Analysis of the process of representing clinical statements for decision-support applications: a comparison of openEHR archetypes and HL7 virtual medical record

González-Ferrer, Arturo; Peleg, Mor; Marcos, Mar; Maldonado, José Alberto

doi:10.1007/s10916-016-0524-3

Cited by 14 publications

(7 citation statements)

References 21 publications

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“…Many demonstration pilots have been successfully tested including the Center for Disease Control (CDC) Opioid CDS Demonstration Project that encodes CDC's guidelines for Opioid prescription for chronic pain management 28 29 . Archetypes, the clinical information models developed by the international virtual standards development agency, openEHR, were found to contain more granular representational detail and structure compared to HL7's vMR, thus providing better access and integration to patient's data, and some potential for applications beyond CDS 30 31 .…”

Section: Resultsmentioning

confidence: 99%

Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017

Shankar

Anderson

2018

Yearb Med Inform

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Section: Resultsmentioning

confidence: 99%

Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017

Shankar

Anderson

2018

Yearb Med Inform

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“…González-Ferrer et al report positive results about the adequacy of openEHR archetypes within the specification of decision support-specific clinical statements [14]. Chen et al demonstrate initial successes in deploying a regional clinical decision support system based on GDL [15].…”

Section: Discussionmentioning

confidence: 99%

Applying openEHR’s Guideline Definition Language to the SITS international stroke treatment registry: a European retrospective observational study

Anani

Mazya

Chen

et al. 2017

BMC Med Inform Decis Mak

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BackgroundInteroperability standards intend to standardise health information, clinical practice guidelines intend to standardise care procedures, and patient data registries are vital for monitoring quality of care and for clinical research. This study combines all three: it uses interoperability specifications to model guideline knowledge and applies the result to registry data.MethodsWe applied the openEHR Guideline Definition Language (GDL) to data from 18,400 European patients in the Safe Implementation of Treatments in Stroke (SITS) registry to retrospectively check their compliance with European recommendations for acute stroke treatment.ResultsComparing compliance rates obtained with GDL to those obtained by conventional statistical data analysis yielded a complete match, suggesting that GDL technology is reliable for guideline compliance checking.ConclusionsThe successful application of a standard guideline formalism to a large patient registry dataset is an important step toward widespread implementation of computer-interpretable guidelines in clinical practice and registry-based research. Application of the methodology gave important results on the evolution of stroke care in Europe, important both for quality of care monitoring and clinical research.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0401-5) contains supplementary material, which is available to authorized users.

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“…The WFDB analytical server software on Linux system performed arrhythmia detection and classification tasks. A JSON (JavaScript Object Notation) structure [12] that encapsulated the sample readings was adopted which passed the samples to the HAPI (HL7 API) FHIR test server [33][34] to be logged to EHR if anomalies were detected ( Figure 5). The ECG samples encapsulated with JSON data structures were transmitted over 10 seconds intervals as ECG rhythm strips are generally for a 10 seconds duration [2][3] and ECG readings conformed to HL7 and FHIR standards specification [13].…”

Section: A Signal Processing Dataset Preparation and Analysismentioning

confidence: 99%

Personalized wearable systems for real-time ECG classification and healthcare interoperability: Real-time ECG classification and FHIR interoperability

Walinjkar

Woods

2017

2017 Internet Technologies and Applications (ITA)

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Abstract-Continuous monitoring of an individual's health using wearable biomedical devices is becoming a norm these days with a large number of wearable kits becoming easily available. Modern wearable health monitoring devices have become easily available in the consumer market, however, realtime analyses and prediction along with alerts and alarms about a health hazard are not adequately addressed in such devices. Taking ECG monitoring as a case study the research paper focusses on signal processing, arrhythmia detection and classification and at the same time focusses on updating the electronic health records database in realtime such that the concerned medical practitioners become aware of an emergent situation the patient being monitored might face. Also, heart rate variability (HRV) analysis is usually considered as a basis for arrhythmia classification which largely depends on the morphology of the ECG waveforms and the sensitivity of the biopotential measurements of the ECG kits, so it may not yield accurate results. Initially, the ECG readings from the 3-Lead ECG analog front-end were de-noised, zero-offset corrected, filtered using recursive least square adaptive filter and smoothed using Savitzky-Golay filter and subsequently passed to the data analysis component with a unique feature extraction method to increase the accuracy of classification. The machine learning models trained on MITDB arrhythmia database (MIT-BIH Physionet) showed more than 97% accuracy using kNN classifiers. Neuralnet fitting models showed mean-squared error of as low as 0.0085 and regression value as high as 0.99. ECG abnormalities based on annotations in MITDB could be classified and these ECG observations could be logged to a server implementation based on FHIR standards. The instruments were networked using IoT (Internet of Things) devices and ECG event observations were coded according to SNOMED coding system and could be accessed in Electronic Health Record by the concerned medic to take appropriate and timely decisions. The system emphasizes on 'preventive care rather than remedial cure' as the next generation personalized health-care monitoring devices become available.

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Analysis of the process of representing clinical statements for decision-support applications: a comparison of openEHR archetypes and HL7 virtual medical record

Cited by 14 publications

References 21 publications

Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017

Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017

Applying openEHR’s Guideline Definition Language to the SITS international stroke treatment registry: a European retrospective observational study

Personalized wearable systems for real-time ECG classification and healthcare interoperability: Real-time ECG classification and FHIR interoperability

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