scholarly journals Development of a Master Health Facility List in Nigeria

2014 ◽  
Vol 6 (2) ◽  
Author(s):  
Olusesan Ayodeji Makinde ◽  
Aderemi Azeez ◽  
Samson Bamidele ◽  
Akin Oyemakinde ◽  
Kolawole A Oyediran ◽  
...  
Keyword(s):  
Health Information ◽  
Health Facility ◽  
Health Information Systems ◽  
Facility Management ◽  
Health Facilities ◽  
Unique Identifier ◽  
Explicit Processes ◽  
National Health Information Infrastructure ◽  
Multi Level ◽  
State Code

Introduction: Routine Health Information Systems (RHIS) are increasingly transitioning to electronic platforms in several developing countries. Establishment of a Master Facility List (MFL) to standardize the allocation of unique identifiers for health facilities can overcome identification issues and support health facility management. The Nigerian Federal Ministry of Health (FMOH) recently developed a MFL, and we present the process and outcome.Methods: The MFL was developed from the ground up, and includes a state code, a local government area (LGA) code, health facility ownership (public or private), the level of care, and an exclusive LGA level health facility serial number, as part of the unique identifier system in Nigeria. To develop the MFL, the LGAs sent the list of all health facilities in their jurisdiction to the state, which in turn collated for all LGAs under them before sending to the FMOH. At the FMOH, a group of RHIS experts verified the list and identifiers for each state.Results: The national MFL consists of 34,423 health facilities uniquely identified. The list has been published and is available for worldwide access; it is currently used for planning and management of health services in Nigeria.Discussion: Unique identifiers are a basic component of any information system. However, poor planning and execution of implementing this key standard can diminish the success of the RHIS.Conclusion: Development and adherence to standards is the hallmark for a national health information infrastructure. Explicit processes and multi-level stakeholder engagement is necessary to ensuring the success of the effort. 

scholarly journals Producing routine malaria data: an exploration of the micro-practices and processes shaping routine malaria data quality in frontline health facilities in Kenya

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
George Okello ◽  
Sassy Molyneux ◽  
Scholastica Zakayo ◽  
Rene Gerrets ◽  
Caroline Jones
Keyword(s):  
Information Systems ◽  
Data Collection ◽  
Health Information ◽  
Low Income ◽  
Health Workers ◽  
National Level ◽  
Health Information Systems ◽  
Health Facilities ◽  
Careful Examination ◽  
Data Generation

Abstract Background Routine health information systems can provide near real-time data for malaria programme management, monitoring and evaluation, and surveillance. There are widespread concerns about the quality of the malaria data generated through routine information systems in many low-income countries. However, there has been little careful examination of micro-level practices of data collection which are central to the production of routine malaria data. Methods Drawing on fieldwork conducted in two malaria endemic sub-counties in Kenya, this study examined the processes and practices that shape routine malaria data generation at frontline health facilities. The study employed ethnographic methods—including observations, records review, and interviews—over 18-months in four frontline health facilities and two sub-county health records offices. Data were analysed using a thematic analysis approach. Results Malaria data generation was influenced by a range of factors including human resource shortages, tool design, and stock-out of data collection tools. Most of the challenges encountered by health workers in routine malaria data generation had their roots in wider system issues and at the national level where the framing of indicators and development of data collection tools takes place. In response to these challenges, health workers adopted various coping mechanisms such as informal task shifting and use of improvised tools. While these initiatives sustained the data collection process, they also had considerable implications for the data recorded and led to discrepancies in data that were recorded in primary registers. These discrepancies were concealed in aggregated monthly reports that were subsequently entered into the District Health Information Software 2. Conclusion Challenges to routine malaria data generation at frontline health facilities are not malaria or health information systems specific; they reflect wider health system weaknesses. Any interventions seeking to improve routine malaria data generation must look beyond just malaria or health information system initiatives and include consideration of the broader contextual factors that shape malaria data generation.

scholarly journals Institutionalized Data Quality Assessments: A Critical Pathway to Improving the Accuracy of Integrated Disease Surveillance Data in Sierra Leone

2020 ◽  
Author(s):  
Charles Kuria Njuguna ◽  
Mohamed Vandi ◽  
Malimbo Mugagga ◽  
Joseph Kanu ◽  
Evans Liyosi ◽  
...  
Keyword(s):  
Data Quality ◽  
Health Information ◽  
Sierra Leone ◽  
Health Facility ◽  
Disease Surveillance ◽  
Virus Disease ◽  
Health Facilities ◽  
Data Accuracy ◽  
Critical Pathway ◽  
Quality Assessments

Abstract Background Public health agencies require valid, timely and complete health information for early detection of outbreaks. Towards the end of the Ebola Virus Disease (EVD) outbreak in 2015, the Ministry of Health and Sanitation (MoHS), Sierra Leone revitalized the Integrated Disease Surveillance and Response System (IDSR). Data quality assessments were conducted to monitor accuracy of IDSR data. Methods Starting 2016, data quality assessments (DQA) were conducted in randomly selected health facilities. Structured electronic checklist was used to interview district health management teams (DHMT) and health facility staff. We used malaria data, to assess data accuracy as malaria was endemic in Sierra Leone. Verification factors (VF) calculated as the ratio of verified malaria cases in health facility registers to the number of malaria cases in the national health information database, were used to assess data accuracy. Allowing a 5% margin of error, VF <95% were considered over reporting while VF >105 was underreporting. Differences in the proportion of accurate reports at baseline and subsequent assessments were compared using Z-test for two proportions. Results Between 2016 -2018, four DQA were conducted in 444 health facilities where 1,729 IDSR reports were reviewed. Registers and IDSR technical guidelines were available in health facilities and health care workers were conversant with reporting requirements. Overall data accuracy improved from over- reporting of 4.7% (VF 95.3%) in 2016 to under-reporting of 0.2% (VF 100.2%) in 2018. Compared to 2016, proportion of accurate IDSR reports increased by 14.8 % (95% CI 7.2%, 22.3%) in May 2017 and 19.5% (95% CI 12.5% -26.5%) by 2018. Over reporting was more common in private clinics and not- for profit facilities while under-reporting was more common in lower level government health facilities. Leading reasons for data discrepancies included counting errors in 358 (80.6%) health facilities and missing source documents in 47 (10.6%) health facilities. Conclusion This is the first attempt to institutionalize routine monitoring of IDSR data quality in Sierra Leone. Regular data quality assessments may have contributed to improved data accuracy over time. Data compilation errors accounted for most discrepancies and should be minimized to improve accuracy of IDSR data.

scholarly journals Institutionalized Data Quality Assessments: A Critical Pathway to Improving the Accuracy of Integrated Disease Surveillance Data in Sierra Leone

2019 ◽  
Author(s):  
Charles Kuria Njuguna ◽  
Mohamed Vandi ◽  
Malimbo Mugagga ◽  
Joseph Kanu ◽  
Evans Liyosi ◽  
...  
Keyword(s):  
Data Quality ◽  
Health Information ◽  
Sierra Leone ◽  
Health Facility ◽  
Disease Surveillance ◽  
Virus Disease ◽  
Health Facilities ◽  
Data Accuracy ◽  
Critical Pathway ◽  
Quality Assessments

Abstract Background Public health agencies require valid, timely and complete health information for early detection of outbreaks. Towards the end of the Ebola Virus Disease (EVD) outbreak in 2015, the Ministry of Health and Sanitation (MoHS), Sierra Leone revitalized the Integrated Disease Surveillance and Response System (IDSR). Data quality assessments were conducted to monitor the accuracy of data generated through the IDSR system.Methods Starting 2016, regular data quality assessments (DQA)were conducted in randomly selected health facilities. A structured electronic checklist was used to interview district health management team (DHMT) members and health facility staff. We used malaria data to assess data accuracy as malaria was endemic in Sierra Leone. Verification factors (VF) calculated as the ratio of verified malaria cases in the health facility register to the number of malaria cases recorded in the national health information database, were used to assess data accuracy. Allowing a 5% margin of error, VF <95% were considered over reporting while a VF >105 was underreporting. Differences in the proportion of accurate reports in the first and fourth assessments were compared using Z-test for two proportions.Results Between 2016 -2018, four DQA were conducted in 444 health facilities where 1,729 IDSR reports were reviewed. Registers and IDSR technical guidelines were widely available in health facilities and health care workers were conversant with reporting requirements. Overall data accuracy improved from VF of 95.3% in 2016 to 100.2% in 2018. Compared to the baseline in 2016, the proportion of accurate IDSR reports in 2018 increased by 19.5% (CI 12.5% -26.5%). Over reporting was more common in private clinics and not for profit facilities while under-reporting was more common in lower level government health facilities. Leading reasons for data discrepancies included counting errors in 358 (80.6%) health facilities, and missing source documents in 47 (10.6%) health facilities.Conclusion This is the first attempt to institutionalize routine monitoring of IDSR data quality in Sierra Leone. Regular data quality assessments may have contributed to improved data accuracy over time. Data compilation errors accounted for most discrepancies and should be minimized to improve accuracy of IDSR data.

scholarly journals Measles Data Reporting in the District Health Information System: A Case Study of Gombe State

2019 ◽  
Vol 11 (11) ◽  
pp. 109
Author(s):  
Nnamdi Usifoh ◽  
Toby Yak ◽  
Ivy Dooga ◽  
Raymond Dankoli ◽  
Olufemi Ajumobi ◽  
...  
Keyword(s):  
Information System ◽  
Health Information ◽  
Health Facility ◽  
Health Information System ◽  
Data Entry ◽  
Health Facilities ◽  
World Health ◽  
District Health ◽  
Targeted Interventions ◽  
Data Entry Error

BACKGROUND: The District Health Information System (DHIS2) is a modular, cloud-based data management system designed for use in integrated health information systems. In Nigeria, it serves as the repository for routine health data, including measles. A first dose of measles is given routinely in most countries, however, for a country to include a second dose of measles in the routine immunization schedule, it must meet certain criteria set by the World Health Organization (WHO). Unfortunately, Nigeria falls into the category of countries that haven&rsquo;t met the criteria. Despite this, MCV2 data can be seen on the DHIS2 platform. Data from DHIS2 also shows that Gombe State has the highest number of health facilities that reported MCV2 data at least once from 2015 to 2017. The aim of the study was to determine the reasons for the MCV2 reporting on DHIS2 platform for Gombe State. METHOD: We conducted a cross-sectional study among health workers in selected health facilities and LGA RI Officers at the LGA level in Gombe State. Health facility registers were reviewed, and data consistency was ascertained. We reviewed and conducted secondary data analysis of MCV2 data for Gombe State from January 2015 to December 2017. RESULTS: Of the 22 health facilities assessed, 14 health facilities (12 public and 2 private) reported offering MCV2 during the health facility-level interviews. At the LGA level, 5 LGAs out of the 11 LGAs reported during the LGA-level interviews that a second dose of measles is part of the RI schedule in their respective LGAs. For the 6 LGAs that reported not offering a second dose of measles as part of the RI schedule, 3 LGAs identified data entry error as the possible reason for having MCV2 data in the DHSI2 platform while the remaining 3 LGAs reported that the MCV2 data in the DHIS2 platform can be attributed to recording children who didn&rsquo;t receive a first dose of measles at 9 months but received at 18&ndash;23 months as second dose of measles. CONCLUSION: Data entry error and knowledge gap on how to record measles data were identified factors responsible for MCV2 data on the DHIS2 platform. There is a need for targeted interventions towards improving the quality of RI data in Nigeria.

scholarly journals Institutionalized Data Quality Assessments: A Critical Pathway to Improving the Accuracy of Integrated Disease Surveillance Data in Sierra Leone

2020 ◽  
Author(s):  
Charles Kuria Njuguna ◽  
Mohamed Vandi ◽  
Malimbo Mugagga ◽  
Joseph Kanu ◽  
Evans Liyosi ◽  
...  
Keyword(s):  
Data Quality ◽  
Health Information ◽  
Sierra Leone ◽  
Health Facility ◽  
Disease Surveillance ◽  
Virus Disease ◽  
Health Facilities ◽  
Data Accuracy ◽  
Critical Pathway ◽  
Quality Assessments

Abstract Background Public health agencies require valid, timely and complete health information for early detection of outbreaks. Towards the end of the Ebola Virus Disease (EVD) outbreak in 2015, the Ministry of Health and Sanitation (MoHS), Sierra Leone revitalized the Integrated Disease Surveillance and Response System (IDSR). Data quality assessments were conducted to monitor accuracy of IDSR data. Methods Starting 2016, data quality assessments (DQA) were conducted in randomly selected health facilities. Structured electronic checklist was used to interview district health management teams (DHMT) and health facility staff. We used malaria data, to assess data accuracy, as malaria was endemic in Sierra Leone. Verification factors (VF) calculated as the ratio of confirmed malaria cases recorded in health facility registers to the number of malaria cases in the national health information database, were used to assess data accuracy. Allowing a 5% margin of error, VF <95% were considered over reporting while VF >105 was underreporting. Differences in the proportion of accurate reports at baseline and subsequent assessments were compared using Z-test for two proportions. Results: Between 2016 -2018, four DQA were conducted in 444 health facilities where 1,729 IDSR reports were reviewed. Registers and IDSR technical guidelines were available in health facilities and health care workers were conversant with reporting requirements. Overall data accuracy improved from over- reporting of 4.7% (VF 95.3%) in 2016 to under-reporting of 0.2% (VF 100.2%) in 2018. Compared to 2016, proportion of accurate IDSR reports increased by 14.8 % (95% CI 7.2%, 22.3%) in May 2017 and 19.5% (95% CI 12.5% -26.5%) by 2018. Over reporting was more common in private clinics and not- for profit facilities while under-reporting was more common in lower level government health facilities. Leading reasons for data discrepancies included counting errors in 358 (80.6%) health facilities and missing source documents in 47 (10.6%) health facilities. Conclusion This is the first attempt to institutionalize routine monitoring of IDSR data quality in Sierra Leone. Regular data quality assessments may have contributed to improved data accuracy over time. Data compilation errors accounted for most discrepancies and should be minimized to improve accuracy of IDSR data.

Rationale and Design Considerations for a Semantic Mediator in Health Information Systems

1998 ◽  
Vol 37 (04/05) ◽  
pp. 518-526 ◽  
Author(s):  
D. Sauquet ◽  
M.-C. Jaulent ◽  
E. Zapletal ◽  
M. Lavril ◽  
P. Degoulet
Keyword(s):  
Information Systems ◽  
Software Engineering ◽  
Health Information ◽  
Rapid Development ◽  
Heterogeneous Systems ◽  
Health Information Systems ◽  
Semantic Interoperability ◽  
Point Of View ◽  
Current State ◽  
Engineering Environment

AbstractRapid development of community health information networks raises the issue of semantic interoperability between distributed and heterogeneous systems. Indeed, operational health information systems originate from heterogeneous teams of independent developers and have to cooperate in order to exchange data and services. A good cooperation is based on a good understanding of the messages exchanged between the systems. The main issue of semantic interoperability is to ensure that the exchange is not only possible but also meaningful. The main objective of this paper is to analyze semantic interoperability from a software engineering point of view. It describes the principles for the design of a semantic mediator (SM) in the framework of a distributed object manager (DOM). The mediator is itself a component that should allow the exchange of messages independently of languages and platforms. The functional architecture of such a SM is detailed. These principles have been partly applied in the context of the HEllOS object-oriented software engineering environment. The resulting service components are presented with their current state of achievement.

Data Security in Health Information Systems by Applying Software Techniques

1979 ◽  
Vol 18 (04) ◽  
pp. 214-222
Author(s):  
K. Sauter
Keyword(s):  
Information Systems ◽  
Health Information ◽  
Data Security ◽  
Health Information Systems ◽  
Database Systems ◽  
Modern Computer ◽  
Proper Design ◽  
Software Construction ◽  
Organizational Procedures ◽  
Software Measures

The problems encountered in achieving data security within computer-supported information systems increased with the development of modern computer systems. The threats are manifold and have to be met by an appropriate set of hardware precautions, organizational procedures and software measures which are the topic of this paper. Design principles and software construction rules are treated first, since the security power of a system is considerably determined by its proper design. A number of software techniques presented may support security mechanisms ranging from user identification and authentication to access control, auditing and threat monitoring. Encryption is a powerful tool for protecting data during physical storage and transmission as well.Since an increasing number of health information systems with information-integrating functions are database-supported, the main issues and terms of database systems and their specific security aspects are summarized in the appendix.

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