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21 Apr 2021

Efficiently Accessing Waveform Data Can Yield Stronger Clinical Research and Outcomes

By Teresa Soman, Cathleen Olguin and John Zaleski

Waveform data are crucial to clinical surveillance. Although such high-frequency data is relied on to drive point-of-care decisions and for retrospective research, it is, nonetheless, still often underutilized. That is because hospitals and researchers often face challenges with waveform data such as lack of accessibility and significant technical requirements to store and extract relevant information for analysis.

A 2017 study on waveform data from mechanical ventilators characterized the process of managing such data as “obtrusive” and that it required “high levels of technical expertise.” As such, researchers noted, waveform data management is “often cost-prohibitive, limiting their use and scalability for research applications.”[i]

Healthcare organizations, however, are automating waveform data collection and access to support clinical surveillance and research – in a fraction of the time required by manual processes, resulting in more efficient research[ii] and improved outcomes[iii]. Above all, streamlined and comprehensive inclusion of waveform data to drive clinical decisions and research can enable healthcare organizations to evolve from reactive to proactive care.

RELATED READING: How Clinical Workflow and Early Detection Technologies Transform Patient Care

Time-Consuming Process

Even in 2021, utilizing waveform data for clinical research can be tedious. For example, some research institutions still print the waveform images and manually enter discrete observations into the clinical record or research database. Such manual and high-touch processing makes scaling the use of waveform data unwieldy in cases involving hundreds or even thousands of patients. Such inefficient data capture and analysis workflows can significantly extend the time and cost of investigations. Even when waveforms are digitized, capturing these records and searching through them to identify and extract pertinent information can be equally time-consuming. It is no wonder that many clinicians opt for paper and pencil, given the time-consuming potential of digital abstraction of waveform images.

Despite the current technical challenges associated with the capture and processing of waveform data,  clinicians who work with electrocardiograms, mechanical ventilator or capnography waveforms are typically experienced and highly skilled at combining together waveform interpretation with other discrete data to detect clinical risks and deliver safe and effective care. Automated analytic tools and alerts can assist the frontline clinician by performing some of this integration in an effort to determine trends and correlated interactions among discrete measurements to provide a “tap on the shoulder” as a potential indicator of clinical deterioration, ensuring that such deterioration is not overlooked by an otherwise distracted frontline clinician.

Case Example: Sepsis 

Sepsis is one such condition that all hospitals are eager to prevent or, at least, recognize and control early in its onset. That is because sepsis represents a growing burden on U.S. hospitals, which saw a 40% increase in the rate of Medicare beneficiaries hospitalized with the condition over seven years, carrying an estimated cost of more than $41.5 billion, according to the U.S. Department of Health and Human Services.[iv]

Heart rate variability and its relation to sepsis through cardiovascular decompensation has been a subject of research over the years. One recently published study, for example, found that an algorithm incorporating electrocardiogram and arterial blood pressure waveforms can help clinicians detect sepsis within the first hour of an intensive care unit (ICU) stay.[v] Additional studies have shown that insights into trending decompensation are facilitated through electrocardiogram waveform capture and analysis.[vi] [vii] [viii] 

Likewise, researchers can conduct studies of such waveform data to develop an algorithm or protocol that can help point-of-care clinicians recognize sepsis early, which could potentially save patients’ lives. The lack of accessibility and usability of such data, however, is an obstacle, especially when waveforms are produced by different devices from different vendors.

RELATED READING: Improve Sepsis Outcomes: Incorporate An Early Warning Scoring System Into Bedside Care

Removing the Boulders

Leading healthcare organizations are finding new ways to capture meaningful information from the waveform data. Incorporating this information delivers a deeper context into the patient’s care journey than what is available from the discrete data in the electronic health record (EHR) or other devices alone.

Waveform data complements clinical context surrounding the patient and provides perspective into the patient’s health status, enabling clinicians to recognize adverse events sooner, act more quickly and deliver more precise care. This is particularly true of electrocardiograms, end-tidal capnography, and plethysmography, where the shapes of the waveforms provide information into underlying chronic conditions and data integrity.

To efficiently harness waveform data, healthcare organizations are utilizing solutions such as Capsule Technologies’ Advanced Integration tools, which enable point-of-care clinicians and researchers to capture data from multiple devices, tailor the data by frequency and format—including waveforms—to meet the requirements of each downstream system, including smart alarm systems, analytics software and research databases.

Capsule’s solutions normalize the captured waveforms, so regardless of the vendor’s device, the receiving systems can reconstitute waveforms reliably. Waveforms are also timestamped enabling clinicians to reconstitute and correlate data from multiple devices to pinpoint patterns or clinical events across those physiological signals.

By automating waveform data capture and analysis, healthcare organizations can significantly reduce the time and cost of research and improve clinical surveillance efficiency, which over the long term will deliver better outcomes to hospitalized patients.

To learn more about the vendor-neutral interoperability solutions of the Capsule Medical Device Information Platform, please contact us.

About the authors

Teresa Soman, MBA, PMP is director of product management; Cathleen Olguin, MBA, BSN, RN, CNOR is senior clinical solutions executive; and John Zaleski, Ph.D., NREMT, CAP, CPHIMS is head of clinical informatics at Capsule Technologies

G.B. Rehm, B.T. Kuhn, J.P. Delplanque, E.C. Guo, M.K. Lieng, J. Nguyen, N.R. Anderson, J.Y. Adams, “Development of a research-oriented system for collecting mechanical ventilator waveform data.” Journal of the American Medical Informatics Association, Volume 25, Issue 3, March 2018, Pages 295–299.
Prasad, V., Guerrisi, M., Dauri, M. et al. Prediction of postoperative outcomes using intraoperative hemodynamic monitoring data. Sci Rep 7, 16376 (2017).
U.S. Department of Health and Human Services. “Largest Study of Sepsis Cases Among Medicare Beneficiaries Finds Significant Burden.” Press release. February 14, 2020.
M. Mollura, G. Mantoan, S. Romano, L.W. Lehman, R.G. Mark, R. Barbieri, “The role of waveform monitoring in Sepsis identification within the first hour of Intensive Care Unit stay,” July 2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO), Pisa, Italy.
W.H. Cooke, J. Salinas, J.G. McManus, K.L. Ryan, “Heart period variability in trauma patients may predict mortality and allow remote triage.” Aviation, Space and Environmental Medicine, 77(11):1107-12, December 2006.
M.M. Corrales, B delaCruz Torred, A.G. Esquivel, M.A.G. Salazar, J.N. Orellana, “Normal values of heart rate variability at rest in a young, healthy and active Mexican population.” Health. Vol. 4, No. 7 pp 377-385 (2012).
Y.C. Liu, J.H. Liu, Z.A. Fang, G.L. Shan, J. Xu, Z.W. Qi, H.D. Zhu, Z. Wang, X.Z. Yu, “Modified shock index and mortality rate of emergency patients.” World J Emerg Med. 2012;3(2):114-7.

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