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Toward a Learning Health-care System - Knowledge Delivery at the Point of Care Empowered by Big Data and NLP.

Kaggal VC, Elayavilli RK, Mehrabi S, Pankratz JJ, Sohn S, Wang Y, Li D, Rastegar MM, Murphy SP, Ross JL, Chaudhry R, Buntrock JD, Liu H - Biomed Inform Insights (2016)

Bottom Line: Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS.We compared the advantages of big data over two other environments.Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

View Article: PubMed Central - PubMed

Affiliation: Division of Information Management and Analytics, Mayo Clinic, Rochester, MN, USA.; Biomedical Informatics and Computational Biology, University of Minnesota, Rochester, MN, USA.

ABSTRACT
The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

No MeSH data available.


A high-level architecture of big data-empowered analytics in LHS. Big data architecture at Mayo consists of three layers: (i) data ingestion layer that reads data from real-time feeds from the EMR and archived data, (ii) big data analytics layer that does stream processing for analyzing the data, and (iii) data storage and retrieval that stores the information and knowledge that are generated through big data analytics and facilitate retrieval at the appropriate time for clinical use.
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f3-bii-suppl.1-2016-013: A high-level architecture of big data-empowered analytics in LHS. Big data architecture at Mayo consists of three layers: (i) data ingestion layer that reads data from real-time feeds from the EMR and archived data, (ii) big data analytics layer that does stream processing for analyzing the data, and (iii) data storage and retrieval that stores the information and knowledge that are generated through big data analytics and facilitate retrieval at the appropriate time for clinical use.

Mentions: One of the major reasons for the big data initiative at the Mayo Clinic is the ability to extract information from the EHR near real time to meet the information needs at the point of care. Figure 3 shows a high-level architecture of the Mayo big data implementation.


Toward a Learning Health-care System - Knowledge Delivery at the Point of Care Empowered by Big Data and NLP.

Kaggal VC, Elayavilli RK, Mehrabi S, Pankratz JJ, Sohn S, Wang Y, Li D, Rastegar MM, Murphy SP, Ross JL, Chaudhry R, Buntrock JD, Liu H - Biomed Inform Insights (2016)

A high-level architecture of big data-empowered analytics in LHS. Big data architecture at Mayo consists of three layers: (i) data ingestion layer that reads data from real-time feeds from the EMR and archived data, (ii) big data analytics layer that does stream processing for analyzing the data, and (iii) data storage and retrieval that stores the information and knowledge that are generated through big data analytics and facilitate retrieval at the appropriate time for clinical use.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4920204&req=5

f3-bii-suppl.1-2016-013: A high-level architecture of big data-empowered analytics in LHS. Big data architecture at Mayo consists of three layers: (i) data ingestion layer that reads data from real-time feeds from the EMR and archived data, (ii) big data analytics layer that does stream processing for analyzing the data, and (iii) data storage and retrieval that stores the information and knowledge that are generated through big data analytics and facilitate retrieval at the appropriate time for clinical use.
Mentions: One of the major reasons for the big data initiative at the Mayo Clinic is the ability to extract information from the EHR near real time to meet the information needs at the point of care. Figure 3 shows a high-level architecture of the Mayo big data implementation.

Bottom Line: Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS.We compared the advantages of big data over two other environments.Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

View Article: PubMed Central - PubMed

Affiliation: Division of Information Management and Analytics, Mayo Clinic, Rochester, MN, USA.; Biomedical Informatics and Computational Biology, University of Minnesota, Rochester, MN, USA.

ABSTRACT
The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

No MeSH data available.