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

Flow Dominance and Factorization of Transverse Momentum Correlations in Pb-Pb Collisions at the LHC

We present the first measurement of the two-particle transverse momentum differential correlation function, P2≡ ΔpTΔpT/ pT2, in Pb-Pb collisions at sNN=2.76 TeV. Results for P2 are reported as a function of the relative pseudorapidity (Δη) and azimuthal angle (Δφ) between two particles for different collision centralities. The Δφ dependence is found to be largely independent of Δη for |Δη|≥0.9. In the 5% most central Pb-Pb collisions, the two-particle transverse momentum correlation function exhibits a clear double-hump structure around Δφ=π (i.e., on the away side), which is not observed in number correlations in the same centrality range, and thus provides an indication of the dominance of triangular flow in this collision centrality. Fourier decompositions of P2, studied as a function of the collision centrality, show that correlations at |Δη|≥0.9 can be well reproduced by a flow ansatz based on the notion that measured transverse momentum correlations are strictly determined by the collective motion of the system

Inference of effects.

<p>The 90% confidence interval (90% C.I.) for the observed change in primary outcome variables within each group and the P-value (P) for a one-sample t-test with the null-hypothesis µ = 0. A subjectively chosen threshold for the minimal change that could be considered beneficial (Threshold) was used to calculate the chance that the observed changes would be beneficial, trivial or harmful (Inference). One group performed usual swim-training (CON) and another group reduced their training volume by 50% and more than doubled the amount of high-intensity training (HIT).</p

Training intensity and volume.

<p>Training time and milage averaged for every 2^nd week and split into three major training categories. CON: Control groups; INT: intervention group. Li-Aerobic: Technical training, Recovery and low to moderate aerobic training with % of maximal heart rate < 70%. Hi-Aerobic: Intense aerobic training aiming at eliciting close to maximal heart rate and >90% of VO_2max. HIT: “High-Intensity Training” with maximal effort for 20–90 s and a rest: work ratio > 4.</p