Entrepreneurship and Early Innovation in Cardiology Data Systems

I was recruited from Marquette by a group of cardiologists to co-found a cardiology data management company focused on remote ECG analysis. This role marked my first experience building a healthcare technology business from the ground up.

We developed a distributed system that collected trans-telephonic ECG data from over 50 hospitals and clinics across a multi-state region. At the time, this architecture ran on DEC mainframe systems, but by today’s standards, it would be recognized as an early form of cloud-based cardiology infrastructure.

The platform ingested analog ECG signals, digitized and analyzed them, and returned interpreted results back to clinicians—enabling remote cardiac diagnostics at a time when such capabilities were still emerging.

Building on this experience, I founded my first company, Compumedics, focused on Holter analysis systems. By combining off-the-shelf hardware with custom data acquisition and processing, we transformed standard computing platforms into scalable ambulatory ECG analysis solutions.

Marquette Electronics: From Systems to Data-Driven Cardiology

In 1992, I returned to Marquette Electronics as Product Manager for Exercise (Stress) Testing Systems, where I was responsible for advancing product strategy within a rapidly evolving cardiology market. Over time, I expanded my role to include ECG management systems, working across product lines to improve clinical workflows and data utilization.

One of my key contributions was enabling early forms of clinical predictive analytics in stress testing. Working closely with engineering, I developed a method to extract physiological data directly from the CASE stress system via its ASCII interface—initially to support research at Hartford Hospital.

While simple by today’s standards, this approach represented one of the earliest integrations of automated data capture from diagnostic systems into research workflows—laying groundwork for what would later become standard in clinical data interoperability.

Building on this foundation, we transformed the research capability into a production tool. The result was MuseWord, a Microsoft Word–based reporting and formatting library for the Marquette MUSE cardiology management system. MuseWord became widely adopted for clinical research and reporting, significantly streamlining how cardiology data was formatted, analyzed, and shared.

GE Healthcare: Scaling Enterprise Cardiology Systems

Following GE’s acquisition of Marquette Medical Systems in 1998, I transitioned into a Cardiovascular IT Consultant role, working directly with hospital systems to modernize how cardiology data was stored, integrated, and accessed across enterprise environments.

In this role, I operated at the intersection of technology and clinical operations—helping health systems move from fragmented, device-centric workflows to integrated cardiology platforms capable of supporting enterprise-scale data management.

One of my most significant contributions was leading the implementation of a multi-state enterprise cardiology management system within the VA healthcare network. This included deep integration with the VA’s VistA electronic medical record system across VISN 11 in South Florida—a complex deployment requiring coordination across clinical teams, IT infrastructure, and federal compliance frameworks.

This work required more than technical integration—it involved aligning stakeholders, navigating institutional constraints, and delivering a system that could operate reliably at scale in a highly regulated environment.

In 2005, my work at GE Healthcare contributed to record-setting revenue performance, earning recognition in GE’s “Masters Circle”—an award reserved for top global performers.

Mortara Instrument: Establishing DICOM for ECG

In 2005, I was recruited from GE Healthcare to join Mortara Instrument, a fast-moving medical device company focused on challenging established players in cardiology.

At Mortara, I led initiatives that would ultimately reshape how ECG data was stored and integrated across healthcare systems.

At the time, electrocardiographic data was largely managed in proprietary formats or XML-based workflows, limiting its accessibility and preventing seamless integration with hospital imaging systems. I recognized that for cardiology to scale within enterprise IT environments, ECG data needed to be treated as a first-class clinical object—on par with radiology.

To address this, we re-architected the workflow to enable 12-lead ECGs to be stored and managed within standard Picture Archiving and Communication Systems (PACS) using the DICOM standard.

This work effectively bridged a long-standing gap between cardiology and enterprise imaging, allowing ECG data to be stored, retrieved, and shared alongside other diagnostic modalities within a unified infrastructure.

The impact was immediate and far-reaching. DICOM for ECG moved from concept to practical implementation and went on to become a widely adopted standard across healthcare systems globally.

This initiative was recognized with the Frost & Sullivan Award for Strategic Marketing in 2008, reflecting both its technical innovation and its role in driving industry-wide adoption.

Mortara’s success in advancing interoperable cardiology systems ultimately contributed to its acquisition by Hillrom.

Within a few years, the approach was broadly adopted and helped establish DICOM as the de facto standard for ECG data exchange

Cerner: Bringing DICOM ECG into Enterprise EHR Systems

Following the development of DICOM ECG technology at Mortara, the platform was licensed to Cerner, where it was integrated into Cerner Millennium—one of the largest electronic medical record systems in the world.

I was brought in to help drive the adoption and strategic positioning of this capability, enabling 12-lead ECGs to be managed within the same enterprise workflows used for other diagnostic imaging.

This integration allowed ECG data to be linked directly with Cerner’s Modality Worklist and patient record infrastructure, representing a significant step toward true cardiology interoperability within enterprise healthcare systems.

At this stage, DICOM ECG transitioned from a technical innovation to a globally deployed clinical capability—used across large health systems to standardize how ECG data was stored, accessed, and shared.

However, as adoption grew, limitations of the approach became increasingly clear. While DICOM enabled interoperability, it constrained the richness of ECG metadata and limited flexibility for advanced clinical analytics, particularly in workflows requiring waveform reprocessing and longitudinal data analysis.

Having worked both on the development and deployment of DICOM ECG, I later published perspectives on its limitations—arguing that while it solved the interoperability problem, it did not fully address the emerging needs of data-driven cardiology and predictive analytics.

DICOM ECG transitioned from a technical innovation to a globally deployed clinical capability…

Lumedx: Building a Cardiology Platform from the Ground Up

At Lumedx, I served as Director of Product Management, leading a team responsible for multiple cardiology domains, including PACS, electrophysiology, catheterization, echocardiography, and system interfaces.

My primary mandate was clear: eliminate Lumedx’s dependency on third-party ECG systems and build a fully integrated cardiology data management platform in-house—enabling the company to compete directly with enterprise solutions such as GE MUSE and Epiphany.

To accelerate development, I traveled to Pune, India, where I assembled and led a team of five engineers to build the system from scratch. Within five weeks, we delivered a working proof of concept—establishing the foundation for a complete cardiology information system.

That prototype was brought back to the United States and evolved into a production platform, achieving FDA 510(k) clearance and going live internationally in Saudi Arabia within 18 months.

This effort transformed Lumedx from a dependent bidder into a competitive cardiology platform provider—capable of delivering a fully integrated enterprise solution.

Excel Medical: Real-Time Clinical Data at the Edge

At Excel Medical, I worked on one of the earliest implementations of real-time physiological data integration—capturing live patient monitoring data directly from hospital networks.

Building on foundational UDP-based broadcast architectures used in bedside monitoring systems, we developed a platform capable of listening to continuous data streams from patient monitors and extracting real-time physiological waveforms and vitals without disrupting clinical workflows.

This work led to the development of the WAVE physiological edge server—one of the first FDA-cleared systems designed to deliver near-real-time patient data from bedside monitors into enterprise clinical systems and electronic medical records.

WAVE enabled clinicians to access live waveforms and patient data beyond the bedside, laying the groundwork for modern remote monitoring, telemetry integration, and clinical data streaming architectures.

The platform became a key asset in Excel Medical’s acquisition by Hillrom, driven by its role in advancing real-time interoperability between medical devices and healthcare IT systems.

We built one of the first systems capable of extracting real-time physiological data directly from bedside monitor networks—without requiring changes to clinical infrastructure.

Dassault Systems

At Dassault Systèmes, I worked at the intersection of life sciences, clinical data, and advanced simulation technologies—helping bridge traditional healthcare systems with emerging computational and modeling platforms.

As part of the Life Sciences R&D organization, I contributed to initiatives that connected clinical data environments with Dassault’s broader ecosystem, including Medidata (clinical research), BIOVIA (biopharmaceutical modeling), and SIMULIA (physics-based simulation).

This work explored how real-world clinical data—such as physiological signals and diagnostic information—could be integrated into simulation environments to support drug development, device design, and future digital twin applications.

The role represented a shift from traditional healthcare IT toward computational medicine—where data, modeling, and simulation converge to enable predictive, system-level understanding of human physiology.