A 2024 Heart Rhythm Society perspective piece on CIED data workflows described a problem that rarely makes it into vendor marketing: most cardiac device data is still locked in proprietary, often image- or PDF-based formats that don't talk to each other, forcing clinics and research teams alike to cobble together incompatible systems just to manage it. That paper — co-authored by clinicians and engineers across the device and remote-monitoring industry, including contributors from several of the platforms discussed below — points to standardized data interoperability as the long-term fix. That work is ongoing industry-wide.
For a research coordinator trying to build a cohort or prep a dataset for submission, the gap between "standard eventually" and "usable today" is not an inconvenience. It's the difference between a project that ships on time and one that never gets off the ground. The platforms built for day-to-day clinical triage rarely solve this problem, because it isn't the one they were built to solve.
TL;DR — The best cardiac monitoring software for research needs more than a set of premade dashboards and a large dataset. It needs structured, queryable data that isn't locked behind a request-and-wait process, and, ideally, a vendor that can point to peer-reviewed evidence of its technology's performance, not just its own claims.
Key Takeaways
- Research teams need direct, structured access to monitoring data, not just a clinical review dashboard built for triage
- Ask Atlas™ lets research staff query Octagos's entire cardiac, heart failure, and ambulatory monitoring data in natural language instead of requesting access from a third party
- Multi-site scalability matters for any platform supporting multi-center studies, not just single-institution use
Ask Atlas: Built for This Use Case
Most cardiac remote monitoring platforms treat data access as something you request. You build a hypothesis and wait for someone to build a dashboard that answers your question, or wait on a manual export you'll still need to clean before you can use it.
Ask Atlas takes a different approach: research staff can query Octagos's cardiac, heart failure, and ambulatory monitoring data in natural language, the same way they'd ask a colleague a question, rather than filing an export request and waiting on a manual pull.
A coordinator building a cohort of patients with a specific arrhythmia burden over a defined window, or pulling transmission compliance rates across a multi-site study, can get there directly instead of reconstructing it from raw exports after the fact.
That matters more than it sounds like on paper, because the bottleneck in most research workflows isn't a lack of data — it's the time between "we need this dataset" and "this dataset is available to analyze."
Ask Atlas runs on the same underlying data infrastructure Octagos uses across its remote monitoring platform — infrastructure that includes AI models for transmission processing and alert triage that have been evaluated in two peer-reviewed JACC: Advances studies. Those studies assessed the accuracy and workflow impact of Octagos's AI-driven transmission interpretation. That means Ask Atlas is a query tool built on infrastructure with a track record of peer-reviewed, published scrutiny.
For a research team, the actual pitch is direct access to your data without a middleman, on top of a data platform that's already been through peer review, even as we continue building out published evidence for Ask Atlas specifically.
What Each Platform Offers for Research Use
Octagos — the strongest fit for teams that need to query data directly rather than wait on dashboards or exports. Ask Atlas gives research staff natural-language access to CIED device transmissions, HF, and ambulatory monitoring data instead of manual exports. The underlying AI infrastructure has published, peer-reviewed performance data (JACC: Advances, 2025 — see references), though those studies evaluate transmission processing and alert triage rather than the natural-language query layer specifically.
Implicity — a European vendor with a research-oriented feature set: a dedicated research tool with built-in eCRF and patient consent management, raw (non-PDF) discrete data across manufacturers. Worth a look for teams running formal multi-site studies with consent and case-report-form requirements and want to do that directly through their remote monitoring software.
PaceMate — offers a large aggregated dataset (device, EHR, and operational data combined) with flexible custom filtering in a range of pre-built dashboards.
Murj — offers pre-built dashboards and exportable reports, explicitly marketed for supporting research and quality-improvement work, though the workflow leans more on structured reporting than open-ended querying.
91 Life — advertises AI-powered patient insights and large-scale data access with patient-specific queries. We haven't independently verified the accuracy claims behind this, and haven't found published clinical validation; worth asking directly before relying on it for a study.
RhythmScience (Rhythm360) — the platform's public materials emphasize billing capture, alert triage, and multi-vendor data aggregation rather than research-specific tooling. Recent hiring for a clinical AI research role suggests population/cohort analytics may be in development, but there's no published research-use case study yet.
Frequently Asked Questions
What makes cardiac monitoring software suitable for research versus everyday clinical use? Clinical use prioritizes triage speed and billing capture. Research use prioritizes structured, queryable data and a dataset large enough to draw conclusions from.
What is Ask Atlas and how is it different from standard data export? Ask Atlas lets research staff query your data in natural language rather than filing a manual export request and cleaning the result afterward. It's built to answer specific research questions directly, isolating a patient cohort by arrhythmia type, or pulling compliance rates across a multi-site study, instead of handing back a raw file that still needs to be shaped into something usable.
What security certifications should a research institution require? SOC 2 Type II, SOC 3, HITRUST, and HIPAA compliance are standard requirements for any use of patient data.
Can a platform built for single-institution use support a multi-center study? Not typically. This is where legacy, on-prem platforms tend to fall short. Gathering data across institutions is much more labor-intensive without a cloud-based, multi-site architecture. Confirm that the solution you are considering has role and site functionality that supports multicenter structures.
See How Ask Atlas Handles Your Research Data
Most research delays in cardiac monitoring don't come from a lack of data. They come from the time it takes to turn raw transmission data into something a research team can actually analyze.
If your team is still filing export requests, waiting on manual pulls, or cleaning up data before you can even start building a cohort, it's worth seeing how a query-first approach changes that timeline.
References
- Slotwiner DJ, Serwer GA, Allred JD, et al. 2024 HRS perspective on advancing workflows for CIED remote monitoring. Heart Rhythm O2. 2024;5(12):845-853. https://doi.org/10.1016/j.hroo.2024.09.012
- Bawa D, Ghazal R, Kabra R, et al. Role of Artificial Intelligence in Reducing Data Deluge From Cardiac Implantable Electronic Devices. JACC: Advances. 2025;4(10_Part_2):102057. https://doi.org/10.1016/j.jacadv.2025.102057
- Katapadi A, Chelikam N, Rosemas S, et al. Impact of Artificial Intelligence-Enhanced Insertable Cardiac Monitors on Device Clinic Workflow and Resource Utilization. JACC: Advances. 2025;4(4):101656. https://doi.org/10.1016/j.jacadv.2025.101656