Date post: | 24-Dec-2014 |
Category: |
Government & Nonprofit |
Upload: | wget2013 |
View: | 184 times |
Download: | 0 times |
• Michael Smith MD, MPH• twitter: @docmikesmith skype: docmjsmith
Uses of ICT to Impact Organizational and Operational Challenges in Disaster Management
(transformational technologies)
Current uses of ICT in disasters
• Patient tracking (akin to logistics )• Assessments - epidemiologic / need / damage• Geolocating areas of need • Communications within organizations• Capturing and sharing data • Incident management
Patient tracking (logistics)
Patient tracking (logistics)
WIISARD
LA Lenert, D Kirsh, WG Griswold, C Buono, J Lyon, R Rao, TC Chan. Design and evaluation of a wireless electronic health records system for field care in mass casualty settings. J Am Med Inform Assoc. 2011;18(6):842-52. Epub 2011 Jun 27
Assessments - epidemiologic / needs / damage
Assessments - epidemiologic / needs / damage
Geolocating areas of need
Communications within organizations
Capturing and sharing data
Capturing and sharing data
Challenges to Sharing Information
• Structure and motivation of the agencies• Leadership capabilities• Social / political structures of the affected area• Needs drive communication – needs for
fundraising lead to competitive motivations
Signs of poor collaboration
• Process is confused with results • Misallocation of resources • Maldistribution of teams • Resupplies not coordinated between groups• Survivor problems evolve to a critical state
Incident management
Leading ICT challenges in disaster response
• perceiving survivors’ needs in near real-time
• assessing damage geographically
• creating greater horizontal and vertical integration of the response community
Refunite
Perceiving survivors’ needs in near real time
Perceiving survivors needs – text analysis
• Natural Language Processing - the Holy Grail• Currently uses two methods:
probalistic topic models parallel bilingual corpora
• Mechanical Turk method of crowdsourcing translations not sustainable
Probalistic topic models
Start with 2 billion tweetsTrain a binary classifier to search for all terms related to “flu” with 5,100 training examples
fever/ sneeze/ cough/ pain, etc= 1.63 million hits,
Tweets categorized as ailments, symptoms, and treatments. Excludes “confusers” – “I got Bieber fever”
QCRI’s people trained algorithm classifies large volumes of texts into various categories for individualized attention by programs like micromappers, and Crisis Trackers
Bilateral parallel corpora
Used to teach computers languagesIncludes a known language next to the other ‘slanguage’Builds similarities
(essentially the Rosetta stone in cyber age)
Providing integration – horizontal and vertical
Integration requires communication
Organization requires situational awareness