The ROI of Continuous Surveillance

by Brian McAlpine,
Vice President Strategy and Business Development

The successful implementation of real-time patient safety initiatives has long been a goal of health system leaders. Unfortunately, parsing alarms 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 identify signs of deterioration puts that achievement out of reach for many hospitals and health systems.

There are a number of hospital-acquired conditions (HACs) that could be prevented by continuous clinical surveillance. Sepsis and respiratory compromise are among the most costly in terms of resources , morbidity and mortality.

For example, respiratory failure that requires emergency mechanical ventilation occurs in 44,000 patients per year in the United States.¹ The cost to U.S. hospitals for opioid-induced respiratory depression (OIRD) interventions are estimated at nearly $2 billion per year.² In addition, ventilator-associated complications (VAC) can lead to longer stays in the ICU and greater rates of readmission. VAC adds approximately $40,000 in costs to each case—or $1.2 billion in total costs annually.³

Complications due to OIRD are not confined to the ICU, either. In a 2014 study, 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.⁴

Trending Toward Best Practices

Continuous surveillance is the obvious best practice—and one recommended by the Centers for Medicare and Medicaid Services⁵, the Joint Commission⁶, the Anesthesia Patient Safety Foundation⁷ and other healthcare advocates and agencies. Though it is more often utilized in high-acuity settings, such as intensive care units, ever-increasing emphasis on detecting potential adverse clinical events makes enterprise-wide continuous surveillance an enticingsolution.

The emerging utilization of real-time data to drive continuous surveillance offers health systems a quantitative estimate of whether a patient’s condition is going to get worse over time. In contrast to traditional patient monitoring, 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.⁸

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.

Return on Investment

More than a patient safety measure, continuous surveillance is a viable and sustainable solution to the negative costs associated with patient deterioration⁹, 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., 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.¹⁰

In a study to determine the efficacy of continuous capnography monitoring on emergency rescues, Stites et al., 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.”¹¹ 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., observed that “continuous monitoring on a [MED-SURG] unit was associated with a significant decrease in total length of stay in the hospital and in intensive care unit days for transferred patients, as well as lower code blue rates.”¹²

Technology. Continuous surveillance can be accomplished largely with modest net-new technology investments; hospitals with critical care units or ICUs already have continuous surveillance infrastructure in place.¹³ 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—will yield 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 and predict 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 alarm floods and costly interventions are required.

Starting Point

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.

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. 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.