High-risk patients, especially those who are in respiratory distress or require continuous monitoring, are found across the patient care continuum, not just high-acuity areas. Identifying which patients are vulnerable to deterioration is critical to patient safety measures that emphasize timely interventions over costly emergency rescues.
Continuous clinical surveillance, which applies advanced analytics to real-time medical device data and retrospective electronic health record (EHR) data to intercept adverse events and prevent costly care escalations, present a significant opportunity for health systems to integrate real-time patient safety into clinical workflow.
However, this capability requires medical device integration (MDI) to capture continuous streams of data from multiple sources, such as bedside monitors, EKGs and live-streaming waveforms. These data streams must be aggregated with retrospective data stored in EHRs, then filtered through an intelligent, rules-based engine that uncovers clinically relevant trends in the patient’s condition.
Fortunately, this is not a concept for the future. Continuous clinical surveillance, advanced analytics and timely intervention based upon evolving trends in a patient’s condition is attainable in today’s care setting.
RELATED READING: The Right Medical Device Integration Strategy Can Increase the Efficiency of Medical Device Security
Continuous surveillance is a systematic, goal-directed process in which clinicians apply past medical history together with real-time vital signs monitoring to prompt real-time decision-making based upon evolving patient trends.
One of the objectives of analytics is to surface connections among seemingly unrelated sources of data to determine the onset of an adverse event that would not normally be visible by observing a single parameter or multiple parameters individually. This process is performed within the scope and guidance of physiological and clinical evidence, protocols, and subject to oversight by licensed clinical staff. Predictive models based on multiple sources of data can help clinicians anticipate adverse events much more reliably than data from a single source and underscore the multiple findings or measurements of various normally-monitored parameters that could indicate impending compromise in the health of a patient.
Creating the environment to facilitate continuous surveillance and intervention, however, is challenged by the fact that medical device data are often isolated, with each device having unique communication protocols, physical connections, update rates, and terminology.
Data derived from integrated medical devices can be employed for decision making, both as part of the standard patient care management processes, such as charting, and proactive continuous surveillance. Enabling medical devices for continuous surveillance should be included in any medical device integration initiative.
The transformative power of medical device integration mitigates the shortcomings of conventional monitoring practices, including alarm fatigue, significant monitoring gaps and data delivery delays.
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.
For hospitals and health systems, especially those that are breaking ground on a net-new medical device integration program, the formidable task list that comes with any MDI initiative requires the input and expertise of a project team, which ideally, should be comprised of leadership from myriad departments, including IT, networking, facilities, clinical staff, and biomedical engineering.
This team will be responsible for every phase of deployment—acquisition, rollout, implementation and transition to live operations. The team will determine the hospital’s objectives and integration goals, as well as the devices, device types, business and clinical requirements, risk management concerns, patient safety goals, and costs.
This reinforces the need to have a comprehensive and forward-looking approach to selecting an MDI and middleware provider that can support the technical and clinical needs of your healthcare organization both in terms of the immediate needs associated with an EHR implementation, as well as enabling real-time patient surveillance and intervention to improve patient safety and overall quality of care.