In many low income countries health information systems are poorly equipped to provide detailed information on hospital care and outcomes. Information is thus rarely used to support practice improvement. We describe efforts to tackle this challenge and to foster learning concerning collection and use of information. This could improve hospital services in Kenya.We are developing a Clinical Information Network, a collaboration spanning 14 hospitals, policy makers and researchers with the goal of improving information available on the quality of inpatient paediatric care across common childhood illnesses in Kenya. Standardised data from hospitals’ paediatric wards are collected using non-commercial and open source tools. We have implemented procedures for promoting data quality which are performed prior to a process of semi-automated analysis and routine report generation for hospitals in the network.In the first phase of the Clinical Information Network, we collected data on over 65 000 admission episodes. Despite clinicians’ initial unfamiliarity with routine performance reporting, we found that, as an initial focus, both engaging with each hospital and providing them information helped improve the quality of data and therefore reports. The process has involved mutual learning and building of trust in the data and should provide the basis for collaborative efforts to improve care, to understand patient outcome, and to evaluate interventions through shared learning.We have found that hospitals are willing to support the development of a clinically focused but geographically dispersed Clinical Information Network in a low-income setting. Such networks show considerable promise as platforms for collaborative efforts to improve care, to provide better information for decision making, and to enable locally relevant research.

BackgroundImproved hospital care is needed to reduce newborn mortality in low/middle-income countries (LMIC). Nurses are essential to the delivery of safe and effective care, but nurse shortages and high patient workloads may result in missed care. We aimed to examine nursing care delivered to sick newborns and identify missed care using direct observational methods.MethodsA cross-sectional study using direct-observational methods for 216 newborns admitted in six health facilities in Nairobi, Kenya, was used to determine which tasks were completed. We report the frequency of tasks done and develop a nursing care index (NCI), an unweighted summary score of nursing tasks done for each baby, to explore how task completion is related to organisational and newborn characteristics.ResultsNursing tasks most commonly completed were handing over between shifts (97%), checking and where necessary changing diapers (96%). Tasks with lowest completion rates included nursing review of newborns (38%) and assessment of babies on phototherapy (15%). Overall the mean NCI was 60% (95% CI 58% to 62%), at least 80% of tasks were completed for only 14% of babies. Private sector facilities had a median ratio of babies to nurses of 3, with a maximum of 7 babies per nurse. In the public sector, the median ratio was 19 babies and a maximum exceeding 25 babies per nurse. In exploratory multivariable analyses, ratios of ≥12 babies per nurse were associated with a 24-point reduction in the mean NCI compared with ratios of ≤3 babies per nurse.ConclusionA significant proportion of nursing care is missed with potentially serious effects on patient safety and outcomes in this LMIC setting. Given that nurses caring for fewer babies on average performed more of the expected tasks, addressing nursing is key to ensuring delivery of essential aspects of care as part of improving quality and safety.

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ObjectiveTo share approaches and innovations adopted to deliver a relatively inexpensive clinical data management (CDM) framework within a low-income setting that aims to deliver quality pediatric data useful for supporting research, strengthening the information culture and informing improvement efforts in local clinical practice.Materials and methodsThe authors implemented a CDM framework to support a Clinical Information Network (CIN) using Research Electronic Data Capture (REDCap), a noncommercial software solution designed for rapid development and deployment of electronic data capture tools. It was used for collection of standardized data from case records of multiple hospitals’ pediatric wards. R, an open-source statistical language, was used for data quality enhancement, analysis, and report generation for the hospitals.ResultsIn the first year of CIN, the authors have developed innovative solutions to support the implementation of a secure, rapid pediatric data collection system spanning 14 hospital sites with stringent data quality checks. Data have been collated on over 37 000 admission episodes, with considerable improvement in clinical documentation of admissions observed. Using meta-programming techniques in R, coupled with branching logic, randomization, data lookup, and Application Programming Interface (API) features offered by REDCap, CDM tasks were configured and automated to ensure quality data was delivered for clinical improvement and research use.ConclusionA low-cost clinically focused but geographically dispersed quality CDM (Clinical Data Management) in a long-term, multi-site, and real world context can be achieved and sustained and challenges can be overcome through thoughtful design and implementation of open-source tools for handling data and supporting research.

BackgroundHospital 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.DesignThe survey was a cross-sectional, cluster sample study conducted in 22 hospitals in Kenya. The statistical analysis was descriptive with adjustment for clustering.ResultsMost 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.ConclusionsStudy 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.