How to enable cardiac device patient growth without adding headcount
Cardiac clinics apply data integration best practices to address information management complexities and reduce administrative burdens.
Cardiac data is expanding rapidly in healthcare provider organizations. Driven by rising patient volumes and increasingly complex medical devices, cardiac implantable electronic device (CIED) data management presents a formidable administrative and cost management hurdle for cardiac clinics and health IT leaders.
For example, the American Heart Association projects that more than 12.1 million patients will get an arrhythmia diagnosis by 2030. Filtering artifacts within the cardiac device data versus true arrhythmia indications is a constant challenge for electrophysiologists and cardiologists. Larger patient volumes create additional pressure on cardiac device clinics that are already struggling with staffing shortages and administrative complexity.
Provider organizations must find new ways to maintain service levels without proportionally increasing personnel costs. Here are three lessons learned at a large academic health system in the Southeast that expanded their cardiac service line while also managing workforce gaps. More patients required care, but fewer cardiac clinic professionals were available to meet the demand on the service line.
Its experience highlights a broader issue confronting many healthcare organizations. New digital tools can improve efficiency, but the benefits often depend on successful collaboration with existing systems. This includes seamless Epic and Cerner integration, bi-directional EHR workflows and automated discrete data transfers.
Understanding the data deluge
Disparate data streams from CIEDs including pacemakers, ICDs, Holter monitors and remote telemetry are likely to test even the most efficient and experienced cardiac clinic teams. These lifesaving CIEDs generate continuous alerts for arrhythmias and heart conditions across a growing volume of clinic patients.
The core problem isn't simply data ingestion; it’s data triage.
Massive, multi-source data streams must be filtered to bring attention to only the critical signals requiring immediate clinical intervention. Data management in modern cardiac clinics requires surfacing critical clinical signals in real time to ensure prompt patient care.
Without scalable data pipelines and automated signal-to-noise filtering, care teams are likely to encounter alert fatigue, resulting in delayed clinical communications when seconds count. The first step for IT leaders is to build greater efficiency across disparate CIED data workflows.
Three lessons from the front lines
These challenges become increasingly difficult when electrophysiology (EP) device clinics expand. One large Southeast health center’s clinic monitored thousands of patients with implanted cardiac devices, generating large volumes of data that needed to be reviewed, documented and integrated into clinical and billing workflows.
Leaders recognized that existing processes were unsustainable. Staff spent considerable time on manual administrative work, including document scans, order creation and device-monitoring scheduling. At the same time, the organization faced pressure to ensure that billable monitoring services were properly captured and submitted to receive proper reimbursement for care delivered.
In early 2023, the health system implemented a cloud-based management platform designed to support CIED monitoring workflows. The vendor-neutral CIED monitoring platform unified multiple-OEM device data and consolidated fragmented device clinic workflows.
Initial goals included greater consistency across monitoring programs, reduced manual work and automation of portions of the documentation process.
The early results were encouraging. Staff reported improvements in workflow standardization. Several labor-intensive processes were reduced or eliminated. Device data was consolidated more effectively. Routine administrative tasks required less manual intervention.
However, post-implementation performance reviews revealed that the technology deployment alone was not enough. The new CIED data management platform functioned as intended, but data exchange between the monitoring system and the organization’s electronic health records system remained incomplete. Consequently, clinical, operational and revenue cycle systems did not always receive information in a seamless manner.
What initially appeared to be a software implementation problem evolved into an interoperability and workflow barrier.
Lesson 1: Resolve EHR integration
The organization launched a second phase of the project focused specifically on workflow, data integration and operational alignment. Rather than relying primarily on large stakeholder meetings, project leaders adopted a more targeted approach that connected technical experts directly with the appropriate departmental resources.
Those discussions revealed issues that had gone unnoticed previously. For example, deeper conversations with revenue cycle leaders uncovered thousands of billable events that were trapped between systems. Billing had not progressed through the workflow as expected, contributing to revenue leakage and operational inefficiency.
Lesson 2: Implement ongoing monitoring of data flows and interfaces
Leaders also implemented ongoing monitoring processes to identify future integration problems before they became significant operational issues. Weekly reports now flag transmission failures early and address problems quickly.
Continuous oversight reflects a growing recognition among healthcare organizations that interoperability requires active management. Interfaces that appear stable can still experience failures that disrupt workflows and delay reimbursement if organizations lack visibility into system performance.
Lesson 3: Keep cardiac clinic teams accountable
Large projects often involve multiple departments and external vendors, making ownership difficult to establish. Designate clear responsibility for coordination, resolution and measurement to accelerate progress.
One of the most significant gains involved billing cycle performance. The average time required to move remote monitoring services from the date of service to billing was reduced substantially over a two-year period. Faster charge and verified claim capture improved operational efficiency and accelerated revenue recognition, while also tracking for new CPT code remote monitoring thresholds as patient volumes also grew.
For example, the number of monitored patients increased by approximately 67 percent over the period examined. Despite that increase, staffing levels remained unchanged.
This outcome may be particularly important for healthcare leaders facing workforce constraints and looking for new ways to reduce burnout. Rather than adding personnel to accommodate growth, the organization absorbed increased demand through workflow improvements, automation and more reliable cardiac device data integration. Monitoring activity also moved consistently through clinical, operational and billing workflows.
Efficient data management needs expand
As healthcare organizations continue expanding cardiac remote monitoring programs and other data-intensive services, the lessons learned are likely to become increasingly relevant to manage cardiac data flows and device outputs.
The challenge facing providers has passed from gathering more patient information to ensuring that information moves efficiently across clinical, operational and financial systems. Cardiac device monitoring intelligence and interoperability with EHRs are essential to balance growth, manage data, achieve efficiency and deliver quality patient care.
Michael Dufresne, MBA, BSE, is vice president of medical affairs and clinical education at Murj.