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March 21, 2018 Readers Write 3 Comments

Continuous Clinical Surveillance: An Idea Whose Time Has Come
By Janet Dillione

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Janet Dillione is CEO of Bernoulli Health of Milford, CT.

It’s no secret that the general acuity of hospitalized patients is increasing as the overall US population continues to age (hello, Baby Boomers). Many patients who would have been in an ICU in the past are now found in lower-acuity areas of the hospital. We foresee that the hospital of tomorrow, in terms of monitoring and surveillance capabilities, will need to be more like an enterprise-wide ICU.

A significant problem with such a transformation is that hospitals will not be able to staff their entire facility like an ICU. In most hospitals, there is simply no money to add more staff. Even if there were sufficient funds, doctors and nurses are in short supply. Hospitals will have no choice — they will need new technological tools to help clinicians manage these rising levels of acuity.

One type of technology that holds promise in this regard is continuous clinical surveillance. In contrast to electronic monitoring — which includes observation, measurement, and recording of physiological parameters — continuous clinical surveillance is a systematic, goal-directed process that detects physiological changes in patients early, interprets the clinical implications of those changes, and alerts clinicians so they can intervene rapidly. (1)

Just a few years ago, continuous clinical surveillance would have been impossible because there was no way to integrate data from different monitoring devices, apply analytics to that information in real time, and communicate alerts to physicians and nurses beyond the nearest nurse’s station. But today, medical device data can be aggregated and analyzed in a continuous stream, along with other relevant data such as patient data from the EHR. In addition, many clinicians now carry mobile devices that allow them to be alerted wherever they are.

Early Warning System

A continuous clinical surveillance system uses multivariate rules to analyze a variety of data, including real-time physiological data from monitoring devices, ADT data, and retrospective EHR data. When its surveillance analytics identify trends in a patient’s condition that indicate deterioration, the system sends a “tap on the shoulder” to the clinicians caring for the patient.

For example, opioid-induced respiratory depression accounts for more than half of medication-related deaths in care settings. (2) Periodic physical spot checks by clinical staff can leave patients unmonitored up to 96 percent of the time. (3) By connecting bedside capnographs and pulse oximeters to an analytic platform to detect respiratory depression and instantly alert the right clinicians, continuous surveillance can shorten the interval between a clinically significant change and treatment of the patient’s condition.

A recent study found that compared to traditional patient monitoring and spot checks, continuous clinical surveillance reduced the average amount of time it took for a rapid-response team to be deployed by 291 minutes in one clinical example. In addition, the median length of stay for patients who received continuous surveillance was four days less than that of similar patients who were not surveilled. (4)

Another condition that requires early intervention is severe sepsis, which accounts for more than 250,000 deaths a year in the US. (5) The use of continuous clinical surveillance can help predict whether a patient’s condition is going to get worse over time. By aggregating data from monitoring devices and other sources and applying protocol-driven measures for septicemia detection, a multivariate rules-based analytics engine can identify a potentially deteriorating condition and notify the clinical team.

Reduction in Alarm Fatigue

Repeated false alarms from multiple monitoring devices often cause clinicians to disregard these alerts or arbitrarily widen the alarm parameters. Continuous surveillance can significantly reduce the number of alarms that clinicians receive.

An underlying factor that produces alarm fatigue is that the simplistic threshold limits of physiologic devices — like patient monitors, pulse oximeters, and capnographs — are highly susceptible to false alarms. Optimization of the alarm limits on these devices and silencing of non-actionable alarms is not enough to eliminate this risk. The challenge is achieving a balance between communicating essential patient information while minimizing non-actionable events.

Continuous clinical surveillance solutions that analyze real-time patient data can generate smart alarms. Identifying clinically relevant trends, sustained conditions, reoccurrences, and combinatorial indications may indicate a degraded patient condition prior to the violation of any individual parameter. In addition, clinicians can leverage settings and adjustments data from bedside devices to evaluate adherence to or deviation from evidence-based care plans and best-practice protocols.

In a study done in a large eastern US health system, researchers sought to establish that continuous surveillance could alert clinicians about signs of OIRD more effectively than traditional monitoring devices connected to a nurse’s station without compromising patient safety. The results showed that a continuous surveillance analytic reduced the number of alerts sent to the clinical staff by 99 percent compared to traditional monitoring. No adverse clinical events were missed, and while several patents did receive naloxone to counter OIRD, all patients at risk were identified early enough by the analytic to be aroused and avoid the need for any rapid response team deployment. (6)

Clinical Workflow

When CIOs are considering a continuous clinical surveillance solution, they should look for a platform that fits seamlessly with their institution’s clinical workflow. This is especially important outside the ICU, where the staff-to-patient ratio is lower than in critical care. In these care settings, a solution that can be integrated with their mobile communication platform ensures that alerts will be received on a timely basis.

In addition, the continuous surveillance solution should have an open interoperability standards based architecture that integrates with the hospital’s EHR, clinical data repository, and other applications. Especially in these times, it must support strict security protocols as part of an overall cybersecurity strategy.

Clinicians are beginning to recognize that continuous clinical surveillance can help them deliver better, more consistent, more efficient, and safer patient care. In this respect, it reminds me of the timeframe after publication of the famous IOM report that highlighted the dangers of medication errors in the US healthcare system. Companies scrambled to provide a solution, and when automated medication administration was first introduced, the technology was unimaginably clunky. As many of us remember, COWs left the pastures and moved onto hospital floors.

I had the opportunity to watch clinicians who had significant doubts about bar coding and scanning try these new tools. It only took that first patient where the technology helped them avoid dispensing an incorrect medication to turn a skeptic into an evangelist. They quickly realized their patients were safer because of the new technology. Clinicians will discover that continuous clinical surveillance offers the same type of patient safety benefits.

Eventually, hospitals will use continuous surveillance with acutely ill patients in all care settings. The ability of analytics to interpret objective physiological data in real time and enable clinical intervention for deteriorating patient conditions that could otherwise be missed is just too powerful to ignore.

REFERENCES

1. Giuliano, Karen K. “Improving Patient Safety through the Use of Nursing Surveillance.” AAMI Horizons. Spring 2017, pp 34-43.

2. Overdyk FJ, Carter R, Maddox RR, Callura J, Herrin AE, Henriquez C. Continuous Oximetry / Capnometry Monitoring Reveals Frequent Desaturation and Bradypnea During Patient-Controlled Analgesia. Anesth Analg. 2007;105:412-8.

3. Weinger MB and Lee La. No patient shall be harmed by opioid-induced respiratory depression. APSF Newsletter. Fall. 2011. Available at: www.apsf.org/newsletters/html/2011/fall/01_opioid.htm.

4. “Improving Patient Safety through the Use of Nursing Surveillance.”

5. Centers for Disease Control and Prevention, “Data & Reports: Sepsis.” https://www.cdc.gov/sepsis/datareports/index.html

6. Supe D, Baron L, Decker T, Parker K, Venella J, Williams S, Beaton L, Zaleski J. Research: Continuous surveillance of sleep apnea patients in a medical-surgical unit. Biomedical Instrumentation & Technology. May/June 2017; 51(3): 236-251. Available at: http://aami-bit.org/doi/full/10.2345/0899-8205-51.3.236?code=aami-site.

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Currently there are "3 comments" on this Article:

  1. Janet – insightful as always. I remain a fan.

    Concerned staffing levels “at max” knowing early pt. surveillance systems (Tele-CC) required credentialed RN / MD support team (local or remote)- costly model, saves lives.

    Tele-team triages massive, complex data sets in “real time”. Algorithms can support subset, but clinician critical thinking, judgment and collaboration key to many intervention decisions. I fear hospitals (and vendors) think tech (AI) can replace clinician critical thinking – ever more data /alerts can mitigate unsafe RN staffing levels.

    RNs consumed with high acuity, co-morbid pt. care, med tech, burdensome documentation, family (lab, dietary, housekeeping) support, continued cacophony of alarms, new care team texting / call systems giving dozens of clinicians (pt. / families) direct access to RNs (via phones not well integrated with EHRs mostly accessed on other devices).

    RNs often laid off reflexively to improve bottom line. Many cite unsafe pt.load, toxic (abusive, dangerous) environment. RN turnover rates and “disengagement” level extremely high – patient care issues technology can’t address without cultural changes. (excuse soapbox.)

  2. Wonderful topic (I’m biased as I strive to implement systems I’d want, prior to my own long-term care becoming imminent).







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