David H Peters1, Manish Kohli, Maya Mascarenhas, Krishna Rao. 1. Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205, USA. dpeters@jhsph.edu
Abstract
OBJECTIVE: The objective was to test whether a decision support technology for non-physicians can increase health care utilization and quality. DESIGN: Before and after measurements were taken from a systematic random sample of patients and staff at randomly assigned intervention and control facilities. SETTING: The study took place at primary health facilities in rural Tamil Nadu, India. PARTICIPANTS: One thousand two hundred and eighty-six patients and 82 staff were interviewed. INTERVENTION: A computer-assisted decision support technology was introduced to assist with patient screening. MAIN OUTCOME MEASURES: Outcome measures included new patient visits per month, a Global Patient Assessment of Care Index, and health worker attitude variables. RESULTS: There was a difference of difference of 430 new patient visits per month at the intervention sites (P = 0.005), an increase from baseline of 18% at intervention sites compared with a decline of 5% at control sites. The intervention was associated with significant improvements in a Global Patient Assessment of Care Index (mean difference of difference 7.9, P < 0.001). The largest gains were made in patient communication, technical quality, and general satisfaction with care. The attitudes of public health workers toward the new technology and their jobs did not change. CONCLUSIONS: Decision support technologies have considerable potential to improve coverage and quality of health care for the poor and where there is no doctor, but the unreceptive attitude of public health workers would need to be overcome. Application of these technologies should take advantage of their popularity with patients and the opportunity to work through the private sector.
RCT Entities:
OBJECTIVE: The objective was to test whether a decision support technology for non-physicians can increase health care utilization and quality. DESIGN: Before and after measurements were taken from a systematic random sample of patients and staff at randomly assigned intervention and control facilities. SETTING: The study took place at primary health facilities in rural Tamil Nadu, India. PARTICIPANTS: One thousand two hundred and eighty-six patients and 82 staff were interviewed. INTERVENTION: A computer-assisted decision support technology was introduced to assist with patient screening. MAIN OUTCOME MEASURES: Outcome measures included new patient visits per month, a Global Patient Assessment of Care Index, and health worker attitude variables. RESULTS: There was a difference of difference of 430 new patient visits per month at the intervention sites (P = 0.005), an increase from baseline of 18% at intervention sites compared with a decline of 5% at control sites. The intervention was associated with significant improvements in a Global Patient Assessment of Care Index (mean difference of difference 7.9, P < 0.001). The largest gains were made in patient communication, technical quality, and general satisfaction with care. The attitudes of public health workers toward the new technology and their jobs did not change. CONCLUSIONS: Decision support technologies have considerable potential to improve coverage and quality of health care for the poor and where there is no doctor, but the unreceptive attitude of public health workers would need to be overcome. Application of these technologies should take advantage of their popularity with patients and the opportunity to work through the private sector.
Authors: Nathan Mensah; Felix Sukums; Timothy Awine; Andreas Meid; John Williams; Patricia Akweongo; Jens Kaltschmidt; Walter E Haefeli; Antje Blank Journal: Glob Health Action Date: 2015-01-27 Impact factor: 2.640
Authors: Felix Sukums; Nathan Mensah; Rose Mpembeni; Jens Kaltschmidt; Walter E Haefeli; Antje Blank Journal: Glob Health Action Date: 2014-10-27 Impact factor: 2.640
Authors: Ashwin Vasan; David C Mabey; Simran Chaudhri; Helen-Ann Brown Epstein; Stephen D Lawn Journal: Health Policy Plan Date: 2017-04-01 Impact factor: 3.344