15 Jan 2019

Why the Difference Between Continuous Surveillance and Patient Monitoring Matters

Mary Jahrsdoerfer, Ph.D., R.N.

The prevention of hospital-acquired illnesses (HAI) have come under greater regulatory scrutiny from the Centers for Medicare & Medicaid Services (CMS). Most notably, the Hospital-Acquired Conditions Reduction Program (HACRP), which penalizes hospitals for high rates of such HAIs as ventilator-associated injuries, preventable readmissions and sepsis.1

Enhancing the safety and outcomes of patients and avoiding costly penalties begins with comprehensive continuous clinical surveillance practices. Now, clinical leaders reading this may push back with an argument that, yes, some reforms could be made at the margins, but such practices already exist within their health systems.

Distinction

Here is where we get into an issue of semantics. In a forthcoming concept analysis to be published by the HIMSS Online Journal of Nursing Informatics (OJNI), I argue that clinicians often use the terms ‘monitoring’ and ‘surveillance’ interchangeably.

“In today’s acute-care environment, multiple internal and external factors give clinical surveillance characteristics distinct from those other terms and have changed how nurses apply critical thinking and decision-making to patient care and safety.”2

The term ‘surveillance’ embodies the following clinical attributes: Attention, Timeliness, Recognition, Intuition, Analysis, Action and Collaboration. While monitoring may have one or more of these attributes, clinical surveillance requires all seven. The core of surveillance is ‘real-time’ attention, recognition and analysis that allows for action and collaboration. It is a paradigm shift from a retrospective clinical response, to a prospective anticipation and planning.

This prospective shift may take one of two forms: 1) artificial intelligence (AI), where the machine (software capability of the monitor) learns the physiological trends for a particular patient and notifies the clinician when a trend takes a different trajectory, or 2) rules-based phenomenon, where a human (clinical expert) sets the rules or combination of rules for which the clinician must be notified, therefore eliminating extraneous non-actionable alarms.

Understanding the distinctions between patient monitoring and continuous clinical surveillance is more than a matter of language. It represents the quintessential basis of good clinical practice, the foundation of comprehensive patient safety initiatives and the ability to comply with—and even exceed—regulatory requirements.

Perception vs. Reality

An analysis that featured interviews with more than 30 clinical informatics executives uncovered a common perception that because patients in critical care units were attached to bedside monitoring devices, clinical surveillance was an established standard of care.3

However, Giuliano notes that “[s]urveillance and monitoring each represent a distinct process in patient care. Monitoring involves observation, measurement and recording of physiological parameters, while surveillance is a systematic, goal-directed process based on early detection of signs of change, interpretation of the clinical implications of such changes, and initiation of rapid, appropriate interventions.”4 This author takes Giuliano’s definition a giant step further, evidenced by the 7 clinical surveillance attributes (listed in previous section) that must be present within a clinical scenario in order to label it as true ‘clinical surveillance’.

Adversely, when characterizing what clinical surveillance is ‘not’, we examine what patient monitoring actually ‘is’. Patient monitoring is both fragmented and episodic, capturing a patient’s condition in ways that are dangerously narrow and incomplete. Most patient monitoring practices involve vital sign spot-checks and responses to notifications sent from individual physiologic devices. Malkary notes that within a “MED-SURG environment, nurses conduct episodic monitoring several times per day or on an as-needed basis. This represents a gross under sampling of what is going on with the patient, which could result in missing subtle changes in the patient’s condition.”5

Additionally, patient monitoring inherently assumes that an HAI, such as opioid-induced respiratory depression (OIRD), will be caught during the narrow windows of time that a clinicians is visually observing a patient; in truth, spots checks can leave patients unmonitored 96 percent of the time.6 Even if a clinical team member (and patient) were to catch deterioration, the danger is active, present and likely requires emergency rescue or escalation to an intensive care unit.

Fig. 1: Jungquist, et al., note that in 42 percent of confirmed OIRD events, “the interval between the last nursing assessment and the detection of respiratory depression was less than two hours, and in 16 [percent] of the cases, it was within 15 minutes.”7

In contrast, continuous clinical surveillance allows the clinical team to see the “forest for the trees” through real-time, continuous data flow from multiple sources that can be filtered and intelligently analyzed for significant trends and prospective intervention. There also is growing evidence that continuous clinical surveillance facilitates interventions long before a life-threatening event occurs.8

The significant difference lay in the acquisition and analysis of essential patient data. Whereas patient monitoring is episodic and littered with potentially consequential gaps, continuous clinical surveillance is ongoing, comprehensive and assimilated for prospective clinical decision-making.

The Power of Prediction

Patient monitoring depends on a team of clinicians, working as individuals, to observe the state of the patient’s health at a particular moment in time, and leans heavily of the (often technical) threshold violations of individual devices.

By contrast, clinical surveillance is team-based, allowing multiple caregivers to assess a holistic portraits of multiple patients from a centralized location or via mobile alarm notifications. Because continuous clinical surveillance relies on multi-variate rules to correlate data; identify clinically relevant temporal trends, sustained conditions, reoccurrences and combinatorial indications; and create new early warning alarms, clinical team members can quickly recognize and respond to signs of distress before the patient’s health is compromised.

“Data collection and analysis are further enhanced when including methods for disseminating, analyzing and distributing these data. These features facilitate better patient care management and clinical workflow by allowing patients to be monitored remotely.”9

Conclusion

As I note in my concept analysis, while healthcare leaders are “beginning to conceptualize the professional meaning of clinical surveillance, a strong need to operationalize the technical aspect of surveillance into [patient safety standards and outcomes] still exists.”10

In a recent report, KLAS indicates that “clinical surveillance tools hold the promise of giving caregivers clinically actionable insights that decrease mortality, reduce readmissions, and improve overall patient outcomes, and clinicians expect these alerts to be embedded directly within their workflow.” 11

In a study of two geographically disperse hospitals, Watkins et al., concludes that continuous clinical surveillance “may have initiated nursing interventions that prevented failure‐to‐rescue events. Nurses surveyed unanimously agreed that continuous vital sign surveillance will help enhance patient safety.”12

Advances in healthcare surveillance technology and distribution have made continuous clinical surveillance not just an achievable reality, but an increasingly essential patient care capability.

References

  1. Centers for Medicare & Medicaid Services. Hospital-Acquired Conditions Reduction Program Available at: www.cms.gov/medicare/medicare-fee-for-service-payment/acuteinpatientpps/hac-reduction-program.html.
  2. Jahrsdoerfer, M. (Winter 2019). Clinical Surveillance, A Concept Analysis: Leveraging real-time data and advanced analytics to anticipate patient deterioration. Bringing theory in practice. Online Journal of Nursing Informatics (OJNI), vol. 23 (1), Available at http://www.himss.org/ojni.
  3. Malkary G. Healthcare without bounds: trends in clinical surveillance and analytics. Spyglass Consulting Group. March 2018.
  4. Giuliano KK. Improving patient safety through the use of nursing surveillance. Biomed Instrum Technol. 2017 Feb;51(s2):34-43.
  5. Ibid.
  6. Rothman, B. Bedside monitoring at Vanderbilt: road to implementation. AAMI Foundation. American Dental Association, Chicago, IL. November 2014.
  7. Jungquist CR, Smith K, Nicely KL, Polomano RC. Monitoring hospitalized adult patients for opioid-induced sedation and respiratory depression. AJN. March 2017; 117(3):S27–S35. Available at: http://journals.lww.com/ajnonline/Fulltext/2017/03001/Monitoring_Hospitalized_Adult_Patients_for.4.aspx
  8. Bernoulli Health. Continuous clinical surveillance: a business and clinical case for creating the foundation for real-time healthcare. March 2018. Available at: http://bernoullihealth.com/continuous-clinical-surveillance-ebook.
  9. Ibid.
  10. Jahrsdoerfer, M. (Winter 2019). Clinical Surveillance, A Concept Analysis: Leveraging real-time data and advanced analytics to anticipate patient deterioration. Bringing theory in practice. Online Journal of Nursing Informatics (OJNI), vol. 23 (1), Available at http://www.himss.org/ojni.
  11. Bernoulli Health. KLAS recognizes Bernoulli in the 2018 clinical surveillance report. December 4, 2018. Available at: http://bernoullihealth.com/klas-recognizes-bernoulli-in-the-2018-clinical-surveillance-report.
  12. Watkins T, Whisman L, Booker P. Nursing assessment of continuous vital sign surveillance to improve patient safety on the medical/surgical unit. Journal of Clinical Nursing. November 2015. Available at: https://onlinelibrary.wiley.com/doi/full/10.1111/jocn.13102.
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