LESSON 2 - RELATION TO MEDTECH

Data Analytics

help in Medical Technology by transforming raw health-related into meaningful insights that supports clinical decision-making, diagnostics, research, hospital operation, and patient care

Electronic Health Record

what does EHR stands for?

Telemedicine

Population Health Management

Drug Discovery & Development

Real-Time Alerting

Electronic Health Records

Genomic Sequencing

Remote Monitoring

Predictive Analytics

Big Data Analytics in Healthcare

Clinical Diagnostics and Imaging

AI-powered analysis of imaging (e.g., MRI, CT scans) improves the identification of tumors, spinal deformities and vascular diseases

Predictive Analytics for Patient Care

Algorithms analyze patient history and vitals to predict risks, such as hospital readmissions or. potential deterioration, allowing for proactive interventions

Wearable Technology and Monitoring

data from continuous health trackers provide real-time, actionable insights into patient health, assisting in chronic disease management

Operational Efficiency

Hospitals use dat analytics to forecast patient demand, manage staffing, and optimized workflows

Drug Discovery and Personalized Medicine

Advanced algorithms analyze large genomic and chemical datasets to. pinpoint drug targets and predict treatment efficacy

Common Data Source

medical data comes from multiple systems within healthcare environments

Laboratory Information System (LIS)

• Blood Chemistry results

• Hematology and urinalysis reports

• Microbiology culture and sensitivity results

Electronic Health Records (EHR)

• patient demographics

• medical history

• Diagnoses and treatment plans

Medical Devices and Instruments

• Autom

Public Health a

Knowledge Discovery in Databases

What does the acronym KDD stands for?

KDD Process

is a structures approach used in data analytics to extract useful knowledge from large datasets

Data Selection

Choosing relevant laboratory and patient data

Data Preprocessing (Cleaning)

Removing missing or duplicate records

Data Transformation

-Normalizing lab values

-Aggregating test results by data or patient group

Data Mining

Applying statistical or machine learning techniques

Pattern Evaluation

-Identifying meaningful and clinically valid patterns

-Eliminating irrelevant findings

Knowledge Presentation

-Visualizing results using charts and dashboards

-Reporting insights to clinicians or administrators