Six benefits of unifying patient monitoring data across your integrated delivery network

Quality information holds the potential to help health systems not just respond to market demands – sicker patients, continued scrutiny of outcomes, unabated demands on resources – but actually drive their missions forward. In practice, though, many systems fail to fully leverage a crucial asset: the robust numerics, waveforms and, yes, alarms comprising their monitoring data.

This oversight signifies a lost opportunity. Not only does monitoring provide a substantial portion of patient data, its (often) continuous nature provides a unique perspective, as other information sources are captured at lower frequency and usually prompted by specific events.

To tap the vast resource that monitoring data represents, health systems must first unify it across the enterprise. That transformation starts with standardizing monitoring across all care sites, with a centralized platform to consistently capture data, structure it, interpret it and share it wherever it needs to go.

Ideally, the solution should close continuous monitoring gaps and even pull in supplementary data from other medical devices. It should support clinicians, researchers and decision-makers across the enterprise while enabling novel platforms for insight extraction and longer-term data storage. And it should promote strong data privacy and security.

With health systems now able to meet these criteria, patient monitoring data can reach its full potential, unlocking six key impacts:

When monitoring data becomes centralized, clinicians are equipped to quickly continue caring for patients transferred from other locations within the system. They don’t need to wait for snapshots from the EMR; instead, they can immediately access a comprehensive record of the patient’s physiologic history and trends.

Likewise, when patients need to move between care areas in a hospital, centralization can reduce the need for manual data entry and help close monitoring gaps during transport. It can also ensure all care teams see complete, up-to-date patient information to make the most informed clinical decisions.

Centralization can enable timely, seamless data access wherever it’s needed in the health system – on a traditional central station, through a computer’s web browser or on a mobile device. In the hospital, that flexibility can free frontline teams from the bedside when not needed, particularly when a mobile solution also allows them to act.

Of course, the data must provide clinical value and staff empowerment rather than simply add to existing noise – calling for scrutiny of related technology before adoption and a discussion with stakeholders about how best to use it. Without data unification, though, such a conversation is moot.

While frontline teams can set up alarms based on best clinical practices, those guidelines may come partly from assumptions and anecdotes. Expectations on how a monitoring system’s alarms will behave may also differ from reality, calling for a data-driven approach.

Connected monitoring allows health systems to capture a trove of comparable alarm data across the enterprise. They can then develop evidence-based paths for safely adjusting alarms for specific types of care units, particular populations and even individual patients – a complex but worthy goal often unattainable in the past.

To meet market demands and reach their goals, health systems must constantly adapt, refine and innovate. However, relying on impressions and assumptions rather than quantitative evidence makes improvement difficult. Also, without enough information to identify patterns, systemic challenges might just seem like isolated events in the moment.

The volume of patient monitoring and the depth of its data can address both challenges when applied at scale. By aggregating data across the enterprise, clinical leaders can review patient outcomes, adherence to protocols, clinical practices and workflow efficiencies to identify opportunities for improvement and ways to change.

On the operational side, leaders can use monitoring data and analytics to match staffing and equipment to patient needs and to respond to challenges such as changing market conditions. Such actionable, data-driven insights have often been hard to come by in the past.

Public health emergencies have shown the need to quickly gather and analyze available monitoring data, to shape interventions and deliver a coordinated response. Developing larger, unified data sets can make it easier to track outcomes, identify at-risk groups and distribute resources where they need to go.

More commonly, the richness of monitoring can power clinical research and population health studies in new ways, to identify trends, risk factors and preventive care strategies.

Researchers previously had to rely on retrospective EMR snapshots, often just a single data point for each parameter every 30 to 60 seconds. Newer solutions can store and share the full breadth of monitoring data through enhanced onsite capabilities or cloud-based solutions.

For patient monitoring, current algorithm development and deployment calls for large, high-quality, comprehensive datasets. So does the future, anticipated integration of artificial intelligence.

By boosting the number of reliable and robust data points, health systems and their partners can take a step closer to goals such as earlier detection of deterioration, smarter alarms and enhanced clinical decision support. It’s an exciting prospect for the field of patient monitoring.