Stop Guessing: How Specialty Labs Find the Exactly Right Doctors Using Diagnosis Data

For specialty labs—especially those dealing with complex areas like genetics, cancer, or rare diseases—the sales game has changed in 2026. The old way of doing things doesn’t work anymore.

Unlike basic labs that just want a high volume of routine tests, specialty labs need to find a needle in a haystack. You don’t need 5,000 doctors ordering one test a year. You need to find the narrow 1% of specialists who are treating the exact types of sick patients who need your specific diagnostic test every single week.

The problem is, these “perfect targets” are really hard to find using old methods. You can’t just look at a doctor’s job title. One “Oncologist” might treat basic cases, while another down the street is handling the complex, late-stage cases that require your lab’s advanced testing.

If you rely on titles alone, you miss the nuance trapped in fragmented systems. Overcoming this requires breaking down data silos in healthcare organizations to see what’s really happening.

So, how do you find them? You have to look at the patients they treat. You do this by using ICD-10 diagnosis codes. By grouping specific diagnosis codes together, labs can pinpoint exactly which doctors are seeing the patients who need their help right now.

Here is a simple guide on how to use diagnosis patterns to find these high-value doctors so your sales team can stop guessing and start selling.

Why the “Spray and Pray” Approach Fails

In the world of expensive, advanced diagnostics, generic targeting is a waste of time. A complex genetic test that costs thousands of dollars is only necessary for a very specific type of patient.

If your sales team is just targeting every doctor listed under “Hematology/Oncology,” they are wasting most of their day. The difference in what those doctors actually do day-to-day is massive.

To grow efficiently, specialty labs need to stop looking at who the doctor is (their title) and start looking at who the doctor treats (their patients). This requires a fundamental shift in how you analyze healthcare providers, looking past the diploma on the wall and into the reality of their daily practice.

Building “Diagnosis Clusters”: Defining the Patient

Step one is translating what your test does into the language of medical claims.

Patients who need advanced testing rarely have just one simple problem. They usually have a complex profile. Therefore, you can’t just look for one single diagnosis code. You need to build “Clusters”—groups of codes that, when seen together, paint a picture of the exact patient you are looking for. This approach is critical in highly targeted sectors like life sciences commercial intelligence.

Think of it like a recipe:

• The Routine Patient: Might just have the code for “Lung Cancer.”
• The Complex Patient (Your Target): Has the code for “Lung Cancer” + a code for “Metastasis to the Liver” + a code for “Previous Treatment Failure.”

By grouping these codes, you create a “digital fingerprint” of the exact clinical scenario where your test is necessary.

Finding the 1%: Who Sees the Most of These Patients?

Once you know what kind of patient you are looking for, you need to find who is treating them.

Many doctors across the country might see one or two patients fitting your profile a year. Those aren’t your best targets. You want to find the “power-users”—the doctors where these complex patients are concentrated.

By filtering national data against your specific diagnosis clusters, you can rank every doctor based on how many of these high-acuity patients they manage. You will likely find that a tiny percentage of doctors are treating the majority of your relevant patients. Focusing on them is the essence of maximizing ROI with targeted healthcare marketing strategies.

Helping Your Sales Team and Ensuring Payment

Selling complex tests isn’t like selling office supplies; it’s a clinical consultation. Your specialized sales staff (like Medical Science Liaisons) need to have high-level conversations.

When you give them this diagnosis data, you change their entire approach. They aren’t knocking on doors hoping to find a relevant patient. They are walking in armed with proof that the doctor is currently struggling with a difficult patient population that aligns with your lab’s test. Using data to anticipate needs like this is a prime example of the role of predictive analytics in healthcare sales.

Furthermore, this helps with insurance. Insurance companies often have strict rules about when they will cover expensive tests. By targeting doctors whose patients already match the insurance coverage guidelines, you make it much more likely your lab will get paid. This strategic alignment is a core component of modern MedTech commercial intelligence.

The Simple Workflow: Finding the Needle in the Haystack

How does a lab actually do this? It requires a platform like Alpha Sophia that can handle complex data. Here is the simple workflow:

Step 1: Define the Patient Profile

Your commercial and medical teams decide which combination of diagnosis codes spells “our ideal patient.”

Step 2: Find the Doctors

The platform searches national claims data to find every doctor managing patients who match that exact profile. You sort the list to find the doctors seeing the most of them.

Step 3: Check Current Behavior

Look at what those top doctors are currently ordering. Are they using an outdated test? Are they not testing at all? By analyzing these ICD-10 diagnosis trends alongside procedure data, you can spot the gaps your lab can fill.

Step 4: Activate the Sales Team

Give your field team the list of these top 1% targets, along with the data on why they were chosen. This becomes their “pre-call plan” for a highly targeted conversation.

Conclusion

For specialty labs, the era of chasing volume for volume’s sake is over. The path to success lies in precision—finding the specific clinical scenarios where your high-value test is a necessity. By shifting your strategy to look at diagnosis clusters, you ensure your expensive sales resources are spending their time only with the physicians who truly need to hear your story.

Frequently Asked Questions on Specialty Lab Targeting

Strategy & Concept

1. Why isn’t checking what tests they already ordered (CPT data) enough? CPT data shows what test was ordered, but not why. Diagnosis data (ICD-10) tells you the clinical reason. For complex labs, you need to know if the patient is sick enough to justify your high-value test, even if the doctor hasn’t ordered a similar test before.

2. What is an “ICD-10 Cluster”? It’s a group of diagnosis codes that, when seen together, define a specific type of complex patient. It goes beyond just naming a disease to include symptoms, complicating factors, or previous treatment failures that make the patient a candidate for your test.

3. How does this differ from finding “Key Opinion Leaders” (KOLs)? Traditional KOLs are often famous academics who publish papers. This data finds “clinical KOLs”—the doctors in the trenches who may not be famous but are managing the highest volume of relevant patients in the real world.

4. Why is “patient density” so important for specialty labs? Because selling these tests takes a long time and costs a lot of money. It’s inefficient to spend sales effort on a doctor who only sees one relevant patient a year. You need targets with a high density of relevant patients to justify the investment.

5. Can this help find patients for rare diseases? Yes, it’s often the best way. By grouping seemingly unrelated symptom codes that characterize an undiagnosed rare condition, labs can find physicians who are likely struggling to diagnose these patients.

Data & Workflow

6. How many codes should be in a cluster? There’s no set rule. It depends on how complex the condition is. It could be 3-5 codes for a specific cancer stage, or 10+ codes for a rare disease presentation.

7. How current is the diagnosis data? Like all claims data, it typically has a 60-90 day lag. This is standard and sufficient for identifying established clinical practice patterns and patient panel characteristics.

8. Can we see the specific patients’ names? No. Never. All data is completely de-identified to comply with HIPAA privacy laws. You see counts of unique patients managed by a doctor, but never any personal information.

9. How do we know which codes to pick for our cluster? Your team needs to look at the official clinical guidelines for your test. What conditions or symptoms make a patient eligible for it? Those are the codes you should be using.

Commercial Application

10. How do Medical Science Liaisons (MSLs) use this data? Sales reps might use volume for targeting. MSLs use the specific diagnosis data to prepare for high-level clinical discussions, tailoring their scientific conversation to the exact types of complex patients the physician is treating.

11. Does this help with getting paid by insurance? Yes. By targeting physicians whose patient diagnoses match insurance coverage guidelines, you increase the chances of getting paid and reduce paperwork headaches later.

12. Can this help launch a brand new test? Absolutely. This is its main use for new launches. Since no one has ordered the new test yet (no CPT history), you must find the physicians treating the target patient population using diagnosis data.

13. How does this integrate with our CRM (like Salesforce)? Modern platforms allow you to export these highly segmented lists of “power-users” along with their profile data, which can then be uploaded directly into your CRM for your field team.

14. How often should we update our target lists? Clinical guidelines change. You should review your ICD-10 clusters and re-run your targeting lists every 6 months or whenever there is a significant update to guidelines in your therapeutic area.

15. Is this approach good for routine labs? It’s less necessary for routine labs where volume is driven by general health needs. This approach is specifically designed for labs with high-value, specialized tests where the clinical need is narrow and specific.