An Agent-Oriented Data Sharing and Decision Support Service for Hubei Provincial Care Platform

2015 ◽  
pp. 429-440 ◽  
Author(s):  
Liang Xiao
Keyword(s):  
Decision Support ◽  
Data Sharing ◽  
Support Service

A Pharmacogenomics Clinical Decision Support Service Based on FHIR and CDS Hooks

2018 ◽  
Vol 57 (S 02) ◽  
pp. e115-e123 ◽  
Author(s):  
R.H. Dolin ◽  
A. Boxwala ◽  
J. Shalaby
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Genetic Data ◽  
Gene Interaction ◽  
Clinical Decision ◽  
Support Service ◽  
Sequencing Data ◽  
National Human ◽  
Functional Prototype ◽  
The U.S

Objectives Pharmacogenomics (PGx) is often considered a low-hanging fruit for genomics–electronic health record (EHR) integrations, and many have expressed the notion that drug–gene interaction checking might one day become as much a commodity in EHRs as drug–drug and drug–allergy checking. In addition, the U.S. Office of the National Coordinator has recognized the trend toward storing complete sequencing data outside the EHR in a Genomic Archiving and Communication System (GACS) and has emphasized the need for “pilots that test Fast Healthcare Interoperability Resources (FHIR) Genomics for GACS integration with EHRs.” We sought to develop a PGx clinical decision support (CDS) service, leveraging the emerging FHIR and CDS Hooks standards, and based on an assumption that pharmacogene sequencing data would be stored alongside the EHR in a GACS. Methods We developed a PGx CDS service as a functional prototype. The service is triggered by a medication order in the EHR. When evoked, the service looks for relevant genetic data in a GACS and returns corresponding recommendations back to the ordering clinician. Where the patient has no genetic data on file, the service can recommend pretreatment genetic testing where applicable. Results Overall, we were able to meet our objectives and deploy a functional prototype, interfaced with a commercial EHR. We identified several areas where FHIR or CDS Hooks lacked necessary semantics or have implementation ambiguity. Primary FHIR challenges included multiple ways to say the same thing, which exacerbated the complexity of variant to allele conversion and lack of representation of deoxyribonucleic acid region(s) studied. Primary CDS Hooks challenges included the complexity of executing an authenticated query against one system (GACS) upon being triggered by a different system (the EHR), and limitations in the types of actionable recommendations that can be returned to the EHR. Conclusions In conclusion, we have found that PGx CDS based on FHIR and CDS Hooks appears to represent a promising means of genomics–EHR integration. More real-world testing along with a set of use-case driven GACS interface requirements will push us closer to the U.S. National Human Genome Research Institute vision of a plug-in PGx app.

scholarly journals Enhanced Situational Awareness through a Decision Support Service for Optimal Allocation of Resources and Response Capacity

2017 ◽  
Vol 32 (S1) ◽  
pp. S229
Author(s):  
Irene Christodoulou ◽  
George M. Milis ◽  
Panayiotis Kolios ◽  
Christos Panayiotou ◽  
Marios Polycarpou ◽  
...  
Keyword(s):  
Decision Support ◽  
Optimal Allocation ◽  
Situational Awareness ◽  
Support Service ◽  
Allocation Of Resources ◽  
Response Capacity

scholarly journals A Robotcar-based Proof of Concept Model System for Dilemma Zone Decision Support Service

2014 ◽  
Vol 13 (4) ◽  
pp. 57-62
Author(s):  
Hyukjoon Lee ◽  
◽  
Young-uk Chung ◽  
Hyungkeun Lee
Keyword(s):  
Decision Support ◽  
Support Service ◽  
Model System ◽  
Proof Of Concept ◽  
Dilemma Zone ◽  
Concept Model

scholarly journals Decentralized Digital Health Services Caught Between the Pressure for Innovation and the Burden of Regulations

2021 ◽  
Author(s):  
Lea Meier ◽  
Kevin Tippenhauer ◽  
Murat Sariyar
Keyword(s):  
Decision Support ◽  
Health Services ◽  
Digital Health ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Support Service ◽  
Third Party ◽  
Trade Offs ◽  
External Software ◽  
Multiple Challenges

Multiple challenges await third-party digital health services when trying to enter the health market. Prominent examples of such services are clinical decision support systems provided as external software. Uncertainty about their challenges, technical as well as legal, pose serious hurdles for many innovations to be adopted early on. There are many options and trade-offs to provide digital healthcare solutions as a third-party service. This paper discusses them by referring to a pharmacogenetic decision support service. By providing best-practices, scenario descriptions and templates designed for third-party services with respect to legal and technical issues, obstacles and uncertainties can be reduced, which will have an impact on better diagnoses and treatments in the healthcare system.

An Efficient Data Analytics-Cloud Integrated Decision Support Service for Smart Agriculture

2018 ◽  
Vol 3 (2) ◽  
pp. 1-12 ◽  
Author(s):  
Balaji Prabhu B V ◽  
◽  
Dakshayini M ◽  
Keyword(s):  
Decision Support ◽  
Data Analytics ◽  
Support Service ◽  
Efficient Data ◽  
Smart Agriculture

scholarly journals Achieving data privacy for decision support systems in times of massive data sharing

2022 ◽  
Author(s):  
Rabeeha Fazal ◽  
Munam Ali Shah ◽  
Hasan Ali Khattak ◽  
Hafiz Tayyab Rauf ◽  
Fadi Al-Turjman
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Data Sharing ◽  
Data Privacy ◽  
Support Systems ◽  
Massive Data

Introducing SNOMED-CT* Coding into an Electronic Health Record: Impact on Clinicians, Data Sharing and Research potential *Systemised Nomenclature of Medicine Clinical Terminology (Preprint)

2021 ◽  
Author(s):  
Tanya Pankhurst ◽  
Felicity Evison ◽  
Jolene Atia ◽  
Suzy Gallier ◽  
Jamie Coleman ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Data Sharing ◽  
Clinical Decision ◽  
Health Record ◽  
Coding System ◽  
Snomed Ct ◽  
Healthcare Settings ◽  
Electronic Health ◽  
Systematized Nomenclature Of Medicine

BACKGROUND This study describes the conversion within an existing Electronic Health Record (EHR) from the coding system International Classification of Diseases version 10 (ICD-10) to the Systematized Nomenclature Of MEDicine - Clinical Terms (SNOMED-CT), for collection of patients’ history and diagnoses. The setting is a large acute hospital, designing and building its own EHR. Well-designed EHRs create opportunities for continuous data collection which can be utilised in Clinical Decision Support rules to drive patient safety. Collected data can be exchanged across healthcare systems to support patients in all healthcare settings. Data can be used for research to prevent disease and protect future populations. OBJECTIVE To migrate a current electronic health record, with all relevant patient data, to the coding system, Systematized Nomenclature of Medicine - Clinical Terms, to optimise clinical utilisation and clinical decision support, and facilitate data sharing across organisational boundaries for national programmes, and remodelling of medical pathways. METHODS The study used qualitative and quantitative data to understand the successes and gaps in the project, clinician attitudes to the new tool, and future use. RESULTS The new coding system (“tool”) was well received and immediately widely used in all specialities. It resulted in increased, accurate and clinically relevant data collection. Clinicians appreciated the increased depth and detail of the new coding, welcomed the potential for both data sharing and research, and gave extensive feedback for further development. CONCLUSIONS Successful implementation aligned the Trust with national strategy and can be used as a Blueprint for similar projects in other healthcare settings. CLINICALTRIAL NA

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