The job posting said quality analyst. The job is spreadsheets.

The job posting said "quality analyst." The job is spreadsheets.

Read enough of these listings and the pattern shows up. Master's preferred. Clinical background a plus. Quality reporting experience required. Strong analytical skills. The posting describes someone who can look at how a population is doing on its quality measures and help a care team change it.

Then you talk to the person who took the job, and here is the actual week. Pull the measure file. Pull the supplemental file. Pull the provider roster. Match them together. Chase down the rows that didn't reconcile. Rebuild last month's report because the specification changed. Send it to several people who each want it cut a different way. Then start again.

Nobody wrote that week into the posting. Nobody designed it, either. It is simply what the role became once the data underneath it stopped agreeing with itself.

Why does a quality analyst spend the week reconciling files?

Because the systems underneath never agreed on anything, and a person is the cheapest available adapter between them.

Think about what a single quality rate rests on. Claims that arrive weeks after the visit. Supplemental data that comes in on its own schedule. Lab results, chart records, and payor quality files, each with its own way of naming a patient and a provider. A roster that may already be out of date by the time it lands, a problem we walked through in why the roster is wrong on the day it's received. Before anyone can say how a measure is doing, someone has to decide which version of each patient is the real one.

In many quality departments, that someone is the analyst. This is what a great many quality departments actually are right now: large analyst teams stitching results together in spreadsheets. The workbook becomes the integration layer. The analyst's judgment becomes the matching rule. And because the matching lives in a person's head and a set of fragile formulas, it has to be done again every cycle.

None of this is a failure of the people. Reconciling several sources against measure specifications is careful, difficult work, and the numbers have to be right. The trouble is that it is the wrong work for the people doing it, and it never ends.

What does it cost to staff reconciliation with clinical talent?

There are a couple of costs here, and neither is comfortable.

The first is the plain one. Organizations pay clinical-grade salaries for data-reconciliation work, and the people doing it know it. Someone who came into quality to improve how patients are cared for, and who spends the week chasing rows that won't match, eventually notices the distance between the posting and the job. That distance is a large part of why good analysts leave. And when they leave, the undocumented rules in the workbook leave with them, and the next hire spends a long stretch learning to rebuild a report rather than learning the population.

The second cost is quieter. Every hour spent reconciling is an hour not spent on the member who needed a call.

The cost isn't the hour. It's the call that never happened.

Picture a composite case. An analyst at a health plan notices on a Thursday that a group of members with diabetes have not had their eye exam, and that most of them see the same handful of practices. That is exactly the kind of pattern a quality team exists to find. But Thursday is also the day the monthly report is due, and the supplemental file didn't match the roster again. The pattern goes into a note to look at later. Later turns into weeks. By the time it reaches a care manager, the year is shorter, the list is staler, and some of those members have gone a good deal longer without care they needed.

Nobody decided those members mattered less. The reconciliation simply came first, because the report was due and the report always comes first. We saw the same shape in a quality analyst's October, where the day went to measuring where the year stood and very little of it went to changing it.

Will hiring more analysts fix the problem?

It is the natural instinct. The reports are late, the team is stretched, so add headcount. More analysts will produce the same reports faster, and maybe with fewer late nights.

But the work stays the same. A bigger team reconciling the same disconnected sources still spends its week reconciling. The adapter gets more expensive without getting any more useful, and the member who needed a call is still waiting behind the file that didn't match.

There is a second-order effect, too. When a quality team's output is a stack of reports, everyone downstream learns to argue about the reports. Provider meetings turn into debates about whose number is right, a pattern we described in the provider quality scorecard meeting that keeps arguing about the data. More analysts produce more versions to argue about.

What does a quality team's week look like instead?

The organizations pulling ahead in value-based care are not the ones with the biggest quality teams. They're the ones whose quality teams spend the week on patients, because the reconciliation already happened somewhere else.

That changes the analyst's week in ways that are easy to picture:

• Monday starts with which members need something this month, not with which file needs pulling.
• The question "which number is right" mostly stops coming up, because everyone is working from the same continuously updated view.
• The analyst's judgment goes into finding patterns, such as a practice falling behind on a screening or a group of members who keep slipping through, and handing them to the care team while there is still time to act.
• Reporting becomes a by-product of work the team was already doing, instead of a separate project rebuilt by hand every cycle.

That is closer to the job the posting described. It is also a job people stay in, because the work they were hired for is the work they actually do. And it moves effort earlier in the year, when a member identified in the spring still has time to be reached, scheduled, seen, and confirmed.

As we argued in the dashboard era of population health is over, describing a population is not the same as changing what happens to it. A quality team that spends its week describing is doing the first job well and never getting to the second.

A question worth asking this week

Open your best analyst's calendar and just look at it. If most of it is pulling, matching, and rebuilding, you are not short on talent. You are spending it on being the adapter between systems that should have agreed in the first place. That is a solvable problem.

ElevateCare 360 is built to be the place teams track, work, and report quality measures, replacing spreadsheet reconciliation with a single, continuously updated view. You can see how that works on our care gap closure page.