What happened to patients
already treated.
The procedure, the medications, the side effects, the course afterward. It is all written down, in narrative, across encounters, in records nobody has time to read at scale. We follow that thread through the record and show the passage behind every finding.
Retrospective, not predictive. Evidence, not discovery.
The most underused clinical dataset is the one already in the chart.
Structured fields capture what is billable. The clinically interesting part (how the patient responded, what was tolerated, what was stopped and why, what the physician noticed and wrote down) lives in narrative text. Reading a chart properly takes most of an hour, which is why it is done on a sample of high-value records and not on the rest.
Manual
Comprehensive chart review is measured in tens of minutes per record. That cost sets the ceiling on how many records get looked at.
Inconsistent
Two competent reviewers reading the same chart do not always extract the same facts. Inter-rater variability is a known limitation of manual abstraction.
Incomplete
Under time pressure, information buried deep in a long record is missed, and the buried part is often the part that mattered.
Unshared
What a health system learns about how a therapy performed in practice rarely reaches the people who made it, in any usable, structured form.
Why "throughline"
A throughline is the single thread that runs through a whole story. In a patient's record it runs across encounters: what was done, what was given, what was tolerated, what changed and why. Each visit is written down separately. We assemble them into one line under a de-identified token, so the course of treatment can be read as a course.
Three audiences, three different questions.
Throughline is in development. What follows is what it is designed to answer, stated as design intent rather than as a shipped feature list.
For clinicians and quality teams
Better ways to treat and diagnose. Across a population you already serve: which treatment pathways were followed, what varied between them, and what the documented course looked like afterward. Where a diagnosis was reached late, what appeared in the record before it.
- Care-pathway variation surfaced from the notes, not from claims alone
- Documented outcomes and complications assembled per cohort
- Earlier-signal review: what was present in the record before the diagnosis was made
For research organizations
Structured evidence out of unstructured records. Cohort assembly against real inclusion criteria, with abstraction that shows its work: each extracted variable traceable to the passage it came from, so a reviewer can verify rather than trust.
- Cohort identification from narrative, not just coded fields
- Variable-level provenance for every abstracted value
- Consistent extraction across reviewers and across time
For manufacturers
Real-world usage feedback. De-identified, aggregated signal on how a product was actually used and tolerated in practice: dosing as administered rather than as labeled, concomitant therapy, documented side effects, and the reasons treatment was changed or stopped.
- Usage patterns in practice versus label
- Documented adverse experience and tolerability signal
- Discontinuation and switching, with the stated reason where the record gives one
On manufacturer reporting specifically
Anything returned to a manufacturer is de-identified and aggregated at the population level, under the customer's own governance and data-use terms, and never as patient-level records. Signal that could constitute a reportable adverse event is a human escalation path, not an automated output: the system routes it to a qualified reviewer and does not decide for them. Pharmacovigilance reporting is a regulated obligation belonging to the parties who hold it, and we do not discharge it on anyone's behalf.
In development, and saying so.
We would rather be believed than impressive. Throughline is not in production, and there is no published result or validation study yet.
Design and build. The method is specified: longitudinal assembly under a de-identified token, passage-anchored extraction, and escalation rules written as configuration.
Cohort-level analysis against a design partner's real questions, with every extracted variable reviewable by a qualified person before anything is reported.
A clinical or research counterpart to define the questions worth asking. We will not invent clinical priorities on our own.
If you hold the records and have the question, talk to us.
Health systems, quality teams, research organizations, and medical affairs groups. Tell us what you are trying to learn from care that has already been delivered.