Forecast The Work. Then Forecast The People.
Why Patient Support Capacity Planning Needs More Than A Volume Forecast
Strategic plans are getting tested. Senior leaders are asking their teams to plug numbers into sprawling, multi-tab Excel files. Budgets are being arm-wrestled into place.
And Patient Support leaders are trying to figure out what they are actually going to need next year, what they can defend, and what somebody else is going to decide they should live without.
Across completely unrelated manufacturers, therapeutic areas and brands, I keep getting versions of the same question:
How many people are we going to need?
One of the stranger things about consulting in Patient Support is how often completely different engagements start to rhyme. I can be working with manufacturers in different therapeutic areas, with different mechanisms of action, different brands and completely different access challenges, and still find myself thinking, didn’t I just have this conversation last week?
Lately, that conversation has been about capacity. Can you help us model this? At what volume do we need to add people? What happens if we grow faster than expected? What happens if we get a new indication, formulation, device or route of administration?
The question is reasonable. What worries me is how often headcount becomes the starting point before anyone has mapped the work.
Operational Excellence Has A Messaging Problem
I have liked almost every operational excellence person I have worked with. They tend to be very reasonable people. They do time studies. They ask good questions. They want to understand how work actually gets done. Maybe in a past life I was an engineer, because I find a lot of that stuff fascinating.
The trouble usually starts when the work gets translated upward.
Organizations often do a terrible job of selling operational excellence. I have yet to see it introduced without half the employees immediately thinking, “Ugh. Well, maybe I’ll get a package.” And then they start looking. Shopping around a little. Seeing who’s hiring.
That may have nothing to do with what leadership intends. Somehow, “operational excellence” still gets communicated as a euphemism for cuts are coming.
Then you watch a thoughtful analysis of where work is duplicative, where processes are broken, where technology is failing people, or where teams are spending time on things that add very little value get translated into: Great. How many heads can we take out?
That part is nails on a chalkboard for me.
The useful version of operational excellence gets very specific about the work. What should disappear? What can technology handle? Where have broken processes created unnecessary labor? Which activities actually improve the Patient experience or access outcome and deserve to be protected?
Budgets are finite. Patient Support leaders have to make responsible decisions about how resources are used. Volume alone tells you remarkably little about the people required to run the program.
A 20 percent increase in Patient volume might create roughly 20 percent more work. It could also create far less or far more, depending on what is changing for those Patients.
Take something as seemingly straightforward as expanding an existing product through a new indication, formulation, device or route of administration.
A new formulation can move the benefit from medical to pharmacy. A different route of administration can change the site of care. New CPT or HCPCS coding can create questions while health plans get their systems configured. A product billing under a misc-J code until a permanent code is assigned can create additional documentation requirements, manual follow-up and payer-specific headaches. Add a new autoinjector or another device and you may create an entirely different set of questions from providers, Patients and payers.
A standard BV may no longer answer everything the team needs to know. Someone may need to call the payer, figure out whether the system is actually set up correctly, follow up, escalate and then follow up again.
The Patient count may not have changed very much. The work per Patient has.
January Is More Than Recertification
The beginning of the plan year is another good example. Patient Support teams understandably plan for the annual recertification rush, but recertification is only part of what January brings.
Employer groups change benefit designs. New carve-outs and formularies go live. Patients move into copay accumulator or maximizer programs, or get routed into Alternative Funding Programs (AFPs), which remain my personal arch nemesis.
Those changes do not show up the same way operationally. An accumulator can create distressed calls later in the year when assistance runs out. A maximizer may barely touch your phones while manufacturer copay spend climbs, as does the gross-to-net hit. An AFP can suddenly drive Patient Assistance Program (PAP) applications, free goods requests and eligibility work from Patients whose insurance status looks unchanged on paper.
The enrollment file can look almost identical while the workload underneath it changes substantially.
In annual planning, that difference matters because a spike in activity can have several explanations. You may need permanent headcount, temporary support, better technology, or a process fix that should happen before anyone gets added to payroll.
Before adding people, look hard at the work already sitting in the model.
Is your Customer Relationship Management (CRM) system actually doing what you need it to do, or are people compensating for technology that was never built around the way the program operates? Are people performing manual work because it truly requires judgment, or because nobody ever fixed the process? Has an old workaround quietly become standard operating procedure?
The same scrutiny should apply to field activity. If your Field Reimbursement team is touching more cases, does that intervention change the Patient’s access outcome? Are they resolving something the Hub cannot resolve? Are they reducing downstream work? Or have you added another touchpoint, handoff and activity that now has to be staffed, tracked and included in the capacity model?
Who Is Paying For All Of This?
Eventually, annual planning gets to the question that tends to make the room a little more interesting.
Patient Support often works as a shared capability across brands. That makes perfect sense right up until everyone starts figuring out who pays for what.
Some expenses are easy to tie directly to a brand. Others are shared across a portfolio: technology, leadership, training, quality, reporting, Hub infrastructure and sometimes the people doing the work.
When Patient Support is treated as a cost center, that may be perfectly defensible from an accounting perspective. It also changes the conversation. People start looking for ways to control the cost, even though the function exists to solve access problems.
And once the brands start looking at their allocations, things can get interesting.
If shared Patient Support costs are allocated based simply on Patient volume, revenue, prescriptions or some other easy denominator, somebody is going to feel screwed.
The mature brand with relatively stable coverage may look across the table and wonder why it is subsidizing the launch brand generating complicated reimbursement questions, repeated escalations and a lot more human intervention.
The launch brand may reasonably argue that shared infrastructure exists precisely so every new product does not have to build a Patient Support organization from scratch.
Both of them are right, which is what makes it a fight instead of a discussion.
That is usually when I start looking at the denominator.
Two brands can have exactly the same number of Patients and create wildly different amounts of work. One may move through a relatively predictable access pathway. Another may require repeated payer calls, coding clarification, provider education, escalations and substantial field intervention.
If we have not taken the time to understand what is actually consuming capacity, the cost allocation methodology starts driving the operating model instead of reflecting it.
A brand that believes it is subsidizing the rest of the portfolio may push for dedicated resources it does not actually need. Shared teams get carved up because the accounting is easier. Technology gets duplicated. Processes get recreated. Everyone becomes very focused on whose budget pays for what while losing sight of whether the structure makes any sense for the Patient.
Marriages, Divorces And Blended Families
I’ve been working in Life Sciences Patient Support for about fourteen years, and I’ve seen the tide move in and out a few times now.
We’re one company. It should be one Patient Support experience across brands. Everyone will be working together!
Three years later:
No one knows our Patients, our health care providers and our access challenges like the brands. We need to divide and conquer.
I’ve seen marriages, divorces and blended families in Patient Support. I’ve seen a shared services model dismantled and rebuilt multiple times, with the same slides making both cases.
There’s a lot of spin masquerading as strategy on PowerPoint decks. The tell is usually that the reorganization has a name before it has a capacity model. And every time the pendulum swings, somebody has to rebuild that model around the new org chart. At some point, somebody still has to put something in a spreadsheet.
Before you walk into annual planning and say, “We expect 20 percent more Patients, so we need 20 percent more people,” I would back up and figure out what is actually changing.
What work will that change create? What existing work can disappear? Is the added demand temporary or structural? How much of it truly requires a human being? And if the work is shared across brands, does the allocation methodology reflect what is consuming capacity or simply use the easiest denominator?
Then we can talk about headcount.
Forecast the work first. Then forecast the people.
A note on AI use
I wrote this piece. I used AI during the editing process to pressure-test its structure, clarity, accuracy, and brand risk. The voice, opinions, examples, and final editorial calls are mine.
No one is requiring me to disclose this. I believe in being honest about how I use AI. Used well, it helps me sharpen the work; it does not replace the thinking, judgment, or two decades I spent working in hospitals and life sciences.