This lecture describes the uses of Computers in Epidemiology and Health. The topic has been made considering the basics for the undergraduate, and third-year students.
The unusual occurrence in a community or region of disease, specific health related behaviour (eg. Smoking) or other health related events (eg. Traffic accidents) clearly in excess of “expected occurrence.
Data
Information
Intelligence
Health information system
Sources of data
Census
Registration of vital events
Sample registration system
Notification of diseases
Hospital records
Disease registers
Record linkage
Epidemiological surveillance
Other health service records
Environmental health data
Health manpower statistics
Population surveys
Other routine statics related to health
Non – quantifiable information
Health management information system
Central Bureau of health Ingelligence
National health profile
WHO Reports
Global Health Observatory
World bank
Health stats
The unusual occurrence in a community or region of disease, specific health related behaviour (eg. Smoking) or other health related events (eg. Traffic accidents) clearly in excess of “expected occurrence.
Data
Information
Intelligence
Health information system
Sources of data
Census
Registration of vital events
Sample registration system
Notification of diseases
Hospital records
Disease registers
Record linkage
Epidemiological surveillance
Other health service records
Environmental health data
Health manpower statistics
Population surveys
Other routine statics related to health
Non – quantifiable information
Health management information system
Central Bureau of health Ingelligence
National health profile
WHO Reports
Global Health Observatory
World bank
Health stats
This presentation contains in brief about various Non-communicable diseases (NCDs) and International interventions to combat NCDs. It also contains recent updates on current problem statement of common NCDs and updates on National Programme for Prevention and Control of non-Communicable Diseases (NP-NCDs).
National framework for malaria elimination in indiaAparna Chaudhary
outlines India’s strategy for elimination of the disease by 2030. The framework has been developed with a vision to eliminate malaria from the country and contribute to improved health and quality of life and alleviation of poverty.
The purpose of community diagnosis is to define existing problems, determine available resources and set priorities for planning, implementing and evaluating health action, by and for the community.
This presentation contains in brief about various Non-communicable diseases (NCDs) and International interventions to combat NCDs. It also contains recent updates on current problem statement of common NCDs and updates on National Programme for Prevention and Control of non-Communicable Diseases (NP-NCDs).
National framework for malaria elimination in indiaAparna Chaudhary
outlines India’s strategy for elimination of the disease by 2030. The framework has been developed with a vision to eliminate malaria from the country and contribute to improved health and quality of life and alleviation of poverty.
The purpose of community diagnosis is to define existing problems, determine available resources and set priorities for planning, implementing and evaluating health action, by and for the community.
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
With the promises of predictive analytics in big data, and the use of machine learning algorithms,
predicting future is no longer a difficult task, especially for health sector, that has witnessed a great
evolution following the development of new computer technologies that gave birth to multiple fields of
research. Many efforts are done to cope with medical data explosion on one hand, and to obtain useful
knowledge from it, predict diseases and anticipate the cure on the other hand. This prompted researchers
to apply all the technical innovations like big data analytics, predictive analytics, machine learning and
learning algorithms in order to extract useful knowledge and help in making decisions. In this paper, we
will present an overview on the evolution of big data in healthcare system, and we will apply three learning
algorithms on a set of medical data. The objective of this research work is to predict kidney disease by
using multiple machine learning algorithms that are Support Vector Machine (SVM), Decision Tree (C4.5),
and Bayesian Network (BN), and chose the most efficient one.
Describes Indian Council of Medical Research, ICMR Institutes, importance of IT in health care, Health Information System and Mobile based Surveillance Quest using IT. For more information visit: http://www.transformhealth-it.org/
Telemedicine refers to the use of telecommunications technology, such as video conferencing and remote monitoring, to provide medical services and support at a distance. In the context of clinical research, telemedicine has become increasingly important and relevant.
Advantages of Telemedicine in Clinical Research:
Improved Patient Access: Telemedicine enables researchers to reach a broader pool of participants, including those in remote or underserved areas, enhancing the diversity of study populations.
Increased Convenience: Participants can engage in research activities from the comfort of their homes, reducing the need for frequent in-person visits and associated travel burdens.
Real-Time Data Collection: Researchers can gather real-time data from patients using telemedicine tools, enhancing the efficiency of data collection and reducing potential delays.
Enhanced Patient Engagement: Telemedicine can facilitate regular communication between researchers and participants, leading to improved compliance and more comprehensive study results.
Cost and Time Savings: By reducing the need for physical infrastructure and frequent site visits, telemedicine can lead to cost and time savings in clinical research.
Remote Monitoring: Telemedicine allows for remote monitoring of patients' health status and treatment adherence, leading to better safety and efficacy evaluations.
Ensuring Continuity: During unexpected events or emergencies, telemedicine can ensure continuity in clinical research activities, minimizing disruptions.
An Adaptive Technique in Electronic Health Record for Clinical Decision Makin...ijtsrd
Cloud computing is a collection of several computer resources that consists of both software and hardware. It is a type of service that is delivered over the internet and can be accessible from anywhere. 1 The data and services can be accessed through the internet. 4 These services are managed by the third party over the internet. They eventually provide access to the servers and resources. Health records consist of patient’s data regarding health. This data is usable by both the hospitals and patients. 6 8 This can be eventually used to track the medical history of patients. Data Visualization is a graphical depiction of the data. It implicates producing images that advertise the link among the data that the users view. Hence, they are used for clinical decision making. In this paper we will be discussing how cloud can be used to maintain health records electronically. Meghana Prakash | Vignesh S "An Adaptive Technique in Electronic Health Record for Clinical Decision Making Based on Data Visualization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30699.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30699/an-adaptive-technique-in-electronic-health-record-for-clinical-decision-making-based-on-data-visualization/meghana-prakash
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The prostate is an exocrine gland of the male mammalian reproductive system
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These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
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2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
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2. Specific Learning Objectives—(60mts)
At the end of the topic, the student should be able to tell
1. Introduction and a brief history(5mts)
2. Enumerate various use of computers in epidemiology(5mts)
3. Describe the uses in detail. (25mts)
4. Challenges faced during the application (5mts)
5. Problems and constraints(10mts)
6. Limitations of computer use(10mts)
7. C
2. VARIOUS USES OF COMPUTERS IN
EPIDEMIOLOGY AND HEALTH –
ENUMERATE
8. Health Management
Information System
Developing Databases
For Epidemiological
Research
Statistical Analysis Of
Epidemiological And
Health Research Data
Disease Surveillance Telemedicine
Electronic Medical
Records
Geographic
Information System
10. 1. HEALTH MANAGEMENT
INFORMATION SYSTEM
DEFINITION: a tool that helps in
gathering, aggregating, analysing,
using the information generated for
taking actions to improve the
performance of health systems.
11. Domains/fields of health information
system
•It includes demography, vital statistics, health system
input, output, health determinants, health economics,
health status, health infrastructure, resources and
outcome, financial statistics, environmental health
statistics.
13. Sources of HMIS
• Census
• Registration of vital events( birth, death, marriage etc)
• Notification of diseases and disease registers.
• Records and reports of hospitals
• Statistics regarding environmental health.
• Statistics regarding health resources and services.
14. Sources of HMIS
• Sample survey( national sample survey organization)
• Population survey
• Statistics regarding efforts to check epidemiological diseases and
research in this field.
• School record
• Economic planning
• Plans of social security
16. 2. DEVELOPING DATABASES
FOR EPIDEMIOLOGICAL
RESEARCH
Modern medical and epidemiological research is
heavily dependent on computers for entering,
correcting, validating and storing the research for
further analysis.
Usually, database applications such
as “EXCEL” are used.
Some statistical applications have an
inbuilt capability to enter and store
the data.
17. Various databases used in epidemiology
Support of Survey and Questionnaire Data Collection
•Epi info (CDC, Atlanta, GA)
•Microsoft Access((Microsoft
Corp., Redmond, WA)
•REDCap (Vanderbilt University,
Nashville, TN)
•Google form
18.
19. •Free public- domain suite of software tools designed and maintained by CDC for public health
practitioners and researchers
•Easy to set up; can be used to support mobile data collection also Web- based and cloud- optimized
components for data collection
•Contains customizable data entry forms and database construction
•Enables data analyses with epidemiologic statistics, maps, and graphs for public health professionals
who lack an IT background
•Used in outbreak investigations and for developing small- to- mid– sized disease surveillance systems
•Useful for public health field investigators to know and use because of its capabilities
•Available for free download at http://www.cdc.gov/epiinfo
20. Microsoft Acesss
Microsoft Access (Microsoft
Corp., Redmond, WA) •Database management system and
part of the Microsoft Office suite
•Makes data easy to store and
manipulate
•Limitation: single- user data entry
•Additional information available
at https://www.microsoft.com/en-
us/external icon
22. Applications for Analysis, Visualization, and Reporting (AVR)
SAS (Statistical Analysis System; SAS Institute, Inc.,
Cary, NC)
•Statistical analysis software suite for advanced
analytics, multivariate analyses, business
intelligence, data management, and predictive
analytics
•Highly powerful software application
•Additional information available
at https://www.sas.com/en_
us/home.htmlexternal icon
23. Applications for Analysis, Visualization, and Reporting (AVR)
SPSS (IBM Corporation,
Armonk, NY)
Additional information available
at http://www.ibm.com/spssext
ernal icon
• Analytic software widely used in social
science studies
•In addition to statistical analysis, features
data management (e.g., selecting cases,
reshaping files, or creating derived data)
and data documentation (e.g., metadata
dictionary stored in the data file)
24. Applications for Analysis, Visualization, and Reporting (AVR)
ESSENCE (Electronic Surveillance System for the
Early Notification of Community-based Epidemics)
Additional information about the National
Syndromic Surveillance Program and ESSENCE is
available
at https://www.cdc.gov/nssp/news.ht
ml#ISDS
•Syndromic surveillance system
operational in many jurisdictions and
nationally as part of CDC’s National
Syndromic Surveillance Program
•Developed by the Johns Hopkins
University Applied Physics Laboratory
•Enhancements developed through a
collaboration among CDC, state and local
health departments, and the Applied
Physics Laboratory
25. Applications for Analysis, Visualization, and Reporting
(AVR)
HealthMap (Boston Children’s Hospital,
Boston, MA)
Available for use
at http://www.healthmap.orgexter
nal icon
• Free mapping utility
•Uses informal Internet sources (e.g., online news
aggregators, eyewitness reports, expert- curated
discussions, and validated official reports) for
disease outbreak monitoring and real- time
surveillance of emerging public health threats to
achieve a unified and comprehensive view of the
current global state of infectious diseases
26. 4. DISEASE
SURVEILLANCE
Computer-based surveillance help in:
• Rapid transmission
• Monitoring progress
• Analysis of morbidity data
E.g., IDSP in which data are transmitted by state
health department computers to central surveillance
units through electronic mail for rapid analysis and
display of results and for prevention of outbreaks.
27.
28. 5. TELEMEDICINE
DEFINITION: Delivery of healthcare services,
by healthcare professionals using information and
communication technologies for the exchange of
valid information of diagnosis, treatment and
prevention of diseases
• Good alternative for delivering healthcare for
rural and geographically distant population.
29.
30.
31. SUPPORT
In India, telemedicine programs are being actively supported by:
• Department of Information Technology (DIT)
• Indian Space Research Organization
• NEC Telemedicine program for North-Eastern states
• Apollo Hospitals
• Asia Heart Foundation
• State governments
• Telemedicine technology also supported by some other private organizations
32. Evolution of Telemedicine
Point to
Point
• One patient
connected to one
doctor
• Within same
hospital
Point to
Multi
Point
•One patient end at a
time connected to
many specialist doctors
•Within the same
hospital
Multipoint
to
Multipoint
•Several patient ends
connectedto several
different specialist
doctors
•At different hospitals, in
different geographical
distances
Telemedicine :
ways of
communication
33. The telemedicine system consists:
• TELEMEDICINE PLATFORM - computer
or even digital mobile phone
• TELEMEDICINE SOFTWARE - for
capturing of patient’s information including
images, at both patient’s and doctor’s end
along with availability of communication
media.
34. 6.ELECTRONIC MEDICAL RECORDS(EMR)
EMR is a secure and
confidential method of
keeping medical records, as
compared to hardcopies of
paper-based medical records.
Ensures completeness, quick retrieval, much less
space requirement, as well as fast retrieval of
specific data for research, besides allowing for
tracking specific patients.
E.g., recently initiated
MCTS- Mother and Child
tracking system is a good
example of electronic health
record system
36. 7.GEOGRAPHIC INFORMATION SYSTEM (GIS)
• DEFINITION: Automated systems of software and hardware for the capture,
storage, retrieval, analysis and display of spatial and non-spatial data.
• GIS is a computer-based information system that is used to digitally represent
and analyze the geographic features present on the earth’s surface and the
events that are taking place on it.
• It utilizes various techniques such as cartography, statistical analysis, and
database technology, utilizing information from digital maps, global
positioning surveys, census data, and data from other sectors.
• Presently, GIS is being utilized for the prevention and control of vector-borne
diseases.
43. Examples of e-Health India
• National Health Portal of India, Gateway to Authentic Health
Information (nhp.gov.in)
• Pradhan Mantri Surakshit Matritva Abhiyan | PMSMA (nhp.gov.in)
• Nikshay
• Official Website Ayushman Bharat | HWC (nhp.gov.in)
47. Problems or constraints in India
1. Structural
• Multiplicity of institutions and departments
• Fragmentation of data.
• Lack of infrastructural facilities for storage and
maintenance of records.
48. Problems or constraints-cont.
2. Procedural
• Excessive information
• Encryption/hidden issues
• Exhaustive information, seldom used.
• Overburden of collection and recording of data along with General
health care.
• Incomplete, unreliable and intentionally managed information.
49. Problems or constraints-cont.
2. Procedural
• Repetition of general information
• Inappropriate forms/cards/reports
• Less interest of users in information
• Time consuming procedure
• Confusing coding, long list of indices
• Absence of feedback to information suppliers.
50. Problems or constraints.-cont.
3. Related to content
• Mostly service utilization statistics.
• Only summarized information reaches at higher level.
• Less emphasis on socioeconomic information.
• no user friendly
51. Problems or constraints –cont.
4. Related to human resource
• Absence or lack of skilled medical record professionals
• Lack of opportunity for in service training for the staff.
• Health care providers/nurses/biomedical trained persons
are collecting and preparing data.
• Lack of motivation/extra incentives
52. Problems or constraints-cont.
5. Technological
• Much manual paper based system.
• Absence or lack of computerized data base
system
56. References
1. PVD Shetty,M Khapre –Short notes in Community Medicine, third edition
2. K Park –Park textbook of preventive and Social Medicine,24th edition
3. geographicinformationsysteminpublichealthbikram-190426111355.pdf
4. Health Services and Outcomes Research Methodology (2021) 21:339–362 Standard
electronic health record (EHR) framework for Indian healthcare system |
SpringerLink
5. Application of GIS in public health in India: A literature-based review, analysis, and
recommendations - PubMed (nih.gov) Indian J Public Health. 2016 Jan-
Mar;60(1):51-8. doi: 10.4103/0019-557X.177308.
