A mixed-methods assessment of Routine Health Information System (RHIS) Data Quality and Factors Affecting it, Addis Ababa City Administration, Ethiopia, 2020

AbstractBackground: Effective and efficient health care services need evidence-based decisions, and these decisions should rely on information from high-quality data. However, despite a lot of efforts, routine health data is still claimed to be not at the required level of quality. Previous studies have primarily focused on organization-related factors while little emphasis was given for perception and knowledge of service providers' gaps. Therefore, this study aims to evaluate the quality of data generated from routine health information systems and factors contributing to data quality from diverse aspects. Objective: This study aims in assessing the quality of routine health information system data generated from health facilities in Addis Ababa city administration, providing the level of data quality of routine health information system, and factors affecting it. Method: A cross-sectional study was conducted on 568 health professionals from 33 health centers selected randomly using a two-stage sampling method. A qualitative study was also conducted using 12 key informants. Result: The overall regional data quality level was 76.22%. Health professionals' motivation towards routine health care data have shown a strong association with data quality, (r (31) =.71, p<.001). Lack of adequate Health information system task competence, non-functional PMT, and lack of supervision was also commonly reported reasons for poor data quality. Conclusion: This review has documented the data quality of routine health information systems from health centers under Addis Ababa city. Overall data quality (76.22%) was found to be below the national expectation level, which is 90%. The study emphasized the role of behavioral factors in improving the quality of routine health care data. [Ethiop. J. Health Dev. 2021; 35(SI-1): 15-24 ] Keywords: RHIS, Accuracy, completeness, timeliness, consistency, Addis Abab

Data completeness in healthcare: A literature survey.

As the adoption of eHealth has made it easier to access and aggregate healthcare data, there has been growing application for clinical decisions, health services planning, and public health monitoring with daily collected data in clinical care. Reliable data quality is a precursor of the aforementioned tasks. There is a body of research on data quality in healthcare, however, a clear picture of data completeness in this field is missing. This research aims to identify and classify current research themes related to data completeness in healthcare. In addition, the paper presents problems with data completeness in the reviewed literature and identifies methods that have been adopted to address those problems. This study has reviewed 24 papers (January 2011–April 2016) published in information and computing sciences, biomedical engineering, and medicine and health sciences journals. The paper uncovers three main research themes, including design and development, evaluation, and determinants. In conclusion, this paper improves our understanding of the current state of the art of data completeness in healthcare records and indicates future research directions.N

Health management information system (HMIS) data quality and associated factors in Massaguet district, Chad

Background Quality data from Health Management Information Systems (HMIS) are important for tracking the effectiveness of malaria control interventions. However, HMIS data in many resource-limited settings do not currently meet standards set by the World Health Organization (WHO). We aimed to assess HMIS data quality and associated factors in Chad. Methods A cross-sectional study was conducted in 14 health facilities in Massaguet district. Data on children under 15 years were obtained from the HMIS and from the external patient register covering the period January-December 2018. An additional questionnaire was administered to 16 health centre managers to collect data on contextual variables. Patient registry data were aggregated and compared with the HMIS database at district and health centre level. Completeness and accuracy indicators were calculated as per WHO guidelines. Multivariate logistic regressions were performed on the Verification Factor for attendance, suspected and confirmed malaria cases for three age groups (1 to < 12 months, 1 to < 5 years and 5 to < 15 years) to identify associations between health centre characteristics and data accuracy. Results Health centres achieved a high level of data completeness in HMIS. Malaria data were over-reported in HMIS for children aged under 15 years. There was an association between workload and higher odds of inaccuracy in reporting of attendance among children aged 1 to < 5 years (Odds ratio [OR]: 10.57, 95% CI 2.32-48.19) and 5- < 15 years (OR: 6.64, 95% CI 1.38-32.04). Similar association was found between workload and stock-outs in register books, and inaccuracy in reporting of malaria confirmed cases. Meanwhile, we found that presence of a health technician, and of dedicated staff for data management, were associated with lower inaccuracy in reporting of clinic attendance in children aged under five years. Conclusion Data completeness was high while the accuracy was low. Factors associated with data inaccuracy included high workload and the unavailability of required data collection tools. The results suggest that improvement in working conditions for clinic personnel may improve HMIS data quality. Upgrading from paper-based forms to a web-based HMIS may provide a solution for improving data accuracy and its utility for future evaluations of health interventions. Results from this study can inform the Ministry of Health and it partners on the precautions to be taken in the use of HMIS data and inform initiatives for improving its quality

Improving the quality of routine maternal and newborn data captured in primary health facilities in Gombe State, Northeastern Nigeria: a before-and-after study.

OBJECTIVES: Primary objective: to assess nine data quality metrics for 14 maternal and newborn health data elements, following implementation of an integrated, district-focused data quality intervention. SECONDARY OBJECTIVE: to consider whether assessing the data quality metrics beyond completeness and accuracy of facility reporting offered new insight into reviewing routine data quality. DESIGN: Before-and-after study design. SETTING: Primary health facilities in Gombe State, Northeastern Nigeria. PARTICIPANTS: Monitoring and evaluation officers and maternal, newborn and child health coordinators for state-level and all 11 local government areas (district-equivalent) overseeing 492 primary care facilities offering maternal and newborn care services. INTERVENTION: Between April 2017 and December 2018, we implemented an integrated data quality intervention which included: introduction of job aids and regular self-assessment of data quality, peer-review and feedback, learning workshops, work planning for improvement, and ongoing support through social media. OUTCOME MEASURES: 9 metrics for the data quality dimensions of completeness and timeliness, internal consistency of reported data, and external consistency. RESULTS: The data quality intervention was associated with improvements in seven of nine data quality metrics assessed including availability and timeliness of reporting, completeness of data elements, accuracy of facility reporting, consistency between related data elements, and frequency of outliers reported. Improvement differed by data element type, with content of care and commodity-related data improving more than contact-related data. Increases in the consistency between related data elements demonstrated improved internal consistency within and across facility documentation. CONCLUSIONS: An integrated district-focused data quality intervention-including regular self-assessment of data quality, peer-review and feedback, learning workshops, work planning for improvement, and ongoing support through social media-can increase the completeness, accuracy and internal consistency of facility-based routine data

The measurement of pneumonia incidence and mortality in Malawi in children under five

Background Globally, pneumonia is a leading cause of death in children under-five. Although pneumonia is responsible for nearly a million under-five deaths worldwide each year, there is limited information on community-level pneumonia incidence. To guide data-informed policies and resource allocation, policymakers require nationally representative, regularly available, sub-national estimates of disease incidence and mortality. We explore the use of available health sector data, including routine data captured in Malawi’s Health Information System (HIS) and survey data, to construct sub-national estimates of pneumonia incidence and mortality. As an initial step in this process, we explore the quality of routine HIS diagnosis data. Methods Our methods vary by research questions. In Chapter 2, we estimate district-level pneumonia mortality in children 1-59 months using the Lives Saved Tool (LiST). We estimate district-level coverage of preventive and curative health sector interventions to determine how change in intervention coverage has impacted pneumonia mortality over time and across districts. In Chapter 3, we use a mixed methods study to explore the quality and use of routine HIS data. Guided by the World Health Organization data quality metrics, we describe the quality of routine acute respiratory infection (ARI) data collected through the HIS. We use qualitative methods to understand how the data collection process contributes to quality and use of the ARI data. In Chapter 4, we use Bayesian estimation methods to estimate district-level pneumonia incidence. Bayesian estimation techniques allow us to use available health sector information—the ARI data that we explored in Chapter 3, the intervention coverage data that we used in Chapter 2, and census data on under-five population—to estimate community-level pneumonia incidence, an unknown quantity. Results Pneumonia mortality has declined from 2000 to 2014 across all districts in Malawi. The decline is attributed to preventive interventions (Haemophilus influenzae type B and pneumococcal vaccines), treatment of pneumonia with antibiotics, and reductions in stunting and wasting. Pneumonia mortality is <4 per 1,000 children under-five in all districts in Malawi in 2014. We estimate community-level under-five pneumonia incidence to be 66.5 per 1,000 (SD: 23.2 per 1,000) across Malawi’s 28 districts in June 2015. Pneumonia incidence increases slightly over time and demonstrates seasonal variation. Routine data on ARI diagnosis in children under five, used to estimate pneumonia incidence, is available, complete, and consistent over time. However, data in the HIS are an overestimate of number of cases recorded in the register as verified by our study team (mean difference in number of cases: 94.0). Conclusion We demonstrate that Malawi’s available health information includes information that can be used to create sub-national estimates of incidence and mortality. District-level variation and trends over time can be used for the directed allocation of resources and to identify and respond to areas of above-average disease burden

Evaluating the quality of routine data in primary health facilities for monitoring maternal and newborn care in Gombe State, northeastern Nigeria

Background. Accurate data are essential for monitoring progress and course correction. Good quality facility-based routine data can be used at the facility, district, national, and global levels to improve quality of care and care policies. However, poor routine data quality has been an ongoing challenge. This thesis aimed to evaluate the quality of routine data for monitoring maternal and newborn care in primary health facilities in Gombe State, Nigeria. Methods. To examine the quality of routine monitoring data, in Study 1 we assessed facility-reported data in the District Health Information Software, version 2 (DHIS 2) according to three routine data quality dimensions: completeness and timeliness, internal consistency, and external consistency. Using direct observations as a gold standard, in Study 2 we assessed the validity of data in facility registers as well as women’s recall of childbirth events. For 21 months (April 2017-December 2018), we implemented a data quality intervention, working with all 11 local government area (district-equivalent) monitoring and evaluation officers and maternal and child health program coordinators of Gombe State which oversee 492 primary health facilities. The intervention included regular self-assessment of data quality, learning workshops, and planning for improvement. In Study 3, we quantified the changes in data quality using before-and-after analyses, comparing the intervention period to the 21-month pre-intervention period (July 2015-March 2017). Results. Twelve of 14 priority facility-based indicators were available in Gombe’s health information system to monitor maternal and newborn care. However, the facility data were incomplete and showed inconsistencies over time, between related indicators, between internal and external data sources. Contact indicators had higher data quality than indicators reflecting the content of care. Though there were challenges with the quality of facility-reported data, the validity study demonstrated that health workers were able to record valid information for some aspects of maternal and newborn care. When compared to childbirth observations, health workers documented accurately in maternity registers for the following indicators: the cadre of main birth attendant; maternal background characteristics, and newborn outcomes. Lastly, the data quality intervention was associated with improved completeness, timeliness, consistency between related data, and accuracy of facility reporting. Conclusion. Facility-based routine data in Gombe State can monitor priority service provision indicators for mothers and newborns. To realize the potential of these data, opportunities to improve data quality include: expanding data quality assessments beyond completeness and accuracy; maximizing the reporting and specificity of existing data; refining supervision feedback on the data quality metrics; and optimizing the digitization of facility data in information systems such as DHIS 2. Further research opportunities include: deepening our understanding of how health workers directly engage with facility documentation to perform clinical care tasks; and developing a composite score to summarize the multi-dimensionality of routine data as a measure for continuous data quality monitoring and as an outcome for data quality interventions

Health Care Manager Electronic Medical Record Systems Implementation Strategies to Improve Patient Outcomes

Ineffective implementation of electronic medical record systems (EMRS) among health care outpatient clinics results in substantial financial loss. Health care managers (HCMs) in outpatient clinics who fail to implement EMRS adversely affect employee usage and performance. Grounded in fayolism theory, the purpose of this qualitative multiple case study was to explore the strategies health care managers used to implement EMRS in their organizations. The participants were six HCMs in a U.S East Coast city. Thematic analysis was used to analyze the data from semistructured interviews and internal company documents. Five themes emerged: internal communication, overcoming barriers, time management, compensation improving productivity, and data organization. A key recommendation for HCMs is to ensure that employees have adequate EMRS training to implement EMRS in health care outpatient clinics. The implications for positive social change include the potential to decrease societal health care costs and optimize health care information management for improved patient safety and wellbeing

Describing Documentation of Electronic Health Records (EHR) in Anti-Retroviral Therapy (ART) Clinics to Improve Data Quality for Healthcare Processes in Malawi

In healthcare, documentation deals with recording of significant patient encounters within a healthcare information system. Documentation has evolved from paper-based tools to electronic means such as Electronic Health Record (EHR) systems. The EHR may optimize healthcare processes by facilitating communication, offering decision support, and allocating and distributing resources to health facilities for patient care. Faced with the Human Immunodeficiency Viruses that leads to the Acquired ImmunoDeficiency Syndrome (HIV/AIDS) pandemic, Malawi has embraced an EHR for its Anti-Retroviral Therapy (ART) clinics since 2006. The 90-90-90 HIV/AIDS eradication strategy which Malawi adopted with the leadership of the World Health Organization (WHO) United Nations against AIDS (UNAIDS) may benefit from this EHR. In this initiative, identification and enrollment of HIV positive clients into ART clinics is essential for viral load suppression. The EHR benefits rely on good data quality, which has been lacking in Low-Income Countries (LICs) including Malawi. Although data quality has many definitions, a widely accepted concept is “data quality is fit for purpose.” This concept has not been used in many data quality assessments, particularly in LICs. I asked the following research question: How can the varying purposes of EHR data use for different stakeholders inform interventions for EHR data quality improvement? I had the following research aims: • To understand the importance of data quality characteristics in the context of EHR stakeholders’ purposes of data use • To conduct a data quality assessment and understand the drivers of the observed data quality I conducted this research at primary, secondary and tertiary level ART clinics that manage over 200 patients a day in Malawi. My study used an observational study design with mixed methods. I conducted semi-structured interviews with 34 stakeholders comprised of nurses and clinicians as well as public health officials and donors who support the EHR. Additionally, I assessed 160,647 patient records that had 549,826 visits across 10 different health facilities, extracted from existing EHR data. My work established that stakeholders have clinical facing purposes or administrative purposes of data use. Stakeholders with clinical facing purposes of data use expected their data to be plausible. On the other hand, stakeholders with administrative purposes of data use expected completeness first. I found variation in the observed proportions of the data quality characteristics of completeness and plausibility. Completeness ranged from 5% to 99% while plausibility ranged from 40% to 99% across variables essential to the 90-90-90 initiative. After integrating my qualitative and quantitative results, some results indicated agreement or convergence between different sources of information about data quality. Data quality was high for the preferred characteristic of plausibility for patient health tracking. Conversely, there were divergent results between findings in my first and second aims for data management and use. For this purpose of data use, I found that plausibility was highly prioritized by participants, but in the data assessment, I found that only 40% of the records were plausible. The recommendations that follow from my work regarding interventions to improve data quality include: • Consider priorities based on importance of specific data quality characteristics for different stakeholder purposes of data use. • Assess observed data quality proportions after conducting a quantitative data quality assessment to identify where interventions should first be implemented in relation with expected stakeholder data characteristics and purposes of data use.PHDHlth Infrastr & Lrng Systs PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168001/1/ojgadabu_1.pd

Factors to improve data quality of electronic medical records

Electronic Medical Record (EMR) systems have been identified as having the potential to improve health care and allow the health care sector to reap a number of benefits when implemented successfully. These benefits include enabling quick and easy access to patient files and also reducing the problem of misplaced or lost patient files. Such EMRs allow for patient records to be up to date, provided that health care practitioners capture standard and consistent data in the relevant fields. In Africa, there are only a few countries that have successfully implemented EMR systems due to social and technological challenges. Social factors include lack of computer skilled health workers, lack of adequate training, physician’s resistance to shift from using paper records to electronic records, either due to complex systems or the fear of being replaced by the systems. On the other hand, the technological factors include lack of Information Technology (IT) and clinical resources, lack of internet access, financial barriers to purchase the necessary technological hardware and implementation costs. A few South African health care institutions have implemented EMR systems, however, most of the public health care facilities still make use of a manual system to capture patient information. In the case where public health care facilities do have an EMR system implemented, there are problems with the consistency of the data that is captured. The inconsistency is caused by the different understandings that the health care professionals have regarding the importance of capturing the necessary information that is collected at various points in health care institutions, thus affecting data quality. For the successful implementation and use of EMR systems, everything within the health care organisation should be integrated. In other words, the steering committee and workgroup, the equipment, the product, the processes, the system and the facility design and construction should be incorporated to work together. The common problems identified in literature regarding data quality in EMRs include misspelled words, inconsistent word strings, inaccurate information entered on the record and incompleteness of the record. These problems lead to poor quality information, lack of accessibility of the record, poorly organised notes and inaccurate information about the patient. The South African strategy aims to implement a National Health Insurance (NHI) which will provide citizens with equitable access to health care. For the successful implementation of the NHI strategy, South African health care sectors should address the barriers which were identified and learn from other African countries that have successfully implemented EMR systems and had positive outcomes. Therefore, this study investigates how data quality can be improved on electronic medical records in public health care in South Africa? The qualitative research methodology approach was used for this study. Interviews were conducted with eight health care professionals at Klerksdorp, in the North West province to obtain data regarding the factors they would deem important for the improvement of data quality in EMRs. The Data Quality Framework (DQF) was applied in this study and six dimensions were identified as the factors to improve data quality. These dimensions include completeness, accuracy, consistency, conformity, timeliness, and integrity. From the analysis of the interview responses, it was discovered that there were, in fact, data quality issues experienced at the public health care facilities of South Africa. A need was identified for the use of data quality assessment tools and solutions to address the data quality issues or challenges that health care practitioners are faced with during their daily jobs. Seven barriers were also identified as having an impact on the successful implementation of EMRs at health care institutions. These barriers, together with the data quality issues, influence the successful use of EMRs and should not be overlooked. From these barriers the study developed seven Critical Success Factors which can be used by the National Department of Health to improve the quality of EMRs.Thesis (MCom) -- Faculty of Management and Commerce, 201

Factors to improve data quality of electronic medical records

Electronic Medical Record (EMR) systems have been identified as having the potential to improve health care and allow the health care sector to reap a number of benefits when implemented successfully. These benefits include enabling quick and easy access to patient files and also reducing the problem of misplaced or lost patient files. Such EMRs allow for patient records to be up to date, provided that health care practitioners capture standard and consistent data in the relevant fields. In Africa, there are only a few countries that have successfully implemented EMR systems due to social and technological challenges. Social factors include lack of computer skilled health workers, lack of adequate training, physician’s resistance to shift from using paper records to electronic records, either due to complex systems or the fear of being replaced by the systems. On the other hand, the technological factors include lack of Information Technology (IT) and clinical resources, lack of internet access, financial barriers to purchase the necessary technological hardware and implementation costs. A few South African health care institutions have implemented EMR systems, however, most of the public health care facilities still make use of a manual system to capture patient information. In the case where public health care facilities do have an EMR system implemented, there are problems with the consistency of the data that is captured. The inconsistency is caused by the different understandings that the health care professionals have regarding the importance of capturing the necessary information that is collected at various points in health care institutions, thus affecting data quality. For the successful implementation and use of EMR systems, everything within the health care organisation should be integrated. In other words, the steering committee and workgroup, the equipment, the product, the processes, the system and the facility design and construction should be incorporated to work together. The common problems identified in literature regarding data quality in EMRs include misspelled words, inconsistent word strings, inaccurate information entered on the record and incompleteness of the record. These problems lead to poor quality information, lack of accessibility of the record, poorly organised notes and inaccurate information about the patient. The South African strategy aims to implement a National Health Insurance (NHI) which will provide citizens with equitable access to health care. For the successful implementation of the NHI strategy, South African health care sectors should address the barriers which were identified and learn from other African countries that have successfully implemented EMR systems and had positive outcomes. Therefore, this study investigates how data quality can be improved on electronic medical records in public health care in South Africa? The qualitative research methodology approach was used for this study. Interviews were conducted with eight health care professionals at Klerksdorp, in the North West province to obtain data regarding the factors they would deem important for the improvement of data quality in EMRs. The Data Quality Framework (DQF) was applied in this study and six dimensions were identified as the factors to improve data quality. These dimensions include completeness, accuracy, consistency, conformity, timeliness, and integrity. From the analysis of the interview responses, it was discovered that there were, in fact, data quality issues experienced at the public health care facilities of South Africa. A need was identified for the use of data quality assessment tools and solutions to address the data quality issues or challenges that health care practitioners are faced with during their daily jobs. Seven barriers were also identified as having an impact on the successful implementation of EMRs at health care institutions. These barriers, together with the data quality issues, influence the successful use of EMRs and should not be overlooked. From these barriers the study developed seven Critical Success Factors which can be used by the National Department of Health to improve the quality of EMRs.Thesis (MCom) -- Faculty of Management and Commerce, 201