Combitech PHC project
Routine health information systems (RHIS) as a tool in Low and middle income settings with potential for health system performance measurement have been inadequately utilised in several LMIC settings, often traceable to poor planning and quality of the data.( 1) Health Information System was narrowly focused on just the Ministry of Health without proper interconnection of related institutions. Proper interconnection and joint planning between all institutions involved in health information management is likely to substantially minimise duplication and reduce wastage of resources ( 2) . The increased availability and adoption of ICT to consolidate the management of different data sources call for better collaboration and coordination. The challenges related to Health Information system include very weak capacity for health information systems at health facilities, its operation at the local government areas , provision of facilities , untimely production / reporting of routine data and inadequate use of available data for planning and decision making and inadequate allocation of funds to the health information system by Federal and State governments .
The Federal Ministry of Health established its national health management information system ( DHIS2) for routine information , the level of compliance and implementation is low with varying reporting rates across the states. The overall completion rates of national DHIS 2 database is just 60%. (3)
The quality of data is suboptimal and data quality assessments are neither regularly nor consistently conducted. There are often large variations in the values of the indicators from different data sources. There is poor validation of the data collected on the platform, hence, the quality is suboptimal, thereby limiting its use by policy makers and implementers. (4)
Previous studies have reported that the quality of data collected by DHIS platform has been poor. A study carried out in Oyo State, Nigeria reported incomplete, inaccurate and non-consistent data over time between related indicators in facility paper summaries as well as in the electronic databases (5). A similar study conducted in Lagos reported some disparities between the data from the DHIS and NDHS which were attributed to the poor quality of data on the DHIS (6). The limited availability of reliable data to generate health statistics tended to increase reliance on the use of estimations that are based on assumptions with resultant associated errors and questionable reliability. (7)
One of the issues the FMOH sought to redress was the realisation that the NHMIS policy was narrowly and exclusively focused on routine health data which was a subsystem of a health information system as defined by WHO. The DHIS tracks about 1000 data elements but produces only 85 summary indicators. Many of these data elements are rarely analysed.(3) Some PHC service delivery indicators such as provider absence rate and perceived barriers to care due to distance and cost used in measuring PHC performance are not currently captured by the DHIS platform.
Data for the Nigeria HIS reside across multiple institutions and systems. These institutions and their systems neither routinely communicate with one another nor share data. (8,9) Previous studies had identified weak HIS leadership and governance, turf rivalries, unclear assignment of roles and responsibilities, lack of financial incentives and limited technical skills; the absence of interagency coordination and collaboration and inefficiency in the HIS. (8,10)
Information or data systems on the DHIS platform are poorly and irregularly communicated with others. There is poor feedback mechanism from higher levels to the lower levels which might be due to poor communication systems between different levels of care. (11) Unhindered communication between different levels of data collection is essential to proper functioning of the health information management system. The routine analysis of data and the provision of timely feedback mechanisms are inadequate as a result , efforts in data use for policymaking are deficient.(3)
There is weak capacity for the health information system at the health facility and its operations at the local government areas. Some health workers are not properly trained to handle data. This leads to poor standards of data collection. A study carried out in Kaduna State, Nigeria reported that there was inadequate training of the primary health care data officers and subsequent improved training significantly increased the overall data accuracy rate, data completeness and timeliness of reporting .(12) In an assessment conducted, it was observed that several computers were found in their supply boxes untouched while computations were done by hand. (13) Frontline health workers complained of parallel data collection tools across disease programmes which made completion difficult and time consuming. (14)
PHC facilities in Nigeria are currently under-utilized with a utilization rate of less than 20% (15), attributed to perceptions of poor quality of services at PHC facilities and resulting in poor community participation in PHC. (16) Studies assessing the quality of routine health data from primary health centres have shown persistent challenges in terms of incomplete and untimely reporting, incomplete indicator-level data, inaccurate facility reporting, and imprecise target population estimates for coverage.(14) Health facilities have a central role in the health system and successfully deploying a process to manage their registration and deregistration in an information system is important for determining the number of active health facilities .This is necessary for the reliable calculation of routine health indicators that require the number of active health facilities as the denominator (e.g. report completeness rates).
There is poor and irregular funding of the data collection process. Almost a quarter (23.7%) and almost one-sixth (15.8%) of health workers involved in data collection for disease surveillance and notification in South-East Nigeria reported lack of adequate training and inadequate/irregular finance for transportation to submit hard copies of collected data respectively as some of their challenges. (18) There is limited availability of resources (financial and technical) across government departments.
Poor IT support at different levels.
Transmission of data from the Local Government to the State Ministry of Health was challenged because of poor access to internet services, resulting in data backlog and poor quality data. (8) Inadequate planning can fuel barriers towards an interconnected HIS in an evolving environment that increasingly uses information and communications technology(ICT). Barriers to interconnection of the HIS also included lack of standards, inadequate political will, economic challenges, limited infrastructure and constrained human resource skills. (19, 13)
References
Lippeveld, T. (2017). Routine health facility and community information systems: Creating an information use culture.Global Health: Science and Practice,5(3), 338–340
Makinde, O. A., Meribole, E. C., Oyediran, K. A., Fadeyibi, F. A., Cunningham, M., Fajugbagbe-Hussein, Y., ... Mullen, S. (2018). Duplication of effort across development projects in Nigeria: An example using the Master Health Facility List. Online Journal of Public Health Informatics, 10(2)
Federal Ministry of Health, the National Health Policy 2016
Ohiri, K., Ukoha, N. K., Nwangwu, C. W., Chima, C. C., Ogundeji, Y. K., Rone, A., & Reich, M. R. (2016). An assessment of data availability, quality, and use in malaria program decision making in Nigeria. Health Systems & Reform, 2(4), 319-330
Adejumo, A. (2017). An assessment of data quality in routine health information systems in Oyo State, Nigeria.
Nwankwo B, Sambo MN. Can training of health care workers improve data management practice in health management information systems: a case study of primary health care facilities in Kaduna State, Nigeria. Pan Afr Med J [Internet]. 2018 Aug 24 [cited 2022 Jul 20];30(3):289–304. Available from: /pmc/articles/PMC6320474/
Makinde, O. A., & Oyediran, K. A. (2015). Rethinking HIV prevalence determination in developing countries. AIDS Care, 27(2), 240–243. https://doi.org/10.1080/09540121.2014.952219.
Asangansi, I. (2012). Understanding HMIS implementation in a developing country ministry of health context – an institutional logics perspective. Online Journal of Public Health Informatics, 4(3). Retrieved 8 March 2014 from http://ojphi.org/ojs/index.php/ojphi/article/view/4302
Federal Ministry of Health, Nigeria. (2014). National Health Information System Strategic Plan (2014-2018). Federal Government of Nigeria. Retrieved 22 December 2017 from http://www.health.gov.ng/doc/National%20HIS%20Strategic% 20Plan.pdf
Makinde, O. A., Mami, M. I., Oweghoro, B. M., Oyediran, K. A., & Mullen, S. (2016b). Investing in health information management: The right people, in the right place, at the right time. HIM Journal, 45(2), 90–96. https://doi.org/10.1177/ 1833358316639447
Bhattacharya AA, Umar N, Audu A, Felix H, Allen E, Schellenberg JR, Marchant T. Quality of routine facility data for monitoring priority maternal and newborn indicators in DHIS2: a case study from Gombe state, Nigeria. PloS one. 2019 Jan 25;14(1):e0211265
Austin Q, Akeju A, Chukwu D. Case study: Strengthening data quality for diagnosis, decision-making and implementation in Lagos state, Nigeria [Internet]. Clinton Health Access Initiative. 2022 [cited 2022 Jul 20]. p. 1–3. Available from: https://www.clintonhealthacces...
Makinde, O. A., Enemuo, J. C., Adeleke, O., Ohadi, E. M., Dieng, A., & Osika, J. S. (2012b). Assessment of the Routine Health Management Information System in Imo State, Federal Republic of Nigeria. Bethesda, MD: Abt Associates Inc. Retrieved from https://www.hfgproject.org/ass... ral-republic-nigeria/
Bosch-Capblanch, X., Auer, C., Njepuome, N., Saric, J., Jarrett, C., Guterman, A., ... Garba, A. B. (2017). Characterisation of the health information system in Nigeria. Retrieved 19 May 2018 from paperbased.info/wp-content/uploads/2017/10/ PHISICC3_NGA_Report_v08.pdf
Gupta et. al., Decentralised delivery of primary health services in Nigeria; Survey evidence from Lagos and Kogi States. The World Bank.
Alenoghena et. al., Primary health care in Nigeria; Strategies and constraints in implementation: International Journal of Community Research, 2014.
- Nnebue CC, Onwasigwe CN, Adinma ED, Adogu POU. Challenges of data collection and disease notification in Anambra State, Nigeria. Trop J Med Res. 2014 Jan 30;17(1):1–10.
- Kumar, M., Gotz, D., Nutley, T., & Smith, J. B. (2017). Research gaps in routine health information system design barriers to data quality and use in low- and middle-income countries: A literature review. The International Journal of Health Planning and Management, 33(1), e1–e9. https://doi. org/10.1002/hpm.244
The solution will make use of the decision support tools that have been already developed in Nigeria DHIS2 platform and introduction of mobile phone technology in communication and transmission of health data
The solution will involve the use of personnel and technology to improve the collection and transmission of monthly service delivery indicators for PHC performance from the primary healthcare facilities on the existing DHIS-2 data collection and transmission model. Also use of personnel and technology to improve quality of routine PHC service delivery at all levels
This will involve the use of dedicated mobile phones in a Closed User Group (CUG) powered by solar power banks by the Community focal persons , Ward focal persons/supervisors, head of facility/record officers and the LGA Monitoring and Evaluation officers for transmission of information and data to the next higher level to generate monthly PHC service delivery indicators in South West Nigeria. Also to provide guidance and support to staff at the lower level.
The Community focal persons and Ward focal persons will be trained to obtain information on additional key indicators of PHC performance (perceived access barriers due to treatment costs, perceived access barriers due to distance and provider absence rate) using an observational checklist. They will be trained on appropriate checklists used to record key information regarding gaps and challenges in the quality of PHC services at the health facility units. The ward focal persons will be trained on how to collate data from all health facilities in the ward and transmission to the local government level using the CUG.
In addition, the facility and Local Government focal persons will be trained on collation and analysis of the monthly service delivery indicators for PHC performance and the additional key indicators for PHC performance (perceived barriers to health care due to distance, cost and provider absence rate).The local government focal persons will be trained on supervisory support for staff at the lower level.
Independent data validators will be recruited to coordinate peer review validation of the information on the additional key indicators obtained from the Community focal persons and the Ward focal persons ; and the monthly service delivery data generated by the facility focal persons, at the first week of the following month using a monthly validation summary sheet. The independent validators will pay quarterly visits to the PHC facilities and communicate with the Community focal person, Ward focal person and facility focal persons ( head of facility /record officers) through the use of CUG .
The validated data will be shared with the lGA Monitoring and Evaluation Officer before transmission to the State Ministry of Health.
There will be regular feedback from the higher level (State Ministry of Health) to the local government, independent validators at the LGA, the facility focal persons in PHC , Ward focal persons and the Community focal persons.
The personnel (Community focal persons, facility focal persons, Ward focal persons and independent validators) will be trained on the use of the checklists and the summary sheet for data transmission and the use of CUG for communication .
Dedicated closed user group (CUG) phones (not laptops or any other high-technology gadget) will be used for communication on the monthly service PHC delivery indicators and additional key indicators for PHC performance and feedback mechanism at all levels during official hours.
These processes will ensure cost effectiveness and efficiency of the already-existing systems and the data transmission process.
The solution will serve the facility focal persons (head of facilities / record officers ) the Community focal persons, LGA focal persons (M&E officer) , Independent validators (Health Information Managers) as target population by building their capacities on data collection and supervisory visits to ensure quality of data transmission . The solution will also improve the feedback mechanism from the higher levels on the data collected and transmitted and support for the lower level staff.
The solution will ensure the availability and accessibility of quality data that will assist in planning, research and advocacy. In addition, the improvement in data quality will assist the policy makers in the health sector to put in place measures that will enhance the PHC performance for quality health service delivery. In the study above carried out in Lagos, policy makers reported that poor quality data from the local governments reflected in Health policy decision making[8].
The solution will provide timely , complete and non-duplicated data for planning and improving PHC performance at community levels . This might provide baseline information for planning, resource allocation and evidence generation for funders who may be interested in funding PHC service delivery
The solution might translate to improvement in the quality of PHC service delivery data and at the ultimate the improvement in the quality of life of the members of the community who utilise the PHC services. These include children below five years, school children, adolescents and adults (especially women of reproductive age) that utilise promotive, preventive and curative health services in the PHCs. These services include maternal and child health services as well as the treatment of communicable and non-communicable diseases (especially hypertension and diabetes mellitus).
The team is a multidisciplinary team of public health physicians, state government M&E officer, health informatician, CBOs and Ward Development Committees representatives
The Team is involved in community health service delivery, planning, healthcare data collection , validation and supervisory functions
The needs of the target population will be met through their involvement in collection of monthly PHC service delivery data and supervision. The solution will input provision of support in terms of funds and capacity building and direct engagement of stakeholders of health.
Advocacy visits will be made to the Community Based Organisations and Ward Development Committees, Directors of PHC, Executive Secretary to the State Primary Health Care Development Board and Officers in charge of the facilities to get their buy-in and involvement in planning and implementation of the solution.
- Employ unconventional or proxy data sources to inform primary health care performance improvement
- Provide improved measurement methods that are low cost, fit-for-purpose, shareable across information systems, and streamlined for data collectors
- Leverage existing systems, networks, and workflows to streamline the collection and interpretation of data to support meaningful use of primary health care data
- Provide actionable, accountable, and accessible insights for health care providers, administrators, and/or funders that can be used to optimize the performance of primary health care
- Pilot
Financial barrier – funding in terms of dedicated phones, solar power banks, checklist /data collection template, training of the personnel at the levels, sustaining the stipends of the personnel at the facility, ward and LGA level.
Technical Barrier: There is deficiency in the capacity of available staff in proper documentation, data recording and transmission to the next higher level. Where needed, task-shifting to a lower cadre staff will be done, hence, the need for training . The Community focal persons will be trained on the use of observation check-lists to record the additional key indicators , The ward focal persons will be trained on their role in the collection of facility based data, use of data collection tools and low cost technology such as mobile phones for subsequent transmission of data collected to the next higher level. The independent validators will be trained on support supervisory visit and provision of support to staff at lower level.
This solution helps to effectively provide information on the additional key PHC indicators not currently captured nor analysed by DHIS 2 platform for improvement of PHC performance. Indicators such as provider absence rate cannot be measured effectively through cross-sectional surveys, and the other indicators such as Perceived access barriers due to treatment costs and Perceived access barriers due to distance are community based.
The solution will use trained Community focal persons from communities in the wards in the urban and rural LGAs where the 10 model PHC resides in each of the states in south west region, they will use the checklist to record information on provider absence rate and communicate to the next higher level through the use of CUG after validation and feed them into the existing DHIS system for measuring the indicator.
The Community focal persons from communities in neighboring wards will be rotated through all other wards within the same LGA to ensure random selection, maintain anonymity and build objectivity into the measurement of data on provider absence rate. The information on the two community based indicators will be captured by the community focal persons from 10 mothers selected by simple random sampling ,asking questions on access barriers due to treatment costs and access barriers due to distance.
The community focal persons will access the communities in the neighboring wards through the CUG , collate the information and communicate with the independent validators and LGA focal persons through the caller user group. This will prevent poor communication because individuals who are responsible for data collection enhance limitless conversations between the individuals.
The facility focal persons will communicate and transmit the monthly PHC service delivery data to the ward focal persons. The ward focal persons will collect and collate data from the model health PHC facilities in the wards and record on a summary sheet and communicate with the independent validators and the LGA focal persons. In addition , the independent validators will make quarterly visits to the model PHC facilities and coordinate peer review validation of the data received from community focal persons and ward focal persons. The data validated will be transmitted to the LGA M& E which will be communicated to state level. The solution will build in a continuous feedback system from the state level to the community focal person.
These approaches will help improve the quality of the data and help build team spirit and a sense of collective responsibility among the staff in the health information management system. These approaches will improve the information flow and reduce bottlenecks in flow of data across different levels.
Impact goals for next year are to :
Increase the proportion of skilled health information management officers at all levels
Improve the data transmission process at all levels
Improve the feedback mechanism system
Impact goals for next five years are to :
Improve the quality of monthly PHC service delivery data available and accessible to health care administrators, partners and policy makers in South west , Nigeria
Provide new evidence for additional key indicators for measuring quality PHC service delivery
Insitutionalise the additional key PHC indicators ( provider absence ratio, perceived access barriers due to treatment costs and perceived access barriers due to distance) as routine monthly PHC service delivery data in the existing DHIS platform in South west, Nigeria
Institutionalize an integrated and sustainable health information system for decision making at all levels in South west , Nigeria
Improve funding of data transmission process at the health facility level
Increase in Donor funding available for improvement in PHC service delivery
The measurable indicators are :
Proportion of Health management information officers trained at the model PHCs
Proportion of Community focal persons and Ward focal persons trained
Proportion of facility focal persons and LGA focal persons trained
Proportion of communities within the model PHC catchment with trained Community focal persons capturing the additional key PHC perfomance indicators.
Proportion of model PHCs with completely filled facility based registers
Proportion of model PHCs with timely and complete data to the LGA by 5th day of the following month.
Proportion of Ward focal persons with collated and complete model PHCs data and supervisory checklist
Proportion of independent validators who made quarterly visits to the facilities
Proportion of LGAs with timely and complete data to the state level by 10th day of the following month.
Proportion of model PHCs who have a facility based display of PHC performance indicators for their facility for the last 3 month.
Proportion of LGAs capturing the additional key indicators.
Proportion of states capturing the additional key indicators.
Inputs: Money, phones, Solar power banks, data collection documents ( checklists and summary sheets, health personnel at different levels
Processes/Activities: Procurement and distribution of the CUG phones and solar power banks to the health information management personnel, logistics for peer review validation, development and distribution of tools for capturing the additional indicators for community focal persons and spreadsheets for independent validators, training of the health information management personnel.
Outputs (direct and indirect): Observational checklist, and reports of the community survey, spreadsheets, training materials and availability of trained health information management personnel.
Short-term outcome (immediate): Availability of Quality routine monthly PHC service delivery data , improved data/ information flow and Sustained feedback system
Long-term outcome (impact): institutionalization of the additional key PHC performance indicators as part of the routine monthly service delivery data. Reduction in infant, Under-5 and maternal mortality.
The core technology driving this innovation is a GSM technology that is built into a closed caller user-group for use of communication of data collected / information by community focal persons and ward focal persons with the independent validators at the LGAs. Also , communication of routine data on monthly PHC service delivery from the model PHC to the LGA focal person.. It is expected to provide a cheap calling rate financed by the employers/funders. The innovation will make use of a minimum of edge(2G) technology to work and it is deployable in all LGAs in the south west region.
- A new application of an existing technology
- Software and Mobile Applications
- 3. Good Health and Well-being
- Nigeria
- Nigeria
The community focal persons, health facility focal persons , ward focal persons , independent validators at LGA and local government focal persons will provide information/ data on additional key performance indicators for PHC service delivery and monthly service delivery data from model PHCs and communities in the wards in the urban and rural LGAs in each state in the south west zone, Nigeria.
The community focal persons will collect information from the model PHCs on provider absence rates using a checklist and the members of the community on their perceived barriers to health care due to cost, due to distance and due to the absence of the health providers at the facilities.
The facility focal persons will collect data from the registers in the model primary health centers on routine monthly PHC service delivery data / performance indicators.
The ward focal persons will collate the monthly PHC service delivery data from all the model PHCs in the ward
The independent validators at the LGA will make supervisory visits to the model PHC quarterly and coordinate the peer review validation of the data collated by the ward focal persons
The LGA focal persons will receive and integrate the validated data ( additional key PHC service delivery indicator and monthly PHC service delivery data) from the community, facility and ward focal persons for transmission to the State Ministry of Health.
Their incentives will include the provision of CUG phones with airtime and monthly stipend.
- Hybrid of for-profit and nonprofit
The solution includes sustainable approaches to ensure gender equity as regards deployment of technology driven monitoring of PHC performance. Half of independent monitors shall be female. Special considerations will be put in place to ensure equity in all training and capacity building activities especially as regards number of women and inclusion of persons with special needs and disabilities.
The environment where the solution will be applied is the south west zone of Nigeria, it is one of the six zones in Nigeria with an estimated population of 38 million. The six states in the south west zones are divided into senatorial districts which are constituted by local government areas which are stratified into urban and rural. Though most Yoruba speak, there are different dialects and have boundaries as south east and north central zones and neighboring countries like Badagry and Cotonou. The cities like Lagos, Ibadan Abeokuta and Akure are cosmopolitan and commercial centres and this allows for diversity in social and cultural attributes. The Director PHC & state M& E officer will be requested to recruit the health information management personnel at the LGA , ward , and model PHCs. There exist some relationships with the community based organisation , they will assist in recruiting the representatives and community focal persons. The team of professionals are also diverse in terms of state of origin , social and cultural attributes .
Service subsidization business model. This model is integrated with the non-profit organization; the business activities and social programs overlap. The organisation is involved in consulting for local and international organizations. The range of services offered include training on data collection and analysis, program planning, execution and evaluation.
The funds realized from the consulting are used to sustain the organization's running cost, advocacy, publishing and funding novel ideas. The beneficiaries are provided opportunities for job as data clerks at the firm, access to the data after seeking permission and being part of the research publications in addition to monthly stipends realised from the projects.
- Individual consumers or stakeholders (B2C)
The consulting firm will provide consultancy services to government and non governmental agencies and seek grants for projects from donors and donations / funding from philanthropists through advocacy. The firm will also attract field surveys , small and large scales and data analysis services for individuals and organizations
As the PI of the consulting firm, I have been part of teams that attracted grants from university and have been hired on contracts by non government, government organisation and foundations. l have served as independent evaluator for TY Danjuma’s health foundations, technical experts as coordinator of Miccom Cancer foundation and survey coordinator on the assessment of catastrophic cost of Tuberculosis treatment to individuals and households in Nigeria.
Prof