scholarly journals Constructing data-derived family histories using electronic health records from a single healthcare delivery system

2019 ◽  
Vol 30 (2) ◽  
pp. 212-218
Author(s):  
Maya Leventer-Roberts ◽  
Ilan Gofer ◽  
Yuval Barak Corren ◽  
Ben Y Reis ◽  
Ran Balicer
Keyword(s):  
Family History ◽  
Electronic Health Records ◽  
Clinical Decision Making ◽  
Healthcare Delivery ◽  
Clinical Value ◽  
Healthcare Delivery System ◽  
Family History Information ◽  
Health Records ◽  
History Information ◽  
Electronic Health

Abstract Background In order to examine the potential clinical value of integrating family history information directly from the electronic health records of patients’ family members, the electronic health records of individuals in Clalit Health Services, the largest payer/provider in Israel, were linked with the records of their parents. Methods We describe the results of a novel approach for creating data-derived family history information for 2 599 575 individuals, focusing on three chronic diseases: asthma, cardiovascular disease (CVD) and diabetes. Results In our cohort, there were 256 598 patients with asthma, 55 309 patients with CVD and 66 324 patients with diabetes. Of the people with asthma, CVD or diabetes, the percentage that also had a family history of the same disease was 22.0%, 70.8% and 70.5%, respectively. Conclusions Linking individuals’ health records with their data-derived family history has untapped potential for supporting diagnostic and clinical decision-making.

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

scholarly journals Population-based identification and temporal trend of children with primary nephrotic syndrome: The Kaiser Permanente nephrotic syndrome study

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257674
Author(s):  
Rishi V. Parikh ◽  
Thida C. Tan ◽  
Dongjie Fan ◽  
David Law ◽  
Anne S. Salyer ◽  
...  
Keyword(s):  
Nephrotic Syndrome ◽  
Electronic Health Records ◽  
Healthcare Delivery ◽  
Population Based ◽  
Healthcare Delivery System ◽  
Kaiser Permanente ◽  
Health Records ◽  
Primary Nephrotic Syndrome ◽  
Electronic Health ◽  
Asian Pacific

Introduction Limited population-based data exist about children with primary nephrotic syndrome (NS). Methods We identified a cohort of children with primary NS receiving care in Kaiser Permanente Northern California, an integrated healthcare delivery system caring for >750,000 children. We identified all children <18 years between 1996 and 2012 who had nephrotic range proteinuria (urine ACR>3500 mg/g, urine PCR>3.5 mg/mg, 24-hour urine protein>3500 mg or urine dipstick>300 mg/dL) in laboratory databases or a diagnosis of NS in electronic health records. Nephrologists reviewed health records for clinical presentation and laboratory and biopsy results to confirm primary NS. Results Among 365 cases of confirmed NS, 179 had confirmed primary NS attributed to presumed minimal change disease (MCD) (72%), focal segmental glomerulosclerosis (FSGS) (23%) or membranous nephropathy (MN) (5%). The overall incidence of primary NS was 1.47 (95% Confidence Interval:1.27–1.70) per 100,000 person-years. Biopsy data were available in 40% of cases. Median age for patients with primary NS was 6.9 (interquartile range:3.7 to 12.9) years, 43% were female and 26% were white, 13% black, 17% Asian/Pacific Islander, and 32% Hispanic. Conclusion This population-based identification of children with primary NS leveraging electronic health records can provide a unique approach and platform for describing the natural history of NS and identifying determinants of outcomes in children with primary NS.

scholarly journals Desiderata for computable representations of electronic health records-driven phenotype algorithms

2015 ◽  
Vol 22 (6) ◽  
pp. 1220-1230 ◽  
Author(s):  
Huan Mo ◽  
William K Thompson ◽  
Luke V Rasmussen ◽  
Jennifer A Pacheco ◽  
Guoqian Jiang ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Language Processing ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Relational Algebra ◽  
Common Data Model ◽  
Health Records ◽  
Electronic Health ◽  
Value Sets ◽  
Text Searching

Abstract Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM). Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms. Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility. Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

scholarly journals Integrating Genomic Resources with Electronic Health Records using the HL7 Infobutton Standard

2016 ◽  
Vol 07 (03) ◽  
pp. 817-831 ◽  
Author(s):  
Casey Overby ◽  
Guilherme Del Fiol ◽  
Wendy Rubinstein ◽  
Donna Maglott ◽  
Tristan Nelson ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Search Interface ◽  
Health Records ◽  
Knowledge Resources ◽  
Genomic Resources ◽  
Full Compliance ◽  
Genomic Knowledge ◽  
Electronic Health

SummaryThe Clinical Genome Resource (ClinGen) Electronic Health Record (EHR) Workgroup aims to integrate ClinGen resources with EHRs. A promising option to enable this integration is through the Health Level Seven (HL7) Infobutton Standard. EHR systems that are certified according to the US Meaningful Use program provide HL7-compliant infobutton capabilities, which can be leveraged to support clinical decision-making in genomics.To integrate genomic knowledge resources using the HL7 infobutton standard. Two tactics to achieve this objective were: (1) creating an HL7-compliant search interface for ClinGen, and (2) proposing guidance for genomic resources on achieving HL7 Infobutton standard accessibility and compliance.We built a search interface utilizing OpenInfobutton, an open source reference implementation of the HL7 Infobutton standard. ClinGen resources were assessed for readiness towards HL7 compliance. Finally, based upon our experiences we provide recommendations for publishers seeking to achieve HL7 compliance.Eight genomic resources and two sub-resources were integrated with the ClinGen search engine via OpenInfobutton and the HL7 infobutton standard. Resources we assessed have varying levels of readiness towards HL7-compliance. Furthermore, we found that adoption of standard terminologies used by EHR systems is the main gap to achieve compliance.Genomic resources can be integrated with EHR systems via the HL7 Infobutton standard using OpenInfobutton. Full compliance of genomic resources with the Infobutton standard would further enhance interoperability with EHR systems. Citation: Heale BSE, Overby CL, Del Fiol G, Rubinstein WS, Maglott DR, Nelson TH, Milosavljevic A, Martin CL, Goehringer SR, Freimuth RR, Williams MS. Integrating genomic resources with electronic health records using the HL7 Infobutton standard.

scholarly journals Family history information extraction via deep joint learning

2019 ◽  
Vol 19 (S10) ◽  
Author(s):  
Xue Shi ◽  
Dehuan Jiang ◽  
Yuanhang Huang ◽  
Xiaolong Wang ◽  
Qingcai Chen ◽  
...  
Keyword(s):  
Family History ◽  
Language Processing ◽  
Family Members ◽  
Decision Making Process ◽  
Family History Information ◽  
Health Records ◽  
Joint Learning ◽  
Clinical Text ◽  
History Information ◽  
Entity Identification

Abstract Background Family history (FH) information, including family members, side of family of family members (i.e., maternal or paternal), living status of family members, observations (diseases) of family members, etc., is very important in the decision-making process of disorder diagnosis and treatment. However FH information cannot be used directly by computers as it is always embedded in unstructured text in electronic health records (EHRs). In order to extract FH information form clinical text, there is a need of natural language processing (NLP). In the BioCreative/OHNLP2018 challenge, there is a task regarding FH extraction (i.e., task1), including two subtasks: (1) entity identification, identifying family members and their observations (diseases) mentioned in clinical text; (2) family history extraction, extracting side of family of family members, living status of family members, and observations of family members. For this task, we propose a system based on deep joint learning methods to extract FH information. Our system achieves the highest F1- scores of 0.8901 on subtask1 and 0.6359 on subtask2, respectively.

Handoff Communication and Electronic Health Records

2014 ◽  
Vol 3 (1) ◽  
pp. 162-169 ◽  
Author(s):  
Julie Apker ◽  
Christopher Beach ◽  
Kevin O’Leary ◽  
Jennifer Ptacek ◽  
Dickson Cheung ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Patient Care ◽  
Healthcare Delivery ◽  
Communication Style ◽  
Health Records ◽  
Practical Applications ◽  
Sensemaking Theory ◽  
Making Sense ◽  
Study Results ◽  
Electronic Health

When transferring patient care responsibilities across the healthcare continuum, clinicians strive to communicate safely and effectively, but communication failures exist that threaten patient safety. Although researchers are making great strides in understanding and solving intraservice handoff problems, inter-service transition communication remains underexplored. Further, electronic health records (EHRs) figure prominently in healthcare delivery, but less is known about how EHRs contribute to inter-service handoffs. This descriptive, qualitative study uses Sensemaking Theory to explore EHR-facilitated, inter-service handoffs occurring between emergency medicine and internal/hospitalist medicine physicians. The researchers conducted six focus groups with 16 attending physicians and medical residents at a major Midwestern academic hospital. Findings suggest clinicians hold varied expectations for information content and relational communication/style. Their expectations contribute to making sense of uncertain handoff situations and communication best practices. Participants generally perceive EHRs as tools that, when used appropriately, can enhance handoffs and patient care continuity. Ideas for practical applications are offered based on study results.

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