How Pharma Teams Use Diagnosis Trends to Identify Growing Specialty Markets

The standard way to size an indication starts by counting the patients diagnosed today, ranking territories by current volume, and prioritizing the markets that already look big.

A snapshot shows you the market at the moment you can least afford to enter it, once demand is obvious, priced in, and crowded with competitors.

Diagnosis trends work on a different axis. They track the rate and direction of clinical activity over time, so a specialty market that is accelerating shows up in the data well before it shows up in a revenue forecast.

Colorectal cancer makes the point. Among adults aged 45 to 49, incidence had been creeping up by about 1.1% a year for over a decade. Then, from 2019 to 2022, that rate jumped to 12% a year, driven almost entirely by a surge in early-stage detection.

A team reading that slope in 2021 saw a specialty market forming across gastroenterology, oncology, and diagnostics. A team reading a static patient count saw a small number and moved on. The gap between those two readings is the difference diagnosis trends analysis makes.

The same logic scales far beyond one cancer, though rarely as a single clean line. A specialty market usually holds several trends at once, moving at different rates by stage, molecular subtype, age group, and geography, so the signal sits in the segments that are accelerating, not in one slope for the whole category.

Those segment-level movements show up in diagnosis data months or years before they register as revenue.

Why Diagnosis Trends Matter When Evaluating Specialty Markets

Specialty is where the money and the movement now sit, which raises the cost of reading these markets late. Broad primary-care categories grow slowly and predictably. Specialty categories lurch, and they lurch on the back of new approvals, new diagnostics, and shifting clinical guidelines.

That’s why timing a specialty entry off last year’s volume is how teams end up launching into a market that has already been divided.

Specialty Now Drives the Majority of Drug Spend and Almost All of Its Growth

By 2021, specialty drugs already made up 50% of total drug spending in the United States, according to an analysis from the HHS Office of the Assistant Secretary for Planning and Evaluation.

The trajectory has not flattened since. Retail prescription drugs were the fastest-growing category of health spending from 2020 to 2024, per the Peterson-KFF Health System Tracker, and total US drug spending rose 12.7% in 2025 to $915 billion, one of the sharpest single-year jumps in two decades, according to the American Society of Health-System Pharmacists.

Cancer drugs were the largest and fastest-growing category, and clinic spending grew 19%, pulled up by specialty and injectable therapies including new treatments for rare diseases like ALS and amyloidosis that had few options until recently.

The GLP-1 wave shows the same pattern at full volume, with that single drug class reaching roughly $132 billion, about 14% of all US drug spending, and the report describing the market as still on the steep part of the curve.

In Specialty Markets, Spending Growth and Patient Volume Move Separately

Here is the trap in counting patients. The same ASPE analysis found specialty drug spending grew 43% between 2016 and 2021 while the number of specialty prescriptions rose just 0.5%.

Almost all the growth came from price and mix, not from more patients filling scripts. A team sizing that market by patient count would have concluded nothing was happening. A team reading spend, diagnosis, and product-mix trends would have seen a market reshaping itself underneath a flat volume line.

In specialty, the patient count and the market value drift apart, so diagnosis data analysis has to look at the slope and its composition, not the headline number.

What Diagnosis Data Can Reveal About Specialty Market Growth

Diagnosis data is the closest thing pharma has to a real-time reading of where clinical demand is building.

Every diagnosis leaves a coded trace in claims, an ICD-10 record tied to a rendering provider and a site of care. Aggregate those traces over time and three distinct signals emerge, each useful for a different part of specialty market analysis.

Rising Diagnosis Volume Marks Where Patient Demand Is Building

The first and most direct signal is a rising count of diagnoses for a given condition. The early-onset cancer picture is a clean example. An NCI-led study found that rates of 14 cancer types increased in at least one under-50 age group between 2010 and 2019, and the average age at colorectal cancer diagnosis has fallen from 72 to 67 within a single generation of clinicians.

For a company with a product indicated for a younger patient population, that shift redraws the addressable market and moves it into specialties that historically saw few of these cases. Disease prevalence data tells you the market exists. The diagnosis growth rate tells you it is coming toward you.

A New Test or Treatment Can Turn a Hidden Population Into a Visible Market

Diagnosis volume often lags true disease burden, then jumps when a diagnostic or a therapy arrives to make diagnosis worthwhile.

Metabolic dysfunction-associated steatohepatitis, or MASH, sat under-diagnosed for years, in part because there was no incentive to diagnose a condition with no approved treatment.

When the FDA approved the first MASH therapy in March 2024, the calculus flipped, and the diagnosed population became a market that decision-makers now size to assess the budget impact of new therapeutics. Neurology is running the same play.

The FDA cleared the first blood test for Alzheimer’s diagnosis in May 2025, a far less invasive alternative to PET scans, arriving alongside disease-modifying treatments and a patient population that has now crossed 7 million for the first time. When a test or a drug lowers the barrier to diagnosis, the diagnosis curve bends upward, and a previously invisible population becomes a commercial one.

Using Claims Data to Distinguish Emerging Markets From Established Demand

The hardest judgment in pharma market intelligence is telling an emerging one from a mature one, and telling a real change in disease from a change in how it gets counted. Claims data supports both judgments because it carries the slope, the composition, and the provider behavior behind a number, instead of just the number itself.

Emerging Markets Show a Steep Slope, Established Ones Show a Plateau

Established demand looks like high, stable volume. The market is large and familiar, the competitors are entrenched, and year-over-year diagnosis counts barely move.

Emerging demand looks nothing like it, carrying lower absolute volume but a steep and sustained climb. Colorectal cancer contains both patterns at once. Incidence in older adults has been flat or falling for years, a saturated segment, while the under-50 curve rises steeply, a market still forming.

Average the two together and you erase the only part worth acting on, which is why a good deal of diagnosis trends analysis comes down to separating the flat lines from the climbing ones.

Read the Cause of a Diagnosis Spike Before You Act on It

A rising diagnosis count can have more than one cause, and the cause changes what the trend is worth.

Sometimes incidence is genuinely climbing. Sometimes the spike reflects better detection rather than more disease, as in colorectal cancer, where screening among adults 45 to 49 rose sharply once the recommended screening age dropped from 50 to 45, a shift the American Cancer Society ties directly to the jump in early-stage diagnoses.

Clinician behavior moves the numbers too, as more physicians decline to rule out a diagnosis on the basis of age alone. Claims data also carries a limitation worth stating plainly, since it only reflects diagnoses that were coded and billed and therefore undercounts whatever goes undiagnosed.

In one Medicare claims analysis reported by AJMC, diagnosed MASH prevalence read near 1.49% while true prevalence was estimated around 5.30%.

A team that treats claims as a complete census will misjudge the market floor. Read them instead as a lagged, detection-sensitive signal, and the slope stays legible under the noise.

There is a second gap worth reading, between the patients who are diagnosed and the patients who are actually treated. A large diagnosed population with low treatment rates is a market waiting on the right product, better access, or more awareness to convert it, whereas a population that is both fully diagnosed and fully treated sits much closer to saturation.

MASH lived in that first state for years, a sizeable diagnosed group with nothing approved to offer them, until a treatment arrived and turned latent demand into something a commercial team could act on.

Finding Geographic Pockets of Growing Clinical Activity

National diagnosis trends are averages, and averages bury the variation a commercial team most needs to see. A condition rising 8% a year nationally can be flat across most metros and climbing at double digits in a handful of counties.

Both the baseline burden and its rate of change vary by place, so healthcare market analysis that stops at the national figure leaves most of the opportunity unread.

Disease Burden Clusters, and So Does Its Growth

Disease does not spread evenly. Chronic disease prevalence clusters heavily in the southeastern US, according to a CDC analysis that maps prevalence down to the zip-code level.

Diabetes is the textbook example, concentrated in a CDC-defined diabetes belt of 644 counties across 15 mostly southern states, where diagnosed prevalence runs 11.7% against 8.5% everywhere else.

For an endocrinology or cardiometabolic product, that concentration doubles as a map of where diagnosis volume, and often diagnosis growth, sits highest. National patient counts answer only half the question. The other half is local, in the specific places where those numbers are climbing fastest.

Local Trend Data Changes Which Territories Are Worth Building

Geography also decides whether a national trend is executable at all. The same report notes that organizations focused on specialty and rare disease will see far steeper cost growth than those built around routine care, which is why it calls local data analysis essential to planning.

A rep territory drawn from a national average will overcover the flat regions and undercover the pockets where clinical activity is actually accelerating.

Build territories instead around observed diagnosis and procedure activity, and around the real driving distances between provider clusters, and a trend becomes a route a field team can work.

Connecting Diagnosis Trends With the HCPs and Specialties Treating Patients

A diagnosis trend stays an abstraction until it is tied to the specific providers managing those patients, and this is where a lot of specialty market analysis quietly breaks down. Teams reach for specialty labels as a shortcut, and specialty labels are a coarse proxy for who actually treats a condition.

Specialty Labels Undercount Who Actually Manages a Condition

Two physicians can share a taxonomy code and run completely different panels, one endocrinologist deep in complex diabetes and metabolic liver disease while another barely touches it.

Diagnosis exposure, measured as ICD-10 diagnosis volume grouped into CCSR categories, tells them apart in a way a label never can. A rising trend also rarely respects specialty boundaries.

The early-onset cancer shift is being absorbed across primary care, gastroenterology, and oncology at the same time, as clinicians in every setting change how they evaluate younger patients.

A 2023 CDC survey of nearly 1,500 primary care providers examined how they recognize colorectal symptoms and decide on workups in patients under 50. Gastroenterology moved at the guideline level, with the US Multi-Society Task Force updating its recommended screening age to 45.

Oncology takes those diagnoses downstream through multidisciplinary early-onset programs spanning gastroenterology, medical oncology, and surgery. Targeting by label alone would miss most of the physicians riding the trend.

Diagnosis Exposure Maps the Real Prescriber and Referral Network

Anchoring a trend to HCP diagnosis data sharpens the whole commercial picture.
Cross-referencing which providers diagnose a condition, at what volume, and over what trailing period surfaces the working prescriber base, and claims sequencing exposes the referral pathways feeding it. Alzheimer’s shows why this matters when a market is in motion.

As blood-based biomarker testing pushes diagnosis earlier and into specialty care, and as the field writes clinical guidelines around who gets tested and when, the set of relevant providers keeps expanding and shifting, so a neurology map built on last year’s referral patterns is already out of date.

Diagnosis and treatment also happen in different places, and claims sequencing exposes the hand-offs between them. A condition might be diagnosed by a primary-care physician or a community specialist and then referred to an academic center for management, which makes the diagnosing provider and the prescribing decision-maker two separate targets in two separate settings.

Reading the referral pathways behind a diagnosis trend puts both ends of that flow in front of a commercial team, which is where field and medical resources actually need to land.

Turning Diagnosis Trends Into Pharma Market Prioritization

Reading trends is only half the job. What a pharma commercial team needs out the other end is a ranked set of markets, indications, and territories, ordered by where to commit resources first, and diagnosis trends feed straight into that ranking as long as the trend is weighed against the factors that decide whether growth can be captured.

Rank Markets by Trajectory Alongside Current Size

Current size measures how big a market is today. Trajectory answers a different question, whether entering it now is early, on time, or late, and the two answers often point in opposite directions.

A large but flat market is a share fight against incumbents, while a smaller market climbing steeply is an opening, and getting there before the slope becomes obvious is most of the advantage on offer. This is the move from static, volume-based targeting toward forward-looking prioritization that reads where activity is heading rather than where it has been.

Ranking indications and territories by diagnosis slope, held alongside absolute size, surfaces the markets worth entering before they price themselves out.

Weight the Trend Against Treatability and Competitive Density

A steep diagnosis curve is necessary but never sufficient on its own. MASH shows why treatability belongs in the ranking, since the market only turned commercially real once an approved therapy existed to make diagnosis actionable.

Competitive density carries the same weight, because a fast-growing market that three companies already contest can rank below a slower one nobody has entered yet.

A workable prioritization holds all of it together, the diagnosis trend, the treatable population behind it, the competitive field already in place, and the geographic pockets where the slope runs steepest, and that combination, rather than any single metric, separates a defensible entry sequence from a list of big numbers.

In practice an indication with a moderate but steeply rising curve, a newly approved therapy, and few entrenched competitors will often outrank a much larger one whose curve has flattened and whose prescriber base several competitors already share.

The bigger market looks safer on a size chart and is usually the worse place to spend a launch budget.

How Alpha Sophia Helps Pharma Teams Analyze Diagnosis and Market Activity

All of this depends on being able to read diagnosis activity at the provider level, across payors, and over time.

Alpha Sophia is built to supply exactly that. It is an external, NPI-anchored view of all-payor US medical claims covering more than 4 million providers across Medicare, Medicaid, and commercial payors, regularly refreshed, that pharma teams query to see and compare markets.

The interpretation and the prioritization stay with the team. Alpha Sophia supplies the ground truth they read it from.

Query Diagnosis Activity by Individual ICD-10 Code Across an All-Payor View

Teams can filter providers by individual ICD-10 diagnosis codes, grouped into CCSR categories, alongside CPT and HCPCS procedure codes, taxonomy, affiliations, open payments, publications, and clinical trials.

Because the diagnosis data is granular, a team can identify every physician diagnosing a given condition, ranked by volume, and see the actual provider base behind a trend rather than an estimate of it.

Anchoring every diagnosis to an individual NPI turns a national trend into a named, reachable set of providers.

Compare Cohorts and Geographies to See Where Activity Is Moving

Reading a trend means comparing the state of a market over time and across place, and the cohort analysis is built for exactly that, letting teams contrast groups of providers to see how diagnosis and procedure activity differ and shift.

The Territory Manager adds the geographic layer, building and managing territories nationwide with heat-map analysis and driving-distance calculations, so a team can pinpoint the pockets where clinical activity is both concentrated and climbing.

Lead lists export to Excel, native HubSpot integration sits alongside Salesforce export and open API access, and the diagnosis-driven segments drop into the workflow a commercial team already runs.

Conclusion

Static market sizing will always tell a team which markets are big today, and it will always be weaker at flagging which ones are about to matter. Diagnosis trends fill that gap by exposing the rate and direction of clinical activity, which is where growing specialty markets announce themselves first.

Teams that read those trends well separate steep slopes from plateaus, tell real disease increases from detection artifacts, locate the geographic pockets where activity is concentrating, and tie every trend back to the providers actually treating patients.

None of this replaces judgment about treatability, competition, and fit. It gives that judgment something current and specific to work from, which is the whole difference between entering a specialty market as it forms and arriving once it has already been divided.

FAQs

What are diagnosis trends in healthcare?
Diagnosis trends are changes over time in how often specific conditions are diagnosed, usually tracked through coded claims data using ICD-10 diagnosis codes. They show the rate and direction of clinical activity for a condition rather than a single point-in-time count.

How can pharma companies use diagnosis data?
Because each diagnosis ties to a provider and a site of care, teams can measure diagnosis volume by physician, segment providers by the conditions they actually manage, and prioritize outreach around real clinical activity instead of specialty labels alone.

How does claims data help identify growing specialty markets?
Claims data captures diagnoses, procedures, and prescribing over time, so teams can measure whether activity for a condition is accelerating or flat. A steep, sustained upward slope in diagnoses signals an emerging market, while high but stable volume signals established demand.

What is specialty market analysis in pharma?
Specialty market analysis is the process of evaluating a focused clinical market, such as a therapeutic area or indication, to decide whether and how to enter it. It combines disease prevalence, diagnosis and procedure trends, the treatable population, competitive presence, and geography.

How can diagnosis trends identify geographic opportunities?
Disease burden and its growth cluster geographically rather than spreading evenly, so national averages hide local variation. Mapping diagnosis activity by region reveals pockets where a condition is most common or climbing fastest, such as the concentration of diabetes across southern states.

How does Alpha Sophia help pharma teams analyze healthcare market opportunities?
Teams can filter by individual ICD-10 codes, CPT and HCPCS procedures, taxonomy, and affiliations, then use cohort analysis and the Territory Manager to compare activity across provider groups and geographies.