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Amplifying Domain Expertise in Clinical Data Pipelines
Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert w...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677017/ https://www.ncbi.nlm.nih.gov/pubmed/33151150 http://dx.doi.org/10.2196/19612 |
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author | Rahman, Protiva Nandi, Arnab Hebert, Courtney |
author_facet | Rahman, Protiva Nandi, Arnab Hebert, Courtney |
author_sort | Rahman, Protiva |
collection | PubMed |
description | Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert who informs the design of the data pipeline and consumes its results for decision support. Although there are multiple data interaction tools for data scientists, few exist to allow domain experts to interact with data meaningfully. Designing systems for domain experts requires careful thought because they have different needs and characteristics from other end users. There should be an increased emphasis on the system to optimize the experts’ interaction by directing them to high-impact data tasks and reducing the total task completion time. We refer to this optimization as amplifying domain expertise. Although there is active research in making machine learning models more explainable and usable, it focuses on the final outputs of the model. However, in the clinical domain, expert involvement is needed at every pipeline step: curation, cleaning, and analysis. To this end, we review literature from the database, human-computer information, and visualization communities to demonstrate the challenges and solutions at each of the data pipeline stages. Next, we present a taxonomy of expertise amplification, which can be applied when building systems for domain experts. This includes summarization, guidance, interaction, and acceleration. Finally, we demonstrate the use of our taxonomy with a case study. |
format | Online Article Text |
id | pubmed-7677017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-76770172020-11-23 Amplifying Domain Expertise in Clinical Data Pipelines Rahman, Protiva Nandi, Arnab Hebert, Courtney JMIR Med Inform Viewpoint Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert who informs the design of the data pipeline and consumes its results for decision support. Although there are multiple data interaction tools for data scientists, few exist to allow domain experts to interact with data meaningfully. Designing systems for domain experts requires careful thought because they have different needs and characteristics from other end users. There should be an increased emphasis on the system to optimize the experts’ interaction by directing them to high-impact data tasks and reducing the total task completion time. We refer to this optimization as amplifying domain expertise. Although there is active research in making machine learning models more explainable and usable, it focuses on the final outputs of the model. However, in the clinical domain, expert involvement is needed at every pipeline step: curation, cleaning, and analysis. To this end, we review literature from the database, human-computer information, and visualization communities to demonstrate the challenges and solutions at each of the data pipeline stages. Next, we present a taxonomy of expertise amplification, which can be applied when building systems for domain experts. This includes summarization, guidance, interaction, and acceleration. Finally, we demonstrate the use of our taxonomy with a case study. JMIR Publications 2020-11-05 /pmc/articles/PMC7677017/ /pubmed/33151150 http://dx.doi.org/10.2196/19612 Text en ©Protiva Rahman, Arnab Nandi, Courtney Hebert. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.11.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Viewpoint Rahman, Protiva Nandi, Arnab Hebert, Courtney Amplifying Domain Expertise in Clinical Data Pipelines |
title | Amplifying Domain Expertise in Clinical Data Pipelines |
title_full | Amplifying Domain Expertise in Clinical Data Pipelines |
title_fullStr | Amplifying Domain Expertise in Clinical Data Pipelines |
title_full_unstemmed | Amplifying Domain Expertise in Clinical Data Pipelines |
title_short | Amplifying Domain Expertise in Clinical Data Pipelines |
title_sort | amplifying domain expertise in clinical data pipelines |
topic | Viewpoint |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677017/ https://www.ncbi.nlm.nih.gov/pubmed/33151150 http://dx.doi.org/10.2196/19612 |
work_keys_str_mv | AT rahmanprotiva amplifyingdomainexpertiseinclinicaldatapipelines AT nandiarnab amplifyingdomainexpertiseinclinicaldatapipelines AT hebertcourtney amplifyingdomainexpertiseinclinicaldatapipelines |