To identify potential participants for clinical trials, electronic health records (EHRs) are searched at potential sites. As an alternative, we investigated using medical devices used for real-time diagnostic decisions for trial enrollment.

To project cohorts for a trial in acute coronary syndromes (ACS), we used electrocardiograph-based algorithms that identify ACS or ST elevation myocardial infarction (STEMI) that prompt clinicians to offer patients trial enrollment. We searched six hospitals’ electrocardiograph systems for electrocardiograms (ECGs) meeting the planned trial’s enrollment criterion: ECGs with STEMI or > 75% probability of ACS by the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI). We revised the ACI-TIPI regression to require only data directly from the electrocardiograph, the e-ACI-TIPI using the same data used for the original ACI-TIPI (development set n = 3,453; test set n = 2,315). We also tested both on data from emergency department electrocardiographs from across the US (n = 8,556). We then used ACI-TIPI and e-ACI-TIPI to identify potential cohorts for the ACS trial and compared performance to cohorts from EHR data at the hospitals.

Receiver-operating characteristic (ROC) curve areas on the test set were excellent, 0.89 for ACI-TIPI and 0.84 for the e-ACI-TIPI, as was calibration. On the national electrocardiographic database, ROC areas were 0.78 and 0.69, respectively, and with very good calibration. When tested for detection of patients with > 75% ACS probability, both electrocardiograph-based methods identified eligible patients well, and better than did EHRs.

Using data from medical devices such as electrocardiographs may provide accurate projections of available cohorts for clinical trials.

The use of complementary and alternative medicine (CAM) is increasing. Access to CAM through primary care referral is common with some of these referrals occurring through existing NHS contracts. Yet currently little is understood about General Practitioners (GPs) referrals to CAM via an NHS contract.

This exploratory qualitative study was designed to explore UK GPs experiences of referring patients to CAM under an NHS contract.

Semistructured interviews were conducted with 10 GPs in the UK, purposively sampled, who referred patients under an NHS contract to a private CAM clinic, staffed by medically qualified CAM practitioners. Qualitative methodology making use of the framework approach was used to undertake the interviews and analysis.

The decision of GPs to refer a patient to CAM through an NHS contract is complex and based on negotiation between patient and GP but is ultimately determined by the patients’ openness and motivation towards CAM. Most GPs would consider referral when there are no other therapeutic options for their patients. Various factors, including clinical evidence, increase the likelihood of referral but two overarching influences are crucial: (a) the individual GPs positive attitude to, and experience of CAM, including a trusting relationship with the CAM practitioner; and (b) the patient’s attitude towards CAM. In-depth knowledge of CAM was not a vital factor for most GPs in the decision to refer.

A CAM referral only took place if the patient readily agreed with this therapeutic approach, and in this respect it may differ from referrals by GPs to conventional medicinal practitioners. Such an approach, then, relies on patients having a positive view of CAM and as such could result in inequity in treatment access. Increasing knowledge about and evidence for CAM will assist GPs in making appropriate referrals in a timely manner. We propose a preliminary model that explains our findings about referrals considering patients need as well as the medical process. As data saturation may not have been achieved, further investigation is warranted to confirm or refute these suggestions.