AI-Powered Document Processing for Medical Malpractice Litigation

A single medmal case can generate thousands of pages of hospital records, SOAP notes, lab results, and imaging reports spanning years of treatment. Finding the clinical details that prove a breach in care means reading all of it. Medical malpractice record analysis at this volume is where cases stall or accelerate.

Beyond volume, medmal discovery presents unique analytical complexity. Your team needs to map the differential diagnosis process, evaluate treatment decisions against clinical practice guidelines, identify where the standard of care was breached, and trace a causal chain from the provider's actions to the patient's harm. In states requiring a pre-suit certificate of merit, you need this analysis organized before you can even file.

Doing all of this manually (reading every page, cross-referencing providers and dates, building a coherent timeline from disorganized records) consumes weeks of paralegal and nurse reviewer time per case. AI-powered medmal discovery processing eliminates that bottleneck.

Dodonai extracts key clinical events, organizes them chronologically, and produces detailed medical chronologies so you can verify any claim against the source document. This gives your team a structured foundation for identifying standard of care deviations, informed consent gaps, and the causation evidence your experts need.

With organized chronologies and deposition summaries generated in minutes, your attorneys can focus on evaluating whether the differential diagnosis was reasonable, whether clinical practice guidelines were followed, and whether the causal chain holds up.

Medical malpractice attorneys hold their work product to a higher standard than most practice areas, and they should. When a case hinges on whether a physician deviated from accepted clinical practice at a specific moment in the treatment timeline, a generic AI summary that skips dates or glosses over provider notes is worse than useless. We hear this concern often, and it shaped how we built the platform.

Dodonai does not produce a single fixed summary and call it done. Every medical chronology includes the granular clinical detail medmal attorneys depend on: individual provider entries, medication changes, lab values, diagnostic imaging findings, referral patterns, and informed consent documentation, each tied to a specific source page in the original records. If a nurse's note at 2:14 AM documents a change in patient status that the attending didn't act on until morning rounds, that gap shows up in the chronology with citations to both entries.

Where Dodonai goes further is complete customization of your output. Every firm works differently. Some want chronologies broken out by treating provider. Others need entries grouped by body system or organized around specific allegations. You can define custom data fields, choose narrative or tabular formats, build reusable templates that match your firm's preferred layout, and adjust the level of detail from high-level executive summaries down to line-by-line clinical extraction. The output conforms to your workflow, not the other way around.

For firms that want an extra layer of confidence, Dodonai supports a human-in-the-loop review process. Use AI to do the heavy lifting of organizing and extracting thousands of pages of records, then have your nurse reviewers or paralegals verify the structured output against the source documents in our side-by-side viewer. Your team spends their time on quality assurance and clinical judgment instead of data entry. It is the difference between spending 40 hours building a chronology from scratch and spending 4 hours reviewing one that is already 95% there.

The firms getting the most from AI-powered medical malpractice document processing are not the ones replacing human review entirely. They are the ones using AI to eliminate the repetitive extraction work so their experienced reviewers can focus on what actually requires medical and legal expertise: identifying the standard of care breach, evaluating causation, and building the narrative that wins at trial.