By Michael Wong, JD (Founder and Executive Director, Physician-Patient Alliance for Health & Safety)
The successful implementation of continuous surveillance monitoring may have substantial patient benefits. Unfortunately, analyzing notifications from individual medical devices, reliance on physical spot checks of patients, and the lack of rules-based advanced analytics to assess a patient’s current condition in real-time or to identify signs of deterioration is a goal that many hospitals and health systems still have not attained.
There are a number of hospital-acquired illnesses that could be identified by continuous surveillance monitoring. Sepsis and respiratory compromise are among the most costly in terms of resources and morbidity and mortality.
For example, respiratory failure that requires emergency mechanical ventilation occurs in 44,000 patients per year in the United States. The cost to U.S. hospitals for opioid-induced respiratory depression (OIRD) interventions are estimated at nearly $2 billion per year. In addition, ventilator-associated complications (VAC) can lead to longer stays in the ICU and greater rates of readmission. VAC complications add approximately $40,000 in costs to each case—or $1.2 billion in total costs annually.
Complications due to OIRD are not confined to the ICU. In a 2014 study, “The return on investment of implementing a continuous monitoring system in general medical-surgical units,” Slight et al., found that between 2008 and 2012, more than 10,000 patients who suffered an in-hospital cardiopulmonary arrest (IHCA) did so on the general care floor, “which is where patients with relatively stable conditions are placed.”
Trending Toward Best Practices
Continuous surveillance monitoring is more often utilized in high-acuity settings, such as intensive care units. However, detecting potential adverse clinical events enterprise-wide, throughout the hospital, potentially offers even greater benefits.
The emerging utilization of real-time data and continuous surveillance offers health systems a quantitative estimate of whether a patient’s condition is going to get worse over time. Continuous 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.
Moreover, data collection and its 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.
Return on Investment
More than a patient safety measure, continuous surveillance monitoring is a viable and sustainable solution to the negative costs associated with patient deterioration, including resource utilization, emergency transfers to ICUs, length of stay and hospital readmissions.
Early Intervention. In a 21-month study on the impact of pulse oximetry surveillance on rescue events and ICU transfers, Taenzer et al., in “Impact of pulse oximetry surveillance on rescue events and intensive care unit transfers: a before-and-after concurrence study” observed that continuous surveillance techniques decreased rescue events from 3.4 to 1.2 per 1,000 patient discharges and ICU transfers from 5.6 to 2.9 per 1,000 patient days.
In a study to determine the efficacy of continuous capnography monitoring on emergency rescues, Stites et al., in “Continuous Capnography Reduces the Incidence of Opioid-Induced Respiratory Rescue by Hospital Rapid Resuscitation Team” observed that “the pre-intervention incidence of OIRD in the setting of rapid response was 0.04% of patients receiving opioids. After the implementation of capnography, the incidence of OIRD in the setting of rapid response was reduced to 0.02%, which was statistically significant.” In addition, the authors found that continuous surveillance also reduced transfers to higher levels of care was reduced by 79% (baseline, 7.6 transfers/month; post-intervention, 1.6 transfers/month).
Length of Stay. There are a number of studies that point to continuous clinical surveillance resulting in a statistically significant impact on a patient’s length of stay (LOS) in a hospital.
During an 18-month clinical trial in a 33-bed inpatient MED-SURG unit, Brown et al., in “Continuous monitoring in an inpatient medical-surgical unit: a controlled clinical trial” observed that “continuous monitoring on a [MED-SURG] unit was associated with a significant decrease in total LOS in the hospital and in intensive care unit days for transferred patients, as well as lower code blue rates.” PPAHS interviewed Dr. Eyal Zimlichman, one of the co-authors, about this study in a clinical education podcast, “Return on Investment of Continuous Electronic Monitoring.”
Technology. Hospitals with critical care units or ICUs already have continuous surveillance infrastructure in place. Optimizing that infrastructure’s capabilities and incorporating it into existing clinical workflows is the real heavy lift.
Continuous monitoring from multiple data sources—EKGs, vital signs, laboratory tests, etc.—will yield to better predictive models than data from a single source. One of the goals of the advanced analytics that come with continuous clinical surveillance is to connect the dots from among seemingly unrelated, individual data sources. This ability enables clinicians to observe a potentially adverse course in the patient’s condition over time, prior to the violation of the limit threshold of any individual parameter, and respond before costly interventions are required.
EHRs form the foundation of how most hospitals are approaching surveillance—and make for a natural starting point. For example, EHRs store retrospective data, but there is value to augmenting surveillance strategies by adding real-time data captured from patient-connected devices. For example, real-time clinical surveillance and analytics solutions can collect and aggregate clinician-validated retrospective data from the EHR including patient demographics and lab values, and correlate it with real-time streaming data including temperature, heart rate, oxygenation levels and blood pressure.
Analytics based on multiple sources of data also can help offset the problem of alarm fatigue by filtering out false or artifact signals that typically invade the high-fidelity data at the core of continuous surveillance.
In a recent clinical education podcast, “Improving Patient Safety and Reducing Alarm Fatigue,” Leah Baron, MD (Chief of The Department of Anesthesiology, Virtua Memorial Hospital) spoke with me about the experience of Virtua Memorial Hospital in improving patient safety and reducing alarm fatigue.
Dr. Baron says that what began as a project to implement capnography monitoring to address opioid-induced respiratory depression quickly turned into a project to reduce nuisance alarms when monitoring resulted in too many false alarms:
“when we first introduced capnography to monitor patients for respiratory depression related to their opioid therapy, we very quickly found out that the amount of alarms that were bombarding our health care workers was unmanageable. And, the reality was that they could not respond to all of them, but a lot of them were just pure noise. And, that’s why we realized that if we want to use this effectively, we needed to figure out how to identify these actionable alarms and filter the noise, and that’s why, subsequently, we decided that we’re going to do another study and see if we can achieve better results with that.”
By connecting capnography to middleware, Virtua Memorial Hospital was able to distinguish between actionable and non-actionable alarms and help them to escalate the alarms when they occurred. As Dr. Baron describes:
“So, we selected patients with what we thought have a higher chance of having this respiratory depression – patients with significant serious sleep apnea undergoing major surgery. And, we created these algorithms that we wanted to test on our med-surg floors and see if they meet our expectations. So, that was our goal in this study. And, we actually were able to significantly reduce our alarms, without having a single patient event that went unnoticed.”
Details of Dr. Baron’s study have been recently published in AAMI’s Biomedical Instrumentation and Technology magazine in the article, “Continuous Surveillance of Sleep Apnea Patients in a Medical Surgical Unit.”
Beyond high-acuity areas, healthcare systems are creating a foundation for other real-time healthcare innovations, including clinical surveillance modules, medical device integration in an EHR and virtual ICUs.
Combining analysis with real-time data at the point of collection creates a powerful tool for prediction and clinical decision support. Clinicians cannot only use this data to identify gaps in patient care being provided, but it may also serve as documentation that treatment has been provided. The ability to track patients throughout the hospital, continuously add new devices, and distribute real-time patient monitoring to centralized dashboards and mobile devices should be a major consideration for CIOs tasked with achieving real-time healthcare capabilities.