PIMA Program

Problem statement

The latest estimates of maternal deaths from the Maternal Mortality Estimation Interagency Group (MMEIG) estimated that 295,000 maternal deaths occurred globally, of which 196,000 (66%) were from sub-Saharan Africa, SSA (WHO, 2019). The maternal mortality rates are reported to vary around 351 per 100,000 live births, with 27 to 35·1 stillbirths per 1,000 births, and 28 to 45 neonatal deaths per 1,000 live births (Aftab et al., 2021; AMANHI study group., 2018). The SSA maternal mortality ratio (MMR) was 542 deaths per 100,000 live births compared to a global ratio of 216 deaths per 100,000 live births (Musarandega et al., 2021). This means immediate action is needed to meet the ambitious SDG 2030 target (Alkema et al., 2016). On the other hand up of 2019, there were 63 countries at risk of missing the target. If current trends continue with 48·1 million under-5 deaths are projected to occur between 2020 and 2030, almost half of them are projected to occur during the neonatal period (Sharrow et al., 2022).  This means SSA countries must continue to invest in health information systems that collect and publishes comprehensive quality maternal and newborn care with an emphasis on data use at Primary health care (PHC) facilities.

 Tanzania is one of the SSA countries with high MMR as the rates in Tanzania vary from 524-556 per 100,000 live births (UNICEF, 2019, Index mundi, 2019a) which is 78 times higher compared to that of the UK  (Index mundi, 2019b).  In Mbeya region of Tanzania, only half of the pregnant women receive the minimum standard for obstetric care at PHCs while MMR in Mbeya is as high as  776 per 100,000 live births (UNICEF, 2015).  There is a big debate if these patterns are true and realistic in Tanzania mainly because of poor data capturing mechanisms causing these indices to fluctuate back and forth from time to time.

 While it might be puzzling over the number of deaths, a number of people with access to essential health services in a range of prevention, diagnosis, treatment and prevention of end-stage diseases is only half of the world’s population (7.3 billion people of the world). About 12% of the world's population use at least 10% of their household budgets for out-of-pocket health expenditures at PHC facilities (WHO, 2017). On the other hand, almost 180 million people spend a quarter or more of their household budgets on health. This population is increasing at a rate of almost 5% per year, with women in SSA including Tanzania being the worst affected (Lozano et al., 2020; UICC, 2022).

There is limited feedback from health managers and policymakers to PHC facilities because feedback is limited to the information reported by PHC facilities, which, apart from the possibility of being incomplete, it also does not enable healthcare managers to have a real-time experience of what is happening at PHC facilities. The lack of reliable electronic information systems with effective sharing mechanisms from PHC facilities to different levels of administration and this barrier exacerbated the problem of data use in Tanzania.

 Tanzania has established national and integrated health information systems at the focus of district aggregation of health system data i.e. District Health Information Software 2 (DHIS2) platform (Simba et al., 2022). The DHIS2 uses reporting tool for health management data however this system did not include the PHCPI and WHO PHC measurement frameworks. Even though the DHIS2 gather and analyze data across Tanzania’s 169 districts there have been challenges in optimizing data quality. Usually, facility (and sometimes district) data officers enter data manually, which makes it prone to human errors. Data quality assessments at facility and district levels vary regarding training and availability of personnel, which results in issues with data timeliness, completeness, consistency, and accuracy to continue being the main issue.  As result most of the time data is unreliable, and data visualization is limited. On the other hand, insufficient human resources also resulted in barriers to accessing, analyzing, and interpreting the data.

Quality of care in a primary health facility has been affected by the poor quality of data to guide improvements. On the other hand, sub-optimal quality of care has been hard to improve often the sub-optimal quality of care has pushed mothers to attend antenatal clinic services maternal health services, and postnatal care.  As result, about two-thirds of maternal deaths are due to limited or discouraged to access PHC services. This means there is a sub-optimal quality of care that demands regular reviews of quality improvement strategies at PHC i.e. 85% of recommendations (Said, Pembe, et al., 2021), so that we can reduce deaths in the future (Said et al., 2020).  

 At large, PHC facilities in Tanzania present affected quality elements that involve WHO pillars and embraced in the Donabedian model of quality of care (Donabedian, 1988, 2005) has been the root cause. It is clear that more fine-tuning of data strategies is needed for greater effectiveness in efforts to reduce maternal and newborn mortality rates (Izugbara, 2021).

The schematic determinants of health systems are first, i.e. sub-optimal structures that include Governance, limited adjustments to population health needs, limited PHC financing, and PHC inputs that include unfriendly physical infrastructures, limited health workforce, lower levels of medicine, poor health information systems, and lack of digital technologies utilities. The service delivery often is explained by difficult models of health care provision, missing systems for improving quality of care, lack of resilience in PHC facilities, and services that generally affects access to PHC which creates a barrier to the community to access the PHC (Arsenault et al., 2020), especially in antenatal services which is the entry point of quality RMNH (Downe et al., 2019) and so countries difficulty in attaining SDG.3.

In the missed structures for good governance to maintain quality standards of health care finance and people need (Donabedian, 2005) the geographic locations have affected two-thirds of primary health facilities located >5 kms in Tanzania (half dispensaries and a third health centers (Kapologwe, Kibusi, et al., 2020). Reports show that more than 75% of patients missed appointments in maternal health services at PHC facilities due to limited opportunities for patients feedback (Thomas et al., 2021) and service providers' feedback (Mselle et al., 2021)  which has created a big burden multiple layers of PHC performance improvement.

The missed structures have caused little guidance on health financing needs and adjustment for population health needs has failed to guide the designs of implementation strategies that are targeting vulnerable groups are critical elements. Evidence exists that direct health financing (DHFF) is the financing method that increases the number of medical supplies, equipment, and reagents necessary to provide maternal health services by contributing 33.6%. Unfortunately, the utility of DFF in patient safety and QI at PHC facility have received little attention in countries like Tanzania (Tukay et al., 2021).

 The distribution of inputs of vaccines, antibiotics, anti-diarrhoeal, anti-malarial, and essential medical supplies are proportionally non-dominance compared to program-based financed inputs like ant-TB, uterotonics, and anti-retroviral therapy (ART) and anti-hypertensive drugs. This means there are inequities for PHC in the distribution of health care inputs across public primary care facilities. This gap highlights the need to ensure a better coordinated and equitable distribution of inputs through regular monitoring of the availability of health care inputs and strengthening of reporting systems (Kuwawenaruwa et al., 2017).

 Published reports have stated that between 2015 and 2019 there has been an improvement in the physical infrastructure of PHC buildings as a result of construction, upgrading, and equipping the facilities, and decentralization (Scholz et al., 2015)  to offer safe surgery and related diagnostic services in most of the populated areas. Despite these achievements, still there is a high demand for good physical infrastructure statuses and the functioning of primary health facilities with the capacity to offer essential services in Tanzania towards achieving UHC (Kapologwe, Meara, et al., 2020).

 Human resources for health (HRH) at PHC are among the most important components of a health system's inputs (Kumar & Khan, 2014). The performance of a PHC facility depends ultimately on adequate staffing levels and on the knowledge, skills, and motivation of the team responsible for delivering services which are lower in most PHC facilities. The demand for Human resources for health at PHC has remained to be a multi-disciplinary issue with staff categories ranging from community health workers to nurses and doctors. Often HRH needs improvements that involve regular assessments, mentoring, training, and access to loans, to improve clinical quality and facility business performance (King et al., 2021).

 The health management information system in most developing countries has faced completeness and timeliness gaps that range from as low as 32% to as high as 75% (Ouedraogo et al., 2019; Rumisha et al., 2020). The PHC in Tanzania presents high variations in the Health Management Information System (HMIS) utilization and data accuracy at facilities and local council levels. The routine HMIS is often weak and data at the district level are inaccurately compiled in view of what is available at the source (Rumisha et al., 2020). These gaps have made inadequate attempts for data analysis and poor data utilization and a lack of standard operating procedures on data use for quality improvement (Mboera et al., 2021).On the other hand, there have been very few matrices to generate indicators from the data sources and use them for quick the quality measurement aspect from the use of the current data and the possibility of sharing the final message to the policymakers and communities. This is an organizational challenge, that has affected the performance of preventive health care, treatments, and rehabilitation services for better patient health outcomes (Fourneyron et al., 2018). These results highlight the need to design tailored and inter-service strategies for improving data quality.

Generally, these HMIS problems are explained by the lack of comprehensive vision and the use of recommended measurements frameworks at the national level, regional, and district level; Poor quality improvement becomes impractical due to the missing well-framed data; Most reported measurements are primarily informing the funders rather than practical data to guide the performance and PHC improvements; Inconsistent connectivity and adoption of real-time digital tools; Information or data systems are not communicating with others; and there is a heavy burden of data collection by frontline health workers at PHC. We believe the Tanzanian HMIS is missing simplified feedback mechanisms that often include nurses and doctors as primary sources of data as data collectors themselves and recipients of feedback and reminders to improve PHC performance of data.

For many decades service delivery has not been measured in terms of friendliness and respect (Manu et al., 2021), time taken to offer services (Shelley et al., 2019) and patient-centered concerns that individuals and families are in need (Gourlay et al., 2014).  For many decades patients have not contributed their experiences and opinions and feedback on primary health care performances. The lack of patient-reported experiences and patient-reported outcome measures has affected the overall well-being of communities and has lost ownership in terms of capacity, empowerment, leadership, the value of health service, aspirations, and participation, again, financial commitment and contributions (Sarriot & Shaar, 2020).

 The process measures also reflect that medicines expenditure is at 57% mainly by out-of-pocket in public facilities and over 45% at private facilities as well as consultation fees with 22% at public facilities and 40% of at private facilities (Rao & Pilot, 2014). Despite its foundational role in communities, primary health care remains out of reach for millions of people, just because there are few existing community-based insurance schemes and financial support to reduce pocket payment schemes in the country. On the other hand, facility-based financing from the central government has been reduced time after time demanding PHC to fund themselves using the business model.

 Lastly, there is the challenge of the use of new technical ICT tools that are approved by the authorities with legal concerns. For progress to continue, legal binding to the diverse health care stakeholders, including patients, frontline health workers, policymakers, and everyone in between to be responsive and understand data and measurement for improvements are needed. 

Preamble 

PIMA is a Swahili (Tanzanian Language) word that means measure and so is the acronym for Primary health care’s Improvement by Measurement for Access (PIMA).

 Primary health care (PHC) services are the initial health services to the people that provide a crucial foundation of Universal Health Coverage (UHC) in Reproductive Maternal Newborn Child and Adolescent Health (RMNCAH).  Unfortunately, most of the patients in low-income countries have been repelled by sub-optimal care at PHC. Again, patients and service providers impinge with limited opportunities to provide feedback that affects multiple layers of health systems from the district councils to the national level. On the other hand, the authorities have been failing to improve the quality of PHC due to limited access to completed and timely released data from validated tools through a common sharing platform to the policymakers and reverse to health care providers and communities.

 The PIMA project will test the effectiveness of automated feedback and reminders (SMS and emails) from the newly validated iCare EMR that will be tested in 12 PHC facilities. Data will be collected by 12 data clerks and 12 hospital coordinators in 2 regions of Tanzania using the PHC. The program will use Primary Health Care Performance Initiative (PHCPI) indicators (Bitton et al., 2017; Jeremy Veillard et al., 2017) and WHO PHC performance measurements frameworks and indicators (WHO et al., 2020).

Implementation of the Solution 

Step 1: PIMA Tanzania will align PHC sources of data, monitoring, and evaluation tools within the DHIS2 tools (Simba et al., 2022) and facility surveys targeting r structure, inputs, process, outputs, outcome, and impacts. Data will also be collected from laboratory integrated management systems (LMIS) and Human Resource Information systems (HRIS)  (Mghamba et al., 2004). This innovation has been supported by the Ministry of Health and President's Office, Regional Administration, and Local Government Tanzania (PO-RALG). This endeavor is also in line with the existing Tanzanian health sector strategic plan V on the use of technologies to improve the quality of care (MOHCGGEC, 2021).  The Tanzanian health sector strategic plan provides the basis for monitoring and evaluation of PHC priority planning and resource allocation for quality health services provision (Maluka et al., 2018). 

 Step 2: PIMA Tanzania will conduct a situation analysis to inform baseline values of the program using mixed methods (both qualitative and quantitative data) through the modified PHC measurement framework scores (35 indicators)  combined with Tanzanian health service needs of assessments  (MOHCGGEC, 2021), PHCPI 25 key indicators (Vital Signs) and 56-second sets of diagnostic indicators (Bitton et al., 2017; Jeremy Veillard et al., 2017) and WHO proposed global-level reporting indicators (WHO et al., 2020). These indicators will use mixed sources and will include the use of HCD techniques. The proposed initial work will identify and guide the drivers of the program's success in PHC community coverage, people’s demand, and consumption of PHC services for possible modification of the indicators. Situational analysis will inform the willingness of the government and its legislation actors to adopt feedback and reminders for PHC quality improvements and to what extent. PIMA will then develop measures of opportunities for policy change and public-private partnerships benefits in PHC service improvements in attaining UHC and achieving SDG.3. The Situational analysis will therefore focus on epidemiological, political, socioeconomic, and organizational attributes of the current PHC performances while paying special attention to equity issues.  PIMA Tanzania targets will work with the Ministry of Health and President's Office, Regional Administration, and Local Government Tanzania (PO-RALG) in Tanzania to guide and support evidence-based guides for automated feedback and reminders drivers of success by the PIMA program.

PIMA program in Tanzania will embed and align PHC monitoring and evaluation within existing Tanzanian national health sector strategic planning V for monitoring and review of PHC performance to enable UHC outcome and SDG attainment as an impact (MOHCGGEC, 2021; UN, 2020; Wagstaff & Neelsen, 2020) PIMA program will link the measurements of implementation of PHC with the PIMA software that generate automated SMS and emails to health care providers, health system administrators, and patients.  (Van Den Heuvel et al., 2018; Yeates et al., 2017) to build the maternal and perinatal death surveillance and response (MPDSR) (Kashililika & Moshi, 2021) in Tanzania as an alternative approach to traditional paper-based systems (UDSM-COICT, 2006).

 Step 3: PIMA Tanzania will use modified 35 PHC framework indicators (Bitton et al., 2017; Jeremy Veillard et al., 2017; MOHCGGEC, 2021; WHO et al., 2020; WHO & UNICEF, 2022) to compare the PHC facility,  district, regional and later on countries to countries performances.  The indicators can be further modified by the community stakeholders upon initial conduct of interviews with women, adolescents, parents of both genders, not-for-profit NGO and Civil Servant organizations (CSO) working on Maternal and Newborn Health through Human-Centered Designs (HCD) review meetings). The PHCs will then be able to track their own progress by collecting data using 12 data clerks and 12 PHC coordinators. PIMA will also track users’ health demands through HCD as an approach suited to the maturity of their health systems and health information systems quality.

 Step 4: PIMA Program will install 12 computers in 12 PHC of Mbeya and Dodoma region of Tanzania. The program will use the validated iCare EMR as the main data source together with LMIS and HRMIS and electronic surveys integrated into the Local Area Network (LAN) of the PHC.  The iCare EMR made by the University of Dar es Salaam - College of Information Communication and Technology (UDSM-COICT) will link up PIMA software to test the PHC performance. The testing will be done through the use of 35 modified performance measurement indicators covering structures, inputs, process, output, and outcome and impact measures. From this list of these indicators, PIMA software will capture accurate data from the up-to-date data from a broad range of functioning health management information systems HMIS, surveys, and HCD electronic data. PIMA software will then perform analytics of structures, inputs, processes, outputs, outcomes and impacts at monthly intervals. PIMA software will also check data quality in terms of completeness, accuracy, consistency, and timely sharing with red flags (worse and inappropriate and yellows scores (needs corrections). The descriptive and analytic findings will then be shared automatically in the form of SMS and emails as feedback and reminders for quick Quality Improvement (QI) actions.  The feedback and reminders will be produced in colorful plots to health system administrators and policymakers and also back to health care providers and patients. At the PHC facility level, the nurses and doctors will be followed for their mistakes and errors scores while giving them the opportunity to improve. At the council level, the PHC facilities will be followed for their performance in summarized monthly performances. The council health management teams and policymakers will put special comments that are measured as a way to construct feedback and reminders to the PHC health care provider teams. This is anticipated to produce timely release and completeness of data in a progressive manner that bears feedback.

 The PIMA  program will therefore use the combination of PHCPI and the WHO proposed global-level reporting indicators for PHC (WHO et al., 2020) in the PIMA agreed indicators that will collect data progressively for 18 months in  2 regions of Tanzania for the PHC facility-level structure, process and outcome data, mixed methods policy surveys, facility level routine RMNCAH data, and the routine health management information systems (HMIS). At the policy level, PIMA will undertake national and subnational qualitative key informant surveys to robustly assess PHC capacities, including in the areas of PHC legislation, mechanisms for engagement with communities, multi-sectorial coordination and action, regulatory systems and models of care.

 A combination of facility-level and community-based HCD data will provide necessary local- and national-level data on guiding PHC performance. Regular monthly facility surveys, such as Qualitative assessments with PHC facility managers will be implemented to provide objective performance measures on structures, inputs, process, output, outcome, and impacts

The added indicators from Global reporting indicators (WHO et al., 2020; WHO & UNICEF, 2022) are; Existence of national health policy oriented to PHC and UHC, the Existence of policy, strategy, or plan for improvement of quality and safety, Coordination mechanisms with multi-stakeholder participation and community engagement, Per capita total health expenditure (and PHC specific)*, Government PHC spending as a percentage of government health expenditure, Health facility density and distribution (primary care, public/private mix)*, Availability of basic water, sanitation and hygiene (WASH) amenities*, Health worker density and distribution (by occupation, public or private) [SDG 3.c.1]*, Availability of essential medicines (percentage of primary care facilities and other types) [SDG 3.b.3]* , Service package meeting criteria, Outpatient visits (primary care), Admissions for ambulatory care sensitive conditions* 

Tanzania has a strong policy towards tackling maternal mortality and morbidity (MOHCGGEC, 2021). The Tanzanian health policy, maternal and child health were considered the ‘prime targets for health care delivery’. In the last 10 years, a number of plans and programs were implemented to address the problem. The National Road Map Strategic Plan to Accelerate Reduction of Maternal, Newborn and Child Deaths  (One Plan II)(MOHCDGEC, 2016; MOHCGGEC, 2021) reflects the current global approach to maternal mortality reduction based on the continuum of care at PHC. However, SDG 3.1 which aims to reduce maternal mortality to less than 70 deaths per 100,000 live births is enormous and very ambitious (WHO, 2016).  This is because the demand for maternal newborn, child, and adolescents health care services in primary health facilities is high unfortunately with the high level of antenatal visits (96%), declining facility delivery (50%), and first postpartum follow-up visit (35%) (Mahiti et al., 2015).

 The program will target women of reproductive age, newborns and children, and adolescents in the scope of Reproductive Maternal Newborn, Child and Adolescent Health (RMANCAH). This is because there is a high demand for Reproductive and HIV, SIT and contraceptive services among adolescent victims (Yussuf et al., 2020). Again, women's utilization of PHC facilities has been a problem due to perspectives of neglect and violation of women’s rights (Mpembeni et al., 2019). On the other hand, despite the focus of health policy on RMNCAH rural areas (where 80% of the Tanzanian population lives) (NBS & ICF Macro, 2010), urban areas remain far better covered by PHC RMNCAH compared to rural settings (Isangula, 2022). Large inequalities exist in institutional delivery and postpartum coverage between women depending on their education, socio-economic status and geographical barriers (Bintabara, 2021; Bishanga et al., 2018).

Previous research in Tanzania showed that: 1) the decision regarding where to seek PHC care during delivery is not in the hands of pregnant women alone, but depends strongly on advice from relatives, traditional birth attendants (TBAs), and health professionals (2) the quality of health care service in which human errors in the management of labor is as high as in 93.1% repel most mothers from PHC use (Said et al., 2020). This gap is regarded as deficient by most mothers, thus discouraging many women from going to PHC facilities for delivery and 3) interactions between health care providers and mothers, the distance of the facility and associated costs of travel, the advice of health workers on the place of delivery, and knowledge of danger signs, were associated with the type of delivery and use of PHC services (Bohren et al., 2014; Mbwele et al., 2013; Said, Sirili, et al., 2021).

 Much previous research on maternal health in Tanzania has favored the perspectives of health service providers at PHC. Published studies exploring women's perspectives on maternal health care services at PHC remain scarce. This means while we address the data quality issue for maternal and newborn health care at PHC we need also to measure the women's and children's demands so that the health system can provide what women need to endure UHC is attained and targets for SGD 3 are well reached. However, women's perceptions of the services available are especially relevant to informing program implementation.

The aim of this program is to target the population of women of reproductive age, newborns under-five children, and adolescents to solve the Reproductive Maternal Newborn Child And Adolescent Health (RMNCAH) barriers in accessing PHC services in Tanzania.  

This program is called Primary Health Care Improvement by Measurement for Access (PIMA Program) and it will measure the performances of 6 PHC facilities in Mbeya (3 dispensaries, 2 health centers and, one district hospital) and 6 PHC facilities Dodoma regions of Tanzania (3 dispensaries, 2 health centers and, one district hospital). 

There are two main custodians of PIMA program implementation in Tanzania. The University of Dar es Salaam – Mbeya College of Health and Allied Sciences (UDSM-MCHAS) with proximities in Mbeya and the Ministry of Health with the allied President Office for Regional Authorities and Local Governments (PO-RALG) with proximities in Dodoma.

PIMA program is the make of the University of Dar es Salaam – Mbeya College of Health and Allied Sciences (UDSM-MCHAS) and The University of Dar es Salaam – College of Information and Communication Technologies, UDSM - COICT.   The Principal Investigator (PI) is coming from the UDSM MCHAS who will oversee the program implementation at the 12 PHC facilities includes (6 Dispensaries, 4 health centers, and 2 district hospitals in Mbeya and the 6 PHC in Dodoma with the close follow-up by MOH and PO-RAL. The UDSM-COICT will provide the technical installation of iCare EMR and integration of PIMA software in iCare EMR for tracking the PHC performance indicators and computing the analytics for the trends of PHC performances.  The effectiveness testing and implementation science will be done by the team of Vijiji Tanzania public health officers. Vijiji Tanzania will guide the training and orientation of the teams in the 12 facilities for the collection of data for structures, inputs, services, outputs, and outcomes. UDSM - COICT will continue to monitor the quality of the automated feedback and reminders (SMS and emails) from the iCare EMR that will be tested in 12 PHC facilities using the modified PHCPI and WHO validated measurements of PHC performance during the program implementation in Tanzania.

 Northwestern University will provide the statistical inputs in the design of the PIMA software and Harvard University, Primary Health Care Unit of  Ariadne Labs, at Brigham and Women's Hospital of Harvard T.H. Chan School of Public Health will further guide the measurements of PHC in Tanzania.

The team members from the University of Dar es Salaam - Mbeya College of Health and Allied Sciences, UDSM-MCHAS are Dr. Bernard Mbwele, the clinical researcher and public health specialist, Dr. Khanafi Said, and Prof. Projestine C. Muganyizi (Male) and Prof. Mwajabu Possi (Female), the consultant obstetrician. The team members from the University of Dar es Salaam – College of Information Communication and Technology, UDSM-COICT are Dr. Honest Kimaro, the ICT consultant and Dr. Leonard Peter, the ICT specialist. From the Ministry of Health Tanzania, we have Dr. Aifello Sichalwe, the Chiefe Medical Officer of the Ministry of Health. And from the President's Office - Regional Administration and Local Government, PO-RALG we have Dr. Ntuli Kapologwe, the Director of Health and Nutrition PORALG. From Kijiji Tanzania, Dr. Amani Twaha (Male) and Ms. Catherine Qaresi (Female) will lead the implementation science and from Northwestern University, Dr. Salva Balbale (Female)  and Prof Melissa Simons (Female) will guide health outcome service measurements and last but not least Prof Melissa Simons will guide measurement of social determinant of maternal health in Tanzania.

Collectively the team will conduct the mixed methods surveys to answer the questions about why and how these patterns are seen from the iCare and PIMA software data.  The team will work closely with Regional health management teams and councils health management teams to make sure  that the community stakeholders are well involved for the maximum engagement of the communities as progressively community feedback through HCD and Patient-centered outcome measures will be needed

Rationale 

The importance of Primary health care (PHC), was initially described in the Declaration of Alma-Ata in 1978 as the platform that offers a core foundation for health system improvement (WHO, 1988; WHO Europe, 1978).  Exactly 40 years later, in  2018 World Health Organization (WHO) and United Nations Children’s Fund (UNICEF) urged institutions working for quality healthcare improvement to return to the Declaration of Alma-Ata into practice (WHO, 2018, 2020). In this movement, there is a vision for primary health care in the 21st century: toward universal Health Coverage (UHC) and the Sustainable Development Goals (WHO & UNICEF, 2022). There is now a need to work on the strategies that reinforce a whole-of-government and whole-of-society approach to PHC that combines three core components: multi-sectorial policy and action; empowered people and communities for health access; primary care and essential public health functions as the core of integrated health services measures.

By bringing together these components, PHC creates the foundation for the achievement of universal health coverage (UHC)(Hussein, 2015; Yaya & Sanogo, 2019) and the health-related Sustainable Development Goals (SDGs) (Kruk et al., 2018; UN, 2020). In 2015, Primary Health Care Performance Initiative, (PHCPI) published the conceptual framework for PHC to illustrate PHC performance measures in 5 areas of  a) systems of governments, people needs and financing b) Inputs of drugs, workforce and funds, c) service delivery, d) output and e) outcomes to support primary health care functions of first-contact accessibility, comprehensiveness, coordination, continuity, and person-centeredness (Bitton et al., 2017; PHCPI, 2015).

There have been reinforcements for investing more resources for PHC measures while targeting the people served with limited success (Hanson et al., 2022). The use of electronic feedback and reminders upwards from patients to service providers then to local policymakers at the district level to the national policymakers and reverse is thought to provide opportunities for primary health systems strengthening. The emergence of mobile phone networks and computers for email access across the globe presented a novel opportunity for rapid improvement in the global health technology revolution.  The University of Dar es Salaam and ICT companies initially developed the district health information system in Tanzania, DHIS2 (Simba et al., 2022; UDSM-COICT, 2006) and AfyaCare /GOTHOMIS (MSH, 2018) for electronic medical records and reporting. Unfortunately, these platforms there still have quality check errors and they are missing in PHC measurement frameworks in Tanzania.  UDSM recognizes the opportunity for synchronizing feedback and reminders (Coma et al., 2019) as the best way to connect patients, health service providers, and national administrators in a summarized format so as to capture the core health information, even in hard-to-reach areas; and compress the time between a crisis and an appropriate response from the local authorities and the national authorities (Labrique et al., 2018). This approach can help countries like Tanzania equitably track the level and distribution of health services and well-being by focusing on people’s needs and preferences (both as individuals and communities).  In this way,  PHC measures that are quickly available as feedback and reminders can facilitate countries in attaining UHC and achieving SDG 3, with the largest gains for the poorer section of society (Moreno-Serra & Smith, 2012).

Specific financial barriers anticipated include the full stretch of the PIMA software for departs of PHC as more computers will be needed for installing the EMR for PIMA use in all facilities.

Technical barriers anticipated includes technical updates of healthcare providers' codes of ID as healthcare provider often are shifted or transferred to other facilities.

The current scale involved the key actors from the Ministry of Health and PORALG however; we expect to have delays in running the program in all facilities in the future as an extensive discussion will be needed. Therefore the legal barriers include full authority to run iCare and PIMA in the whole country.

The proposed program will demand the use of computerized doctors' and nurses' notes for the integration of the data from laboratory LMIS and Human resources for health data from national or regional databases; the culture of using computers might delay the up-taking of the program. 

The PIMA program team would like to test the use of iCare and PIMA software with other African countries and other lower-middle-income countries (LMICs) in Asia and Latin America for a market extension.

The University of Dar es Salaam (UDSM) with support from Northwestern University and partly from Harvard School of Public Health, the PHC unit has offered guidance and UDSM has collaborated with the Ministry of Health and PORALG in the process to develop a data PHC performance measurement system. The program will use with real-time data quality assurance DQA tool integrated with the national DHIS-2 and PHC facility iCare EMR.

Following data entry in the PIMA dashboard at the PHC facility and then the transfer to the district level, data officers use PIMA software for reviewing the automated use of quality checks. The PIMA software will highlight data that require further investigation, which reduces hours of data quality assessment from week time to approximately 15 minutes. The automated identification of outliers, implausible values, or inconsistencies with other data elements PIMA software will enable the data officers at the PHC and district level to target their data review on specific data quality elements.

PIMA software will significantly reduce the time required to complete in-depth data reviews and ensures that all submitted data are reviewed systematically and comprehensively. PIMA software also includes visual features that enable rapid identification of data quality issues of the report. Using the findings from PIMA software, PHC monitoring and evaluation staff can deploy targeted strategies to improve data quality and use them for quality improvement (QI) in any customized areas like RMNCAH to attain UHC and SDGs.

Innovation

The PIMA program in Tanzania will use a separate dashboard of iCare EMR that is embedded with software (PIMA Software) for combining key information from different sources of PHC in the areas of Structures, Inputs, and Service process delivery, Outputs, Outcome on UHC and Impact on SDG. The iCare EMR is set to collect data enabling measurements for health systems determinants (for monitoring capacity of PHC), service delivery (for monitoring performance of PHC), and health system objectives (for monitoring impact) that will be analyzed by PIMA software. In each PHC facility, data will be entered by a data clerk and PHC teams using survey tools, and then enter the data into the PIMA dashboard which is software for PHC measurement calculations. PIMA software will not submit any incomplete form and it will always refer to the previously entered data to ask the data clerk if they are sure about what they are feeding into the system. PIMA software will prompt the data clerk for any incomplete, inconsistent, inaccurate, or delayed data. The program will validate the entry point indicators during the data inputs needed for the measurement framework for maternal newborn, child, and adolescent health in Tanzania.

At the PHC facilities, from different angles of health systems determinants, service delivery, and health system objectives, the electronic PIMA dashboard will perform automated calculations of indices in terms of descriptive percentages and analytical statistical inferences from each of the PHC using PIMA software.

PIMA software will then generate graphics and short statements as summarized results of PHC permanence measurements. Summarized data visualization will be available at the PHC for local quality improvement planning. Execution of change ideas and follow up on the performance changes on a monthly basis, quarterly, and annually.  These results will be shared periodically with health care providers through automated generated SMS and automated emails. This means without internet performance changes can be seen at the facility using local networks from one unit to the other. On the other hand, a special agreement to the mobile phone company will serve SMS generation and lastly with little bandwidth of internet emails can generate to the health care providers and to designated authorities at the district councils  (Council Health Management Teams, CHMTs). The leadership of CHMT, the DMO  will authorize the transfer of records by inserting an electronic signature in the office computer that reports have been received and verified for further action and forward the reports to the Regional Health Management Team, RHMT). PIMA software will then calculate summarized reports from all the CHMTS to RHMTs, giving an opportunity for the Regional Medical Officer, RMO to authorize and forward the reports by inserting an electronic signature in the office computer as a way to transfer forward the feedback reports to the Chief Medical Officer (CMO) at the Ministry of Health.

The Ministry of Health will write summarized comments electronically to the RHMTs. All RHMT will receive automated mobile phone short messages (SMS) notifying them that comments have been shared in the system while at the same time they will receive emails with actions to follow up. These reports will then be summarized in an electronic fashion and transferred to the CHMTs from which all the PHC will get a final action plan that was constructed electronically through PIMA software.

 PIMA software will work on the modified PHCPI and WHO global indicators as a way to offer data visualization on each PHC facility's performance PIMA software will offer opinions and experience reports as well as summarized outcomes directly given by them. Summarized as patients reported experiences (PRE) and patients reported outcomes (PRO).   The patients will be able to share comments through their SMS to a special number linked to the iCare and PIMA software.  The system will perform tracking and summarize the scores of  PRE and PRO comments that will be shared from one level of the health system to other for initiating appropriate meetings to respond to the patient’s needs.  In the end, there will be other comments from higher policy-making authorities to review opportunities to respond to the PRE and PRO progressively.  

The biggest benefit of using PIMA software is that without the internet more summarized reports will be generated automatically and shared in each PHC facility involved using the Local Area Network (LAN). Again there is an option for sharing automated SMS  to patients who gave PRE and PRO,  health care providers, District council Health system managers, and regional health system managers to the national policy-making body.  On the other hand, the internet is ubiquitously available in Tanzania and emails will not require high bandwidth connectivity to give automated feedback and reminders.

PIMA program designation reflects how end-to-end automated data monitoring and lineage accelerate data adoption to make PHC more data-driven for the quality of health care improvement at PHCs after 1 year of implementation.

When the PHC will start using validated data that is complete and timely available, there will be a promotion and feasibility of quality improvement teams tracking their progress in RMNCAH changes.  This means in the next 2 years there will be reliable and significant measures of quality improvement in the PHC of Tanzania.

In the next 3 years, most of the Council Health Management Teams at the District level (CHMTs) in Tanzania will be receiving and processing significant amounts of data across complex levels and volume of health data from the PHC.  Installing iCare and PIMA software into the PHC will provide valuable applications for electronic data sharing and analytics in which PHC data quality is paramount including machine learning models that give feedback and reminders to patients, health care providers, and health system administrators and managers. On the other hand, real-time analytics improve the quality of data. This means in the first year of running the PIMA program we anticipate seeing an improvement in data quality in the PHC of Tanzania.

Lastly, in the next 5 years will assume the rest of  Tanzania uses iCare that is integrated into PIMA software as an opportunity to improve Reproductive and Maternal Newborn Child and Adolescent Health progress and other health-related global agenda at PHC. This means in the next 3 years, the health care delivery at PHC will be more attractive compared to the way they are now. Again shortly after deploying the PIMA program, there will be significant changes in an array of health system elements that facilitate data measurements for health systems determinants (for monitoring capacity of PHC), service delivery (for monitoring performance of PHC), and health system objectives (for monitoring impact) regularly.  This means through the application of the PIMA program at the PHCs, there will be improved monitoring and support of quality health services in Tanzania. In the next 5 years, the use of evidence-based patients’ needs will ultimately attract communities to use PHC for their health needs and so attain the UHC requirement.

 We believe that in the next 5 years PIMA program will be a catalyst for Sustainable Development Goal 3 (SDG 3 or Global Goal 3), regarding Good Health and Well Being. This will is particularly important for the reduction of maternal and newborn deaths (SDG 3.1 (Maternal Health), SDG3.2 (Neonatal Health), SDG 3.7 (Reproductive Health) SDG 3.8 (UHC), SDG 3.C (PHC financing), and SDG 3.D (Global Health Risks like COVID-A9)) in the rest of Tanzanian PHC. This achievement will mainly be the product of sufficient metrics for the PHC performance that will be consciously built as the new indicators are adopted.

PIMA program will measure the PHC performance through the use of 36 indicators derived from the three blocks Health systems determinants, service delivery and Health system objectives, and six domains of structures, inputs, process, outputs, outcome,  and impact.  From these domains, a total of 18 subdomains were identified from the WHO PHC performance framework. The 18 subdomains created an opportunity of identifying 35 initial primary PHC performance indicators for Tanzania.

The 35 initial primary PHC performance indicators for Tanzania are as follows; 

1. Health in all Policies with multi-sectorial coordination
2. Existence of national health policy oriented to PHC and UHC 
3. Existence of policy, strategy, or plan for improvement of quality and safety
4.  Priority setting is informed by data & evidence    
5. Existence of an M&E framework for national health plan meeting criteria
6. Per capita total health expenditure (and PHC specific)*
7. Government PHC spending as a percentage of government health expenditure
8. Health facility density and distribution (primary care, public/private mix)*
9. Availability of basic water, sanitation, and hygiene (WASH) amenities* 
10. Is there an adequate and trained primary care workforce?
11. Health worker density and distribution (by occupation, public or private) [SDG 3.c.1]*
12. Has the availability of medicines, diagnostics, and supplies improved
13. Are facility registers and reporting complete?
14. Has the use of digital technologies increased in facilities?
15. Are services designed in an integrated way across main delivery platforms?
16. Are there linkages with community and social services?
17. Do facilities have quality improvement processes in place?
18. Do facilities meet resilience criteria?
19. Has access to services improved?
20. Has equity in access improved?
21. Are comprehensive services available at the point of care?
22. Are they meeting minimum standards of the services?
23. Are services responsive to patient needs?
24. Is the provision of care based on adherence to standards?
25. Has patient safety improved?
26. Waiting time to get service
27. Has service coverage improved?
28. Are people protected from financial risk?
29. Have risk behaviors reduced?
30. Has health security improved?
31. Have health outcomes improved?
32. Are populations protected from health emergencies?
33. Have health inequities reduced?
34. Are health systems responsive and resilient
35. Are the Health-related SDGs achieved?

PIMA program is will be guided by the performance measurement framework of the program that has 5 layers of accomplishment 1. Fulfilling the Objectives, 2. Immediate Outputs, 3. Intermediate Outputs, 4. Ultimate Outcomes and 5.The  Impact

Main Objective: The main objective of the proposed PIMA program is to test the usefulness of automated feedback and reminders over the PHC performance measurement framework in maternal newborn, child and adolescent health in Tanzania.

Specific Objectives : The specific objectives of the PIMA program is to work on 5 schematic areas of PHC performance measurements which are;

1. To develop progressive suitable indicators with inputs from community stakeholders for the consolidated PHC measurement framework in in maternal newborn, child and adolescent health in Tanzania.
2. To provide progressive evidences for the levels of structural performances of PHC services in in maternal newborn, child and adolescent health in Tanzania.
3. To provide progressive evidences for the levels of inputs for PHC services i in maternal newborn, child and adolescent health in Tanzania.
4. To provide progressive evidences for the levels of service processes for PHC in in maternal newborn, child and adolescent health in Tanzania.
5. To provide progressive evidences for the levels of UHC output of PHC services in in maternal newborn, child and adolescent health in Tanzania.

Program Immediate Outputs

PIMA program will track immediate outputs on the quality of data after 6 months of implementation in terms of completeness, consistency, accuracy, and timely collection and sharing.  PIMA software will use Immediate Outputs Indicators to measure the Immediate Outputs.

Immediate Outputs Indicators;

1. Monthly proportion of PHC facilities that have submitted complete data of 5 schematic areas of PHC performance measurements
2. Proportion of complete data of 5 schematic areas of PHC performance measurements in a PHC facility
3. Monthly proportion of PHC facilities that have submitted consistent data of 5 schematic areas of PHC performance measurements
4. Proportion of consistent data of 5 schematic areas of PHC performance measurements in a PHC facility
5. Monthly proportion of PHC facilities that have submitted accurate data of 5 schematic areas of PHC performance measurements
6. Proportion of accurate data of 5 schematic areas of PHC performance measurements in a PHC facility
7. Monthly proportion of PHC facilities that have submitted timely data of 5 schematic areas of PHC performance measurements
8. Proportion of timely data of 5 schematic areas of PHC performance measurements in PHC a facility

 Program Immediate Outputs

PIMA program will improve the timely generation of summarized reports of quality improvement activities in 5 schematic areas of PHC performance measurements from 12 PHC and their respective district councils, regional authorities and national level measurements. PIMA will focus on the use of 5 schematic areas of PHC performance measurement in QI agenda within the PHCS. These QI agenda must be for Maternal Newborn, Child, and Adolescents' Health (RMNCAH) in Tanzania, and their use of quality data to provide measures of intermediate indicators.

Intermediate Indicators;

1. Monthly proportion of PHC facilities that have used  inputs from community stakeholders for RMNCAH in QI agenda  
2. Proportion of inputs from community stakeholders for RMNCAH in QI agenda in a PHC facility
3. Monthly proportion of PHC facilities that have used structural performances measures for RMNCAH in QI agenda
4. Proportion of structural performances measures for RMNCAH used in QI agenda in a PHC facility
5. Monthly proportion of PHC facilities that have used the inputs measures for PHC services for RMNCAH in QI agenda
6. Proportion of inputs measures for PHC services for RMNCAH used in QI agenda in a PHC facility
7. Monthly proportion of PHC facilities that have used the  service process measures for RMNCAH in QI agenda
8. Proportion of service process measures for RMNCAH used in QI agenda in a PHC facility
9. Monthly proportion of PHC facilities that have used the UHC service outputs measures for RMNCAH in QI agenda
10. Proportion of UHC service outputs measures for RMNCAH used in QI agenda in a PHC facility

Program Ultimate Outcome

PIMA program will measure the ultimate outputs in terms of universal health coverage service coverage and financial protection.  Then PIMA program will measure the ultimate outputs on the health security of women and children served and saved in the PHC.

Ultimate Outcome Indicators;

1. Quarterly proportion of PHC facilities that have provided levels of UHC  service coverage
2. Proportion of UHC service coverage from each PHC
3. Quarterly proportion of PHC facilities that have provided UHC financial protection.
4. Proportion of UHC financial protection from each PHC
5. Quarterly proportion of PHC facilities that have provided Health Security
6. Proportion of Health security from each PHC

Program Impact

PIMA program will support the reduction of maternal and newborn mortality rates in Tanzania. This means at the end the program will measure the performance of the program versus the attainment of SDG 3 targets.

These SDG 3 targets are:

3.1 By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live births.

3.2 By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births.

3.7 By 2030, ensure universal access to sexual and reproductive health-care services, including family planning, information and education, and the integration of reproductive health into national strategies and programs.

3.8 Achieve universal health coverage, including financial risk protection, access to quality essential healthcare services, and access to safe, effective, quality, and affordable essential medicines and vaccines for all.

3.C Substantially increase health financing and the recruitment, development, training, and retention of the health workforce in developing countries, especially in the least developed countries and small island developing States.

3.D Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction, and management of national and global health risks.

The theory of change is underpinned by the Donabedian model of quality improvement that was initially published in 1966. In this model three components approach for evaluating the quality of care underpins measurement for improvement. The three components are structure, process and outcomes. Measurement for improvement has an additional component – balancing measures (Donabedian, 1988, 2005). On the other hand health system is believed to be is structured around the WHO framework that describes health systems in terms of six core components or “building blocks”: (i) service delivery, (ii) health workforce, (iii) health information systems, (iv) access to essential medicines, (v) financing, and (vi) leadership/governance (WHO, 2010). That provides more definition of the use of the Donabedian model when mixed with these building blocks. Most importantly Andersen's theory of behavioral model of families’ use of health services (1968) is explaining why women get repelled from the PHC facility by sub-optimal care. Andersen stated, that individual health behavior patterns are based on predisposition to care, factors that impede or enable the use of care, and the overall need for care (Andersen, 1995; Travers et al., 2020).

 PIMA program theory of change is based on the fact that the program will track PHC performances using the framework that is underpinned by Donabedian model (Donabedian, 1988, 2005)., WHO building blocks  (WHO, 2010).and Andersen model (Andersen, 1995; Travers et al., 2020)  all built and combined Structures, Inputs, and Service process delivery, Outputs, Outcome, and Impact of each PHC facility (Mbwele et al., 2012). These measures will help facilities track their strengths, weaknesses, threats, and opportunities in improving the care in which this program will track Reproductive Maternal Newborn and Child and Adolescent Health (RMNCAH).

Upon sharing automated feedback and reminders to patients, health care providers, health administrators at the district councils, and administrative regions and then to the ministry, PIMA software will highlight the discrepancies of data using colored flags.

 The red flag will indicate that there is a data error at the spotted PHC facility.  For instance, the dispensary has received laboratory reagents for a certain month PCR machine which is impossible to be done there. When the feedback is received at CHMT or RHMT they will notice an error and insert a comment that will automatically be reported to inform the corresponding PHC facility as automated electronic feedback and reminder. This will help for quick correction of the data in question.

The yellow flags in dashboards will indicate that the data needs further investigation because the positivity is high. For example, a health center is reporting to receive laboratory reagents from a biochemistry analyzer, while it now it is very rare for a health center to have a biochemistry analyzer. This type of input data might be correct, but it is worth taking a second look, and comments from CHMT or RHMT members will add value to improving the quality of data from PHC.

It can be proven that PIMA program will improve the quality of data then the quality of service with better outputs of Accessibility, affordability, acceptability, Service availability and readiness, and again Utilization of RMNCAH services. This means many women will be served through PIMA and many women, children, and adolescents' lives will be saved. 

Most important is the array of indicators which span from Structures, Inputs, Service process delivery, Outputs, Outcome by UHC, and Impact of each PHC facility by SDGs. This means every month PHC performance will be seen moving from one direction to another at all these levels. PIMA software will be able to summarize the performances with summary data visualizations.

In Tanzania, the primary source of PHC performance measures is the routine health information system, which is emphasized by the government to be as much electronic as possible. PIMA program has designed electronic data workflows from different hospital sources of structures, inputs and processes. These will come from  at PHC level and allow real time data sharing electronic-based to district level and then to the nation level. Most importantly, the software will generate feedback to the health care workers and patients in critical scenario that may require health care providers and or patients engagement in a critical manner. PIMA program will incorporate the new iCare EMR in 12 PHC facilities of Mbeya and Dodoma regions and their performance measures through modified PHCPI and who PHC measurement frameworks. PIMA software will collect data from multiple surveys, including spreadsheets, from different hospital units joined from a Local Area Network (LAN) into a single, cloud-based platform, allowing analysis from integrated data, and creation of reports with real-time data sharing. PIMA software will make a statistical descriptive analysis of patients’ demographics, their diagnosis, complications, and missing services with attributes ranging from structures, inputs, processes, and outputs.

 With PIMA software, the PHC data extractions and calculations will be automatically computed under the guidance of the PHC performance framework and shared as mini-reports through automated SMS and automated emails. The software will create visualizations that can narrate a story with charts and graphs with a timeline of data entries in each PHC facility and they're aggregated at the district level, regional level, and national level.  The software can also share alerts by red flags and yellow flags when there is incomplete, inaccurate, inconsistent, and delayed submitted data to the next level of services. The software will share real-time data and set alerts to watch for issues.

 There was an attempt to use the District Health Information Software Version 2 (DHIS2) to collect, analyze, and visualize different aggregate health data, as well as generate different reports required to plan, manage and make different decisions in the health sector in Tanzania. Unfortunately, the theoretical underpinning of quality of care at PHC has been limited. In 2013 DHIS2 was rolled out across the country in Tanzania and is currently health aggregate data from all health facilities across the country. Unfortunately, it is hard to do key performance measures and give feedback to PHC through DHIS2.

The Tanzanian health sector has several EMR solutions attempts AfyaCare and GOTHOMIS for the public and many others for private PHC facilities. However, the EMR solutions existing in Tanzania have a number of challenges. For example, they are not comprehensive enough to address all key aspects of PHC performance, are not interoperable with other key electronic systems in the health sector, and have a number of usability issues. To address these and other EMR challenges in the Tanzanian health sector, the University of Dar es Salaam - College of Information and Communication Technology (UDSM-COICT) at DHIS2 Lab recently developed an EMR solution called iCare. The use iCare EMR has been tested in two hospitals in Dar es Salaam (one private and one public) with remarkable performance in data sharing workflows and metadata integrations from one unit to another irrespective of the size. In PIMA program, iCare will be used to capture key data required to monitor the performance of PHC in Tanzania. The iCare will be linked to the PIMA software that will create automated SMS and emails and feedback and reminders to improve data quality and also proved scores as performance measures.

PIMA program aims to provide an EMR integrated with DHIS2 and a computer-based Human Resource Health Information system (HRHIS) in Tanzania. An integrated EMR system will include a laboratory integrated management system (LMIS) and Human Resources for Health Information System (HRHIS) and additional surveys needed to feed data for the PHC performance framework. In addition, the automated electronic feedback reminders PIMA can offer the platform for personal health tracking, client identification, and medical supply chain tracking to support the proper organization and delivery of healthcare services in the country.

Apart from facilitating the automatic sharing of information from PHC facilities to healthcare managers and policymakers at the district, regional and national levels, the EMR will enable healthcare managers and policymakers to have a real-time experience of what is happening at PHC facilities. Nonetheless, PIMA software integrated into iCare  will share performance feedback to the PHC health care providers and patients,

University of Dar es Salaam – Mbeya College of Health and Allied Sciences (UDSM-MCHAS) and University of Dar es Salaam - College of Information and Communication Technology (UDSM-COICT) will also improve interoperability between EMR at PHC facilities and the DHIS2, to facilitate automatic computation and reporting of indicators required to measure the performance of PHC. In general, our solution will improve diagnostic services and the availability of health information required to assess PHC performance. This solution will be tested in 12 PHC of Mbeya and Dodoma (3 dispensaries, 2 health centers, and one district hospital from each region) in one region of Tanzania mainland before scaling it to the national level.

During n PIMA program implementation, the special data clerks will be deployed in each in every unit PHC among the  12 PHC facilities that will be paid by the program to collect data for 18 months of the program implementation in Mbeya and Dodoma.

Data clerks will be trained on the use of survey tools, qualitative methods of data capturing, and HCD data collection for indicators that span from Structures, Inputs, and Service process delivery, Outputs, Outcome, and Impact of each PHC facility. One of the motivational packages is the Data Management and GCP Training for Data needed in the implementation sciences. Certificates with the University of Dar es Salaam, Northwestern University, and Harvard University will be on their certificates. Additionally, the 12 data clerks and 12 hospital coordinators from the facility will be paid by the PIMA program incentive to upkeep data entries.

The Implementing team of PIMA has noticed many research works with data clerks will get credible data. On the theory hand, the routine health system data from the Health management information system (HMIS) in Tanzania will not have credible data. This is due to the overburden of doctors and nurses to give the PHC services which are presumed to be more important than the data recorded in (HMIS).

The implementation science to test the effectiveness of the program by step wedge methods will offer the opportunity for PHC performance measures in an easy way so as to reduce the burden of health care providers at the PHCs. This means when data clerks will collect these data into systems it will be a movement to test the employment of new cadres for data entries in the PHC facilities. Comparison metrics with the previous quality of data will provide evidence of whether the authorities need to define a new cadre for data entries to measure the PHC performances. In the end, this implementation science will stimulate the government of Tanzania and other African Countries to create a new cadre for data quality in every PHC performance measure.

The PIMA program development has been made by the gender-balanced management team of experts from Tanzania and the United States (half male and half females). 

The design of PIMA implementation has targeted the required collection of data from the diverse communities by age, gender, children's sex, ethnicity, economy, and culture. PIMA program has therefore committed to diversity, equity, and inclusion from reconsiderations of the beneficiary of data and its workflow design, customization, and decision support. For example, implementation required analyzing and redesigning hundreds of clinical workflow patterns of which analysis can be categorized by age, gender (later) now children's gender, ethnicity, economy, and culture.

Additionally, some quality programs in Tanzania collected diversity data using paper case report forms and then transferred the data into an electronic database or EMR systems for linkage with quality data. Most of this quality improvement  (QI) programs' lack of standardization for quality measures and data specifications made some of the tasks even more difficult. With iCare and PIMA software, it is possible for PHC to move forward with collecting more clinical data lined with all diversity data to allow categorization in analysis. 

Our business model is to help people served by PHC under Universal Health Coverage (UHC). This means the PIMA program will work with the government of Tanzania on a national wide scale up the use of PIMA software upon proving that iCare and PIMA software works.  Later on, the PIMA program will sign agreements with special commissions to owners of private hospitals and other governments in LMIC to facilitate as many as possible who are served by PHC facilities to benefit from quality health services, where and when they need them, without suffering financial hardship. PIMA targets 3 key dimensions of UHC through its business model Access to health services: everyone who needs services should get them, not only those who can pay for them. Financial risk protection: people should not fall into debt paying for treatment and care and Quality of services:  services should be adequate and effective.

PIMA team can produce health economic models on Cost-utility analysis. i.e. Financial analysis to guide program impacts conclusion after understanding lives saved, Disability-adjusted life year (DALY) as a way to realize the amount of disease burden saved in the program, and a time-based measure that takes into account changes of combined years of life lost due to premature mortality (YLLs) and years of life lost due to time lived in a situation of less than full health, or changes of individual years of healthy life lost due to disability (YLDs). and lastly, the use of Quality-adjusted life-year (QALY) as the generic measure of disease burden, including both the quality and the quantity of life lived.  

With these measures benefits of the Program can be elucidated. 

The financial sustainability of the PIMA program relies on the business model of other not-for-profit organizations, private hospital organizations, and or government health facilities, that cover both public and private sectors. The service contracts to governments and private PHC owners will be developed.

 The University of Dare Salam will work on policy demands for the Ministry of Health and the Association of Private Health Facilities in Tanzania (APHFTA) in which terms of services will be discussed for approval. PIMA software will need maintenance in the PHC facilities and therefore for the software to become more user-friendly and useful for both private and public facilities, sustainable charges will have to be lad into agreements. . Through APTHA and the Ministry of Health, all PHC facilities directors will have to sign legal documents for service charges upon agreements from both parties. These will cut across all private and public PHCs. Charges will be lower in public compared to the private so as to minimize costs of services in the public facilities.

We were funded by THET for Obstetric hemorrhage management training through Multidisciplinary teams using ICT tools and a website in Mbeya, Tanzania. This was THET funded (Proj. AGP 4.59) we were able to reduce maternal deaths from an average of 32 deaths per month to 9 deaths per month. Our projects have given us the opportunity to design training manuals for Obstetric hemorrhage management in Tanzania.