Emerging Technologies of the Future Lab: A Webinar Recap

In an era where technology is rapidly transforming healthcare, a recent roundtable discussion brought together industry thought leaders to explore the current state of technology and its potential impact on the future of laboratory science. 

The conversation was rich with insights, covering topics from artificial intelligence (AI) in personalized medicine to the shift toward value-based care and the untapped potential of longitudinal data. 

The Panelists

Suren Avenjian, CEO of LigoLab Informatics Platform, moderated the roundtable discussion. The panel comprised:

• Bruce Friedman, Professor Emeritus, University of Michigan Medical School
• Stan Schofield, Managing Principal of The Compass Group
• Khosrow R. Shotorbani, President, Executive Director, Project Santa Fe Foundation - Lab 2.0
• Dennis Winsten, President, Dennis Winsten & Associates, Healthcare Systems Consultants

The Digital Transformation of Laboratory Science

The panelists began by acknowledging the digital transformation sweeping various industries, including laboratory medicine. 

They highlighted how technology advancements such as artificial intelligence, laboratory information system software automation, and digital pathology, have profoundly shaped the modern laboratory landscape.

As clinical laboratories grapple with disruptions from technological advancements, regulatory changes, and evolving healthcare landscapes, the panelists emphasized the need for medical labs to be proactive. This includes adopting new technologies, optimizing lab workflow management, and investing in staff training and development. 

By embracing change and focusing on delivering high-quality, cost-effective services, clinical laboratories and pathology groups can continue to play a vital role in the healthcare ecosystem.

Learn More: Can Your Laboratory Information System Support the Latest LIS System Technology?

The Importance of LIS Laboratory Information System Integration

The panelists then delved into the complexities of laboratory information systems (LIS systems, medical LIS), focusing on the crucial distinction between integration and interfacing. 

The conversation highlighted the importance of using one comprehensive laboratory information system (LIS software, pathology software) for better efficiency, quality, and productivity.

LIS System Integration vs. LIS Interfacing: Understanding the Difference

One of the panelists, Dennis Winsten, emphasized the difference between integration and interfacing. He explained many people use these terms interchangeably, but they should not.

Interfacing involves the transmission of transactions and messages between disparate laboratory software systems, whereas integration implies all data is contained within a single system.

"Lots of times I hear comments about systems being integrated and in fact, they're not integrated - they're interfaced. Interfacing requires the transmission of transactions and messages between the systems, whereas integration is all contained in the single system." 

- Dennis Winsten

The Challenges of Lab Information System Interfacing

Lab information system interfacing is prevalent and comes with its own set of challenges. 

Changes between interfaced lab systems require retesting, potential downtime, and remapping. There can also be inconsistencies in how the two systems present their data.

Also, if one of the interfaced systems goes down, it raises questions about how to resend the data and which system holds the most current and accurate information.

The Benefits of Lab Information System Integration

On the other hand, lab information system integration offers a comprehensive solution where all data is readily available. This eliminates the silos of financial information, clinical information, and clinical lab data, allowing for real-time access throughout the integration.

"Concerning integration, there's one comprehensive system. All the data is there. You don't have a silo of financial information, clinical information, and clinical lab data." 

- Dennis Winsten

LIS system integration ensures that the data used is consistent and unambiguous. It also enhances business intelligence and analytics by allowing them to work across the full spectrum of clinical and financial information, without the need to reconcile disparate information between different laboratory software systems.

Learn More: Why Integrated LIS System and Lab RCM Software is a Catalyst for Growth

AI: The Game Changer in Personalized Medicine

The webinar continued with a robust discussion on the role of artificial intelligence (AI) in personalized medicine. 

Learn More: How AI Can Propel Medical Laboratories into a New Era of Growth

"I have confidence that the lab industry will absorb AI almost effortlessly. Throughout my career, I've seen the lab as a driver for technology and automation. So, I have no qualms about this. 

I’m confident because laboratory personnel and professionals are very comfortable working with automation and technology, and our industry will provide that for us."

- Bruce Friedman

The panelists highlighted AI's potential to revolutionize healthcare by validating data, predicting diseases, and enhancing the clinical decision-making process. 

Ensuring Data Quality with AI

A key point of discussion was the importance of data quality in AI applications. 

Dennis Winsten stressed the importance of AI operating on accurate and reliable data, cautioning against the "garbage in, garbage out" pitfall. 

"The old expression goes back 30 or 40 years, 'garbage in, garbage out,' and artificial intelligence is not going to solve that if it's dealing with garbage."

- Dennis Winsten

He stressed the need for AI to ensure valid data in longitudinal databases suggesting that AI could serve as a quality control officer, identifying inconsistencies in incoming data and ensuring that only good quality data is used for analysis and decision-making.

Predictive Analytics: A Glimpse into the Future of Patient Care

Dennis Winsten took the conversation a step further, envisioning a future where AI, through predictive analytics, could identify potential diseases a patient might develop over time based on their variations within a normal range. 

"We've now moved into - with artificial intelligence - a predictive model. This model has enough capability to determine what could happen, and what is likely to happen, based on the analysis of historical data.”

- Dennis Winsten

This perspective paints AI as a crystal ball, providing a glimpse into the future of an individual's health. 

But Dennis Winsten didn't stop at predictive analytics. He went on to discuss the next evolution in AI: prescriptive analytics.

“Predictive is good, but I think the next step is even more important, and that's prescriptive. 

With artificial intelligence and machine learning, where the machine learns from the new data it's getting, it can alter what it suggests. Prescriptive suggests decision options that are the most likely to optimize outcomes. It indicates what should happen or the best course of action. 

Using mathematical-based techniques, optimization, machine learning, and heuristics, this is a powerful tool."

- Dennis Winsten

The Success of AI: A Data-Dependent Story

The discussion underscored a critical point: the success of AI in revolutionizing personalized medicine hinges on the quality of data it operates on. 

As Dennis Winsten succinctly put it: 

“AI is only as good as the data it's going to operate on.”

This statement serves as a reminder that while AI holds immense potential, its effectiveness is intrinsically tied to the quality, accuracy, and reliability of the data it uses.

The Transition to Value-Based Care: A New Era in Healthcare

The conversation then took a turn toward the future of healthcare, focusing on the transition from volume-based to value-based care. 

“Contracts are moving from fee-for-service to payments for value and it's getting traction.”

- Stan Schofield

The panelists concurred that this shift would necessitate significant changes in healthcare, with laboratories playing a pivotal role.

Laboratories: The Bridge to Patients

The panelists urged laboratories to move from their traditional roles and bridge the gap with patients. They emphasized the need for labs to be active and guide patients through the health system efficiently and cost-effectively. 

Stan Schofield advised:

"Get closer to the patient - work with your data analytics and financial people doing the contracting."

Understanding Costs and Contracting

The panelists also underscored the importance of labs understanding their costs and working closely with data analytics and financial teams. 

They stressed the need for labs to be at the table when value-based care contracts are signed. 

As Stan Schofield put it: 

"No health system should ever sign a value-based care contract without a lab's input."

The Proactive Approach to Value-Based Care

The discussion highlighted the need for medical labs to adopt a proactive approach as they transition to value-based care. 

The panelists emphasized that labs need to be more than just passive providers of test results; they need to be active participants in the healthcare journey, helping to drive patient care and outcomes.

The success of this transition, they emphasized, hinges on labs stepping out of their traditional roles and taking an active role in patient care.

Learn More: Leveraging LigoLab for Optimal Return on LIS Investment: A Guide for Lab Directors

Longitudinal Data: The Power to Predict and Prevent

The power of longitudinal data emerged as a central theme of the webinar. 

"We have to start transforming and utilizing the longitudinal data, which could help as a stepping stone to the future model. This model allows us to do proactive risk stratification even at an asymptomatic stage, which will be required for value-based care."

- Khosrow R. Shotorbani

The panelists discussed the transformative potential of this data, painting a picture of a future where it could serve as a crystal ball, enabling early diagnosis and prevention of diseases, leading to cost savings and improved patient care.

Khosrow R. Shotorbani highlighted the potential of longitudinal data to predict and prevent diseases. 

He noted that this data could even be used to diagnose pre-disease states, a concept that could revolutionize healthcare. 

"We need to stop talking about just a test and start talking about the change in a test which is that longitudinal data even within the normal range."

- Khosrow R. Shotorbani

The Challenge of Retesting Drugs

However, this new approach would not be without its challenges. 

Khosrow R. Shotorbani pointed out that diagnosing pre-disease states would require retesting drugs for these states, posing a new challenge for the industry. 

"Many of our drugs will have to be retested for pre-disease as opposed to the clinical manifestation of disease, turning healthcare on its axis.”

Medical Laboratories: The Unsung Heroes of Patient Care

The role of medical laboratories in patient care was a recurring topic throughout the webinar. 

The panelists emphasized the need for labs to be proactive, understand their costs, and work with data analytics and financial teams. 

They painted a picture of labs as unsung heroes in the healthcare system, sitting on a wealth of raw materials that could be harnessed to improve patient care.

“Lab data is the biggest bargain in healthcare today.”

- Bruce Friedman

Labs as Catalysts

Khosrow R. Shotorbani highlighted the untapped potential of labs in the healthcare system. 

"Labs are sitting on that raw material." 

He suggested labs could act as catalysts in value-based care, helping to guide patients through the health system efficiently and cost-effectively.

The Unidirectional Nature of Labs

A significant point of discussion was the unidirectional nature of labs. As Bruce Friedman explained: 

"The lab has a unidirectional link to patient care. The lab takes samples, does the tests, gets the results, and sends those out. But the lab rarely finds out the specific outcome." 

He further elaborated that while 70 percent of clinical decisions are based on lab data, labs often don't receive feedback on the outcomes of their work. This lack of feedback prevents labs from understanding the full impact of their contributions to patient care.

Bruce Friedman concluded: 

"The unidirectional nature of the lab has been a problem for a long, long time." 

This statement underscores the need for a more integrated approach where labs are kept in the loop about the outcomes of their work, enabling them to understand and enhance their contributions to patient care.

Learn More: Four Game-Changing Business Strategies to Improve Laboratory Processes

Charting the Course for the Future

The webinar concluded with optimism and anticipation for the future. 

The panelists called for continued collaboration and innovation to harness the potential of technology in personalized medicine, to reshape the future of clinical laboratories, and, ultimately, improve patient outcomes.

The insights and connections gained through this roundtable are more than just a recap of a fascinating discussion. They represent a roadmap for the future, a collective effort to navigate the evolving landscape of the clinical laboratory industry. As we continue to explore and innovate, these discussions will be vital in guiding our course.

Looking ahead, the horizon is filled with exciting possibilities. 

From the potential of AI and longitudinal data to the pivotal role of labs in patient care, the future of clinical laboratories promises to be a journey of discovery and innovation.

To watch this webinar on-demand, click HERE. 

Have a topic for a future LIS system or laboratory billing webinar? Then suggest it at Info@LigoLab.com.

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