BACKGROUND: Recruitment to trials in primary care is often difficult, particularly when practice staff need to identify study participants with acute conditions during consultations. The Scottish Acute Recruitment Management Application (SARMA) system is linked to general practice electronic medical record (EMR) systems and is designed to provide recruitment support to multi-centre trials by screening patients against trial inclusion criteria and alerting practice staff if the patient appears eligible. For patients willing to learn more about the trial, the software allows practice staff to send the patient's contact details to the research team by text message. AIM: To evaluate the ability of the software to support trial recruitment. DESIGN OF STUDY: Software evaluation embedded in a randomised controlled trial. SETTING: Five general practices in Tayside and Fife, Scotland. METHODS:SARMA was used to support recruitment to a feasibility trial (the Response to Oral Agents in Diabetes, or ROAD trial) looking at users of oral therapy in diabetes. The technical performance of the software and its utility as a recruitment tool were evaluated. RESULTS: The software was successfully installed at four of the five general practices and recruited 11 of the 29 participants for ROAD (other methods were letter and direct invitation by a practice nurse) and had a recruitment return of 35% (11 of 31 texts sent led to a recruitment). Screen failures were relatively low (7 of 31 referred). Practice staff members were positive about the system. CONCLUSION: An automated recruitment tool can support primary care trials in Scotland and has the potential to support recruitment in other jurisdictions. It offers a low-cost supplement to other trial recruitment methods and is likely to have a much lower screen failure rate than blanket approaches such as mailshots and newspaper campaigns.
RCT Entities:
BACKGROUND: Recruitment to trials in primary care is often difficult, particularly when practice staff need to identify study participants with acute conditions during consultations. The Scottish Acute Recruitment Management Application (SARMA) system is linked to general practice electronic medical record (EMR) systems and is designed to provide recruitment support to multi-centre trials by screening patients against trial inclusion criteria and alerting practice staff if the patient appears eligible. For patients willing to learn more about the trial, the software allows practice staff to send the patient's contact details to the research team by text message. AIM: To evaluate the ability of the software to support trial recruitment. DESIGN OF STUDY: Software evaluation embedded in a randomised controlled trial. SETTING: Five general practices in Tayside and Fife, Scotland. METHODS: SARMA was used to support recruitment to a feasibility trial (the Response to Oral Agents in Diabetes, or ROAD trial) looking at users of oral therapy in diabetes. The technical performance of the software and its utility as a recruitment tool were evaluated. RESULTS: The software was successfully installed at four of the five general practices and recruited 11 of the 29 participants for ROAD (other methods were letter and direct invitation by a practice nurse) and had a recruitment return of 35% (11 of 31 texts sent led to a recruitment). Screen failures were relatively low (7 of 31 referred). Practice staff members were positive about the system. CONCLUSION: An automated recruitment tool can support primary care trials in Scotland and has the potential to support recruitment in other jurisdictions. It offers a low-cost supplement to other trial recruitment methods and is likely to have a much lower screen failure rate than blanket approaches such as mailshots and newspaper campaigns.
Authors: Valentina I Petkov; Lynne T Penberthy; Bassam A Dahman; Andrew Poklepovic; Chris W Gillam; James H McDermott Journal: Exp Biol Med (Maywood) Date: 2013-10-09
Authors: Taylor R Pressler; Po-Yin Yen; Jing Ding; Jianhua Liu; Peter J Embi; Philip R O Payne Journal: BMC Med Inform Decis Mak Date: 2012-05-30 Impact factor: 2.796
Authors: Simon de Lusignan; Rob Navarro; Tom Chan; Glenys Parry; Kim Dent-Brown; Tony Kendrick Journal: BMC Med Inform Decis Mak Date: 2011-10-13 Impact factor: 2.796
Authors: Yizhao Ni; Jordan Wright; John Perentesis; Todd Lingren; Louise Deleger; Megan Kaiser; Isaac Kohane; Imre Solti Journal: BMC Med Inform Decis Mak Date: 2015-04-14 Impact factor: 2.796