Implement American Joint Commission on Cancer (AJCC) checklists via the College of American Pathologist checklists (CAP-eCC) for structured data transmission to health records and analytic repositories in support of precision medicine

2018 ◽  
Author(s):  
J. Mark Tuthill
Keyword(s):  
Precision Medicine ◽  
Data Transmission ◽  
Structured Data ◽  
Joint Commission ◽  
Health Records

scholarly journals Integrating Biomedical Research and Electronic Health Records to Create Knowledge Based Biologically Meaningful Machine Readable Embeddings

2019 ◽  
Author(s):  
Charlotte A. Nelson ◽  
Atul J. Butte ◽  
Sergio E. Baranzini
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Precision Medicine ◽  
Biomedical Research ◽  
Current Knowledge ◽  
Knowledge Network ◽  
Health Records ◽  
Major Barrier ◽  
Learning Platform ◽  
Electronic Health

ABSTRACTIn order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform that information into patient care has not kept at pace. A major barrier preventing this transformation is that multi-dimensional data collection and analysis is usually carried out without much understanding of the underlying knowledge structure. In an effort to bridge this gap, Electronic Health Records (EHRs) of individual patients were connected to a heterogeneous knowledge network called Scalable Precision Medicine Oriented Knowledge Engine (SPOKE). Then an unsupervised machine-learning algorithm was used to create Propagated SPOKE Entry Vectors (PSEVs) that encode the importance of each SPOKE node for any code in the EHRs. We argue that these results, alongside the natural integration of PSEVs into any EHR machine-learning platform, provide a key step toward precision medicine.

scholarly journals Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis

2017 ◽  
Author(s):  
Brett K Beaulieu-Jones ◽  
Daniel R Lavage ◽  
John W Snyder ◽  
Jason H Moore ◽  
Sarah A Pendergrass ◽  
...  
Keyword(s):  
Data Analysis ◽  
Electronic Health Records ◽  
Structured Data ◽  
Health Records ◽  
Electronic Health

scholarly journals Representation of Information about Family Relatives as Structured Data in Electronic Health Records

2014 ◽  
Vol 05 (02) ◽  
pp. 349-367 ◽  
Author(s):  
Y. Lu ◽  
C.J. Vitale ◽  
P.L. Mar ◽  
F. Chang ◽  
N. Dhopeshwarkar ◽  
...  
Keyword(s):  
Family History ◽  
Structured Data ◽  
Free Text ◽  
Snomed Ct ◽  
Family History Information ◽  
Text Documents ◽  
Health Records ◽  
Relative Information ◽  
History Information ◽  
Electronic Health

SummaryBackground: The ability to manage and leverage family history information in the electronic health record (EHR) is crucial to delivering high-quality clinical care.Objectives: We aimed to evaluate existing standards in representing relative information, examine this information documented in EHRs, and develop a natural language processing (NLP) application to extract relative information from free-text clinical documents.Methods: We reviewed a random sample of 100 admission notes and 100 discharge summaries of 198 patients, and also reviewed the structured entries for these patients in an EHR system’s family history module. We investigated the two standards used by Stage 2 of Meaningful Use (SNOMED CT and HL7 Family History Standard) and identified coverage gaps of each standard in coding relative information. Finally, we evaluated the performance of the MTERMS NLP system in identifying relative information from free-text documents.Results: The structure and content of SNOMED CT and HL7 for representing relative information are different in several ways. Both terminologies have high coverage to represent local relative concepts built in an ambulatory EHR system, but gaps in key concept coverage were detected; coverage rates for relative information in free-text clinical documents were 95.2% and 98.6%, respectively. Compared to structured entries, richer family history information was only available in free-text documents. Using a comprehensive lexicon that included concepts and terms of relative information from different sources, we expanded the MTERMS NLP system to extract and encode relative information in clinical documents and achieved a corresponding precision of 100% and recall of 97.4%.Conclusions: Comprehensive assessment and user guidance are critical to adopting standards into EHR systems in a meaningful way. A significant portion of patients’ family history information is only documented in free-text clinical documents and NLP can be used to extract this information.Citation: Zhou L, Lu Y, Vitale CJ, Mar PL, Chang F, Dhopeshwarkar N, Rocha RA. Representation of information about family relatives as structured data in electronic health records. Appl Clin Inf 2014; 5: 349–367 http://dx.doi.org/10.4338/ACI-2013-10-RA-0080

A Technique to Exploit Free-Form Notes to Predict Customer Churn

2014 ◽  
Vol 4 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Gregory W. Ramsey ◽  
Sanjay Bapna
Keyword(s):  
Language Processing ◽  
Resource Planning ◽  
Classification Problem ◽  
Structured Data ◽  
Free Form ◽  
Care Providers ◽  
Health Records ◽  
Hospital Administrators ◽  
Customer Churn ◽  
Processing Techniques

As healthcare costs rise, hospitals are seeking ways to improve operations. This paper examines the usefulness of free-form notes to solve a classification problem commonly associated with customer churn. The authors show that classifiers which incorporate free-form notes, using natural language processing techniques, are up to 9% more accurate than classifiers that are solely developed using structured data. The authors suggest that hospitals and chronic disease management clinics can use structured data and free-form notes from electronic health records to predict which patients are likely to cease receiving care from their facilities. Classification tools for predicting patient churn are of interest to hospital administrators; such information can aid in resource planning and facilitate smoother handoffs between care providers.

scholarly journals Challenges of Identifying Clinically Actionable Genetic Variants for Precision Medicine

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Tonia C. Carter ◽  
Max M. He
Keyword(s):  
Precision Medicine ◽  
Genetic Variants ◽  
Genomic Medicine ◽  
Genetic Mutations ◽  
Healthcare Outcomes ◽  
Health Records ◽  
Data Infrastructure ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing

Advances in genomic medicine have the potential to change the way we treat human disease, but translating these advances into reality for improving healthcare outcomes depends essentially on our ability to discover disease- and/or drug-associated clinically actionable genetic mutations. Integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a big data infrastructure can provide an efficient and effective way to identify clinically actionable genetic variants for personalized treatments and reduce healthcare costs. We review bioinformatics processing of next-generation sequencing (NGS) data, bioinformatics infrastructures for implementing precision medicine, and bioinformatics approaches for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.

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