Preventing Alarm Fatigue in Life Science Facilities with EMS and Smart AI [Stephen Tierney]

Monitoring Is More Than Watching Equipment

Many monitoring programs begin with the equipment itself. Teams measure the temperature of a freezer, the carbon dioxide level in an incubator, or the pressure in a system. This information is useful, but it may not show the full picture.

The room around the equipment can affect its performance. A room filled with many minus 80-degree freezers, for example, can become difficult for the HVAC system to cool. The freezers release heat into the room. If the room becomes too warm, the equipment may work harder or fail to maintain its set point.

This is why monitoring should look at both the equipment and its surroundings. Data from the freezer may show that the internal temperature is changing. Data from the room may explain why. Information about the air handler, pressure, outside temperature, and gas supply can add even more context.

Without this wider view, a team may see the result but miss the cause.

This can be explained by the difference between measuring one part of a process and understanding how the parts affect each other. A strong monitoring program follows the process from research and development through testing, manufacturing, storage, and transport.

Why a Building Management System May Not Be Enough

Building management systems, or BMSs, play an important role in many facilities. They control and manage building services such as heating, ventilation, air conditioning, air pressure, and other building functions.

They are not designed to answer every GXP question.

One common approach is to connect equipment alarms to the BMS. This can seem practical. If a freezer or air handler sends an alarm signal, the BMS receives it and passes it to a facilities team.

The problem is that a single alarm output may not show what is happening inside the equipment. If the output fails, the alarm may never reach the BMS. A BMS may also depend on a sensor that does not sit in the best location.

We can take an example of a minus 80-degree freezer. The built-in temperature sensor may sit near the top of the unit. The temperature there could differ from the temperature in the middle of the freezer. If the team relies only on that one sensor, it may not receive a true picture of the storage conditions.

A BMS also focuses on control. It constantly adjusts systems to meet a target. An environmental monitoring system, or EMS, has a different role. It records independent measurements and reports the conditions at a given time.

That independent view can help a company confirm what is really happening, even when the building control system reports that everything is operating normally.

Environmental Monitoring Adds an Independent Record

An EMS does not replace a BMS. The two systems serve different purposes.

A BMS helps control the building. An EMS monitors conditions and creates a record that can support quality and compliance work. In many GXP settings, an EMS also provides controlled access, audit trails, alarm records, electronic signatures, and other features needed for regulated operations.

This difference matters when a company must show that its data is accurate and trustworthy. The system must be properly designed, tested, and maintained. The sensors must be suitable for the intended use. The data must remain secure and traceable.

An EMS can also monitor conditions that a BMS may not measure in enough detail. This could include carbon dioxide and oxygen levels in an incubator, gas pressure, room conditions, or pressure changes across an air-handling system.

These measurements can reveal links between conditions that may affect a process. For example, carbon dioxide and oxygen readings may support a better understanding of pH conditions in an incubator. Gas pressure readings may show that a supply problem, rather than the incubator itself, caused a change.

The goal is not to gather data for its own sake. The goal is to gather information that helps explain what happened and what action is needed.

Alarm Fatigue Can Hide Serious Problems

A system that sends too many alarms can become almost as dangerous as a system that sends too few.

Facilities teams may receive hundreds or even more than a thousand alarms each day. Some alarms may show a short-lived pressure change or a minor system condition. Others may point to a serious failure. When all alerts appear with the same urgency, people may stop treating them as meaningful.

This is known as alarm fatigue. After seeing the same warnings again and again, a person may ignore a new alarm because it looks like all the others.

 An example can be cited here involving an air handler that failed in a facility with animal housing. The building management system sent an alarm, but the employee receiving alerts already had around 1,300 to 1,400 alarms each day. The warning did not lead to action.

The next day, scientists arrived and found that no air was reaching the animal housing area. The BMS had not clearly shown that the air handler had stopped working. The failure was treated as though the unit still had some function.

An independent monitoring system changed the response. The company installed separate pressure sensors on the air handler. These sensors created another alarm path. When the pressure dropped, the right people received a clear warning.

The new system gave the team an estimated 30 to 45 minutes to respond. That time could allow staff to move animals, protect an experiment, or take other steps before the problem caused greater harm.

The lesson is clear. Better alarm response does not always mean sending more alerts. It means sending fewer, more useful alerts to the people who need them.

The First Question Should Be: What Is the Impact?

When an alarm occurs, teams often follow a familiar path. Facilities receives the alert and works to repair the equipment. This is a logical first step, but it may not be the best first action in a GXP environment.

The first question should be: What is the impact on the work?

We can have a failed vacuum pump as an example. Facilities may need to repair the pump, but scientists may need to know first because the failure could affect an experiment that has taken weeks or months to prepare.

The scientific team may be able to move the work to a backup system, change the order of activities, or protect a sample. They can only do this if they learn about the issue quickly.

In a commercial manufacturing setting, quality, subject matter experts, production, engineering, and regulatory teams may also need to review the event. They may need to decide whether to stop production, place a batch on hold, assess product impact, or begin an investigation.

This calls for a clear alarm response plan. The plan should explain who receives each type of alert, who assesses the impact, who repairs the problem, and who decides whether the event affects product or data.

The plan should also make room for communication between teams. Facilities may know how a system failed. Scientists may know what the failure means for the work. Quality may know what records and assessments are needed. Each group holds part of the answer.

Sensor Quality Starts at the Bottom

Reliable monitoring depends on more than software. It begins with the sensor.

A poor sensor can send poor data into even the best monitoring platform. A small error at the start can affect the full chain of records, alarms, reports, and decisions.

For instance, let’s discuss the use of four-wire PT100 temperature probes. A four-wire design can reduce errors linked to resistance in the wires. Two-wire and some three-wire designs may create less reliable readings in certain applications.

There are also oxygen sensors that can drift over time. A sensor that drifts forces the team to keep checking and correcting its readings. Other sensor designs can offer a more stable reference and may reduce this problem.

The point is not that one sensor type fits every use. The point is that the sensor must match the process, the environment, and the level of accuracy needed.

The connecting hardware matters too. A sensor sends data to a device that collects and transfers the reading. That device must also work correctly. The software then needs to record the data, manage alarms, provide reports, and protect the audit trail.

If a reading looks wrong, the team should be able to identify where the problem may have occurred. Was the sensor damaged? Was the connection device faulty? Did the software lose data? Was the alarm interval set poorly? Did a change in equipment firmware affect the result?

A system built carefully from the sensor upward makes these questions easier to answer.

Sensor Placement Can Change the Result

A good sensor in the wrong location may still produce misleading information.

A pressure sensor placed next to a door may report repeated changes each time someone enters or leaves. A light sensor placed in a dark corner may not represent the area where light exposure matters. An oxygen sensor placed too high in a room may fail to warn people at the level where they are working.

Temperature sensors also need careful placement. A refrigerator or freezer may have different temperatures at the top, middle, and bottom. The middle may provide a more useful reading for the stored materials, but the right location depends on the equipment and the mapping results.

Sensor placement in incubators also needs care. A sensor that measures carbon dioxide or oxygen may need to remain horizontal. Moisture can collect on the sensor tip. If the sensor sits at the wrong angle, water may block the reading until the droplets fall away.

Large warehouses bring more questions. How many sensors are needed? At what height should they sit? Should the team measure near the ceiling, at the storage level, or closer to the floor?

There is no single answer for every site. Teams need to review floor plans, equipment, airflow, storage patterns, and temperature mapping results before choosing locations.

AI Can Find Patterns, But It Should Not Make the Final Call

Monitoring systems can produce huge amounts of data. A company may have thousands of refrigerators and freezers, each sending readings at regular intervals. A person cannot review every data point by hand.

This is where artificial intelligence can help. AI can review large data sets and look for patterns that may be hard for a person to see.

It may identify units that are opened too often, draw unusual amounts of power, show compressor problems, or begin to drift after maintenance. It may also help identify equipment that could fail in the future.

AI can support health scores for equipment or facilities. It may help a team focus on the units that need review instead of treating every unit in the same way.

AI also has limits. It may produce a result that sounds reasonable but does not fit the equipment or process. It may search too far back in time. It may treat an unusual reading as a major problem when the reading has a simple explanation.

The need for human guidance and clear limits is pivotal. People should define the purpose of the review and set rules for what the system can and cannot conclude.

AI also does not produce the same type of result every time when asked an open-ended question. A controlled reporting tool may be better when a company needs the same calculation and report each time.

This makes AI a support tool, not a replacement for experienced scientists, engineers, and quality professionals. People understand the process, the equipment, and the practical reasons behind certain choices.

For example, a scientist may prefer one incubator over several identical units because that unit has produced better results in past work. A simple utilization report may suggest moving work to another unit. Human experience may show why that change could create risk.

Prevention Requires Time and Shared Ownership

Many companies say they want to prevent failures. Prevention takes more than a statement in a policy. It requires time to review data, study failures, check sensor locations, and improve alarm paths.

When a failure happens, teams should ask more than “What broke?” They should ask why the system did not identify the problem sooner. Did the sensor measure the right location? Did the alarm reach the right person? Did someone know what action to take? Did the team understand the effect on the process?

A good review can lead to changes in equipment, sensors, software, procedures, training, and communication.

Quality teams play a key role in this work. They help ensure that the data is reliable and that the company assesses the impact of events in a consistent way. Facilities teams help keep the building and equipment working. Scientists and production teams explain the effect on the work. Each group must share information early.

The strongest programs do not treat quality as a final check. They involve quality, facilities, engineering, and scientific teams before problems occur.

How to Choose a Monitoring Partner

A monitoring supplier should be able to explain more than its software. The supplier should understand sensors, equipment, facilities, data records, alarms, validation, and the needs of GXP operations.

Organizations should ask direct questions before selecting a partner. Can the supplier show how its sensors are calibrated? Can it explain the validation approach? Can it show how the system protects data and records alarms? Can it explain what happens if a sensor, connection device, server, or cloud service fails?

The supplier should also understand the customer’s wider process. A freezer alarm may affect a study. A pressure failure may affect an animal room. A temperature excursion may affect a batch. The supplier should be able to discuss these links.

A strong partner should welcome difficult questions and provide evidence instead of relying on broad claims. The organization should review references, ask for demonstrations, and confirm that the system fits its quality needs.

A Clearer Path Forward

A BMS can control building systems. An EMS can provide independent information about conditions that affect GXP work. Sensors must be accurate and placed with care. Alarms must reach the right people and support fast decisions. Quality, facilities, science, engineering, and production teams must work from the same facts.

AI can help review large data sets and find patterns. Human judgment must remain part of the process, especially when the data could affect product quality, patient safety, animal welfare, or a long-running study.

The best monitoring program does not create the most data. It creates trusted data that people can understand and use.

What does your organization do when a critical alarm occurs? Does the alert reach the person who understands the process impact, or only the person responsible for fixing the equipment? Reviewing that question may be the first step toward a stronger monitoring and response program.