If you’re starting your first job as a software engineer, designer, or product manager in healthcare, welcome. Truly. We need you.
🟢 Greens: Beginner
When you’re new to healthcare, there are a few things you’ll quickly get up to speed on, including types of health data and data privacy.
Types of health data
Start with the basics: the core categories of clinical data.
- A doctor visit → Encounter
- A blood draw → Lab
- An X-ray → Radiology report + image
- A medication → Med list entry
- A blood pressure reading → Vital
Same patient. Same event. Data ends up in different systems depending on the EHR, lab machine, imaging vendor, or workflow.
When you go to the doctor because your knee is unusually sore after a few days on the slopes, the provider is going to ask you questions about how you’re feeling. You may have your blood pressure measured, height and weight recorded, etc. All of this is going into the Electronic Medical Record software they use, which is why the provider is using a laptop and typing. If you have blood drawn, the sample will go to a lab, and the results will be stored in the lab’s software, sent back to the provider, and likely automatically stored in the EMR. If you get an X-ray, the results are stored in the X-ray machine’s software and may or may not be automatically stored in the EMR.
This is your first lesson in interoperability:
Clinical reality is simple; data reality is not.
Your job at this stage is to understand what the data represents, where it lives, and why systems disagree about the “source of truth.”
Data privacy
One of the first things you’ll experience in your first week is HIPAA training. It feels routine until you first see real patient data. A diagnosis. A lab result. A radiology report.
At that moment, something shifts.
You realize you’re being trusted with the most intimate data a human being has. This isn’t “a schema.” It’s someone’s life. And that responsibility will shape the rest of your career.
Also, remember that “the P in HIPAA stands for Portability.” Beyond data privacy, HIPAA addresses health data interoperability and patients’ access to their personal health information. PACS systems, and more.
🟦 Blues: Intermediate
I think of this level as gaining an understanding of APIs, agents, web scrapers, and the real healthcare software ecosystem, and how to retrieve data from EMRs, billing systems, PACS, and more.
Once you understand the data, you begin to understand why it doesn’t move easily.
This is the messy middle:
- FHIR APIs (powerful but inconsistent)
- Vendor APIs (Epic, Cerner, Athena, payer portals)
- Scrapers + agents (still everywhere because… reality)
- Release of Information (ROI) workflows (the backbone no one wants to talk about)
- Manual queues, PDFs, faxes (yes, still)
And you begin to see the ecosystem:
- Providers create and store data
- Payers request and evaluate data
- Patients want control of their data
- HIEs, clearinghouses, and networks move data
- Vendors create their own micro-walled gardens
Interoperability is rarely a technical problem. It’s an incentives problem.
◆ Black Diamond: Expert
This is when policy, standards, and the actual shaping of the future of healthcare are closely intertwined with your day-to-day job.
You begin to see how CMS, ONC/ASTP, and OCR shape the data landscape, payment models, API certification, information blocking, privacy enforcement, and more.
You learn how standards actually get made:
- HL7 workgroups
- FHIR Implementation Guides
- Da Vinci, Argonaut, CARIN
- CMS Interoperability Framework
- The stakeholders that show up in the room (and the ones who don’t)
You understand TEFCA, QHINs, and the meaning of “exchange purposes” in the context of nationwide data flow.
And you start asking different questions:
- How should clinical data move in the U.S.?
- Where should policy intervene vs. where should technology evolve?
- What incentives will finally align interoperability with patient needs?
At this level, you’re helping shape the future, which is pretty darn cool.
⛷️ Good Luck, you’ve got this!
Keep Reading
Nine years ago, I wrote a post titled “The First 90 Days for a Product Manager New To Healthcare”.
It’s fun to look back on that now and reflect on what’s mostly the same and the big improvements.
Below is a short reading list I often share with people who are new to health data or interoperability, or who are trying to understand why everything still feels so… harder than it should be.
1. The Fragmentation of Health Data — Travis May (Datavant)
🔗 https://medium.com/datavant/the-fragmentation-of-health-data-8fa708109e13
A big-picture walkthrough of where health data comes from, why it splinters instantly, and what it takes to stitch it back together.
2. The Graveyard of Interoperability Initiatives — Travis May
A look at decades of “this time interoperability will work!” attempts—and an honest take on what has to change for it to finally stick.
3. The Health API Landscape — Arjun Sethi (a16z)
🔗 https://a16z.com/2017/04/05/health-api-landscape/
A tour of the early API companies that started loosening the grip of legacy EHRs and helped health data move a little more freely.
4. Why the Fax Machine Still Rules American Healthcare — Sarah Kliff (Vox)
🔗 https://www.vox.com/health-care/2017/10/30/16228054/american-medical-system-fax-machines-why
The classic explainer for why healthcare still leans on fax machines—part technology gap, part incentives, part “it’s how we’ve always done it.”
5. The New Rules of Healthcare APIs — Kibbe & Kuraitis
🔗 https://thehealthcareblog.com/blog/2014/12/04/the-new-rules-of-healthcare-apis/
A foundational argument that APIs (and standards like FHIR) change the game by letting systems talk to each other without hand-built integrations.
More if you want to keep going:
6. A Common-Sense Guide to Health Data Interoperability — ONC/HHS
🔗 https://www.healthit.gov/topic/interoperability
A straightforward introduction to what interoperability actually means—beyond the buzzword.
7. The Case for a Health Data Utility — Civitas Networks for Health
🔗 https://www.civitasforhealth.org/insights/the-case-for-a-health-data-utility/
Makes the case for treating health data infrastructure like a utility: shared, reliable, and built to serve everyone.
8. Why Is It So Hard to Share Health Data? — Kaiser Health News
🔗 https://kffhealthnews.org/news/why-is-it-so-hard-to-share-health-data/
A simple, human-centered explanation of the legal, technical, and business friction that keeps data stuck.
9. FHIR Is Not Magic — Grahame Grieve
🔗 https://www.healthintersections.com.au/?p=2944
A gentle reality check from the creator of FHIR on what the standard solves—and what it doesn’t.
10. The Future of Accessing Your Health Data — Travis May
🔗 https://travismay.medium.com/the-future-of-accessing-your-health-data-e2624e63bdd8
A hopeful look at what health data access could be if we build the right pipes, policies, and incentives.
Kelly Taylor's Blog 