SMART on FHIR: a standards-based, interoperable apps platform for electronic health records

Mandel, Joshua C.; Kreda, David; Mandl, Kenneth D.; Kohane, Isaac S.; Ramoni, Rachel

doi:10.1093/jamia/ocv189

Cited by 569 publications

(386 citation statements)

References 5 publications

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“…HL7 (Health Level-7) is a standards agency that develops standards for electronic health resources and FHIR is a standard that enables electronic health records (EHR) interoperability through web-service interfaces. [20] In the next section one such implementation is described that demonstrates real-time updates to an EHR sandbox using FHIR web-services and SNOMED CT coding system, which is a coding system to describe clinical concepts, vocabulary, descriptions and inter-relationships between clinical codes.…”

Section: B Wearable Ecg Kits and Healthcare Interoperabilitymentioning

confidence: 99%

“…The paper also describes the methods used to filter raw ECG waveform and convert it to MITDB format to train ECG classifiers for predictive analysis. A real-time smart IoT (Internet of Things) system has been demonstrated, which is based on a widely accepted HL7 FHIR standard [20]. FHIR standard and its underlying infrastructure is now being adopted by many device manufacturers to enable instruments to update electronic health records in real time.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: B Wearable Ecg Kits and Healthcare Interoperabilitymentioning

confidence: 99%

Section: Introductionmentioning

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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“…The NHS (National Health Services, UK) have an informatics facility in the form of a Developer network hosted by the NHS Digital that provide the specification for data and services interoperability [39] The GP Connect Ecosystem have defined standards and web-services that can be used to upload data to the NHS data centres (offered by NHS GPSoC -GP System of Choice or GP Connect) so the real-time data can be uploaded to NHS cloud and monitored by the patients' General Practitioners. FHIR is a well-known HL7 standard used by health services in several countries across the globe to enable web-services upload and host patient records in Electronic Health Records (EHR) [40]. In order to demonstrate gathering of samples, passing the same to the analytical server and logging these to EHR in a standard recognized format, a publicly available FHIR server instance was identified and a RESTful web-service was hosted in an HSPC sandbox [41][42].…”

Section: B Real-time Data Acquisition and Web-servicesmentioning

confidence: 99%

“…The paper then describes the methods used to train ECG classifiers and their predictive aspect. For a case study, a real-time smart IoT (Internet of Things) system has been proposed which could be integrated with the GP Connect infrastructure provided by NHS, UK and which is based on a widely accepted HL7 FHIR standard [40]. Such infrastructure is now also becoming available in many countries across the globe and the proposed system could be easily integrated to relay patient health status to the General Practitioner (GP) or Physician in real time.…”

Section: Introductionmentioning

confidence: 99%

ECG classification and prognostic approach towards personalized healthcare

Walinjkar

Woods

2017

2017 International Conference on Social Media, Wearable and Web Analytics (Social Media)

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Abstract-A very important aspect of personalized healthcare is to continuously monitor an individual's health using wearable biomedical devices and essentially to analyse and if possible to predict potential health hazards that may prove fatal if not treated in time. The prediction aspect embedded in the system helps in avoiding delays in providing timely medical treatment, even before an individual reaches a critical condition. Despite of the availability of modern wearable health monitoring devices, the real-time analyses and prediction component seems to be missing with these devices. The research work illustrated in this paper, at an outset, focussed on constantly monitoring an individual's ECG readings using a wearable 3-lead ECG kit and more importantly focussed on performing real-time analyses to detect arrhythmia to be able to identify and predict heart risk. Also, current research shows extensive use of heart rate variability (HRV) analysis and machine learning for arrhythmia classification, which however depends on the morphology of the ECG waveforms and the sensitivity of the ECG equipment. Since a wearable 3-lead ECG kit was used, the accuracy of classification had to be dealt with at the machine learning phase, so a unique feature extraction method was developed to increase the accuracy of classification. As a case study a very widely used Arrhythmia database (MIT-BIH, Physionet) was used to develop learning, classification and prediction models. Neuralnet fitting models on the extracted features showed mean-squared error of as low as 0.0085 and regression value as high as 0.99. Current experiments show 99.4% accuracy using k-NN Classification models and show values of Cross-Entropy Error of 7.6 and misclassification error value of 1.2 on test data using scaled conjugate gradient pattern matching algorithms. Software components were developed for wearable devices that took ECG readings from a 3-Lead ECG data acquisition kit in real time, de-noised, filtered and relayed the sample readings to the telehealth analytical server. The analytical server performed the classification and prediction tasks based on the trained classification models and could raise appropriate alarms if ECG abnormalities of V (Premature Ventricular Contraction: PVC), A (Atrial Premature Beat: APB), L (Left bundle branch block beat), R (Right bundle branch block beat) type annotations in MITDB were detected. The instruments were networked using IoT (Internet of Things) devices and abnormal ECG events related to arrhythmia, from analytical server could be logged using an FHIR web service implementation, according to a SNOMED coding system and could be accessed in Electronic Health Record by the concerned medic to take appropriate and timely decisions. The system focussed on 'preventive care rather than remedial cure' which has become a major focus of all the health care and cure institutions across the globe.

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“…Fortunately, this fact is something widely recognized by the current Health IT community and steps have been directed to tackle these problems. From requirements analysis of data standards [9] and development data integration mechanisms [10] for making DSS interoperable, the emergence of new lightweight web services standards like the HL7 Fast Healthcare Interoperability Resources (FHIR) [11], to substantial investments from public bodies that ended up with real deployments and piloting of patient guidance systems. A good example is MobiGuide [10], [12], a project funded by the European Commission under the seventh framework program (FP7).…”

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confidence: 99%

Generating Big Data Sets from Knowledge-based Decision Support Systems to Pursue Value-based Healthcare

González-Ferrer¹,

Seara²,

Cháfer³

et al. 2018

IJIMAI

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Talking about Big Data in healthcare we usually refer to how to use data collected from current electronic medical records, either structured or unstructured, to answer clinically relevant questions. This operation is typically carried out by means of analytics tools (e.g. machine learning) or by extracting relevant data from patient summaries through natural language processing techniques. From other perspective of research in medical informatics, powerful initiatives have emerged to help physicians taking decisions, in both diagnostics and therapeutics, built from the existing medical evidence (i.e. knowledge-based decision support systems). Much of the problems these tools have shown, when used in real clinical settings, are related to their implementation and deployment, more than failing in its support, but, technology is slowly overcoming interoperability and integration issues. Beyond the point-of-care decision support these tools can provide, the data generated when using them, even in controlled trials, could be used to further analyze facts that are traditionally ignored in the current clinical practice. In this paper, we reflect on the technologies available to make the leap and how they could help driving healthcare organizations shifting to a value-based healthcare philosophy.

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SMART on FHIR: a standards-based, interoperable apps platform for electronic health records

Cited by 569 publications

References 5 publications

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

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

ECG classification and prognostic approach towards personalized healthcare

Generating Big Data Sets from Knowledge-based Decision Support Systems to Pursue Value-based Healthcare

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