Ask any doctor which part of a clinical note takes the longest to write, and most will say the ‘Assessment and Plan’ — the section that requires the highest clinical reasoning and the most careful wording. Ironically, this is also the section where AI can add the most value: not by replacing clinical judgment, but by structuring and articulating it faster than any doctor can type. This article explores how AI is transforming the A&P section from a documentation burden into an auto-drafted clinical output that doctors can review and finalise in seconds.
Why the Assessment and Plan Is the Hardest Section to Write
The A&P section is where the doctor synthesises history, examination findings, investigations, and clinical reasoning into a coherent narrative — and then translates that narrative into a specific, actionable management plan. For a straightforward case like hypertension follow-up, this might take 90 seconds to write. For a complex multi-morbid patient with diabetes, CKD, and cardiovascular disease, it may require 5–7 minutes of careful documentation.
In high-volume Indian OPD settings, where a doctor may see 40 patients in 4 hours, spending even 3 minutes per A&P section adds up to 2 hours of pure documentation — per day. Many doctors resort to abbreviated, template-driven entries (‘DM2 — continue medications, review in 3 months’) that are fast to write but clinically thin, creating risk in medico-legal situations and limiting the usefulness of records for future care.
How AI Structures the Assessment Section
AI scribes trained on clinical corpora can extract the key elements of a consultation and formulate an Assessment section that mirrors how a well-trained clinician would write it. By analysing the conversation for chief complaint, history, examination findings (if verbalised), and prior diagnoses, the AI can generate text such as: ‘This is a 58-year-old male with known T2DM and hypertension presenting with 3-day history of worsening bilateral leg oedema. Assessment consistent with early decompensated cardiac failure on a background of poorly controlled DM2 (last HbA1c 9.2%).’
This level of documentation would take a busy doctor 2–3 minutes to write manually. The AI produces it in seconds, with the doctor needing only to verify accuracy and add any nuances the AI missed. The system also flags when critical information appears absent — for example, if an A&P references cardiac failure but no echocardiogram result was mentioned, the AI may prompt: ‘Echo findings not captured — please add if available.’
Auto-Generating the Plan Section with Clinical Safety Checks
The Plan section presents additional complexity: it must include medications (with doses and frequencies), investigations, referrals, lifestyle advice, and follow-up timing. AI scribe systems integrated with a drug database — such as the NRCeS database of 91,000+ medicines — can auto-populate prescription details as the doctor verbalises them: ‘Start Tab Metformin 500mg twice daily with meals’ becomes a structured, codified prescription entry, cross-referenced for drug interactions with the patient’s existing medications.
This integration between ambient documentation and prescribing is where AI scribes become genuinely transformative. Rather than the doctor writing the note, then separately entering the prescription into the EMR, the two processes merge into one: speak once, get both the clinical note and the structured prescription simultaneously. In pilot studies, this merged workflow reduces total per-patient administrative time by up to 60%.
Customising A&P Templates by Specialty
One size does not fit all in clinical documentation. A cardiologist’s A&P for a patient with atrial fibrillation looks very different from a paediatrician’s plan for a child with recurrent otitis media. AI scribes with specialty-specific template libraries allow doctors to pre-define the structure they want: NYHA classification for heart failure, GINA step for asthma, WHO growth percentile for paediatric cases.
When these templates are loaded into the AI system, the draft A&P comes pre-structured for the specialty — the doctor simply verifies that the fields are correctly populated rather than building the note from scratch. DoctorScribe.ai supports custom template creation for over 25 medical specialties, with pre-built templates for the most common conditions in each — designed specifically for Indian clinical practice and epidemiology.
📊 Key Facts & Statistics
| Metric | Data / Finding |
| Time to write A&P for complex patient (manual) | 5–7 minutes |
| Time to write A&P for routine case (manual) | 1.5–3 minutes |
| AI draft generation time for A&P section | 15–30 seconds |
| Doctor review and approval time (AI draft) | 20–45 seconds |
| Reduction in per-patient admin time (merged workflow) | Up to 60% |
| Specialty templates available in DoctorScribe.ai | 25+ specialties |
| NRCeS drug database integration (drug name lookup) | 91,000+ medicines |
🔄 AI-Assisted Assessment and Plan Generation Flow
| Step | Input | AI Action | Doctor Action |
| 1. Consultation | Doctor speaks with patient | AI listens and captures entities | Focus on patient |
| 2. Draft generation | Extracted symptoms + Dx history | Generates A&P draft in 15–30 sec | Continue with next patient |
| 3. Review | AI draft presented on screen | Highlights gaps and prompts | Read and verify draft |
| 4. Prescriptions | Verbally stated medications | Cross-checks drug interactions | Confirm or modify |
| 5. Finalise | Reviewed draft + edits | Formats into EMR-ready note | Sign and save (< 1 min) |
✅ Key Takeaways
- The A&P section is the most time-consuming part of clinical documentation and the highest-value target for AI.
- AI scribes generate structured A&P drafts in 15–30 seconds based on the spoken consultation.
- Drug database integration turns verbally stated prescriptions into structured, checked EMR entries.
- Specialty-specific templates ensure the AI draft fits cardiologists, paediatricians, and all others.
- The merged note-and-prescription workflow can reduce per-patient admin time by up to 60%.
📚 References
- Payne TH, et al. Transition from Paper to Electronic Inpatient Physician Notes. J Am Med Inform Assoc. 2010;17:108–111.
- Kaufman DR, et al. Supporting Clinical Cognition: A Human-Centered Approach to Medical Knowledge Engineering. AMIA; 2003.
- NRCeS National Formulary of India. New Delhi: National Resource Centre for EHR Standards; 2023.
- Holden RJ, et al. Cognitive Performance-Altering Effects of Electronic Medical Records. Applied Ergonomics. 2011;42(4):523–530.
- Soto-Perez-de-Celis E, et al. Clinical Documentation in Oncology. Lancet Oncol. 2017;18(4):e199.