Introduction to HealthInformatics
• Health Informatics integrates IT with
healthcare to improve the quality, safety, and
efficiency of patient care. It involves the
acquisition, storage, and use of health data for
decision-making.
It bridges the gap between clinical practice and information technology, enabling better patient outcomes
through data-driven insights.
2.
History and Evolution
•Initially focused on billing systems and hospital
records. Evolved into EHRs, telemedicine, and
AI-driven diagnostics. Now supports data-
driven healthcare systems.
The evolution reflects the growing need for integrated systems that support clinical workflows and patient
engagement.
3.
Core Objectives
• •Enhance healthcare delivery and outcomes
• • Improve communication among care
providers
• • Support clinical decision-making
• • Facilitate health research and policy-making
These objectives align with global efforts to digitize healthcare and make it more patient-centric.
4.
Electronic Health Records(EHRs)
• Digitized patient records accessible in real
time. Enable coordination, reduce errors, and
streamline workflows.
EHRs also support population health management and facilitate research through structured data.
5.
Telemedicine and Remote
Monitoring
•Use of technology to deliver care at a distance.
Includes video consultations, wearable
devices, and mobile health apps.
Remote monitoring helps manage chronic diseases and reduces hospital readmissions.
6.
Clinical Decision SupportSystems
(CDSS)
• Software that provides evidence-based
recommendations to clinicians. Enhances
diagnosis, treatment, and patient safety.
CDSS tools are integrated into EHRs to provide alerts, reminders, and diagnostic support.
7.
Health Information Exchange(HIE)
• Enables secure sharing of patient data across
institutions. Essential for continuity of care
and public health surveillance.
HIEs are critical during emergencies and for patients receiving care from multiple providers.
8.
Mobile Health (mHealth)
•Use of mobile devices to monitor health,
provide reminders, and collect health data.
Empowers patients and supports preventive
care.
mHealth apps promote self-care and allow real-time communication with healthcare providers.
9.
Big Data inHealthcare
• Large-scale analysis of health data to detect
trends, predict outbreaks, and personalize
treatments.
Big data analytics can identify at-risk populations and optimize resource allocation.
10.
Artificial Intelligence inHealth
Informatics
• AI is used for predictive analytics, medical
imaging, diagnostics, and automating
administrative tasks.
AI also assists in drug discovery and robotic surgery planning.
11.
Privacy and Security
•Protecting patient data is crucial. Regulations
like HIPAA and GDPR ensure data
confidentiality, integrity, and access control.
Cybersecurity measures include encryption, access logs, and multi-factor authentication.
12.
Interoperability Challenges
• Lackof standardization hinders system
integration. HL7 and FHIR protocols address
these issues to enable data exchange.
Interoperability ensures that patient data follows them across different care settings.
13.
Patient Portals
• Onlineplatforms that let patients view
records, book appointments, and
communicate with healthcare providers.
Patient portals increase transparency and encourage active participation in care.
14.
Wearable Technology
• Deviceslike smartwatches track vital signs,
activity levels, and chronic conditions, feeding
data into health systems.
Wearables contribute to early detection of health issues and personalized care plans.
15.
Health Analytics
• Analyzingpatient and population-level data to
improve care, reduce costs, and support
public health initiatives.
Analytics dashboards help administrators monitor performance and outcomes.
16.
Genomics and Personalized
Medicine
•Use of genetic information to tailor
treatments to individuals. Health informatics
manages and analyzes genomic data.
Genomic data is used to predict disease risk and guide preventive strategies.
17.
Training and Workforce
Development
•Healthcare professionals need training in
informatics tools, data literacy, and ethical
data handling.
Training programs include certifications in health informatics and data governance.
18.
Ethical and LegalConsiderations
• Concerns include consent, data misuse,
algorithm bias, and equitable access to digital
health tools.
Ethical frameworks guide the responsible use of AI and patient data.
19.
Future Trends
• Growthof AI, blockchain in medical records,
integrated IoT devices, and global health
informatics networks.
Future systems will be more adaptive, intelligent, and globally connected.
20.
Conclusion and KeyTakeaways
• Health Informatics is vital to modern
healthcare. It improves care quality, enhances
safety, and enables innovation through data.
Continued innovation in informatics will shape the future of healthcare delivery.
21.
Laboratory Information Systems
(LIS)
•LIS are specialized systems used to manage lab
operations and patient test data.
• Functions include:
• • Sample tracking and labeling
• • Test ordering and result reporting
• • Integration with EHRs and other systems
• • Quality control and audit trails
LIS also support regulatory compliance and laboratory accreditation processes.
