Podcast
Questions and Answers
CDSS is designed to increase the likelihood of late diagnoses.
CDSS is designed to increase the likelihood of late diagnoses.
False
CDSS can help improve operational efficiency by streamlining clinical workflows.
CDSS can help improve operational efficiency by streamlining clinical workflows.
True
AI systems can make decisions independently by learning on their own.
AI systems can make decisions independently by learning on their own.
True
The integration of wearable technology in patient-facing systems aids in glucose monitoring for diabetes management.
The integration of wearable technology in patient-facing systems aids in glucose monitoring for diabetes management.
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The Dartmouth AI Workshop was held in 1970.
The Dartmouth AI Workshop was held in 1970.
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Knowledge-Based Systems rely solely on real-time data analysis for generating solutions.
Knowledge-Based Systems rely solely on real-time data analysis for generating solutions.
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Automated tumor grading assists in cancer diagnosis and treatment planning.
Automated tumor grading assists in cancer diagnosis and treatment planning.
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IBM’s Deep Blue defeated Gary Kasparov in a game of Go.
IBM’s Deep Blue defeated Gary Kasparov in a game of Go.
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CDSS does not provide tailored diagnostic suggestions based on patient data.
CDSS does not provide tailored diagnostic suggestions based on patient data.
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Model-Based Systems are particularly flexible in handling unknown scenarios.
Model-Based Systems are particularly flexible in handling unknown scenarios.
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One of the benefits of CDSS is the ability to reduce duplicate tests.
One of the benefits of CDSS is the ability to reduce duplicate tests.
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Machine Learning can help in filtering out large volumes of irrelevant information.
Machine Learning can help in filtering out large volumes of irrelevant information.
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Deep learning in imaging is unable to detect diabetic retinopathy with high accuracy.
Deep learning in imaging is unable to detect diabetic retinopathy with high accuracy.
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CDSS assists healthcare providers by automating the selection of diagnostic codes.
CDSS assists healthcare providers by automating the selection of diagnostic codes.
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Alpha Go Zero developed its own strategies through learning after playing against human players.
Alpha Go Zero developed its own strategies through learning after playing against human players.
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The advancement of AI has been influenced by fields such as psychology and linguistics.
The advancement of AI has been influenced by fields such as psychology and linguistics.
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Classifiers are trained on unlabelled data sets to categorize new data accurately.
Classifiers are trained on unlabelled data sets to categorize new data accurately.
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Decision trees are a type of artificial neural network.
Decision trees are a type of artificial neural network.
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Natural Language Processing (NLP) is used to analyze structured data.
Natural Language Processing (NLP) is used to analyze structured data.
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Speech analysis extracts insights from recorded speech in a manner similar to image analysis.
Speech analysis extracts insights from recorded speech in a manner similar to image analysis.
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Clinical Decision Support Systems (CDSS) are designed solely for administrative functions in healthcare.
Clinical Decision Support Systems (CDSS) are designed solely for administrative functions in healthcare.
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Drug-Drug Interaction (DDI) alerts are part of patient safety applications in healthcare.
Drug-Drug Interaction (DDI) alerts are part of patient safety applications in healthcare.
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Diagnostic Decision Support Systems (DDSS) have been reported to achieve diagnostic accuracy as high as 93%.
Diagnostic Decision Support Systems (DDSS) have been reported to achieve diagnostic accuracy as high as 93%.
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Healthcare practices require decision-making that cannot be supported by computer programs.
Healthcare practices require decision-making that cannot be supported by computer programs.
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Study Notes
Introduction to AI
- AI is a computer program or system that can make decisions on its own, either through training or learning ability.
- AI has evolved from simply being faster than humans to making decisions using rules (expert driven) and learned data (data driven).
Brief History of AI
- AI advancements are driven by fields like engineering, biology, psychology, mathematics, philosophy, and linguistics.
- Alan Turing questioned the potential for computers to learn in 1950.
- The Dartmouth AI Workshop was held in 1956.
- While computers continue to advance, they often lack strategic thinking.
- IBM's Deep Blue defeated Gary Kasparov in chess in 1997 using pre-programmed rules and algorithms.
- Google's DeepMind created Alpha Go Zero in 2017, which defeated Go grandmasters by developing its own strategies through learning.
Foundational Concepts of AI
- Machine Learning (ML): An automated system that processes vast data volumes to extract information for practical problem-solving, including decision support.
- Knowledge-Based Systems: Use expert knowledge (rules/guidelines) for predefined solutions in established scenarios. They're best for known issues but can be prone to errors in novel situations.
- Data-Driven Systems: Leverages AI and machine learning to analyze large datasets, identify trends, and aid diagnosis. Highly adaptive but struggles with unknown scenarios and lacks flexibility.
- Model-Based Systems: Utilize simulations and mathematical models for predicting outcomes and optimizing decisions, ideal for planning and scenario analysis, but limited to pre-defined models.
- Classifiers (predictions): Processes classifying data into categories known by training on labeled data,allowing categorization of new unlabeled data.
- Decision Trees: A decision support method illustrating the decisions and paths based on attributes/characteristics.
- Linear Regression: A statistical method for modeling the relationship between variables displayed on a graph.
- Logistic Regression: A statistical method displaying the probability of an event occurring, displayed on a graph.
- Artificial Neural Networks: A machine learning algorithm modeled on biological nervous systems, comprising interconnected neurons that receive, process input and generate output (predictions).
- Natural Language Processing (NLP): Automated language analysis intended to parse unstructured text, respond to queries and extract organized data.
- Image Analysis: Extracts meaningful information from images, especially useful in visual analysis and identifying features.
- Speech Analysis: Similar to image analysis, extracting meaningful information from recorded speech.
Deep Learning Workflow
- Training data: Used for initial learning
- Feature extraction: The system extracts key features to understand the data.
- Machine learning model classification: The machine learning model sorts the data.
- Test data: Used to evaluate learning effectiveness.
Value: Information Synthesis
- Too much patient data, data complexity, and medical literature can make processing and analyzing information challenging.
Value: Augmenting Human Performance
- Rare diseases/unusual presentation issues can be difficult to diagnose.
- Pressure to make quick decisions is sometimes present.
- Interaction effects of different variables (medical, diseases etc.) can sometimes be hard to account for.
Value: Surveillance
- Public health concerns must be tracked on a rapid and accurate basis.
- Speedy spreading via travel can cause significant problems.
- Processes like post-market medical device safety must be well managed.
What is Clinical Decision Support System (CDSS)?
- A system that supports decision-making and provides healthcare providers to analyze clinical evidence/suggestions to improve healthcare and decision effectiveness.
Basis of CDSS
- Computers have long been appreciated for their ability to process vast amounts of data, including symptoms and possible treatment options.
- Effective healthcare relies on decision-making, hence computers have a direct (or indirect) impact on the quality of healthcare decision-making.
Applications of CDSS
-
Patient Safety:
- Drug-Drug Interaction (DDI) Alerts: Prevents harmful medication combinations.
- Barcode Medication Administration: Ensures accurate drug dispensing and administration.
-
Diagnostics:
- Diagnostic Decision Support Systems (DDSS): Peripheral neuropathy diagnosis (93% accuracy).
- Imaging Assistance: Guides appropriate imaging requests for various conditions (e.g., lumbar MRI for back pain).
- Laboratory Support: Combines test results for non-invasive liver fibrosis diagnosis.
-
Cost Optimization:
- Suggests cost-effective medication alternatives.
- Reduces duplicate tests/inpatient stays for increased efficiency.
-
Patient-Facing Systems:
- Personal Health Records (PHR): Tools for self-management.
- Wearable Technology Integration: Monitors glucose for diabetes management.
-
AI & Machine Learning:
- Deep Learning in Imaging: Detects diabetic retinopathy and tumors.
- Automated Tumor Grading: Assists in cancer diagnosis/treatment planning.
Benefits of CDSS
-
Diagnosis:
- Reduces human error in diagnostic decision-making.
- Prevents late diagnoses via automating test result analysis.
- Assists practitioners in interpreting medical images/pathology results.
- Tailored diagnostic suggestions based on patient data.
- Improves detection of complex conditions like cancer.
-
Prevention:
- Increases screening rates by automating/guiding prevention protocols.
- Facilitates early diagnosis & reduces long-term healthcare costs/complications.
- Mitigates disease severity via predictive algorithms & adherence to clinical guidelines.
-
Management & Planning:
- Streamlines clinical workflows, saving time, and reducing costs.
- Enhances hospital resource management (beds, surgical rooms, equipment).
- Improves documentation via automation (diagnostic codes, medical records).
- Assists in human resource planning (staff allocation).
- Supports drug & blood bank management.
- Guides patients in making informed decisions based on real-time data.
-
Prescriptions:
- Ensures safer & more effective medication practices by supporting drug selection, dosing, and monitoring.
Barriers to CDSS
- Interoperability Issues: Challenges integrating diverse systems/data effectively.
- Data Quality & Completeness: Inconsistent or incomplete data can lead to errors.
- Usability Concerns: Difficulty using or implementing the system effectively.
- Ethical/Regulatory Barriers: Concerns about patient privacy, accountability.
- Resistance to Adoption: Physicians may resist change due to concerns.
- Focus on Early Decision-making Phases: May not support more complex cases effectively.
- Insufficient Stakeholder Involvement: A lack of collaboration can hinder the system's effectiveness.
- Evaluation/Feedback Mechanisms: The lack of thorough assessment/feedback can prevent improvement.
CDSS in Malaysia
- Adoption is slower than Electronic Medical Records (EMR) in Malaysia.
- Some doctors feel threatened by CDSS as it may impact their workflow.
- Doctors' existing knowledge could be duplicated in CDSS.
- There are concerns about receiving instructions from the system.
- There's a misconception that CDSS will replace physicians.
Legal and Regulatory Considerations
- Uncertainty about liability for decisions made.
- Concerns about the safety of using these systems in healthcare.
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