What to prescribe for a patient in general practice when the choice of treatments has a limited evidence base? Tjeerd-Pieter van Staa and colleagues argue that using electronic health records to enter patients into randomised trials of treatments in real time could provide the answer

Background Pragmatic trials compare the effects of different decisions in usual clinical practice. Objectives To develop and evaluate methods to implement simple pragmatic trials using routinely collected electronic health records (EHRs) and recruiting patients at the point of care; to identify the barriers and facilitators for general practitioners (GPs) and patients and the experiences of trial participants. Design Two exemplar randomised trials (Retropro and eLung) with qualitative evaluations. Setting Four hundred and fifty-nine English and Scottish general practices contributing EHRs to a research database, of which 17 participated in the trials. Participants Retropro aimed to recruit 300 patients with hypercholesterolaemia and high cardiovascular risk and eLung aimed to recruit 150 patients with a chronic obstructive pulmonary disease exacerbation. Interventions Retropro randomised between simvastatin and atorvastatin and eLung between immediate antibiotics and deferred or non-use. eLung recruited during an unscheduled consultation using EHR flagging. Main outcome measure Successful trial completion with implementation of information technology (IT) system for flagging and data processing and documentation of operational and scientific experiences. Data sources EHR research database. Results The governance approval process took over 3 years. A total of 58.8% of the practices (n = 270) expressed interest in participating. The number of interested practices dropped substantially with each stage of the governance process. In Retropro, 6.5% of the practices (n = 30) were eventually approved and 3.7% (n = 17) recruited patients; in eLung, these numbers were 6.8% (n = 31) and 1.3% (n = 6) respectively. Retropro successfully completed recruitment (301 patients) whereas eLung recruited 31 patients. Retropro recruited 20.6% of all statin starters in recruiting practices and 1.1% in the EHR database; the comparable numbers for eLung were 32.3% and 0.9% respectively. The IT system allowed for complex eligibility criteria with central on and off control of recruitment and flagging at a practice. Good Clinical Practice guidelines, governance and consent procedures were found to have substantially affected the intended simple nature of the trials. One qualitative study of 13 clinicians found that clinicians were generally positive about the principle of computerised trial recruitment (flagging during consultation). However, trials which did not include patients with acute illness were favoured. The second qualitative process evaluation interviewed 27 GPs about their actual experiences, including declining, recruiting and non-recruiting GPs. Opportunistic patient recruitment during a routine GP consultation was found to be the most controversial element. The actual experiences of recruiting patients during unscheduled consultation were generally more positive than the hypothetical views of GPs. Several of the recruiting GPs reported the process took 5 minutes and was straightforward and feasible on most occasions. Almost all GPs expressed their strong support for the use of EHRs for trials. Ten eLung participants were interviewed, all of whom considered it acceptable to be recruited during a consultation and to use EHRs for trials. Conclusions EHR point-of-care trials are feasible, although the recruitment of clinicians is a major challenge owing to the complexity of trial approvals. These trials will provide substantial evidence on clinical effectiveness only if trial interventions and participating clinicians and patients are typical of usual clinical care and trials are simple to initiate and conduct. Recommendations for research include the development of evidence and implementation of risk proportionality in trial governance and conduct. Trial registration Current Controlled Trials ISRCTN33113202 and ISRCTN72035428. Funding This project was funded by the NIHR Health Technology Assessment programme and the Wellcome Trust and will be published in full in Health Technology Assessment; Vol. 18, No. 43. See the NIHR Journals Library website for further project information.

A content-centric network is one which supports host-to-content routing, rather than the host-to-host routing of the existing Internet. This paper investigates the potential of caching data at the router-level in content-centric networks. To achieve this, two measurement sets are combined to gain an understanding of the potential caching benefits of deploying content-centric protocols over the current Internet topology. The first set of measurements is a study of the BitTorrent network, which provides detailed traces of content request patterns. This is then combined with CAIDA's ITDK Internet traces to replay the content requests over a real-world topology. Using this data, simulations are performed to measure how effective content-centric networking would have been if it were available to these consumers/providers. We find that larger cache sizes (10,000 packets) can create significant reductions in packet path lengths. On average, 2.02 hops are saved through caching (a 20% reduction), whilst also allowing 11% of data requests to be maintained within the requester's AS. Importantly, we also show that these benefits extend significantly beyond that of edge caching by allowing transit ASes to also reduce traffic.

Objective Biomedical research increasingly relies on the integration of information from multiple heterogeneous data sources. Despite the fact that s

The learning health care system refers to the cycle of turning health care data into knowledge, translating that knowledge into practice, and creating new data by means of advanced information technology. The electronic Primary Care Research Network (ePCRN) was a project, funded by the U.S. National Institutes of Health, with the aim to facilitate clinical research using primary care electronic health records (EHRs). We identified the requirements necessary to deliver clinical studies via a distributed electronic network linked to EHRs. After we explored a variety of informatics solutions, we constructed a functional prototype of the software. We then explored the barriers to adoption of the prototype software within U.S. practice-based research networks. We developed a system to assist in the identification of eligible cohorts from EHR data. To preserve privacy, counts and flagging were performed remotely, and no data were transferred out of the EHR. A lack of batch export facilities from EHR systems and ambiguities in the coding of clinical data, such as blood pressure, have so far prevented a full-scale deployment. We created an international consortium and a model for sharing further ePCRN development across a variety of ongoing projects in the United States and Europe. A means of accessing health care data for research is not sufficient in itself to deliver a learning health care system. EHR systems need to use sophisticated tools to capture and preserve rich clinical context in coded data, and business models need to be developed that incentivize all stakeholders from clinicians to vendors to participate in the system.