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Medical Record Summarization

Description: Summarize patient data and get concise information based on medical history Problem In Argentina, 33% of a physician’s time  during consultations is spent...

by Jose Zea•2 min read

Description: Summarize patient data and get concise information based on medical history

Problem

In Argentina, 33% of a physician’s time  during consultations is spent recording and reviewing data in Electronic  Health Records (EHRs) instead of direct interaction with patients [1].

In Colombia, EHR design does not always  facilitate the immediate understanding of complex data, leading to errors in clinical decision-making [2].

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Problem Size

Globally:Physicians spend an estimated 2–4 hours daily using EHRs, equating to 50% oftheir workday [3].

In Latin America: Studies highlight uneven adoption of EHRs, frequent errors, andprolonged data retrieval times [3].

Human Cost:Administrative burdens lead to burnout in 63% of physicians using EHRs [4].

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Solution

Automated Summaries: Extract and present the most relevant clinical history  information in a condensed and prioritized format.

Intelligent Data Analysis: Identify patterns and correlations in patient data to  support decision-making.

Ease of Integration: Operates on existing EHR platforms without disrupting  workflows [3].

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Opportunity Cost

Time Saved: Physicians could save 2–3 hours daily, allowing more patient care or focus on criticaltasks [1].

Efficiency: Lack of optimized tools perpetuates diagnostic errors, increasing healthcare costs by up to 50% per patient due to unnecessary tests and treatments [2].

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Impact

Administrative Time Reduction: AI can cut EHR management timeby 40% [1].

Improved Diagnostic Precision: Enhances identification of complex conditions by 25% through automatic detection of critical data [3].

Professional Satisfaction: Reduces burnout by 30% through decreased administrative workload, improving work-life balance [4].

Economic Impact: Healthcare systems could save up to$12,000 per physician annually by optimizing resources and reducing errors [2].

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Data Sources

The assistant is based on PubMed, which offers a wide range of biomedical research data, ensuring evidence-based insights. Additionally, the assistant integrates electronic health records (EHR), clinical trial databases, and patient medical records to provide a multifaceted view of each patient's medical history, ensuring summaries are accurate, relevant, and useful in the diagnostic process.

Citations

  1. Fundación Femeba. (2022). Time Spent by Physicians on the Use of Electronic Health Records During Outpatient Visits. Retrieved     from https://www.fundacionfemeba.org.ar/blog/farmacologia-7/post/tiempo-del-medico-empleado-en-el-uso-de-la-historia-clinica-electronica-durante-los-encuentros-ambulatorios-47475
  2. National Association of Medicine of Colombia. (2023). Electronic Health Records: A Challenge for Medicine in Colombia. Retrieved     from https://anmdecolombia.org.co/historia-clinica-electronica/
  3. El Bosque University. (2021). Evaluation of the Impact of Electronic Health Record Use on Medical Care in Colombia. Retrieved     from https://repositorio.unbosque.edu.co/server/api/core/bitstreams/9b73dc76-bc39-4cae-aaa4-7ef91051124e/content
  4. IntraMed. (2023). Time and Quality in Health Record Management: What Does the Evidence Say? Retrieved from https://www.intramed.net/content/95371

Video

https://www.tella.tv/video/clw9vh65p003q09k150lb4gf3