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

1. Lippeveld, T. (2017). Routine health facility and community information systems: Creating an information use culture.Global Health: Science and Practice,5(3), 338–340

2. 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)

3. Federal Ministry of Health, the National Health Policy 2016 

4. 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

5. Adejumo, A. (2017). An assessment of data quality in routine health information systems in Oyo State, Nigeria. 

6. 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/

7. Makinde, O. A., & Oyediran, K. A. (2015). Rethinking HIV prevalence determination in developing countries. AIDS Care, 27(2), 240–243. https://doi-org.ezproxyberklee.flo.org/10.1080/09540121.2014.952219.

8. 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 

9. 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

10. 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.ezproxyberklee.flo.org/10.1177/ 1833358316639447

11. 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

12. 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...

13. 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/

14. 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

15. Gupta et. al., Decentralised delivery of primary health services in Nigeria; Survey evidence from Lagos and Kogi States. The World Bank.

16.  Alenoghena et. al., Primary health care in Nigeria; Strategies and constraints in implementation: International Journal of Community Research, 2014.

17. 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.
18. 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.

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 :

1. Increase the proportion of skilled health information management officers at all levels  

2. Improve  the data transmission process at all levels 

3. Improve the feedback mechanism system 

Impact goals for next five years are to :

1. Improve the quality of  monthly PHC service delivery data  available and accessible to health care administrators, partners and policy makers  in South west , Nigeria

2. Provide new evidence for additional key indicators for  measuring  quality PHC service delivery

3. 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 

4. Institutionalize an integrated and sustainable health information system  for decision making at all levels in South west , Nigeria 

5. Improve funding of data transmission process at the health facility level

6. Increase in Donor funding available for improvement in PHC service delivery

The measurable indicators  are :

1. Proportion of Health management information officers trained at the model  PHCs

2. Proportion of Community focal persons  and Ward focal persons  trained

3. Proportion of facility focal persons  and LGA focal persons trained

4. Proportion of communities within the model  PHC catchment with trained Community focal persons capturing  the additional key PHC  perfomance indicators.

5. Proportion of model  PHCs with completely  filled facility based registers

6. Proportion of model PHCs with timely and complete data  to the LGA by 5th day of the following month.

7. Proportion of Ward focal persons with collated and complete model PHCs data and supervisory checklist 

8. Proportion of independent validators who made quarterly visits to the facilities 

9. Proportion of LGAs with timely and complete data to the state level by 10th day of the following month.

10. Proportion of  model PHCs who have a facility based display of PHC  performance indicators for their facility for the last 3 month.

11. Proportion of LGAs capturing the additional key indicators.

12. 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.

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.   

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. 

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.