Greg Brady, Founder and CEO of Connect4Patients – Interview Series

Greg Brady

, founder and CEO of Connect4Patients, is a seasoned technology executive and serial entrepreneur known for building large-scale enterprise platforms, with a career spanning leadership roles at Oracle and i2 Technologies before founding One Network Enterprises, where he served as CEO for nearly two decades and later as Chairman; today, he is applying decades of experience in data integration, AI, and network-based systems to healthcare through Connect4Patients, aiming to solve industry-wide inefficiencies by unifying fragmented data and enabling more intelligent, patient-centered care.

Connect4Patients

is a healthcare technology platform focused on transforming the medical industry through a unified, patient-centric data ecosystem that addresses longstanding issues such as fragmented records, limited preventative care, and inefficient diagnostics; its core offering, “The Healthcare Network,” functions as a digital infrastructure that creates a single, accurate patient record accessible across providers, while embedded AI tools enhance clinical decision-making, improve patient understanding, and reduce medical errors, ultimately aiming to lower costs, improve outcomes, and shift healthcare from reactive treatment toward proactive, longitudinal health management.

You previously founded One Network Enterprises, building a global supply chain platform connecting over 150,000 trading partners and ultimately leading to its roughly $839M acquisition by Blue Yonder. What lessons from designing large-scale, multi-party data networks influenced your decision to launch the healthcare platform and pursue the idea of a unified patient record?

Supply chains are highly distributed across contract manufacturers, suppliers and logistics companies.

Before the deployment of One Network, companies were focused on a manual process across internal operations and with their suppliers and logistics partners, but the data they were all working from was different. There was no single version of the truth. This increased lead times and created service level problems in supporting customers. It also resulted in lost sales.

Providing a real-time single version of the truth was necessary to reduce time and cost and improve service levels.

If you compare this to the medical industry, the problem is the same. Data is fragmented across hospitals, primary care providers, specialists, and most importantly, the patient. This approach generates huge costs, because multiple manual processes must be deployed across participants. This takes time away from patient care as each provider attempts to manually create a complete medical record rather than spending quality time with the patient. Most importantly, this manual process yields a higher number of medical errors, which can be fatal.

The government and industry have unsuccessfully tried to solve this problem for many years, but a network and a patient-centric application is the only viable solution.

My experience in deploying a network that reflects a single version of truth uniquely qualifies me to solve this problem, and that’s where The Healthcare Network comes in. The Healthcare Network functions as a digital superhighway to enable any care provider, including hospitals, physicians, laboratories, imaging facilities and more, to onboard seamlessly and access a single complete patient record inclusive of family history and wellness data. This data is then translated by the AI into a language and description the patient can understand. This allows the patient to help cleanse the data in the network and manage their own preventative and treatment-based care.

Healthcare has invested heavily in electronic health records, yet clinicians still struggle with fragmented data across systems. Why has interoperability remained such a persistent challenge in healthcare compared to other industries that successfully unified their data platforms?

Interoperability has gone through many phases, from data exchanges and standardizations to statewide Healthcare Information Exchanges (HIE). Yet all of them failed – not because doctors and systems don’t want to share data, but because of industry customizations and cumbersome processes of integration across multiple facilities, specialties and demographics.

Bottom-line? HIEs just don’t work.

There are hundreds of thousands EMR implementations in the US and the approach to share data was defined as a point-to-point solution. But the number of connections in a point-to-point solution is overwhelming, and data security is a significant concern. The secret sauce is a many-to-many network where each node onboards only once and can immediately connect to anyone else on the network with no additional integration.

Your vision centers around creating a “single source of truth” for patients. From a systems architecture perspective, what does that actually look like in practice, and how do you ensure that the data flowing into such a system is trustworthy and validated?

Residing on top of The Healthcare Network is a patient-centric, HIPAA-compliant solution: Connect4Patients. In conjunction with the patient, Connect4Patients defines each of their care providers and manages the security across the subnetwork of providers. The Network runs in a secure HIPAA-compliant data center.

A patient is connected to the network and he/she quickly defines their care providers and Orion, the AI, fuses the data and cleanses the data, translates it into a language the patient can understand and further engages the patient to refine it. This activity creates the SVOT and a patient centric-application with an AI supporting all participants. The Healthcare Network and patient-centric application augments existing EMRs and doesn’t require an expensive IT project to enable it.

Our main focus is to benefit patients; the record is theirs, and they should be able to access and help manage it.

Connect4Patients emphasizes permission-based data sharing and patient participation. How do you design a platform that gives patients control over their health data while still enabling clinicians and health systems to make rapid, data-driven decisions?

The AI system translates the data into language the patient can understand and creates a separate one for the caregiver. The document types are permission-based, allowing the patient to share only the information he/she approves. This patient-centric approach enables the patient to take an active role in their own treatment-based care and guides them to a preventative care approach early on. This preventative care approach will be the step function change in the US.

This single version of the truth is shared by all parties; the care providers can use the AI for diagnostic support and drug interaction analysis. AI learning models and an AI research assistant can focus on the complete patient record for immediate data-driven decisions.

Many AI systems in healthcare struggle with incomplete or inconsistent datasets. How does having a unified patient narrative change the way machine learning models can be applied in clinical decision-making?

Once a complete and single version of the truth is created for the US treatment-based market, AI systems can assist in acute and chronic care to include diagnostic support, drug interactions, prescribed dietary and workout programs. The learning models can identify the necessary changes to support a preventative care model. For example, it might suggest what ingredients should be removed from the food supply; what drugs are negatively affecting patients and should be removed from the marketplace; and which movement and workout programs are appropriate for different patient populations.

With complete and cleansed medical records, the AI system will be much more useful and much more accurate. We have only a cursory understanding of all the ways an AI system can be utilized once a complete lifestyle record that includes medical and wellness data is made available to the AI system.

In supply chains, you demonstrated how multi-party networks can synchronize complex ecosystems. Do you see healthcare evolving toward a similar network model where hospitals, insurers, pharmacies, and patients operate on a shared data infrastructure?

Yes. The Healthcare Network integrates the entire medical industry. That includes hospitals, clinics, doctors, insurance companies and patients, all of whom can subscribe with minimal effort.

From a technical standpoint, what are the biggest barriers to integrating clinical data, operational data, and patient-generated data into a single intelligent platform?

Hospitals have mostly adopted a FHIR standard for medical language, but that doesn’t mean that workflows and infrastructure also align. These differences are within and across all electronic medical records. Additionally, systems do not populate the FHIR resource standards equally. For example, some may include medical history, while others may leave this resource blank choosing to allow providers to keep this information in a clinical note.

Patient-reported data, such as wellness and digital information, is even more challenging, as it is often unstructured across patients and vendors and is rarely included in the traditional medical record. Even when patients want to share this information, doctors often don’t have access to a standard, structured location or may not know where to enter it. At best, they might add it to the clinical note or throw it away once the patient leaves. Integrating this structured and unstructured data in a complete, clean, and comprehensive way is the solution and our secret sauce.

AI-driven healthcare platforms often raise concerns around privacy and governance. How do you balance advanced analytics and interoperability with the strict regulatory requirements around patient data?

All patient data is protected, secure and HIPAA-compliant.

The AI aggregates and anonymizes the data, thus eliminating the security concerns, so the AI system can learn cause and effect. This information can be used to suggest preventative measures, like dietary and workout programs or new drug protocols. But this information is available across the system – not just to an individual patient.

At the patient level, privacy and governance is not a concern, since the user is in control of security and permissions.

As healthcare systems increasingly adopt AI tools, do you believe the industry needs a new foundational data architecture before those tools can reach their full potential?

The Healthcare Network and C4P patient app can augment the existing infrastructure to make it far more efficient. I believe that reimaging the healthcare approach and its infrastructure will take place when the industry sees the power of the network and the data it can provide. For now, the network and patient app just improve the existing systems.

Looking ahead, if healthcare successfully achieves a unified patient record and intelligent data network, how could that transform outcomes over the next decade—from preventative care to population health and personalized medicine?

Healthcare is costing billions and harming hundreds of thousands of patients annually. I believe putting the patient at the center will significantly cut these outrageous medical costs and more importantly, save lives. We can easily imagine what even a fraction of improvement will do: prevent harm to hundreds of thousands and save billions annually, so that monies can be more appropriately invested (not wasted) in better lifestyle and prevention-focused care for everyone.

Thank you for the great interview, readers who wish to learn more should visit

Connect4Patients

.