Improving quality and use of routine health information system data in low- and middle-income countries: A scoping review

Common data quality elements for health information systems: a systematic review

BACKGROUND

Data quality in health information systems has a complex structure and consists of several dimensions. This research conducted for identify Common data quality elements for health information systems.

METHODS

A literature review was conducted and search strategies run in Web of Knowledge, Science Direct, Emerald, PubMed, Scopus and Google Scholar search engine as an additional source for tracing references. We found 760 papers, excluded 314 duplicates, 339 on abstract review and 167 on full-text review; leaving 58 papers for critical appraisal.

RESULTS

Current review shown that 14 criteria are categorized as the main dimensions for data quality for health information system include: Accuracy, Consistency, Security, Timeliness, Completeness, Reliability, Accessibility, Objectivity, Relevancy, Understandability, Navigation, Reputation, Efficiency and Value- added. Accuracy, Completeness, and Timeliness, were the three most-used dimensions in literature.

CONCLUSIONS

At present, there is a lack of uniformity and potential applicability in the dimensions employed to evaluate the data quality of health information system. Typically, different approaches (qualitative, quantitative and mixed methods) were utilized to evaluate data quality for health information system in the publications that were reviewed. Consequently, due to the inconsistency in defining dimensions and assessing methods, it became imperative to categorize the dimensions of data quality into a limited set of primary dimensions.

Self-reported follow-up care needs can be met in both facility and self-managed abortion: Evidence from low- and middle-income countries

OBJECTIVES

To understand in-facility follow-up care-seeking behavior among both people who self-managed medication abortions and those who obtained facility-managed care in low-and-middle-income countries. We explore factors that contribute to meeting individual self-reported follow-up care needs, core to person-centered care.

STUDY DESIGN

We conducted a qualitative, codebook thematic analysis of 67 in-depth interviews conducted with people who self-managed medication abortions or obtained facility-managed medication abortion care. We first classified individuals as having their follow-up care needs met (not seeking care when the participant felt confident that additional care was not warranted or desired or receiving care if it was desired) or not. Our a priori analytic domains came from the Anderson model of health services utilization - predisposing, enabling, or need factors (perceived and evaluated need for health services) that contributed to having follow-up care needs met or not. We also describe emergent themes within each domain.

RESULTS

Most participants (n=59, 88%) had their follow-up care needs met; half (n=33, 49%) sought follow-up care in a facility. Prior birth or abortion experiences emerged as predisposing factors for having follow-up care needs met. Having accompaniment support (from activists or hotlines who provide abortion guidance outside of clinical settings), knowing what to expect, and information sources were key enabling factors for having follow-up care needs met. Need factors included flexible follow-up care guidelines. Those who did not have their follow-up care needs met described predisposing negative health system experiences; enabling factors including health system challenges, stigma from providers, and legal risk; and need factors of required follow-up care guidelines.

CONCLUSIONS

Medication abortion follow-up care experiences are diverse, and individual needs can be met both in and outside of health facilities. Understanding prior experiences, enabling accompaniment support, and considering flexible follow-up care guidelines can support meeting individual follow-up care needs, which is essential to person-centered abortion care.

IMPLICATIONS

Follow-up care needs, essential to ensuring access to high-quality abortion services, can be met in both self-managed and in-facility medication abortion models. Policies that require follow-up care when it is not needed or desired by the person can reinforce ideas that self-managed abortion is not safe or effective, despite existing evidence.

Data Quality-Driven Improvement in Health Care: Systematic Literature Review

BACKGROUND

The promise of real-world evidence and the learning health care system primarily depends on access to high-quality data. Despite widespread awareness of the prevalence and potential impacts of poor data quality (DQ), best practices for its assessment and improvement are unknown.

OBJECTIVE

This review aims to investigate how existing research studies define, assess, and improve the quality of structured real-world health care data.

METHODS

A systematic literature search of studies in the English language was implemented in the Embase and PubMed databases to select studies that specifically aimed to measure and improve the quality of structured real-world data within any clinical setting. The time frame for the analysis was from January 1945 to June 2023. We standardized DQ concepts according to the Data Management Association (DAMA) DQ framework to enable comparison between studies. After screening and filtering by 2 independent authors, we identified 39 relevant articles reporting DQ improvement initiatives.

RESULTS

The studies were characterized by considerable heterogeneity in settings and approaches to DQ assessment and improvement. Affiliated institutions were from 18 different countries and 18 different health domains. DQ assessment methods were largely manual and targeted completeness and 1 other DQ dimension. Use of DQ frameworks was limited to the Weiskopf and Weng (3/6, 50%) or Kahn harmonized model (3/6, 50%). Use of standardized methodologies to design and implement quality improvement was lacking, but mainly included plan-do-study-act (PDSA) or define-measure-analyze-improve-control (DMAIC) cycles. Most studies reported DQ improvements using multiple interventions, which included either DQ reporting and personalized feedback (24/39, 61%), IT-related solutions (21/39, 54%), training (17/39, 44%), improvements in workflows (5/39, 13%), or data cleaning (3/39, 8%). Most studies reported improvements in DQ through a combination of these interventions. Statistical methods were used to determine significance of treatment effect (22/39, 56% times), but only 1 study implemented a randomized controlled study design. Variability in study designs, approaches to delivering interventions, and reporting DQ changes hindered a robust meta-analysis of treatment effects.

CONCLUSIONS

There is an urgent need for standardized guidelines in DQ improvement research to enable comparison and effective synthesis of lessons learned. Frameworks such as PDSA learning cycles and the DAMA DQ framework can facilitate this unmet need. In addition, DQ improvement studies can also benefit from prioritizing root cause analysis of DQ issues to ensure the most appropriate intervention is implemented, thereby ensuring long-term, sustainable improvement. Despite the rise in DQ improvement studies in the last decade, significant heterogeneity in methodologies and reporting remains a challenge. Adopting standardized frameworks for DQ assessment, analysis, and improvement can enhance the effectiveness, comparability, and generalizability of DQ improvement initiatives.

Determinants of translating routine health information system data into action in Mozambique: a qualitative study

INTRODUCTION

Routine health information systems (RHISs) are an essential source of data to inform decisions and actions around health facility performance, but RHIS data use is often limited in low and middle-income country contexts. Determinants that influence RHIS data-informed decisions and actions are not well understood, and few studies have explored the relationship between RHIS data-informed decisions and actions.

METHODS

This qualitative thematic analysis study explored the determinants and characteristics of successful RHIS data-informed actions at the health facility level in Mozambique and which determinants were influenced by the Integrated District Evidence to Action (IDEAs) strategy. Two rounds of qualitative data were collected in 2019 and 2020 through 27 in-depth interviews and 7 focus group discussions with provincial, district and health facility-level managers and frontline health workers who participated in the IDEAs enhanced audit and feedback strategy. The Performance of Routine Information System Management-Act framework guided the development of the data collection tools and thematic analysis.

RESULTS

Key behavioural determinants of translating RHIS data into action included health worker understanding and awareness of health facility performance indicators coupled with health worker sense of ownership and responsibility to improve health facility performance. Supervision, on-the-job support and availability of financial and human resources were highlighted as essential organisational determinants in the development and implementation of action plans. The forum to regularly meet as a group to review, discuss and monitor health facility performance was emphasised as a critical determinant by study participants.

CONCLUSION

Future data-to-action interventions and research should consider contextually feasible ways to support health facility and district managers to hold regular meetings to review, discuss and monitor health facility performance as a way to promote translation of RHIS data to action.

Comprehensive assessment of pediatric acute and inpatient care at a tertiary referral hospital in Malawi: opportunities for quality improvement

BACKGROUND

Despite the reduction in global under-5 mortality over the last decade, childhood deaths remain high. To combat this, there has been a shift in focus from disease-specific interventions to use of healthcare data for resource allocation, evaluation of performance and impact, and accountability. This is a descriptive analysis of data derived from a prospective cohort study describing paediatric admissions to a tertiary referral hospital in Malawi for the purpose of process evaluation and quality improvement.

METHODS

Using a REDCap database, we collected data for patients admitted acutely to Kamuzu Central Hospital, a tertiary referral centre in the central region. Data were collected from 17 123 paediatric inpatients from 2017 to 2020.

RESULTS

Approximately 6% of patients presented with either two or more danger signs or severely abnormal vital signs. Infants less than 6 months, who had the highest mortality rate, were also the most critically ill on arrival to the hospital. Sepsis was diagnosed in about 20% of children across all age groups. Protocols for the management of high-volume, lower-acuity conditions such as uncomplicated malaria and pneumonia were generally well adhered to, but there was a low rate of completion for labs, radiology studies and subspecialty consultations required to provide care for high acuity or complex conditions. The overall mortality rate was 4%, and 60% of deaths occurred within the first 48 hours of admission.

CONCLUSION

Our data highlight the need to improve the quality of care provided at this tertiary-level centre by focusing on the initial stabilisation of high-acuity patients and augmenting resources to provide comprehensive care. This may include capacity building through the training of specialists, implementation of clinical processes, provision of specialised equipment and increasing access to and reliability of ancillary services. Data collection, analysis and routine use in policy and decision-making must be a pillar on which improvement is built.

Factors impacting clinical data and documentation quality in Australian aged care and disability services: a user-centred perspective

BACKGROUND

Research has highlighted a need to improve the quality of clinical documentation and data within aged care and disability services in Australia to support improved regulatory reporting and ensure quality and safety of services. However, the specific causes of data quality issues within aged care and disability services and solutions for optimisation are not well understood.

OBJECTIVES

This study explored aged care and disability workforce (referred to as 'data-users') experiences and perceived root causes of clinical data quality issues at a large aged care and disability services provider in Western Australia, to inform optimisation solutions.

METHODS

A purposive sample of n = 135 aged care and disability staff (including community-based and residential-based) in clinical, care, administrative and/or management roles participated in semi-structured interviews and web-based surveys. Data were analysed using an inductive thematic analysis method, where themes and subthemes were derived.

RESULTS

Eight overarching causes of data and documentation quality issues were identified: (1) staff-related challenges, (2) education and training, (3) external barriers, (4) operational guidelines and procedures, (5) organisational practices and culture, (6) technological infrastructure, (7) systems design limitations, and (8) systems configuration-related challenges.

CONCLUSION

The quality of clinical data and documentation within aged care and disability services is influenced by a complex interplay of internal and external factors. Coordinated and collaborative effort is required between service providers and the wider sector to identify behavioural and technical optimisation solutions to support safe and high-quality care and improved regulatory reporting.

Effect of Performance-Based Nonfinancial Incentives on Data Quality in Individual Medical Records of Institutional Births: Quasi-Experimental Study

BACKGROUND

Despite the potential of routine health information systems in tackling persistent maternal deaths stemming from poor service quality at health facilities during and around childbirth, research has demonstrated their suboptimal performance, evident from the incomplete and inaccurate data unfit for practical use. There is a consensus that nonfinancial incentives can enhance health care providers' commitment toward achieving the desired health care quality. However, there is limited evidence regarding the effectiveness of nonfinancial incentives in improving the data quality of institutional birth services in Ethiopia.

OBJECTIVE

This study aimed to evaluate the effect of performance-based nonfinancial incentives on the completeness and consistency of data in the individual medical records of women who availed institutional birth services in northwest Ethiopia.

METHODS

We used a quasi-experimental design with a comparator group in the pre-post period, using a sample of 1969 women's medical records. The study was conducted in the "Wegera" and "Tach-armacheho" districts, which served as the intervention and comparator districts, respectively. The intervention comprised a multicomponent nonfinancial incentive, including smartphones, flash disks, power banks, certificates, and scholarships. Personal records of women who gave birth within 6 months before (April to September 2020) and after (February to July 2021) the intervention were included. Three distinct women's birth records were examined: the integrated card, integrated individual folder, and delivery register. The completeness of the data was determined by examining the presence of data elements, whereas the consistency check involved evaluating the agreement of data elements among women's birth records. The average treatment effect on the treated (ATET), with 95% CIs, was computed using a difference-in-differences model.

RESULTS

In the intervention district, data completeness in women's personal records was nearly 4 times higher (ATET 3.8, 95% CI 2.2-5.5; P=.02), and consistency was approximately 12 times more likely (ATET 11.6, 95% CI 4.18-19; P=.03) than in the comparator district.

CONCLUSIONS

This study indicates that performance-based nonfinancial incentives enhance data quality in the personal records of institutional births. Health care planners can adapt these incentives to improve the data quality of comparable medical records, particularly pregnancy-related data within health care facilities. Future research is needed to assess the effectiveness of nonfinancial incentives across diverse contexts to support successful scale-up.

Analyzing Global Cancer Control: Progress of National Cancer Control Programs through Composite Indicators and Regression Modeling

Aim

Cancer is a significant public health concern, and National Cancer Control Programs (NCCPs) are crucial for reducing its burden. However, assessing the progress of NCCPs is challenging due to the complexity of cancer control outcomes and the various factors that influence them. Composite indicators can provide a comprehensive and accurate assessment of NCCP progress.

Materials and Methods

The dataset was compiled for 144 countries and comprised eight composite indices and two high-level comparative indicators (mortality-to-cancer incidence ratio [MIR] and 5-year cancer prevalence-to-incidence ratio [PCIR]) representing NCCP outcomes. Two large databases and six annual composite index reports were consulted. Linear regression analysis and Pearson correlation coefficients were used to establish a relationship between indicators and NCCP outcomes. A multiple regression machine learning model was generated to further improve the accuracy of NCCP outcome prediction.

Results

High-income countries had the highest cancer incidence, whereas low-income countries had the highest MIR. Linear regression analysis indicated a negative trend between all composite indicators and MIR, whereas a positive trend was observed with PCIR. The Human Development Index and the Legatum Prosperity Index had the highest adjusted R 2 values for MIR (0.74 and 0.73) and PCIR (0.86 and 0.81), respectively. Multiple linear regression modeling was performed, and the results indicated a low mean squared error score (-0.02) and a high R 2 score (0.86), suggesting that the model accurately predicts NCCP outcomes.

Conclusions

Overall, composite indicators can be an effective tool for evaluating NCCP, and the results of this study can aid in the development and keeping track of NCCP progress for better cancer control.

Perceived progress toward scale of 14 maternal, newborn, child health, and nutrition (MNCHN) assets at subnational level in Burkina Faso, Ethiopia, India, Kenya, and Nigeria

Inequitable coverage of evidence-based MNCHN interventions is particularly pronounced in low and middle income countries where access and delivery of these interventions can vary dramatically at the subnational level. We conducted health system assessments in nine subnational geographies in five countries (Burkina Faso, Ethiopia, India, Kenya and Nigeria) to explore progress toward scale of 14 evidence-based MNCHN interventions (iron-folic acid, oxytocin, magnesium sulfate, misoprostol; 7.1% chlorhexidine for umbilical cord care, neonatal resuscitation, kangaroo mother care, community regimen for the treatment of possible severe bacterial infection; amoxicillin dispersible tablets, multiple micronutrient supplements, balanced energy protein supplementation, early and exclusive breastfeeding, feeding of small and sick newborns, and management of severe and moderate acute malnutrition in children less than five years old). Between March and October 2021, we conducted key informant interviews with a purposive sample of 275 healthcare providers and 94 district health management (DHMT) staff to better understand bottlenecks, facilitators and uptake of the interventions across varied subnational settings. Across all interventions and geographies, providers and DHMT staff perceived lack of robust HMIS data as the most significant barrier to scale followed by weak facility infrastructure. DHMT staff viewed limited budget allocation and training as a much larger barrier than healthcare providers, most likely given their purview as subnational managers. Healthcare providers were focused on supply chain and staffing, which affect workflows and service provision. Understanding provider and health facility management views of why interventions do or do not advance towards effective coverage can assist in creating enabling environments for the scale of best practices. These types of data are most helpful when collected at the subnational level, which allows for comparisons both within and between countries to show health disparities. Importantly, this strategic data collection can provide a starting point for improvement efforts to address existing health system gaps.

Implementation of REDCap mobile app in an oral HIV clinical study

BACKGROUND

In Peru, HIV cases are highly concentrated among men who have sex with men (MSM). Despite the availability of anti-retroviral therapy, people living with HIV (PWH) have higher levels of oral diseases. Alcohol use disorder (AUD) is significantly present among PWH. Our overarching goal was to generate foundational evidence on the association of AUD and oral health in MSM with HIV and enhance research capacity for future intersectional research on AUD, oral health and HIV. Our specific aim was to implement an on-site electronic data collection system through the use of a REDCap Mobile App in a low-middle income country (LMIC) setting.

METHODS

Five validated surveys were utilized to gather data on demographics, medical history, HIV status, alcohol use, HIV stigma, perceived oral health status, and dietary supplement use. These surveys were developed in REDCap and deployed with the REDCap Mobile App, which was installed on ten iPads across two medical HIV clinics in Lima, Peru. REDCap app as well as the protocol for data collection were calibrated with feedback from trial participants and clinical research staff to improve clinical efficiency and participant experience.

RESULTS

The mean age of participants (n = 398) was 35.94 ± 9.13y, of which 98.5% identified as male, and 85.7% identified as homosexual. 78.1% of participants binge drank, and 12.3% reported being heavy drinkers. After pilot testing, significant modifications to the structure and layout of the surveys were performed to improve efficiency and flow. The app was successfully deployed to replace cumbersome paper records and collected data was directly stored in a REDCap database.

CONCLUSIONS

The REDCap Mobile App was successfully used due to its ability to: (a) capture and store data offline, (b) timely translate between multiple languages on the mobile app interface, and (c) provide user-friendly interface with low associated costs and ample support.

TRIAL REGISTRATION

1R56DE029639-01.

Adapting national data systems for donor transition: comparative analysis of experience from Georgia and China

Health management information systems (HMISs) are essential in programme planning, budgeting, monitoring and evidence-informed decision-making. This paper focuses on donor transitions in two upper-middle-income countries, China and Georgia, and explores how national HMIS adaptations were made and what facilitated or limited successful and sustainable transitions. This comparative analytical case study uses a policy triangle framework and a mixed-methods approach to explore how and why adaptations in the HMIS occurred under the Gavi Alliance and the Global Fund-supported programmes in China and Georgia. A review of published and grey literature, key informant interviews and administrative data analysis informed the study findings. Contextual factors such as the global and country context, and health system and programme needs drove HMIS developments. Other factors included accountability on a national and international level; improvements in HMIS governance by establishing national regulations for clear mandates of data collection and reporting rules and creating institutional spaces for data use; investing in hardware, software and human resources to ensure regular and reliable data generation; and capacitating national players to use data in evidence-based decision-making for programme and transition planning, budgeting and outcome monitoring. Not all the HMIS initiatives supported by donors were sustained and transitioned. For the successful adaptation and sustainable transition, five interlinked and closely coordinated support areas need to be considered: (1) coupling programme design with a good understanding of the country context while considering domestic and external demands for information, (2) regulating appropriate governance and management arrangements enhancing country ownership, (3) avoiding silo HMIS solutions and taking integrative approach, (4) ensuring the transition of funding onto domestic budget and enforcing fulfilment of the government's financial commitments and finally (5) investing in technologies and skilled human resources for the HMIS throughout all levels of the health system. Neglecting any of these elements risks not delivering sustainable outcomes.

Exploring approaches to weighting estimates of facility readiness to provide health services used for estimating input-adjusted effective coverage: a case study using data from Tanzania

The ideal approach for calculating effective coverage of health services using ecological linking requires accounting for variability in facility readiness to provide health services and patient volume by incorporating adjustments for facility type into estimates of facility readiness and weighting facility readiness estimates by service-specific caseload. The aim of this study is to compare the ideal caseload-weighted facility readiness approach to two alternative approaches: (1) facility-weighted readiness and (2) observation-weighted readiness to assess the suitability of each as a proxy for caseload-weighted facility readiness. We utilised the 2014-2015 Tanzania Service Provision Assessment along with routine health information system data to calculate facility readiness estimates using the three approaches. We then conducted equivalence testing, using the caseload-weighted estimates as the ideal approach and comparing with the facility-weighted estimates and observation-weighted estimates to test for equivalence. Comparing the facility-weighted readiness estimates to the caseload-weighted readiness estimates, we found that 58% of the estimates met the requirements for equivalence. In addition, the facility-weighted readiness estimates consistently underestimated, by a small percentage, facility readiness as compared to the caseload-weighted readiness estimates. Comparing the observation-weighted readiness estimates to the caseload-weighted readiness estimates, we found that 64% of the estimates met the requirements for equivalence. We found that, in this setting, both facility-weighted readiness and observation-weighted readiness may be reasonable proxies for caseload-weighted readiness. However, in a setting with more variability in facility readiness or larger differences in facility readiness between low caseload and high caseload facilities, the observation-weighted approach would be a better option than the facility-weighted approach. While the methods compared showed equivalence, our results suggest that selecting the best method for weighting readiness estimates will require assessing data availability alongside knowledge of the country context.

From purists to pragmatists: a qualitative evaluation of how implementation processes and contexts shaped the uptake and methodological adaptations of a maternal and neonatal quality improvement programme in South Africa prior to, and during COVID-19

BACKGROUND

Despite progress, maternal and neonatal mortality and still births remain high in South Africa. The South African National Department of Health implemented a quality improvement (QI) programme, called Mphatlalatsane, to reduce maternal and neonatal mortality and still births. It was implemented in 21 public health facilities, seven per participating province, between 2018 and 2022.

METHODS

We conducted a qualitative process evaluation of the contextual and implementation process factors' influence on implementation uptake amongst the QI teams in 15 purposively selected facilities. Data collection included three interview rounds with the leaders and members of the QI teams in each facility; intermittent interviews with the QI advisors; programme documentation review; observation of programme management meetings; and keeping a fieldwork journal. All data were thematically analysed in Atlas.ti. Implementation uptake varied across the three provinces and between facilities within provinces.

RESULTS

Between March and August 2020, the COVID-19 pandemic disrupted uptake in all provinces but affected QI teams in one province more severely than others, because they received limited pre-pandemic training. Better uptake among other sites was attributed to receiving more QI training pre-COVID-19, having an experienced QI advisor, and good teamwork. Uptake was more challenging amongst hospital teams which had more staff and more complicated MNH services, versus the primary healthcare facilities. We also attributed better uptake to greater district management support. A key factor shaping uptake was leaders' intrinsic motivation to apply QI methodology. We found that, across sites, organic adaptations to the QI methodology were made by teams, started during COVID-19. Teams did away with rapid testing of change ideas and keeping a paper trail of the steps followed. Though still using data to identify service problems, they used self-developed audit tools to record intervention effectiveness, and not the prescribed tools.

CONCLUSIONS

Our study underscores the critical role of intrinsic motivation of team leaders, support from experienced technical QI advisors, and context-sensitive adaptations to maximise QI uptake when traditionally recognised QI steps cannot be followed.

Refining the Performance of Routine Information System Management (PRISM) framework for data use at the local level: An integrative review

INTRODUCTION

Foundational to a well-functioning health system is a strong routine health information system (RHIS) that informs decisions and actions at all levels of the health system. In the context of decentralization across low- and middle-income countries, RHIS has the promise of supporting sub-national health staff to take data-informed actions to improve health system performance. However, there is wide variation in how "RHIS data use" is defined and measured in the literature, impeding the development and evaluation of interventions that effectively promote RHIS data use.

METHODS

An integrative review methodology was used to: (1) synthesize the state of the literature on how RHIS data use in low- and middle-income countries is conceptualized and measured; (2) propose a refined RHIS data use framework and develop a common definition for RHIS data use; and (3) propose improved approaches to measure RHIS data use. Four electronic databases were searched for peer-reviewed articles published between 2009 and 2021 investigating RHIS data use.

RESULTS

A total of 45 articles, including 24 articles measuring RHIS data use, met the inclusion criteria. Less than half of included articles (42%) explicitly defined RHIS data use. There were differences across the literature whether RHIS data tasks such as data analysis preceded or were a part of RHIS data use; there was broad consensus that data-informed decisions and actions were essential steps within the RHIS data use process. Based on the synthesis, the Performance of Routine Information System Management (PRISM) framework was refined to specify the steps of the RHIS data use process.

CONCLUSION

Conceptualizing RHIS data use as a process that includes data-informed actions emphasizes the importance of actions in improving health system performance. Future studies and implementation strategies should be designed with consideration for the different support needs for each step of the RHIS data use process.

Data Quality in Health Care: Main Concepts and Assessment Methodologies

INTRODUCTION

 In the health care environment, a huge volume of data is produced on a daily basis. However, the processes of collecting, storing, sharing, analyzing, and reporting health data usually face with numerous challenges that lead to producing incomplete, inaccurate, and untimely data. As a result, data quality issues have received more attention than before.

OBJECTIVE

 The purpose of this article is to provide an insight into the data quality definitions, dimensions, and assessment methodologies.

METHODS

 In this article, a scoping literature review approach was used to describe and summarize the main concepts related to data quality and data quality assessment methodologies. Search terms were selected to find the relevant articles published between January 1, 2012 and September 31, 2022. The retrieved articles were then reviewed and the results were reported narratively.

RESULTS

 In total, 23 papers were included in the study. According to the results, data quality dimensions were various and different methodologies were used to assess them. Most studies used quantitative methods to measure data quality dimensions either in paper-based or computer-based medical records. Only two studies investigated respondents' opinions about data quality.

CONCLUSION

 In health care, high-quality data not only are important for patient care, but also are vital for improving quality of health care services and better decision making. Therefore, using technical and nontechnical solutions as well as constant assessment and supervision is suggested to improve data quality.

Feasibility, usability and acceptability of a novel digital hybrid-system for reporting of routine maternal health information in Southern Tanzania: A mixed-methods study

Health information systems are important for health planning and progress monitoring. Still, data from health facilities are often of limited quality in Low-and-Middle-Income Countries. Quality deficits are partially rooted in the fact that paper-based documentation is still the norm at facility level, leading to mistakes in summarizing and manual copying. Digitization of data at facility level would allow automatization of these procedural steps. Here we aimed to evaluate the feasibility, usability and acceptability of a scanning innovation called Smart Paper Technology for digital data processing. We used a mixed-methods design to understand users' engagement with Smart Paper Technology and identify potential positive and negative effects of this innovation in three health facilities in Southern Tanzania. Eight focus group discussions and 11 in-depth interviews with users were conducted. We quantified time used by health care providers for documentation and patient care using time-motion methods. Thematic analysis was used to analyze qualitative data. Descriptive statistics and multivariable linear models were generated to compare the difference before and after introduction and adjust for confounders. Health care providers and health care managers appreciated the forms' simple design features and perceived Smart Paper Technology as time-saving and easy to use. The time-motion study with 273.3 and 224.0 hours of observations before and after introduction of Smart Paper Technology, respectively, confirmed that working time spent on documentation did not increase (27.0% at baseline and 26.4% post-introduction; adjusted p = 0.763). Time spent on patient care was not negatively impacted (26.9% at baseline and 37.1% at post-intervention; adjusted p = 0.001). Health care providers described positive effects on their accountability for data and service provision relating to the fact that individually signed forms were filled. Health care providers perceived Smart Paper Technology as feasible, easy to integrate and acceptable in their setting, particularly as it did not add time to documentation.

Routine health information system utilization and associated factors among health professionals in public health facilities in Dire Dawa, eastern Ethiopia: A cross-sectional study

Background

Using reliable evidence from routine health information system (RHIS) over time is a vital aid to improve health outcome, tackling disparities, enhancing efficiency, and encouraging innovation. In Ethiopia, utilization of routine health data for improving the performance and quality of care was not well-studied in grassroot health facilities.

Objective

This study was conducted to determine the level of RHIS utilization and associated factors among health professionals in public health facilities of Dire Dawa, eastern Ethiopia.

Methods

An institution-based cross-sectional study was conducted among 378 health professionals from June 10 to July 20, 2020. Self-administered pretested-structured questionnaire was used to collect data from the participants. Data were entered using EpiData 3.1 and analyzed using Stata 16.0. Descriptive statistics was used to describe the basic characteristics of the participants, and multivariable logistic regression analysis was conducted to identify factors associated with RHIS utilization. Adjusted odds ratio (AOR) (95% CI) was used to report association and significance declared at a P-value <0.05.

Results

Good RHIS utilization among health professionals was 57.7% (95% CI: 52.6%, 62.6%). Good organizational support (AOR = 3.91, 95% CI: 2.01, 7.61), low perceived complexity of RHIS formats (AOR = 2.20, 95% CI: 1.23, 3.97), good self-efficacy (AOR = 2.52, 95% CI: 1.25, 5.10), and good decision-making autonomy (AOR = 3.97, 95% CI: 2.12, 7.43) were important factors associated with good RHIS utilization.

Conclusions

The level of good RHIS utilization among health professionals was low. Lack of self-confidence and empowerment, complexity of RHIS formats, and poor organizational support were significantly reducing RHIS utilization. Therefore, improving self-efficacy and decision-making capacity of health professionals through comprehensive training, empowerment, and organizational support would be essential.

Determining the requirements of a medical records electronic deficiency management system: a mixed-method study

Purpose

Despite the presence of electronic medical records systems, traditional paper-based methods are often used in many countries to document data and eliminate medical record deficiencies. These methods waste patient and hospital resources. The purpose of this study is to evaluate the traditional deficiency management system and determine the requirements of an electronic deficiency management system in settings that currently use paper records alongside electronic hospital information systems.

Design/methodology/approach

This mixed-method study was performed in three phases. First, the traditional process of medical records deficiency management was qualitatively evaluated. Second, the accuracy of identifying deficiencies by the traditional and redesigned checklists was compared. Third, the requirements for an electronic deficiency management system were discussed in focus group sessions.

Findings

Problems in the traditional system include inadequate guidelines, incomplete procedures for evaluating sheets and subsequent delays in activities. Problems also included the omission of some vital data elements and a lack of feedback about the documentation deficiencies of each documenter. There was a significant difference between the mean number of deficiencies identified by traditional and redesigned checklists (p < 0.0001). The authors proposed an electronic deficiency management system based on redesigned checklists with improved functionalities such as discriminating deficiencies based on the documenter’s role, providing systematic feedback and generating automatic reports.

Originality/value

Previous studies only examined the positive effect of audit and feedback methods to enhance the documentation of data elements in electronic and paper medical records. The authors propose an electronic deficiency management system for medical records to solve those problems. Health-care policymakers, hospital managers and health information systems developers can use the proposed system to manage deficiencies and improve medical records documentation.

Health information system concept in health services in the national health insurance (JKN) era in Indonesia: An environment and one health approach

The health information system is a component of the healthcare system. The health information system in health services in Indonesia has experienced many problems in getting support for policy making, the implementation of the industrial revolution 4.0, and national health insurance (JKN). To answer the above problems, it is necessary to make a concept of health information systems in health services that based on environment and one health perspectives. This research was part of the thematic research of the 2019 JKN National Health Facilities Survey (Rifaskes) in Indonesia. The systems approach and cross-sectional research were carried out by collecting quantitative data. A structural equation model with Lisrel 88 software was used to model the health information system. The health information system produced a concept that included the following structured input components: governance, human resources, infrastructure, types of information system (IS) (program, JKN, management), and financing; process components: funding, technical guidance, and verification and validation; and output components: open access, standards and quality, utilization, bridging, and security. The concept for strengthening the health information system prioritizes improving the output components (standards, utilization, bridging, open access, and security) in the process components (funding, verification, technical guidance) while the input components (financing, human resources, governance, IS programs, infrastructure, IS JKN, IS management).

Routine use of DHIS2 data: a scoping review

Background

In regard to health service planning and delivery, the use of information at different levels in the health system is vital, ranging from the influencing of policy to the programming of action to the ensuring of evidence-informed practices. However, neither ownership of, nor access to, good quality data guarantees actual use of these data. For information to be used, relevant data need to be collected, processed and analysed in an accessible format. This problem of underused data, and indeed the absence of data use entirely, is widespread and has been evident for decades.

The DHIS2 software platform supports routine health management for an estimated 2.4 billion people, in over 70 countries worldwide. It is by far the largest and most widespread software for this purpose and adopts a holistic, socio-technical approach to development and implementation. Given this approach, and the rapid and extensive scaling of DHIS2, we questioned whether or not there has been a parallel increase in the scaling of improved information use. To date, there has been no rigorous review of the documentation on how exactly DHIS2 data is routinely being used for decision-making and subsequent programming of action. This scoping review addresses this review gap.

Methods

The five-stage approach of Arksey and O’Malley progressed by Levac et al. and Peters was followed. Three databases (PubMed, Web of Science and Embase) were searched, along with relevant conference proceedings and postgraduate theses. In total, over 500 documents were reviewed and data from 19 documents were extracted.

Results

Overall, DHIS2 data are being used but there are few detailed descriptions of this usage in peer reviewed or grey literature. We find that, commonly, there exists a centralised versus decentralised pattern of use in terms of access to data and the reporting of data ‘up’ in the system. We also find that the different conceptualisations of data use and how data use is conceptualised are not made explicit.

Conclusions

We conclude with some suggestions for a way forward, namely: i) the need to document in more detail and share how data are being used, ii) the need to investigate how data were created and who uses such data, iii) the need to design systems based on work practices, and in tandem develop and promote forums in which ‘conversations’ around data can take place.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08598-8.

Posyandu Application in Indonesia: From Health Informatics Data Quality Bridging Bottom-Up and Top-Down Policy Implementation

The community’s mother and child health (MCH) and nutrition problems can be overcome through evidence-based health policy. Posyandu is an implementation of community empowerment in health promotion strategies. The iPosyandu application (app) is one of the health informatics tools, in which data quality should be considered before any Posyandu health interventions are made. This study aims to describe and assess differences in data quality based on the dimensions (completeness, accuracy, and consistency) of the secondary data collected from the app in Purwakarta Regency in 2019–2021. Obstacles and suggestions for improving its implementation were explored. This research applies a mixed-method explanatory approach. Data completeness was identified as the number of reported visits of children under five per year. Data accuracy was analyzed using WHO Z-score anthropometry and implausible Z-score values. Data consistency was measured using Cronbach’s alpha coefficient, followed by qualitative research with focus group discussions, in-depth interviews, and field observation notes. The quantitative study results found that some of the data were of good quality. The qualitative research identified the obstacles experienced using the iPosyandu app, one of them being that there were no regulations governing the use of iPosyandu to bridge the needs of the government, and provided suggestions from the field to improve its implementation.

Barriers and Facilitators to Data Use for Decision Making: The Experience of the African Health Initiative Partnerships in Ethiopia, Ghana, and Mozambique

BACKGROUND

Three African Health Initiative (AHI) partnership projects in Ethiopia, Ghana, and Mozambique implemented strategies to improve the quality and evaluation of routinely collected data at the primary health care level and stimulate its use in evidence-based decision making. We compare how these programs designed and carried out data for decision-making (DDM) strategies, elaborate on barriers and facilitators to implementation success, and offer recommendations for future DDM programming.

METHODS

Researchers from each project collaboratively wrote a cross-country protocol based on these objectives. By adapting the Consolidated Framework for Implementation Research (CFIR) through a qualitative theme reduction process, they harmonized lines of inquiry on the design of the respective DDM strategies and the barriers and facilitators of effective implementation. We conducted in-depth interviews and focus group discussions with stakeholders from the primary health care systems in each country, and we carried out multistage, thematic analyses using a deductive lens.

RESULTS

Effective implementation of DDM depended on whether implementers felt that DDM was adaptable to context, feasible to trial, and easy to introduce and maintain. The prevailing policy and political environment in the wider health system, learning climate and absorptive capacity for evidence-based change in DDM settings, engagement of external change agents and internal change leaders, and promotion of opportunities and means for team-based reflection and evaluations of what works influenced the success or failure of DDM strategies.

CONCLUSION

Opportunities for team-based capacity building and individual mentorship led to effective DDM programming. External policies and associated incentives bolstered this but occasionally led to unintended consequences. Leadership engagement and availability of resources to act on recommendations; respond to capacity-building needs; and facilitate collaborations between peers, within hierarchies, and across the local health system proved crucial to DDM, as was encouraging adaptation and opportunities for iterative on-the-job learning.

Lessons Learned From the Capacity-Building and Mentorship Program to Improve Health Information Systems in 11 Districts of Ethiopia

Health information systems strengthening interventions, such as the capacity-building and mentorship program used, leverages the expertise of stakeholders from multiple disciplines and can help improve data quality and information use at health facilities.

Introduction:

Methods:

Results:

Discussion:

Maternal Service Coverage and Its Relationship To Health Information System Performance: A Linked Facility and Population-Based Survey in Ethiopia

Coverage for most maternal services showed promising performance. Improving the health information system performance can further improve maternal service uptake and quality.

Background:

Studies in Ethiopia show an increasing trend in maternal health service use, such as having at least 4 visits of antenatal care (ANC4+) and skilled birth attendance (SBA). Improving the health information system (HIS) is an intervention that can improve service uptake and quality. We conducted a baseline study to measure current maternal service coverage, HIS performance status, and their relationship.

Methods:

We conducted a linked health facility-level and population-based survey from September 2020 to October 2020. The study covers all regions of Ethiopia. For the population-based survey, 3,016 mothers were included. Overall, 81 health posts, 71 health centers, and 15 hospitals were selected for the facility survey. A two-stage sampling procedure was applied to select target households. The study used modified Performance of Routine Information System Management tools for the facility survey and a structured questionnaire for the household survey. Multilevel logistic regression was employed to account for clustering and control for likely confounders.

Results:

Maternal service indicators, ANC4+ visits (54.0%), SBA (75.8%), postnatal care (70.6%), and cesarean delivery (9%) showed good service uptake. All data quality and use indicators showed lower performance compared to the national target of 90%. Maternal education and higher levels of wealth index were significantly and positively associated with all selected maternal service indicators. Longer distance from health facilities was significantly and negatively associated with SBA and the maternal care composite indicator. Among HIS-related indicators, availability of electronic HIS tools was significantly associated with maternal care composite indicator and ANC4+.

Conclusions:

Maternal service indicators showed promising performance. However, current HIS performance is suboptimal. Both service user and HIS-related factors were associated with maternal service uptake. Conducting similar research outside of the project sites will be helpful to have a wider understanding and better coverage.

Framework synthesis to inform the ideation and design of a paper-based health information system (PHISICC)

Background

Health information systems (HIS) are meant to support decision‐making at all levels of the system, including frontline health workers. In field studies in Côte d’Ivoire, Mozambique and Nigeria, we observed health workers' interactions with the HIS and identified twelve decision‐making components of HIS. The objective of this framework synthesis is to portray these components in HIS research, in order to inform the ideation of a paper‐based HIS intervention (PHISICC).

Methods

We searched studies in the Cochrane Central Register of Controlled Trials (CENTRAL), Embase, Epistemonikos, Medline, in‐Process on the Ovid platform, OpenGrey, PDQ  Evidence (“pretty darnd quick” Evidence), the World Health Organization (WHO) Global Health Library and included studies focussing on HIS interventions, data quality, information support tools and data use for decision‐making in the context of the governmental health care sector. We assessed the methodological quality of studies using the Critical Appraisal Skills Programme tool. We synthesised the findings based on the decision‐making components of HIS and thematic areas.

Results

The search identified 6784 studies; 50 were included. Most of the 50 studies had quality concerns. All studies included at least one of the decision‐making components: the most prominent were the technical aspects of ‘recording’ and ‘reporting’. Data use for decision‐making was much less represented.

Conclusion

HIS research focuses on the more technical aspects of HIS. Further research on HIS, given the strong push towards HIS digitalisation, should consider putting at the centre the human experience of decision‐making and data use, in order to make HIS relevant for quality of care.

Across the health system, Health Information Systems (HIS) are meant to support data management and use, which are key activities of health care delivery.

Informed by existing frameworks and by fieldwork in three African countries, we identified twelve decision‐making components of HIS.

In this framework synthesis, we found that the most common decision‐making components in the HIS literature were those related to the technical aspects of data ‘recording’ and ‘reporting’; data use for decision‐making was much less represented.

For HIS instruments to facilitate improved health care delivery, clinical and public health decision‐making components of HIS should be put at the centre for HIS research, ideation and implementation.

Utilisation of health management information and its determinant factors among health professionals working at public health facilities in North Wollo Zone, Northeast Ethiopia: a cross-sectional study

OBJECTIVE

The study aimed to assess health management information utilisation and associated factors among health professionals working at public health facilities in North Wollo Zone, Northeast Ethiopia.

SETTING

The study was conducted at public health facilities in the North Wollo Zone, Northeast Ethiopia.

PARTICIPANTS

A total of 664 (56.3% male and 43.7% female) health professionals participated in the study. All health professionals permanently working in North Wollo Zone were included in this study. However, health professionals who were not present during the data collection period by any means and who had less than 6 months of experience were not included in this study.

PRIMARY AND SECONDARY OUTCOME MEASURES

The main outcome measure was health management information utilisation.

RESULT

About 58.4% (n=388) (95% CI: 54.4% to 62.0%) of the study participants use health management information. The multivariable logistic regression model indicated that participants who had managerial positions are more likely to use health management information with an adjusted OR (AOR) of 3.11 and 95% CI 1.84 to 5.24. Similarly, having a good motivation level (AOR=4.42 (95% CI: 2.82 to 6.93)), perceived good culture of health information (AOR=6.17 (95% CI: 3.35 to 11.36)), a standard set of indicators (AOR=4.11 (95% CI: 2.65 to 6.38)), having good governance of health information system (AOR=1.75 (95% CI:1.13 to 2.72)) and health management information system (HMIS) training (AOR=3.10 (95% CI: 1.89 to 5.07)) were the predictors positively associated with higher utilisation of health management information.

CONCLUSION

This study revealed that utilisation of health management information was still inadequate. Enhancing motivation, building a culture of information use, having standardised indicators, strengthening the governance of health information systems and comprehensive HMIS training were measures to be taken to improve utilisation of health management information in this study setting.

Has the Volume-Based Drug Purchasing Approach Achieved Equilibrium among Various Stakeholders? Evidence from China

Volume-based drug purchasing by China’s health insurance system currently represents the largest group purchasing organization worldwide. After exchanging the market that accounted for nearly half of the volume of the healthcare system for the ultra-low-price supply of limited drugs, what are the effects on patient and funding burdens, drug accessibility, and clinical efficacy? We aimed to verify the effectiveness of the policy, explore the reasons behind the problem and identify regulatory priorities and collaborative measures. We used literature and reported data from 2019 to 2021 to conduct a stakeholder analysis and health impact assessment, presenting the benefit and risk share for various dimensions. The analysis method was a multidimensional scaling model, which visualized problematic associations. Seventy-nine papers (61 publications and 18 other resources) were included in the study, with 22 effects and 36 problems identified. The results indicated favorable affordability and poor accessibility of drugs, as well as high risk of reduced drug quality and drug-use rationality. The drug-use demand of patients was guaranteed; the prescription rights of doctors regarding clinical drug use were limited; unreasonable evaluation indicators limited the transformation of public hospitals to value- and service-oriented organizations; the sustainability of health insurance funds and policy promotion were at risk; and innovation by pharmaceutical companies was accelerated. The problems associated with high co-occurrence frequencies were divided into the following clusters: cost control, drug accessibility, system rationality, policy fairness, drug quality, and moral hazards. These findings suggested that China has achieved short-term success in reducing the burden on patients and reducing fund expenditure. However, there were still deficiencies in guaranteed supply, quality control, and efficacy tracking. The study offers critical lessons for China and other low- and middle-income countries.

Contribution of health information system to child immunization services in Ethiopia: baseline study of 33 woredas

Background

Monitoring progress using appropriate data, with a functional health information system (HIS), believed to be very crucial for success of immunization program. Baseline study was conducted to assess, immunization service coverage, HIS performance status, and their relationships.

Methods

A linked facility and population-based survey was conducted concurrently from September 21 to October 15, 2020. A total of 3016 households were reached to interview mothers having children aged 12–23 months in the 33 woredas. Overall 81 health posts, 71 health centers, and 15 hospitals were selected for the facility survey. The study used modified Performance of Routine Information System Management (PRISM) tools for the facility survey and a structured questionnaire for the household survey. Using STATA 14.0 software, mixed effect modeling was employed to control the effect of clustering and potential confounders.

Results

The proportion of fully immunized children was 58%. Coverages of measles (at least one dose) and penta3 immunization (received all 3 doses of DPT-HepB-Hib vaccine) were 86%, and 85% respectively. About 27% of mothers had missed their child immunization card mainly due to misplacing or lost. Except ‘source document completeness’ (85%) and ‘use of data for planning and target setting’ (84%), other data quality and use indicators like ‘data accuracy’ (63%), ‘data use for performance review and evidence-based decision making’ (50%), and ‘data use to produce analytical reports’ (31%) show low performance. The odds of fully immunized children is 37% lower in Muslims compared with Orthodox Christians (AOR, 0.63; 95%CI: 0.46, 0.88), higher by 42% with father’s secondary education compared with no education (AOR, 1.42; 95%CI: 1.05, 1.92), and highest wealth quintile compared with lowest quintile (AOR, 2.49; 95%CI: 1.54, 4.03). For each additional score of HIS infrastructure availability, the odds of fully immunized children increased by 22% (AOR:1.22; 95% CI: 1.03, 1.44).

Conclusions

Child immunization coverages are promising However, the current HIS performance is suboptimal. Both service user and HIS related factors are important for immunization service uptake. Documenting required information and advising mothers to keep immunization cards by health workers, and working to have functional HIS are recommended.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01796-8.

Direct financial assistance for improved maternal and child health data: a pilot study supporting the health management information system in Malawi

Background

The health management information system (HMIS) is an integral component of a strong health care system. Despite its importance for decision-making, the quality of HMIS data remains of concern in low- and middle-income countries. To address challenges with the quality of maternal and child health (MCH) data gathered within Malawi’s HMIS, we conducted a pilot study evaluating different support modalities to district-level HMIS offices. We hypothesized that providing regular, direct financial assistance to HMIS offices would enable staff to establish strategies and priorities based on local context, resulting in more accurate, timely, and complete MCH data.

Methods

The pilot intervention was implemented in Mwanza district, while Chikwawa, Neno, and Ntchisi districts served as control sites given support received from other institutions. The intervention consisted of providing direct financial assistance to Mwanza’s HMIS office following the submission of detailed budgets and lists of planned activities. In the control districts, we performed interviews with the HMIS officers to track the HMIS-related activities. We evaluated the intervention by comparing data quality between the post- and pre-intervention periods in the intervention and control districts. Additionally, we conducted interviews with Mwanza’s HMIS office staff to determine the acceptability and appropriateness of the intervention.

Results

Following the 10-month intervention period, we observed improvements in MCH data quality in Mwanza. The availability and completeness of MCH data collected in the registers increased by 22 and 18 percentage points, respectively. The consistency of MCH data between summary reports and electronic HMIS also improved. In contrast, 2/3 control districts noted minimal changes or reductions in data quality after 10 months. The qualitative interviews confirmed that, despite some challenges, the intervention was well received by the participating HMIS office. HMIS staff preferred our strategy to other conventional strategies that fail to give them the independence to make decisions.

Conclusions

This pilot intervention demonstrated an alternative approach to support HMIS offices in their daily efforts to improve data quality. Given the Ministry of Health’s (MoH) interest in strengthening its HMIS, our intervention provides a strategy that the MoH and local and international partners could consider to rapidly improve HMIS data with minimal oversight.

Análise da qualidade da informação sobre óbitos por neoplasias no Brasil, entre 2009 e 2019

RESUMO Objetivo: Avaliar a qualidade das informações sobre mortalidade por neoplasias no âmbito do Sistema de Informação sobre Mortalidade. Métodos: Estudo descritivo observacional com avaliação da qualidade do Sistema de Informação sobre Mortalidade, com desfecho referente aos dados de óbitos por neoplasias ocorridos entre os anos de 2009 e 2019 na população brasileira (≥15 anos). A qualidade da informação (QI) foi mensurada para o Brasil e para as Unidades Federativas por meio das dimensões: cobertura, especificidade e completude dos dados. Resultados: A qualidade da dimensão cobertura variou entre “boa” e “excelente” nas abrangências nacional e estadual. A dimensão especificidade foi classificada como inadequada predominantemente nos Estados das Regiões Norte e Nordeste. A proporção de causas mal definidas foi classificada como de “baixa” qualidade na maioria das unidades de análise ao longo da série. A completude dos dados variou de acordo com o indicador utilizado, os indicadores sexo e idade mostraram-se “excelentes” para todo o período e unidades de análise, a escolaridade apresentou variação de qualidade tanto nas unidades como nos períodos e o estado civil apresentou melhoria da qualidade de seu registro ao longo do período, assim como o indicador raça/cor. Conclusões: A qualidade dos dados de mortalidade por neoplasias na população brasileira (≥15 anos) é, em sua maioria, adequada, mas há lacunas importantes que merecem ser preenchidas, pois a ampliação da QI busca dar visibilidade à condição de saúde da população brasileira, bem como propor ações públicas para sua melhoria.

REDCap mobile data collection: Using implementation science to explore the potential and pitfalls of a digital health tool in routine voluntary medical male circumcision outreach settings in Zimbabwe

Background

Digital data collection tools improve data quality but are limited by connectivity. ZAZIC, a Zimbabwean consortium focused on scaling up male circumcision (MC) services, provides MC in outreach settings where both data quality and connectivity is poor. ZAZIC implemented REDCap Mobile app for data collection among roving ZAZIC MC nurses. To inform continued scale-up or discontinuation, this paper details if, how, and for whom REDCap improved data quality using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework.

Methods

Data were collected for this retrospective, cross-sectional study for nine months, from July 2019 to March 2020, before COVID-19 paused MC services. Data completeness was compared between paper- and REDCap-based tools and between two ZAZIC partners using two sample, one-tailed t-tests.

Results

REDCap reached all roving nurses who reported 26,904 MCs from 1773 submissions. REDCap effectiveness, as measured by data completeness, decreased from 89.2% in paper to 76.6% in REDCap app for Partner 1 (p < 0.001, 95% CI: -0.24, -0.12) but increased modestly from 86.2% to 90.3% in REDCap for Partner 2 (p = 0.05, 95% CI: -.007, 0.12). Adoption of REDCap was 100%; paper-based reporting concluded in October 2019. Implementation varied by partner and user. Maintenance appeared high.

Conclusion

Although initial transition from paper to REDCap showed mixed effectiveness, post-hoc analysis from service resumption found increased REDCap data completeness across partners, suggesting locally-led momentum for REDCap-based data collection. Staff training, consistent mentoring, and continued technical support appear critical for continued use of digital health tools for quality data collection in rural Zimbabwe and similar low connectivity settings.

Analysis of quality of information about deaths from neoplasms in Brazil between 2009 and 2019

ABSTRACT Objective To assess the quality of information about mortality from neoplasm within the Mortality Information System. Methods: Descriptive observational study evaluating the quality of the Mortality Information System, with an outcome referring to data on deaths from neoplasm between 2009 and 2019 in the Brazilian population (≥15 years). Information Quality (IQ) was measured through coverage, specificity and completeness of data, at national and state level. Results: The quality of the coverage dimension ranged from “good” to “excellent” in the national and state coverages. Specificity was classified as inadequate mainly in the states of the North and Northeast regions. The proportion of ill-defined causes was classified as “poor” quality in most units of analysis throughout the series. Data completeness varied according to indicator. Gender and age indicators were proven “excellent” for the entire period and units of analysis, while educational level varied in quality across units and periods, marital status had its quality improved over the period, as well as ethnicity/skin color. Conclusions: The quality of data on mortality from neoplasm in the Brazilian population (≥15 years) is mostly adequate, but there are important gaps to be filled, as the expansion of IQ seeks to give visibility to the health condition of the Brazilian population and to propose public actions for its improvement.

Exploring data quality and use of the routine health information system in Ethiopia: a mixed-methods study

OBJECTIVE

A routine health information system (RHIS) enables decision making in the healthcare system. We aimed to analyse data quality at the district and regional level and explore factors and perceptions affecting the quality and use of routine data.

DESIGN

This was a mixed-methods study. We used the WHO toolkit for analysing data quality and interviewed staff at the point of data generation and along with the flow of data. Data were analysed using the Performance of Routine Information System Management framework.

SETTING

This study was performed in eight districts in four regions of Ethiopia. The study was nested within a 2-year programme of the Operational Research and Coaching for government Analysts.

PARTICIPANTS

We visited 45 health posts, 1 district hospital, 16 health centres and 8 district offices for analysis of routine RHIS data and interviewed 117 staff members for the qualitative assessment.

OUTCOME MEASURES

We assessed availability of source documents, completeness, timeliness and accuracy of reporting of routine data, and explored data quality and use perceptions.

RESULTS

There was variable quality of both indicator and data element. Data on maternal health and immunisation were of higher quality than data on child nutrition. Issues ranged from simple organisational factors, such as availability of register books, to intricate technical issues, like complexity of indicators and choice of denominators based on population estimates. Respondents showed knowledge of the reporting procedures, but also demonstrated limited skills, lack of supportive supervision and reporting to please the next level. We saw limited examples of the use of data by the staff who were responsible for data reporting.

CONCLUSION

We identified important organisational, technical, behavioural and process factors that need further attention to improve the quality and use of RHIS data in Ethiopia.

Strengthening the national health information system through a capacity-building and mentorship partnership (CBMP) programme: a health system and university partnership initiative in Ethiopia

Background

A strong health information system (HIS) is one of the essential building blocks for a resilient health system. The Ministry of Health (MOH) of Ethiopia is working on different initiatives to strengthen the national HIS. Among these is the Capacity-Building and Mentorship Partnership (CBMP) Programme in collaboration with public universities in Ethiopia since November 2017. This study aims to evaluate the outcomes and share experiences of the country in working with universities to strengthen the national HIS.

Methods

The study employed a mixed-methods approach that included 247 health organizations (health offices and facilities) of CBMP-implementing woredas (districts) and 23 key informant interviews. The programme focused on capacity-building and mentoring facilities and woreda health offices. The status of HIS was measured using a connected woreda checklist before and after the intervention. The checklist consists of items related to HIS infrastructure, data quality and administrative use. The organizations were classified as emerging, candidate or model based on the score. The findings were triangulated with qualitative data collected through key informant interviews.

Results

The results showed that the overall score of the HIS implementation was 46.3 before and 74.2 after implementation of the programme. The proportion of model organizations increased from 1.2% before to 31.8% after the programme implementation. The health system–university partnership has provided an opportunity for higher education institutions to understand the health system and tune their curricula to address real-world challenges. The partnership brought opportunities to conduct and produce local- and national-level evidence to improve the HIS. Weak ownership, poor responsiveness and poor perceptions of the programme were mentioned as major challenges in programme implementation.

Conclusion

The overall HIS has shown substantial progress in CBMP implementation woredas. A number of facilities became models in a short period of time after the implementation of the programme. The health system–university partnership was found to be a promising approach to improve the national HIS and to share the on-the-ground experiences with the university academicians. However, weak ownership and poor responsiveness to feedback were the major challenges identified as needing more attention in future programme implementation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12961-021-00787-x.

Effects of routinely collected health information system variables on the readmission of patients with type 2 diabetes

OBJECTIVES

This research explores the association between variables routinely collected in a health information system and the readmission of patients with type 2 diabetes within 30 days of discharge.

METHODS

This retrospective cohort study was conducted at King Fahd Hospital of the University (KFHU) in Al-Khobar, KSA. The study population comprised patients with type 2 diabetes who were admitted to the hospital from January 2016 to November 2016. Data were obtained from the hospital's information system at KFHU. The association between the readmission of patients with type 2 diabetes and routinely collected health information system variables such as demographics, type of diabetes, length of stay, and discharge type were analyzed.

RESULTS

A total of 497 cases met the inclusion criteria. Of these, 31 (6.2%) cases were readmitted within 30 days. Type 2 diabetes was the only variable found to be significantly associated with readmission within 30 days (χ2 (1, N = 497) = 6.116, p = 0.0134). Diabetes type (p = 0.0133) and discharge type (p = 0.0403) were the only variables that displayed significance utilizing a logistic regression model.

CONCLUSION

Overall, the routinely collected demographic, diagnostic, and administrative variables were found to be poor predictors of 30-day readmission for type 2 diabetes at the institution studied. Nonetheless, the only significant variables in the prediction of 30-day readmission were diabetes type and discharge type. To determine the predictors of readmission, it is recommended that future studies include height and weight to the routinely collected health information system variables. We also suggest that future studies be based on data collected over several years or on pooled data collected from several hospitals.

Assessment of quality of routine health information system data and associated factors among departments in public health facilities of Harari region, Ethiopia

Background

Despite the improvements in the knowledge and understanding of the role of health information in the global health system, the quality of data generated by a routine health information system is still very poor in low and middle-income countries. There is a paucity of studies as to what determines data quality in health facilities in the study area. Therefore, this study was aimed to assess the quality of routine health information system data and associated factors in public health facilities of Harari region, Ethiopia.

Methods

A cross-sectional study was conducted in all public health facilities in the Harari region of Ethiopia. The department-level data were collected from respective department heads through document reviews, interviews, and observation checklists. Descriptive statistics were used to data quality and multivariate logistic regression was run to identify factors influencing data quality. The level of significance was declared at P value < 0.05.

Result

The study found good quality data in 51.35% (95% CI 44.6–58.1) of the departments in public health facilities in the Harari Region. Departments found in the health centers were 2.5 times more likely to have good quality data as compared to those found in the health posts. The presence of trained staffs able to fill reporting formats (AOR = 2.474; 95% CI 1.124–5.445) and provisions of feedbacks (AOR = 3.083; 95% CI 1.549–6.135) were also significantly associated with data quality.

Conclusion

The level of good data quality in the public health facilities was less than the expected national level. Lack of trained personnel able to fill the reporting format and feedback were the factors that are found to be affecting data quality. Therefore, training should be provided to increase the knowledge and skills of the health workers. Regular supportive supervision and feedback should also be maintained.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01651-2.

Assessment of RHIS Quality Assurance Practices in Tarkwa Submunicipal Health Directorate, Ghana

Background. Routine health information system (RHIS) quality assurance has become an important issue, not only because of its significance in promoting high standard of patient care, but also because of its impact on government budgets for the maintenance of health services. Routine health information system comprises healthcare data collection, compilation, storage, analysis, report generation, and dissemination on routine basis at the various healthcare settings. The data from RHIS give a representation of health status, health services, and health resources. The sources of RHIS data are normally individual health records, records of services delivered, and records of health resources. Using reliable information from routine health information systems is fundamental in the healthcare delivery system. Quality assurance practices are measures that are put in places to ensure the health data that are collected meet required quality standards. Routine health information system quality assurance practices ensure that data that are generated from the system are fit for use. This study considered quality assurance practices in the RHIS processes. Methods. A cross-sectional study was conducted in eight health facilities in Tarkwa Submunicipal health service in the western region of Ghana. The study involved routine quality assurance practices among the 90-health staff and management selected from facilities in Tarkwa Submunicipal who collect or use data routinely from 24th December, 2019, to 20th January, 2020. Results. Generally, Tarkwa Submunicipal health service appears to practice quality assurance during data collection, compilation, storage, analysis, and dissemination. The results show some achievement in quality control performance in report dissemination (77.6%), data analysis (68.0%), data compilation (67.4%), report compilation (66.3%), data storage (66.3%), and collection (61.1%). Conclusions. Even though Tarkwa Submunicipal health directorate engages some control measures to ensure data quality, there is the need to strengthen the process to achieve the targeted percentage of performance (90.0%). There was significant shortfall in quality assurance practices performance especially during data collection, with respect to the expected performance.

Feasibility, acceptability and validation of wearable devices for climate change and health research in the low-resource contexts of Burkina Faso and Kenya: Study protocol

As the epidemiological transition progresses throughout sub-Saharan Africa, life lived with diseases is an increasingly important part of a population's burden of disease. The burden of disease of climate-sensitive health outcomes is projected to increase considerably within the next decades. Objectively measured, reliable population health data is still limited and is primarily based on perceived illness from recall. Technological advances like non-invasive, consumer-grade wearable devices may play a vital role in alleviating this data gap and in obtaining insights on the disease burden in vulnerable populations, such as heat stress on human cardiovascular response. The overall goal of this study is to investigate whether consumer-grade wearable devices are an acceptable, feasible and valid means to generate data on the individual level in low-resource contexts. Three hundred individuals are recruited from the two study locations in the Nouna health and demographic surveillance system (HDSS), Burkina Faso, and the Siaya HDSS, Kenya. Participants complete a structured questionnaire that comprises question items on acceptability and feasibility under the supervision of trained data collectors. Validity will be evaluated by comparing consumer-grade wearable devices to research-grade devices. Furthermore, we will collect demographic data as well as the data generated by wearable devices. This study will provide insights into the usage of consumer-grade wearable devices to measure individual vital signs in low-resource contexts, such as Burkina Faso and Kenya. Vital signs comprising activity (steps), sleep (duration, quality) and heart rate (hr) are important measures to gain insights on individual behavior and activity patterns in low-resource contexts. These vital signs may be associated with weather variables-as we gather them from weather stations that we have setup as part of this study to cover the whole Nouna and Siaya HDSSs-in order to explore changes in behavior and other variables, such as activity, sleep, hr, during extreme weather events like heat stress exposure. Furthermore, wearable data could be linked to health outcomes and weather events. As a result, consumer-grade wearables may serve as a supporting technology for generating reliable measurements in low-resource contexts and investigating key links between weather occurrences and health outcomes. Thus, wearable devices may provide insights to better inform mitigation and adaptation interventions in these low-resource settings that are direly faced by climate change-induced changes, such as extreme weather events.

Key informant perspectives on the challenges and opportunities for using routine health data for decision-making in Senegal

Background

Increasing the performance of routine health information systems (RHIS) is an important policy priority both globally and in Senegal. As RHIS data become increasingly important in driving decision-making in Senegal, it is imperative to understand the factors that determine their use.

Methods

Semi-structured interviews were conducted with 18 high- and mid-level key informants active in the malaria, tuberculosis and HIV programmatic areas in Senegal. Key informants were employed in the relevant divisions of the Senegal Ministry of Health or nongovernmental / civil society organizations. We asked respondents questions related to the flow, quality and use of RHIS data in their organizations. A framework approach was used to analyze the qualitative data.

Results

Although the respondents worked at the strategic levels of their respective organizations, they consistently indicated that data quality and data use issues began at the operational level of the health system before the data made its way to the central level. We classify the main identified barriers and facilitators to the use of routine data into six categories and attempt to describe their interrelated nature. We find that data quality is a central and direct determinant of RHIS data use. We report that a number of upstream factors in the Senegal context interact to influence the quality of routine data produced. We identify the sociopolitical, financial and system design determinants of RHIS data collection, dissemination and use. We also discuss the organizational and infrastructural factors that influence the use of RHIS data.

Conclusions

We recommend specific prescriptive actions with potential to improve RHIS performance in Senegal, the quality of the data produced and their use. These actions include addressing sociopolitical factors that often interrupt RHIS functioning in Senegal, supporting and motivating staff that maintain RHIS data systems as well as ensuring RHIS data completeness and representativeness. We argue for improved coordination between the various stakeholders in order to streamline RHIS data processes and improve transparency. Finally, we recommend the promotion of a sustained culture of data quality assessment and use.

Routine health management information system data in Ethiopia: consistency, trends, and challenges

Background: Ethiopia is investing in the routine Health Management Information System. Improved routine data are needed for decision-making in the health sector. Objective: To analyse the quality of the routine Health Management Information System data and triangulate with other sources, such as the Demographic and Health Surveys. Methods: We analysed national Health Management Information System data on 19 indicators of maternal health, neonatal survival, immunization, child nutrition, malaria, and tuberculosis over the 2012-2018 time period. The analyses were conducted by 38 analysts from the Ministry of Health, Ethiopia, and two government agencies who participated in the Operational Research and Coaching for Analysts (ORCA) project between June 2018 and June 2020. Using a World Health Organization Data Quality Review toolkit, we assessed indicator definitions, completeness, internal consistency over time and between related indicators, and external consistency compared with other data sources. Results: Several services reported coverage of above 100%. For many indicators, denominators were based on poor-quality population data estimates. Data on individual vaccinations had relatively good internal consistency. In contrast, there was low external consistency for data on fully vaccinated children, with the routine Health Management Information System showing 89% coverage but the Demographic and Health Survey estimate at 39%. Maternal health indicators displayed increasing coverage over time. Indicators on child nutrition, malaria, and tuberculosis were less consistent. Data on neonatal mortality were incomplete and operationalised as mortality on day 0-6. Our comparisons with survey and population projections indicated that one in eight early neonatal deaths were reported in the routine Health Management Information System. Data quality varied between regions. Conclusions: The quality of routine data gathered in the health system needs further attention. We suggest regular triangulation with data from other sources. We recommend addressing the denominator issues, reducing the complexity of indicators, and aligning indicators to international definitions.