Table 1.
Issues warranting addressing for AI-clinical documentation
| Issue | Potential strategies to address |
|---|---|
| Need to better characterise qualities of a good clinical note, particularly related to diagnostic assessment. | ► Convening discussions and promoting research among clinicians, educators, patients and quality, safety and informatics experts to leverage AI to better define, standardise and optimise note content, organisation and workflow efficiencies. |
| Balance note production efficiency versus clinician tailoring of notes in own voice. Reliance on assessment plans generated by AI may contribute to cognitive biases and diagnostic errors. | ► Time-saving/efficiencies of AI notes should be partially reallocated to enable dedicated reflection of clinical encounters and crafting/editing time. ► Use of direct dictation of assessment (eg, by clinician using voice recognition after visit) rather than relying on AI capture during visit. User-friendly ways to customise AI note templates to match the clinician’s narrative style and practice needs. |
| Errors in AI notes: how to minimise, detect and mitigate. | ► While generally rare, occasionally serious; requires vigilance to ensure careful MD review of notes before signing. ► Potential of advanced AI review tools to help detect and correct. ► Patients can play a role in identifying errors in notes, thereby improving note accuracy and patient safety.22 |
| How will trainees learn to write good notes? Risk of atrophy of note-writing skills. | ► Standardised curriculum and training requirements (including minimum number of supervised notes) on clinical documentation best practices before learners access AI to generate notes.23 ► Developing and teaching new paradigms for how to write notes in the AI era. |
| Omission of important elements (eg, richer social history (‘chit chat’), physical examination general description, diagnostic assessments) that may not be verbalised during encounters. | ► Vendor attention to this design shortcoming. ► Workflow during and after encounters to ensure missing elements are captured. Retraining clinicians to better verbalise for patients during encounters and ways to add post encounters.24 |
| Loss of information continuity from prior notes if starting with blank AI notes each visit. | ► Listening to and understanding clinicians who copy-pasted prior notes. ► Engineering AI note encounter workflow (including prepopulating information) to ensure prior problems are not lost. |
| Limitations of ‘one-size fits all’ AI notes | ► Deployment of new AI features that allow customisable templates, editing, pausing to insert dictated corrections. ► Making such customisation easier for users to unlock such features. |
| Balance/optimise note comprehensiveness and succinctness to achieve readability and usefulness. Achieve patient-friendly language while preserving medical precision/accuracy. | ► Collapsible note synoptic sections to view more versus less detail. ► Leverage AI capabilities to produce note summaries and versions tailored to patient’s health literacy level. |
| Patient privacy and trust (eg, risk of privacy breaches, erosion of patient trust with robotised AI notes) | ► Enhanced data privacy protections (regulatory, institutional, monitoring). ► Attention to AI note language and careful clinician editing of the AI-drafted notes before signing off is especially important in the era of open notes. ► User-friendly and responsive systems for patient feedback on any concerns/questions they may have regarding the content of their notes. |
| Dominance of for-profit commercial products limiting cooperative learning. | ► Public reporting of errors and transparency about shortcomings, including collating experience and understanding why some clinicians who have tried (often briefly) but abandon AI documentation. ► Regulation and oversight of standards. |
| Climate impacts of AI electricity and water demand. | ► While not unique to AI documentation, quality and safety informed healthcare need to be mindful of environmental impacts, thus work with broader efforts to minimise harms.25 |
| AI, artificial intelligence. | |