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Objective: OpenMRS (www.openmrs.org) is a configurable open source electronic medical record application developed and maintained by a large network of open source developers coordinated by the Regenstrief Institute and Partners in Health and mainly used for HIV patient and treatment information management in Africa. Our objective is to develop an open Implementers Network for OpenMRS to provide regional support for the growing number of OpenMRS implementations in Africa and to include African developers and implementers in the future growth of OpenMRS., Methods: We have developed the OpenMRS Implementers Network using a dedicated Wiki site and e-mail server. We have also organized annual meetings in South Africa and regional training courses at African locations where OpenMRS is being implemented. An OpenMRS Internship program has been initiated and we have started collaborating with similar networks and projects working in Africa. To evaluate its potential, OpenMRS was implemented initially at one site in South Africa by a single implementer using a downloadable OpenMRS application and only the OpenMRS Implementers Network for support., Results: The OpenMRS Implementers Network Wiki and list server have grown into effective means of providing implementation support and forums for exchange of implementation experiences. The annual OpenMRS Implementers meeting has been held in South Africa for the past three years and is attracting successively larger numbers of participants with almost 200 implementers and developers attending the 2008 meeting in Durban, South Africa. Six African developers are presently registered on the first intake of the OpenMRS Internship program. Successful collaborations have been started with several African developer groups and projects initiated to develop interoperability between OpenMRS and various applications. The South African OpenMRS Implementer group successfully configured, installed and maintained an integrated HIV/TB OpenMRS application without significant programming support. Since then, this model has been replicated in several other African sites. The OpenMRS Implementers Network has contributed substantially to the growth and sustainability of OpenMRS in Africa and has become a useful way of including Africans in the development and implementation of OpenMRS in developing countries. The Network provides valuable support and enables a basic OpenMRS application to be implemented in the absence of onsite programmers.

We developed adaptive turnaround documents (ATDs) to address longstanding challenges inherent in acquiring structured data at the point of care. These computer-generated paper forms both request and receive patient tailored information specifically for electronic storage. In our pilot, we evaluated the usability, accuracy, and user acceptance of an ATD designed to enrich a pediatric preventative care decision support system. The system had an overall digit recognition rate of 98.6% (95% CI: 98.3 to 98.9) and a marksense accuracy of 99.2% (95% CI: 99.1 to 99.3). More importantly, the system reliably extracted all data from 56.6% (95% CI: 53.3 to 59.9) of our pilot forms without the need for a verification step. These results translate to a minimal workflow burden to end users. This suggests that ATDs can serve as an inexpensive, workflow-sensitive means of structured data acquisition in the clinical setting.

Advances in optical character recognition (OCR) software and computer hardware have stimulated a reevaluation of the technology and its ability to capture structured clinical data from preexisting paper forms. In our pilot evaluation, we measured the accuracy and feasibility of capturing vitals data from a pediatric encounter form that has been in use for over twenty years. We found that the software had a digit recognition rate of 92.4% (95% confidence interval: 91.6 to 93.2) overall. More importantly, this system was approximately three times as fast as our existing method of data entry. These preliminary results suggest that with further refinements in the approach and additional development, we may be able to incorporate OCR as another method for capturing structured clinical data.