Binding SNOMED CT Terms to Archetype Elements

2015 ◽  
Vol 54 (01) ◽  
pp. 45-49 ◽  
Author(s):  
J. Bermudez ◽  
A. Illarramendi ◽  
I. Berges
Keyword(s):  
Electronic Health Records ◽  
Health Systems ◽  
The Other ◽  
Snomed Ct ◽  
Health Records ◽  
Large Size ◽  
Level Of Confidence ◽  
Comparison Methods ◽  
String Comparison ◽  
Text Information

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.Background: The proliferation of archetypes as a means to represent information of Electronic Health Records has raised the need of binding terminological codes – such as SNOMED CT codes – to their elements, in order to identify them univocally. However, the large size of the terminologies makes it difficult to perform this task manually.Objectives: To establish a baseline of results for the aforementioned problem by using off-the-shelf string comparison-based techniques against which results from more complex techniques could be evaluated.Methods: Nine Typed Comparison Methods were evaluated for binding using a set of 487 archetype elements. Their recall was calculated and Friedman and Nemenyi tests were applied in order to assess whether any of the methods outperformed the others.Results: Using the qGrams method along with the ‘Text’ information piece of archetype elements outperforms the other methods if a level of confidence of 90% is considered. A recall of 25.26% is obtained if just one SNOMED CT term is retrieved for each archetype element. This recall rises to 50.51% and 75.56% if 10 and 100 elements are retrieved respectively, that being a reduction of more than 99.99% on the SNOMED CT code set.Conclusions: The baseline has been established following the above-mentioned results. Moreover, it has been observed that although string comparison-based methods do not outperform more sophisticated techniques, they still can be an alternative for providing a reduced set of candidate terms for each archetype element from which the ultimate term can be chosen later in the more-than-likely manual supervision task.

scholarly journals Meaningful Information in the Age of Big Data: A Scoping Review on Social Determinants of Health Data Collection for Electronic Health Records

2019 ◽  
Author(s):  
Kelsey Berg ◽  
Chelsea Doktorchik ◽  
Hude Quan ◽  
Vineet Saini
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Electronic Health Records ◽  
Social Determinants Of Health ◽  
Health Systems ◽  
Social Determinants ◽  
Scoping Review ◽  
Determinants Of Health ◽  
Health Records ◽  
Automated Technology

Abstract Background: Electronic Health Records (EHRs) are key tools for integrating patient data into health information systems (IS). Advances in automated data collection methodology, particularly the collection of social determinants of health (SDOH), provide opportunities to advance health promotion and illness prevention through advanced analytics (i.e. “Big Data” techniques). We ask how current data collection processes in EHRs permit SDOH data to flow throughout health systems. Methods: Using a scoping review framework, we searched through medical literature to identify current practices in SDOH data collection within EHR systems. We extracted relevant information on data collection methodology, specifically focusing on uses of automated technology. We discuss our findings in the context of research methodology and potential for health equity. Results: Practitioners collect a variety of SDOH data at point of care through EHR, predominantly via embedded screening tools and clinical notes, and primarily capturing data on financial security, housing status, and social support. Health systems are increasingly using digital technology in data collection, including natural language processing algorithms. However overall use of automated technology is limited to date. End uses of data pertain to improving system efficiency, patient care-coordination, and addressing health disparities. Discussion & Conclusion: EHRs can realistically promote collection and meaningful use of SDOH data, although EHRs have not extensively been used to collect and manage this type of information. Future applied research on systems-level application of SDOH data is necessary, and should incorporate a range of stakeholders and interdisciplinary teams of researchers and practitioners in fields of health, computing, and social sciences.

scholarly journals An exploration of the properties of the CORE problem list subset and how it facilitates the implementation of SNOMED CT

2015 ◽  
Vol 22 (3) ◽  
pp. 649-658 ◽  
Author(s):  
Kin Wah Fung ◽  
Julia Xu
Keyword(s):  
Electronic Health Records ◽  
Growth Pattern ◽  
Problem List ◽  
Snomed Ct ◽  
Health Records ◽  
The Core ◽  
Core Subset ◽  
Electronic Health ◽  
Systematized Nomenclature Of Medicine ◽  
Core Problem

Abstract Objective Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is the emergent international health terminology standard for encoding clinical information in electronic health records. The CORE Problem List Subset was created to facilitate the terminology’s implementation. This study evaluates the CORE Subset’s coverage and examines its growth pattern as source datasets are being incorporated. Methods Coverage of frequently used terms and the corresponding usage of the covered terms were assessed by “leave-one-out” analysis of the eight datasets constituting the current CORE Subset. The growth pattern was studied using a retrospective experiment, growing the Subset one dataset at a time and examining the relationship between the size of the starting subset and the coverage of frequently used terms in the incoming dataset. Linear regression was used to model that relationship. Results On average, the CORE Subset covered 80.3% of the frequently used terms of the left-out dataset, and the covered terms accounted for 83.7% of term usage. There was a significant positive correlation between the CORE Subset’s size and the coverage of the frequently used terms in an incoming dataset. This implies that the CORE Subset will grow at a progressively slower pace as it gets bigger. Conclusion The CORE Problem List Subset is a useful resource for the implementation of Systematized Nomenclature of Medicine Clinical Terms in electronic health records. It offers good coverage of frequently used terms, which account for a high proportion of term usage. If future datasets are incorporated into the CORE Subset, it is likely that its size will remain small and manageable.

scholarly journals Phenotyping issues for exploring electronic health records to design clinical trials

2020 ◽  
Vol 17 (4) ◽  
pp. 402-404
Author(s):  
Jill Schnall ◽  
LingJiao Zhang ◽  
Jinbo Chen
Keyword(s):  
Clinical Trials ◽  
Electronic Health Records ◽  
Electronic Health Record ◽  
Statistical Methods ◽  
Gold Standard ◽  
The Other ◽  
Health Record ◽  
Health Records ◽  
Electronic Health ◽  
Downstream Analysis

For utilizing electronic health records to help design and conduct clinical trials, an essential first step is to select eligible patients from electronic health records, that is, electronic health record phenotyping. We present two novel statistical methods that can be used in the context of electronic health record phenotyping. One mitigates the requirement for gold-standard control patients in developing phenotyping algorithms, and the other effectively corrects for bias in downstream analysis introduced by study samples contaminated by ineligible subjects.

scholarly journals Documentation of social determinants in electronic health records with and without standardized terminologies: A comparative study

2018 ◽  
Vol 28 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Karen A Monsen ◽  
Joyce M Rudenick ◽  
Nicole Kapinos ◽  
Kathryn Warmbold ◽  
Siobhan K McMahon ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Free Text ◽  
Snomed Ct ◽  
Health Records ◽  
Behavioral Determinants ◽  
Omaha System ◽  
Standardized Terminology ◽  
Electronic Health ◽  
Data Elements ◽  
Improve Health

Background: Electronic health records (EHRs) are a promising new source of population health data that may improve health outcomes. However, little is known about the extent to which social and behavioral determinants of health (SBDH) are currently documented in EHRs, including how SBDH are documented, and by whom. Standardized nursing terminologies have been developed to assess and document SBDH. Objective: We examined the documentation of SBDH in EHRs with and without standardized nursing terminologies. Methods: We carried out a review of the literature for SBDH phrases organized by topic, which were used for analyses. Key informant interviews were conducted regarding SBDH phrases. Results: In nine EHRs (six acute care, three community care) 107 SBDH phrases were documented using free text, structured text, and standardized terminologies in diverse screens and by multiple clinicians, admitting personnel, and other staff. SBDH phrases were documented using one of three standardized terminologies ( N = average number of phrases per terminology per EHR): ICD-9/10 ( N = 1); SNOMED CT ( N = 1); Omaha System ( N = 79). Most often, standardized terminology data were documented by nurses or other clinical staff versus receptionists or other non-clinical personnel. Documentation ‘unknown’ differed significantly between EHRs with and without the Omaha System (mean = 26.0 (standard deviation (SD) = 8.7) versus mean = 74.5 (SD = 16.5)) ( p = .005). SBDH documentation in EHRs differed based on the presence of a nursing terminology. Conclusions: The Omaha System enabled a more comprehensive, holistic assessment and documentation of interoperable SBDH data. Further research is needed to determine SBDH data elements that are needed across settings, the uses of SBDH data in practice, and to examine patient perspectives related to SBDH assessments.

Monitoring inbaskets with feedback to providers enhances the timeliness of patient care.

2017 ◽  
Vol 35 (8_suppl) ◽  
pp. 132-132
Author(s):  
Kerin B. Adelson ◽  
Amelia Anne Trant ◽  
Michael Strait ◽  
Rory Kaplan ◽  
Vanna Dest ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Medical Records ◽  
Weighted Average ◽  
The Other ◽  
Financial Impact ◽  
Multidisciplinary Communication ◽  
Health Records ◽  
Phone Calls ◽  
Or Practice ◽  
Future Work

132 Background: The adoption of Electronic Health Records (EHRs) has changed the workflow for providers. The “inBasket” feature supplants telephone and email communications, replaces paper results review and has become the way to track incomplete encounters. Failure to promptly complete medical records leads to unread notes and impairs multidisciplinary communication. Also, unclosed encounters lead to potential penalties and uncollected fees. One benefit of the inBasket, is that it can be monitored. Methods: We created a report that tracked EPIC inBasket activities by provider including numbers of open charts, unchecked results, phone calls, incomplete dictations and orders needing cosign. The report shows the number and length of time each activity has been delinquent. In December 2015 we began emailing automated reports to all providers with their monthly results. In January 2016 we began an enforcement process for providers with >50 open charts: 30 days to complete charts or practice would be closed to new patients; if >50 remained after 30 days providers had 60 days before complete suspension from practice. We modeled the financial impact of chart closure by creating a weighted average by payor of fees collected per visit. For charts that would have been delinquent beyond payor filing deadlines this represents actual increased revenue. For charts that would have eventually been closed before the filing deadline this represents earlier revenue capture. Results: See table. Conclusions: Inbasket monitoring with structured feedback to providers led to improvement in the number of open charts, the focus of corrective action. We did not see consistent improvements in the other inbasket metrics. Future work will involve a similar approach to all inBasket metrics. Closing charts also has a significant financial impact; we estimate that $483,049 in fees that could have been lost due to exceeding payor deadlines became collectable. [Table: see text]

scholarly journals Evaluation of Smoking Status Identification Using Electronic Health Records and Open-Text Information in a Large Mental Health Case Register

PLoS ONE ◽  
2013 ◽  
Vol 8 (9) ◽  
pp. e74262 ◽  
Author(s):  
Chia-Yi Wu ◽  
Chin-Kuo Chang ◽  
Debbie Robson ◽  
Richard Jackson ◽  
Shaw-Ji Chen ◽  
...  
Keyword(s):  
Mental Health ◽  
Electronic Health Records ◽  
Smoking Status ◽  
Health Records ◽  
Case Register ◽  
Text Information ◽  
Electronic Health

Use of SNOMED CT® and LOINC® to standardize terminology for primary care asthma electronic health records

2017 ◽  
Vol 55 (6) ◽  
pp. 629-639 ◽  
Author(s):  
M. Diane Lougheed ◽  
Nicola. J. Thomas ◽  
Nastasia. V. Wasilewski ◽  
Alison. H. Morra ◽  
Janice. P. Minard
Keyword(s):  
Primary Care ◽  
Electronic Health Records ◽  
Snomed Ct ◽  
Health Records ◽  
Electronic Health ◽  
Standardize Terminology

The Work Practice of Medical Secretaries and the Implementation of Electronic Health Records in Denmark

2005 ◽  
Vol 34 (4) ◽  
pp. 104-111 ◽  
Author(s):  
Pernille Bertelsen ◽  
Christian Nøhr
Keyword(s):  
Electronic Health Records ◽  
Work Practice ◽  
The Other ◽  
Health Record ◽  
Administrative Staff ◽  
Health Records ◽  
Detailed Understanding ◽  
Professional Groups ◽  
Work Tasks ◽  
Electronic Health

The introduction of electronic health records will entail substantial organisational changes to the clinical and administrative staff in hospitals. Hospital owners in Denmark have predicted that these changes will render up to half of medical secretaries redundant. The present study however shows that medical secretaries have a great variety of duties, and often act as the organisational ‘glue’ or connecting thread between other professional groups at the hospital. The aim of this study is to obtain a detailed understanding of the pluralism of work tasks the medical secretaries perform. It is concluded that clinicians as well as nurses depend on medical secretaries, and therefore to reduce the number of secretaries because electronic health record systems are implemented needs very careful thinking, planning and discussion with the other professions involved.

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