It’s Tuesday morning. Your IDT is 20 minutes in. Eleven disciplines around the table, roughly 30 seconds per participant, and the meeting is starting to slide from care planning into status updates.
The nurse is scrolling one screen. The social worker has a paper chart. The therapist is working from memory. The dietitian pulls up a spreadsheet someone updated last Friday. Before anyone discusses the participant, someone asks the question that quietly haunts too many meetings: “Are we sure that’s the latest version?”
This is normal. It should not be.
Nearly every operational decision in PACE is a data decision. Data isn’t just how PACE measures performance, it’s how PACE delivers care.
This probably looks like a tagline. Admittedly, I write a lot of taglines as Head of Marketing, but I assure you that this one is not. It’s the operating reality of every program in the country. And over the next 5 years, the gap between the programs that thrive and the ones that struggle will come down to their data infrastructure.
Why data matters more in PACE than almost anywhere else
There are five structural reasons PACE runs on data in a way much of healthcare doesn’t:
1. Full-risk capitation
Every unmanaged admission comes out of your margin.
2. The IDT model
Care is collective by design — nurses, social workers, PTs, OTs, dietitians, physicians, and more, all making shared decisions. The quality of those decisions is capped by the quality of the information in front of them and how easy it is to access.
3. Care coordination across settings
PACE doesn’t just deliver clinic care. Programs coordinate specialists, hospitals, SNFs, home care, transportation, pharmacy, social services, and caregivers. Every handoff creates data. Every missed handoff creates risk.
4. Participant complexity
Dual-eligibles have more comorbidities, more transitions, and more moving parts than any other population. Managing that complexity without a full picture forces teams to rely on experience and incomplete information.
5. HPMS and CMS oversight (and the revenue tied to it)
Compliance reporting is the visible part. The bigger story is risk adjustment. If the clinical picture in the EMR isn’t captured, translated, and submitted accurately as encounter data, your HCC scores drop and CMS underpays you for the complexity you’re managing. That’s not a reporting problem. That’s a revenue problem.
Each of these five starts with data, but the impact is operational, financial, and clinical.
The questions PACE teams actually need to answer
Forget dashboards for a second. Think about the questions that actually get asked in a PACE program on any given Tuesday.
The reactive ones — the “what happened” questions — are table stakes:
- Who was hospitalized last month?
- What drove our ED spike in Q2?
- Are we meeting our quality thresholds?
With the right tools in place, these are answerable in minutes, but for many programs, it might take a week and three exports to Excel.
The diagnostic ones are where PACE outcomes actually move:
- Did our new post-discharge protocol reduce readmissions?
- Are the participants using our new transportation vendor showing better attendance?
- How do outcomes compare for participants who attend day center daily vs. weekly?
- What combinations of conditions and social factors are producing our highest-cost care?
Executives have their own version of the list:
- Where are we losing margin, and what’s driving it?
- Which centers are performing differently, and why?
- Are we staffing to demand—by shift, by discipline, by center?
When answers do arrive, they’re often met with another question: Is this the right number? When finance, quality, and clinical operations each have their own reports (and those reports don’t agree) the conversation shifts from making decisions to reconciling data. That’s time the IDT, leadership, and analysts never get back.
Why PACE programs struggle to answer forward-looking questions today
Two root causes:
Data lives in more places than any one system can hold.
Many PACE programs have invested in reporting and population health tools. Those investments are certainly a step in the right direction, but they solve only part of the problem. Critical information still lives in pharmacy, labs, transportation, home care, quality tools, finance, and workforce systems. Anything that spans those sources gets pulled together by hand, on a Thursday, by someone who has 40 other things to do.
The symptom: shadow systems.
When data doesn’t come together, tribal knowledge does. Every discipline builds its own workaround. Spreadsheets tracking DME deliveries. Sticky notes on wound care progression. Personal databases for referral follow-up. A shared drive folder that only two people know about. It works — until one of those two people leaves. Then the institutional memory walks out the door.
Over time, these workarounds create another problem: confidence starts to erode. When every department maintains its own spreadsheet or report, no one is quite sure which version reflects reality. Teams spend valuable time validating numbers instead of acting on them, not because anyone distrusts each other, but because everyone is working from a slightly different picture.
When data does exist, it isn’t always fresh enough to matter.
Freshness varies wildly by source. Some feeds arrive next-day. Others lag by weeks. The gap between what a program can report on and what it can act on is where outcomes get lost. Knowing that a participant was hospitalized three weeks ago helps a Quality Improvement report. Knowing they were admitted yesterday changes today’s care plan.
One thing to be clear about: PACE teams are extraordinarily resourceful. The manual processes, the tribal knowledge, the spreadsheets updated by hand — that isn’t evidence of poor operations. It’s evidence of people compensating for missing pieces of infrastructure. It’s not a failure of effort. It’s a failure of infrastructure.
What good data operations looks like in PACE
The maturity curve for PACE data operations has four stages. Most programs are somewhere on it. Even the most advanced haven’t reached the end.
Accessible data
The people making decisions can get data predictably without jumping through hoops each time they need it.
Reliable data
Trust is the foundation for everything else that follows. Different reports don’t tell different stories. Finance, quality, and clinical leadership aren’t bringing conflicting numbers into the same meeting. The conversation starts with “What should we do?” instead of “Which report is right?”
Connected data
The sources that matter live together. It’s not just EMR and claims data, but pharmacy, transportation, and everything else a full participant picture requires.
Actionable data
Answers show up where people are already working, not in a dashboard they have to remember to check later. And teams can answer the questions no vendor could have anticipated: your program’s questions, about your population, in the format your team needs. The ceiling isn’t dashboards. The ceiling is how fast the right insight gets to the right person in the right workflow.
What comes next
The next generation of successful PACE organizations won’t necessarily have larger teams or bigger budgets. They’ll make faster, more confident decisions because they can trust the data behind them, and because they can answer the questions unique to their program, not just the ones a vendor happened to build a report for.
Getting there won’t happen overnight. Most programs have built impressive processes around disconnected systems, manual reporting, and countless workarounds. But as PACE programs continue to grow, those workarounds become harder to sustain. At some point, data alone isn’t enough. Programs need data that is trusted, connected, and available when decisions are being made, not days or weeks later.
That’s the shift we’re seeing across the industry: data moving from a back-office reporting function to a core part of how PACE organizations operate.
In our next post, we’ll look at what that shift looks like in practice, and how we built DataHub to help PACE programs make it.
