Integrating Electronic Health Records in Clinical Decision Support Systems

2015 ◽  
pp. 407-416 ◽  
Author(s):  
Eider Sanchez ◽  
Carlos Toro ◽  
Manuel Graña
Keyword(s):  
Decision Support ◽  
Electronic Health Records ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Health Records ◽  
Electronic Health

scholarly journals Use of Electronic Health Records and Clinical Decision Support Systems for Antimicrobial Stewardship

2014 ◽  
Vol 59 (suppl_3) ◽  
pp. S122-S133 ◽  
Author(s):  
Graeme N. Forrest ◽  
Trevor C. Van Schooneveld ◽  
Ravina Kullar ◽  
Lucas T. Schulz ◽  
Phu Duong ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Records ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Antimicrobial Stewardship ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Health Records ◽  
Electronic Health

Mining Electronic Health Records to Guide and Support Clinical Decision Support Systems

2017 ◽  
pp. 184-201 ◽  
Author(s):  
Jitendra Jonnagaddala ◽  
Hong-Jie Dai ◽  
Pradeep Ray ◽  
Siaw-Teng Liaw
Keyword(s):  
Decision Support ◽  
Text Mining ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Association Studies ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Genome Wide ◽  
Electronic Health

Clinical decision support systems require well-designed electronic health record (EHR) systems and vice versa. The data stored or captured in EHRs are diverse and include demographics, billing, medications, and laboratory reports; and can be categorized as structured, semi-structured and unstructured data. Various data and text mining techniques have been used to extract these data from EHRs for use in decision support, quality improvement and research. Mining EHRs has been used to identify cohorts, correlated phenotypes in genome-wide association studies, disease correlations and risk factors, drug-drug interactions, and to improve health services. However, mining EHR data is a challenge with many issues and barriers. The aim of this chapter is to discuss how data and text mining techniques may guide and support the building of improved clinical decision support systems.

scholarly journals Merits, features, and desiderata to be considered when developing electronic health records with embedded clinical decision support systems in Palestinian hospitals: a consensus study

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Ramzi Shawahna
Keyword(s):  
Decision Support ◽  
Electronic Health Records ◽  
Analytic Hierarchy Process ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Analytic Hierarchy ◽  
Health Records ◽  
Hierarchy Process

Abstract Background Electronic health records (EHRs) with embedded clinical decision support systems (CDSSs) have the potential to improve healthcare delivery. This study was conducted to explore merits, features, and desiderata to be considered when planning for, designing, developing, implementing, piloting, evaluating, maintaining, upgrading, and/or using EHRs with CDSSs. Methods A mixed-method combining the Delphi technique and Analytic Hierarchy Process was used. Potentially important items were collected after a thorough search of the literature and from interviews with key contact experts (n = 19). Opinions and views of the 76 panelists on the use of EHRs were also explored. Iterative Delphi rounds were conducted to achieve consensus on 122 potentially important items by a panel of 76 participants. Items on which consensus was achieved were ranked in the order of their importance using the Analytic Hierarchy Process. Results Of the 122 potentially important items presented to the panelists in the Delphi rounds, consensus was achieved on 110 (90.2%) items. Of these, 16 (14.5%) items were related to the demographic characteristics of the patient, 16 (14.5%) were related to prescribing medications, 16 (14.5%) were related to checking prescriptions and alerts, 14 (12.7%) items were related to the patient’s identity, 13 (11.8%) items were related to patient assessment, 12 (10.9%) items were related to the quality of alerts, 11 (10%) items were related to admission and discharge of the patient, 9 (8.2%) items were general features, and 3 (2.7%) items were related to diseases and making diagnosis. Conclusions In this study, merits, features, and desiderata to be considered when planning for, designing, developing, implementing, piloting, evaluating, maintaining, upgrading, and/or using EHRs with CDSSs were explored. Considering items on which consensus was achieved might promote congruence and safe use of EHRs. Further studies are still needed to determine if these recommendations can improve patient safety and outcomes in Palestinian hospitals.

Integration of Hospital Information and Clinical Decision Support Systems to Enable the Reuse of Electronic Health Record Data

2017 ◽  
Vol 56 (03) ◽  
pp. 238-247 ◽  
Author(s):  
Georgy Kopanitsa
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Data ◽  
Health Record ◽  
Electronic Health

SummaryBackground: The efficiency and acceptance of clinical decision support systems (CDSS) can increase if they reuse medical data captured during health care delivery. High heterogeneity of the existing legacy data formats has become the main barrier for the reuse of data. Thus, we need to apply data modeling mechanisms that provide standardization, transformation, accumulation and querying medical data to allow its reuse.Objectives: In this paper, we focus on the interoperability issues of the hospital information systems (HIS) and CDSS data integration.Materials and Methods: Our study is based on the approach proposed by Marcos et al. where archetypes are used as a standardized mechanism for the interaction of a CDSS with an electronic health record (EHR). We build an integration tool to enable CDSSs collect data from various institutions without a need for modifications in the implementation. The approach implies development of a conceptual level as a set of archetypes representing concepts required by a CDSS.Results: Treatment case data from Regional Clinical Hospital in Tomsk, Russia was extracted, transformed and loaded to the archetype database of a clinical decision support system. Test records’ normalization has been performed by defining transformation and aggregation rules between the EHR data and the archetypes. These mapping rules were used to automatically generate openEHR compliant data. After the transformation, archetype data instances were loaded into the CDSS archetype based data storage. The performance times showed acceptable performance for the extraction stage with a mean of 17.428 s per year (3436 case records). The transformation times were also acceptable with 136.954 s per year (0.039 s per one instance). The accuracy evaluation showed the correctness and applicability of the method for the wide range of HISes. These operations were performed without interrupting the HIS workflow to prevent the HISes from disturbing the service provision to the users.Conclusions: The project results have proven that archetype based technologies are mature enough to be applied in routine operations that require extraction, transformation, loading and querying medical data from heterogeneous EHR systems. Inference models in clinical research and CDSS can benefit from this by defining queries to a valid data set with known structure and constraints. The standard based nature of the archetype approach allows an easy integration of CDSSs with existing EHR systems.

Mining Electronic Health Records to Guide and Support Clinical Decision Support Systems

2016 ◽  
pp. 252-269 ◽  
Author(s):  
Jitendra Jonnagaddala ◽  
Hong-Jie Dai ◽  
Pradeep Ray ◽  
Siaw-Teng Liaw
Keyword(s):  
Decision Support ◽  
Text Mining ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Association Studies ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Genome Wide ◽  
Electronic Health

Clinical decision support systems require well-designed electronic health record (EHR) systems and vice versa. The data stored or captured in EHRs are diverse and include demographics, billing, medications, and laboratory reports; and can be categorized as structured, semi-structured and unstructured data. Various data and text mining techniques have been used to extract these data from EHRs for use in decision support, quality improvement and research. Mining EHRs has been used to identify cohorts, correlated phenotypes in genome-wide association studies, disease correlations and risk factors, drug-drug interactions, and to improve health services. However, mining EHR data is a challenge with many issues and barriers. The aim of this chapter is to discuss how data and text mining techniques may guide and support the building of improved clinical decision support systems.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

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