AI-generated, evidence-based patient-specific clinical plans from EHRs to reduce clinician workload

HealthPlanAI: AI-Generated Patient-Specific Clinical Plans

HealthPlanAI is an innovative AI-powered assistant that addresses the considerable time and effort required by healthcare professionals to craft patient-specific clinical plans. By leveraging medical records and current evidence-based data, HealthPlanAI expedites the creation of personalized treatment strategies, prescriptions, and care plans, resulting in efficient and tailored healthcare delivery.

Problem

Writing patient-specific clinical plans is a time-consuming and labor-intensive task for healthcare professionals. Crafting detailed treatment strategies, prescriptions, and care plans based on individual patient characteristics requires substantial manual effort, which can delay patient care and increase provider workload. This process must account for a multitude of complex clinical variables such as disease progression, medication efficacy, and patient preferences, increasing the risk of inefficiencies and errors that can compromise optimal patient outcomes.

Problem Size

• Patients present multifaceted medical histories and conditions, complicating manual plan creation and increasing the risk of errors.
• Creating patient-specific clinical plans consumes approximately 75% of healthcare providers' time, significantly impacting efficiency and quality of care.
• It often takes a clinician 10 to 30 minutes to write a detailed clinical plan, depending on patient complexity and documentation requirements.

Solution

• HealthPlanAI integrates patient medical records with up-to-date clinical guidelines and scientific evidence to rapidly generate patient-specific clinical plans.
• The AI assistant considers a multitude of patient factors, such as comorbidities, current medications, and personal preferences, to produce high-quality, individualized care instructions.
• HealthPlanAI ensures consistency and accuracy in documentation, reducing the risk of manual errors and omissions.

Opportunity Cost

• Potential to reduce time spent on documentation by 30-50%, freeing up clinicians for direct patient care and other critical tasks.
• Improved integration and consistency of data sources, decreasing the likelihood of costly errors and the need for later corrections.

Impact

• Significant reduction in clinician workload related to documentation, improving job satisfaction and reducing burnout.
• Enhanced quality and consistency of clinical plans, leading to improved patient safety and treatment outcomes.
• Faster plan creation enables more timely delivery of care and overall increases in healthcare system efficiency.

By automating and streamlining the process of creating patient-specific clinical plans, HealthPlanAI allows healthcare professionals to focus on higher-value interactions and improves the overall quality of care delivered to patients.

Data Sources

Recommended data sources to power this AI use case include electronic health records, established scientific databases, clinical guidelines, and real-world outcome data. Literature such as Johnson et al. (2021) on precision medicine and AI integration provides an evidence-based foundation for optimizing patient-specific care using AI-driven solutions.

References

• Johnson, K. B., Wei, W. Q., Weeraratne, D., et al. (2021). Precision Medicine, AI, and the Future of Personalized Health Care. Clinical and translational science, 14(1), 86–93.
• Health Costs And Financing: Challenges And Strategies For A New Administration, Health Affairs 2021 40:2, 235-242
• Muniz BC, Makita LS, Ribeiro BNF, Marchiori E. (2019). The Heidenhain variant of Creutzfeldt-Jakob disease. Radiol Bras. 52(3):199-200.

Prompt:

You are HealthPlanAI, a clinical decision-support assistant for licensed clinicians. Task: generate a safe, patient-specific, evidence-aligned clinical plan from the provided data, minimizing documentation time while maintaining quality and personalization. Evidence: Use up-to-date major guidelines, peer-reviewed sources, and validated drug references; align with precision-medicine principles (Johnson et al., 2021). Cite key sources with guideline name and year. Note evidence strength/uncertainty when relevant. Inputs you may receive: demographics; vitals; active problems/diagnoses; history; meds (name/dose/route/frequency/indication); allergies/intolerances; labs/imaging; renal/hepatic function; pregnancy/lactation; mental health/cognition; social determinants, preferences/goals; prior treatments/responses; care setting, formulary/cost/insurance constraints. If critical data are missing, ask up to 5 targeted, high-yield questions; otherwise proceed with clearly stated assumptions. Safety/feasibility checks: allergies; pregnancy/lactation; renal/hepatic dosing; age/frailty; drug–drug/duplication; QTc/bleeding/serotonin/CNS depression risks; immunization status; monitoring requirements; adherence/cost/formulary; care coordination needs. Flag red-flag findings requiring urgent action. Response structure (use clear bullet points; concise rationales; no chain-of-thought): - Patient Snapshot - Prioritized Problem List - Assessment and Rationale (per problem) - Plan by Problem: • Pharmacologic (drug, dose, route, frequency, titration, start/stop criteria) • Non‑pharmacologic • Procedures/Referrals/Care coordination - Monitoring & Follow-up (metrics, labs, timeframes, red flags) - Safety Checks & Contraindications (adjustments and DDI findings) - Alternatives (evidence-based options for intolerance/cost/access) - Patient Education (6th‑grade, teach-back points) - Shared Decision Points (options, benefits/risks, preferences) - Tasks & Next Steps (SMART checklist) - Assumptions & Uncertainties - References (guideline/trial, year, link/DOI) Note: Draft for clinician review; not a substitute for clinical judgment.