Diagnosis-to-framework in under a minute
Care plan drafting
PMC12195667: AI care plans in 35 seconds vs. about 30 minutes by hand (p < 0.001)
NANDA-I 2024-2026 has 267 approved nursing diagnoses. AI can draft a NANDA-compliant PES-format care plan (Problem, Related To, As Evidenced By) from a diagnosis in under a minute. This lesson teaches the care plan prompt pattern, shows a worked example with a real nursing diagnosis, and covers the one verification step that makes AI-generated care plans safe to use.
Worth knowing:The AI draft is a starting point, not a finished plan. The clinical value is in what you edit out, not what the AI generated. Knowing what to cut is the skill.
Staff nurses who write care plans manuallyNursing managers looking to reduce documentation burdenCNS and NP students learning NANDA documentation
Start Lesson 1 →3 minutes instead of 10
SBAR shift handoffs
SBAR reduces handoff communication errors by 75.5% (PMC12668328)
SBAR (Situation, Background, Assessment, Recommendation) is the clinical standard for shift handoffs. AI can produce a complete SBAR from a brief nurse-authored summary in under 3 minutes. This lesson teaches the handoff prompt pattern, covers HCA's finding that AI-assisted handoffs achieve 86% first-attempt accuracy, and shows what information must come from you for the output to be safe.
Worth knowing:The SBAR prompt is also a forcing function for your own clinical thinking. Structuring the input makes you notice what you do not know before the handoff.
Floor nurses on high-turnover unitsCharge nurses who receive handoffs from multiple staffAny unit working to reduce handoff-related adverse events
Start Lesson 2 →Grade 5.6, not Grade 12
Patient education at the right reading level
12% of US adults have proficient health literacy
Standard AHA patient education materials average Grade 12 reading level. Claude reduced them to Grade 5.6 in a NYU Langone pilot. This lesson teaches how to specify reading level, cultural context, and health literacy tier in a patient education prompt, and how to verify the output is actually readable, not just shorter.
Worth knowing:The Flesch-Kincaid score is not the full picture. A Grade 6 text with medical jargon is still inaccessible. This lesson shows the two-step check: score first, then jargon scan.
Nurses who write or review patient discharge instructionsPublic health nurses working with underserved populationsAnyone who has watched a patient nod at instructions they did not understand
Start Lesson 3 →AI as your second set of eyes
Medication reconciliation
81.6% of warfarin patients have an undocumented DDI at discharge
Medication reconciliation errors cause more preventable adverse events than any other nursing documentation task. This lesson teaches how to use an approved AI tool as a second-check tool for drug-drug interactions, contraindications, and dosing anomalies, and how to document the check in a way that protects the patient and your license. Includes the de-identification protocol: age range and medication list only, never a patient name.
Worth knowing:AI does not replace the pharmacist. It surfaces the questions worth asking the pharmacist. The workflow is: AI flags, nurse escalates, pharmacist confirms.
Med-surg and ICU nurses managing polypharmacy patientsNurses on units with high discharge volumesAnyone who has caught a reconciliation error after discharge
Start Lesson 4 →Defensible by design
Documentation that protects your patient and your license
NSO malpractice claims average $210,513; most involve documentation gaps
Defensible documentation is specific, objective, timely, and complete. AI can help with all four, but only if you give it the right inputs. This lesson teaches the documentation prompt pattern that produces legally defensible nursing notes, covers the AMREC framework (98.3% documentation accuracy in a 2025 study), and walks through a documented $9M nursing-documentation case as a failure autopsy.
Worth knowing:The documentation prompt is not a time-saver. It is a structured interview that forces you to articulate what happened before you write it. That structure is what makes the note defensible.
Nurses on high-acuity units where documentation is litigation exposureNursing managers who review documentation for completenessAnyone who has had a documentation gap brought up in a peer review
Start Lesson 5 →