Volume 40 Number 3 | June 2026
Summary

The article outlines a vision for the “Laboratory of the Future” as a technologically advanced, data-driven, and collaborative environment. It emphasizes integrating AI and data science to improve efficiency, compliance, and financial outcomes, while repositioning laboratories as strategic assets. Empowered professionals, stronger clinical partnerships, and proactive advocacy are key to advancing patient care and the profession.

Maria Sue Abiera, MS, MLS(AMT), Volunteer Contributor

Maria Sue AbieraIf you’ve ever felt buried in paperwork, frustrated by inefficient processes, or misunderstood by clinical colleagues, you’re not alone. Our labs are at an inflection point. The future is not just about more work; it’s about smarter work, greater recognition, and a stronger voice. Based on conversations with colleagues across the healthcare spectrum, here is what the “Laboratory of the Future” must look like.

Embracing the Efficiency Revolution: A View from the Front Lines

Technology must move beyond automation to true integration. All clinical lab areas both performing routine and specialized tests rely on technology that is evolving fast. The laboratory information system (LIS) that stores patient demographics and test results and provides computational methods to track quality control, estimate reference ranges, and support clinical decisions are no longer sufficient to manage the growing need for documentation efficiency and organization in meeting regulatory requirements amid staffing shortages and recent CLIA overhaul. Software companies are producing innovative digital solutions for record management, personnel training, and competency and internal audits.

According to Flor Manalili-Black, senior manager of lab operations at Advent Health Sebring, her laboratory uses MediaLab extensively, though, she thinks an inventory management system based on predictive analytics and a cellphone app will complete the package. Carl Abiera, a former chemistry supervisor at a major reference laboratory in New York City, expressed the same opinion. AI applications that render smart monitoring and reorder of reagents and supplies alleviate workload stress. Although their system was a customized program adapted to their unique laboratory environment, he found it insufficient to eliminate more tedious tasks such as barcode scanning of reagents, so the inventory data is updated.

Many tech solutions exist and are continually advancing. The challenge is the ability to keep up with upgrades and novel capabilities, while maximizing their benefits.

“When we champion technologies that enhance quality, efficiency, and financial integrity, we give hospital or physician office leadership the business case to invest in us.”

Data Science: Embracing a Powerful Tool

Association for Diagnostics & Laboratory Medicine (ADLM)’s definition of laboratory data science on its website states: “It is an interdisciplinary field which centers on leveraging data to improve the quality and efficiency of laboratory medicine. It improves patient outcomes and reduces healthcare costs.” It further adds that data science in laboratory medicine encompasses data governance, informatics, data processing, data analytics, machine learning, and artificial intelligence.

Let’s take a hypothetical case study that centers on a pain point common to all clinical laboratories to illustrate the power of this soon-to-be critical tool in medical laboratory science—How Data Science Could Have Averted a Joint Commission Finding.

Scenario

A hospital laboratory is in the process of acquiring accreditation from the Joint Commission. One of the survey findings was under standard DC.02.01.01 EP2 and EP3 where tracers for a point-of-care (POC) test from the ER department did not have orders in the patient medical records. The process where the doctor places an order in the patient’s medical record was not followed. When interviewed on two occasions there was pushback from the nursing staff who performed these tests. This is a requirement to be followed by all testing personnel, so in essence, the laboratory is responsible for the nursing staff performing these POC tests to follow the correct procedure where a doctor’s order should be in patient records before performing the test. This hospital currently has 25 POC instruments used in multiple hospital departments.

1. Preventing the Finding: Predictive Analytics as an Early Warning System

A data science-driven system would move beyond periodic audits to continuous, real-time surveillance. Here’s how it could have worked:

  • Automated Order-Verification Monitoring: By integrating data from the 25 POC instruments with the electronic medical record (EMR), a simple algorithm could run daily (or hourly) to flag every POC test result without a corresponding physician order in the patient’s chart. This creates an automatic, objective audit trail.
  • Predictive Risk Scoring: Beyond flagging, a predictive model could analyze contextual factors to identify when and where non-compliance is most likely:
    • Unit and Shift Analysis: Does the ER have a higher rate on night shifts? Does noncompliance spike during shift changes or high patient volume?
    • User Patterns: Are there specific operators or groups with higher exception rates?
    • Test-Type Analysis: Are certain urgent tests (e.g., glucose in a crisis) more likely to be performed without a prior order?

With this system, the lab manager or POC supervisor would have received automated alerts months before the survey, showing: “Non-compliance rate for POC order documentation in the ER has exceeded the 5 percent threshold for three consecutive weeks. Predicted risk remains high on weekend night shifts.” This allows for targeted retraining or workflow adjustments before an inspector arrives.

2. Determining if the Issue is Systemic: From Anecdote to Evidence

Data science moves the conversation from “the nurses push back” to an objective, system-wide assessment.

  • Cross-Departmental Benchmarking: The model would analyze data from all 25 instruments across every department (ER, ICU, floors, etc.). It would not just identify the ER as a problem, but could reveal, for example: “While the ER has the highest volume of exceptions, the Cardiac ICU has a 40 percent exception rate for stat lactate orders, indicating a potential workflow breakdown specific to that unit and test.”
  • Root Cause Correlation: By correlating the non-compliance data with other hospital data sets (nurse-to-patient ratios, EMR usability metrics, training completion records), the lab could present evidence on whether the cause is:
    • Training Gap: Concentrated in new hires or specific units.
    • Workflow Issue: Linked to a cumbersome order-entry process in the EMR.
    • Cultural/Procedural: A widespread misunderstanding of the requirement.

This data-driven answer shifts the focus from disciplining individuals to fixing broken processes, which is far more effective for lasting correction.

3. Securing the CFO’s Buy-In: Speaking the Language of Finance

To get the CFO’s ear and compel nursing leadership to the table, the lab must translate compliance risk into financial impact. Data science can provide numbers—the cost component, how to calculate and present it to the CFO.

Here’s a hypothetical example of how to present the cost of non-compliance:

Cost Component How to Calculate and Present It
Direct Revenue Loss Our data shows an average of 350 POC tests per month are performed without a corresponding billable. At a conservative reimbursement rate of $25 per test, this represents a direct monthly loss of $8,750—or over $105,000 annually unrealized revenue.
Corrective Action Costs The labor required to retrospectively research and create orders for these tests involves 40 hours/month of combined lab and nursing staff time, costing an additional $2,000+ monthly in wasted productivity.
Regulatory and Reputational Risk A Joint Commission Finding can trigger follow-up surveys, mandatory action plans, and increased scrutiny. While hard to quantify, a single significant citation can impact hospital accreditation status and reputation, affecting market share and patient volumes.

Recommended Action for the Lab:

Create a one-page dashboard for the CFO with three key visuals:

  • A trend line showing the monthly volume of unpaid tests and the associated revenue loss.
  • A bar chart comparing exception rates across the top five hospital departments.
  • A projection showing the 12-month financial impact if the issue is not corrected.

This data-driven, financial narrative transforms the problem from a “lab critical issue” to a hospital-wide revenue integrity and risk management issue, ensuring executive support for the necessary interdisciplinary corrective action.

This hypothetical case demonstrates that in the “Laboratory of the Future,” data science is a critical tool that empowers labs to govern decentralized testing, predict compliance failures, and advocate for resources using the universal language of data and dollars. It turns the lab from a cost center struggling with enforcement into a strategic asset that safeguards both quality and the hospital’s financial health.

Reframing the Narrative: Investment, Not Expense

We must champion this shift in perception. This approach is the foundation of independent startup labs and physician office labs: laboratory owners put money into testing with the expectation of a return on investment and earning profits. Hospital labs operate similarly. The lab needs to be aware of the trends in diagnostic testing and position itself as a major player in patient care and one of the pillars to the hospital’s bottom line.

This leads to the crucial mindset shift: the laboratory must be perceived as a strategic investment, not a cost center. Consider the data science tool described earlier. Implementing it requires an upfront investment in software and analyst time. However, its payoff is tangible: it prevents revenue loss by ensuring billable tests are properly ordered, avoids costly regulatory citations, and frees up staff time from manual audits for higher value work. This is the same return-on-investment logic applied to an independent lab’s new sequencing instrument or a physician office lab’s POC instrument. When we champion technologies that enhance quality, efficiency, and financial integrity, we give hospital or physician office leadership the business case to invest in us.

Unleashing the DCLS: The Missing Link in Test Stewardship

The Doctorate in Clinical Laboratory Science (DCLS) is our profession’s secret weapon for the future. They should be integrated to guide test ordering, reducing unnecessary tests, and ensuring the right ones are ordered at the right time. Here are a couple of examples where DCLS can become the missing link in test stewardship.

According to a paper entitled, “Rationale for Cost-Effective Laboratory Medicine,” by Ann Robinson, published in the Clinical Microbiology Reviews 1994, doctors account for major overutilization, misutilization, and underutilization of hospital resources including the laboratory. In a study published in the National Library of Medicine in August 2020, ordering the wrong tests leading to misdiagnosis and delayed treatment costs hospitals, S1.7M a month! Those with a doctorate in CLS can point doctors in the right direction when ordering laboratory tests for more efficient, cost-effective, and timely patient care.

A webinar by G2 Intelligence entitled, “Strategies for Clinical Labs to Get Paid More for Genetic Test Claims,” focusing on new test offerings in pharmacogenetics or oncology for example, a DCLS can spearhead in the preparations needed when offering a new test that results in successful reimbursement. It presented the need for the laboratory to know beforehand the test’s clinical validity and utility, identifying the targeted patient and a strategy to properly educate ordering doctors and providers of the test. A DCLS has the potential to perform the above functions and answer provider questions about the new test.

Fostering Clinical Collaboration: The MLS as a Collaborative Allied Professional

The “Laboratory of the Future” requires a fundamental shift in mindset: from a siloed technical expert to a collaborative allied health professional. This means being open to and understanding the concerns of our closest allied health professionals, the nurses, and viewing them not as “customers” or “users” of our services, but as essential partners in a shared mission—patient care.

The article, “Nursing and the Laboratory: Relationship Issues That Affect Quality of Care,” was co-authored by an MLS and a nurse and published in 2006 by Medscape General Medicine. The article discusses relationship issues between the lab and nursing that affect patient care. It highlights our perception of how we relate to nurses and their appreciation of the lab’s critical role in medical management of patients. It also identifies areas of improvement to help them deliver better quality of care. It bears the hope that the article can foster better communication, collaboration, and understanding between our roles as healthcare professionals.

A collaborative MLS professional actively:

  • Communicates Proactively—proactively notifies nurse stations when there are changes in policy or procedures. For example, specimen previously not collected in ice now requires ice.
  • Partners in Problem-Solving: Works with nursing units on data-driven projects to reduce contamination rates or improve test utilization.
  • Engages in continuous improvement: Streamlines processes to improve STAT test TAT to help nurses optimize the best outcome for the patient.

By embracing this identity, the MLS becomes the indispensable linchpin of diagnostic stewardship, ensuring the right test is ordered, collected, and interpreted correctly—ultimately modulating a direct, positive impact on patient care.

Unified Standards: A Call for Regulatory Stewardship

The “Laboratory of the Future” needs to operate under a nationally uniform interpretation of CLIA standards. This means leveling off the playing field, so all laboratories are held to the same high standards, regardless of state. Professional organizations need to participate in this stewardship, where labs share best practices, audit tools, and compliance strategies to help each other excel, moving from an inspection mindset to a continuous improvement culture.

Building Our Legacy: Advocacy Starts Early

For our profession to thrive, we must be its loudest advocates. This means that MLS professionals visit local schools to introduce students to the fascinating world of diagnostic medicine during their biology and chemistry courses. This shifts the perception from a “hidden” career to an exciting, STEM-based profession that is critical to modern healthcare.

Other significant ways we can advocate for our profession are found in an article authored by Angela Tomei-Robinson, MS, MLS(ASCP)CM, and Rodney Rohde, PhD, SM(ASCP)CM, SVCM,MBCM, FACSC.

On March 12, in celebration of Women’s History Month, I was invited to speak on the Women in STEM panel at a local theater with four other panelists from different fields. It was followed by a screening of the film, Hidden Figures. I used the opportunity in the panel discussion to educate the audience on the critical role MLSs play in patient care, how we produce data that drives 70 percent of medical decisions, that we are the brains behind the scenes, which makes us hidden figures of healthcare. I call on all MLSs to take every opportunity to speak at events outside the MLS community to reach the public who need to know what we do.

The “Laboratory of the Future” is within our grasp. It’s a lab powered by intelligent technology, led by empowered professionals, and recognized as an indispensable clinical and financial asset. By advocating for and embracing these necessary changes, we won’t just improve our workdays—we’ll secure the future of our profession for generations to come.

Maria Sue Abiera is Founder and Technical Lab Consultant at Clinical Laboratory Scientist Consulting in Staten Island, New York.