Baobab spring 2015 usability and contextual inquiry

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Context: social, organizational, etc.

System:desktop, mobile,tablet, etc. User

Tool

Task Data analysis? Writing? Graphing?

Iterative process

“All models are wrong…”

Key Points

Key Questions & Tradeoffs●

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Who: Stakeholder AnalysisRosson & Carroll 2002

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Stakeholders - Challenges

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Example: Cancer Deep Phenotyping Extraction from Electronic Medical Records

G. Savova, and R. Jacobson co-PIs

DeepPhe Stakeholders

DeepPhe Stakeholder:Translational Scientist with “Dry Bench” Bioinformatics skillsBackground• PhD trained scientist in wide range of fields relevant to cancer (e.g. genetics, pharmacology, molecular

biology, immunology)• Analytically trained and familiar with statistical methods, including genomics/bioinformatics.• Unfamiliar with NLP Concepts• Unfamiliar with NLP tools and resources• Limited familiarity with OO programming languages• Familiar with text manipulation languages ( e.g. Python, Perl, Ruby)

Premise/story• Cancer biologists are unraveling the genomic and molecular changes that drive tumors towards

specific behaviors such as progression and metastasis. Identifying these molecular drivers will require information about the specific cancer behaviors that they produce. This class of users will examine data for case finding and to classify cases based on outcome.

DeepPhe Stakeholder:Translational Scientist with “Dry Bench” Bioinformatics skillsExpectations • Population-level statistics, summarization, and comparisons.• Graphical displays, including bar charts, error bars, etc.• Inferential statistics• Export to statistical software (SAS,SPSS,RapidMiner, R)

Information needs• Demographic data• Treatment data• Disease progression, metastasis and other outcomes (e.g. RECIST criteria)• Available biomarkers and other clinical molecular information not in structured format (e.g.

Oncotype Scores)

Current tools and limitations• Mac desktop, Linux and Windows computing• Some familiarity with DBMS and data management principles• Knowledge and use of statistical software (e.g. SAS, SPSS, RapidMiner, R), but time required

to extract and format data is substantial.• Routine access to PHI clinical text for work, able to interpret clinical text reports, but in-

depth review is too error-prone and time-consuming.

Checklist: Stakeholder Identification

KCH Lab Stakeholders?

Stakeholder definition issues?

Data collection

“talking to the users..”

Observation

Types of Observations

Passive Observation

Checklist: Observation Planning

Checklists: Observations

Checklists: Observations

Checklists: Observations

Drawbacks of observations

Contextual Interviews

Synthesis Lectures in Human-Centered InformaticsMorgan & Claypool 2014

Contextual Interviews

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Principles of contextual inquiry modified from Beyer & Holtzblatt 1998, 2014

ContextBeyer & Holtzblatt 1998,2014

PartnershipBeyer & Holtzblatt 1998, 2014

InterpretationBeyer & Holtzblatt 1998, 2014

FocusBeyer & Holtzblatt 1998, 2014

AccomplishmentBeyer & Holtzblatt 1998, 2014

IdentityBeyer & Holtzblatt 1998, 2014

SensationBeyer & Holtzblatt 1998, 2014

The “Triangle of Joy in Use”Beyer & Holtzblatt 2014

Checklist: Contextual Inquiries

Challenges

Contextual Inquiry Example

Analysis exercise

Recording

How many participants?

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When to involve users?

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Baobab Contextual Inquiry Exercise

Participatory Design

Kemmis & McTaggart (1982) reprinted in Clemensen, et al. 2007

Clemensen, et al. 2007

•Users involved throughout•Scenario design between CD and PD

•Pros and Cons?

Ethnography●

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Tradeoffs

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Rapid EthnographyMillen, 2000

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Rapid Ethnography

Eliciting Feedback

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Discussion of LIMS

Checklist for LIMS: Observation Planning

Where are we?

Scenario-Based Design Rosson & Carroll 2001, 2002

“Five Reasons for Scenario-Based Design” J.M. Carroll, 1995

Scenario-based design Rosson & Carroll 2001, 2002

Problem scenario: visit to a science fiction club meetingRosson & Carroll 2002

Checklist: Problem Scenarios