A senior managing partner reviewing financial performance charts with an associate in a warm sunlit conference room, natural tones

— PRACTICE GROWTH & PROFIT INSIGHTS

Should You Grow Your Practice Right Now - Or Not?

Or is "growth" the wrong question?

4. What Has to Be True for the Investment to Work?

This may be one of the most neglected questions in practice decision-making.

Not because physicians can’t do the math.

The problem is that the math is often built on assumptions and we don't always stop to ask which assumptions are actually facts.

Consider buying a new piece of diagnostic or treatment equipment. The economics can look terrific on paper:

The procedure reimburses $X. There are plenty of patients who could benefit from it. Five procedures a week could pay for the equipment in less than a year.

Sounds pretty good. But wait. What has to be true for that to actually happen?

There have to be enough appropriate patients. Patients have to accept treatment. The physician has to be willing and able to incorporate it into the clinical workflow.

Staff have to be trained and available. The equipment has to be used often enough to justify the investment. The payer mix has to support the expected reimbursement. Procedures have to be coded and documented correctly.

Money has to actually be collected.

Most importantly, the revenue needs to be genuinely new, not simply revenue moved from something you were already doing. And after the additional costs of the investment, it needs to produce new profit on its own merits.

Five procedures a week sounds like one utilization assumption. It is an assumption. It isn't necessarily a fact. And it isn’t actually even one assumption. It's a chain of assumptions.And that's where investment decisions can get interesting.

The problem isn't doing the math. The problem is forgetting that the math is often built on assumptions.

So instead of asking only, “What's the ROI?”, ask:

“What has to be true for me to get the result I'm expecting?”

Then identify which of those conditions you know to be true and which you're assuming.

If you believe you need five procedures a week to make the investment work, don't start by buying the equipment. Start by testing whether five procedures a week is actually realistic.

Look at your patient population. Identify how many potential candidates you've seen recently. Determine how many would likely accept the procedure (ask some of them and get their feedback). Check actual reimbursement against your payer mix. Look at the staffing and workflow implications.

You may confirm the opportunity. Or you may discover that the opportunity isn't quite what it appeared to be. So, for an economic opportunity, there are 2 distinct tests:

A. Is the revenue genuinely new?
If the new equipment simply moves procedures/revenue from an existing service into the new service, you may have added equipment expense without adding meaningful economic value.

B. Does the new revenue produce new profit?
Even if the revenue is genuinely new, the investment still needs to cover its additional costs - equipment expense, financing, supplies, staffing, maintenance, etc. - and leave something worthwhile behind.

The outcome here is to gain a realistic sense of value before you've spent the money.

The purpose isn't to make you more cautious. It's to make you better at investing - to help you distinguish between calculated risk and assumed risk.

Calculated risk weighs potential benefits against possible losses before making a decision, so you can (and should) plan how to mitigate any potential loss before you dive in.

Assumed risk refers to knowingly accepting the dangers of an activity or investment without that mitigation plan.

There is no such thing as “certainty” in these kinds of decisions. “Doing nothing” (almost always) wastes opportunity that could result in massive gains, so that is not a viable decision baseline. “Calculated risk” is what you’re going for.

The same thinking applies to AI, but with an important distinction.

Suppose an AI documentation tool saves a physician 45 minutes a day. That's valuable.

But the value doesn't necessarily come from turning those 45 minutes into additional patient visits.

The physician might use the time to leave earlier, reduce after-hours charting, spend more time with patients, or simply end the day feeling less like a data-entry clerk and more like a physician.

Those are all legitimate outcomes and each one has value. They just aren't the same outcome.

Other applications of AI, like revenue cycle management, coding, scheduling, authorization workflows, or other revenue-generating or cost-reducing processes, can be evaluated much more directly in economic terms.

The point is not to make every investment pass an ROI test. The point is to be clear about what result you're actually buying.

So, start with asking, “What has to be true for me to get that result?”

Once you identify those conditions, you might be able to test them cheaply before you spend too much money.

Whatever the answer is, it’s useful to know before you've committed.

5. What happens if we're wrong?

Every investment has downside risk. Sometimes it's financial. Sometimes it's time. Sometimes it's disruption. Or opportunity cost. Or the loss of confidence that comes from implementing one more initiative that doesn't work.

The question isn't whether you can eliminate risk. You can't.

The question is whether you understand it well enough to manage it.

6. How quickly will we know whether it's working?

Suppose you're considering two investments.

One costs $30,000 and can give you meaningful evidence about whether it's working within 90 days, and could provide an OK ROI.

The other adds $200,000 a year in fixed overhead and may take two or three years before you really know whether the decision was right. But if you are, there could be significant ROI.

Those aren't simply two different-sized investments. Those are two different opportunities to learn, and mitigate risk. That matters when the economic environment is uncertain.

A decision that lets you learn quickly gives you something valuable beyond the initial result: the ability to adjust.

So, even though the time horizon for realizing ultimate ROI is vastly different in these 2 scenarios, structuring the management so you can adjust as quickly as possible is a key advantage to risk mitigation, and actually even in shortening an ROI realization timetable.

7. What will cause us to stop, change course or accelerate?

Don't wait until you've spent the money to decide what success looks like. Decide beforehand.

If the investment produces X, we continue. If it produces Y, we modify it. If it produces Z, we stop. That’s good management.

A Practical Example

Suppose a practice is considering spending $30,000 on an AI-enabled revenue-cycle solution because the owner believes it can reduce denials and accelerate collections.

Instead of asking, “Will this work?” and making a yes/no decision based on the sales pitch, the owner defines what the potential cost benefit is, then what evidence should appear within 90 days.

For example:

Current monthly net collections: $150,000
Current denial rate: 8%
Current average days in A/R: 48 days
Current Net Collection Ratio: 91%
Expected improvement: reduce denials to 5%, reduce A/R days to under 30, and produce a 5% increase in net collections.

We’ll assume that there won’t be any decrease in current billing employee spending, as someone will still need to run the AI, and be the human-in-the-loop for decisions and work the AI can’t perform.

Here’s the math:

Decreasing the Denial Rate

POTENTIAL RESULT: Decreasing the Denial Rate decrease from 8% to 5% generates an additional $54,000 a year.

The denial-rate improvement is 3 percentage points:

At 8% denials: $150,000 × 8% = $12,000 denied
At 5% denials: $150,000 × 5% = $7,500 denied
Reduction in denied revenue: $12,000 − $7,500 = $4,500 per month

So the practice gains approximately $4,500 per month, or $54,000 annually, assuming the recovered claims are successfully resubmitted and collected.

With 3.4% inflation, the annual gain’s approximate purchasing-power value is: $54,000×(1−0.034)=$52,164

This is additional collectible revenue, not necessarily pure profit; billing labor, appeals, payer adjustments, and other collection costs may reduce the net gain.

Reducing Average Days in AR

No one much thinks about the real world effects of having days in AR extend beyond the best possible benchmark. So, here’s the (painfully detailed – sorry) math:

POTENTIAL RESULT: Reducing days in accounts receivable from 48 to 29 days frees up about $95,000 in cash.

The boring calculation:

· Average daily collections: $150,000 ÷ 30 days ≈ $5,000

· Reduction in A/R days: 48 − 29 = 19 days

· Cash released: 19 × $5,000 = $95,000

In real-world terms, the practice converts approximately $95,000 of previously outstanding receivables into available cash, improving liquidity without increasing monthly revenue.

Accounting for 3.4% (2026 August inflation), the purchasing-power-adjusted value is approximately:

$95,000 × (1−0.034) =$ 91,770

So the practical effect is roughly $95,000 in nominal working capital, or $91,770 in current purchasing power. This is generally a one-time cash-flow improvement, not a recurring $95,000 increase in monthly collections.

Increasing Net Collections

POTENTIAL RESULT: 5% Increase in Net Collections from 91% to 96% (still below benchmark BTW. Benchmark is 97%+. Just trying to keep it real here.)

A 5% increase in net collections on $150,000 of monthly revenue: $150,000×0.05=$7,500

The practice gains approximately $7,500 per month, or $90,000 / year

This is additional collected cash. The actual profit increase may be lower if achieving it requires extra billing staff, software, collection fees, or other expenses.

CONCLUSION: Looks good so far.

There is potentially $145,000 in ongoing revenue increase (from better denial management and net collection ratios), and a one-time increase from moving days in AR to under 30.

Could you get that increase without AI automation? Maybe. Depends on why the current metrics are the way they are. For the sake of our example, let’s say you’ve optimized what you can without AI, and the "before" metrics are your best. In this case, AI automation could be viable.

Next Step: Establish Checkpoints

30 days: Is the system actually being used? Are staff following the new workflow? Are the types of denials changing? Have net collections improved at all?

60 days: Are overall denial rates beginning to move? Is A/R aging improving? Are previously missed claims or coding issues being identified?

90 days: Has the practice produced enough evidence to determine whether the investment is generating the expected financial improvement?

Now the decision becomes much easier.

If the numbers are moving in the right direction, continue and optimize.

If they're not, figure out why. Maybe the technology works but staff aren't using it correctly. Maybe the problem wasn't denials - it was something else in the revenue cycle. Or perhaps the vendor's promised improvement simply isn't materializing.

The important thing is that the physician doesn't discover 18 months later that the investment didn't work.

The faster you can obtain meaningful evidence, the more intelligently you can adjust.

You don't necessarily need certainty before acting. You need a way to learn.

So, Should You Grow?

Maybe. Maybe not.

I don't think uncertainty is a good reason to stop making decisions. It is a good reason to make better decisions.

You don't need to know exactly what Medicare will do next year.

You don't need to know exactly where AI will be five years from now.

You don't need to predict the economy.

You need to know your own practice well enough to always understand where the next dollar, hour and employee can produce the greatest return.

And perhaps most importantly: You need to know what not to do.

Because in a practice with finite money, finite people and finite physician time, every dollar you spend solving the wrong problem is a dollar you didn't spend solving the right one.

That's why I believe the first step in practice growth isn't growth. It's diagnosis.

Find out what's really happening. Find out what's limiting you. Find out where the opportunity is. Then decide what to do about it.

You don't need to change everything. You need to know what matters.

There is a lot to be uncertain about right now.

Reimbursement.

Labor.

The economy.

Artificial intelligence.

Competition.

The cost of capital.

And, for independent physicians, the future of independent practice itself.

If you're a physician-owner who has been thinking about expanding, adding another provider, opening another location, investing in technology, hiring staff, or simply putting more money back into the practice, the obvious question is:

Is now really the time to grow?

I think that's the wrong question.

Growth Is Not the Same Thing as “Getting Bigger”

We tend to use the word "growth" as though everyone agrees on what it means.

They don't.

A practice can grow revenue and become less profitable.

It can add physicians, become more difficult to manage and lose profit too.

It can see more patients while giving the owner less control over his or her working life, and reduce revenue per visit due to less physician capacity.

It can add employees, equipment, locations and technology and often end up with less cash at the end of the year.

That's not the kind of growth most physician-owners have in mind, but can be the outcome if growth is not consciously defined at the outset . Rather, the outcome is assumed, a dangerous beginning, by the way. And so "growth" gets a bad name, for many. Too much risk. Too much work. Dubious outcome.

And so too many practice owners opportunity, lose profit and lifestyle they could have had, if they only approached it a bit differently

Here's my point:

The purpose of growth is not to make the practice "bigger."

The purpose of growth is to make the practice more economically valuable, more capable and more rewarding to own.

That can mean more revenue. It can mean more profit. It can mean more physician capacity. It can mean a practice that operates with less owner involvement.

It can mean creating the financial strength to weather reimbursement changes. It can mean creating the kind of practice you eventually want to sell.

And sometimes it can mean all of those things at once.

But they are not automatically produced by "growth."

Revenue growth, profit growth, capacity growth and owner freedom are related. But they are not the same thing.

That's important because it changes how you think about the next investment you make.

Before You Decide What to Invest In, Find Out What's Actually Holding You Back

Suppose you want to increase revenue. What’s stopping you?

Maybe you don't have enough patients. Maybe you have plenty of patients but not enough physician capacity. Maybe you actually have physician capacity but your schedule is inefficient.

Maybe your physicians have capacity but spend too much time doing work that someone else could do.

Maybe you're seeing plenty of patients but losing revenue through documentation, coding, billing or collections. Maybe your revenue is fine and your real problem is margin.

Or maybe the practice is profitable but the owner is still doing everything.

Those are very different problems. And they require very different solutions.

If your true constraint is physician capacity, improving marketing may simply give you more patients you can't efficiently accommodate.

If your problem is revenue-cycle management, adding another physician may simply produce more charges that aren't collected effectively and you lose profit.

If the physician is spending hours every evening charting, buying another piece of technology may not be the first thing to fix.

If the practice has plenty of capacity but can't fill the schedule, hiring another physician isn't a growth strategy. It's an expense.

This all sounds obvious. It isn't, for a reason I've never quite been able to wrap my head around.

Physicians are trained to diagnose problems before treating them. It's a deeply ingrained process. Diagnose first. Then treat.

Somehow, when we get into the business of the practice, that discipline often disappears. Doctors tell me they weren't trained in management. True enough. I'm not talking about that.

We see something we don't like and immediately start looking for a solution. The otherwise deeply ingrained process of diagnosis-before-treatment disappears.

So, the immediate response becomes ...new employee. New software. New location. New provider. New marketing. New technology. New consultant.

Whatever.

The question we should ask first is much simpler: What is actually limiting the practice right now?

A practice doesn't have to fix everything. It has to fix the thing that is limiting it now.

The Most Expensive Decision May Be the One That Solves the Wrong Problem

There is another reason this matters.

Every practice has limited resources: limited cash. Limited physician time. Limited staff capacity. Limited management attention.

And, perhaps most importantly, limited tolerance for failure.

A physician-owner can survive making a wrong decision once in a while.

But a practice that continually invests in things that don't address its real constraint will eventually discover that "busy" and "successful" are not synonyms.

I've seen practices work very hard at improving things that were not actually their most important problem.

The staff gets trained. Software gets purchased. Workflow gets redesigned. Meetings are held. Everyone is busy.

Six months later, the economic needle has barely moved. Everyone is frustrated and exhausted. And no one wants to do “that” again. So the next pearl you come home with from a conference is doomed out of the gate. No one wants to “do that” again.

There is nothing particularly mysterious about this.

They solved something.

It just wasn't the thing that needed solving.

AI Makes This More Complicated, and More Interesting

Artificial intelligence adds another layer to this.

There are now tools that can potentially and massively change how clinical decisions, documentation, scheduling, communication, billing, administrative work and many other activities are performed, for the better.

That's exciting. It is also dangerous.

Not because AI is inherently dangerous. It actually isn’t (no matter the doomer stories. What evil humans might do with it is another matter altogether).

It’s because having more possible solutions can make it easier to choose the wrong one.

Technology can solve a task without solving the problem. This is a very important distinction.

Consider documentation.

If a physician spends two hours every evening completing charts, reducing that time has obvious value.

But what happens next?

Does the physician see more patients? Maybe, unless documentation is done outside of patient sessions, which happens more often than not.

Does the doctor leave the office earlier? Maybe. But not during usual office hours. So no personal gain there, though going home on time when the day is done is an obvious benefit.

Spend more time with family? That’d be good. Spend more time working on the practice? Maybe.Less fatigue and burnout? Probably, so that’s good too.

To some, lifestyle improvement is a primary goal. So, no charting at night at home (or god forbid, sitting in your practice’s office, alone, in the dark and cold, without snacks) could be life-changing. To growth oriented owners with big ambitions, working at night might not really bother them.

All of those outcomes could be legitimate. But they aren't the same result. And the value of the investment depends upon what you actually intend to accomplish.

The same thing is true of administrative automation.

Suppose you can automate a task that currently consumes ten hours of staff time every week. Great! And what happens to those now-available ten hours?

Do you eliminate the work? What do you do with the employee now?

Eliminate the position? Probably not, if they are full-time and you intend to grow. Move the employee into work that produces greater value? Sounds good. Is that actually possible? Maybe.

Will that improve patient service? Increase physician capacity? Provide an opportunity elsewhere?

If you don't answer these questions, you've automated a task. You haven't necessarily improved the economics of the practice.

That distinction becomes increasingly important as AI makes more and more tasks automate-able.

The question isn't "Can AI do this?" The better question is: "If AI does this, what economically valuable thing happens next?"

That is the question I would ask before implementing almost any new technology.

Growth Decisions Should Begin With the End in Mind

Before spending money on growth, I would start with the result you actually want.

Not the tool. Not the employee. Not software. Not the building.

The result.

For example: "I want to increase physician capacity by 20% without increasing physician work hours." That's a meaningful objective. Now start working backward from that result.

What is preventing that capacity today? Where is physician time being consumed? Which activities actually require a physician? Which don't?

What happens if those activities are delegated, eliminated, streamlined or automated?

What does the change cost? What additional capacity should it create? What is that capacity worth?

Now you have something you can evaluate.

This is very different from saying, "I've heard AI documentation can save physicians time. Maybe we should get an AI scribe." **

Maybe you should. But first figure out what you're trying to accomplish.

**My apologies: AI documentation may be too simple an example of “figuring out what you want to accomplish first” – most doctors these days seem to think that using ambient AI documentation is a no-brainer. I think it might be as well if used slightly differently than I see it in use at the moment, so it leaves me fairly circumspect, with my tin foil hat firmly on my pointy head.

The Growth Investment Test

Here's a simple test I suggest using before making a significant investment in the practice.

You don't need a consultant to use it. You don't need special software. You don't even need a particularly sophisticated financial model to get started.

Take the investment you're considering and answer these seven questions.

1. What are we actually trying to accomplish?

Be specific. More revenue? More profit? More physician capacity? A new market?

Less owner dependency? Better patient access? More personal time? A stronger position for future growth?

If the answer is "all of the above," narrow it down. What is the primary result?

2. What is preventing us from accomplishing it now?

This is the diagnosis. Don't skip it.

If you want more revenue, why don't you have it? If you want more profit, where is the margin being lost? If you want more capacity, what is consuming the capacity you already have? If you want less owner dependency, what decisions or activities still require you personally?

There may be several contributing problems. There is usually one that matters more than the others.

Find it.

3. What changes if we solve that problem?

This is where you turn operational change into an economic proposition. If we solve the problem, how much additional capacity should we have? How much additional revenue could that capacity produce? What happens to margin?

What happens to staff utilization? What happens to the owner's time? What happens to the patient experience?

Don't settle for "it should make things better." Define what "better" means.

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I’ve always counseled clients that some yearly growth is a must, if only to the current inflation rate, so your profit is actually true and not being eroded unbeknownst to you.

So in that respect, some growth is always good. Mostly that’s just a few percent annually, achieved without much in the way of significant investment.

Likewise, I don’t believe anyone should blindly invest money in a practice simply because they could buy up a new location cheap.

Or because there’s FOMO (fear of missing out) in them thar hills in AI automation.

Or because some practice in your area just went corporate and you’re worried they’ll eat up your patient base.

The better question is: What would have to be true for growth to make economic sense for your practice?

That's a very different question.