Overcoming Challenges in Point-of-Care Testing to Meet Quality Goals

Over the past few decades, the availability and use of point-of-care testing (POCT) has steadily increased, in line with the market overall. POC diagnostics tests global sales reached $29.5 billion in 2020 and are expected to reach $50.6 billion by 2025. In 2020, glucose monitoring applications represented the largest POC market segment (55.4%), followed by cardiac (10%) and infectious diseases (9.8%). Being performed near the point of patient care (as the name suggests), POCT offers considerable advantages over central laboratory testing, such as shorter turnaround times, elimination of sample transportation and simpler sample requirements and handling. POCT can improve the accessibility and uptake of diagnostic testing, enable physicians to provide timely advice and reduce the need of follow-up visits by providing treatment right from the start.

Despite its benefits, POCT carries a higher cost-per-test compared to the central lab, it is resource-intensive, and it requires a disciplined implementation of quality control procedures, training and operating protocols (outside of the typical lab environment). To deliver reliable results, organizational and quality assurance (QA) challenges must be addressed to implement POCT in any healthcare environment.

Challenges of POCT Implementation

One of the factors that strongly affects adoption is the quality and accuracy of test results. The root causes of result errors are due to:

• User errors in clerical or use methods, including training effectiveness
• Underlying measurement technology (sensitivity and specificity or measurement failure)
• Lack of a robust quality control process, including calibration of non-waived devices; measuring, recognizing and eliminating drift or bias in results; or errors in the calibration method

Laboratory personnel exist within a professional environment that promotes and focuses on test procedures and test-result integrity. For lab-based instrumentation, proficiency testing (which involves validating test protocols, evaluating training and a quality control process) can ensure errors in use, device, results bias and calibration are minimized.

In contrast, the operators of non-laboratory POCT are focused primarily on patient care–often caring for multiple patients at once, tasked with non-testing responsibilities and exposed to important, but distracting, environmental factors (in the case of emergency medical technicians). Their training and professional focus is not on the laboratory medicine procedures that ensure quality results. This increases the importance of enabling, encouraging and enforcing procedures and infrastructure to maintain integrity in the testing process (pre-analytical, analytical and post-analytical phases and training). POCT occurs “in the field” with a large and heterogeneous user base and field conditions, and as such, can affect factors that impact results.

Putting POCT into Practice

Given these challenges, how can healthcare providers adopt a human-centered and risk-based approach to POCT?

Here are some quick tips to get you started.

1. Consider the human as the central point in your design and deeply apply human factors and usability engineering within your service design and product design processes.
   This will enable the design team to identify and eliminate factors that not only affect safety, but also address accuracy, effectiveness and user satisfaction. The usability engineering process considers these factors as interface characteristics related to safety or potential use errors, identifies hazards and hazardous situations, and teaches the designer to differentiate between perception, cognition and action. The process generates and confirms design weaknesses and unanticipated use errors through experimental methods (such as formative and summative testing).
   
   Note that the usability process will inform not just the device design, but the training and quality process itself, yielding a superior result. Consider that training materials and workflow are design artifacts too and can be designed to improve perception, cognition and action.
   
   
2. Enable and establish a robust clinical governance infrastructure.
   This encompasses not only addressing distributed personnel, their roles and responsibilities and process, but also the type of data and network infrastructure in support of your POC device. Data connectivity and availability have their own inherent challenges. Many POC devices are not connected at all: They lack connectivity to the hospital or other local area network (LAN), further requiring enhanced data security and privacy capabilities if connected, and lack the proper IoT architecture (such the ability to properly identify patients or have bi-directional data transfer to a central data infrastructure).
   
   Proper data connectivity, data availability and a clear governance model with a POC coordinator are a must. This enables the POC coordinator to be informed of and act on the status of the device operation, with compliance to the quality control process. A targeted POC device design can expose the POC operator to the steps of a robust quality system, enforce proper user credentials based on identification and training status, and report on device condition.
   
   
3. Take advantage of your enhanced data infrastructure to improve proficiency testing and quality control.
   A recent study of global healthcare providers concluded that the majority of healthcare POCT providers believe improvements in delivery need to be made. They cited opportunities in the major phases of POCT: pre-analytic (initial and subsequent training, enforcement against expired materials); analytic (verification of device/method, built-in quality and function checks) and post-analytic (POC coordinator oversight, external QA and proficiency testing).
   
   While all quality practices are important for POCT results, there are a few that almost every provider can agree on: having trained and competent personnel, testing and verifying device operation, and pursuing ongoing quality assessments are the key factors for ensuring quality. With your established robust governance infrastructure, a strong back-end digital infrastructure that leverages artificial intelligence (AI) and machine learning (ML) can be used to identify trained (and systematically re-trained) personnel, ensure device self-test and subsequent actions, enforce a QA process and execute external QA and its resulting actions. 

Conclusion

Supported by this ever-expanding POC test menu, healthcare providers will continue to shift routine laboratory testing to the site of patient care to reap the benefits of efficiency and cost-reduction. Institutions need to adapt their overall care strategy, finding a new balance between central and decentralized testing and addressing the practical challenges of implementing POCT, including its attendant quality control. The reality is that functional device design, back-end connectivity and the presence of AI/ML in mining digital data is not enough. Your healthcare organization must implement a strong POC governance model and POC coordinators that are enabled by actionable data to deliver the most reliable results.