Pre-analytical phase: The automated ProTube device supports quality assurance in the phlebotomy process

Artificial intelligence in the pre-analytical phase: State-of-the art and future perspectives

The use of artificial intelligence (AI) has become widespread in many areas of science and medicine, including laboratory medicine. Although it seems obvious that the analytical and post-analytical phases could be the most important fields of application in laboratory medicine, a kaleidoscope of new opportunities has emerged to extend the benefits of AI to many manual labor-intensive activities belonging to the pre-analytical phase, which are inherently characterized by enhanced vulnerability and higher risk of errors. These potential applications involve increasing the appropriateness of test prescription (with computerized physician order entry or demand management tools), improved specimen collection (using active patient recognition, automated specimen labeling, vein recognition and blood collection assistance, along with automated blood drawing), more efficient sample transportation (facilitated by the use of pneumatic transport systems or drones, and monitored with smart blood tubes or data loggers), systematic evaluation of sample quality (by measuring serum indices, fill volume or for detecting sample clotting), as well as error detection and analysis. Therefore, this opinion paper aims to discuss the state-of-the-art and some future possibilities of AI in the preanalytical phase.

Charting the Next Road Map for CSF Biomarkers in Alzheimer's Disease and Related Dementias

Clinical prediction of underlying pathologic substrates in people with Alzheimer's disease (AD) dementia or related dementia syndromes (ADRD) has limited accuracy. Etiologic biomarkers - including cerebrospinal fluid (CSF) levels of AD proteins and cerebral amyloid PET imaging - have greatly modernized disease-modifying clinical trials in AD, but their integration into medical practice has been slow. Beyond core CSF AD biomarkers (including beta-amyloid 1-42, total tau, and tau phosphorylated at threonine 181), novel biomarkers have been interrogated in single- and multi-centered studies with uneven rigor. Here, we review early expectations for ideal AD/ADRD biomarkers, assess these goals' future applicability, and propose study designs and performance thresholds for meeting these ideals with a focus on CSF biomarkers. We further propose three new characteristics: equity (oversampling of diverse populations in the design and testing of biomarkers), access (reasonable availability to 80% of people at risk for disease, along with pre- and post-biomarker processes), and reliability (thorough evaluation of pre-analytical and analytical factors influencing measurements and performance). Finally, we urge biomarker scientists to balance the desire and evidence for a biomarker to reflect its namesake function, indulge data- as well as theory-driven associations, re-visit the subset of rigorously measured CSF biomarkers in large datasets (such as Alzheimer's disease neuroimaging initiative), and resist the temptation to favor ease over fail-safe in the development phase. This shift from discovery to application, and from suspended disbelief to cogent ingenuity, should allow the AD/ADRD biomarker field to live up to its billing during the next phase of neurodegenerative disease research.

Design, Development, and Evaluation of the Blood Collection Management Workstation

Purpose

To design and develop a blood collection management workstation with high usability to reduce the risk of preanalytical errors and improve patient safety.

Methods

A five-phase mobile application development lifecycle model (MADLC) and experience-based co-design (EBCD) were used for design and development. Subsequently, the blood collection management workstation was evaluated using the Chinese System Usability Scale (SUS) in a general ward setting from January to June 2021.

Results

It was used on 2593 in-patients who underwent phlebotomy with 12,378 tubes being labeled. The rate of errors and meantime for blood sampling were decreased compared with the same period in the previous year. A total of 14 nurses agreed to participate in the evaluation, and the overall raw SUS score was 69.26 ± 10.39, which indicated above average results.

Conclusion

The blood collection management workstation has shown the potential to decrease errors and improve working efficiency in a clinical setting. The study also identified some weaknesses, which will be amended in the future.

A Socio-Analytical Approach to the Integration of Drones into Health Care Systems

The integration of drones into health care as a supplement to existing logistics methods may generate a need for cooperation and involvement across multiple resource areas. It is currently not well understood whether such integrations would merely represent a technical implementation or if they would cause more significant changes to laboratory services. By choosing socio-technical theory as the theoretical lens, this paper intends to harvest knowledge from the literature on various organizational concepts and examine possible synergies between such theories to determine optimal strategies for introducing the use of drones in a health care context. Our particular interest is to examine whether the insights generated from the multi-level perspective (MLP) may have the potential to create dynamic spin-offs related to the organizational transitions associated with the implementation of drones in health services. We built our study on a scoping literature review of topics associated with the MLP and socio-technical studies from differing arenas, supplemented with studies harvested on a broader basis. The scoping review is based on 25 articles that were selected for analysis. As a way of organizing the literature, the niche, regime, and landscape levels of the MLP are translated to the corresponding health care-related terms, i.e., clinic, institution, and health care system. Furthermore, subcategories emerged inductively during the process of analysis. The MLP provides essential knowledge regarding the context for innovation and how the interaction between the different levels can accelerate the diffusion of innovations. Several authors have put both ethical topics and public acceptance into a socio-technological perspective. Although a socio-technical approach is not needed to operate drones, it may help in the long run to invest in a culture that is open to innovation and change.

Exact time of venous blood sample collection - an unresolved issue, on behalf of the European Federation for Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for Preanalytical Phase (WG-PRE)

Objectives An accurate knowledge of blood collection times is crucial for verifying the stability of laboratory analytes. We therefore aimed to (i) assess if and how this information is collected throughout Europe and (ii) provide a list of potentially available solutions. Methods A survey was issued by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group on Preanalytical Phase (WG-PRE) in 2017, aiming to collect data on preanalytical process management, including sampling time documentation, in European laboratories. A preceding pilot survey was disseminated in Austria in 2016. Additionally, preanalytical experts were surveyed on their local setting on this topic. Finally, the current scientific literature was reviewed on established possibilities of sampling time collection. Results A total number of 85 responses was collected from the pilot survey, whilst 1347 responses from 37 European countries were obtained from the final survey. A minority (i.e. ~13%) of responders to the latter declared they are unaware of the exact sampling time. The corresponding rate in Austria was ~70% in the pilot and ~30% in the final survey, respectively. Answers from 17 preanalytical experts from 16 countries revealed that sampling time collection seems to be better documented for out- than for in-patients. Eight different solutions for sample time documentation are presented. Conclusions The sample collection time seems to be documented very heterogeneously across Europe, or not at all. Here we provide some solutions to this issue and believe that laboratories should urgently aim to implement one of these.

Defining and Managing the Preanalytical Phase With FMECA: Automation and/or "Human" Control

OBJECTIVE

Our scope is to provide methodological elements on how to manage effectively the preanalytical phase in the laboratory testing process, by objectively measuring the risk connected to the phases handled by man with respect to those managed by machines.

BACKGROUND

Preanalytical errors account for most of the mistakes related to laboratory testing and can affect patient care. Hence, it is necessary to manage the risk connected to the preanalytical phase, as required by certification and accreditation bodies. The risk assessment discloses the steps at greater risk and gives indications to make decisions.

METHOD

We have reviewed the state of art in the automation of the preanalytical phase, addressing needs and problems. We have used the proactive risk assessment methodology FMECA (Failure Mode, Effects, and Criticality Analysis) to identify the most critical phases in our preanalytical process and have calculated the risk associated.

RESULTS

The most critical phases were the human controlled ones. In particular, the highest risk indexes were associated to manual acceptance of test orders, identification of the patients, tube labeling, and sample collection.

CONCLUSION

Automation in the preanalytical phase is fundamental to replace, support, or extend the human contribution. Nevertheless each organization is different about workloads and competencies, so the most suitable management must be tailor-made in each context.

APPLICATION

We present a method by which each organization is able to find its best balance between automation and human contribution in the control of the preanalytical phase.

System-related and cognitive errors in laboratory medicine

Current efforts focusing on better defining the prevalence of diagnostic errors, their causes and remediation strategies should address the role of laboratory testing and its contribution to high-quality care as well as a possible source of diagnostic errors. Data collected in the last few years highlight the vulnerability of extra-analytical phases of the testing cycle and the need for programs aiming to improve all steps of the process. Further studies have clarified the nature of laboratory-related errors, namely the evidence that both system-related and cognitive factors account for most errors in laboratory medicine. Technology developments are effective in decreasing the rates of system-related errors but organizational issues play a fundamental role in assuring a real improvement in quality and safety in laboratory processes. Educational interventions as well as technology-based interventions have been proposed to reduce the risk of cognitive errors. However, to reduce diagnostic errors and improve patient safety, clinical laboratories have to embark on a paradigmatic shift restoring the nature of laboratory services as an integral part of the diagnostic and therapy process.

Blood sampling guidelines with focus on patient safety and identification – a review

It has been well documented over recent years that the preanalytical phase is a leading contributor to errors in the total testing process (TTP). There has however been great progress made in recent years due to the exponential growth of working groups specialising in the field. Patient safety is clearly at the forefront of any healthcare system and any reduction in errors at any stage will improve patient safety. Venous blood collection is a key step in the TTP, and here we review the key errors that occur in venous phlebotomy process and summarise the evidence around their significance to patient safety. Recent studies have identified that patient identification and tube labelling are the steps that carry the highest risk with regard to patient safety. Other studies have shown that in 16.1% of cases, patient identification is incorrectly performed and that 56% of patient identification errors are due to poor labelling practice. We recommend that patient identification must be done using open questions and ideally three separate pieces of information. Labelling of the tube or linking the identity of the patient to the tube label electronically must be done in the presence of the patient whether it is before or after sampling. Combined this will minimise any chance of patient misidentification.

Patient and Sample Identification. Out of the Maze?

Patient and sample misidentification may cause significant harm or discomfort to the patients, especially when incorrect data is used for performing specific healthcare activities. It is hence obvious that efficient and quality care can only start from accurate patient identification. There are many opportunities for misidentification in healthcare and laboratory medicine, including homonymy, incorrect patient registration, reliance on wrong patient data, mistakes in order entry, collection of biological specimens from wrong patients, inappropriate sample labeling and inaccurate entry or erroneous transmission of test results through the laboratory information system. Many ongoing efforts are made to prevent this important healthcare problem, entailing streamlined strategies for identifying patients throughout the healthcare industry by means of traditional and innovative identifiers, as well as using technologic tools that may enhance both the quality and efficiency of blood tubes labeling. The aim of this article is to provide an overview about the liability of identification errors in healthcare, thus providing a pragmatic approach for diverging the so-called patient identification crisis.

Managing the patient identification crisis in healthcare and laboratory medicine

Identification errors have emerged as critical issues in health care, as testified by the ample scientific literature on this argument. Despite available evidence suggesting that the frequency of misidentification in vitro laboratory diagnostic testing may be relatively low compared to that of other laboratory errors (i.e., usually comprised between 0.01 and 0.1% of all specimens received), the potential adverse consequences remain particularly worrying, wherein 10-20% of these errors not only would translate into serious harm for the patient, but may also erode considerable human and economic resources, so that the entire healthcare system should be re-engineered to act proactively and limiting the burden of this important problem. The most important paradigms for reducing the chance of misidentification in healthcare entail the widespread use of more than two unique patient identifiers, the accurate education and training of healthcare personnel, the delivery of more resources for patient safety (i.e., implementation of safer technological tools), and the use of customized solutions according to local organization and resources. Moreover, after weighing advantages and drawbacks, labeling blood collection tubes before and not after venipuncture may be considered a safer practice for safeguarding patient safety and optimizing phlebotomist's activity.

Significant Reduction in Preanalytical Errors for Nonphlebotomy Blood Draws After Implementation of a Novel Integrated Specimen Collection Module

OBJECTIVES

Most preanalytical errors at our institution occur during nonphlebotomy blood draws. We implemented an electronic health record (EHR), interfaced the EHR to the laboratory information system, and designed a new specimen collection module. We studied the effects of the new system on nonphlebotomy preanalytical errors.

METHODS

We used an electronic database of preanalytical errors and calculated the number and type of the most common errors in the emergency department (ED) and inpatient nursing for 3-month periods before (August-October 2014) and after (August-October 2015) implementation. The level of staff compliance with the new system was also assessed.

RESULTS

The average monthly preanalytical errors decreased significantly from 7.95 to 1.45 per 1,000 specimens in the ED (P < 0001) and 11.75 to 3.25 per 1,000 specimens in inpatient nursing (P < 0001). The rate of decrease was similar for mislabeled, unlabeled, wrong specimen received and no specimen received errors. Most residual errors (80% in the ED and 67% in inpatient nursing) occurred when providers did not use the new system as designed.

CONCLUSIONS

Implementation of a customized specimen collection module led to a significant reduction in preanalytical errors. Improved compliance with the system may lead to further reductions in error rates.