Artificial Intelligence (AI) is rapidly transforming healthcare, including one of the most critical aspects of medical malpractice and personal injury litigation: medical documentation. For attorneys, understanding how AI influences medical records is essential when evaluating case merit, preparing for trial, or conducting discovery.
AI in Medical Documentation
Modern electronic health record (EHR) systems increasingly integrate AI to improve efficiency. Common applications include:
Voice-to-text transcription: Physicians use AI-powered dictation to generate clinical notes in real time. Smart templates and auto-complete: AI suggests diagnoses, treatment plans, or billing codes based on partial entries. Predictive analytics: Systems may “flag” potential complications or recommend orders to providers. Clinical decision support: AI prompts reminders or cautions about medication interactions, lab results, or diagnostic pathways.
While these tools can save time and reduce clerical burden for clinicians, they also introduce unique legal implications.
Potential Legal Impacts for Attorneys
Accuracy vs. Automation Errors AI-generated notes may contain transcription mistakes, irrelevant “boilerplate” language, or incorrect auto-filled data. This can obscure what actually happened during a patient encounter, complicating causation analysis. Over-documentation and “Note Bloat” AI can generate lengthy, standardized notes that bury critical information under repetitive text. Attorneys may struggle to identify key decision points or deviations from the standard of care. Authorship and Responsibility If an AI system inserts information into the chart, questions arise: Who is responsible—the provider or the system vendor? Attorneys must carefully assess whether the record reflects the provider’s independent judgment. Metadata and Audit Trails AI-driven EHRs often store metadata showing when entries were created, modified, or auto-generated. This can provide valuable evidence in cases involving delays in diagnosis or treatment, but it also requires attorneys to request complete audit logs in discovery. Bias and System Limitations AI recommendations are only as strong as the data they’re trained on. If an algorithm reflects biases (for example, underdiagnosing in certain demographic groups), this may influence medical decision-making—and ultimately, case outcomes.
Practical Considerations for Attorneys
Ask for the Right Records: Don’t stop at the PDF printouts. Request audit trails, metadata, and AI system notes to uncover how documentation was created. Engage Experts Early: A legal nurse consultant or medical expert familiar with AI-driven documentation can help parse what was authored by the provider versus generated by the system. Scrutinize Templates: Identify whether “copy-and-paste” or automated fields may have obscured omissions, contradictions, or a lack of clinical reasoning. Challenge Assumptions: Don’t assume documentation is error-free simply because it looks polished. AI can make mistakes that human reviewers miss. Consider Vendor Liability: In some cases, AI vendors may be relevant to discovery if their software materially influenced documentation.
Final Thoughts
AI is reshaping healthcare documentation, offering both benefits and challenges. For attorneys, this evolution means approaching medical records with heightened scrutiny. By understanding how AI shapes the clinical narrative, legal teams can more effectively identify strengths, weaknesses, and liabilities in a case.
At Calm Journey, LLC, we specialize in helping attorneys navigate these complexities—analyzing medical records, uncovering hidden details, and clarifying how AI-driven documentation may influence your case strategy.
Heather
Heatheranne@calmjourney.org
Calm Journey 