes
Practice (2 pts maximum): 2
Outcome measures (2 pts. maximum): 0; Face Validity -Outcome measure confounded by practice itself (no comparison)

Results/findings (3 pts maximum;: 1 ; Appropriateness of statistical Analysis- Insufficient data to allow/verify calculation of effect size without comparison practice

Hill PM [1,2], Mareniss D [1,2], Murphy P [2], Gardner H [2], Hsieh Y [1], Levy F [1,2], Kelen GD [1,2]
- 2010
- Annals of Emergency Medicine
[1] Dept. of Emergency Medicine, Johns Hopkins Univ. School of Med.
[2] Johns Hopkins Hospital
- Funding: Self-financed

- Description: 2-component intervention: ED electronic documentation and information system integrated with the LIS including physician order entry combined with bar-code technology linked to patient's identity; physician order entry generates printed barcode specimen labels near patient's room. Not used for blood bank, tissue, Level I trauma and severe critical care specimens
- Duration: 17 months (5/2008- 9/2009); ongoing
- Training: Not reported
- Staff/Other Resources
Staff: Nurses and clinical technicians; Other Resources: Electronic documentation and information system integrated with a laboratory information system and physician order entry system, patient wristbands containing a patient identify barcode, label printers, specimen containers, scanners and labels
- Cost: Not reported

- Description: Specimen processing error rate (total) including 4 separate types: unlabeled/mislabeled/wrong patient specimen or requisition; tabulated monthly
- Recording method Monthly monitoring reports from normal quality assurance program clinical pathology information system records. Data from the Pre and Post periods were tabulated monthly.

Limitation: Outcome measure includes unlabeled specimens - barcoding practice associated with barcoded labels

- Pretest-Posttest
- Findings/Effect Size: error rate;
Pre: 0.42% (3,007/724,465)
Post: 0.11% (379/334,039)

Absolute decrease: 0.3%
Relative decrease: 73.8%
➢ OR = 3.67 (CI: 3.30 - 4.08)
- Stat. Significance/Test(s)
0.31% absolute reduction; 95%
C.I.: 0.28% to 0.32%; Chi-squared test

- Results/Conclusion Bias: None

- Morrison AP [1,2], Tanasijevic MJ [2], Goonan EM [2], Lobo MM [2], Bates MM[1], Lipsitz SR [1], Bates DW [1], Melanson SEF [2]
- 2010
- American Journal of Clinical Pathology
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA
[1] Dept. of Medicine
[2]Dept. of Pathology, Clinical Laboratories Division
- Funding: Self-financed

- Description: Incorrectly labeled samples - monthly number and rate:
(1) Mislabeled
(2) Unlabeled
(3) Overall = (1)+ (2)
- Recording Method
Pre: Lab staff compared patient identifiers on specimen label and requisition form at receipt. Recorded as mislabeled if identifiers did not match or later determined sample from a different patient; without labels recorded as unlabeled.
Post: Audit data collected by electronic ID system detected mismatches when scanning patient wristband before specimen collection; created a mismatch alert preventing a wrong- patient sample draw.

-Pretest-Posttest
- Findings/effect size: Overall labeling error rate per 10,000
Pre: 5.45 (95% CI: 4.47 - 6.63)
Post: 3.20 (95% CI: 2.48 - 4.14)
Absolute decrease: 0.02%
Relative decrease: 41.3%
➢ OR = 1.70 (CI : 1.23-2.35)
Mislabeled: 43% reduction
Pre: 0.030% (55 errors)
Post: 0.017% (32 errors)
Unlabeled: 38% reduction
Pre: 0.024% (44 errors)
Post: 0.015% (27 errors)

- Stat. Significance Test: Logistic regression used to model rates over time and for statistical significance and confidence interval estimates; before and after changes tested via Wald statistics; p = 0.0013
- Results/Conclusion Bias: None noted

Study (3 pts maximum): 0;
- Study Setting: Sufficiently distinctive that results unlikely generalizable - Bone marrow transplant unit transfusions (−2)
- Potential Study Bias: Study time period and sample selection may introduce bias affecting results - Study time period not reported (−1)

Practice (2 pts maximum): 1 ;
- Adequacy of practice description: Important aspect likely to critically affect implementation of the practice is not well described - No practice duration specified

Outcome measures (2 pts maximum): 1 ; Recording method is not adequately described.

Results/findings (3 pts maximum): 0
- Sample size: Measurement period not reported; sample size likely too small for a robust estimate of practice impact.
- Appropriateness of statistical analysis: Data insufficient to allow effect size calculation(non comparative)

Study (3 pts maximum): 1 ;
- Study Setting: Sufficiently distinctive that results may not be generalizable - hematology outpatient transfusions
- Potential study bias: Time period and sample selection

Practice (2 pts maximum): 1 ;
- Adequacy of practice description: Important aspect likely to critically affect implementation of the practice is not well described - No practice duration specified

Outcome measures (2 pts maximum): 1 ;
- Face validity: Process outcomes lack correspondence to evidence review PSID error rate outcome

Results/findings (3 pts maximum): 1 ;
- Sample size: Measurement period not reported; sample size likely too small for robust estimate.
- Uncontrolled deviations: Results not clearly attributable to practice - training may have an impact

Study (3 pts maximum): 2 ;
- Study Setting: Sufficiently distinctive that results may not be generalizable to other settings/specimens - Surg. path. workflow redesign
Practice (2 pts maximum): 2 Outcome measures (2 pts maximum): 2 Results/findings (3 pts maximum): 3

LMBP EVIDENCE REVIEW PSID BARCODING SYSTEMS PRACTICE UNPUBLISHED STUDIES

Laboratory Medicine Best Practices Body of Evidence Table

Practice: POCT Bar
Coding Systems

Study Quality Rating

Effect Size
Rating

Overall
Consistency

Overall Strength of
Body of Evidence

Study

Practice

Measures

Results

Total

Rating

Unpublished

Unpub B 2009

1

2

2

0

5

Poor

N/A
Yes High

LMBP EVIDENCE REVIEW PSID POINT-OF-CARE TEST BARCODING PRACTICE UNPUBLISHED STUDIES

APPENDIX C. LMBP Barcoding Systematic Review Eligible Studies

Barcoding Systems

Published

Brown JE, Smith N, Sherfy BR. Decreasing mislabeled laboratory specimens using barcode technology and bedside printers. Journal of nursing care quality. 2010 [[PubMed] Google Scholar]

Unpublished

Large academic medical center, Southern CA; 2009.

Point-of-Care Test Barcoding

Published

Colard DR. Reduction of patient identification errors using technology. Point of Care. 2005;4:61-3. [Google Scholar]

Rao AC, Burke DA, Dighe AS. Implementation of bar coded wristbands in a large academic medical center: Impact on point of care error rates. Point of Care. 2005;4:119-22. [Google Scholar]

Unpublished

Midwest Academic Medical Center, Minnesota; 2009.

Footnotes

a See Glossary for more detailed information on ID errors and associated terms.

bSee Appendix A for the LMBP Patient Specimen Identification Expert Panel Members. LMBP Workgroup members are listed at: https://www.futurelabmedicine.org/about/lmbp_workgroup/

cMore information on submission of unpublished studies to the Laboratory Medicine Best Practices Initiative is available at www.futurelabmedicine.org

d See Glossary for more information on odds ratios.

eThe criteria for a Substantial effect size rating: OR > 2.0 and significantly different from OR =1.0 at p = 0.05 (i.e., the lower limit of the 95 percent confidence interval is > 1.0).

f Random-effects model assumes there is no common population effect size for the included studies and the studies' effect size variation follows a distribution with the studies representing a random sample. This is in contrast to the fixed-effects model which assumes a single population effect size for all studies and that observed differences reflect random variation.

g Similarly, additional searches covering the following databases completed in September 2011 identified no economic evaluations: EconLit, Cost Effectiveness Analysis (CEA) Registry, and the U.K. National Health Service (NHS) National Institute for Health Research Centre for Reviews and Dissemination (CRD) Economic Evaluation Database (EED) and Health Technology Assessment (HTA) Database.

* Laboratory Medicine Best Practices Workgroup member

Disclaimer: The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention/the Agency for Toxic Substances and Disease Registry (CDC/ATSDR).

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Effectiveness of Barcoding for Reducing Patient Specimen and Laboratory Testing Identification Errors: A Laboratory Medicine Best Practices Systematic Review and Meta-Analysis

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This is the first systematic review of the effectiveness of barcoding practices for reducing patient specimen and laboratory testing identification errors.The CDC-funded Laboratory Medicine Best Practices Initiative systematic review methods for quality ...

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This proactive approach not only reinforces organizational commitment to quality care but also builds trust, and leads to substantial cost savings for an institution.

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Medical Errors Are Killing Us | Theresa Sabo | TEDxStanleyPark

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The implementation of barcode scanning benefits internal customers by streamlining processes and creating a more efficient working environment, while external customers experience improved healthcare services and potentially lower costs

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Seeking greater lab efficiency? Lab sample barcoding could be the ideal upgrade to elevate your sample management to a higher standard. Barcoding involves the use of a scanner with barcode labels to update and track labeled items. In a laboratory, virtually anything can benefit from barcoding, but it's most commonly employed for sample tracking and inventory management. In general, labs consider barcode scanners invaluable due to their precision, efficiency and robust capabilities.

Barcoding Helps Lab Efficiency

Using barcode scanners helps anyone interacting with samples work quickly, with metadata and lineage data for a sample loading instantaneously into your system with a quick scan. This simplifies and speeds the handling process when accessioning new sample shipments, processing and aliquoting, freezer and storage organizing, and any exporting of samples or sample data. Additionally, barcode scanners help efficiency when searching for stored samples, helping lab workers quickly identify and verify the correct lineage of samples.

Improved Accuracy

Having data transferred via barcode scan reduces errors during registration and handling. Barcoding allows lab workers to avoid typing mistakes and errors due to misidentified handwriting. The scanner and software transfer the data directly to the software needed for whichever task is being done. The simplified data transfer also allows for samples to have less transition time between freezers in transport or between benches, further reducing the opportunity for mistakes and helping to preserve the integrity of samples.

Enhanced Traceability

Using lab sample barcoding enhances traceability as part of a comprehensive data management solution. Barcoding ensures each sample is connected to a unique ID, making it easy to store, find, and study the information about the samples. It also helps keep track of actions taken with samples and by whom.

What Else Can I Do in the Lab With Barcoding Equipment?

Barcoding is a versatile system of tools that is only limited by the lab manager's creativity and ability to get others on board.

Manage More Metadata: Barcode equipment can be used to track important metadata, such as calibration dates, repair dates, and model numbers, ensuring accurate and up-to-date records.

Streamline Efficient Inventory Management: inventory management by using barcodes to automatically keep track of stock levels, making it easier to monitor supplies and reorder when needed.

Initiate Instrument Automation: Utilize barcodes to kickstart instrument automation, simplifying and speeding up various lab processes for greater efficiency and accuracy.

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What's Possible With Lab Sample Barcoding?

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Barcoding in the lab is a wide-spread practice for sample tracking that allows for high accuracy and efficient transfer of data.

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1. Project Manager:

Oversees the project

Ensures timeline adherence

Encourages effective communication

2. Clinical Informaticist:

Collaborates on workflow optimization

Proposes optimized diagrams

3. IT Specialist:

Addresses technical requirements

Establishes necessary IT infrastructure

4. Quality and Compliance Specialist:

Ensures healthcare regulation adherence

5. Financial Specialist:

Conducts technology and financial analysis

6. Lab Personnel:

Implements barcode technology

7. End-User Testing Specialist:

Conducts assessments

Identifies and fixes system flaws

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Project Initiation Phase:

Commences on January 16, 2024.

Objectives setting and project team assembly.

Project Charter Development:

By January 28, 2024.

Gains stakeholder approval for a clear project direction.

Current State Workflow Mapping:

Starts on January 22, 2024.

Focus on handwritten specimen labeling.

Identifies existing processes and potential inefficiencies.

Future State Workflow Design:

Begins post-completion of the mapping phase.

Incorporates barcode scanning technology.

Aims to improve accuracy, efficiency, and patient safety.

Project Completion:

Targeted for February 11, 2024.

Includes testing, training, and final adjustments.

Structured Timeline:

Provides a systematic and organized approach.

Flexibility:

Allows for adjustments based on unforeseen circumstances or evolving project requirements.

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It is not uncommon to see health care facilities managers directly or indirectly responsible for building projects.

Image by Getty Images

Facilities projects on a health care campus can range from simple office moves to very complex construction and system implementations. Some major construction projects can take years to plan and cost hundreds of millions of dollars to complete. To add complexity to these projects, they often need to be completed while the building continues to see or treat patients.

In the health care field, it is not uncommon to see facilities managers directly or indirectly responsible for building projects. This makes sense given the facilities manager's broad range of responsibilities as well as their thorough understanding of the multidisciplinary activities within the built environment.

However, what does not make sense is to assume a facility manager has the required training and skills to manage health care construction projects or lead those who manage them. Risk factors are too numerous to count. These projects demand expert leadership to ensure the safety of patients, staff and visitors, and achieve ultimate project success.

Facility project work

Facilities managers in almost all industries are responsible for many different aspects of operations. Whether they work in manufacturing, retail, transportation, production or hospitals, they wear many hats and must know an incredibly diverse range of topics. They must be able to discuss high-pressure steam systems one minute and then provide input on carpet selections the next. They must be able to direct a maintenance crew, understand a financial statement and support countless thousands of square feet of building space.

Much of the work they coordinate could be considered project work. A project, as defined by the Project Management Institute (PMI), is a temporary endeavor undertaken to create a unique product, service or result. These words are very important when it comes to understanding the tools and techniques needed for project success in health care. For example, replacement of an air handler serving a hospital inpatient floor can offer significant challenges for the facilities manager. By PMI's definition, it would not technically be a project because the result is not unique.

One could argue, however, that the facilities manager would implement project-type planning to replace the air handler without interruption to patient care. It would then be reasonable to see how a facilities manager's role could evolve to include more and incrementally complex project oversight. As project complexity increases, so do the risks associated with poor performance and unsuccessful projects managed by untrained people.

Defining project success

How does a health care organization define project success? Is it an agreed upon policy statement that is reviewed routinely? Is it just assumed that everyone knows? Time, cost and scope are almost universally identified when the topic of project success is discussed. This is not to say they are not valid components of a good project, but there are factors outside of the "three-legged stool" of time, cost and scope that need to be considered.

What if all three of these criteria are met but the user does not like the product? Imagine a surgeon walking into a brand-new operating room and saying it does not work, even when every scope item was included. If someone is hurt during the project, is it still successful? Consider a project that comes in under budget. If millions of dollars were allocated to a project that were not needed, most likely there were other projects that could not be completed because finances were not available.

The point is that project success can mean different things to different organizations. How project success is measured must be established by the organization before a project starts and may even vary as appropriate. It is critical that a facilities manager responsible for project management understand how success is defined and how accountability will be determined. This information then should cascade as a key message to stakeholders.

Scheduling components

While multiple factors can determine project success, one key element of projects that heath care facilities managers should fully understand is project scheduling. The goal of any project schedule should be to aid the facilities manager and stakeholders in identifying task durations as well as establish overall project duration.

The project schedule is vital in health care and is extremely important for several reasons. First, the schedule is used at a high level to determine the length of the project and determine when operations in the affected area will return to normal. In health care, the schedule indicates when patients can be seen or treated. Second, the schedule is used to identify and plan for interruptions in service utilities such as domestic water, heating, cooling and life safety features.

The facilities manager also can use the project schedule to routinely report on project progress and identify risks to the project based on tasks that appear to be lagging and manage changes as they occur. A schedule of values can be built into a project schedule to assist the facilities manager in identifying actual work performed versus work scheduled. This will ensure that payment is made based on earned value (work completed).

However, a project schedule is only as effective as its inputs. It is not uncommon for the project schedule to be established by the contractor based on input from their trades and subcontractors. This is not to say a schedule prepared by the contractor is inherently inaccurate, but more than likely these schedules are built on pessimistic or worst-case scenarios.

A facilities manager should understand that all schedules are not created equally, and scrutiny with routine monitoring of schedules is necessary as a component of a successful project. Level of detail in project schedule also should be identified in the early stages of project planning.

Scheduling techniques

According to PMI's Project Management Body of Knowledge Guide ( PMBOK® Guide), a project schedule is "the planned dates for performing scheduled activities and the planned dates for meeting scheduled milestones." The goal of any project schedule should be to aid the facilities/project manager and stakeholders in identifying task durations as well as establishing the overall project duration.

As noted, there are several methods of project scheduling that are available to the facilities manager. There are different scheduling techniques available, such as "program evaluation review technique," "activity on node" and "schedule network analysis." Project schedules also can be established simply by ordering activities in sequence to determine project duration.

Critical path schedules shown on Gantt charts or bar charts are arguably the most widely used and recognized scheduling techniques in health care. However, if the facilities manager is not fully fluent in the scheduling technique being used, project success is at risk. Project schedules may be created with the beginning date and end date fixed. The activities of the project can then be inserted to create a timeline.

Project schedules also can be established simply by ordering activities in sequence to determine project duration. This may be done if the project end date is not fixed or the if the project's start date is flexible.

The key to good project scheduling is providing accurate activity duration estimates. The project scheduler must accurately determine activity lengths so that the project schedule is useful. The schedule baseline provides a means to measure and report project performance by validating value of work completed (earned value).

Critical path methodology

A very common scheduling technique, developed in the 1950s by the Dupont Corp., is the critical path method. In the critical path method, the overall project duration is generally determined by the activity paths with the longest durations, according to the PMBOK Guide. The duration of the critical path usually determines the duration of the entire project.

TOP: Critical chain methodology focuses on bottlenecks to improve schedule reliability. BOTTOM: A traditional critical path schedule in Gantt form with critical activities shown in red.

Credit goes here

This technique is useful in determining the amount of flexibility in the schedule on logical task paths. Any delay of a terminal element on the critical path directly impacts the planned project completion date. Also, there is no "float" in a critical path activity, which is the duration a project activity can be delayed without causing project completion to be delayed. Each activity in a critical path schedule is determined by one time estimate.

Frequently, the estimate is pessimistic, which allows for "slack," or the amount of time a project can be delayed without delaying subsequent tasks. The bottom figure on page 34 is an example of a traditional critical path schedule in Gantt form. Critical activities are shown in red.

The red critical activities and their dependencies constitute the critical path. Again, activity durations are given by one estimate. By estimating the worst-case scenario of each activity, the project has slack or float built in. A good example of this would be installing flooring in a hospital corridor. The facilities manager in charge of the project knows that the task could take as little as three days, should take five days, but could take seven days. Therefore, to ensure the schedule does not slip, the estimate for the task is scheduled for seven days. Any associated activities are scheduled similarly.

One primary problem with critical path scheduling is that time estimates are subjective. Inaccurate activity durations can cause the project to be longer than necessary. Padding task durations is done to ensure a high probability of task completion. The trend is then to waste any padding or slack time at the beginning of the activity. If a problem occurs later, it may be too late to recover. Starting work as early as possible is also common, and this can cause multitasking or jumping from task to task. The result is that tasks then usually take longer and rarely finish early.

There are advantages to critical path scheduling as well. Where the project needs to be accelerated, the critical path helps the facilities manager identify which project steps should accelerate to complete the project within the available time. They are mathematically simple, requiring no complex formulas to determine activity durations. Also, many scheduling software programs support critical path methodology.

Critical chain methodology

An alternative to critical path project scheduling is the concept of critical chain. Research indicates that Eliyahu Goldratt, Ph.D., developed and publicized critical chain, according to the article "A critical look at critical chain project management," which appeared in the December 2003 issue of Project Management Journal.

Goldratt is also known as the inventor of the theory of constraints, which is the philosophy behind critical chain. The theory of constraints is a tool used in managing repetitive systems based on the principle that every system has a constraint. System performance can only be enhanced by improving or correcting the restraining resource, according to the Project Management Journal article.

Like the theory of constraints, critical chain focuses on bottlenecks to improve schedule reliability. Critical chain recognizes that task durations are not exact. Therefore, task durations in critical chain scheduling have a probability of distribution centered on "most likely" values. Uncertainty is managed by scheduling backwards from when a project is needed to ensure that work that needs to be done is done, and is only done when needed.

A key component of critical chain includes reducing activity duration estimates by 50%. Activity durations are normal estimates, which are typically known to be high probability and contain excessive safety time. The time that is cut from individual tasks is aggregated and inserted as buffers in the project and eliminates resource contentions by leveling the project plan. The critical chain can then be identified as the longest chain of path and resource dependencies (see top figure on page 34).

A project buffer is inserted at the end of the project to aggregate critical chain contingency time. It is then also important to provide resources with activity durations and estimated start times, not milestones. This encourages resources to pass on their work when done.

The project duration in the critical chain figure is the same. However, activity durations have been reduced by 50% to eliminate "safety time" built into each task. Safety time is combined and inserted as a "project buffer." By using critical chain, the project schedule is only five weeks yet allows for three weeks of buffer. Combined buffer can be less than the sum of individual safety times.

The major advantage of using critical chain scheduling is that it battles Parkinson's law, or "student syndrome," which states that work expands to fill the time available for its completion. Even though activities are scheduled to start as early as possible, they rarely do. Another advantage is that the buffers are more realistic, mathematically speaking, than subjective buffers built into critical path schedules. Project times are shortened due to realistic buffering and focus.

There are also drawbacks to critical chain scheduling. For instance, administrators or program managers may be reluctant to approve project schedules that reflect buffers. Their thought may be to remove buffers and shorten overall project duration. Another challenge discussed in the Project Management Journal article is that critical chain only focuses on one of the legs of the three-legged stool: time.

Planning for success

Facilities managers involved in health care projects should understand and develop schedules that are realistic to ensure smooth project performance, mitigate burden on resources and avoid tying up organizational dollars unnecessarily.

Critical path and critical chain scheduling are options available to assist the project team in monitoring project activities and identifying when interventions may be needed. Where time constraints are crucial, critical chain may be a favorable option. Ease of use must also be considered, especially if software is involved.

Schedules are important, and the key to any technique is the accuracy of the data: Garbage in equals garbage out.

The term "project management" is used loosely in many fields, and health care is no exception. People in any number of formal roles are assigned project management as if it is a skill borne of experience in the field or analogous to other educational or professional backgrounds.

When one reviews any job description for facilities managers, there is a very good chance that "project management" is listed as desired experience, direct responsibility or indirect oversight.

Professional-level project management requires specific skills, tools and techniques so that risk is mitigated and project success is achieved. Patient safety, the ability of staff members to perform their roles at a high level, and stewardship of an organization's finances demand a very high level of competency from those who manage projects in health care.

Professional membership groups associated with health care facilities management, such as the American Society for Health Care Engineering, do an exceptional service by providing training for job functions that are subject to audit and tailored to meet the requirements of enforceable codes and standards.

Aligning with codes and standards is the interweaving of project management fundamentals that aim to ensure the safety of patients, staff and visitors while project work is being done in occupied settings. For example, documented pre-construction risk assessments (PCRAs) and interim life safety measures (ILSMs) are required in accredited health care facilities.

These assessments and mitigation measures are often, and should be, the work of a multidisciplinary group of subject matter experts in their respective disciplines. However, the needs of the patient demand that these efforts are led by a highly competent facilities manager who has the training and tools needed to ensure project success.

Project management-specific training should be sought or required by those who are responsible for health care projects. It is in the best interest of patients, staff, visitors and organizations.
Gordon Howie, MSPM, CHFM, CHC, is regional director of facilities and construction services at Mayo Clinic Health System in Eau Claire, Wis., and an American Society for Health Care Engineering Advisory Board member. He can be reached at howie.gordon@mayo.edu.

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It is not uncommon to see health care facilities managers directly or indirectly responsible for building projects. Image by Getty Images Facilities projects on a health care campus can range from simple office moves to very complex construction and system implementations. Some major construction projects can take years to plan and cost hundreds of millions of dollars to complete.

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References

Alavi, N., Khan, S. H., Saadia, A., & Naeem, T. (2020). Challenges in Preanalytical Phase of Laboratory Medicine: Rate of Blood Sample Nonconformity in a Tertiary Care Hospital. EJIFCC, 31(1), 21–27.

Alcantara, J. C., Alharbi, B., Almotairi, Y., Alam, M. J., Muddathir, A. R. M., & Alshaghdali, K. (2022). Analysis of preanalytical errors in a clinical chemistry laboratory: A 2-year study. Medicine, 101(27), e29853. https://doi.org/10.1097/MD.0000000000029853

Banks, P., Brown, R., Laslowski, A., Daniels, Y., Branton, P., Carpenter, J., Zarbo, R., Forsyth, R., Liu, Y. H., Kohl, S., Diebold, J., Masuda, S., Plummer, T., & Dennis, E. (2017). A Proposed Set of Metrics to Reduce Patient Safety Risk From Within the Anatomic Pathology Laboratory. Laboratory medicine, 48(2), 195–201. https://doi.org/10.1093/labmed/lmw068

Eker P. (2022). Direct cost analysis for 32,783 samples with preanalytical phase errors. Northern Clinics of Istanbul, 9(4), 391–400. https://doi.org/10.14744/nci.2022.73555

Eye On Tech (n.d.) What is a Project Scope? Effectively Planning a Project. Retrieved February 3,2024 from https://www.youtube.com/watch?v=nXOCnDQ05kA

Howie, G. (2021, October 31). Scheduling for Project Success. Healthcare Facilities Management. https://www.hfmmagazine.com/articles/4332-scheduling-for-project-success

Huang, H., Yin, H., Wang, X., Wang, Q., Xiao, M., & Zhao, Q. (2022). Design, development, and evaluation of the Blood Collection Management Workstation. Risk Management and Healthcare Policy, Volume 15, 2015–2022. https://doi.org/10.2147/rmhp.s384866

Johnson, J. (2019). Laboratory Specimen ID Report January 2019. pdc. Retrieved 1/31/2024 from www.pdchealthcare.com

Knox, L., & Brach, C. (2015, September). Primary Care Practice Facilitation Curriculum (Module 20). AHRQ Publication No. 15-0060-EF, Rockville, MD: Agency for Healthcare Research and Quality.

LabKey. (2023, October 31). What’s Possible With Lab Sample Barcoding? LabKey. https://www.labkey.com/lab-sample-barcoding/#:~:text=Using%20lab%20sample%20barcoding%20enhances,with%20samples%20and%20by%20whom.

PDC Healthcare. (2019). Laboratory Specimen ID Report January 2019. https://www.pdchealthcare.com/media/literature/pdf/2019-Lab-Specimen-ID-Report.pdf

PEAK TECHNOLOGIES (n.d.) Barcodes in the Lab. PEAK TECHNOLOGIES. Retrieved February 3, 2024 from https://www.dasco.com/resources/learning-center/2020/03/18/barcodes-in-the-lab/

Sabo, T. (Speaker). (2018, April 10). Medical Errors Are Killing Us [Video]. TEDxStanleyPark. https://www.youtube.com/watch?v=l3FkYdnc-FY&t=1s

Snyder, S. R., Favoretto, A. M., Derzon, J. H., Christenson, R. H., Kahn, S. E., Shaw, C. S., Baetz, R. A., Mass, D., Fantz, C. R., Raab, S. S., Tanasijevic, M. J., & Liebow, E. B. (2012). Effectiveness of barcoding for reducing patient specimen and laboratory testing identification errors: a Laboratory Medicine Best Practices systematic review and meta-analysis. Clinical biochemistry, 45(13-14), 988–998. https://doi.org/10.1016/j.clinbiochem.2012.06.019

The Joint Commission. (2024). Laboratory Services: 2024 National Patient Safety Goals https://www.jointcommission.org/standards/national-patient-safety-goals/laboratory-services-national-patient-safety-goals/

Third Wave Analytics. (2023, September 29). The power of barcodes in LIMS and LIS systems mastering the art and science of modern sample management. https://thirdwaveanalytics.com/blog/lims-barcode-integration/

Yu, M., Lee, T., & Mills, M. E. (2019). The Effect of Barcode Technology Use on Pathology Specimen Labeling Errors. AORN Journal, 109(2), 183–191. https://doi-org.ju.idm.oclc.org/10.1002/aorn.12585

Zayas‐Cabán, T., Okubo, T. H., & Posnack, S. (2022). Priorities to accelerate workflow automation in health care. Journal of the American Medical Informatics Association, 30(1), 195–201. https://doi.org/10.1093/jamia/ocac197

Zebra Technologies. (2019). Boosting laboratory specimen management with barcoding.

https://www.zebra.com/content/dam/zebra_new_ia/en-us/solutions-verticals/vertical-solutions/healthcare/brief-application/healthcare-brief-application-laboratory-management-en-gb.pdf

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Medical Errors Are Killing Us | Theresa Sabo | TEDxStanleyPark

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What quality improvement tools can be used for healthcare workflow redesign?

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What quality improvement tools can be used for healthcare workflow redesign?

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As you go live with a new system implementation, you need to take the organizations' expectations into account. Testing should occur prior to "Go Live".

Different forms of testing:

Unit Testing

Integration Testing

Acceptance Testing

Alpha testing & Beta Testing

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Testing and evaluation will be paramount, prior to, during, and after implementation of the new workflow redesign.

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Design, Development, and Evaluation of the Blood

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Design, Development, and Evaluation of the Blood

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Collection Management Workstation

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Training on the new workflow using a variety of methods and training styles, including virtual classes, externally contracted trainers, in-house trainers, vendors, system analysts, and users with previous experience.

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Effectiveness of Barcoding for Reducing Patient Specimen and Laboratory Testing Identification Errors

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Effectiveness of Barcoding for Reducing Patient Specimen and Laboratory Testing Identification Errors

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Laboratory Testing Identification Errors

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According to an internal survey of PDC laboratory label users, with an average 5,500 specimens passing through laboratories monthly, accurate identification is critical, and drastically impacts your lab's operational performance.

According to a study published in the Archives of Pathology and Laboratory Medicine, specimen labeling errors account for 55.5% of identification errors in the lab. ¹ Specimen labeling errors continue to threaten all healthcare facilities, no matter their size or amount of specimens processed. No laboratory is immune to the damaging effects yielded by specimen labeling errors. In fact, 66% of surveyed lab clinicians claim specimen labeling improvements are needed in their facilities.

Improving patient safety and reducing specimen errors continue to be top priorities among laboratories, with 90% of surveyed labs stating specimen labeling errors are a key concern for their labs. Mislabeled, unlabeled, or incomplete specimen labels often go unnoticed, generating damaging repercussions for your laboratory. Shedding light on such an important and widespread issue will help your facility gain awareness, so tools can be employed to help reduce specimen labeling errors.

Here are 3 Ways Specimen Labeling Errors Threaten Your Laboratory:

Patient safety is the utmost concern among healthcare facilities, and specimen labeling errors can place your patient in dire life-or-death situations. Taking into consideration that up to 70% of treatment options are determined by lab results, ² it is imperative that all specimen labels be labeled correctly with the proper patient identification, so that all lab results correspond to the correct patient. Errors in specimen labeling lead to a higher number of inaccurate test results (e.g. wrong patient-wrong results), which can delay patient's results and treatment options. This is not only dangerous to the health of the patient, but negatively impacts their satisfaction with your facility and compromises their peace of mind.

Increase Lab & Hospital Expenses

Specimen labeling errors not only compromise the safety of your patient, but also increase lab and hospital expenses. Mislabeled or misaligned labels lead to added costs for retesting and supplementary supplies. Lab retests caused by errors may not be reimbursed, leaving your lab and hospital susceptible to unnecessary hidden expenses that can add up quickly. Increased staff hours and length of patient stay are also costly consequences associated with specimen labeling errors.

3. Inefficiency Slows Down Your Lab

The threats specimen labeling errors have on your laboratory surpass monetary increases, and hinder the efficiency of your lab. Valuable staff time is wasted by performing label error corrections. When staff must perform numerous patient sample re-draws and retesting, the efficiency and productivity of your lab diminishes, and staff productivity and satisfaction is negatively affected.

Any specimen labeling error, no matter how small it may seem at the time, leaves your lab vulnerable and at risk, ultimately compromising the safety of your patients.

Below are 3 best practices for reducing specimen labeling errors in your laboratory:

Use at Least Two Patient Identifiers

Specimens must be properly labeled at the time of collection, by using positive patient identification prior to administering the patient's test, and applying the label to the specimen. Medication containers must be in the presence of your patient when labeling, to ensure it is the right patient and test. To comply with Joint Commission National Patient Safety Goals (NPSG 01.01.01) and IQCP, your specimen must always be labeled with at least two patient identifiers (e.g. patient's name full legal name, date of birth, medical record number, and sample source).

2. Enforce Periodic Quality Audits for Proper Labeling

In addition to having at least two patient identifiers on the specimen label, it is imperative to confirm that specimens are properly labeled and legible. All specimens must have a quality label affixed to them, with no missing pertinent patient information. Be sure the label has the correct patient name, patient information, contents, and specimen source. Avoid handwritten labels, since legibility is essential, and misinterpreting handwriting can greatly impact patient safety. It is important to conduct proper training and periodic audits for all staff, to ensure that all specimens are labeled correctly every time.

3. Implement Barcode ID System

Since handwritten specimen labels run the risk of negatively affecting your facility's efficiency and decreasing patient safety, it is highly advantageous to implement a facility-wide barcode ID system. In fact, effectiveness of barcoding for reducing patient specimen and lab testing identification errors is part of the CDC's Laboratory Medicine Best Practices (LMBP). 3 When establishing a barcode ID system, consistent scanning is vital. 86% of reported labs use barcoding, but an overwhelming 44% do not scan consistently, and 14% never scan their label's barcode or patient ID wristband. Be sure to implement facility-wide quality management protocols to ensure staff consistently follows the designated process for scanning patient and specimen barcodes.

Ultimately, it is important that you purchase quality labels that can withstand harsh hospital environments. No matter how efficient your system, if labels are falling off your blood tubes, or if print is smearing or fading, your facility is at a higher risk for specimen identification errors.

Resources:

1,2,3 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: 1106-1113

Jess Johnson is the Senior Brand Manager for Healthcare at PDC, helping to communicate to healthcare customers how PDC solutions can help them with workplace and patient safety. She joined PDC in 2019, with a background in B2B marketing communications and content creation. Jess has Marketing and Journalism degrees from Miami University in Oxford, OH. Originally from Northeast Ohio, Jess now lives in Santa Clarita, CA with her husband and pit-lab mix, Cashmere.

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3 Ways Specimen Labeling Errors Threaten Your Laboratory - ConnectID

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Specimen labeling errors compromise the safety of your patients. Here are 3 ways they can threaten your lab, and 3 best practices for reducing such errors.

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Software testing can be performed at different levels of the software development process. Performing testing activities at multiple levels help in the early identification of bugs and better quality of software product. In this tutorial, we will be studying the different levels of testing namely - Unit Testing, Integration Testing, System Testing, and Acceptance Testing.

Here, we will describe the different testing levels of testing in brief, and in the next tutorials, we will explain each level individually, providing an example and detailed explanation.

Unit Testing

Unit Testing is the first level of testing usually performed by the developers.

In unit testing, a module or component is tested in isolation.

As the testing is limited to a particular module or component, exhaustive testing is possible.

Advantage - Error can be detected at an early stage saving time and money to fix it.

Limitation - Integration issues are not detected in this stage, modules may work perfectly on isolation but can have issues in interfacing between the modules.

Integration Testing

Integration testing is the second level of testing in which we test a group of related modules.

It aims at finding interfacing issues b/w the modules i.e. if the individual units can be integrated into a sub-system correctly.

It is of four types - Big-bang, top-down, bottom-up, and Hybrid.

1. In big bang integration, all the modules are first required to be completed and then integrated. After integration, testing is carried out on the integrated unit as a whole.

2. In top-down integration testing, the testing flow starts from top-level modules that are higher in the hierarchy towards the lower-level modules. As there is a possibility that the lower-level modules might not have been developed while beginning with top-level modules.

So, in those cases, stubs are used which are nothing but dummy modules or functions that simulate the functioning of a module by accepting the parameters received by the module and giving an acceptable result.

3. Bottom-up integration testing is also based on an incremental approach but it starts from lower-level modules, moving upwards to the higher-level modules. Again the higher-level modules might not have been developed by the time lower modules are tested. So, in those cases, drivers are used. These drivers simulate the functionality of higher-level modules in order to test lower-level modules.

4. Hybrid integration testing is also called the Sandwich integration approach. This approach is a combination of both top-down and bottom-up integration testing. Here, the integration starts from the middle layer, and testing is carried out in both directions, making use of both stubs and drivers, whenever necessary.

System Testing

System Testing is the third level of testing.

It is the level of testing where the complete integrated application is tested as a whole.

It aims at determining if the application conforms to its business requirements.

System testing is carried out in an environment that is very similar to the production environment.

Acceptance Testing

Acceptance testing is the final and one of the most important levels of testing on successful completion of which the application is released to production.

It aims at ensuring that the product meets the specified business requirements within the defined standard of quality.

There are two kinds of acceptance testing- alpha testing and beta testing.

1. When acceptance testing is carried out by testers or some other internal employees of the organization at the developer's site it is known as alpha testing.

2. User acceptance testing done by end-users at the end-user's site is called beta testing.

This concludes our tutorial on the different levels of testing. You can continue with our Software Testing Tutorial course to study these levels of software testing in detail, in the coming tutorials.

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Software testing can be performed at different levels of the software development process. Performing testing activities at multiple levels help in the early identification of bugs and better quality of software product.

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Measuring Clinical Workflow to Improve Quality and Safety

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Measuring Clinical Workflow to Improve Quality and Safety

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Health Analytics Types, Functions and Levels

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Health Analytics Types, Functions and Levels

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4 steps of workflow analysis

Understanding and documenting the workflow

Collecting data

Analyzing the data

Deriving possible workflow improvements