Lecture 17 — EHR and claims data: the modality

Lecture 17 — EHR and claims data: the modality#

⚠️ AI-synthesized; not fully reviewed by course staff. Treat as a study aid; released slides, notebooks, and lecture recordings are authoritative.

Tue Mar 24, 2026 · Part 3 — Health data modalities · §3.1 EHR & claims

What this lecture is about#

This is the lecture where the course pivots from “ML in general” to “ML for health data.” Electronic health records (EHRs) and claims data are the largest and most-studied data modality in clinical ML, and they are also the modality where “the data are generated, not given” hits hardest.

We open with a statistical-trap warm-up — a Berkson’s-paradox-style example. Among hospitalized patients, two diseases that are independent in the general population can appear negatively correlated, simply because being hospitalized requires having one or the other (or both) seriously enough. The point: the moment you condition on “appears in the EHR,” you have changed the distribution you’re studying.

Then we walk through the data modality in detail:

  • What an EHR actually is. Diagnosis codes, procedure codes, medication orders, lab results, vital signs, clinical notes (covered in L19-L20), imaging (L21-L22), demographics. Each is its own sub-modality with its own data-quality profile.

  • What claims data are, and how they differ. Claims are billing records, not clinical records. They tell you what was billed, not what happened. They are also more comparable across institutions because billing standards are tighter than EHR conventions.

  • Coding systems and their hierarchies. ICD-9/10, CPT, RxNorm, LOINC, SNOMED-CT. Each is a tree (or a DAG) with its own granularity, ambiguity, and historical revisions. “The patient has ICD-10 code I50.9” is coarser information than “the patient has ICD-10 code I50.21.”

  • Who’s actually in the data. Insurance status, geographic catchment, language, socioeconomic status, healthcare-seeking behavior — all of these select who shows up in any given EHR. The observed cohort is not the population.

  • Health event streams. A patient’s record is a sequence of timestamped events at irregular times. The timing of an event (lab ordered, code recorded) is itself informative — sicker patients have more recordings.

  • Cohort selection as a modeling problem. “Patients with diabetes” sounds simple. Defining it from EHR data requires choosing a code set, an observation window, an exclusion list, and a denominator. Two reasonable definitions can yield very different cohorts, very different conclusions, and very different models.

  • Standards and interoperability. OMOP CDM (the OHDSI standard), MEDS (the modality-agnostic event-stream format), FHIR (the API standard). They help; they don’t eliminate the problems.

The paradox flagged is the Coastline paradox — a question about how the apparent length of a coastline depends on the resolution at which you measure it. The same is true of “number of features in an EHR”: at one level of aggregation a patient has 50 features; at another level, 50,000.

Why it matters#

Three reasons this lecture is the spine of the second half of the course:

The data are byproducts of care, not designed measurements. A vital sign appears in the EHR because someone took it. They took it because the patient was sick enough, or it was a routine check-in, or the protocol required it. Whether a measurement exists is informative. Whether a code is recorded is informative. Whether the patient came back for a follow-up is informative. None of this is iid sampling from a population distribution.

Cohort selection is where most ML-for-EHR papers go wrong before they start. “Predict mortality in patients with sepsis” — what counts as sepsis? Who’s excluded? What window? When does the prediction time start? Two papers can claim to study the same problem and not be comparable.

Standards (OMOP, MEDS, FHIR) are imperfect but worth investing in. They don’t make the data clean; they make it exchangeable. A model trained against MEDS-formatted data has a chance of running against another site’s MEDS-formatted data. A model trained against an institution-specific schema does not.

Things you should walk away believing#

  • The EHR is a record of care delivery and billing, not a record of biology. Patterns in it reflect both.

  • Missingness, frequency-of-measurement, and code-presence are themselves data, often the most informative data.

  • Cohort definitions are modeling choices. Document them. Validate them against external definitions when possible.

  • The observed population is not the target population. If you don’t think about this explicitly, your model will assume they are.

  • Coding systems are hierarchical and revisable. ICD-10 came after ICD-9, ICD-11 came after ICD-10 (2022 release, in transition globally); SNOMED-CT is updated quarterly. Models that ignore this break across institutions and across time.

  • OMOP, MEDS, and FHIR are worth the upfront cost. They are the closest thing the field has to a lingua franca.

How this connects to the rest of the course#

  • L18 (the next lecture) takes “EHR pathology” as given and asks: given that, how should we model with it?

  • L19-L20 (clinical text) zoom into one EHR sub-modality.

  • L21-L22 (medical imaging) zoom into another.

  • L23 (population health and survival) is what you reach for when EHR alone isn’t enough.

  • L24 (causality and fairness) returns to who’s in the data and why.

  • L28 (recap) revisits the EHR-as-byproduct claim.

Study guide#

Key terms, self-check questions, and additional resources for active recall.

Key Terms#

Term

Definition

EHR (Electronic Health Record)

Clinical record system used by hospitals/clinics; contains diagnoses, procedures, medications, labs, vitals, notes, imaging. Created by clinicians during care.

Claims data

Billing records submitted to payers. Tells you what was billed, not what happened. More comparable across institutions because billing standards are tighter.

ICD-9 / ICD-10 / ICD-11

International Classification of Diseases. Hierarchical disease-code system. ICD-10 is widely deployed (the US uses ICD-10-CM, which transitioned in October 2015); WHO released ICD-11 in 2022 and many countries are now in transition. Models trained on one revision do not directly read another.

CPT

Current Procedural Terminology. Procedure / service codes used for billing.

RxNorm

Standardized vocabulary for clinical drugs.

LOINC

Logical Observation Identifiers Names and Codes — for labs and clinical observations.

SNOMED-CT

Systematized Nomenclature of Medicine — Clinical Terms. Comprehensive clinical terminology.

OMOP CDM

Observational Medical Outcomes Partnership Common Data Model. The OHDSI-led standard for harmonizing EHR/claims data.

MEDS

Medical-Event-Data-Standard. Modality-agnostic event-stream format (timestamp, code, value).

FHIR

Fast Healthcare Interoperability Resources — HL7 standard for clinical-data APIs.

Berkson’s paradox

Conditioning on a collider (e.g., “patient was hospitalized”) creates spurious correlations. The canonical EHR pitfall.

Cohort selection

Defining the population a study/model applies to (e.g., “diabetic patients ≥ 40 with a recent A1C measurement”). A modeling decision, not preprocessing.

The Central Claim#

EHR data are byproducts of care, not designed measurements

A vital sign is in the EHR because someone took it. Someone took it because the patient was sick enough, or it was routine, or the protocol required it. Whether a measurement exists is informative. Whether a code is recorded is informative. Whether a patient came back is informative. None of this is iid sampling from a population distribution.

Cohort Selection As a Modeling Problem#

“Patients with diabetes” sounds simple

…but defining it from EHR data requires choosing: a code set (which ICD-10 codes? include E11.x but exclude E10.x?), an observation window (when?), an exclusion list (gestational diabetes? prediabetes?), a denominator (all patients in the system? those with at least one encounter?). Two reasonable definitions can yield very different cohorts and different conclusions. Document them.

Self-Check Questions#

  1. Patient A has a diagnosis of hypertension with three encounters in 2024. Patient B has the same diagnosis and one encounter. Why might these patients have systematically different downstream outcomes that are not about disease severity?

  2. Why is “missing lab value” not the same kind of missingness as “missing answer on a survey”? Give one concrete clinical example where the fact of measurement is more informative than its value.

  3. ICD-9 → ICD-10 transition (US: October 2015) added many codes and changed the structure. What does this do to a model trained on pre-2015 data and deployed in 2025?

  4. OMOP, MEDS, FHIR. Match each to: “common data model for harmonizing EHR + claims across sites” (OMOP is a CDM, not a billing-only dataset), “modality-agnostic event-stream format,” “HL7 API standard for clinical interoperability.”

  5. Berkson’s paradox in one sentence: how can you avoid it when defining your study cohort?

Additional Resources#

Clinical Context#

Who’s actually in your dataset?

Insurance status, geographic catchment, language, socioeconomic status, healthcare-seeking behavior — all select who shows up in any given EHR. The observed cohort is not the population. If you don’t account for this, your model assumes they are the same.