Integrating Telemedicine Solutions with Electronic Health Records; Evaluation of Alternatives based on the Proposed Reference Architecture for Norway. Report 02-2016

This report studies the way forward for how a telemedicine solution can be integrated for exchange of data with an existing Electronic Health Record (EHR) system. The solution used an example for this report is based on a telemedicine solution for COPD patients (Chronic Obstructive Pulmonary Disease) developed in the project “Collaborative Point-of-Care Services Agder: Follow-up of COPD patients as part of the United4Health EU Project», with financial support from the Research Council of Norway. In addition, the EHR solution from DIPS ASA is used as an example of an existing system for integration. Important parameters for choosing way forward on how to are: urgency with regards to timeline level of structuring of the data. compliance with the reference architecture1proposed by the Norwegian Directorate of eHealth (NDE) Three alternative ways forward are discussed in this report, based on four different scenarios with their respectively defined use-cases. Possibilities of integration exists already today which may support one of the use cases in the simplest way, but may not be a futureproof solution regarding functionality and recommended standards. Such a solution is supported by DIPS Classic as well as DIPS Arena by using HL7 V3 interface in DIPS. The journal data may be stored in an unstructured way as a PDF document in a patients EHR. To send structured data from a Telemedicine System to an EHR will be the preferred way for the future, and will support several use cases in a more efficient way. This will require more work in total and is dependent on other parties (external storage, DIPS etc) for building infrastructure and new interfaces. Such solutions will still be of high interest in the future. This report describes two different scenarios for how such solutions can be implemented in the future using either external storage and XDS.b or using FHIR/OpenEHR. Which of these alternatives that will be the leading standard or best practice is hard to predict, since it will highly depend on how the user requirements from the health care market will request such solutions, and how the standardization requirements from National authorities evolves in the next years. In addition, it depends on how the developers/vendors of both telemedicine solutions and EHR-systems will responds to these requirements

Outcome measures in chronic obstructive pulmonary disease (COPD): strengths and limitations

Current methods for assessing clinical outcomes in COPD mainly rely on physiological tests combined with the use of questionnaires. The present review considers commonly used outcome measures such as lung function, health status, exercise capacity and physical activity, dyspnoea, exacerbations, the multi-dimensional BODE score, and mortality. Based on current published data, we provide a concise overview of the principles, strengths and weaknesses, and discuss open questions related to each methodology. Reviewed is the current set of markers for measuring clinically relevant outcomes with particular emphasis on their limitations and opportunities that should be recognized when assessing and interpreting their use in clinical trials of COPD

A Mobile ECG Monitoring System with Context Collection

Preventative health management represents a shift from the traditional approach of reactive treatment-based healthcare towards a proactive wellness-management approach where patients are encouraged to stay healthy with expert support when they need it, at any location and any time. This work represents a step along the road towards proactive, preventative healthcare for cardiac patients. It seeks to develop a smart mobile ECG monitoring system that requests and records context information about what is happening around the subject when an arrhythmia event occurs. Context information about the subject’s activities of daily living will, it is hoped, provide an enriched data set for clinicians and so improve clinical decision making. As a first step towards a mobile cardiac wellness guideline system, the authors present a system which can receive bio-signals that are wirelessly streamed across a body area network from Bluetooth enabled electrocardiographs. The system can store signals as they arrive while also responding to significant changes in Electrocardiogram activity. The authors have developed a prototype on a handheld computer that detects and responds to changes in the calculated heart rate as detected in an ECG signal. Although the general approach taken in this work could be applied to a wide range of bio-signals, the work focuses on ECG signals. The components of the system are, - A Bluetooth receiver, data collection and storage module - A real-time ECG beat detection algorithm. - An Event-Condition-Action (E-CA) rule base which decides when to request context information from the user. – A simple user interface which can request additional information from the user. A selection of real-time ECG detection algorithms were investigated for this work and one algorithm was tested in MATLAB and then implemented in Java. In order to collect ECG signals (and in principle any signals), the generalised data collection architecture has also been developed using Java and Bluetooth technology. An Event-Condition-Action (ECA) rule based expert system evaluates the changes in heart beat interval to decide when to interact with the user to request context information

EHR: a Sensing Technology Readiness Model for Lifestyle Changes

Interest in developing user-centered sensing technologies for personalized behavior change has gained significant momentum. However, very little research work has been done to understand issues relative to user readiness and adoption of the sensing technologies to change their behaviors, especially the motivations as well as the concerns and impediments for adoption. We have developed a model called EHR (e-health readiness), to understand and explain the relationship between user habits, perceived healthiness and beliefs towards sensing technologies, and how these factors influence user readiness to use sensing technologies to manage their wellness. We then validate the model using psychometric methods by a large-scale user study (N = 541). Results show overall readiness to sensing technologies is positively influenced by readiness to monitor health conditions, share data within social networks, and receive recommendations. Additionally, readiness is significantly impacted by perceptions of healthiness, technology satisfaction and usefulness of such technology. Finally, we summarize user motivations and concerns for pervasive sensing tools through qualitative analysis on their comments. We present this model and the results of this survey to shed light on designing future sensing technologies for behavior change