IT Trends and Issues in Healthcare

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Questions and Answers

What does AI help to improve in radiology?

  • Increase patient wait time
  • Prevent doctors from seeing patients
  • Reduce interaction time (correct)
  • Minimize use of CT and MRI

AI algorithms have no impact on the early detection of cancers.

False (B)

Name one area in psychiatry where AI applications are utilized.

Anxiety or depression

AI uses Medical Learning Classifiers (MLCs) to aid in _____ diagnosis.

<p>disease</p> Signup and view all the answers

Which imaging technique is enhanced by Deep Learning in dermatology?

<p>Face photography (B)</p> Signup and view all the answers

Natural Language Processing (NLP) can help identify drug interactions.

<p>True (A)</p> Signup and view all the answers

What is one key development area for AI technologies in primary care?

<p>Predictive modelling</p> Signup and view all the answers

Match the following AI applications with their healthcare areas:

<p>Google DeepMind = Breast cancer detection Natural Language Processing = Drug interactions Medical Learning Classifiers = Disease diagnosis Deep Learning = Dermatology improvements</p> Signup and view all the answers

What is the primary role of Artificial Intelligence in Electronic Health Records (EHR)?

<p>To interpret records and provide updated information about diseases (A)</p> Signup and view all the answers

Predictive modeling of EHR can achieve up to 50% accurate data.

<p>False (B)</p> Signup and view all the answers

What does EHR stand for?

<p>Electronic Health Records</p> Signup and view all the answers

Artificial Intelligence can help distinguish between a heart attack and __________.

<p>myocardial infarction</p> Signup and view all the answers

Match the following concepts with their descriptions:

<p>Electronic Health Records (EHR) = Digitalized patient data management Artificial Intelligence = Data interpretation and predictive analysis Predictive modeling = Risk assessment based on historical data Traditional alternatives = Prior non-digital healthcare methods</p> Signup and view all the answers

Which of the following is an application of Artificial Intelligence in healthcare?

<p>Analyzing treatment techniques (C)</p> Signup and view all the answers

Robotics in healthcare is primarily used for administrative tasks.

<p>False (B)</p> Signup and view all the answers

What technology is primarily used in precision medicine to predict successful treatment protocols?

<p>Machine Learning</p> Signup and view all the answers

Natural Language Processing helps in analyzing unstructured clinical notes and preparing _____ reports.

<p>clinical</p> Signup and view all the answers

Match the AI technologies with their functions in healthcare:

<p>Machine Learning = Predicts treatment protocols Natural Language Processing = Classifies clinical documents Robotics = Assists in surgeries Robotic Process Automation = Updates patient records</p> Signup and view all the answers

What is a common use of Rule-based Expert Systems in healthcare?

<p>Guiding clinical decision-making (D)</p> Signup and view all the answers

Robotic Process Automation (RPA) is primarily used for decision-making in complex patient cases.

<p>False (B)</p> Signup and view all the answers

What role do electronic health records play in AI's use in healthcare?

<p>They gather past data for disease prevention and diagnosis.</p> Signup and view all the answers

Flashcards

EHR in healthcare

Electronic Health Records (EHR) are a key digital tool in modern healthcare, used to store and manage patient information.

AI in EHR

Artificial Intelligence (AI) helps interpret EHR data for disease diagnosis and personalized treatment plans.

Predictive modelling

Using EHR data to predict future health risks for patients and their families.

EHR data accuracy

Studies suggest that predictive modeling using EHR data can achieve up to 75% accuracy.

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EHR growth

The number of online health records is predicted to double every five years.

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Machine Learning (in AI)

Predicting the best treatment for a patient based on their characteristics and treatment context. A key part of precision medicine.

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Natural Language Processing (NLP)

Analyzing and classifying medical documents and notes. Helps in preparing reports.

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Robotics in Healthcare

Using physical robots with AI to help surgeons in operations, improving their vision and precision.

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Rule-Based Expert System

A system using if-then rules, commonly used for tasks like Electronic Health Records (EHR).

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Robotic Process Automation (RPA)

Automating repetitive tasks like updating patient records or billing, often with other tech.

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Electronic Health Records (EHR)

Storing and managing patient health information digitally. Part of healthcare recordkeeping system.

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Precision Medicine

Using patient characteristics to predict and tailor treatment protocols.

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AI in Radiology

AI in radiology reduces doctor interaction time, letting them see more patients by using imaging technologies like CT and MRI.

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AI in Screening

AI algorithms, like Google DeepMind, assist in early cancer detection (breast and prostate), surpassing human expertise.

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AI in Psychiatry

AI is being explored to understand and analyze anxiety and depression, but is still in the proof-of-concept phase.

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AI in Primary Care

AI is used in primary care for predictive modeling, analytics, and decision support.

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AI in Disease Diagnosis

AI uses Medical Learning Classifiers (MLCs) to aid doctors in diagnosing patients using electronic health records.

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AI in Dermatology

AI enhances dermatology by employing deep learning in image processing, making it more effective than traditional methods.

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AI in Drug Interaction

AI algorithms, using NLP and machine learning, help identify and understand potential drug interactions.

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AI in Drug Manufacturing

AI accelerates drug development, particularly for conditions like OCD, significantly improving the speed and efficiency over traditional methods.

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Study Notes

  • Learning Objectives:
    • Recognize IT trends in healthcare issues.
    • Recognize IT issues emerged in healthcare.

AI in Healthcare

  • Artificial Intelligence (AI): A branch of computer science aiming to enable computer systems to perform tasks similar to humans.
  • Healthcare Applications:
    • Analyzing treatment techniques for diseases and prevention.
    • Used in diagnosis, drug research, patient monitoring, etc.
    • Gathering past data from electronic health records for disease prevention and diagnosis.

AI Technologies in Healthcare

  • Machine Learning (Neural Network/Deep Learning): Primarily used for precision medicine, predicting the best treatment protocol based on patient characteristics and treatment context.
  • Natural Language Processing (NLP): Creates, understands, and classifies clinical documents and research to analyze unstructured clinical notes for reports.
  • Robotics: Enables physical robots to perform tasks in healthcare, like surgical procedures (gynecological, prostate, and head/neck surgery) improving surgical accuracy and efficiency.
  • Rule-Based Expert Systems: A collection of "if-then" rules widely used in the commercial sector and electronic health care (EHR). Rules are created by human experts/knowledge engineers. However, if knowledge changes, rules become complex and time-consuming.
  • Robotic Process Automation (RPA): Used for repetitive tasks like updating patient records or billing, and can extract data when combined with other technologies.

Roles of AI in Healthcare

  • The presentation shows different roles of AI in healthcare, including radiology, screening, psychiatry, primary care, and electronic health records (EHR). The presentation also lists specific subcategories, such as disease diagnosis, dermatology, drug interaction, or manufacturing drugs.
  • Radiology: CT and MRI technology, reduced interaction time, and see more patients simultaneously.
  • Screening: Example, an AI algorithm called Google DeepMind which can detect breast cancer at early stages, potentially preventing severe stages. Accurate detection of prostate cancer.
  • Psychiatry: AI applications study anxiety and depression, highlighting ethical and regulatory questions.
  • Primary Care: AI technologies such as predictive modeling, business analytics, and supportive decision-making.
  • Disease Diagnosis: AI uses Medical Learning Classifiers (MLCs) to aid doctors in diagnosis, leveraging electronic health records (EHR).
  • Dermatology: An advanced imaging technique utilizing deep learning in image processing offering improved efficiency and diagnosis compared to traditional methods. AI can detect skin cancer from face photographs.
  • Drug Interaction: Algorithm improves using NLP; Machine learning and Deep learning techniques are being used for identifying, extracting, and discovering drug interactions.
  • Drug Manufacturing: AI helps expedite the processes for drug molecules, especially for conditions like obsessive-compulsive disorder (OCD).

Electronic Health Records (EHR)

  • Digitalization of Healthcare: EHRs are a critical component for developing a fully digitalized healthcare sector.
  • Predictive Capabilities: AI can analyze patient data, including family history, to predict disease risks and improve diagnosis accuracy. The AI uses existing patient data to create new rules for diagnosis and identify patterns that help with diagnosis.
  • Predictive modeling via EHR data can provide up to 75% accurate data and this increases the number of online health records.

Question

  • Given continuous IT trends, identify possible issues in healthcare.

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