Y F van der Heijden1,2, J Hughes3, D W Dowdy4, E Streicher5, V Chihota6,7, K R Jacobson8, R Warren5, G Theron5. 1. Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA. 2. Vanderbilt Tuberculosis Center, Nashville, TN, USA. 3. Médecins Sans Frontières, Khayelitsha, South Africa. 4. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 5. DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa. 6. The Aurum Institute, Johannesburg, South Africa. 7. School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. 8. Section of Infectious Diseases, Boston University School of Medicine, Boston, MA, USA.
Abstract
SETTING: Tuberculosis (TB) diagnosis and treatment requires patients to have multiple encounters with health care systems and the different stakeholders who play a role in curing them to coordinate their efforts. To optimize this process, high-quality, readily available data are required. Data systems to facilitate these linkages are a neglected priority which, if weak, fundamentally undermine TB control interventions. OBJECTIVE: To describe lessons learnt from the use of programmatic data for TB patient care and research. DESIGN: We did a survey of researcher and clinical provider experiences with information systems and developed a tiered approach to addressing frequently reported barriers to high-quality care. RESULTS: Unreliable linkages, incomplete data, lack of a reliable unique patient identifier, and lack of data management expertise were the most important data-related barriers to high-quality patient care and research. We propose the creation of health service delivery environments that facilitate, prioritize, and evaluate high-quality data entry during patient or specimen registration. CONCLUSION: An integrated approach, focused on high-quality data, and centered on unique patient identification will form the foundation for linkages across health systems that reduce patient management errors, bolster surveillance, and enhance the quality of research based on programmatic data.
SETTING: Tuberculosis (TB) diagnosis and treatment requires patients to have multiple encounters with health care systems and the different stakeholders who play a role in curing them to coordinate their efforts. To optimize this process, high-quality, readily available data are required. Data systems to facilitate these linkages are a neglected priority which, if weak, fundamentally undermine TB control interventions. OBJECTIVE: To describe lessons learnt from the use of programmatic data for TB patient care and research. DESIGN: We did a survey of researcher and clinical provider experiences with information systems and developed a tiered approach to addressing frequently reported barriers to high-quality care. RESULTS: Unreliable linkages, incomplete data, lack of a reliable unique patient identifier, and lack of data management expertise were the most important data-related barriers to high-quality patient care and research. We propose the creation of health service delivery environments that facilitate, prioritize, and evaluate high-quality data entry during patient or specimen registration. CONCLUSION: An integrated approach, focused on high-quality data, and centered on unique patient identification will form the foundation for linkages across health systems that reduce patient management errors, bolster surveillance, and enhance the quality of research based on programmatic data.
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