Health systems are generating more patient data than at any point in their history. Bedside monitors, ambulatory devices and home care technologies are producing a continuous stream of readings, signals and clinical indicators.
But most of it isn’t translating automatically into better decisions.
Lavonia Thomas, chief nursing informatics and innovation officer at the University of Texas MD Anderson Cancer Center in Houston, has spent years working at that gap. Her diagnosis is blunt: the problem was never a shortage of data.
“The places that it is creating real insight are the places that’s fixing a problem,” Ms. Thomas said during a recent episode of the “Becker’s Healthcare Podcast.” “You hear a lot about early warning systems for things like sepsis. That’s fixing a real problem, and it’s also accompanied with infrastructure. When the signals come, they have to land on the right person to take the right action at the right time. Signals without that effective infrastructure fail and basically create noise.”
The infrastructure gap she describes runs deeper than interoperability checklists suggest. For nurses, pulling together information from radiology, cardiology, pathology and other departments has always been part of the work. The scale of the problem changed when that information started living in disconnected systems, producing data volumes too large for human synthesis, routed through screens that are difficult to interpret in real time.
“For nurses, we’ve often pulled together disparate places of information to bear on our patient care. Where this becomes a challenge is when there’s different systems that do not talk to one another,” she said. “Pulling that data together is hard because it’s such a data plentiful environment in one screen that the human brain can’t interpret it.”
That disconnection doesn’t just slow care delivery. It creates meaningful risk at the moments when continuity matters most: during transitions. Ms. Thomas points to the window between hospital discharge and home as the point in the care journey where clinical context most reliably disappears.
“Any transition is really capable of creating lost data,” she said. “But when we think about leaving the hospital, going to the home, there is a portion of time that the patient may not be hooked up to any of this technology, and we lose a piece of the story. Where’s that data going? What repository? Who’s monitoring? And do they have the infrastructure to know who to contact? It’s really the infrastructure and the connection of the technology that drives success.”
The same logic governs how Ms. Thomas evaluates AI use cases and where she sees health systems most likely to go wrong. The failure mode is an algorithm applied to broken systems.
“Without a proper definition of a problem and the proper workflow mapped, the AI technology will only layer on a level of complexity and then generate data that may not be useful,” she said.
Preventing that outcome, in her view, starts with who is in the room before any technology decision is made. Nurses are the clinical role that has historically carried the burden of synthesizing fragmented patient information across a care episode. That’s what makes nurses the right people to identify the problem that AI is supposed to solve.
“My role is integral in ensuring that the nurses’ voice is at the table,” she said. “When the initial decisions are being made, only a nurse understands who the right players are to bring to the table. So having that person at that level before the decisions are even made is crucial.”
The principle extends to vendor relationships, and Ms. Thomas thinks it requires work on governance and procurement. Nurses should be part of the conversations about deciding which companies to work with and drive the implementation. The go-live is not the end of the accountability window.
“Transparency and agreement to continue to work together and iterate based on our defined problems,” she said. “It’s really as simple as that.”
For health systems working to extend that continuous view of the patient beyond the hospital, Ms. Thomas works to define the population, map the geography, and match the technology to the infrastructure that actually exists in those communities.
“Defining your pain points as an organization, whether that be a certain patient population that’s at higher risk of readmissions or some other criteria, defining what that population looks like, and then digging into your actual patients to determine what areas of the country, for example, or in your local community these patients tend to live to determine what’s the technologies in those areas and what best to support,” she said. “And then how can you scale existing resources across the organization and expand them into the ambulatory setting, virtual nursing being one such use case that I could see.”
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