Quality Moves in Medical Laboratories

By Dr Puneet Kumar Nigam, Chief Quality Officer, Metropolis Healthcare

“Medicine is a science of uncertainty and an art of probability,” claimed William Osler. Multiple steps in evaluation, like history, physical examination, imaging, electrocardiogram, and laboratory investigations, are undertaken to manage the uncertainties and reach the correct diagnosis.

Any discussion of laboratory errors cannot be understood until we also consider inherent variability within the human body due to diurnal variation, response to drugs and chemicals ingested through food or nutraceuticals, seasonal variation, impact of stress and mood, lifestyle and effect of exercise, and subclinical infections.

Many times, variations due to these factors are perceived as laboratory errors simply because tests conducted at different time points show different results. The largest contribution comes from the pre-examination phase, ~ 70%. While labs plan improvement in all phases, the weaker areas will always get more attention. Another way to look at lab operations is through the 5 M’ approach – Manpower, Machine, Measurement, Material, and Methods.

Manpower is the most important component since they are either directly involved in an activity or using a device, instrument, or utility. To achieve the best outcome, the focus should be on selecting the right talent, experience depending on job requirements, attitude to learn and upskill, and discipline to implement defined procedures and policies. Provide ongoing training for all laboratory personnel to update them on the latest techniques, technologies, and safety protocols.

Machine includes all equipment for performing tests, processing samples, data analytics, and software utilities. To achieve optimal equipment performance, it is important to select the right equipment (fitness of purpose), qualify them at installation, and do periodic maintenance and calibration, as the manufacturer recommends. The use of software tools can help in capturing all incidents, breakdowns, and flags. Moreover, artificial intelligence (AI) can predict when instruments need maintenance, reducing downtime and errors due to faulty equipment. Real-time monitoring is now possible to ensure they are functioning correctly with manual or AI-based diagnostic support to preempt problem processes and suggest corrective actions.

Measurement is at the heart of generating a correct report. Also, statistically robust quality control measures should be defined and implemented. Participating in external quality assessment schemes (EQAS) helps in getting an unbiased measure of test accuracy. Ongoing verification helps ensure consistency in testing over longer periods of time. All errors and failures should be promptly acted upon so they do not recur. Employ a system of double-checking critical steps in manual testing processes, such as sample labeling and data entry, to catch potential errors before they affect results. Equipment interfacing with the laboratory information system (LIS) prevents many transcription errors. AI algorithms can cross-check data to ensure consistency and accuracy, especially if data is available digitally, for individual labs, or at multiple locations.

Material like reagents, QC material, calibrators, and consumables must all be ordered and received according to specifications and business requirements. Acceptance testing of all reagents helps identify any lot-to-lot variation. Supply chain efficiency is a major quality indicator. Important considerations are receipt at appropriate conditions and storage at the correct temperature, acceptance testing, availability of instructions for use, and the provision of a system of adverse incident reporting.

Methods, when well documented and implemented (SOPs), bring standardization and consistency. The measurement methods should be validated and verified by the laboratory before being put to routine use. Following good laboratory documentation and labeling practices helps prevent many potential failures. If a laboratory uses different methods for one test, comparison studies should be done to ensure comparability of methods.

Manual processes are prone to human error due to fatigue, distraction, or simple oversight. Process automation is now available for repetitive tasks to improve turnaround time and deliver consistent output around the clock, even with large workloads. For example, in pre-examination phase tracks, the system can manage sample sorting, centrifugation, aliquoting, and transportation to different departments.

Automation in the examination phase is commonly used in biochemistry and hematology, and it is increasingly being adopted in histopathology, cytogenetics, and microbiology. LIS enhancements and middleware bring operational control, better resource utilization, inventory management, patient-based real-time quality control, delta checks, standardization of testing & reflex testing protocols. Reporting has become faster with the application of auto-verification. Digital pathology is transforming image-based reporting and second-opinion access. Finally, integrating automation with laboratory information management systems (LIMS) facilitates seamless data management with audit trails. Automation in training through Learning management systems offers flexibility to users and better knowledge sharing.

An internal audit management system is paperless and transparent, making periodic monitoring easy. Utilities in sample collection and transportation allow labs to better meet turnaround time requirements. Environmental control systems, with the support of AI, can manage and adjust environmental conditions to optimal levels and adjust lab conditions in real time.

Offering customer-centric services means labs need to adapt ways to collect feedback from all users. Software solutions are also available for customer support/call centers, taking feedback, e.g., Net Promoter Score (NPS), apart from calling, web-based, or app-based options.

In conclusion, technology and software tools are helping laboratories deliver better-quality services even as they expand geographically and numerically. The new ISO 15189:2022 Standard also ushers a greater focus on risk management, ethics, and safety. With more adoption of machine learning, digital solutions, and AI, things look promising in the future.