What's missing, what's late, and what it means for the case.
The attorney sends the production on Monday. ABSON.ai typically ingests the full set overnight — 3,200 pages from seven facilities, including three hospitals, two outpatient clinics, a pharmacy, and a nursing facility.
3,200 pages from seven facilities — three hospitals, two outpatient clinics, a pharmacy, and a nursing facility.
Structured chronology by provider and facility, automated trend analyses for hemoglobin and creatinine, flagged omissions from the Standards Engine, medication reconciliation across facilities, and a draft expert memorandum with page citations.
The expert reviews the structured data, verifies flagged findings against source documents, refines the causation analysis, and edits the draft memorandum.
A completed opinion letter is released through the platform's secure work-product mechanism.
When ABSON's extraction layer reads a 58-year-old patient with a family history of colorectal cancer and no documented colonoscopy, it flags a finding. When the Standards Engine compares that patient's age and family history against ACS screening criteria and calculates a 13-year delay, it produces a cited, deterministic result — arithmetic applied to published guidelines, not a probabilistic inference.
Under oath, the distinction is significant. Extraction findings require the expert's independent verification. Standards Engine findings are independently auditable against the published standard that generated them. Each finding in the platform identifies which layer produced it so the expert knows exactly what to verify and what to cite.
The Standards Engine checks patient data against published screening criteria and intervention thresholds including USPSTF, ACC/AHA, ACS, ACG, and ASGE, with NCCN oncology algorithms planned under commercial license. When it flags a screening omission, that finding is arithmetic. The expert can cite the specific guideline and recommendation grade in their opinion.
Document classification, entity extraction, lab trending, medication reconciliation — pulled from source pages with citations.
Expert verifies against sourceArithmetic applied to published criteria — USPSTF, ACC/AHA, ACS, ACG, ASGE. Cited to the recommendation and its grade.
Auditable against the standardIn federal court and most state courts, expert testimony must satisfy the Daubert reliability standard. The tool the expert uses to analyze records becomes part of the methodology subject to scrutiny.
Doctor, you used ChatGPT to help form your opinions? Can you verify that the hemoglobin values it cited are accurate and not fabricated? Are you aware these models sometimes generate information that appears in no source document? Do you know whether your patient's records were retained after your session?
I used a HIPAA-compliant medical record analysis platform that extracted structured data from the records and checked it against published clinical guidelines. The comparison is deterministic — arithmetic applied to published criteria. The platform provides page-level citations for extracted data points, and I verified each one independently.
The expert's clinical judgment is the deliverable. The 35 hours of reading, extracting, cross-referencing, and organizing that precede that judgment are the cost.
ABSON compresses the mechanical work — document classification, entity extraction, chronology construction, lab trending, medication reconciliation — so the expert arrives at the clinical analysis phase with structured, cited findings rather than raw pages. The expert reviews what the platform extracted. The expert verifies what the Standards Engine flagged. The expert forms the opinion, drafts the conclusions, and signs the report.
The physician is always in the loop. The platform handles extraction, organization, and citation. The physician handles interpretation, judgment, and testimony. This structure also satisfies the Daubert reliability standard. The expert can explain their methodology as physician-verified, AI-assisted analysis rather than AI-generated opinion.
Document classification, entity extraction, chronology construction — pulled from the production into structured form.
Lab trending, medication reconciliation across facilities, cross-provider timelines — assembled with citations.
Clinical judgment, causation analysis, opinion formation — applied to structured, cited findings rather than raw pages.
The expert verifies, drafts the conclusions, and signs the report. The methodology is physician-verified, AI-assisted analysis.
What ABSON found in a synthetic malpractice file. A plaintiff firm uploaded a medical malpractice production on Monday evening — 3,200 pages from three hospitals, two outpatient clinics, a pharmacy, and a nursing facility.
Values trended across 22 lab panels from two facilities, showing a 14-month decline from 13.2 to 9.8 g/dL that three providers documented but none acted on. In manual review, constructing this trend requires holding 22 data points across sessions spanning weeks of reading.
22 panels · 2 facilities · 14 monthsScreening colonoscopy indicated at age 42 per ACS criteria, first performed at age 55. A 13-year delay. Calculated against the published guideline. The expert can cite the ACS recommendation, version, and recommendation grade.
ACS · 13-year delay398 days between a positive FIT result and the follow-up colonoscopy. This required cross-referencing a lab order in one provider's records against a procedure note 3,400 pages later in a different provider's records. In manual review, this gap is visible only if the expert happens to remember the FIT result when they reach the procedure note hours or days later.
398-day gap · cross-providerThe expert spent approximately 12 hours verifying findings, refining the causation analysis, and editing the memorandum. Total expert engagement: under 15 hours. Estimated time for manual review: 40 to 55 hours. The value was thoroughness as much as speed. Findings that depend on full-record context across providers and years were identified automatically rather than discovered by chance during a 50-hour manual review.
Projected timelines based on platform capabilities applied to a synthetic record set. Results on actual cases may vary. In any conflict between this summary and the Terms of Service, the Terms of Service control.
Economic projections based on typical case parameters. Results vary by case complexity, record volume, and workflow.
Platform pricing from $299/case. Experts pay nothing on firm-shared cases.
20-minute walkthrough. Bring a redacted case file or use our demo records. No commitment beyond the call.