By Alyx Arnett

A March 2025 report from the Department of Veterans Affairs Office of Inspector General (OIG) has found that staff at the Carl Vinson VA Medical Center in Dublin, Georgia, reprocessed and used surgical instruments with visible damage, despite policies prohibiting such practices. 

The report was prompted in part by a spring 2024 incident in which surgical tools with visible damage—including staining and pitting—were discovered in a rectal tray that had already been used during a patient procedure. While it remains unclear whether the nonconforming instruments were used on the patient, the entire tray had been reprocessed and returned to use, and staff failed to remove the visibly damaged tools prior to the procedure, according to the report. According to VA policy, if one item in a surgical tray is contaminated or nonconforming, the entire tray is considered compromised.

The event occurred after a prior inspection in 2022 had identified similar issues at the facility and after VA leaders had agreed to implement corrective actions.

During the latest inspection, OIG staff found additional nonconforming instruments in randomly selected surgical trays and determined that reprocessing and using visibly damaged tools was an ongoing practice at the facility. According to the report, both current and former Sterile Processing Services (SPS) chiefs allowed the practice, citing factors such as pressure to deliver complete trays, unclear responsibility for instrument replacement, and what one leader described as “staff complacency.”

Training Delays and Policy Noncompliance

As part of its review of the rectal tray incident, the OIG assessed how facility leaders responded after the damaged instruments were discovered in spring 2024. According to the report, the chief of SPS conducted refresher training for sterile processing staff and posted visual reminders in the work area. However, operating room staff did not initially receive similar training, despite internal documentation noting the need for “more rigid inspection” of surgical equipment.

When asked why this training was delayed, the operating room nurse manager cited limited staff time and expressed concern about leadership deprioritizing essential safety efforts, according to the report. The OIG described the lack of timely training as inconsistent with expectations and a missed opportunity to prevent continued use of damaged instruments.

Preventive Maintenance Program Missing Until 2024

Inspectors also found that the facility had failed to implement a preventive maintenance program for surgical instruments, despite a 2016 VA policy requiring one. The program, which involves routine sharpening, repair, and replacement, was only put in place in May 2024 after years of leadership turnover, according to the report.

During initial servicing under the new contract, more than 800 surgical instruments were identified as “beyond repair,” including four from the same rectal tray involved in the spring 2024 incident.

Incomplete Fixes from Prior Oversight

The March 2025 inspection also assessed the facility’s progress on recommendations issued in an earlier March 2024 OIG report. Key corrective actions remained incomplete:

• CensiTrac, an electronic instrument tracking system, had not been fully implemented. OIG found missing documentation, unmarked instruments, and discrepancies between instrument trays and count sheets.
• The CensiTrac coordinator, responsible for overseeing the system and instrument marking, had longstanding performance issues that were not formally addressed by supervisors.
• A room meant for training and meetings continued to be used for meals and breaks, raising contamination concerns. Despite verbal claims of repurposing, inspectors observed food and drink in the room during their visit.

The report cites high leadership turnover as a major contributor to the continued deficiencies. The SPS chief position changed hands four times from early 2022 to late 2023. At the time of inspection, all members of the executive leadership team were serving in acting roles, according to the report. Separately, the VISN chief sterile processing officer noted poor communication and unfilled critical positions within the SPS department.

Recommendations and Response

The OIG made five new recommendations:

1. Ensure proper identification and removal of nonconforming instruments.
2. Provide training for operating room staff on recognizing nonconforming tools.
3. Review whether any patients may have been affected by the approximately 800 nonconforming surgical instruments.
4. Evaluate whether administrative actions are warranted for staff involved in the deficiencies.
5. Strengthen oversight of corrective actions and ensure long-term compliance.

Leaders from both the Carl Vinson VA Medical Center and the VA Southeast Network—which oversees the facility—concurred with the recommendations and submitted action plans. The OIG stated it will continue monitoring until all corrective measures are complete.

In a statement to 24×7, a representative from the Carl Vinson VA Medical Center says the facility is working to address the issues outlined in the OIG reports: “VA is under new leadership and is committed to solving the kinds of problems highlighted in these two OIG reports, which resulted from inspections in 2022 and 2024. The Carl Vinson VA Medical Center and VA Southeast Regional Network are well on their way to addressing all the recommendations in the reports.”

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Related Read:

Summary: ECRI’s 2025 report names AI-enabled health technologies as the top healthcare hazard, citing risks like bias, false results, and safety concerns, while urging careful integration alongside other critical challenges such as home care support gaps, cybersecurity threats, and medical device safety.

Key Takeaways

• AI Risks in Healthcare: Artificial intelligence tops ECRI’s 2025 hazards list, with concerns about patient safety risks, bias, and false results that can disproportionately affect marginalized communities.
• Broader Technology Hazards: ECRI identifies additional risks, including unmet technology support for home care, cybersecurity vulnerabilities, and issues with medical devices and supplies.
• Call for Proactive Measures: ECRI emphasizes the need for healthcare stakeholders to critically evaluate AI integration and adopt strategies to mitigate risks outlined in its Top 10 Health Technology Hazards report.

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Artificial intelligence, now integral to healthcare, leads ECRI’s 2025 list of top health technology risks. While AI promises improved efficiency and outcomes, ECRI warns of significant patient safety risks if not carefully managed. Originally focused on medical imaging, AI now spans diagnostics, documentation, and scheduling. Even unregulated applications in ancillary systems can significantly impact patient care, ECRI emphasizes.

The Downside of AI

“The promise of artificial intelligence’s capabilities must not distract us from its risks or its ability to harm patients and providers,” says Marcus Schabacker, MD, PhD, president and CEO of ECRI. “Balancing innovation in AI with privacy and safety will be one of the most difficult, and most defining, endeavors of modern medicine.”

ECRI experts say AI systems can produce false or misleading results, or “hallucinations,” and the quality of their output can vary across different patient populations. AI models can perpetuate any bias built into them, posing significant risks for underrepresented and historically marginalized communities.

“AI is only as good as the data it is given and the guardrails that govern its use,” says Schabacker. “Healthcare stakeholders at all levels must think critically about the integration of AI, as they would with any new technology.”

The Top 10 Health Technology Hazards

Rounding out the list are technology hazards identified in home care and acute care settings, information security applications, and the medical device supply chain. ECRI’s Top 10 Health Technology Hazards for 2025, in rank order, are:

1. Risks with AI-enabled health technologies
2. Unmet technology support needs for home care patients
3. Vulnerable technology vendors and cybersecurity threats
4. Substandard or fraudulent medical devices and supplies
5. Fire risk from supplemental oxygen
6. Dangerously low default alarm limits on anesthesia units
7. Mishandled temporary holds on medication orders
8. Poorly managed infusion lines
9. Harmful medical adhesive products
10. Incomplete investigations of infusion system incidents

The full Top 10 Health Technology Hazards report, accessible to ECRI members, provides detailed steps that organizations and industry can proactively take to reduce risk and improve patient safety. An executive brief version is available for complimentary download at this link: Top 10 Health Technology Hazards for 2025 Executive Brief.

ECRI will host a live webcast about the top 10 hazards, open to the public, on December 5 at noon ET. A panel of medical device and healthcare safety experts will discuss the hazards’ consequences for patient safety, clinician efficiency, and operational effectiveness, plus strategies for mitigating these risks. Register for the webcast at this link: Top 10 Health Technology Hazards for 2025.

Key Takeaways:

• FleetView helps healthcare providers enhance device uptime, streamline workflows, and reduce costs.
• The system provides valuable insights, including environmental data, improving operational efficiency in healthcare and life sciences.

Getinge has introduced the FleetView web application, offering healthcare providers and life science companies a tool to navigate large amounts of service data and statistical insights. FleetView is designed to help customers reduce total cost of ownership and enhance service reliability and uptime.

Device Life-Cycle Management with FleetView

Getinge is introducing the option for customers to connect select generations of product lines to this device life-cycle management online tool.

“By connecting devices to FleetView, data becomes actionable information, enabling multiple users to collectively enhance device uptime and streamline service workflows,” said Charlotte Enlund, vice president digital health solutions at Getinge. “This results in a more cost-effective and sustainable process that removes bottlenecks and saves resources.”

Remote Access for Troubleshooting and Maintenance

FleetView is accessible from any web-enabled computer, tablet, or smartphone, providing service data, remote troubleshooting, and scheduling of maintenance. This ensures that medical equipment is ready for use across all hospital areas, from sterile reprocessing and the operating room to intensive care and life science.

“The opportunity to connect devices to FleetView helps healthcare providers make sure that devices are always ready to perform when needed. It paves the way for streamlined services, device uptime, less stress, and reduced costs,” said Enlund. “By supporting streamlined workflows, FleetView helps free up time for providers to focus on patient care and efficiency in life science.”

The Growing Role of Software in Healthcare Operations

For specific products, such as in anesthesia, FleetView provides insights on anesthetic agent and gas consumption, helping to reduce the hospital’s environmental footprint.

“FleetView is an example of how software plays an increasingly important role in our customer’s operations, and we will continue to explore how data collection from connected devices can open up many possibilities,” said Enlund.

Key Takeaways:

• Glassbeam’s Service Analytics solution will be used to provide real-time data and predictive insights, improving medical equipment performance at VA facilities.
• The partnership will support VA’s mission to enhance patient care through advanced technology and training environments like the SimLEARN National Simulation Center.

Glassbeam has signed an agreement with The Department of Veterans Affairs (VA) Healthcare Technology Management (HTM) program office to expand the breadth of systems providing real-time data and predictive analytics.

Utilizing Glassbeam’s Service Analytics Solution

Engineers within HTM and Glassbeam will utilize Glassbeam’s Service Analytics solution to connect systems, ingest log data, and develop predictive signatures. The teams will partner to leverage the Simulation Learning, Education, and Research Network (SimLEARN) National Simulation Center to expand the portfolio of medical systems monitored by Glassbeam’s technology. These capabilities increase equipment uptime and improve workforce efficiency by providing real-time data to service teams, resulting in improved patient care and clinical capacity.

“We are proud to work with HTM in utilizing our technology to deliver actionable insights,” said Rich Jones, Glassbeam CEO. “Service Analytics provides a suite of applications to parse and interpret machine data, enabling improved diagnosis and issue resolution. We are committed to elevating equipment service from a break-fix model to a predictive service model in supporting the healthcare industry.”

Role of the VHA HTM Program

The VHA HTM program office leverages the SimLEARN National Simulation Center to serve as an operational hub for coordinating national VHA clinical simulation activities. The center provides a high-fidelity training environment by replicating actual patient treatment areas, including outpatient and inpatient settings.

Supporting Superior Veteran Care with Advanced Technology

“This partnership supports our efforts to provide superior support to the veterans we serve and aligns with our pursuit to provide a high technology training environment in support of VA medical centers across the country,” said Connor Walsh, director, VHA Medical Device Networking and Cybersecurity Division. “The ability to monitor systems in real-time to anticipate service needs and leverage analytics to reduce downtime enables our mission of providing patient-focused technology.”

Key Takeaways:

• CMS Initiative: ECRI applauds CMS’s introduction of the Patient Safety Structural Measure, which aims to improve safety practices in healthcare by encouraging a systems-based approach.
• Call for Cultural Change: ECRI emphasizes the need for healthcare institutions to reassess safety protocols and commit to a culture of continuous learning and improvement, driven by the new CMS measure.

ECRI has expressed support for the Centers for Medicare and Medicaid Services (CMS) following the approval of a new Patient Safety Structural Measure as part of the hospital quality reporting program update.

Measure’s Impact on Healthcare Safety

The measure is part of an ongoing effort to prioritize patient and workforce safety across healthcare institutions. By introducing a standardized framework, the measure encourages hospitals to adopt a systems-based approach that could lead to substantial improvements in safety practices.

Preventable medical errors are responsible for an estimated 100,000 deaths annually in the United States, according to ECRI, highlighting a critical issue within the healthcare industry.

Statements from ECRI

In a statement from ECRI president and CEO Dr. Marcus Schabacker, he said that there has been growing concern that the industry has become alarmingly complacent regarding avoidable fatalities and adverse events that jeopardize patient safety. The root cause of most of these adverse events is often systemic failures, which are challenging to address effectively when safety hazards are tackled in isolated, reactive ways.

ECRI has advocated for a more comprehensive approach. This advocacy is embodied in ECRI’s Total Systems Safety (TSS) framework, which emphasizes redesigning system elements through clinically informed human factor engineering to improve patient and workforce safety on a programmatic level.

“This CMS structural measure could be a catalyst for healthcare institutions to revisit their approach to safety, assess where they stand, and commit to addressing long-standing issues with a heightened level of transparency and urgency,” said Schabacker.

A Call for Action

Schabacker added that the introduction of this structural measure by CMS provides a crucial opportunity for healthcare institutions to reevaluate their safety protocols, confront enduring challenges, and commit to a culture of continuous learning and improvement. This initiative brings safety to the forefront, calling for systemic and cultural changes that foster resilience within healthcare systems.

He commended CMS for taking this bold step and said it stands in solidarity with health systems striving to achieve zero preventable harm in the delivery of patient care.

By Steven Martinez

It seems like artificial intelligence (AI) is everywhere, creating surreal videos, helping college students cheat on essays, and improving efficiency within certain industries. HTM is no different, and software vendors are already exploring AI to help healthcare facilities maintain their equipment and reduce the workload on biomeds.

“On the show floors of [the Healthcare Information and Management Systems Society] and AAMI this year, every vendor seems to incorporate AI into their products in one way or another, from improving images to optimizing workloads to sniffing network chatter for device analytics,” says Erin Sparnon, MEng, CSSBB, AAMIF, FACCE, artificial intelligence business integration strategist for ICA, Inc.

AI is still a new technology, and it may be a while before the dust settles on this latest tech boom. But healthcare technology experts already see exciting potential applications.

“One of the most exciting potential applications of AI is fully capitalizing on the wealth of data collected in the healthcare industry,” says Brad Jobe, TRIMEDX’s chief information officer. “Empowering critical health system staff to perform their best work with the support of AI can reframe this complex technology as an enhancement for clinical operations instead of a disruptive force.”

Understanding AI

One reason AI is discussed so often is that it is a somewhat broad term. Terms like large language models (LLMs), machine learning (ML), and deep learning (DL) are often used to describe how an AI works. But in a fundamental sense, AI is complex computer software and hardware designed to simulate human intelligence. An AI is presented with a data set to analyze, which it interprets to offer new insights in return.

Depending on the veracity of the data and complexity of the AI being used, it can result in something as important as learning to spot cancer in X-ray images or something as novel as generating a five-paragraph book report.

“Most people, when talking about AI, are referring to generative AI, which has seen immense growth and brought AI to the public eye in a significant way due to the release of ChatGPT 3 and several other similar implementations,” says Aaron Hanna, chief technology officer at NVRT Labs in San Antonio. “Generative AI refers to algorithms and models designed to generate new data samples that resemble a given dataset. This is giving AI the ability to parse data and reason over it and create new data, unlocking the incredible potential for all businesses, including HTM.”

According to ICA’s Sparnon, AI is most used in HTM as a ML algorithm to extract insight from large datasets. For example, she says, AI can be used to crunch CMMS data to predict failure modes and support alternative equipment maintenance (AEM) decisions or to track uptime and utilization analytics.

“Machine learning could provide transformational benchmarking insights from maintenance and repair data, in terms of predicting needed maintenance, reducing downtime, and giving clarity to the real service burden and useful life of any particular model,” she adds.

AI in Predictive Maintenance

One area in which AI could make an impact in HTM is predictive maintenance. An AI trained on repair and service data could provide insights into what equipment could be headed for failure, guiding biomeds to where their efforts are most needed.

If an AI detects an imminent failure or malfunctioning equipment, it could go a step further by proactively sending an automated work order to clinical engineering. According to Jobe, the AI could help biomeds optimize preventative maintenance and maximize uptime while minimizing interference with patient care. By streamlining PMs and reducing unpredictability in maintenance schedules, AI could help HTM staff be more efficient with their resources.

“For example, if a certain part needs to be replaced on a machine, AI-powered systems could also warn the BMET that there is a high probability that another part will need to be replaced within three weeks,” says Jobe. “This allows the BMET to order and replace both parts at the same time, instead of working on the machine twice in a matter of weeks.”

AI could also help HTM shops validate supplier recommendations with actual data from the field or track lifecycle management by comparing repair history against industry benchmarks to identify devices that are being used more intensively.

Sparnon cautions that to reach these kinds of capabilities, AI needs to be properly trained on a big enough data pool, and users will need a solid data science skill set to complement their HTM experience for relevant and practical insights. But if the data already exists and is being tracked, the potential of AI to optimize operations is compelling.

“Think of all the analyses you’d love to do if you had access to really good cross-industry, vendor-neutral benchmarked data and a team of data scientists to run analytics for you,” says Sparnon. “When properly deployed on a big enough data pool, AI and ML can help get you most of the way there.” 

How AI Can Affect the HTM Shortage

Another compelling use of AI is training new HTM professionals. NVRT Labs, a software development and extended reality training company, offers HTM training, and Aaron Hanna says that AI’s ability to personalize training could impact the workforce.

“Personalized training that adjusts to the user is already a reality in other verticals, and I believe it will present more effective and efficient ways to train students and fill the knowledge gaps of seasoned workers,” says Hanna. “AI could also provide a way to generate and run real-world scenarios during the education of new techs and provide practical training even before getting into the field.”

Hanna suggests personalized training could be combined with immersive experiences through virtual or augmented reality, providing biomeds with more realistic training. This enhanced training approach can also help address another challenge for HTM: the shortage of new workers.

TRIMEDX’s Brad Jobe has observed firsthand that the aging workforce and declining number of new technicians threaten the health system’s ability to maintain equipment. Additionally, as experienced BMETs retire, their valuable knowledge and expertise risk being lost.

AI can address this problem in two ways: by expediting and enhancing quality training and supplementing the shrinking labor pool. According to Jobe, AI has the potential to increase a BMET’s efficiency in daily tasks and allow them to focus on their skill set and responsibilities.

“The majority of all data is private, and in our industry, an immense amount of vital information is stored in the brains of experienced BMETs,” Hanna adds. “When they retire, this information is lost. At NVRT, we are developing AI with the intent to retain as much of this information as possible and make it accessible to technicians everywhere in a way that is simple to interact with and provides significant value to their day-to-day.”

Challenges for AI

For all its promise as an efficiency booster, implementing AI into HTM is likely to present new challenges. That’s why Jobe says it’s crucial to recognize and address the risks associated with new digital technologies.

Sparnon concurs, saying AI is only as good as the data you give and the questions you ask. 

One reason for this is that raw data often contains statistical noise that AI might not recognize as easily as a human with real-world experience. As miraculous as AI seems on the surface, it’s still important to remember that AI is only simulating intelligence at its current level, Sparnon says. Ask ChatGPT a question, and it will quickly give you a thorough answer, but if you push it further, you may find the facade of intelligence slip.

Sparnon says that creating useful and relevant insights from medical device data will require both data science and HTM skillsets working together, as not all findings are insights. Take infusion pump repairs, for example.

“If a data scientist finds that there’s a peak of infusion pump repairs every October, we need the HTM to remember that [October] is pump PM month, so of course, we’re going to find things when we touch 700 devices at once,” says Sparnon.

That leads to the last challenge, which is training people to use AI effectively. Aside from the costs of implementing new technology, the job of an HTM professional has only become more complex, requiring new skill sets and knowledge to adapt, and AI will be no different.

For now, Sparnon thinks that the most successful AI applications in HTM will be site-specific, narrow applications that provide insights into current work by comparing it against prior work within a single facility. By keeping the focus narrow, the likelihood of errors and misunderstandings is reduced. However, to unlock AI’s full potential, she notes that investment in data science may be necessary.

“Data scientists have common sense and can explain how they got to their conclusions, which is why we need them more than ever to evaluate HTM-focused AI applications for safety, effectiveness in local conditions, and efficiency,” says Sparnon. “I’d love to see data science and analytics grow into just another tool in the HTM box over time, as did network management and cybersecurity.” 

After all, she says, growing and understanding the system ensures the safety of information, devices, and, most importantly, patients.

Key Takeaways

• Glassbeam’s patented technology processes diverse log files to predict and prevent medical device failures, ensuring high availability and reducing unplanned downtime.
• The company emphasizes moving from reactive break-fix approaches to proactive AI and predictive analytics, improving diagnosis and equipment performance.

Glassbeam announced it has received a new patent for its methodology to predict medical device failure based on operational log data.

Supporting Multiple Manufacturers and Modalities

This patented technology can support complex medical equipment from multiple manufacturers and modalities, reducing unplanned downtime and improving the efficiency of engineering teams servicing these devices.

“With this technology, OEMs and service providers can better ensure their products and service support meets the needs of the healthcare industry for clinical equipment with high availability,” said Mohammad Guller, vice president of machine learning.

Moving to AI and Predictive Analytics

The patent covers Glassbeam’s process of ingesting non-homogeneous log files (historical and real-time) from multiple systems to predict failure events and prescribe preventative actions to reduce downtime.

“This patent is a significant step forward on our journey as we work with our customers to move asset performance from an inefficient break-fix world to one that is driven by AI and predictive analytics,” said Rich Jones, Glassbeam CEO. “Our Service Analytics suite of applications utilizes this capability to deliver improved diagnosis and issue resolution. As a key vendor to those providing care, we are committed to supporting improved equipment uptime performance and utilization.”

By Rick Schrenker 

I was recently reminded of my pre-retirement risk management work and how the writings of Henry Petroski and others influenced it. “Things work because they work in a particular configuration, at a particular scale, and in a particular context and culture.”

Design Changes Must Be Analyzed for New Failure Modes

Construction of the Francis Scott Key Bridge commenced the same year as my Baltimore high school commencement: 1972.  I had attended what would today be called a STEM school. In my sophomore year, our class was the first to take a “computer math” course. We did some FORTRAN programming using punch cards. Surely, reader, you remember using them.

“Any design change…can introduce new failure modes or bring into play latent failure modes. Thus, it follows that any design change, no matter how seemingly benign or beneficial, must be analyzed with the objectives of the original design in mind.”

While the design of the Key Bridge did not change over its almost 50 years of service, the type of container ship that caused the collapse of the bridge did not exist when the bridge was conceived. Time will tell how inadequate 20th-century standards-compliant safeguards in the Key Bridge were to deflect and absorb the impact of an out-of-control 21st-century ship.

But what does the Key Bridge disaster have to do with medical technology?

Old Rules Challenge Modern Medical Technology

Consider this: The law that continues to serve as the foundation for the regulation of medical devices in the United States, the Medical Device Amendments to the Food Drug and Cosmetics Act of 1938, dates to 1976..

The age of the Medical Device Amendments is relevant is because devices granted 510k clearance since 1976 must be substantially equivalent to an approved predicate device.The initial set of predicate devices, known as preamendment devices, were commercially available before 1976.

(In my snarkier moments, I’ve been known to argue that if applied to NASA, this would support a claim that a Space Shuttle is substantially equivalent to a Wright Flyer.)

Now consider this observation from a 2006 article in IEEE Computer concerning issues that arose with the simultaneous presence of multiple generations of IT in a healthcare environment.

“… We observe that many clinical systems currently in use were created prior to the recent, dramatic changes in healthcare delivery. Integrated health networks with more complex workflows and a greater need for seamless movement of patient data on demand, anywhere within the network, have for the most part replaced free-standing hospitals, clinics, and group practices. Retrofitting yesterday’s systems to meet today’s needs can only result in a “solution” that falls short … Software engineers have long known that extensive retrofitting causes software to age very rapidly. Considering what we do know about building complex software systems and in the light of these dramatic changes in the industry, it is unfortunate that the prevailing sentiment among healthcare professionals seems to be that legacy information systems, their developers, and their vendors are failing to meet the needs of physicians and hospitals.”

But that was years ago too, right? These kinds of simultaneous old and new mixes of technology no longer occur, right?

Boeing 737 MAX Crashes Linked to Design Changes

Consider the more recent (and mind-bogglingly ongoing) series of consequential failures around Boeing’s 737 MAX. Technically, the 2018 and 2019 fatal crashes tie back to a series of design changes aimed at increasing the efficiency and range of the 737 MAX so Boeing’s products could better compete with those of Airbus. But there is more to the story. For me, the most telling aspect of how Boeing’s corporate culture sacrificed attention to safety was that it successfully lobbied to weaken FAA oversight of design processes, allowing Boeing to self-regulate.

And yet here we are in 2024, reading about yet more Boeing failures.

“It wasn’t that long ago that Boeing’s reputation was that of a staid industrial giant, known for building the safest, most advanced planes in the sky. It helped introduce the world to commercial jet travel.

Pilots and others in the industry, as well as members of the flying public, summed up their confidence in the company with the expression, “If it’s not Boeing, I’m not going.” The company still sells coffee cups and tee shirts with that slogan.”

“Boeing was once known for safety and engineering. But critics say an emphasis on profits changed that.”

But the point is that this is nothing new.

“Petroski returns time and again to that theme, describing it as ‘… the myopia that can occur in the wake of prolonged and remarkable success…’  Intellectually we know that a safe past does not guarantee a safe future, but Petroski drives the point home by describing how success-fueled hubris took the National Aeronautics and Space Administration (NASA) from the glory years of the Apollo program to and through the failures of Mars satellites and two space shuttle disasters. And it is worth remembering that many of the space program failures were not associated so much with design as with program management.”

Healthcare Delivery Faces High Consequential Risks

Consequential failures of technology happen. Consequential accidents happen. Consequential management decisions happen. And they happen in every safety-critical line of business. The delivery of healthcare is not immune. 

And while failures that occur during the delivery of healthcare rarely, if ever, result in shutting down a harbor that employs thousands or plane crashes that kill hundreds, in total they can be far more consequential. Keep in mind that it was just over 30 years ago that the National Institute of Medicine estimated that somewhere between 44,000 and 98,000 U.S. citizens died annually because of failures in the delivery of care.

Yes, changes have been instituted that have improved safety, some as simple as verifying a patient’s birthdate before starting any caregiving activity. On the other hand, the symbiotic working relationships among healthcare workers in general, and nursing and CE/HTM in particular, are bound to be increasingly stressed by staffing shortages. Certainly, the workload has not lessened, leading to increasing production pressure on everyone involved. And this can increase the likelihood of scenarios that place patients at increased risk.

HTM Faces Increasing Challenges

The CE/HTM world has always had a lot of priorities to juggle, but from my now admittedly at-a-distance perspective it has never been more challenging. Still, we should remember that the profession originated to address patient safety issues associated with technology. There is a lot of technology safety history from which to learn, some very recent. The Key Bridge and Boeing stories are ongoing, and CE/HTM can learn from them, especially since many technology failures are often attributable to management failures.

The warning from Santayana is no less important now than it has ever been: “Those who cannot remember the past are condemned to repeat it.”

Rick Schrenker is a former systems engineering manager for Massachusetts General Hospital. Questions and comments can be directed to 24×7 Magazine chief editor Keri Forsythe-Stephens at editor@24x7mag.com.

References:

1. Petroski, H. Success through Failure—The Paradox of Design Princeton University Press. 2006; 167.
2. Petroski, H. Design Paradigms—Case Histories of Error and Judgment in Engineering Cambridge University Press. 1994. 57.
3. LaPlante P. et al, Healthcare Professionals’ Perceptions of Medical Software and What to Do About It, pp 28 – 29, IEEE Computer, April 2006.
4. Robison P, “Flying Blind – The 737 Tragedy and the Fall of Boeing”, p 117, Anchor Books, 2022.
5. https://www.cnn.com/2024/01/30/business/boeing-history-of-problems/index.html . Last accessed April 11, 2024.
6. Schrenker, R, Learning from Failure: The Teachings of Petroski, Biomedical Instrumentation and Technology, 2007, https://array.aami.org/doi/full/10.2345/0899-8205%282007%2941%5B395%3ALFFTTO%5D2.0.CO%3B2, Last accessed April 11, 2024.
7. Phillips J, et al, Nursing and Patient Safety, PSNet, April 2021, Last accessed April 11, 2024.
8. Holt C, Confronting the BMET Staffing Shortage, 24×7, August 17, 2018. https://24x7mag.com/professional-development/department-management/succession-planning/confronting-bmet-staffing-shortage/, Last accessed April 11, 2024.

Key Takeaways:

• Predictive Maintenance: Predictive work systems help identify equipment issues early, reducing unexpected breakdowns.
• Efficient Tracking: RTLS improves equipment tracking and maintenance efficiency, reducing search times.
• Data-Driven Insights: AI provides actionable insights for optimizing equipment performance and minimizing disruption to patient care.

By Rob Moorey

Equipment failures in healthcare can have serious consequences, including delays in diagnosis or treatment, scheduling disruptions, and patient safety risks. Health systems should empower clinical engineering teams with technology that helps proactively identify potential failures.

This will allow health systems to gain visibility into the performance of their equipment and take action to prevent failures before they occur. Avoiding equipment failures leads to shorter turnaround times for maintenance, fewer disruptions to patient care, improved patient safety, and reduced stress for BMETs.

Benefits of Predictive Work Systems

Health systems can significantly decrease the number of unforeseen equipment breakdowns by providing BMETs with predictive work systems. This advanced technology combines remote device monitoring, service expertise, and data science to recognize common preventable equipment problems before a failure happens.

For example, an MRI could be losing helium without anyone realizing it. If this were to continue, the magnet could reach a critical point resulting in quench and significant unplanned downtime, as well as lost revenue and impacted patient care. A predictive work system would alert technicians that the helium is approaching a critical level, allowing them to take preventative actions and schedule planned downtime that’s convenient for clinicians and the clinical engineering team.

A predictive work system could also analyze conditions that indicate air bubbles are developing within a CT machine, even when it appears to be working normally. This is a problem that can typically be difficult to anticipate. However, a predictive work system can notify BMETs there is a problem before it interferes with patient care.

Improving Preventative Maintenance with RTLS

An effective way to streamline preventative maintenance is by taking advantage of Real-Time Location Systems (RTLS). Regular preventative maintenance is one of the most reliable ways to lessen the risk of equipment failures and reduce costs, but clinical engineering teams can struggle to keep track of large, complex, and mobile equipment inventories. An RTLS allows BMETs to know exactly where devices are located, find them, and perform the needed preventative maintenance.

The RTLS will reduce the time spent searching for a device and increase the number of preventative maintenance tasks they’re able to complete each month. TRIMEDX has found that an RTLS can reduce the time spent searching for devices by up to 50%. Without that capability, if a BMET hasn’t been able to find a device to service it, the device could fail when a clinician tries to use it on a patient. This may lead to disrupted scheduling and clinician and patient frustration.

Automation in Equipment Testing

Traditionally BMETs have relied on pen and paper to record equipment test results. After running the tests, they compare the written results to acceptable limits and enter the results manually into a database. This process is time-consuming and filled with opportunities for human error, resulting in incorrect readings, unexpected equipment problems, or issues that could negatively impact patient care and safety.

If BMETs have the latest technology, they can instead run tests through a mobile app that automatically feeds results into the system. The app can eliminate handwritten documentation, quickly validate that results are within permissible limits, and allow technicians to complete proactive maintenance at the time of testing. When equipped with this type of technology, health systems will see fewer incorrect test results and improved failure diagnostics, data handling, and compliance reporting.

Leveraging AI for Enhanced Equipment Performance

There are a many ways artificial intelligence (AI) can empower clinical engineering teams to proactively avoid equipment downtime. Automated equipment testing can also collect valuable data. Machine-learning engines can then collect and analyze the vast amounts of data generated during testing to continuously improve testing accuracy. This process ultimately helps minimize unforeseen failures and ensures equipment is consistently in working order.

By adding machine-learning tools to their toolbox, health systems can have access to real-time data and analysis about their clinical assets. By contrast, in the time a health system manually analyzes data about equipment usage, part replacement, and failure diagnostics–that data is likely out of date. Manually analyzing such large amounts of data is time-consuming and expensive. AI can collect and analyze data in real-time, giving health systems more visibility into equipment performance and allowing them to make smarter decisions.

AI also allows technicians to optimize their preventative maintenance to keep equipment and devices up and running while minimizing interference with patient care. If a certain part needs to be replaced on a machine, AI-powered systems could warn the BMET there is a high probability another part will need to be replaced within three weeks. This allows the BMET to order and replace both parts at the same time, instead of working on the machine twice in a matter of weeks.

Conclusion

By strengthening the connection between technology and BMET expertise, health systems can avoid costly equipment outages and reduce the stress on overworked technicians. An expert clinical engineering team armed with the latest technology will improve patient care and safety while producing cost-savings for the health system.

About the Author

Rob Moorey serves as president of clinical engineering for TRIMEDX. Moorey has been with TRIMEDX for over 10 years and has served as senior vice president of customer delivery and division vice president during that time. Before joining TRIMEDX, he spent eight years working for Aramark Healthcare Technologies in various leadership roles.

Studies show the pandemic disrupted the traditional hands-on, in-person educational experiences of new clinicians, an issue compounded by healthcare workforce shortages. According to ECRI, without sufficient preparation, support, and training throughout the transition into practice, new clinicians may be especially prone to loss of confidence, burnout, and reduced mindfulness around a culture of safety. The convergence of these factors can result in failures to address preventable harm or incidents that cause adverse events for patients, according to ECRI researchers.

Difficulties accessing maternal and perinatal care also made the list, along with the decline of healthcare workers’ wellbeing, and unintended outcomes of adopting new technologies. The Top 10 Patient Safety Concerns report serves as a guide for helping providers and systems reduce risks and improve outcomes for their patients and workforce. ECRI researchers compiled the report by drawing on evidence-based research, data, and expert insights.

“Through no fault of their own, clinicians who started practicing medicine in the last several years didn’t have the same early experience as those who came before them – before the pandemic laid bare critical weaknesses in our healthcare system,” said Marcus Schabacker, MD, PhD, president and CEO of ECRI. “ECRI’s top patient safety concern is a call to action to set new clinicians up for success through a Total Systems Safety approach and assess and redesign the environments in which clinicians are trained, onboarded, mentored, and supported.”

The top 10 patient safety concerns for 2024 are:

1. Transitioning new clinicians from education to practice
2. Workarounds with barcode medication administration systems
3. Access to maternal and perinatal care
4. Unintended consequences of technology adoption
5. Physical and emotional well-being of healthcare workers
6. Complexity of preventing diagnostic error
7. Equitable care for people with physical and intellectual disabilities
8. Drug, supply, and equipment shortages
9. Misuse of parenteral syringes to administer oral liquid medications
10. Preventing patient falls

ECRI’s experts identify several recommendations for these concerns, especially to better prepare new clinicians for practice, including collaborative partnerships among academic and healthcare institutions, robust transition-to-practice programs with intense preceptorships, and simulation-based education to supplement live hands-on learning.

“Hospitals and health systems that are ECRI members have reported to us they’re very concerned about the current state of how newly trained clinicians are transitioned into practice,” said Dheerendra Kommala, MD, chief medical officer at ECRI. “More research is needed to quantify how this issue impacts patient safety outcomes. In the meantime, early indicators warn us to act now. Investments in this new generation of healthcare professionals will establish a stronger, more resilient workforce for decades to come.”