Bernoulli CNO and CAO co-author article on continuous monitoring of patients at risk for respiratory depression published in AAMI Horizons Spring 2017 issue
The use of middleware for the continuous monitoring of patients at risk of respiratory depression was the subject of a paper just published by Bernoulli Chief Analytics Officer John Zaleski, PhD, CAP, CPHIMS, and Chief Nursing Officer Jeanne Venella, DNP, MS, RN, CEN, CPEN, in the Spring 2017 issue of AAMI Horizons.
“Using Middleware to Manage Smart Alarms for Patients Receiving Opioids” explores the risks hospitals and health systems are exposed to due to inadequate monitoring of post-operative patients who receive opioids.
Although continuous monitoring of these patient populations is recommended as a best practice by the Joint Commission, Anesthesia Patient Safety Foundation, the Association for the Advancement of Medical Instrumentation, and other healthcare advocates and governing agencies. However, continuous monitoring of these patient populations, particularly outside the critical care unit setting, remains the exception to the rule.
Why? The adoption of this best practice is beset by significant business and clinical challenges, including the implementation of costly physiologic device technology, the possible addition of full-time direct-care clinical staff, and the difficulty of capturing holistic, real-time patient data in order to facilitate early intervention.
Zaleski and Venella explore the viability of comprehensive and cost-effective solutions, including:
- The use of Smart Alarm technology to provide caregivers with an accurate, real-time picture of a patient’s condition, while attenuating alarm signals that communicate contextual patient-safety specific information and minimize spurious signals that can lead to alarm fatigue.
- Leveraging multi-parameter physiologic monitors—such as capnography and pulse oximetry—as a sensitive and early indicator of opioid-induced respiratory depression.
- Assessing the core standards required of device-agnostic middleware platforms for interfacing with bedside devices and combining it with other data from the patient record to create a more holistic and complete picture of the current patient state.
Write the authors: “Combining analysis with real-time data collection at the point of collection creates a powerful tool for prediction and decision support, particularly if this tool can also integrate data from other sources (e.g.: laboratory, electronic health record) to bring more context to bear relative to the patient under consideration.”