Safety in Numbers? Try Connectivity

January/February 2012

Safety in Numbers? Try Connectivity

How medical device integration increases patient safety.

Oh, to be a CIO at a U.S. hospital today. No doubt your job is challenging. But your skill set is in very high demand. Plus, you have an opportunity to flex your informatics muscles and escort your hospital towards meaningful use. But first you need board-approved funding for medical device integration, or device connectivity.

Why? Device connectivity automates, or channels, the data generated by medical devices directly into the electronic health record (EHR). The result is a more “meaningful” EHR. And the more meaningful a hospital’s EHR, the more poised it is to receive stimulus dollars.

Still, many hospitals hesitate. As of November 2010, approximately 75% of U.S. hospitals were not pursuing meaningful use, according to a HIMSS Analytics survey (2010). So what’s a CIO to do when stimulus dollars alone cannot make the sale for device connectivity? Talk solutions and show how device connectivity scratches one of the hospital board’s biggest itches: patient safety.

More Accurate Device Data Capture
Traditionally, nurses carry most of the weight when it comes to capturing patient data. Today’s nurse moves from bed to bed, acquiring device and patient data, and then entering this information on paper charts. Unfortunately, these paper charts are often unavailable. In these cases, a nurse will enter patient data on just about anything, including the back of a folder or a packet of antiseptic wipes.

Manually transcribing patient data in this way is inherently problematic. Troubles such as indecipherable handwriting, data entered in the wrong chart, and lost notes are all too common. In fact, a Welch Allyn presentation delivered in January of 2009 stated that 10 to 15% of all transcribed test results are errant.

When it comes to patient safety, errant results in the EHR can be worse than no results at all. Here’s the good news: device connectivity reduces the risk of errant transcriptions through seamless data capture. No handwritten notes. No transcribing. No paper. When a device connectivity solution is in place for, say, patient monitors, clinicians are able to review, authenticate, and then import vital signs data directly into the electronic record.

True, patient-monitor integration is relatively straightforward; brand-dependent gateways for monitors have been around for some time. On the other hand, standalone devices such as ventilators (and a plethora of others) are much more complex. These devices are not network enabled; they were not designed to connect to any kind of centralized system—brand dependent or otherwise.

Regardless, these standalone devices generate incredible amounts of critical patient data. Here’s some more good news: there are connectivity solutions available today that are capable of bringing these standalone devices online. In fact, Jefferson Regional Medical Center (JRMC), a 475-bed, not-for-profit, private hospital in Pine Bluff, Arkansas, connected 13 non-networked, standalone ventilators in 2010 to its CIS using one such software-based solution.

“Our approach was to start with the devices that are the most used and collect the most data,” says JRMC Director of Clinical Informatics Leah Wright. “The GE Dinamaps were a given. Plus, our respiratory department was manually collecting a lot of data from ventilators.”

In connecting non-networked standalone devices (as well as networked ones) to the EHR, transcription errors are no doubt greatly reduced. It’s important to note, however, that although automated data is technically error-free data, it is of little value if it is flowing into the wrong patient’s chart. Just as paper charts can end up in the wrong patient file, automated data can flow into the wrong digital record, too. For this reason, device integration solutions rely on association solutions to ensure positive patient identification and increased safety.

Specifically, some association solutions are patient-centric, using barcode or radio frequency identification (RFID) methods to ensure that the right device data is associated with the right patient. Other integration solutions use location-specific associations; they rely on bed numbers or room numbers to match patients to important device data.

In high-acuity environments like the ICU, location-specific associations often suffice, as patients are not moved on a regular basis. However, these dedicated high-acuity environments are becoming more and more full due to overcrowding in hospitals and the general trend of rising patient acuity (ACCE, 2006). Consequently, variable acuity units are springing up more and more. In these units, patient movement is perpetual. In these ever-changing environments, patient-specific association solutions lead to superior data accuracy, reliability, and patient safety.

Decrease Documentation, Increase Direct Care
In the absence of device integration solutions, clinicians spend incredible amounts of time charting and documenting patient information. Take MetroSouth Medical Center, for example. Located on Chicago’s South Side, MetroSouth Medical Center is a community hospital that boasts a national reputation as a leader in cardiovascular primary care.

Consider an angioplasty patient in the cardiac recovery unit of this impressive facility. Post-operation vital signs are tracked every 5 minutes in the first 30 minutes, and every 10 minutes thereafter for 4 hours. That’s 27 checks per patient.
MetroSouth Medical Center Nurse Informaticist John Ratko estimated that just the data entry portion of this task alone took 30 seconds per entry, or about 14 minutes per patient. That means that a nurse with 10 patients (a likely estimate) spent more than 2 hours per shift entering data!

That’s because at each of these checks, a nurse acquired the patient’s vital signs from a device, wrote them down a piece of paper, then entered them into the hospital’s information system later in multiple-patient batches. Due to the immensity of this documentation task, MetroSouth Medical Center implemented a connectivity solution to automate the flow of data from these medical devices directly into the CIS. According to Ratko, the integration efforts are saving nurses in the cardiac recovery unit approximately 2 hours per nursing shift.

“What do you do with 2 hours of found time per nurse? Increase direct care. It’s all about getting the nurse away from the busy work and back to the patients,” says Ratko. “I believe there are profound ripples, hospital-wide, as direct care increases.”

Because device connectivity lightens clinicians’ data documentation burdens, they have more time to spend at the bedside delivering direct patient care. Clinicians at JRMC experienced this same shift from documentation to direct care following the hospital’s integration efforts. In fact, time spent delivering direct care increased by almost 1 minute per patient interaction.

Likewise, clinicians at Texas-based Wise Regional Health System (WRHS) reported that they spent up to 5% more time (an average of 30 minutes per shift) delivering direct patient care following the hospital’s implementation of a connectivity solution.
“My dream is that we’ll see a comment someday indicating that our nurses spend too much time in the rooms,” says Ratko. “More direct care means patients get better faster.”

Advantages of Real-Time Data
While automated data flows decrease documentation time and increase time spent delivering direct care, it also moves data to the medical record faster. That means that clinicians—hospital wide—have access to more up-to-date information about their patients. This, in turn, allows them to make better-informed decisions related to patient care and safety.

Let’s assess bedside patient monitors again. In the absence of a device connectivity solution, clinicians typically acquire a patient’s vital signs from a device, write them down on a piece of paper, and then enter them into the EHR later in multiple-patient batches.
How much later? It depends. Factors such as the hospital’s staffing levels, the ease of use of its CIS, and the clinician’s personal process can impact the data’s transcribed latency. When WRHS hired a third-party consulting firm to assess its speed in this regard, findings revealed that an average of 12 hours passed between the time a patient monitor generated data and when that data was validated in the EHR.


Despite the stimulus-based incentives and the hospital-wide benefits of device connectivity, many hospitals hesitate to move forward with medical device integration. Implementing connectivity involves change, effort, and costs. Furthermore, there are multiple solutions on the market. It is helpful to consider the four Cs as defined below when evaluating a potential connectivity solution.

Coverage. Does the solution work in all clinical environments? What about bedside and mobile devices? Will it collect all the data, regardless of patient location? Many connectivity solutions are location-based, meaning they rely on location information, or bed numbers, to associate a patient to a device. Other solutions are more patient-based and allow devices to be associated to a patient regardless of his or her location.

Compatibility. Many point-of-care devices are designed to run as standalone networks. Connectivity for these kinds of devices is achieved in several ways. In assessing your connectivity options, ask whether or not the solution will leverage existing investments in hardware (such as laptops and workstations-on-wheels). Some solutions require new, single-use hardware investments, which can be costly and impractical.

Confidence. Will it ensure data is documented to the correct patient through barcode scanning or RFID? How does the solution deal with information before it is charted? For clinicians to make decisions with confidence, they have to know the EHR is always complete, accurate, and current.

Costs. Look at implementation costs, proprietary (single-use) hardware requirements, implementation and training time, ease of use, and scalability. Additionally, assess whether or not the solution can embed into your existing clinical information system. This will shorten the learning curve for clinical staff, saving time and money.

The connectivity solution selected greatly impacts outcomes. So does hospital-wide acceptance of the endeavor. Get the right people onboard and invested in the project from the beginning. Include clinicians, IT, and engineering as soon as possible. If you’re not sure whether or not to include a particular department, ask them. The response might surprise you.

Once again, there is good news: after implementing a software-based device connectivity solution, data was validated and in the EHR in 2 hours, not 12—an improvement of 10 hours! At the aforementioned JRMC, similar improvements in data latency followed the implementation of a device connectivity solution. An average of 90 minutes used to pass between the time a bedside monitor generated patient data and when that data was validated in the EHR. Today, however, that data now reaches the record in nearly real-time.

These data latency improvements enable doctors and caregivers to make decisions—from diagnoses to prescriptions—based on comprehensive, up-to-date EHRs. At JRMC, this connectivity and data availability has a particularly significant impact on patient safety. JRMC has 15 physician clinics with remote access to the EHR. As patients move in and out of the facilities within the JRMC enterprise, their up-to-date records move with them, a fact that greatly increases the level of care they receive.

It’s important to note that real-time data in the EHR is also the cornerstone of future advances in clinical decision support systems (CDSS). These software-based systems link health observations with health knowledge. Imagine if, at the point of care, a clinician could review a chart that integrated a patient’s symptoms, medical history, family history, genetics, historical and geographical trends, and published clinical data. The sky is the limit when it comes to CDSS, but it all starts with robust EHRs (Sittig, 1999).

In addition, better data in the EHR aids hospitals’ rapid response teams. “Our integration allows us to create a custom report that shares patient data with our rapid response team,” explains Wright. “If there’s a nurse on the floor and his gut says someone isn’t doing well, he can call in someone from the team to prevent that patient from crashing. The better the report they receive, the more valuable they can be when they arrive to help the patient.”

Enhanced Alarm Systems
When it comes to patient safety, few issues carry as much weight in the medical world as clinical alarms. In fact, clinical alarms have taken either first or second place for the past 3 years, including 2011, on the ECRI Institute’s annual list of Top Ten Health Technology Hazards (2010, 2009, 2008).

No doubt, today’s clinician works in an alarm-laden environment. According to a Critical Care Medicine piece titled “Intensive care alarms—how many do we need?”  roughly 40% of all alarms do not accurately communicate a patient’s condition and can be classified as false; only 15% of all alarms can be considered clinically relevant (Siebig et al, 2010).

Of course, alarm validity is device-dependent. The environment in which the alarm is being used dictates its efficacy as well. It’s also worth mentioning that many nuisance alarms are the result of improper alarm configurations. These factors aside, however, nuisance alarms (or false-positive alarms) are incredibly troublesome.

How troublesome? In 2004, the American College of Clinical Engineering (ACCE) Healthcare Technology Foundation surveyed 1,327 clinicians, engineers, technical staff, and managers to answer that question. In doing so, the ACCE learned that most of the professionals surveyed agreed or strongly agreed that nuisance alarms occur too frequently (81%); disrupt patient care (77%); and reduce trust in alarms and cause caregivers to disable them (78%). These findings were published in a white paper titled, Impact of Clinical Alarms on Patient Safety (2006).

Fortunately, just as device connectivity improves access to real-time data in the EHR, so too does it improve clinical alarm systems. Connectivity between devices, communications systems, and information systems within hospitals greatly augments alarm systems through alarm extensions, descriptive alerts, and smart alarms.

Consider a perfectly configured, clinically relevant alarm related to patient X sounding behind a closed door in a room at one end of an L-shaped, variable acuity unit. Now imagine the clinician on duty is at the other end of the L helping patient Y.

In an integrated hospital, the alarm could reach the clinician in a number of ways. Perhaps the alarm is extended to a fully integrated communications system that then routes it to the clinician’s smart phone. In a similar fashion, perhaps the clinician notices the alarm for patient X while viewing patient Y’s monitor. Because the two monitors are integrated, the clinician becomes aware of the alarm for patient X while viewing patient Y’s monitor.

Similarly, device integration enables the use of descriptive alarms. In the past, medical device alarms sounded when a patient’s readings fell outside of an established parameter. But they didn’t say by how much. Today, clinicians can receive text messages or emails stating, “Patient X’s upper limit for heart rate has been exceeded. The current heart rate is Y.” Clearly, this kind of notification provides more context than say, an audible-only notification. This additional information provides the clinician with the information he or she needs to make an informed decision about how to respond to the patient’s needs and safety.

“If you’re an RN, and you’re in someone else’s room, and you receive an alert indicating that another patient is crashing, that’s a lot different than an alert about the CO2 level being a little low,’’ says Jena Milan, product manager at device integration company iSirona. “When clinicians have an idea as to why that alarm is sounding, they can prioritize and better manage the care of both patients—the one they’re providing care to at the moment as well as the patient whose alarm is sounding.”

Device integration also opens the door for “smart alarms,” or alarm systems that integrate parameters from multiple, disparate devices to evaluate the validity of a single alarm. These systems are “smart” enough to emit one alarm—instead of, say, five—per adverse event, drastically reducing the number of repeat alarms. Do clinicians and engineers trust these smart alarms? The aforementioned ACCE-led survey indicated that 80% of respondents support smart alarms (2006).

Device connectivity will continue to augment alarm systems and patient safety in positive ways, especially as more and more hospitals develop up-to-date EHRs. For example, consider ventilator disconnect alarms. As any respiratory technician will tell you, these are often false alarms. But their validity can be corroborated by oxygen saturation values, which of course come courtesy of another device. Imagine if every ventilator disconnect alarm was routed to the CIS, which was programmed to bundle the alert with real-time data about the patient’s oxygen saturation values before pushing it to the respiratory therapist. This kind of inter-device connectivity will undoubtedly increase patient safety.

Patient safety in clinical environments is a sensitive issue, as it should be. A patient’s well-being is paramount in any hospital. As discussed, device connectivity can improve patient safety through accurate device data captures, increases in direct care, up-to-date EHRs, and integrated alarm systems.

Ultimately, however, patient safety is an industry-wide issue. It requires a dedication to quality, and this dedication must be alive and present in every staff member at a hospital—from the board of directors to the technicians to the connectivity vendors.

Dave Dyell is CEO of iSirona, a provider of simplified solutions for medical device connectivity. Visit iSirona’s blog at or email Dyell at

ECRI Institute’s Annual List of Top Ten Health Technology Hazards. (2010, 2009, 2008).  [Press releases] Retrieved from ECRI Institute’s web site:

HIMSS Analytics: Hospitals Ready to Meet Some Components of Meaningful Use. (2010, November) [Press release] Retrieved from HIMSS Analytics web site:

American College of Clinical Engineering (ACCE) Healthcare Technology Foundation. (2006). Impact of clinical alarms on patient safety. [White paper]. Available at

Siebig, S., Kuhls, S., Imhoff, M., Gather, U., Schölmerich, J., Wrede, C. E. (2010). Intensive care alarms—how many do we need? Critical Care Medicine, 38 (2), 451-456. doi: 10.1097/CCM.0b013e3181cb0888.

Sittig, D. F. (1999). Prerequisites for a real-time clinical decision support system. The Informatics Review. Retrieved from:

Welch Allyn Device Connectivity. (2009, January). [Presentation]. Retrieved from: