AI-Powered EHR Summaries in Latin America, Reducing Administrative Burden and Improving Clinical Decisions

AI-Powered Automated Summarization of Electronic Health Records in Latin America

This use case describes the implementation of artificial intelligence (AI) to automate summarization and intelligent analysis of electronic health records (EHRs), aiming to reduce administrative workload for physicians in Latin America and improve clinical decision-making.

Problem

In Argentina, 33% of a physician’s time during consultations is spent recording and reviewing data in EHRs instead of interacting with patients. In Colombia, EHR systems do not always facilitate immediate understanding of complex data, leading to errors in clinical decision-making.

Problem Size

• Globally, physicians spend an estimated 2–4 hours daily using EHRs, representing about 50% of their workday.
• In Latin America, there is uneven EHR adoption, frequent data entry errors, and prolonged data retrieval times.
• Administrative burdens contribute to burnout in 63% of physicians using EHRs.

Solution

• Automated Summaries: Extract and present the most relevant clinical history information in a condensed, prioritized format.
• Intelligent Data Analysis: Identify patient data patterns and correlations to support clinical decisions.
• Ease of Integration: Operate seamlessly on existing EHR platforms without disrupting clinical workflows.

Opportunity Cost

• Time Saved: Physicians could regain 2–3 hours daily, enabling more patient care or attention to critical tasks.
• Efficiency: Without optimized tools, diagnostic errors persist, increasing healthcare costs by up to 50% per patient due to unnecessary tests and treatments.

Impact

• Administrative Time Reduction: AI can decrease EHR management time by 40%.
• Improved Diagnostic Precision: Automated detection of critical data improves diagnosis of complex conditions by 25%.
• Professional Satisfaction: Reduces physician burnout by 30% through lowered administrative workload and improved work-life balance.
• Economic Impact: Healthcare systems could save up to $12,000 per physician annually by optimizing resource use and reducing errors.

This AI-driven workflow offers substantial benefits for healthcare providers by enhancing operational efficiency, diagnostic accuracy, professional satisfaction, and economic performance, addressing major challenges in Latin American health systems. (Assumption: Impact values may vary based on local implementation and EHR adoption rate.)

Data Sources

The solution uses data from PubMed for clinical evidence, electronic health records (EHRs) for real-world patient information, clinical trial databases, and patient medical records for comprehensive insights. These sources ensure accurate, relevant, and practical summaries to improve diagnostic processes.

References

• Fundación Femeba. (2022). Time Spent by Physicians on the Use of Electronic Health Records During Outpatient Visits.
• National Association of Medicine of Colombia. (2023). Electronic Health Records: A Challenge for Medicine in Colombia.
• El Bosque University. (2021). Evaluation of the Impact of Electronic Health Record Use on Medical Care in Colombia.
• IntraMed. (2023). Time and Quality in Health Record Management: What Does the Evidence Say?

Prompt:

You are a clinical summarization and decision-support assistant for LATAM physicians. Goal: cut EHR time, reduce errors, and support precise decisions by producing concise, prioritized summaries and pattern detection from EHR data. Data sources available: the patient’s EHR, clinical trial databases, patient medical records, and PubMed for evidence. Responde en español neutro. Inputs (provided to you): país (ARG/CO/otro LATAM), especialidad, tipo de visita, ventana temporal, motivo de consulta, demografía, antecedentes (PMH/PSH/Fam/Soc), alergias, medicación, problemas activos, signos vitales, laboratorio, imágenes, notas, procedimientos, hospitalizaciones, EHR snippets. Instructions: - Parsear y unificar datos; resolver conflictos; priorizar lo más reciente y de alta confiabilidad; marcar inconsistencias. - Generar un resumen clínico priorizado y una breve línea de tiempo. - Detectar patrones y correlaciones (riesgos, interacciones, duplicidades, criterios diagnósticos probables). - Proponer próximos pasos accionables con racional clínico y nivel de evidencia. - Citar evidencia con PMIDs/DOIs/URLs de PubMed u otras fuentes confiables y anotar procedencia EHR. - Señalar vacíos de información y preguntas de aclaración. - No inventar datos; expresar incertidumbre; no sustituye juicio clínico; preservar privacidad. Output structure (máximo total 500 palabras; resumen ≤250; viñetas, sin redundancia): 1) Ficha del paciente (edad/sexo/país/visita). 2) Prioridades clínicas (máx. 5, con justificación breve). 3) Línea de tiempo (hitos relevantes con fechas). 4) Medicación y alergias (riesgos/interacciones clave). 5) Datos objetivos destacados (vitales, labs, imágenes anormales). 6) Banderas de riesgo y chequeos de seguridad. 7) Evaluación y diferenciales (con confianza). 8) Próximos pasos recomendados (prueba/tratamiento/seguimiento) + racional + nivel de evidencia. 9) Vacíos de datos y preguntas al clínico. 10) Evidencia y citas (PMID/DOI/URL) + procedencia EHR. Además, incluir un bloque JSON compacto: { patient_id, country, top_priorities:[{name, reason, priority, confidence}], risks:[{type, detail}], recommendations:[{type, action, rationale, evidence:{pmid/doi, level}, urgency}], missing_data:[...], conflicts:[...] }