SlideShare ist ein Scribd-Unternehmen logo
1 von 28
ELECTRONIC MOBILE DATA IN GLOBAL HEALTH RESEARCH:  A HANDS ON COURSE Summer 2010 - Bergen, Norway
We came from around the world to learn from each other…
… across the health-tech divide…
… to understand how  mobile   technologies  can make global health research more  efficient ,  cost-effective   and  accessible .
Electronic Health Records Laboratory Information Systems Pharmacy Management Systems Surveillance Mobile Phone applications Patient ID and Tracking (fingerprint) Epidemiology, Monitoring and Evaluation Supply and administration reporting/tracking Education, training, human resources capacity  building, telemedicine, etc. Sample, supply and medication tracking (barcodes) (remember that eHealth includes… )
(…and that mHealth can be linked to all of these)
We discussed tools and approaches available for  data collection ,  data management ,  data analysis ,  and  reporting .
 
 
 
We designed, deployed and tested several open source data collection tools on mobile phones.
work flow analysis YAWL data analysis R electronic patient medical records OpenMRS clinical trial data management OpenClinica mobile data collection OpenXData Uses Tools
 
 
 
and found that some tools worked well ….  and others are more complicated!!
 
 
“ Logic Model” to help understand how mobile technologies can impact health outcomes Inputs ,[object Object],[object Object],[object Object],[object Object],[object Object],Activities ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Outputs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Outcomes ,[object Object],[object Object],[object Object],[object Object],IMPACTS ,[object Object],[object Object],[object Object],[object Object],[object Object],Results & Impacts Implementation
We learned from each other’s experiences - in multiple countries and health research settings - about the challenges and opportunities in the mHealth field.
 
 
 
Identifying where mHealth solutions can address health service and knowledge gaps Source: C.P. Hudson. Bulletin of the World Health Organization, 2001, 79 (1) 100% 50% 35% 30% 6% 4% 3% 1% All women women with sexually  transmitted disease/ reproductive tract infection Symptomatic Seek treatment Go to heath unit Treated correctly Compliant Treatment effective Partner treated ! ! mHealth  solutions?
We built friendships,  identified new collaborations  and, although this is a fast growing field in global health,  the world feels just a little smaller.
 
Thanks to BSRS 2010,  the city of Bergen, and  Jørn and Thorkild  for making this possible
Owais Uddin Ahmed (Pakistan) Ashar Alam (Pakistan) William Avil és Monterrey (Nicaragua) Matthew Berg (USA) Hugh Cameron (USA) Lumbwe Chola (Zambia) Andrew Codlin (USA) Jamil David (India) Nadia Evelyn Garcia Leyva (Perú) Samsom Gejibo (Ethiopia) Mark Gerard Musoba (Uganda) Ali Habib (Pakistan) Benyamin Harefa (Indonesia) Jonny Heggheim (Norway) Julia Irani (Pakistan) Richard Mayanja (Uganda) Garrett Mehl (USA) Herbert Mugooda (Uganda) Farzana Naheed (Pakistan) Arsenio Nhacolo (Mozambique) Claire Pénicaud (France) Marlise Richter (South Africa) Pablo Rodriguez Martinez (Perú) Nauman Safdar (Pakistan) John Wesonga (Kenya) Heather Zornetzer (USA)

Weitere ähnliche Inhalte

Was ist angesagt?

Precision Medicine in the Big Data World
Precision Medicine in the Big Data WorldPrecision Medicine in the Big Data World
Precision Medicine in the Big Data WorldCloudera, Inc.
 
Week5hcs451 presentation1
Week5hcs451 presentation1Week5hcs451 presentation1
Week5hcs451 presentation1AnaJacobs2
 
Precision Medicine: Research Presentation
Precision Medicine: Research PresentationPrecision Medicine: Research Presentation
Precision Medicine: Research PresentationShelagh McLellan
 
The reality of moving towards precision medicine
The reality of moving towards precision medicineThe reality of moving towards precision medicine
The reality of moving towards precision medicineElia Stupka
 
008 LATEST TRENDS IN HEALTH CARE TECHNOLOGY
008 LATEST TRENDS IN HEALTH CARE TECHNOLOGY008 LATEST TRENDS IN HEALTH CARE TECHNOLOGY
008 LATEST TRENDS IN HEALTH CARE TECHNOLOGYBobba Leeladhar
 
NLM Georgia Biomedical Informatics
NLM Georgia Biomedical InformaticsNLM Georgia Biomedical Informatics
NLM Georgia Biomedical InformaticsAlison Aldrich
 
Randomized Controlled Pilot Study Testing Use of Smartphone Technology for Ob...
Randomized Controlled Pilot Study Testing Use of Smartphone Technology for Ob...Randomized Controlled Pilot Study Testing Use of Smartphone Technology for Ob...
Randomized Controlled Pilot Study Testing Use of Smartphone Technology for Ob...Writers Per Hour
 
Graded Discussion 1 PPT
Graded Discussion 1 PPTGraded Discussion 1 PPT
Graded Discussion 1 PPTChristy Wade
 
Information Technology: The Third Pillar of Medical Education
Information Technology: The Third Pillar of Medical EducationInformation Technology: The Third Pillar of Medical Education
Information Technology: The Third Pillar of Medical EducationBen Williams
 
Sample size and sampleing
Sample size and sampleingSample size and sampleing
Sample size and sampleingAmna Khairy
 
A study on “the impact of data analytics in covid 19 health care system”
A study on “the impact of data analytics in covid 19 health care system”A study on “the impact of data analytics in covid 19 health care system”
A study on “the impact of data analytics in covid 19 health care system”Dr. C.V. Suresh Babu
 
Health Informatics Seminar Summary
Health Informatics Seminar SummaryHealth Informatics Seminar Summary
Health Informatics Seminar Summaryjetweedy
 
GuoJian CV2014.6.26提交
GuoJian CV2014.6.26提交GuoJian CV2014.6.26提交
GuoJian CV2014.6.26提交JIan Guo
 
Diagnosis test of diabetics and hypertension by AI
Diagnosis test of diabetics and hypertension by AIDiagnosis test of diabetics and hypertension by AI
Diagnosis test of diabetics and hypertension by AIDr. C.V. Suresh Babu
 
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
 

Was ist angesagt? (20)

Mycin
MycinMycin
Mycin
 
Fjms - keynote at MIE 2015
Fjms - keynote at MIE 2015Fjms - keynote at MIE 2015
Fjms - keynote at MIE 2015
 
Precision Medicine in the Big Data World
Precision Medicine in the Big Data WorldPrecision Medicine in the Big Data World
Precision Medicine in the Big Data World
 
Week5hcs451 presentation1
Week5hcs451 presentation1Week5hcs451 presentation1
Week5hcs451 presentation1
 
Precision Medicine: Research Presentation
Precision Medicine: Research PresentationPrecision Medicine: Research Presentation
Precision Medicine: Research Presentation
 
Integrated health monitoring
Integrated health monitoringIntegrated health monitoring
Integrated health monitoring
 
Patient safety issues
Patient safety issuesPatient safety issues
Patient safety issues
 
The reality of moving towards precision medicine
The reality of moving towards precision medicineThe reality of moving towards precision medicine
The reality of moving towards precision medicine
 
008 LATEST TRENDS IN HEALTH CARE TECHNOLOGY
008 LATEST TRENDS IN HEALTH CARE TECHNOLOGY008 LATEST TRENDS IN HEALTH CARE TECHNOLOGY
008 LATEST TRENDS IN HEALTH CARE TECHNOLOGY
 
NLM Georgia Biomedical Informatics
NLM Georgia Biomedical InformaticsNLM Georgia Biomedical Informatics
NLM Georgia Biomedical Informatics
 
Randomized Controlled Pilot Study Testing Use of Smartphone Technology for Ob...
Randomized Controlled Pilot Study Testing Use of Smartphone Technology for Ob...Randomized Controlled Pilot Study Testing Use of Smartphone Technology for Ob...
Randomized Controlled Pilot Study Testing Use of Smartphone Technology for Ob...
 
Graded Discussion 1 PPT
Graded Discussion 1 PPTGraded Discussion 1 PPT
Graded Discussion 1 PPT
 
Information Technology: The Third Pillar of Medical Education
Information Technology: The Third Pillar of Medical EducationInformation Technology: The Third Pillar of Medical Education
Information Technology: The Third Pillar of Medical Education
 
Sample size and sampleing
Sample size and sampleingSample size and sampleing
Sample size and sampleing
 
A study on “the impact of data analytics in covid 19 health care system”
A study on “the impact of data analytics in covid 19 health care system”A study on “the impact of data analytics in covid 19 health care system”
A study on “the impact of data analytics in covid 19 health care system”
 
Health Informatics Seminar Summary
Health Informatics Seminar SummaryHealth Informatics Seminar Summary
Health Informatics Seminar Summary
 
GuoJian CV2014.6.26提交
GuoJian CV2014.6.26提交GuoJian CV2014.6.26提交
GuoJian CV2014.6.26提交
 
Diagnosis test of diabetics and hypertension by AI
Diagnosis test of diabetics and hypertension by AIDiagnosis test of diabetics and hypertension by AI
Diagnosis test of diabetics and hypertension by AI
 
gursimran kaur
gursimran kaur gursimran kaur
gursimran kaur
 
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...
 

Ähnlich wie BSRS2010 Final Presentation: mHealth course (ppt)

mHealth Empire: The Rise of the Digital Revolution
mHealth Empire: The Rise of the Digital RevolutionmHealth Empire: The Rise of the Digital Revolution
mHealth Empire: The Rise of the Digital RevolutionMedidata Solutions
 
Health Informatics- Module 1-Chapter 1.pptx
Health Informatics- Module 1-Chapter 1.pptxHealth Informatics- Module 1-Chapter 1.pptx
Health Informatics- Module 1-Chapter 1.pptxArti Parab Academics
 
ICEGOV2009 - Tutorial 4 - E-Health Standards in Practice: Challenges and Oppo...
ICEGOV2009 - Tutorial 4 - E-Health Standards in Practice: Challenges and Oppo...ICEGOV2009 - Tutorial 4 - E-Health Standards in Practice: Challenges and Oppo...
ICEGOV2009 - Tutorial 4 - E-Health Standards in Practice: Challenges and Oppo...ICEGOV
 
Running head COURSE PROJECT- ROUGH DRAFT .docx
Running head COURSE PROJECT- ROUGH DRAFT                     .docxRunning head COURSE PROJECT- ROUGH DRAFT                     .docx
Running head COURSE PROJECT- ROUGH DRAFT .docxsusanschei
 
Leverage machine learning and new technologies to enhance rwe generation and ...
Leverage machine learning and new technologies to enhance rwe generation and ...Leverage machine learning and new technologies to enhance rwe generation and ...
Leverage machine learning and new technologies to enhance rwe generation and ...Athula Herath
 
Shared innovations in measurement and evaluation
Shared innovations in measurement and evaluationShared innovations in measurement and evaluation
Shared innovations in measurement and evaluationbikandob
 
Informatics and nursing 2015 2016.odette richards
Informatics and nursing 2015 2016.odette richardsInformatics and nursing 2015 2016.odette richards
Informatics and nursing 2015 2016.odette richardsOdette Richards
 
Technology will save our minds and bodies
Technology will save our minds and bodiesTechnology will save our minds and bodies
Technology will save our minds and bodiesElizabeth Cooper
 
A study of components and practices in
A study of components and practices inA study of components and practices in
A study of components and practices inAlexander Decker
 
Multimodal Machine Learning for Quality of Life Assessment: Throwing Data at ...
Multimodal Machine Learning for Quality of Life Assessment: Throwing Data at ...Multimodal Machine Learning for Quality of Life Assessment: Throwing Data at ...
Multimodal Machine Learning for Quality of Life Assessment: Throwing Data at ...Katarzyna Wac & The QoL Lab
 
Meeting healthcare challenges: what are the challenges and what is the role o...
Meeting healthcare challenges: what are the challenges and what is the role o...Meeting healthcare challenges: what are the challenges and what is the role o...
Meeting healthcare challenges: what are the challenges and what is the role o...Mohammad Al-Ubaydli
 
Critical Research Appraisal AssignmentNUR501 Philosophi
Critical Research Appraisal AssignmentNUR501 PhilosophiCritical Research Appraisal AssignmentNUR501 Philosophi
Critical Research Appraisal AssignmentNUR501 PhilosophiMargenePurnell14
 
Impacts of mobile devices in medical environment
Impacts of mobile devices in medical environmentImpacts of mobile devices in medical environment
Impacts of mobile devices in medical environmentLucas Machado
 
Estimating the Statistical Significance of Classifiers used in the Predictio...
Estimating the Statistical Significance of Classifiers used in the  Predictio...Estimating the Statistical Significance of Classifiers used in the  Predictio...
Estimating the Statistical Significance of Classifiers used in the Predictio...IOSR Journals
 

Ähnlich wie BSRS2010 Final Presentation: mHealth course (ppt) (20)

mHealth Empire: The Rise of the Digital Revolution
mHealth Empire: The Rise of the Digital RevolutionmHealth Empire: The Rise of the Digital Revolution
mHealth Empire: The Rise of the Digital Revolution
 
Health Informatics- Module 1-Chapter 1.pptx
Health Informatics- Module 1-Chapter 1.pptxHealth Informatics- Module 1-Chapter 1.pptx
Health Informatics- Module 1-Chapter 1.pptx
 
ICEGOV2009 - Tutorial 4 - E-Health Standards in Practice: Challenges and Oppo...
ICEGOV2009 - Tutorial 4 - E-Health Standards in Practice: Challenges and Oppo...ICEGOV2009 - Tutorial 4 - E-Health Standards in Practice: Challenges and Oppo...
ICEGOV2009 - Tutorial 4 - E-Health Standards in Practice: Challenges and Oppo...
 
Running head COURSE PROJECT- ROUGH DRAFT .docx
Running head COURSE PROJECT- ROUGH DRAFT                     .docxRunning head COURSE PROJECT- ROUGH DRAFT                     .docx
Running head COURSE PROJECT- ROUGH DRAFT .docx
 
Leverage machine learning and new technologies to enhance rwe generation and ...
Leverage machine learning and new technologies to enhance rwe generation and ...Leverage machine learning and new technologies to enhance rwe generation and ...
Leverage machine learning and new technologies to enhance rwe generation and ...
 
Shared innovations in measurement and evaluation
Shared innovations in measurement and evaluationShared innovations in measurement and evaluation
Shared innovations in measurement and evaluation
 
Pavia wsp october 2011
Pavia wsp october 2011Pavia wsp october 2011
Pavia wsp october 2011
 
Informatics and nursing 2015 2016.odette richards
Informatics and nursing 2015 2016.odette richardsInformatics and nursing 2015 2016.odette richards
Informatics and nursing 2015 2016.odette richards
 
Advancing-OSHMS High-Performance WS in OHM
Advancing-OSHMS High-Performance WS in OHMAdvancing-OSHMS High-Performance WS in OHM
Advancing-OSHMS High-Performance WS in OHM
 
Technology will save our minds and bodies
Technology will save our minds and bodiesTechnology will save our minds and bodies
Technology will save our minds and bodies
 
A study of components and practices in
A study of components and practices inA study of components and practices in
A study of components and practices in
 
Multimodal Machine Learning for Quality of Life Assessment: Throwing Data at ...
Multimodal Machine Learning for Quality of Life Assessment: Throwing Data at ...Multimodal Machine Learning for Quality of Life Assessment: Throwing Data at ...
Multimodal Machine Learning for Quality of Life Assessment: Throwing Data at ...
 
Meeting healthcare challenges: what are the challenges and what is the role o...
Meeting healthcare challenges: what are the challenges and what is the role o...Meeting healthcare challenges: what are the challenges and what is the role o...
Meeting healthcare challenges: what are the challenges and what is the role o...
 
Rise of the Machines
Rise of the Machines  Rise of the Machines
Rise of the Machines
 
Stanford globalm health_p_mechael
Stanford globalm health_p_mechaelStanford globalm health_p_mechael
Stanford globalm health_p_mechael
 
Critical Research Appraisal AssignmentNUR501 Philosophi
Critical Research Appraisal AssignmentNUR501 PhilosophiCritical Research Appraisal AssignmentNUR501 Philosophi
Critical Research Appraisal AssignmentNUR501 Philosophi
 
Research Interest Oct2016
Research Interest Oct2016Research Interest Oct2016
Research Interest Oct2016
 
Impacts of mobile devices in medical environment
Impacts of mobile devices in medical environmentImpacts of mobile devices in medical environment
Impacts of mobile devices in medical environment
 
Quality of life experiences No.2
Quality of life experiences No.2Quality of life experiences No.2
Quality of life experiences No.2
 
Estimating the Statistical Significance of Classifiers used in the Predictio...
Estimating the Statistical Significance of Classifiers used in the  Predictio...Estimating the Statistical Significance of Classifiers used in the  Predictio...
Estimating the Statistical Significance of Classifiers used in the Predictio...
 

BSRS2010 Final Presentation: mHealth course (ppt)

  • 1. ELECTRONIC MOBILE DATA IN GLOBAL HEALTH RESEARCH: A HANDS ON COURSE Summer 2010 - Bergen, Norway
  • 2. We came from around the world to learn from each other…
  • 3. … across the health-tech divide…
  • 4. … to understand how mobile technologies can make global health research more efficient , cost-effective and accessible .
  • 5. Electronic Health Records Laboratory Information Systems Pharmacy Management Systems Surveillance Mobile Phone applications Patient ID and Tracking (fingerprint) Epidemiology, Monitoring and Evaluation Supply and administration reporting/tracking Education, training, human resources capacity building, telemedicine, etc. Sample, supply and medication tracking (barcodes) (remember that eHealth includes… )
  • 6. (…and that mHealth can be linked to all of these)
  • 7. We discussed tools and approaches available for data collection , data management , data analysis , and reporting .
  • 8.  
  • 9.  
  • 10.  
  • 11. We designed, deployed and tested several open source data collection tools on mobile phones.
  • 12. work flow analysis YAWL data analysis R electronic patient medical records OpenMRS clinical trial data management OpenClinica mobile data collection OpenXData Uses Tools
  • 13.  
  • 14.  
  • 15.  
  • 16. and found that some tools worked well …. and others are more complicated!!
  • 17.  
  • 18.  
  • 19.
  • 20. We learned from each other’s experiences - in multiple countries and health research settings - about the challenges and opportunities in the mHealth field.
  • 21.  
  • 22.  
  • 23.  
  • 24. Identifying where mHealth solutions can address health service and knowledge gaps Source: C.P. Hudson. Bulletin of the World Health Organization, 2001, 79 (1) 100% 50% 35% 30% 6% 4% 3% 1% All women women with sexually transmitted disease/ reproductive tract infection Symptomatic Seek treatment Go to heath unit Treated correctly Compliant Treatment effective Partner treated ! ! mHealth solutions?
  • 25. We built friendships, identified new collaborations and, although this is a fast growing field in global health, the world feels just a little smaller.
  • 26.  
  • 27. Thanks to BSRS 2010, the city of Bergen, and Jørn and Thorkild for making this possible
  • 28. Owais Uddin Ahmed (Pakistan) Ashar Alam (Pakistan) William Avil és Monterrey (Nicaragua) Matthew Berg (USA) Hugh Cameron (USA) Lumbwe Chola (Zambia) Andrew Codlin (USA) Jamil David (India) Nadia Evelyn Garcia Leyva (Perú) Samsom Gejibo (Ethiopia) Mark Gerard Musoba (Uganda) Ali Habib (Pakistan) Benyamin Harefa (Indonesia) Jonny Heggheim (Norway) Julia Irani (Pakistan) Richard Mayanja (Uganda) Garrett Mehl (USA) Herbert Mugooda (Uganda) Farzana Naheed (Pakistan) Arsenio Nhacolo (Mozambique) Claire Pénicaud (France) Marlise Richter (South Africa) Pablo Rodriguez Martinez (Perú) Nauman Safdar (Pakistan) John Wesonga (Kenya) Heather Zornetzer (USA)

Hinweis der Redaktion

  1. We had men and women representing 18 countries and a wide range of health and information technology research backgrounds.
  2. We learned to speak each other’s languages, at least a little!
  3. Participants chared their experiences - successes and failures - with a wide range of eHealth and mHealth tools.
  4. Community health workers collecting child count data for tracking and improving malnutrition in rural Uganda
  5. SMS messaging tools were used to help keep track of and monitor malaria bednet distribution efforts in rural Nigeria.
  6. We learned some invaluable field “tricks” about making mobile phones and electronic technologies more sustainable and robust for limited resource settings.