Most hospitals can tell you what happened last quarter. Bed utilization at 78%. OR utilization at 71%. Labor costs in the orthopedics service line running $150K over budget. The data is there — somewhere in Epic, Tableau, or a Power BI dashboard. Looking backward is no longer the problem.
Looking forward isn't either. The CFO's FP&A team built the budget model. Headcount caps are set. Revenue targets exist. Whether it's Anaplan, Workday, or — far more often — a carefully constructed Excel file, the tools to produce numbers already exist.
The problem is what comes next.
The budget is $3.2 million. Headcount cap: 45. Revenue target: $4.6 million. These are constraints. But within those constraints, someone still has to answer: How do we actually deploy staff next quarter? What should the shift mix look like? If we increase the part-time ratio to cut costs, does continuity of care suffer? If demand spikes 15%, do we have capacity to respond — or do we scramble?
This is not a data problem. It's not a planning problem. It's a decision problem.
And in most hospitals, the way this decision gets made is remarkably similar. Three or four senior leaders sit in a conference room. They spread printed spreadsheets across the table and debate for two hours. A plausible conclusion emerges. And then — no record of which alternatives were considered, which assumptions drove the choice, or under what conditions the decision should be revisited.
This is what I call the Decision Gap.
When you break down how hospitals turn information into action, the structure becomes clear.
Stage 1 is reporting. EHRs and BI tools show what happened. The majority of the $200+ billion invested in U.S. healthcare IT sits here. Dashboards, metrics, historical trends. The question this stage answers: "What was?"
Stage 2 is planning. FP&A tools and budget processes project what should be. Budgets, forecasts, headcount targets, revenue goals. The question this stage answers: "What should be?"
Stage 3 is the decision. Given the plan and the constraints, what specific action do you take? Which trade-offs do you accept? What do you give up, and why is that the right call? The output of this stage should be a scenario comparison with explicit trade-offs, documented assumptions, and an auditable record of what was chosen and what was rejected.
Stage 3 barely exists in most hospitals. Not because the people making decisions are incompetent — most are highly capable. It's because there is no infrastructure for this stage. Stages 1 and 2 have dedicated software, dedicated teams, and dedicated budgets. Stage 3 has a conference room.
The cost of this gap is significant, though it rarely shows up in the metrics we typically measure.
The direct cost is suboptimal resource allocation. When staffing decisions are driven by intuition and negotiation rather than structured scenario analysis, the results are predictably uneven. Some service lines are overstaffed while others are stretched thin. Predictable overtime patterns emerge. The part-time to full-time ratio reflects last quarter's power dynamics, not next quarter's demand.
The hidden cost is decision velocity. The conference room process takes weeks. Not because the decision itself is inherently complex, but because there is no shared framework for evaluating options. The CFO is thinking about margins. The CMO is thinking about quality metrics. The COO is thinking about capacity. Without a structured way to make these trade-offs explicit, the conversation goes in circles. The same debate replays at the next meeting.
The structural cost is accountability. When there is no record of why a decision was made — which alternatives were considered, which assumptions were critical, under what conditions it should be revisited — no one can learn from the outcome. Good decisions and bad decisions become indistinguishable after the fact, because the reasoning was never documented. In an industry where regulatory scrutiny is increasing and justification for resource allocation is demanded more frequently, this is not just an operational problem. It's a governance problem.
I studied the policy dimension of this problem during my doctoral research at Harvard. Using Medicare claims data, I analyzed how hospital closures and physician-hospital vertical integration affect quality and cost. What I found repeatedly was that it wasn't the data providers had but the structural incentives and constraints they faced that determined outcomes.
Hospitals didn't make suboptimal decisions because they lacked information. They made them because the decision-making environment itself was poorly designed — misaligned incentives, unclear trade-offs, and no mechanism to audit whether the choices made were actually defensible given the constraints.
The same pattern shows up in day-to-day hospital operations. The data is there. The plan is there. But the decision architecture — a structured process for converting constraints into ranked options with explicit trade-offs — is missing.
The skeptic's response is: "We already do this. That's what management is."
Partly true. Capable leaders intuitively weigh trade-offs, consider alternatives, and make defensible judgments. The problem isn't that people can't make decisions — it's that the process isn't externalized in a way that scales, that transfers, that survives a leadership transition.
When the VP of Operations who "just knows" ICU staffing retires, what leaves with them? Everything. Institutional knowledge, mental models, implicit trade-off frameworks — none of it was ever recorded. The next VP starts from a blank slate, with dashboards that show the past and budgets that describe the future, but no structured record of how their predecessor actually decided.
The same is true when decisions need to be justified. To the board. To regulators. To payers asking why a staffing choice led to a particular outcome. "We discussed it and reached consensus" is not an answer that survives an audit. But it's the only answer most hospitals can give, because the decision-making infrastructure doesn't produce anything better.
I believe this gap is closing. Not because of any single technology, but because the pressure on hospitals to justify resource allocation is intensifying from every direction — regulatory oversight, payer demands, board expectations, and a workforce shortage that makes every staffing decision heavier than it was five years ago.
When the cost of getting a decision wrong goes up, tolerance for unstructured decision-making processes goes down.
The hospitals that solve this first — that build a real decision layer between their planning tools and execution — will have a structural advantage. Not because they have better data or better plans, but because they make better decisions, faster, with a record of why.
The rest will keep making multi-million dollar decisions in conference rooms with no record.