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In recent times in the healthcare industry, the use of quality improvement has become one of the main drivers of efforts to close the gap between current and expected level of performance. At the heart of quality improvement is the emphasis on the use of data. Data is used to show whether a change introduced is an improvement. But data is only fit for this purpose if it is of high quality. This notwithstanding, obtaining quality data from health care facilities presents a challenge and the situation is even direr in resource limited settings. This therefore calls for approaches to ensure that…mehr

Produktbeschreibung
In recent times in the healthcare industry, the use of quality improvement has become one of the main drivers of efforts to close the gap between current and expected level of performance. At the heart of quality improvement is the emphasis on the use of data. Data is used to show whether a change introduced is an improvement. But data is only fit for this purpose if it is of high quality. This notwithstanding, obtaining quality data from health care facilities presents a challenge and the situation is even direr in resource limited settings. This therefore calls for approaches to ensure that data generated by healthcare facilities can be used to drive improvement works. But any of such approaches must not involve many extra resources and should be easy to arrange and do. This is where the use of data quality assessment is relevant. "Challenges of Routine Maternal Health Information: A Data Quality Improvement Project" sheds light on the current data challenges that healthcare facilities face and how data quality assessment can close that gap. It can also guide health personnel handling data or researchers and NGOs who may be considering a projects to improve health data quality.
Autorenporträt
Paul Konka, BSc Saúde Pública: Estudou Informática em Saúde Pública na Universidade Católica do Gana. É Oficial de Controlo de Doenças no Serviço de Saúde do Gana e tem experiência e conhecimentos na melhoria da qualidade, incluindo a avaliação da qualidade dos dados.