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From Data to Information to …
Leslie McIntosh, PhD, MPH
https://edgewatertech.wordpress.com
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Health Informatics

Computer Science/IS

Analytics/Statistics

Health/Medicine
Data

Collection

Analyses

Tools

Primary

Excel

Clinician

SPSS

Secondary

CDMS

Statistician

SAS
Located, Consolidated, and
Standardized the Data
Built a Tool
Trained Clinicians
{This page intentionally left blank}
People
Computer Scientists/Developers
Analysts
Clinical Researchers
In the end, we have…
Developers
Analysts
Clinicians
DATA
Data Drivers
Liaisons facilitating the
transformation of data to
information to knowledge
Skills
Technical
Statistical tool (R or SAS, a little SPSS)
Scripting language (Python)
Writing

Domain
Electronic Medical Record

Research
Analytical and Investigative
Big Data Consumable
Natural Language Processing (NLP)
Custom Analytics
What’s next

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Leslie McIntosh - Disruptive Diner: Natural Approach to Bid Data

Notas do Editor

  1. Google maps
  2. Google maps
  3. Confluence of Analytics, Information Systems/Computer Science, Domain (in this case HealthCare)Generally have:Passion for 1Training in 2Desire to learn 3rd
  4. PeopleStarting teamHiring studentsWillingness to learn
  5. CIDERAggregation of the electronic medical record for BJC and WashU inpatient systemWeb-based systemNot requiring specialized programming skillsTrained End-users (clinicians and clinical research managers) to use it
  6. A Few people used the systemBut mostly not.Moving data to information is a challenge in translational researchBuilding a tool is not sufficient for non-informatics researchers to access meaningful dataMost clinical researchers think in terms of individual healthMost public health researchers are not aware of the data elements in the medical recordCreating a bridge between the tool and researchers provides a chauffeur in moving data to information
  7. Computer science/Information ScienceAnalysts & StatisticiansClinical Researchers
  8. Tend to focus on the software being built from the requirements presented not from the end-users needs
  9. Technical users of the systems built by developers, Hypothesis Agnostictypically in love with dataThose people that love data rarely love people
  10. Many ideas and depth in clinical specialtyResearchers are not generally trained in data collection, management, and analysis.
  11. In the end we have, developers, analysts, and clinicians trying to get data to informationIn my setting…Developers tend not to focus about the meaning of the dataAnalysts don’t understand computer programmingClinicians who don’t take ownership of data
  12. A common thread is data…Struck oil…what do we do with it now?Data are the oil of scienceIn the social network sense, the people who have data are the brokers,Like in social networks, without a broker or bridge, these are isolated nodes in a social network
  13. Varying levels of education (from Phd, to Masters, Undergraduate, and students)
  14. SkillsTechnicalA statistical tool (R or SAS)Scripting language (Python)MedicalMinimal understanding of medical recordResearch
  15. Investigative nature(Sherlock Holmes, without being a sociopath)E.g., Need to determine why data are missing: query is wrong, data are not available, laws changedCommunicationWithin team (fail early, fail often)With investigators
  16. From unstructured and structured medical documents
  17. NSF & NIH (National Science Foundation & National Institutes for Health) funding initiatives for Big Dat and for the first time, big data analystsAcademia &