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15 results on '"Nyamai, Rachel"'

3. Characteristics of admissions and variations in the use of basic investigations, treatments and outcomes in Kenyan hospitals within a new Clinical Information Network

Author

Ayieko, Philip, Ogero, Morris, Makone, Boniface, Julius, Thomas, Mbevi, George, Nyachiro, Wycliffe, Nyamai, Rachel, Were, Fred, Githanga, David, Irimu, Grace, English, Mike, Tuti, Timothy, Gathara, David, Gachau, Susan, Nʼgar Nʼgar, Samuel, Aduro, Nick, Mutai, Beatrice, Mutai, Loice, Emadau, Caren, Mutiso, Cecilia, Nzioki, Charles, Chabi, Martin, Kanyingi, Francis, Mithamo, Agnes, Kuria, Margaret, Otido, Sam, Kariuki, Alice, Njiiri, Peris, Inginia, Rachel, Musabi, Melab, Kigen, Barnabas, Ochieng, Grace, and Thuranira, Lydia

Published
2016
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9. Assessing the ability of health information systems in hospitals to support evidence-informed decisions in Kenya

Author

Kihuba, Elesban, Gathara, David, Mwinga, Stephen, Mulaku, Mercy, Kosgei, Rose, Mogoa, Wycliffe, Nyamai, Rachel, English, Mike, and Wellcome Trust

Subjects
Evidence-Based Medicine, lcsh:Public aspects of medicine, lcsh:RA1-1270, health information system, Kenya, hospital management information system, Cross-Sectional Studies, Hospital Administration, data quality, Research Design, Hospital Information Systems, Electronic Health Records, Humans, Original Article, Health systems, Public Health, RA
Abstract

Background: Hospital management information systems (HMIS) is a key component of national health information systems (HIS), and actions required of hospital management to support information generation in Kenya are articulated in specific policy documents. We conducted an evaluation of core functions of data generation and reporting within hospitals in Kenya to facilitate interpretation of national reports and to provide guidance on key areas requiring improvement to support data use in decision making.Design: The survey was a cross-sectional, cluster sample study conducted in 22 hospitals in Kenya. The statistical analysis was descriptive with adjustment for clustering.Results: Most of the HMIS departments complied with formal guidance to develop departmental plans. However, only a few (3/22) had carried out a data quality audit in the 12 months prior to the survey. On average 3% (range 1–8%) of the total hospital income was allocated to the HMIS departments. About half of the records officer positions were filled and about half (13/22) of hospitals had implemented some form of electronic health record largely focused on improving patient billing and not linked to the district HIS. Completeness of manual patient registers varied, being 90% (95% CI 80.1–99.3%), 75.8% (95% CI 68.7–82.8%), and 58% (95% CI 50.4–65.1%) in maternal child health clinic, maternity, and pediatric wards, respectively. Vital events notification rates were low with 25.7, 42.6, and 71.3% of neonatal deaths, infant deaths, and live births recorded, respectively. Routine hospital reports suggested slight over-reporting of live births and under-reporting of fresh stillbirths and neonatal deaths.Conclusions: Study findings indicate that the HMIS does not deliver quality data. Significant constraints exist in data quality assurance, supervisory support, data infrastructure in respect to information and communications technology application, human resources, financial resources, and integration.Keywords: health information system; hospital management information system; data quality(Published: 31 July 2014)Citation: Glob Health Action 2014, 7: 24859 - http://dx.doi.org/10.3402/gha.v7.24859

Published
2016

11. Characteristics of admissions and variations in the use of basic investigations, treatments and outcomes in Kenyan hospitals within a new Clinical Information Network

Author

Ayieko, Philip, primary, Ogero, Morris, additional, Makone, Boniface, additional, Julius, Thomas, additional, Mbevi, George, additional, Nyachiro, Wycliffe, additional, Nyamai, Rachel, additional, Were, Fred, additional, Githanga, David, additional, Irimu, Grace, additional, and English, Mike, additional

Published
2015
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12. Variation in and risk factors for paediatric inpatient all-cause mortality in a low income setting: data from an emerging clinical information network.

Author

Gathara, David, Malla, Lucas, Ayieko, Philip, Karuri, Stella, Nyamai, Rachel, Irimu, Grace, van Hensbroek, Michael Boele, Allen, Elizabeth, English, Mike, and Clinical Information Network

Subjects
MORTALITY, HOSPITAL care, PEDIATRICS, RESOURCE allocation, LOW-income countries, CLINICAL medicine, DEVELOPING countries, HEALTH services accessibility, HEALTH status indicators, INFORMATION storage & retrieval systems, MEDICAL databases, PUBLIC hospitals, RESEARCH funding, KEY performance indicators (Management), HOSPITAL mortality
Abstract

Background: Hospital mortality data can inform planning for health interventions and may help optimize resource allocation if they are reliable and appropriately interpreted. However such data are often not available in low income countries including Kenya.Methods: Data from the Clinical Information Network covering 12 county hospitals' paediatric admissions aged 2-59 months for the periods September 2013 to March 2015 were used to describe mortality across differing contexts and to explore whether simple clinical characteristics used to classify severity of illness in common treatment guidelines are consistently associated with inpatient mortality. Regression models accounting for hospital identity and malaria prevalence (low or high) were used. Multiple imputation for missing data was based on a missing at random assumption with sensitivity analyses based on pattern mixture missing not at random assumptions.Results: The overall cluster adjusted crude mortality rate across hospitals was 6 · 2% with an almost 5 fold variation across sites (95% CI 4 · 9 to 7 · 8; range 2 · 1% - 11 · 0%). Hospital identity was significantly associated with mortality. Clinical features included in guidelines for common diseases to assess severity of illness were consistently associated with mortality in multivariable analyses (AROC =0 · 86).Conclusion: All-cause mortality is highly variable across hospitals and associated with clinical risk factors identified in disease specific guidelines. A panel of these clinical features may provide a basic common data framework as part of improved health information systems to support evaluations of quality and outcomes of care at scale and inform health system strengthening efforts. [ABSTRACT FROM AUTHOR]

Published
2017
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