Becton Dickinson is a medical technology company with a broad portfolio spanning medical devices, diagnostics, drug delivery, interventional products and laboratory technologies. Its Connected Care segment focuses on linking systems such as medication management, infusion and patient monitoring to give hospitals more real-time visibility across care delivery.
How does BD define connected care in a practical hospital setting?
Healthcare is under constant financial pressure, and institutions are looking to drive efficiencies. Hospitals generate vast amounts of data that are not yet leveraged to bring value back. For technology and transformation officers, connected care means digitalizing the ecosystem and extracting value by bringing high-value data streams into a cohesive framework.
At BD, our portfolio already spans much of this infrastructure. By connecting automated pharmacy robotics, BD Pyxis™ medication dispensing solutions, and BD Alaris infusion and advanced patient monitoring systems, we touch the key components driving the care continuum. Historically, connectivity meant pushing data into an electronic medical record (EMR). But because EMRs are inherently episodic and slow, that lag time cannot unlock predictive capabilities. We are helping hospitals bridge that gap and transition to real-time, predictive care.
How does moving to a real-time model fundamentally change things at the bedside?
Continuous data is the missing link required to unlock true predictability. If a clinician checks on a patient intermittently every quarter-hour, predicting what will happen next is guessing at best because the data is too fragmented. Conversely, when a care team can continuously watch live trends and waveforms, they can determine if a patient is stabilizing or deteriorating and recommend immediate action.
When you apply AI to this environment, that same real-time synthesis happens at a much higher magnitude. By capturing continuous physiological data rather than intermittent snapshots, intelligent systems can analyze complex waveforms. This allows the care team to see a negative clinical trajectory before traditional symptoms manifest, fundamentally changing how clinicians interact with patient data.
Alarm fatigue is a critical problem for hospitals. How does a smarter alert system look under this predictive model?
Today’s hospitals operate on a reactive framework dictated by threshold alarms. A specific parameter goes outside a set range, a notification fires, and a clinician must physically walk into the room. Many of these alarms are non-actionable, creating cognitive burden for nursing staff. By shifting our focus toward the patient’s entire physiology rather than isolated thresholds, we can analyze how different organ systems interact and predict where that patient is heading.
The goal is to intervene much earlier, preventing a threshold alarm from ever occurring. Identifying a deteriorating patient before a traditional alarm sounds allows us to prevent physiological damage entirely. This not only improves patient safety but also optimizes clinical workflows, shifting the hospital environment away from high-stress crisis management and toward proactive, preventative care.
What are hospitals asking BD for today that they were not asking for several years ago?
Hospitals used to purchase medical devices as isolated commodities. Today, both BD and our hospital partners recognize that the true value lies in stitching our existing ecosystem together. Because we manage the technologies that touch medication from selection to administration, we are uniquely positioned to see what is being prepared, dispensed, infused, and monitored simultaneously. Hospitals are now asking us to close the loop on these technologies to unlock comprehensive clinical insights.
Consider diabetes care as a parallel: a glucose monitor or an insulin pump has individual value, but the breakthrough occurs when you connect them to automatically titrate insulin. We are scaling that concept to the broader hospital environment. By syncing real-time data across our systems, we feed smarter AI engines that provide actionable insights to physicians. Crucially, our philosophy is to keep the clinician firmly in the middle of the loop; the technology does not replace human judgment, but supercharges clinical decision-making.
How does your platform, BD Incada, address the industry's ongoing struggles with interoperability?
BD Incada is an advanced, cloud-based software layer that sits on top of our existing hardware solutions, pulling data seamlessly in the background without requiring new physical infrastructure. Through this platform, we are introducing conversational AI tools. Instead of pulling cumbersome, static reports to investigate issues like medication diversion, a pharmacist can use a secure chatbot interface to query the data in natural language and receive instantaneous, customized analytical insights.
Regarding interoperability, the historical roadblock has not just been connecting systems; it has been the validity of the data itself. If the quality of the incoming data stream is poor, you encounter a “garbage in, garbage out” dilemma that renders AI useless. Because BD controls the primary data sources at the bedside, we can qualify and validate the datasets entering the ecosystem. We actively integrate with major EMR platforms like Epic and Oracle to enrich our models, but reject an unverified plug-and-play approach in favor of rigorous data validation.
What does closed-loop medication management look like in practical terms, and what are the hurdles?
In a traditional setting, managing a patient with volatile blood pressure is highly episodic; a nurse notes a shift, administers a manual medication bolus, and returns later to titrate the pump. With a closed-loop system, the clinician defines the target physiological zone and maintains ultimate oversight, but the automated solution manages the micro-dosing and adjustments in real time. This eliminates dangerous physiological spikes, preserves organ health, and optimizes nursing workflows by removing repetitive, manual adjustments.
The clinical future of this technology in areas like anesthesia and opioid management is incredibly exciting, but the path to market involves distinct challenges. The first hurdle is navigating the rigorous regulatory approval process for advanced AI algorithms, an area where BD already has a strong track record. The second challenge is prioritizing our pipeline. Partnering with leading hospitals and healthcare institutions to create viable clinical use cases while moving toward more autonomous medication management.
As care shifts outside the clinic, do these connected solutions apply to the home-care ecosystem?
The landscape of care delivery is going to change dramatically. Right now, the vast majority of healthcare is managed within a hospital, but in the near future, that dynamic will change, and only a small minority of acute care will take place inside a hospital setting. Patients will still visit hospitals for surgical procedures, but both preoperative prep and postoperative recovery will increasingly live in the home. For BD, the home is not a distant vision; it is a current operational reality.
However, delivering care at home is technically and clinically more difficult than in a hospital. In a hospital room, a physical team of experts can observe the patient and instantly validate data anomalies, whereas a home patient is isolated. This means home-based medical devices need to be more accurate and reliable, not less. While consumer wellness wearables are fine for tracking basic metrics, managing a sick family member requires absolute clinical trust, which is why BD is focused on accurate, automated home solutions that reliably connect data back to distant clinicians.
