scholarly journals Data Quality of the Routine Health Management Information System at the Primary Healthcare Facility and District Levels in Tanzania

2020 ◽  
Author(s):  
SUSAN F. RUMISHA ◽  
EMANUEL P. LYIMO ◽  
IRENE R. MREMI ◽  
PATRICK K. TUNGU ◽  
VICTOR S. MWINGIRA ◽  
...  
Keyword(s):  
Information System ◽  
Data Quality ◽  
Management Information System ◽  
Health Management ◽  
Health Centre ◽  
Healthcare Facility ◽  
Health Management Information System ◽  
Management Information ◽  
Service Areas

Abstract Background: Effective planning for disease prevention and control requiresaccurate, adequately-analysed, interpreted and communicated data. This study assessed the quality of routine Health Management Information System (HMIS) data at healthcare facility (HF) and district levels in Tanzania. Methods: HMIS tools used at primary health care facilities (dispensary, health centre, hospital) and district office were reviewed to assess their availability, completeness, and accuracy of collected data. The assessment involved seven health service areas namely, Outpatient department, Inpatient department, Antenatal care, Family Planning, Post-natal care, Labour and Delivery and Provider-initiated Testing and Counselling.Results: A total of 115 HFs in 11 districts were assessed. Registers (availability rate=91.1%; interquartile range (IQR):66.7%-100%) and reportforms (86.9%;IQR:62.2%-100%) were the most utilized tools. There was a limited use of tally-sheets (77.8%;IQR:35.6%-100%). Tools availability at dispensary was 91.1%, health-centre 82.2% and hospital 77.8%, and was poor in urban districts. The availability rate atthe district level was 65% (IQR:48%-75%). Reports were highly over-represented in comparison to registers’ records, with large differences observed at HF phase of the data journey and more profound in hospitals.Tool availability and data quality varied by service-areas, indicators, facility level, and districts, however, with a remarkable improvement over the years.Conclusion: There are high variations and improvements in the tool utilisation and data accuracy at facility and district levels. The routine HMIS is weak and data at district level inaccurately reflects what is available at the HFs. These results highlight the need to design tailored and inter-service strategies for improving data quality.

scholarly journals Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania

2020 ◽  
Author(s):  
SUSAN F. RUMISHA ◽  
EMANUEL P. LYIMO ◽  
IRENE R. MREMI ◽  
PATRICK K. TUNGU ◽  
VICTOR S. MWINGIRA ◽  
...  
Keyword(s):  
Information System ◽  
Data Quality ◽  
Management Information System ◽  
Health Management ◽  
Health Centre ◽  
Healthcare Facility ◽  
Health Management Information System ◽  
Management Information ◽  
Service Areas

Abstract Background Effective planning for disease prevention and control requires accurate, adequately-analysed, interpreted and communicated data. This study assessed the quality of routine Health Management Information System (HMIS) data at healthcare facility (HF) and district levels in Tanzania. Methods HMIS tools used at primary health care facilities (dispensary, health centre, hospital) and district office were reviewed to assess their availability, completeness, and accuracy of collected data. The assessment involved seven health service areas namely, Outpatient department, Inpatient department, Antenatal care, Family Planning, Post-natal care, Labour and Delivery and Provider-initiated Testing and Counselling. Results A total of 115 HFs in 11 districts were assessed. Registers (availability rate = 91.1%; interquartile range (IQR):66.7%-100%) and report forms (86.9%; IQR:62.2%-100%) were the most utilized tools. There was a limited use of tally-sheets (77.8%; IQR:35.6%-100%). Tools availability at dispensary was 91.1%, health-centre 82.2% and hospital 77.8%, and was poor in urban districts. The availability rate at the district level was 65% (IQR:48%-75%). Reports were highly over-represented in comparison to registers’ records, with large differences observed at HF phase of the data journey and more profound in hospitals. Tool availability and data quality varied by service-areas, indicators, facility level, and districts, however, with a remarkable improvement over the years. Conclusion There are high variations and improvements in the tool utilisation and data accuracy at facility and district levels. The routine HMIS is weak and data at district level inaccurately reflects what is available at the HFs. These results highlight the need to design tailored and inter-service strategies for improving data quality.

scholarly journals Quality assurance of health management information system in Kayunga district, Uganda

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Harriet R. Kagoya ◽  
Dan Kibuule
Keyword(s):  
Health Care ◽  
Information System ◽  
Quality Assurance ◽  
Management Information System ◽  
Health Management ◽  
Care Delivery ◽  
Public Health Care ◽  
Health Management Information System ◽  
Management Information

Background: An efficient health management information system (HMIS) improves health care delivery and outcomes. However, in most rural settings in Uganda, paper-based HMIS are widely used to monitor public health care services. Moreover, there are limited capabilities and capacity for quality HMIS in remote settings such as Kayunga district.Objectives: The quality assurance practices of HMIS in health centres (HCs) in Kayunga district were evaluated.Method: A cross-sectional descriptive study design was used to assess the quality of HMIS at 21 HCs in Kayunga district. Data were collected through in-depth interviews of HMIS focal persons as well as document analysis of HMIS records and guidelines between 15 June 2010 and 15 July 2010. The main outcomes were quality assurance practices, the HMIS programmatic challenges and opportunities. The practice of HMIS was assessed against a scale for good quality assurance practices. Qualitative data were coded and thematically analysed, whereas quantitative data were analysed by descriptive statistics using SPSS v22 software.Results: All the 21 HCs had manual paper-based HMIS. Less than 25% of HCs practised quality assurance measures during collection, compilation, analysis and dissemination of HMIS data. More than 50% of HCs were not practising any type of quality assurance during analysis and dissemination of data. The main challenges of the HMIS were the laborious and tedious manual system, the difficulty to archive and retrieve records, insufficient HMIS forms and difficulty in delivering hard copies of reports to relevant stakeholders influenced quality of data. Human resource challenges included understaffing where 43% of participating HCs did not have a designated HMIS staff.Conclusion: The HMIS quality assurance practices in Kayunga were suboptimal. Training and support supervision of HMIS focal persons is required to strengthen quality assurance of HMIS. Implementation of electronic HMIS dashboards with data quality checks should be integrated alongside the manual system.

Cash Transfers for Improved Maternal and Child Health Data: A Pilot Study Supporting Health Management Information System in Malawi

2021 ◽  
Author(s):  
Mariame O. Ouedraogo ◽  
Madalitso Tolani ◽  
Janet Mambulasa ◽  
Katie McLaughlin ◽  
Diego G. Bassani ◽  
...  
Keyword(s):  
Data Quality ◽  
Management Information System ◽  
Health Management ◽  
Cash Transfers ◽  
Health Management Information System ◽  
Management Information ◽  
Pilot Intervention ◽  
And Child Health ◽  
Mch Data

Abstract 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 designed a pilot study consisting of performing regular cash transfers to district-level HMIS offices. We hypothesized that providing regular cash transfers to HMIS offices would empower staff to establish strategies and priorities based on local context, consequently obtaining and maintaining 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. The intervention consisted of providing cash transfers to Mwanza's HMIS office following the submission of detailed budgets and lists of planned activities with their respective targets and outputs. In the control districts, we performed regular interviews with the HMIS officers to track the HMIS-related activities. We evaluated the intervention by comparing data quality between the post-intervention 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 the intervention district (Mwanza). The availability and completeness of MCH data collected in the registers increased by 22% and 18%, respectively. The consistency of MCH data between summary reports and electronic HMIS improved from 73–94%. The qualitative interviews confirmed that, despite some challenges, the intervention was well received by the participating HMIS office. Participants preferred our strategy to other conventional ways of supporting HMIS that fail to give HMIS offices 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 (MoH)'s 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.

scholarly journals Comparing data Reported Using the National Health Management Information System and data Declared Validated on the PBF Declaration Forms in Funding Health Districts in Nasarawa State

2021 ◽  
Vol 9 (2) ◽  
pp. 210-219
Author(s):  
Prince Olueseh Ezekiel
Keyword(s):  
Information System ◽  
Health Service ◽  
Service Delivery ◽  
Management Information System ◽  
Health Management ◽  
Health Service Delivery ◽  
Health Centers ◽  
Health Management Information System ◽  
Management Information

The National Health Management Information System (NHMIS) Was Designed To Provide Timely And Reliable Health Service Delivery Information. The Efficiency And Effectiveness Of Health Service Delivery Is Assessed By The Availability Of Quality, Complete And Timely Data. The NHMIS Policy Review Was Initiated By A Consortium Of Relevant Stake Holders Led By The Department Of Planning, Research And Statistics (DPRS) Of The Federal Ministry Of Health (FMOH) And The National Primary Health Care Development Agency (NPHCDA). The Emphasis Of The NHMIS Is To Strengthen The Health Information System-HIS In The Country And Promote The Use Of Quality Information For Evidence-Based Decision-Making At The Community, LGA, And National Levels. In Spite Of Substantial Investments, The Health Sector In Nigeria Has Made Slow Progress In Improving Its Health Indices. Thus The Nigeria State Health Investment Project(NSHIP), Through Support From WHO, Introduced The Performance-Based Financing –PBF Currently Rolled Out In Three States- Adamawa, Nasarawa, And The Ondo States To Deliver A Result-Based Approach To Improve Quantity And Quality Of Health Services Especially In The Area Of Maternal Health. Health Centers Receive Funds Directly Based On The Number Of Essential Services They Delivered And The Improved Quality Of Care. This Encouraged Health Centers To Focus On Delivering Results, And The New Funds Enabled Them To Improve Their Services. This Study Compared Data Reported Using The NHMIS And Declared Validated On The PBF Declaration Forms In Funding Health Facilities In Nasarawa State For Quarter 1 (Jan.- Mar.)2018 And Quarter 2 (Apr. – June) 2018.

scholarly journals Perceptions and experiences with District Health Information System software to collect and utilize health data in Bangladesh: A qualitative exploratory study

2020 ◽  
Author(s):  
Tahmina Begum ◽  
Shaan Muberra Khan ◽  
Bridgit Adamou ◽  
Jannatul Ferdous ◽  
Muhammad Masud Parvez ◽  
...  
Keyword(s):  
Information System ◽  
Data Quality ◽  
Health Information ◽  
Management Information System ◽  
Program Effectiveness ◽  
Health Management ◽  
Quality Data ◽  
Health Management Information System ◽  
Management Information ◽  
District Health

Abstract Background: Accurate and high-quality data are important for improving program effectiveness and informing policy. Bangladesh’s health management information system adopted the District Health Information Software, Version 2 (DHIS2) in 2009 to capture real-time health service utilization data. However, routinely collected data are being underused because of poor data quality. We aimed to understand the facilitators and barriers of implementing DHIS2 as a way to retrieve meaningful and accurate data for reproductive, maternal and child health (RMCAH) services. Methods: This qualitative study was conducted in two districts of Bangladesh from September 2017 to 2018. Data collection included key informant interviews (n=11), in-depth interviews (n=23), and focus group discussions (n=2). The study participants were individuals involved with DHIS2 implementation from the community level to the national level. The data were analyzed thematically.Results: DHIS2 could improve the timeliness and completeness of data reporting over time. The reported facilitating factors were strong government commitment, extensive donor support, and positive attitudes toward the technology among staffs. Quality checks and feedback loops at multiple levels of data gathering points were helpful to minimize data errors. Introducing a dashboard makes DHIS2 compatible to use as monitoring tool. However, the barriers to effective DHIS2 implementation were lack of human resources, slow Internet connectivity, frequent changes to of DHIS2 versions, and maintaining both manual and electronic system side-by-side. Data in DHIS2 remains incomplete because it does not capture data from private health facilities. Having two parallel management information systems reporting the same RMNCAH indicators threatens data quality and increases the reporting workload. Conclusion: The overall insights from this study are expected to contribute to the development of effective strategies for successful DHIS2 implementation and building responsive health management information system. Focused strategic direction is needed to sustain the achievements of digital data culture. Periodic refresher trainings, incentives for increased performance, and an automated single reporting system for multiple stakeholders could make the system more user-friendly. A national electronic health strategy and implementation framework can facilitate creating a culture of DHIS2 use for planning, setting priorities, and decision making among stakeholder groups.

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