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Building a decision support system for third world needs. Colorado HIMSS Conference May 7 th , 2010 JP Batra , MBA, MSCS, BSEE, PMP. Presentation Outline. Integrated disease management decision support system (DSS) International project – team, locations, countries

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building a decision support system for third world needs

Building a decision support system for third world needs

Colorado HIMSS Conference

May 7th, 2010

JP Batra, MBA, MSCS, BSEE, PMP

presentation outline
Presentation Outline
  • Integrated disease management decision support system (DSS)
    • International project – team, locations, countries
    • Technologies utilized, product description and its applicability to third world countries
  • Ontology, a semantic technology - 2 minute tutorial
  • Challenges, opportunities and lessons learnt
  • Applicability to US projects
  • Questions and Answers (Q&A)
  • Credits
ontology
Ontology
  • “..An ontology is a formal representation of the knowledge by a set of concepts within a domain and the relationships between those concepts.” (source Wikipedia)
    • Describes a domain (e.g. medical billing domain)
    • Stores relationships – e.g. “is-a”, “has-a”
    • Provides knowledge representation about the domain
    • Captures human knowledge into machine representation, enables automated reasoning
ontology example
Ontology Example
  • Drug Example
  • Disease Example
    • Vector Borne- West Nile, Malaria, Dengue
ontology benefits
Ontology Benefits
  • Shared vocabulary – enabling data reuse, shared data, information system data exchanges
  • Abstracts enterprise knowledge
  • Complex decision making and inferences
  • Advantages over relational databases
    • Ease of adding additional relationships without major database re-architecture or changes
    • They cannot store hierarchical relationships between data
disease management video
Disease Management Video
  • Courtsey: youtube, link http://www.youtube.com/watch?v=mSsmW7N_scY, MaximsNewsNetwork: Malaria – New Treatment Guidelines, World Health Organization
integrated dss
Integrated DSS
  • GIS, semantic technologies and data analysis based decision support platform
    • One country and one disease specific module in use
    • Dozens of use cases (scenarios or queries), all data is geo-tagged
    • Deployed at all levels of political structure – e.g. state, district, county, city
      • Forwards local data to a central collection system for aggregate reporting
    • Being expanded to multiple countries, and multiple diseases
  • Dispersed team in Europe, Africa and Americas
  • Modifiable geo-political hierarchy
    • E.g. District, Ward in one country and state, county in another
integrated dss product
Integrated DSS- Product

30

35

20

20

25

18

45

Map from - http://go.hrw.com/atlas/norm_map/zimbabwe.gif

3 rd world country technology challenges
3rd World Country Technology Challenges
  • Internet access
    • Not ubiquitous, limited bandwidth and connectivity
  • Application needs to run on every conceivable machine imagined!
  • Legacy data consolidation – spreadsheets, MS Access, paper
    • Geopolitical entities misspellings
      • E.g., Harare vs. Herare vs. Hararre all meaning Harare
  • Multi-machine installs and data synchronization*
    • Master (top level) changes need to filter down to all levels
    • Data collection from lower levels need to bubble up to Master level for aggregate reporting
  • Localization – language, time representation, context
  • Different country, different language (e.g. Arabic) and code reuse issues
solutions
Solutions
  • Internet ready applications, but assumes no internet
  • Lightweight applications for machines less than 3 year old
  • Freely distributable copies (just as in Adobe reader)
  • Cross platform software – Linux, Apple, Windows
  • Data consolidation using spreadsheet importer
    • uses ontological principles to resolve misspellings of geopolitical entities. User uses combination of
      • Best sounding match AND
      • Using the geo-political ontology
      • New association added to the ontology
opportunities and lessons
Opportunities and Lessons
  • Potential to improve the program
    • Mobile solutions
    • Address multiple diseases using the same platform
  • Lessons
    • Each country, region and zone has its own nuances
    • Embrace low tech where necessary
    • Do not assume infrastructure in place even if told so!
applicability to us
Applicability to US
  • US has a strong infrastructure
  • Initiate disease management program in monitoring, controlling and auditing
    • H1N1
    • West Nile Virus
    • TB and other programs
  • Patient care management
    • CMS
    • Business intelligence
    • Data integration between different Healthcare IT systems using ontologies
conclusion q a
Conclusion, Q&A
  • Third world countries are adopting newer technologies to address healthcare issues
  • They are good at blending technical solutions with local environment and infrastructure to get the desired effects
  • International health and human service organizations and foundations are committed to assist the third world countries in their efforts
  • Questions and Answers
credits
Credits
  • The Integrated Ontologies and GIS Decision Support System was built by:
  • TerraFrame, Inc., 11005 Dover Street, Ste. 1000, Westminster, CO 80021, Tel: 1-877-444-3074
  • Website: http://www.terraframe.com
  • nmceachen@terraframe.com
  • jpbatra1@yahoo.com