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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...
Citations
... Resource availability has an influence on routine health information utilization by healthcare institutions (Gopalan et al., 2013). This sentiment is supported by Kihuba et al., (2014), that HMIS departments are generally poorly financed at facility level. On average only 3% of the total annual income (from cost sharing and government grants) is allocated to the HMIS departments with a range of 18% as opposed to a policy requiring that at least 10% should be allocated to HMIS (Kihuba et al., 2014). ...
... This sentiment is supported by Kihuba et al., (2014), that HMIS departments are generally poorly financed at facility level. On average only 3% of the total annual income (from cost sharing and government grants) is allocated to the HMIS departments with a range of 18% as opposed to a policy requiring that at least 10% should be allocated to HMIS (Kihuba et al., 2014). Even the USAID-Kenya, (2010), noted that there was little allocation of resources for HIS activities, leave alone investment in capacity building and creation of knowledge management that would facilitate learning and sharing of experiences and best practices ...
... There is little, if any, funds allocated for HIS activities and not all health facilities in Mombasa County have HRIOs. The results support sentiments by Kihuba et al., (2014), that HMIS departments within heath facilities are inadequately financed where only 3% of the total annual budget is being allocated to the HMIS departments. Even USAID-Kenya, (2010), noted that there was inadequate resource allocation not only for HMIS but also for some simple activities like publications and distribution of reports. ...
... Ultimately, reproductive health data need to be complete, accurate, and timely (Arts et al., 2002;Kihuba et al., 2014). When services rendered are documented and properly kept, the information will be useful for making decisions and allocating resources that will help develop reproductive health care. ...
Accurate data are crucial for effective decision-making and policy development in health care. However, poor-quality data and the non-use of information can hinder these processes. This study focused on the quality of reproductive health data in South Africa (1998 & 2016) and Nigeria (2013 & 2018) and aimed to identify factors contributing to the non-use of quality data on reproductive health. The study examined the distribution of observable characteristics of women aged 15-49 years in each country, specifically focusing on the timing of the first postnatal checkup for mothers. It explored the relationship between selected variables and the timing of postnatal checkups. The study's conceptual framework highlighted the connection between utilization of health care services, women's knowledge, perception, and behaviour related to reproductive health, as well as the role of managing the health information system in informing policies and programs to enhance reproductive health outcomes. The findings revealed disparities in data, country-specific peculiarities, and variations in data collection methods. In Nigeria, variables such as age, region, place of residence, education, wealth index, age at first birth, preceding birth interval, and place of delivery were associated with the timing of postnatal checks. However, in South Africa, only a few socioeconomic and demographic variables were associated with the timing of postnatal checks. The study emphasizes the importance of regularly assessing data quality to plan, reliably monitor health services, and improve reproductive health outcomes.
... The poor quality of healthcare data in LMIC has signi cant implications for health outcomes, planning, and resource allocation 22 . Several factors contribute to these data quality issues, including inadequate training of healthcare providers in data collection, management, and analysis 5 , challenges in the design and implementation of health information systems, and issues related to healthcare facilities 18 . ...
... Issues related to healthcare facilities, such as inadequate infrastructure, lack of essential equipment, and insu cient sta ng, can also negatively impact data quality 18 . Improving the quality of healthcare facilities by investing in infrastructure, equipment, and human resources can help enhance data collection processes and overall data quality 22 . ...
... Addressing these challenges requires concerted efforts from governments, international organizations, and other stakeholders. This can involve developing and implementing policies that support the improvement of healthcare providers' skills, enhancing the design and implementation of health information systems, strengthening data collection processes, and investing in the quality of healthcare facilities 22 . ...
Background: Accurate and reliable healthcare data are essential for effective policy decision-making, resource allocation, and improved health outcomes. In Tanzania, healthcare data utilization remains low, with various obstacles limiting the effective use of health information for decision-making. This study aimed to identify and understand the key obstacles that limit healthcare data utilization for policy decision-making in Tanzania, based on a qualitative panel discussion.
Methods: A qualitative research approach was employed, focusing on a panel discussion with five experts in the field of health systems and Health Management Information Systems (HMIS) during the 8th Tanzania Health Summit. The panelists were purposively selected, representing diverse backgrounds and expertise in healthcare data utilization. Data were collected from the panelists' presentations and audience interaction, with 400 participants attending the session. A thematic analysis approach was used to identify the key obstacles limiting healthcare data utilization in Tanzania.
Results: The study identifies key obstacles limiting healthcare data utilization in Tanzania, which include unskilled professionals, multiple health information systems, poor data quality, competing donor priorities, poor communication, healthcare staff fatigue, and low working morale. These challenges hinder effective data-driven decision-making and healthcare service delivery. Addressing these obstacles requires a multifaceted approach involving collaboration among stakeholders, investment in capacity building, harmonization of health information systems, improved communication, and prioritization of healthcare worker well-being.
Conclusion: The findings of this study provide valuable insights into the challenges faced in healthcare data utilization for policy decision-making in Tanzania. Addressing these obstacles is critical for enhancing the capacity of healthcare professionals, policymakers, and other stakeholders to make informed decisions based on accurate, high-quality healthcare data. The study's results can serve as a foundation for targeted interventions and policy recommendations aimed at improving healthcare data utilization in Tanzania and in similar resource-limited settings.
... В то же время использование различных медицинских информационных систем здравоохранения в качестве ресурса эпидемиологических сведений имеет некоторые ограничения, например техническую несовместимость, в результате которой медицинские работники испытывают трудности при обмене файлами [6,7], отличающийся/сложный веб-дизайн интерфейса [8,9], особенности требований к обеспечению безопасности данных [10,11], различную функциональность [12,13], недостаточную профессиональную поддержку [14,15], ограничения доступа в систему (например, удаленного) [8,16], низкое качество внесенных данных [17,18]. Большинство указанных проблем возникают вследствие несоблюдения стандартов, процедур и пользовательских рекомендаций [19]. ...
Background . The emergence of new functional capabilities of statistical accounting made it possible to conduct a comparative analysis of the morbidity of allergic pathologies according to the registers of allergists and pediatricians from the Unified Medical Information and Analytical System (UMIAS) of Moscow with data from the Form of Federal Statistical Observation No. 12 (FSO No. 12). The aim of the study is to investigate the potential of using UMIAS for assessing/monitoring the morbidity of allergic diseases, including bronchial asthma in children, using the example of several outpatient clinics (OPCs) in Moscow. Methods . A study of combined design has been carried out. The data of children of several OPCs in Moscow were analyzed — data from UMIAS (observation registers of pediatricians and allergist-immunologists) and from the reporting forms of the FSO No. 12. Results . For a comparative analysis of statistical data from UMIAS and FSO No. 12, we studied the information of 60,851 children under 18 years of age. It was revealed that out of 60,851 children: allergic rhinitis according to FSO No. 12 and UMIAS was established in 1001 and 1059 patients; atopic dermatitis — in 142 and 345; urticaria — in 363 and 33; angioedema — in 4 and 16, respectively; food allergy — in 233 children according (to FSO No. 12) and in none of the children (according to UMIAS). Out of 60,851 children, 619 children were diagnosed with bronchial asthma according to the annual report (FSO No. 12) and 537 according to the pediatrician’s observation registers (UMIAS). At the same time, it was found that the diagnosis of bronchial asthma is not available as a separate nosology in the registry of an allergist-immunologist, and information about children with bronchial asthma is available to this specialist only when analyzing the uploaded information about children with other allergic diseases. Conclusion . A adequate sample ensured a high representativeness of the results obtained. The differences in the incidence rates of allergic diseases revealed by a comparative analysis of data from various sources — UMIAS and FSO No. 12 — indicate the need to improve both the system of statistical registration of incidence and the development of modern algorithms for early diagnosis and dynamic monitoring of children with allergies.
... Historically, animals were used in the training of surgical skills since the Middle Ages throughout modern times (Cooper & Taqueti, 2008). According to Kihuba, et al. (2014), the provision of safe healthcare cannot be guaranteed in a majority of healthcare facilities both in the public and private facilities. In a MoH survey done in collaboration with World Bank, of the 13 healthcare facilities surveyed, the overall Patient Safety compliance was relatively poor at 97% and less than 1% of the public healthcare facilities and only 2% of private facilities met some minimal standards on Patient Safety further widening the gap as far as quality healthcare to all is concerned. ...
Simulation is a teaching learning and assessment strategy used in medical education to prepare medical laboratory students for clinical practice. Simulation-based learning is aimed at bridging the gap between theory and practice through the use of innovative teaching strategies and thus it is considered the best alternative teaching, learning and assessment tool in preparing medical laboratory students for practical and professional life. Traditional methods of educating medical laboratory students are no longer sufficient in the present times largely influenced by the emergence of new infections, technology, and multimedia. In general, scarce literature supports the use of simulation to benefit medical laboratory student in areas of knowledge, value and realism. However, little emphasis has been placed to make application of the method. The aim of the study was to determine the extent of the application of innovative simulation-based medical teaching and learning among staff in selected Kenya medical training campuses offering medical laboratory sciences in Kenya, with a view of improving the application of the simulation strategy. The study employed the census sampling technique. Data collection tools were structured questionnaire, interview, checklist and observation which were used for data collection to obtain information from the respondents. Quantitative data analysis was conducted using the Statistical Package for Social Science (SPSS) software version 22 for windows. Qualitative data was analyzed using content analysis. Both descriptive and inferential statistics particularly the Chi-square test statistics were used in data analysis. P-value was used to test the normality of the spread of the ages. From the findings, majority of the lecturers, 39 (83.0%) indicated that they understood and defined simulated medical laboratory experiences both actual and anticipated. The respondents reported that simulation-based teaching and learning enables them to earn continuous professional development (CPD) points. All the lecturers 47(100%) agreed that educational validity of simulation-based teaching and learning was one among the factors that shaped their decision to implement simulations. The principals cited issues such as untrained simulator instructor staff in MLS, lack of adequate infrastructure, as part of challenges they encountered while implementing innovative simulation-based teaching and learning. From the study, it was concluded that simulation-based teaching and learning was not applied uniformly across the MLS department, indicating a lack of standardization in training hence the knowledge, attitudes as well as skills acquired by the students before they graduate were not in line with the public expectation. There is a need for Kenya Medical Training College (KMTC) management to ensure uniform application of innovative simulation-based teaching and learning across all MLS departments and hence standardization in training of medical laboratory sciences professionals.
... Reliable estimates of motorcycle-related morbidity, hospitalization, severity, and fatalities, as well as their impact on the public health system, are essential for evidence-based policymaking, advocacy, and priority-setting for appropriate and effective interventions, resource mobilization, and future research. [32][33][34] Unfortunately, health information systems are inadequate in many cities, including Kisumu, in LMICs. 21 No published information exists on the burden imposed on health services by different types of motorcycle crash injuries and severity levels in Kisumu City. ...
Background:
In Kenya, the increased use of motorcycles for transport has led to increased morbidity, mortality, and disability. These injuries exert a burden on the public health system, yet little information exists on health care resource usage by motorcycle crash injury patients. We aimed to estimate the burden of motorcycle crash injuries on the health system in Kisumu City.
Methods:
We conducted a 6-month prospective study of all motorcycle crash injury patients who presented to 3 Tier III public and private hospitals in Kisumu City between May and November 2019. We collected data on demographics, emergency department (ED) visits, admissions, anatomic injury site, services used, and injury severity. We reviewed hospital records to obtain denominator data on all the conditions presenting to the EDs.
Results:
A total of 1,073 motorcycle crash injury cases accounted for 2.0%, 12.0%, and 13.6% of total emergency visits, total injuries, and total admissions to the hospitals, respectively. Men were overrepresented (P<.001). The mean age was 29.6 years (±standard deviation [SD] 12.19; range=2-84). The average injury severity score was 12.83. Surgical interventions were required by 89.3% of patients admitted. Of the 123 patients admitted to the intensive care unit, 42.3% were due to motorcycle accident injuries.
Conclusion:
Motorcycle injuries impose a major burden on the Kisumu City public health system. Increased promotion and reinforcement of appropriate interventions and legislation can help prevent accidents and mitigate their consequences. Focusing on motorcycle injury prevention will reduce accident-related morbidity, hospitalization, severity, and fatalities and the impact on the public health system.
... According to Kihuba, et al. [22], the provision of safe healthcare cannot be guaranteed in a majority of healthcare facilities both public and private facilities. In a MoH survey done in collaboration with the World Bank, of the 13 healthcare facilities surveyed, the overall Patient Safety compliance was relatively poor at 97% and less than 1% of the public healthcare facilities and only 2% of private facilities met some minimal standards on Patient Safety further widening the gap as far as quality healthcare to all is concerned. ...
Simulation is a teaching learning and assessment strategy used in medical education to prepare medical laboratory students for clinical practice. Simulation-based learning is aimed at bridging the gap between theory and practice through the use of innovative teaching strategies and thus it is considered the best alternative teaching, learning and assessment tool in preparing medical laboratory students for practical and professional life. Traditional methods of educating medical laboratory students are no longer sufficient in the present times largely influenced by the emergence of new infections, technology, and multimedia. In general, scarce literature supports the use of simulation to benefit medical laboratory students in areas of knowledge, value, and realism. However, little emphasis has been placed to make application of the method. The aim of the study was to determine the extent of the application of innovative simulation-based medical teaching and learning among staff in selected Kenya medical training campuses offering medical laboratory sciences in Kenya, with a view to improving the application of the simulation strategy. The study employed the census sampling technique. Data collection tools were structured questionnaires, interviews, checklists, and observations which were used for data collection to obtain information from the respondents. Quantitative data analysis was conducted using the Statistical Package for Social Science (SPSS) software version 22 for windows. Qualitative data was analyzed using content analysis. Both descriptive and inferential statistics particularly the Chi-square test statistics were used in data analysis. P-value was used to test the normality of the spread of the ages. From the findings, 35 (74.5%) of the lecturers were males while 12 (25.5%) were females. The age of the respondents was not normally distributed but had positive skewness of 0.845. The results showed that there was a significant relationship between gender and the age of the respondents, Pearson Chi-Square 0.016 (p < 0.05). The majority of the lecturers, 39 (83.0%) indicated that they understood and defined simulated medical laboratory experiences as both actual and anticipated. The respondents reported that simulation-based teaching and learning enable them to earn continuous professional development (CPD) points. All the lecturers 47 (100%) agreed that the educational validity of simulation-based teaching and learning was one of the factors that shaped their decision to implement simulations. The principals cited issues such as untrained simulator instructor staff in MLS, and lack of adequate infrastructure, as part of the challenges they encountered while implementing innovative simulation-based teaching and learning. From the study, it was concluded that simulation-based teaching and learning were not applied uniformly across the MLS department, indicating a lack of standardization in training hence the knowledge, attitudes, as well as skills acquired by the students before they graduate, were not in line with the
... 5 According to previous studies, data users have various information needs, require information at various levels of data, and play various roles in the decision-making process. 6 Research conducted in Malawi found that health facilities performed well in selected data quality categories, such as consistency across registers, reports, and DHIS-2 on women completing fourth ANC visits throughout the campaign. However, in some service areas such as ARI, data quality was low. ...
Background: Previous research in developing countries has revealed a variety of issues that may jeopardize data quality in HIS. According to research, many developing countries health information systems are unable to provide the necessary support information. The information produced is of low quality and the information processed is not made good use of to inform decisions. The lack of promotion of information culture harms the performance of HIS. The general objective of the study was to assess factors influencing effective staff performance in improving data management in selected facilities in Mombasa County, Kenya.Methods: The study adopted a cross-sectional research design with a mixed methods approach. Quantitative data were analyzed using frequencies, proportions, mean, standard deviation, coefficient of variation, cross-tabulations, Phi correlation coefficient, and binary logistic regression (at a significance level of 0.05). Qualitative data were analyzed using content analysis.Results: The results indicated that organizational factors (ϕ=0.268, p>0.05), staff effectiveness (ϕ=0.408, OR=0.056, p>0.05) and individual attributes (ϕ=0.141, p>0.05) did not have significant influence on staff performance in improving data management, while knowledge and skills (ϕ=0.535, OR=0.031, p<0.05) was found to have a significant influence on staff performance in improving data management.Conclusions: The study concludes that the knowledge and skills of health care workers are a significant predictor of improvement in data management at the health facilities in Mombasa County.
... Improved delivery of essential interventions in LMICs hospitals can advance the attainment of the Sustainable Development Goal of lowering the neonatal mortality rate considerably [2,3]. A better understanding of hospitals' neonatal mortality coupled with consistent and appropriate information on how this mortality varies may enhance efforts to improve hospital care at scale [4,5]. Without adjustment for patient case-mix efforts to contrast neonatal in-hospital mortality may be misleading because they fail to adjust for neonatal population characteristics [6]. ...
Background
Two neonatal mortality prediction models, the Neonatal Essential Treatment Score (NETS) which uses treatments prescribed at admission and the Score for Essential Neonatal Symptoms and Signs (SENSS) which uses basic clinical signs, were derived in high-mortality, low-resource settings to utilise data more likely to be available in these settings. In this study, we evaluate the predictive accuracy of two neonatal prediction models for all-cause in-hospital mortality.
Methods
We used retrospectively collected routine clinical data recorded by duty clinicians at admission from 16 Kenyan hospitals used to externally validate and update the SENSS and NETS models that were initially developed from the data from the largest Kenyan maternity hospital to predict in-hospital mortality. Model performance was evaluated by assessing discrimination and calibration. Discrimination, the ability of the model to differentiate between those with and without the outcome, was measured using the c-statistic. Calibration, the agreement between predictions from the model and what was observed, was measured using the calibration intercept and slope (with values of 0 and 1 denoting perfect calibration).
Results
At initial external validation, the estimated mortality risks from the original SENSS and NETS models were markedly overestimated with calibration intercepts of − 0.703 (95% CI − 0.738 to − 0.669) and − 1.109 (95% CI − 1.148 to − 1.069) and too extreme with calibration slopes of 0.565 (95% CI 0.552 to 0.577) and 0.466 (95% CI 0.451 to 0.480), respectively. After model updating, the calibration of the model improved. The updated SENSS and NETS models had calibration intercepts of 0.311 (95% CI 0.282 to 0.350) and 0.032 (95% CI − 0.002 to 0.066) and calibration slopes of 1.029 (95% CI 1.006 to 1.051) and 0.799 (95% CI 0.774 to 0.823), respectively, while showing good discrimination with c-statistics of 0.834 (95% CI 0.829 to 0.839) and 0.775 (95% CI 0.768 to 0.782), respectively. The overall calibration performance of the updated SENSS and NETS models was better than any existing neonatal in-hospital mortality prediction models externally validated for settings comparable to Kenya.
Conclusion
Few prediction models undergo rigorous external validation. We show how external validation using data from multiple locations enables model updating and improving their performance and potential value. The improved models indicate it is possible to predict in-hospital mortality using either treatments or signs and symptoms derived from routine neonatal data from low-resource hospital settings also making possible their use for case-mix adjustment when contrasting similar hospital settings.
... A study done in Kenya revealed problems facing data entered into DHIS2 are due to inadequate training for users, low deployment to all facilities, and a lack of management support. Even those that have deployed were not fully utilizing the system to generate important information for use at the facilities (English et al., 2014). ...
Introduction: Sound and reliable information is the foundation of decision-making across all health system building blocks. Strengthening the health information system is a global concern, especially in developing countries where data management is reported to be weak. In Tanzania, the family planning data management process is faced with discrepancies, such as completeness, timeliness, and accuracy thus calling for a need to explore technical factors that influence it. Objective: To explore technical factors influencing the family planning data management process among private hospitals in Ilala Municipal Council. Methodology: It was a cross-sectional explorative study design that used a qualitative approach. In-depth interviews were conducted by using the semi-structured interview guide. Twelve participants were involved. The study participants were purposively sampled. They included the health secretary, reproductive and child health in-charge, a nurse, and the data focal person. An inductive content analysis approach was used during data analysis. Results: Poor data quality characterized by inaccuracy, inconsistency and untimely recording and transferring to DHIS2, inadequate skilled manpower, and poor capacity building were the factors influencing the family planning data management process. Conclusion: The family planning data management process is affected by numerous factors, among which are poor data quality, inadequate skilled manpower, and poor capacity building. The MTUHA book 8 should be reviewed by the Ministry of Health and other implementing partners to ensure curative pill data are being captured.
