A computer-based medical record system and personal digital assistants to assess and follow patients with respiratory tract infections visiting a rural Kenyan health centre

We modified the existing EMR to include details on patients with ARIs. The EMR database was then used to identify patients with ARIs who were prospectively followed up by a research assistant who rode a bicycle to patients' homes and entered data into a PDA.A total of 2986 clinic visits for 2009 adult patients with respiratory infections were registered in the database between August 2002 and January 2005; 433 patients were selected for outcome assessments. These patients were followed up in the villages and assessed at 7 and 30 days later. Complete follow-up data were obtained on 381 patients (88%) and merged with data from the enrollment visit's electronic medical records and subsequent health center visits to assess duration of illness and complications. Symptoms improved at 7 and 30 days, but a substantial minority of patients had persistent symptoms. Eleven percent of patients sought additional care for their respiratory infection.EMRs and PDA are useful tools for performing prospective clinical research in resource constrained developing countries.The potential of health care information technology to support clinical and research activities has not been fully explored in developing countries. Proper assessment of the processes and outcomes of care is required before resources can be appropriately allocated and care interventions can be planned. Having reliable clinical data from community health centers would therefore be invaluable in making decisions and planning interventions.Although electronic medical records are becoming increasingly prevalent in developed countries, they are still rare in developing countries. Using electronic medical records (EMRs) and personal digital assistants (PDAs) to identify patients for prospective investigations and collect outcomes data can increase the efficiency of such investigations. The feasibility of using PDAs for longitudinal data collection in poor rural settings has not been fully explored. Their portability and lo