Why Dashboards Limit Hospitals, And How Curiosity Engine Helps

Every year, a single hospital generates up to 50 petabytes of data in the form of EHR data, RTLS events, IoT metrics, and more. Yet as data volumes grow, insights remain scarce: leaders simply cannot get operational intelligence accurately or rapidly, and questions about entire workflows, departments, and even hospitals remain, such as:

• What are the patterns in ED boarding and is there any relationship to the surgical admission volume?
• Why does my utilization of IV pumps look lower in facility A compared to facility B?

But as AI grows in capability, learning to reason across complex data, it becomes a potent solution for bridging the data-intelligence divide. Enter Curiosity Engine, an insight layer that is trained on your hospital data, accepts questions in plain language, and returns answers in minutes.

Curiosity Engine helps leaders to:

Overcome the outdated systems that hold hospitals back

Traditionally, dashboards were the tool of choice for leaders, enabling them to drill down into data, zoom out for a big picture view, and slice and dice numbers as needed.

But dashboards remain limited, both in terms of capability and effort. Every dashboard requires significant time and effort to create, maintain, and update. Often, hospital networks will have entire business intelligence teams of data engineers and analysts, whose sole responsibility can be a handful of complex, labor-intensive dashboards.

The result is that dashboards can only answer predefined questions and cannot support real-time data. When a single change can require days of work and weeks of waiting, leaders may have to wait days for answers to follow-up questions, leaving interactive exploration off the table.

Model potential solutions and outcomes

In addition, Curiosity Engine provides an additional capability that dashboards lack: the ability to run what-if scenarios, and get instant, accurate answers. A CFO can determine if it’s possible to sell 30% of his hospital network’s IV pumps without incurring facility shortages, while a COO could better understand how they can re-allocate the remaining pumps to work more efficiently. A CNO considering the placement of hand hygiene solutions can experiment with different arrangements for maximum efficiency.

These possibilities aren’t hypothetical; instead, they’re real questions that leaders must wrestle with, on a quarterly (or sometimes even monthly) basis. Importantly, this helps leaders iterate strategy and decision making, testing assumptions, brainstorming progressions, and then evaluating those as well until they arrive at a final decision.

Get hospital-specific context

Every hospital, even those within larger networks, has its own specific context and set of operational truths. Clinicians and leaders alike use slang, nicknames, and shorthand for workflows, units, and resources, often in ways that aren’t captured by databases. As an example, if a COO asks, “How is Tower 3 doing on throughput?” they could be referring to the north tower (Tower 3), and patient flow (throughput).

Curiosity Engine resolves this by building and continuously refining a hospital-specific ontology: a persistent map of how each health system talks about itself, its people, its equipment, and its procedures. When a leader asks a question, Curiosity Engine interprets the intent behind it, maps the informal language to the relevant data, and returns a response grounded in that hospital’s operational reality.

Over time, as leaders use the system and assert their own definitions, the ontology compounds in depth and specificity. The result is a system that does not just answer questions, but also understands the context behind them, in the same way an experienced colleague would.

Remove the distance between question and answer

Curiosity Engine also reduces the effort required to both create questions and understand answers. Leaders can use plain language (rather than technical terms) to ask questions; then, Curiosity Engine will automatically translate questions into technical queries and language, retrieve the required information, and translate it back to natural language.

Here’s an example. A leader asks “what is the utilization rate of our IV pumps?” Using a dashboard to answer this question would require a data analyst to identify the relevant fields (asset tag IDs, manufacturer codes, and device types), locate these fields across various databases, join them together, and render the data visually.

In fact, this fragmented data is not necessarily a technical failure but a structural issue: hospital data is organized by source, rather than purpose (such as the questions that leaders need answered). When a RTLS tracks a device moving from one room to another; a staffing system logs the numbers of nurses and doctors on various shifts; and an EHR records a medication order for a patient, these data points will remain within their respective systems.

This transition from static reporting to conversational intelligence also tightens decision-making cycles in a way dashboards never could. If downstream questions are answered in minutes, context remains relevant and leaders can stop making high-stakes conclusions based on outdated data.

Empower decision makers at all levels

Because they require resources such as dashboards and BI teams, existing analytics tools and decision making processes encourage centralization. However, this means that leaders who are closest to the action, such as charge nurses, directors, and unit heads, are also the furthest from the data they need to improve the experiences of their teams and patients. This forces them to make high-frequency, high-impact decisions on a daily basis, and without the information that a COO or CFO take for granted.

Curiosity Engine changes this dynamic. By pushing a powerful, evidence-based tool to various leaders across units, wings, and facilities, health systems can spread data-driven decision making throughout their organization. Now, nurse managers can access the same, high-quality insights as a CNO when they ask about staffing patterns; or a facilities lead can identify equipment utilization with a quick question.

When data-driven habits are distributed across every unit, wing, and facility rather than concentrated at the top, the entire organisation starts to self-correct faster, spot problems earlier, and align around evidence rather than assumption. Deploying Curiosity Engine broadly does not just give leaders a better analytics tool; it builds an organisation that reasons from data as a default.