A step-by-step view for mapping your extraction requirements onto a clinical-reasoning process. Each step shows the question it asks (its input) and the question it answers (its output), with a worked example and the kind of IT requirement it maps to.
For technical readers · not clinically technicalA discharge summary reliably records the proximal cause of a hospitalization — the thing that happened last. It rarely records the root cause — the change in the patient's life that set the proximal cause in motion. A system that captures only the proximal cause produces a tidy record and a patient who is readmitted next month. A system that surfaces the root cause routes to the intervention that actually prevents the next admission. That gap is where the money and the risk live — in readmission penalties, risk stratification, and care-management targeting. BRIDGE is a repeatable way to make that gap visible, and to say — with a 0–100 confidence score — how sure it is at each step.
A dialysis patient arrives fluid-overloaded 12 hours before their scheduled treatment. The salt in a bag of pork rinds drove the volume overload. Accurate — and useless for preventing the next one.
The spouse recently died; the patient can no longer drive; between buses he walks to the corner store and buys the cheap, salty food he can afford. Something changed — and it will keep changing the outcome until someone asks. That is a social-work referral, not a diet lecture.
Who is this patient at steady state — their usual labs, weight, medications, and routine?
What counts as abnormal for this person — turning a raw value into a signal.
What changed in the days-to-weeks before admission — new meds, foods, missed treatments, life events?
The proximal trigger — and whether the record even contains it. A silence here is itself a finding.
How did the working diagnosis shift during the stay, and what piece of evidence moved it?
The diagnostic trajectory — not just the endpoint — and who/what changed it.
Which objective findings actually prove the diagnosis — a culture, an image, a trended lab?
What is load-bearing vs. merely assumed — and how confident to be (the certainty score).
What was treated, what triggered discharge, and how certain is each diagnosis on the list?
An auditable discharge picture with a calibrated confidence on every diagnosis — no tidy list that hides its own doubt.
Do I believe this story is coherent — and what did the chart never say that I'd need to ask?
A coherence check plus a ranked list of the unasked questions — where the root cause lives.
| If your requirement is… | Step(s) | …this is what to build there |
|---|---|---|
| “Normalize a lab/value against the patient's own history, not a population range” | B | Patient-specific baselines with record linkage across prior encounters. |
| “Detect what changed before the admission (meds, diet, missed care, life events)” | R | Temporal windowing + change detection; ingest outpatient/ED/SDOH notes; flag when the trigger is absent. |
| “Show how the diagnosis evolved, with an audit trail” | I | Timeline/event model with per-claim provenance and note sequencing. |
| “Attach a confidence level and cite the evidence for each finding” | D · G | Evidence grading + calibrated confidence scoring, each claim linked to its source line. |
| “Parse the discharge summary into principal vs. secondary diagnoses” | G | Discharge-summary parser producing coded, certainty-tagged output. |
| “Catch contradictions the chart didn't reconcile” | E | Consistency/contradiction detection across notes (e.g., conflicting values or meds). |
| “Flag missing social-determinant / root-cause information and trigger a referral” | E | SDOH gap detection + a “known-unknowns” generator that emits questions and routes to care management. |
| “Reduce 30-day readmissions / improve risk stratification” | R · E | The proximal-vs-root distinction is the lever — Run-up finds the trigger, Evaluate finds why it happened. |