Optimizing Point-of-Care Testing for Greater Reliability, Efficacy

Point-of-care testing (POCT), most commonly demonstrated as blood glucose monitoring and home pregnancy tests, is any diagnostic tests performed at or near a patient and at the site where care or treatment is provided.¹ Results are rapid and reliable, allowing providers to identify and manage chronic diseases and acute infection treatment.²

These tests vary in complexity, including basic dipsticks for urinalysis and handheld devices like glucose meters or sophisticated molecular analyzers to detect infectious diseases.¹ Because POCT occurs near the point of patient care, it reduces turnaround time, avoids overcrowding at hospitals, and eliminates sample transportation and handling requirements. The streamlined screening enhances patient outcomes and access to care, allowing public health agencies to more effectively reach targeted populations.²

Although POCT has several advantages over traditional laboratory testing, these tests are not immune to challenges which restrict adoption; moreover, there is always room for further optimization.

Challenges of POCT

Though designed to be simple and accurate, POCT is not error-proof. To ensure high-quality results, proper training and close adherence to directions are vital.³ Even experienced health care practitioners must carefully follow test directions and have familiarity with test systems; if performed or interpreted incorrectly, the subsequent treatment decisions can have serious health consequences.¹ As clinical staff and patients perform POCT, their lack of familiarity with quality control and assurance procedures can increase the possibility for results errors.

Likewise, per-test costs for POCT can be higher than for lab testing. Initial set-up of a disciplined approach to quality control procedures, training, and operating protocols can be resource-intensive.⁴ On the device side, the full benefit of POCT can be compromised by an improper Internet of Things (IoT) architecture which affects data management, data security, and privacy.

Adoption of a Human-centered/Risk-based Approach

Adopting a human-centered design and risk-based testing procedure will require health care organizations to leverage a strong governance model that is also human-centered. Providers must deeply consider human elements while emphasizing usability engineering with the service and product design process. Putting human behavior and interactions as the focus of the design allows the design team to identify and eliminate potential safety endangerments, support accuracy and effectiveness, and boost user satisfaction. Health care providers should note that the usability process will yield superior results when applied to the device design and the training and quality process.

Organizations can use artificial intelligence (AI) and machine learning (ML) to refine personnel training procedures. Though quality practices can minimize errors and lead to better results, POCT still needs well-trained and competent personnel. Likewise, providers need to test devices to verify adequate operational efficiency, and quality assessment must be ongoing to ensure high quality. With a strong back-end digital and governance infrastructure incorporating AI and ML, health care entities can train and re-train staff, self-test devices, and enforce quality assurance processes. Additionally, providers can design their training material and workflows to improve perception, cognition, and action.

Finally, to overcome POCT data-related challenges, providers must establish a data connectivity infrastructure through proper IoT architecture. Currently, many POCT devices are not connected at all. However, by establishing data connectivity and availability with a strong governance model and a POC coordinator, POCT will enhance quality control, track device condition, and impose appropriate user authorization via training status and clearance.

POCT and the Evolution of Technology

While POCT will most likely never replace clinical laboratory testing, as demonstrated with COVID-19 antibody and antigen tests, it will continue to evolve and become an essential part of people’s health care experience. And as technology advances, so too will the quality of POCT—case in point, diabetes has radically changed in both delivery and management.¹

The implementation of human-centered design and risk-based testing plus AI and ML has the potential to drastically enhance the accuracy and quality of POCT results. Further, human-centered approaches must be central to the device design, training methods, and quality procedures of the POCT ecosystem in order to realize the full benefit and potential of using POCT for both patients and health care systems.

About the Author

Stuart Perry is a senior director of innovation consulting at EPAM Systems, Inc. Perry has led multi-disciplinary teams in the design of products, including in-vitro diagnostic systems, optical biometric devices, energy-based skin rejuvenation, and corneal shape correction. Perry is also involved in low-level design activities in the areas of analog low- and high-power circuits, algorithm design and implementation, wireless systems, and system simulation. Perry received an MS in electrical engineering and a BE as a double major in electrical engineering and math from Vanderbilt University.

REFERENCE

1. Testing. Point-of-care testing. Testing.com. https://www.testing.com/articles/point-of-care-testing/. November 9, 2021. Accessed December 13, 2021
2. National Community Pharmacist Association. Point-of-care testing (POCT). NCPA.org. https://ncpa.org/point-care-testing-poct. Accessed December 13, 2021.
3. Price, CP. Point of care testing. BMJ. 2001 May 26; 322(7297): 1285–1288. doi: 10.1136/bmj.322.7297.1285
4. Shaw, JLV. Practical challenges related to point of care testing. PLM. 2016;4:22-29.