Difference between revisions of "User Personae"
From HealthNLP-Deepphe
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==Clinical Translational Scientist== | ==Clinical Translational Scientist== | ||
| + | |||
| + | ===Background=== | ||
| + | |||
| + | * MD, DrPH, or RN trained scientist in wide range of clinical specialties (e.g. oncology, surgery, medicine, pathology, epidemiology) | ||
| + | * Expert understanding of clinical oncology, cancer therapeutics, and patient management | ||
| + | * Familiar with statistical methods, including genomics/bioinformatics, but typically relies on statisticians, bioinformaticists and other collaborators and colleagues for analysis. | ||
| + | * Unfamiliar with NLP Concepts | ||
| + | * Unfamiliar with NLP tools and resources | ||
| + | * Unfamiliar with OO programming languages | ||
| + | * Unfamiliar with text manipulation languages ( e.g. Python, Perl, Ruby) | ||
| + | |||
| + | ===Premise/story=== | ||
| + | |||
| + | This class of users is interested in extracting phenotype features from a set of documents, often for correlating specific features, treatments, and/or outcomes with molecular characterizations of tumors. They may also be interested in risk factors and other co-morbidities that provide insight into cancer biology (e.g. immune function). Cares more about the quality of the phenotype information in relation to the quality of the scientific conclusions to be developed,rather than the details of the phenotype extraction methods. | ||
| + | |||
| + | ===Expectations=== | ||
| + | |||
| + | * Basic inferential statistics, summarization, and comparisons. | ||
| + | * Graphical displays, including bar charts, error bars, etc. | ||
| + | |||
| + | ===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=== | ||
| + | |||
| + | * Enterprise Windows computing | ||
| + | * Limited familiarity with DBMS and data management principles | ||
| + | * 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. Most able to interpret complex temporal and other relations in text, but in-depth review is too error-prone and time-consuming. | ||
==Population Health Scientist/Health Care Outcomes Analyst== | ==Population Health Scientist/Health Care Outcomes Analyst== | ||
Revision as of 09:49, 2 August 2016
Presented here is a series of stakeholder or user descriptions - referred to here as personae, which informed preliminary development of the cancer models.
Contents
- 1 Translational Scientist with “Dry Bench” Bioinformatics skills
- 2 Clinical Translational Scientist
- 3 Population Health Scientist/Health Care Outcomes Analyst
- 4 Information Broker
- 5 Informatics Researcher
- 6 NLP Developers
- 7 Domain Specific Application Developers
- 8 Integrative Cancer Biologists and Modelers
Translational Scientist with “Dry Bench” Bioinformatics skills
Background
- 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 Natural Language Processing (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.
Expectations
- 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.
Clinical Translational Scientist
Background
- MD, DrPH, or RN trained scientist in wide range of clinical specialties (e.g. oncology, surgery, medicine, pathology, epidemiology)
- Expert understanding of clinical oncology, cancer therapeutics, and patient management
- Familiar with statistical methods, including genomics/bioinformatics, but typically relies on statisticians, bioinformaticists and other collaborators and colleagues for analysis.
- Unfamiliar with NLP Concepts
- Unfamiliar with NLP tools and resources
- Unfamiliar with OO programming languages
- Unfamiliar with text manipulation languages ( e.g. Python, Perl, Ruby)
Premise/story
This class of users is interested in extracting phenotype features from a set of documents, often for correlating specific features, treatments, and/or outcomes with molecular characterizations of tumors. They may also be interested in risk factors and other co-morbidities that provide insight into cancer biology (e.g. immune function). Cares more about the quality of the phenotype information in relation to the quality of the scientific conclusions to be developed,rather than the details of the phenotype extraction methods.
Expectations
- Basic inferential statistics, summarization, and comparisons.
- Graphical displays, including bar charts, error bars, etc.
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
- Enterprise Windows computing
- Limited familiarity with DBMS and data management principles
- 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. Most able to interpret complex temporal and other relations in text, but in-depth review is too error-prone and time-consuming.
