Efforts to address infant mortality disparities in Ohio have historically been adversely affected by the lack of consistent data collection and infrastructure across the community-based organizations performing front-line work with expectant mothers, and there is no established template for implementing such systems in the context of diverse technological capacities and varying data collection magnitude among participating organizations.Taking into account both the needs and limitations of participating community-based organizations, we created a data collection infrastructure that was refined by feedback from sponsors and the organizations to serve as both a solution to their existing needs and a template for future efforts in other settings.By standardizing the collected data elements across participating organizations, integration on a scale large enough to detect changes in a rare outcome such as infant mortality was made possible. Datasets generated through the use of the established infrastructure were robust enough to be matched with other records, such as Medicaid and birth records, to allow more extensive analysis.While a consistent data collection infrastructure across multiple organizations does require buy-in at the organizational level, especially among participants with little to no existing data collection experience, an approach that relies on an understanding of existing barriers, iterative development, and feedback from sponsors and participants can lead to better coordination and sharing of information when addressing health concerns that individual organizations may struggle to quantify alone.The online version contains supplementary material available at 10.1186/s12913-021-07393-1. The use of community-based organizations to address social determinants of health has a long history . Infant In 2012, the state legislature renewed efforts to address this disparity by convening local partners in the nine counties in Ohio that account for the majority of black infant mortality. In addition, state agencies collaborated to use population data to target areas for outreach and services as well as coordinate efforts among the multiple programs in each community. Infant vitality efforts in these nine counties, also known as the Ohio Equity Institute (OEI) communities [In 2017, the Ohio Department of Medicaid (ODM) began to focus on the implementation of a consistent data collection and infrastructure across programs to facilitate evaluation despite access to Medicaid and population data. The vision refocused on using data to increase surveillance to target areas for outreach and services as well as the coordination of efforts among the multiple programs in each community. To address the lack of consistency across data collection, ODM developed the OEI Community-Based Organization (CBO) Evaluation project. The goal of the OEI CBO Evaluation project is to create a statewide system for routine data collection and conduct analysis on the data collected to determine the extent to which the selected interventions serve high-risk pregnant women and assess the effect of these interventions on health care utilization and birth outcomes. The research team is made up of researchers at The Ohio State University and the Ohio Department of Medicaid, both in Columbus, Ohio. The Ohio State team, composed of health and bioinformatics subject matter experts, was tasked with developing a multimodal data collection system. The research team worked closely with leadership individuals from the CBOs, who attended meetings in person, online, or via phone with the research team in Columbus to ensure the system was created with their input and needs considered.At the start of the evaluation, the research team performed a needs assessment, which noted there was significant heterogeneity among the interventions, at times individuals participated in more than one program, and there was no standard for data collection. Without a multimodal data collection model necessary to support public health and surveillance of infant mortality, there was a great deal of disparity in data collection approaches and infrastructure. Further, there were no standards for reporting such community-based data. This paper describes the multimodal data collection model necessary to support public health and surveillance of infant mortality and serves as an exemplar for how such efforts might be accomplished in other domains.Taken together, concerns about the disparities in the existing approaches to data collection and the associated impact precipitated into a call to action by state agencies to develop a robust data collection infrastructure for OEI. The primary objectives of developing our data model for OEI were twofold: 1) to generate reports that quantify the performance of the CBOs funded by OEI based on key metrics and 2) to dynamically visualize these key metrics using a Tableau dashboard.As part of this project, we sought to design a data model that included characteristics we previously determined to support public health and surveillance of infant mortality in Ohio. Past research encourages using collaborative design processes to ensure user-centered design , as prodA multimodal system is one that allows for data collection and delivery across a number of communication channels: phone, email, fax, mail, and direct data entry. Multimodal systems allow for data collection across CBOs that vary in their technological capabilities and resources. CBO staff have varying levels of comfort with using tools to report data; some are more comfortable using information technology, while others gravitate to using paper forms. The final data are electronic, and paper forms have additional data entry and submission steps; however, many of the community organizations do not have the technology to collect data online, and it may be easier to collect data on paper during in-person visits with participants. Prior research indicates improved data quality when multiple data collection modes are available –20.A standardized data model allows for the collection of a common set of core metrics across CBOs with heterogenous activities and data collection practices, improving cross-program comparability . The widSystematic educational outreach efforts are necessary to train health care providers on how to report data to a CDM, maintain the fidelity of data collection over time, and foster relationships between community health organizations and research teams , 24–26. This paper describes the design, development, deployment, and assessment of a data collection system to improve public health efforts by collecting data from multiple community programs. The goals of our effort were to minimize data collection burden while maximizing the ability to engage in evaluative assessment of the effect of participation in OEI CBOs. Our paper serves as a template for the development of similar systems, in other contexts and other locations, in which existing data infrastructure and inter-organizational coordination are insufficient to meet the needs of a public health effort. We describe the design of the OEI data collection system and data model that supports these goals. The system developed is still being used to collect data from CBOs across the state for the Ohio Department of Medicaid.The OEI CBO Evaluation project focused on programs in nine counties in Ohio with large disparities in infant mortality and significant urban populations: Butler, Cuyahoga, Franklin, Hamilton, Lucas, Mahoning, Montgomery, Stark, and Summit. These include the urban areas in the cities of Hamilton, Cleveland, Columbus, Cincinnati, Toledo, Youngstown, Dayton, Canton, and Akron, respectively. Existing and newly developed community programs received funding to provide consistent data to ODM and could use this funding for hiring, training, and general program needs. The population involved in the development of data collection materials consisted of supervisors and program leaders from OEI CBOs, the research team at Ohio State, and the funders at the Ohio Department of Medicaid. The population assessed encompassed every participant who enrolled in one of the funded programs in the nine OEI counties. The OEI data collection system collects data only for those enrolled in one of the CBOs, but the population enrolled in these programs is largely high-risk, mostly minority women who have the highest need for interventions.The research team at Ohio State for OEI consisted of faculty, skilled professionals, and technical experts tasked to develop, deploy, and evaluate the data collection system. First, in early 2018, our team conducted a needs assessment by reviewing previous program data collection materials, reviewing CBO grant applications, conducting individual and group interviews with CBO leaders, and reviewing data collection materials from similar programs identified during literature review to identify program needs and conceptualize a multimodal data collection system. The semi-structured interview guide asked open-ended questions about how organizations were managed, data collection and reporting methods, frequency of data collection, types of data collected, and abilities to add data points or change data collection methods. Based on our engagements with the CBOs, the research team sought to sketch out for the communities what a robust infrastructure might entail. The needs assessment occurred over a two-month period.Twenty-one interviews were conducted in both individual and group settings, and input was gathered from 57 CBO leaders representing 60 of the 64 OEI organizations. Some leaders represented multiple CBOs, and there were four CBOs that did not participate in interviews due to non-response to interview requests. The research team met and reviewed interview transcripts to categorize responses.Our needs assessment revealed that the CBOs that collect the most data have data collection activities at intake; each time a participant is seen; and, for some programs, after birth and when exiting the program. Data collection is done either during home visits with a participant, at group classes, or in other one-on-one settings such as public offices. Some programs collect most of the intake data at the first visit, while some collect it over a few initial visits. Our OEI data collection system largely mimics this, having forms for intake, birth, exit, and encounter with online and paper data collection options.One of the major gaps noted by our needs assessment process was that without a system of this nature, there was excess variation in data collection approaches between CBOs. For example, initial engagement identified some community programs with advanced electronic data collection systems in place, while other CBOs worked entirely with paper records. We found that some CBOs collected only aggregated attendance data, while others maintained databases of extensive medical, behavioral, environmental, and demographic data. In addition, sufficient identifier information was often not collected, precluding post-processing and subsequent matching of data with community infrastructure.The needs assessment revealed potential benefits of implementing a more robust system: the sponsors would receive more consistent data collection and program evaluation, while CBOs collecting minimal data would receive data collection materials and support to help with evaluation. Most CBOs had performed little evaluation work; employees lacked the time and resources, and the numbers of participants did not allow for robust analysis of outcomes. The absence of this system could be linked to implications for CBOs such as potential suboptimal allocation of resources, not targeting the individuals who may benefit most, and not providing as many referrals or interventions as a participant may need due to being unaware of risks.The research team developed an initial variable list with the sponsors that was revised based on the needs assessment. Our initial variable list included variables measuring demographic data, environmental and behavioral risk data, and data about care received by the mother and infant. The demographic data collected included names, birth dates, and Medicaid or Social Security identification numbers to ensure that Vital Statistics and Medicaid data are matched properly, as well as address information to map where participants reside. The needs assessment revealed a desire from the CBOs to not have an overly burdensome number of data points. Most already collected much of the data and would not be able to significantly add more data points. Many clinical variables and birth-related variables that can be found in other materials were removed from the variable list to reduce data collection burden from participants. Many of the variables not collected are linked into the final dataset from Vital Statistics birth records, including birth weight, gestation at birth, and other health variables. The focus of the OEI data collection materials was to learn more about program participation, including its intensity and the services offered, as well as to learn more about the environmental and demographic factors that may play a role in infant and maternal health. A great deal of the data collected by the new materials are not available in any of the other data sources linked. Data collection materials were developed once the variable list was finalized. These variables would subsequently be collected and curated on a database server that concomitantly allowed for the querying of data through of a common data model. All data collection activities were approved by an Institutional Review Board at the Ohio Department of Medicaid.Figure Between February 2018 and July 2018, our team engaged in the development and deployment of the OEI data collection infrastructure. Critical early milestones during this period were developing an agreed upon timeline for data reporting, piloting data collection forms with select CBOs, and refinement of these forms. The Excel sheet and paper forms were sent to select CBOs that volunteered during their needs assessment interviews to review and pilot test the forms. These CBOs were encouraged to provide feedback on the specific variables measured, ease of use, language and readability, and general look of the forms. After editing the forms based on this feedback, final versions were developed and the online data portal was created using the same questions. Subsequent milestones focused on piloting the data collection system between July 2018 and August 2018 across all CBOs, testing system integration, validating data points, and making system refinements based on the feedback obtained from the previously listed activities. Data collection began in September 2018 for 30 programs, and additional programs were enrolled over time after training and data use agreements were finalized. The data use agreements allowed the organizations to share individualized data with the research team that are aggregated and evaluated before being presented to the sponsors or other programs.During the development period of the data infrastructure, our team concurrently focused on the design considerations and development of the OEI common data model. The data model architecture for our project contains data about the OEI program, its participants, and non-OEI participants. The model consists of elements from our data collection infrastructure  linked to datasets from state databases . The online data collection system consists of a database using Qualtrics, which offers the ability to collect data about multiple participants and from multiple CBOs. CBO members create login information and are directed to a separate landing page for each CBO. On this landing page, participant demographic information can be added and surveys can be filled out and updated about each participant. These Qualtrics surveys follow the same format as the other data collection methods; CBOs create a record of a participant, then have the ability to complete intake, birth, exit, and encounter forms for that participant. These reports are downloaded by the researcher directly and processed and appended. CBOs can also securely send an Excel-readable spreadsheet that is imported and processed, or scanned paper forms that require an additional data entry step into a spreadsheet that is later imported. Another layer to the collection of data through the OEI system is the submission by some CBOs of data formatted for other data collection systems, in our case Care Coordination Systems (CCS) and the Ohio Comprehensive Home Visiting Integrated Data System (OCHIDS). CCS and OCHIDS contain similar data to the OEI system and required some standardizing before appending to the master dataset.CBOs were trained on how to use the data collection materials in July and August 2018. These training sessions consisted of webinars with demonstrations of how to use the Qualtrics data portal, the validated Excel spreadsheet, and the paper forms. After these demonstrations, CBOs were given a choice of how to submit data among those options. After this choice, the CBO was provided with paper forms, the spreadsheet file, or login information to start data collection. After the first month of data collection, the research team conducted the first round of three Plan-Do-Study-Act (PDSA) cycles. The purpose of the PDSA cycles was to gather information about barriers to data collection, answer CBO questions, help CBOs improve their data quality, foster relationships with CBOs, and further improve the data collection infrastructure. The PDSA cycles consisted of emailing all CBOs surveys and inviting them to short phone calls to discuss their concerns with data collection. Phone calls were optional, but they were especially encouraged for organizations that reported many data collection challenges in the survey. As part of these conversations, CBOs were given the opportunity to change data submission preferences, provide input about portal changes desired, and receive additional training.Data collection occurs at four points in time: enrollment, encounter, birth, and exit. Supplementary Table 1 in Additional File Supplementary Table 3 in Additional File Data are currently gathered by CBOs for other purposes. To the extent that the CBO leverages its existing infrastructure to gather these data, we believe that data quality will be higher.Some CBOs may not currently collect data. In these cases, we provided tools that facilitate data entry in a manner that reduces the likelihood of data integrity issues.Some CBOs may not have the capacity to collect data in the local setting. We make no presumption of computational ability in these settings and facilitated data collection in a manner that allows the CBO to meet the reporting criteria with a minimum transaction cost.Data collection is not the focus of these CBOs. Given the community nature of the interventions, the system was developed in a manner that supports ease of use.Data may be collected within a CBO or at a participant’s home. The nature and timing of how CBOs are administered required a data collection approach that is robust across multiple settings.To develop the OEI system and integrate our data model into the system, we accounted for five critical constraints based on the needs assessment interviews:Given those constraints, we developed our multipronged, multimodal data collection and integration approach for the OEI system.The following section outlines data integration use cases and explicates how data were integrated in a single database.Use Case 1: The CBO keeps digital records, collects all required data elements, and those elements conform to the data model specifications Figs.  and 3.FiUse Case 2: The CBO keeps digital records but does not collect data that conforms to the data model Figs.  and 5.FiUse Case 3: The CBO keeps digital records, but it does not collect all required data elements and those elements do not conform to the data model specifications.Modify current systems to gather the additional required information and then conform to either Use Case 1 or 2.Adopt the use of digital or paper forms and associated workflows.In these cases, the CBO modifies the workflow in one of two ways:Use an online data submission system that mimics the paper forms.Use a paper form and send it to our research team for processing.Use a validated Excel spreadsheet created by our research team that mimics one of the forms from one of the other collection modalities.For cases in which a CBO does not keep digital records, it chooses one of three options:Use Case 4A: The CBO does not currently keep digital records and chooses to use an online data submission system Fig. .Fig. 6DaWe provided an online system that allowed the CBO to enter data. The system required a computer with an internet connection. The website required site-specific authentication, and we provided credentials to the CBOs that chose this model. In cases when this approach is used, the workflow looks as follows:Use Case 4B: The CBO does not currently keep digital records and chooses to use a paper-based data collection system Fig. .Fig. 7DaIn the case when a CBO chooses to collect data on paper, we provided the CBO paper forms, and their digital equivalent, which they used to meet their reporting obligations. The forms are 8.5 by 11 in., double sided, and singly designed to be used in all cases. CBOs using paper forms could also choose to adopt the model of Use Case 4A and enter data after the fact. In these cases, our team followed the workflow illustrated below:Starting in October 2018, CBOs were asked to submit their previous month’s data by the 10th day of each month. Data from September 2018 were reported for 41 CBOs, and the number of CBOs reporting data increased to 62 by June 2019. From August 1, 2018, to June 1, 2019, the total number of participants reached across all programs was 10,693, representing 10,074 different people, indicating 619 clients participated in more than one program. Our research team received data from the CBOs and engaged in extensive data processing steps. These involved assessing data validity, accuracy, completeness, consistency, and uniformity. We also inspected the data for duplication of participant information, where we retained the most complete or most recent version of the forms. Once these steps were complete and the datasets were standardized, they were appended to one another. This appended dataset was then matched to birth records and Medicaid records using key identifiers that included the participant’s Medicaid identification number, name, date of birth, and address; non-matching records were retained for our control group. Of the 8984 total participants in the dataset used to match to birth records in May 2019, there were 2829 participants (31.5%) matched to birth records and 5798 participants (64.5%) matched to Medicaid records. Many of the participants had not yet given birth at the time of this matching. All the datasets in the database contained a one- to three-month delay after an event, except for the Medicaid claims data, which has a six-month delay.Our research team continuously updated a data repository with the above-described data that were appended with information from the OEI system and the external state datasets. Periodic extracts from the data repository were used to generate reports for key stakeholders  and used in the Tableau Dashboard to visualize key metrics. The dashboards, built by the research team with regular feedback from stakeholders, display the data in the form of bar charts, pie charts, and tables of relevant information. Each page of the dashboard is able to focus on a different portion of the data, such as the enrollment numbers for the CBOs, gestation data, or risk factors. Making selections within the dashboard modifies the view and presents additional data. By offering the information in a more visual manner, rather than in tabular format, it can be put to use by policymakers and others who may not be as familiar with statistical analysis while allowing them to draw their own conclusions. An example of one of the OEI dashboards is pictured in Fig. During the data processing step, our research team performed assessment of missingness of data for each variable measured. Overall and individual CBOs’ reporting of values for variables were inspected. Data quality assessment consisted of counting the number of variables each CBO collected data for, along with finding the percentage of missing data for eight key participant information variables. CBOs that had poor data quality  were contacted by our research team during one of multiple Plan-Do-Study-Act (PDSA) cycles to identify systematic barriers to data reporting. During our PDSA calls with the CBOs, our research team used this information to recommend solutions to help improve data reporting. We experienced notable improvements after each PDSA cycle, and this helped improve data reporting methods over the aggregate reporting period. For example, two CBOs that reported 5 out of 8 (63%) of the contact information variables in January 2019 were reporting all eight of the variables by May 2019. The total dataset had 95.0% of participants’ first names and 94.8% of their last names in January of 2019, and 99.3% of first names and 99.2% of last names by May 2019. In January, there were 31 programs sending enrollment forms, four sending birth forms, five sending exit forms, 15 sending encounter forms, and 10 sending group encounter forms. By May, 50 programs sent enrollment, 37 sent birth, 33 sent exit, 41 sent encounters, and 13 sent group encounter forms. Many of the programs that sent only minimal contact information and may not have sent any forms other than enrollment or encounter forms at first were community health worker or Other programs, which may have seen participants only once or twice in public settings. Some of these programs did not collect data at all before this project, and they were able to slowly increase the number of questions they asked over time. Many programs still do not collect every type of form and skip questions, but there were vast improvements throughout the first year of the project.Data created for the OEI reports and dashboard were analyzed using statistical software. Descriptive statistics that include counts, means, and confidence intervals, including bootstrapped estimations of these statistics, were calculated using Stata or Tableau. We also report inferential statistics for some of the metrics.The data model created by our research team has been leveraged to generate reports for key stakeholders and provide them with data visualizations using Tableau dashboards. Table Reflecting upon the instantiation of the OEI infrastructure, we identified six core elements that represent structural components and approaches to signal to CBOs that we were working within a specified set of expectations and wanted their input and buy-in to the system Table . We alsoData collection continues to be one of the biggest challenges for community-based organizations . The OEISuccesses experienced in the first year of data collection included the ability to match to state records and improvements in data quality. Almost a third of participants were matched to birth records at the end of the first year. The lag in birth record reporting, the enrollment of participants early in pregnancy throughout the data collection period, and the collection of data about some participants who were not pregnant at enrollment or experienced pregnancy loss were factors that affected the ability to have all the records matched. Over time, the percentage linked to birth records will improve. In addition, over 60% were matched to Medicaid records. As there is not a requirement that participants receive Medicaid to participate in these programs, some participants may be uninsured, on private insurance, or on insurance associated with school or their parents. Finally, as more CBOs collected data and received support through PDSA cycles, the quality of data collection improved, as there were a higher number of variables collected and lower levels of missing data for participant information variables. Many CBOs who were not collecting sufficient data before this project worked with the research team to increase the number of variables collected. Allowing CBOs to submit data in multiple formats, provisioning reporting materials, and training on reporting data helped increase the number of programs submitting data and the completeness of this information. Despite some reluctance to add many new variables to data collection at needs assessment, the programs were able to comply with data collection because of the support they received and their desire for evaluation. The effort required to change data collection systems was largely exerted at the beginning of the project; after a few months of using the system, many organizations had few concerns to report during PDSA cycles. Maintaining the system is relatively low effort, requiring periodic training for new CBO employees or if there are system updates, and the centralized data collection infrastructure is more stable than if organizations separately collected and evaluated their own data. CBOs are also now able to use better data for cost-saving QI activities or to secure future grant funding for their efforts.The OEI data collection system has been developed to assess statewide infant mortality prevention efforts, as there are many programs attempting to effect change but little coordination among programs. In addition, there is a lack of time and resources for state agencies to create user-centered data collection infrastructure and perform extensive evaluation on their own. The collaboration between multiple teams was necessary to create a robust data collection system that worked for multiple programs. Although there are national reporting requirements for certain infant mortality prevention programs, including Nurse Family Partnership and CenteringPregnancy, there is little comparison of programs or compiling of program data. By collecting data for multiple kinds of programs in Ohio, the effects of any infant mortality prevention efforts can be assessed, and programs of similar types can be compared. Infant mortality is a rare outcome that is difficult to assess statistically, leading to difficulties assessing program success for small individual programs . CompiliData collection for community programs also has the potential to lead to coordinated efforts with health care providers. Infant mortality has multifaceted risks that cannot be addressed through the medical model alone. Many of the environmental, social, and behavioral risks that participants may experience are addressed by community programs through provision of referrals to other services, provision of child care supplies, social support, and education that is not standard for prenatal care in a medical setting –31. PartImprovements in public health require information systems for surveillance and effective program implementation and impact, along with partnerships that build public support . BecauseThere is a need to develop an ecosystem of tools to effectively gather data from program participants, community members, community-based organizations, and local and state authorities to facilitate a greater understanding of how public health interventions impact the communities they serve. There also is a need for participant-facing tools to collect information about service quality and patient-reported outcomes without having to report through a community health worker or other intermediaries. Potential data collection tools include smartphone applications, text messaging-based data collection, and interactive voice activated applications. In addition, there is a need for organization-facing tools that report information back to organizations about their performance relative to other organizations and information about whether there are gaps in service. Currently, the Tableau dashboards are used by the program sponsors, and some of these results may be reported back to CBOs; however, these organizations do not have control over what data they see. Systems may be needed to provide regular feedback and data access for analysis for individual CBOs. Organizational engagement will be imperative moving forward to develop more efficient systems of bi-directional reporting.The OEI data collection infrastructure our research team designed and deployed is still in its early stages of system maturity. Our research team continues to learn from its implementation and use as the system evolves over time. The system, although requiring more effort from CBOs, is already demonstrating signs of collective action at the local and state levels to better coordinate and share information on how to best utilize programmatic resources to reduce infant mortality and its associated disparities in across Ohio.Additional file 1. Variable list. This file includes the complete list of variables measured using the enrollment, birth, exit and encounter forms used as part of this study.Additional file 2. OEI Data Dictionary. This file includes the complete data dictionaries for the enrollment, birth, exit, and encounter forms used as part of this study.


Front-line health workers in remote health facilities are the first contact of the formal health sector and are confronted with life-saving decisions. Health information systems (HIS) support the collection and use of health related data. However, HIS focus on reporting and are unfit to support decisions. Since data tools are paper-based in most primary healthcare settings, we have produced an innovative Paper-based Health Information System in Comprehensive Care (PHISICC) using a human-centred design approach. We are carrying out a cluster randomised controlled trial in three African countries to assess the effects of PHISICC compared with the current systems.Study areas are in rural zones of Côte d’Ivoire, Mozambique and Nigeria. Seventy health facilities in each country have been randomly allocated to using PHISICC tools or to continuing to use the regular HIS tools. We have randomly selected households in the catchment areas of each health facility to collect outcomes’ data (household surveys have been carried out in two of the three countries and the end-line data collection is planned for mid-2021). Primary outcomes include data quality and use, coverage of health services and health workers satisfaction; secondary outcomes are additional data quality and use parameters, childhood mortality and additional health workers and clients experience with the system. Just prior to the implementation of the trial, we had to relocate the study site in Mozambique due to unforeseen logistical issues. The effects of the intervention will be estimated using regression models and accounting for clustering using random effects.Ethics committees in Côte d’Ivoire, Mozambique and Nigeria approved the trials. We plan to disseminate our findings, data and research materials among researchers and policy-makers. We aim at having our findings included in systematic reviews on health systems interventions and future guidance development on HIS.PACTR201904664660639; Pre-results. These trials assess the effects of improving paper-based health information systems, which are greatly used particularly in remote, rural areas but which are neglected in research.The paper-based intervention (PHISICC) has been developed using a human-centred design approach, with front-line health workers and designers driving the cocreation process.Despite the complexity of health systems interventions, we have applied robust experimental methods, together with qualitative research, to assess and understand the effects of the paper-based intervention. Robust evidence on health systems is more likely to gain the credibility of policy-makers and to make it into systematic reviews, guidance development and policy and practice.Research targeting front-line health workers in remote, rural areas has to take place where they live and work, which poses serious obstacles in the organisation, management and monitoring of the trials.These obstacles, aggravated by the COVID-19 pandemic, have challenged the mobility of the research team, the availability of the intervention in one of the countries and the duration of the trials.Front-line health workers (HW) in remote, rural health facilities (HF) in many countries are the first contact with the formal health sector of the population and they are confronted with life-saving clinical and public health decisions on a daily basis. Decisions are made by exerting a balanced judgement on the information related to healthcare events, such as making the correct diagnoses or deciding on which vaccinations a child should receive on a given day. In order to properly handle this information, appropriate data support tools and processes are required, referred to as the health information system (HIS), or routine HIS, or health management information system.2Increasing pressure by donors and governments to collect more and more data has aggravated the situation, through the proliferation of data support tools that have overloaded front-line HWs compromising their capacity to deliver good quality of care and to produce good quality dataPromising ‘quick fixes’, such as the scale up of digital HIS, are taking a long time to implement and face enormous challenges related to infrastructure, equipment and services necessary to run them. Besides, research evidence on the effects of digital solutions remains patchy and inconsistent, even in high-income country settings, where complaints about computerisation of clinical care have been raised,Paper-based Health Information System in Comprehensive Care (PHISICC) is a multiyear, multicountry, mixed-methods research project that aims at producing and testing an innovative paper-based HIS to improve data quality and use, decision making and health outcomes, at primary healthcare (PHC). It is being carried out in selected areas within Côte d’Ivoire, Mozambique and Nigeria. The project started in 2015, producing a systematic review on the effects of HIS interventionsThe impact of the PHISICC HIS on data quality and use, quality of healthcare and HW perceptions is being assessed concurrently in rural areas in the three countries. We describe the design of the trial here, consistent with Consolidated Standards of Reporting Trials reporting guidelines10.1136/bmjopen-2021-051823.supp1Supplementary dataThe aim of the trial is to address the research question: what are the effects of an innovative paper-based HIS (PHISICC) on data use and quality, quality of health and HW perceptions compared with the current HIS, in rural PHC settings?There was no public or patient involvement in the design of the study or selection of study areas because the intervention being assessed in these trials target healthcare providers and decision-makers, rather than patients or the public in general. Population in the catchment area of selected HFs, potentially using their services, were only approached in order to assess the studies outcomes.On the other hand, we have involved health systems stakeholders and front-line HWs. Ministries of Health (MOH) at several levels participated in the preparation of the research proposal , in the characterisation of HIS that preceded the trials (countries workshops), and throughout all project components . Front-line HWs in the three countries have cocreated the intervention  through workshops, personal feedback and piloting under real live conditions. Some of them are part of the research team and coauthoring this manuscript.The study is a CRCT in each of the three countries. In each setting, 70 HFs are randomised to intervention or control (35 per arm). The intervention arm HF use the new PHISICC tools  and the control arm HF use the regular HIS tools. The trial is implemented in the real life contexts of HF carrying out their usual duties.The trials started between the end of 2019 and beginning of 2020, depending on the country, when the intervention was installed and the baseline surveys carried out. Data collection will last until mid-2021.MOH officials in several countries were contacted before submitting the proposal to the funding agency in order to explore the willingness to engage in a project focusing on paper-based tools. Officials in several countries rejected the offer on the grounds of upcoming digitalisation plans of the HIS in the country. We partnered with MOH that found the research relevant to their context in three countries.In each country, the eligibility criteria of study areas were that they had to belong to the operational area of research partners; contain a large enough number of HFs and their catchment population; include vulnerable population ; and be comparatively neglected in terms of infrastructure and services. We excluded areas with concurrent research or other types of activities that could conflict with the CRCT  and areas with threats to safety or security that could jeopardise research activities.The study areas are located in Adzopé, Agboville, Tiassalé and Sikensi districts (Côte d’Ivoire); in Funhalouro, Govuro, Homoine, Inhambane, Inharrime, Inhassoro, Mabote, Maxixe and Panda  and in Yala Local Government Authority .The intervention is implemented at the HF level. The eligibility criteria of the HF were that they had to be located in the study areas, belong to the governmental health sector and their main activity should be the delivery of PHC services. HFs were excluded if they had specialised clinical services, inpatients, physicians providing care or with plans for staff turn-over.A ‘master list’ of eligible HFs was prepared based on information provided by the MOH across all study areas. We aimed at selecting 70 of the eligible HF in each country, using simple random techniques in R.Allocation of HF into the intervention and control arms took place in a formal event, gathering research partners and MOH officials to offer transparency and promote study ownership by local and national authorities. Equally sized, folded pieces of paper with the names and codes of included HF written on them were introduced in an opaque receptacle where they were manually and blindly mixed. A second receptacle contained two equally sized pieces of paper, one with the word ‘intervention’ and another one with the word ‘control’. A selected person in the meeting, not belonging to the research team, extracted one piece of paper at a time to reach half the number of included HF. Then, a paper was extracted from the second receptacle to assign those HF to the intervention or control arms. The rest of the papers were extracted as well to verify completeness and no duplication of names, and those HF were assigned to the other arm.Once HFs were selected, all villages or settlements for each health facility catchment area were listed and three in each catchment area were selected. In practice, we selected all villages because the numbers were below (in Côte d’Ivoire) or just above (in Nigeria) the needs. For each village, we used Google satellite maps to identify and geo-locate every visible roof. Where there were many houses per village , a researcher would mark four points in the map slightly beyond the northernmost, southernmost, easternmost and westernmost roofs seen and 30 random points were selected within that square. From the mapped points, 10 per village (with 20 more acting as reserve) were randomly selected and marked on another map used in the field for data collectors to approach households. Where technical problems impeded this approach in a given village, a field supervisor would rotate a bottle on the floor towards the centre of the village and would select at random 10 households in the direction pointed by the bottle, from the outer limit of the village till the centre.11Blinding is only feasible for the research team members carrying out the CRCT data collection and the analyses of the CRCT findings. The intervention  are by design very different from the existing system and it is not possible to blind participants or principal researchers.We already had the agreement of the MOH and selected HF compliant with the inclusion criteria were provided with the intervention shortly after completing the baseline data collection. Therefore, recruitment as such took place at the same time of the allocation of HF into intervention and control arms.The PHISICC paper-based intervention is a full set of paper-based tools to support decision making by front-line HW. These are the only tools to be used by HW in the intervention arm. The PHISICC tools encompass the whole system  and all clinical and public healthcare areas and are characterised by a common visual language , and standardised formats across healthcare areas. To support front-line HWs decision making, the PHISICC tools incorporate specific places to explicitly record critical data items , graphic artefacts to distinguish severity degrees of signs or symptoms ; space to document diagnoses and treatment decisions; and aides memories in the first page of register books.The PHISICC tools have been developed from May 2017 till June 2019, including production, using a HCD approach.The PHISICC tools have been produced in French for Côte d’Ivoire, in Portuguese for Mozambique and in English for Nigeria, which are the official languages used in the health systems in the three countries. They include the official logo of the MOHs. Healthcare areas covered include: family planning, antenatal care, including tetanus toxoid vaccination, delivery, postnatal care, vaccination, sick child, adults outpatient consultation, tuberculosis diagnosis and treatment, and HIV. Referral forms were also designed.The PHISICC tools have three subcomponents: registers, tallies and reports. Registers are formed by seven Deutsches Institut für Normung (DIN) -A3 and one DIN-A4  book covering all healthcare areas except for tuberculosis treatment, for which DIN-A3 cards where used. Register books have 100, 200 or 400 pages depending on the country and healthcare area. They are used to record individual clients’ data for each healthcare event, either of clinical or public health nature. Some register books have clinical notes at the very beginning, as ‘aide memoires’, and an example of a filled-in form, to assist HW when doubting how to proceed.Tallies are DIN-A3 single sheets which contain a list of the indicators to be transferred to higher levels of the health system, with a series of small ovals, grouped in fives, to mark with tally sticks with a pen. In contrast to the current systems that have no tallies or only for vaccination, tallies were created for all healthcare areas. In the middle-right side of the tally, a column accommodates cells aligned with the ovals to insert the count for each indicator; and in the far right of the sheet there is a replica of the count column, separated with a perforated line, which is detached and sent, as part of the monthly report to the higher level in the health system.While current HIS tools are consistently organised in tabular formats and books, where each clinical event is recorded in a row and each variable  in a column, PHISICC tools incorporated several innovations; in summary: a visual language to guide the clinical decisions of HWs based on severity , more space for clinical data , inclusion of all critical information to assess patients , consolidation of information of all antenatal care visits for a single pregnant woman in the same page, among many other formal and contents improvements, including improved aesthetics. A systematic comparison of the new (intervention) and current (control) tools is provided in We aimed at creating ‘a system’ (not just some tools) focusing on decision making by front-line HWs. The epidemiological and public health contexts in the three countries are similar, as confirmed by the similarities in the existing HIS between the three. The visual language and the recording forms where common to the three countries because clinical decisions are common to the three; although forms allowed for specific tests or treatments. The reporting component was adapted to each country set of indicators, although the visual language and reporting processes were harmonised.During 3 or 4 days, HWs were trained on HIS before the start of the trial. In the intervention arm they were trained on the PHISICC tools; and the control arm received a refresher training about the regular tools, during the same number of days.Additionally, given that the regular tools already contained information on past vaccination history of children still to complete their vaccination schedule, we created a mechanism to retrieve data of children’s vaccination status to transcribe into the new vaccination register book in the intervention arm (‘system transition’).Tools were endorsed by MOH, printed in local printing companies and distributed to HW at the end of the training sessions. A digital spreadsheet was created to monitor consumption and order additional tools to cover health facility needs during the life of the trial.There are five primary outcomes . VaccinaSecondary outcomes are classified under the following domains: data quality, data use, mortality, HW experience, client experience and resource consumption:Data quality, assessed in a sample of recordsCompleteness of recording and reporting in specific forms; that is, prevalence of unduly missing data items, partograph used.Accuracy of recorded figures in comparison to real events .Timeliness of reporting, as documented by time stamps in forms.Loss of data or data which does not reach the next upper administrative level.Data use, assessed in a sample of recordsIn terms of knowledge .Cases of different conditions properly treated .Public health decisions: availability of lost to follow-up lists or plans for vaccination, tuberculosis or HIV/AIDS treatment control.Occurrence of stock outs of essential drugs.Overall under-5s mortality and under-5s mortality excluding perinatal mortality,HWs’ ‘human experience’ and satisfaction .District health information officers’ ‘human experience’ .Clients’ ‘human experience’ and satisfaction, in a sample of households in HFs catchment areas.Resources consumption Intervention costs: tools, training, start-up.Time used for recording and reporting .19Cost-effectiveness per unit of additional improvement in outcomes of interest.It is worthwhile to note that outcomes that do not relate to data quality and use will be assessed using additional data collection tools , which are the same for intervention and control HFs. Hence, the effects of the intervention cannot be attributed to the changes in performance of the paper tools routinely used to record healthcare events in intervention and control HFs, which are different by design.In addition, we will consider ‘explanatory outcomes’ that will help to understand how the measured effects have taken place and why. We will look at the details of the interplay between the intervention, the system, the users and the context. Process indicators will be based on the documented activities that have taken place, from the conception of the intervention, up to its implementation, monitoring and evaluation. Process indicators may include: intervention setup and implementation, monitoring of the use of the intervention, special activities targeted at vulnerable populations, district reactions related to the intervention, handling of data coming from the new system, sustainability based on costs information and perceptions, alignment with national health policies and donor priorities. We will also explore healthcare services characteristics looking at generic indicators from HFs, such as human resources profiles and relations with the communities, population characteristics and system and context characteristics captured in early stages of the project, where data are available.The required sample sizes for each primary outcome were determined using simulation to incorporate the clustering  and to t10.1136/bmjopen-2021-051823.supp2Supplementary data10.1136/bmjopen-2021-051823.supp3Supplementary dataFor each country, we require the probability of α, a type I error  to be less 0.05 and the power to be at least 80%.For vaccination adherence, using a sample size of 35 HF per arm, we would have 80% power in each country to detect as significant a difference between a proportion of due vaccines given from 75% in the control to 85% in the intervention arms, assuming one child per household, 30 households per HF and a between-HF variation equivalent to a k of 0.25, where k is equal to the SD divided by the mean. The value of k is unknown, but was chosen in line with general observations by Hayes and Bennett.20For data quality outcomes, with 35 HF per arm we would be able to detect as significant a difference from a ratio of 0.7 (reported:recorded) vaccinations in the control arm to 0.8 with the intervention with 80% power, assuming 100 recorded vaccinations per HF and an SD of 0.25 in the ratios between HF.In terms of diagnostic scope, we would be able to detect an increase in the proportion of child visits with more than one diagnosis from 30% to 35% with 80% power with 35 HF per arm, 60 records per HF and assuming a k of 0.25.We would be able to detect as significant an increase from 50% of treatments having a corresponding appropriate diagnosis to 60% with 80% power assuming 35 HF per arm, 1 treatment per child, 25 children per HF and variation between HF corresponding to k=0.25.For the outcome related to HWs’ satisfaction, we would be able to detect as significant an increase from 50% of HWs satisfied to 75%, with 80% power assuming 35 HF, three HWs per HF and a variation between HF equivalent to k=0.25. Since this variable is measured in the end-line survey only, we used the formula in Hayes and Bennett.20In summary, in each country we require 35 HF per arm, 3 HW per HF, 100 vaccination records per HF, 60 sick child records per health facility and 30 children per health facility catchment area.Data collection took place at baseline and will take place again at the end of the study. Data are collected from HFs, from the households in the catchment areas of the included HFs and also from district offices.For data quality and data use outcomes, HF registers, tallies and reports will be scrutinised. For population-based outcomes, we carry out household surveys at baseline and at end-line. We use standard approaches for these types of surveys.27We use a mix of paper and electronic data (Open Data Kit (ODK)Quality will be assured through several mechanisms: piloting of data collection tools; thorough training of field workers; checking missing data related; double, independent data entry from papers into digital databases; early descriptive analyses to detect potential outliers; fieldworkers tracking and supervision.The analysis will be carried out for each country separately, and based on intention to treat.Baseline population and health facility characteristics  will be summarised. If large imbalances are observed at baseline, the variables can be used to adjust the effect estimate comparisons.29 30The analyses vary for the different primary outcomes due to the unit of measurement and levels of clustering, the type of variable, and whether measurements were taken at baseline and endpoint or endpoint only. We use regression models to allow us to estimate the effect of the outcome while flexibly accounting for these issues and allowing adjustment for potential confounders.Logistic regression will be used for the binary variables: vaccine adherence is measured by determining whether each vaccine due was received, and treatment appropriateness by whether each treatment was correctly prescribed. Data concordance and diagnostic scope are count variables and may be analysed with Poisson regression, depending on their distribution. The regression model for HW satisfaction will depend on how it is distributed.The outcomes have different levels of clustering . We will account for these levels of clustering by including random effects in the regression models.Four of the primary outcomes are measured at baseline and end-line. The effect of the intervention will be estimated using an interaction term between arm and survey in the regression models: that is, is the change in the outcome between baseline and follow-up in the intervention arm different to the change between baseline and follow-up in the control arm? The effect of HW satisfaction in Nigeria, measured only at end-line, will be estimated as the difference between the intervention and control arms.All estimates for the effect of the intervention will be presented with 95% CIs. The analyses will be carried out using R.31Statistical analyses will be carried out blindly, without knowledge of what HFs or population in the catchment area belong to the intervention or control arms. Only when the analysis code is considered as definitive and fixed, will results be shared with the wider investigators team and the arms for HFs and population will be disclosed.Outcome measurement bias may take place where data from the HIS, which is the focus of the intervention, is used to measure outcomes. However, we will minimise this by assessing population based outcomes at household level.Contamination  may happen via the exchanges between HWs from HFs in both arms; for example, in monthly district data quality meetings, managerial meetings; or through inputs from supervisors who influence control HFs with intervention tips encountered in HFs of the intervention arms. One mechanism to address this issue is using a district-based cluster randomisation scheme. However, we consider that (1) contamination, despite increasing the awareness of health works in control HFs, will hardly influence the decision making mechanisms that the HIS intervention focuses on and (2) randomisation at the level of district poses additional challenges that are not worth the marginal benefit of reducing a doubtful contamination.32The spill-over effect A challenge is the Hawthorne effect . Both HWs in the intervention and in the control sites will have an awareness of being observed as data collection activities will be at the same level of intensity in the two arms. Therefore, there should be no differential effect.Analyses will be based on the intention to treat. It is important to closely monitor if the intervention HFs consistently use the new HIS tools and approaches. The data collection team and the trial monitoring team will check if old forms are still being used in the intervention HFs. However, we do not expect HFs to migrate between intervention and control arms, or vice versa, due to feasibility issues. On the other hand, some household members in a given catchment area may decide to seek for healthcare in a health facility belonging to another trial arm. In these cases, households will be analysed as belonging to the original trial arm.Ethics committees in Côte d’Ivoire, Mozambique and Nigeria approved the study in their respective countries. To date, some modifications to the protocol have taken place. In Côte d’Ivoire, we decided to select study areas close to the research institution based on logistics and practical reasons, instead of selecting an area in the north of the country, where poorer health indicators have been described. In Mozambique, the low density of HF per population implied extremely vast distances between HF and this, coupled with the rainy season, made the trial unfeasible in the originally selected Nampula province. After consultations, we decided to move the trial to the province of Inhambane and cancel the household survey. The allocation of HF to the intervention and control arms was completed using random number generation.We plan to disseminate the findings of the trials as one of the few examples of studies assessing the effects of HISs interventions using experimental study designs.40We acknowledge the challenges of carrying out research on healthcare provided to remote, rural communities (in this case in sub-Saharan Africa). However, it is only in these remote areas where research about their specific problems and needs can take place. Challenges included long distances, poor conditions of roads, unreliable communications and limited food and accommodation services, all of them to be proactively handled to keep the quality of work and the morale of researchers and collaborators. We expect that the dissemination of findings in meetings, conferences and publications will contribute to a better understanding of what it takes to make research in challenging contexts.The engagement and ownership of partners within this research has also been instrumental in order to plan and implement the CRCT. The intervention actually targets a governmental subsystem (the HIS) for which we required more than permission but also endorsement and active support. We achieved this level of collaboration by ensuring the participation of key stakeholders in key phases of the whole project, from inception till the implementation of the last phases, through frequent communications and workshops. The PHISICC programme includes targeted activities to keep decision-makers engaged and we are planning to share the findings through workshops as well as online and face-to-face events to disseminate the lessons learnt from the trial and the whole PHISICC research programme.We also expect that the dissemination of our findings among partners and competitors will contribute to the current debates on the digitalisation of HISs. WHO recommendations on the matter are weak since the underlying evidence to support these recommendations is inconsistent.Finally, we expect that the results of the trials, both quantitative and qualitative, will be able to inform policies on how to make HIS responsive to providers’ decision-making needs, particularly in health services where the most vulnerable live.


Medical research is indispensable to develop new treatments or diagnostic methods. An ethics review board reviews the validity of such medical research. However, with the recent advances in medicine, a meaningful review of medical research often requires advanced knowledge. There is thus a growing necessity for a professional who can support ethical review. Therefore, a new system called the Certified Research Ethics Committee Professional (CReP), an Ethical Review Expert, has been established. Knowledge in the fields of medical genetics and molecular biology is being updated day after day, with flowing suggestions for new treatment methods. It is now possible to provide targeted therapy for cancer or rare and intractable diseases by screening genetic information, the characteristics of cancerous cells and compounds. Many patients also benefit from the advancement of optics and catheter technology, such as an endoscopic operation, which has enabled them to receive minimally invasive treatment instead of open-heart surgery or laparotomy. However, it is undeniable that various unethical studies have been found in the history of clinical research (3), “Ethical Guidelines for Human Genome and Genetic Analysis Research” (4), and “Clinical Trials Act” (5).Numerous medical research and clinical trials have born such benefits to patients. After conducting preclinical research involving basic experiments on cells or animals, those with confirmed efficacy and safety may be used on humans as a new treatment method. A researcher who wants to propose a new kind of treatment should follow certain procedures, one of which is going through an ethics and clinical trial reviews. The requirement for the reviews are based on the “Ethical Guideline for Medical Research Involving Human Subjects” (6). The Editors-in-Chief of JMA Journal have permitted the submission of this manuscript.In such a complicated situation, we established a system called the Certified Research Ethics Committee Professional (CReP), a group of experts in ethical review. In this article, we described the role of CReP in ethical review. This article is an English version of a paper already published in Japanese (7). This requirement is established to ensure that the research subjects do not suffer from disadvantages stemming from information asymmetry. The abovementioned guidelines provide that the “research ethics committee” or the “institutional review board” (IRB) is responsible for the review. When conducting a research or clinical trials, the principal investigator should prepare a research protocol and other required documents, including the instructions for the research subjects and the procedure manual necessary to conduct the research, in accordance with relevant laws and guidelines.As stated in the Declaration of Helsinki, when providing new medical treatment to patients, a third party must review the scientific reasonableness and theoretical validity of the treatment, even when there is an agreement between the medical professional who provides the treatment and the patient who receives the treatment that provides for the patient’s consentOn the other hand, to protect the research subjects or promote scientific reasonableness of the research, the members of the ethics review committee examine the validity of conducting a research based on the submitted documents. The committee reviews the scientific reasonableness and the ethical validity while also ensuring that the research is in compliance with relevant laws and guidelines. However, it is not necessarily true that not only the researchers but also the committee members are aware of the details of the standard procedures, such as the review application. Even if the researchers have sufficient knowledge about the treatment and research in their fields of expertise, they often require support to comply with relevant laws and guidelines related to such research or treatment. Moreover, the research ethics committee board members in many institutions are also engaged in their own tasks or research as experts in their fields, and while they have profound knowledge and experience in their own fields of research, conducting ethics review is merely one of the various duties of many of those working in universities, hospitals, and companies.Research institutions should make efforts to ensure the reliability and safety of the research so that it does not create disadvantages for the subjects. However, as the laws and guidelines do not always provide detailed instructions, handling each case, in reality, is not an easy task for the institutions. Moreover, as mandated by the legal regulations on clinical research, a Certified Review Board (CRB) needs four dedicated full-time supporting staff, increasing the importance of the support department. For these reasons, there is an increasing demand for fostering experts who are knowledgeable about clinical research and ethics review.In Japan, office clerks often take on the roles of the research ethics committee board members in many institutions and often review ethics while also working on other assignments. Additionally, the committee’s uniform management and operation become challenging, as employee transfers occur once every few years.(10). CIPs are a group of experts who runs the IRB, that is, the ethics review committee within a research institution, and they aim to improve the quality of the Human Research Protection Program. More specifically, CIPs support the ethics review committee or IRB members with their knowledge on laws, regulations, principles, and guidelines in the United States, as well as general knowledge about ethics. Most IRBs have a CIP, and there are more than 3,000 CIPs in the United States. The collaboration between CReP in Japan and CIP in the United States is a critical initiative. In recent years, more international joint research has been conducted than ever before. Of course, multilateral clinical trials under GCP for regulatory approval are also necessary. The existence of CReP and CIP, who are aware of the circumstances of each country, will become more crucial for the smooth execution of international joint research.Since 2016, as a part of its research integrity project, the Japan Agency for Medical Research and Development (AMED) has supported fostering and training of ethics review experts called CRePs. CRePs are professionals who promote the protection of research subjects and fair research practice. In the United States, there have long been certified IRB professionals called CIPsIn many Japanese institutions, nonmedical clerks check the validity of the content and the insufficient description in the research protocol and the related documents before the review by the REC board member according to the rules of each institution. Since there is no detailed description of ethical review and research implementation in laws and guidelines, each institution often operates based on their own interpretations. Filling the gaps between the texts of laws and guidelines and making efforts to have a common understanding are necessary. Those who are actually involved in ethical review need to have a common view. Therefore, we created a system to certify those who have the necessary knowledge in ethical review as professionals. By summarizing the common items necessary for professionals as core competencies, we aimed to standardize the clerical work of ethical review. Improving the treatment quality for clinical research, especially for certain types of clinical research, is crucial. Expert knowledge is indispensable for increasing the safety and trustworthiness of such treatments. Until now, there has been a limited number of communities for such professionals. By providing information-sharing opportunities for those with CReP qualifications, the quality of support for clinical research will improve and become standardized.By certifying a nonmedical clerk as a professional, a community to which the new professional belongs to was created, and a new network was built. Through this network, professionals can collaborate to exchange and share information on the outline and procedures of actual ethical reviews that they know. Consequently, the knowledge and procedures of the research ethics committee will be standardized, thus also standardizing the research ethics committee’s review. However, it is hard to say that the standardization of ethical examination has been realized yet. CRePs are concentrated in the core institutions of each region, such as core clinical research hospitals, university hospitals, and national centers, and act as leaders in the region. According to the 2019 Static/Dynamic Survey of Medical Institutions and Hospital Report of the Ministry of Health, Labour and Welfare of Japan, there are 401 hospitals with more than 500 beds in Japan . Since it can be inferred that many clinical studies are conducted in large hospitals to which doctors and researchers who present their research at each academic meeting belong to, it is desirable that CRePs be also assigned to each institution.This paper has reviewed the ethical review necessary for conducting clinical research and the new system for CRePs, the ethics review experts. The CReP Accreditation Committee currently accredits 180 people who supports the ethical review of each institution. For the proper implementation of medical research practice, each facility should protect the research subjects and improve research integrity by creating positions for such qualified professionals. For certain types of clinical research that carry high risks, support from qualified experts would become all the more essential.NoneThis work was supported by Research and Development of Model Material/Program for Research Integrity Education from Department of Research Integrity and Legal Affairs, Japan Agency for Medical Research and Development (AMED). AMED No. 1E407 and 1E410.https://www.iyaku.info/magazine/?p=1&sf=1&ca3=67&ca4=10.We would also like to express our gratitude to the project participants, supporters, and research staff at TMDU and associated hospitals. We also thank the Department of Research Integrity and Legal Affairs, Japan Agency for Medical Research and Development (AMED), for their efforts and financial support. This manuscript has already been published in Japanese language . The journal has given permission to publish the English version. We would like to thank the journal editor-in-chief. The original version is available at YE planned and conducted all studies. MY was involved in research planning advice. SY and MY were involved in assisting in writing this article.It is not applicable because it does not correspond to “Medical and Health Research Involving Human Subjects.”


The initial bedside care of premature babies with an intact cord has been shown to reduce mortality; there is evidence that resuscitation of term babies with an intact cord may also improve outcomes. This process has been facilitated by the development of bedside resuscitation surfaces. These new devices are unaffordable, however, in most of sub-Saharan Africa, where 42% of the world’s 2.4 million annual newborn deaths occur. This paper describes the rationale and design of BabySaver, an innovative low-cost mobile resuscitation unit, which was developed iteratively over five years in a collaboration between the Sanyu Africa Research Institute (SAfRI) in Uganda and the University of Liverpool in the UK. The final BabySaver design comprises two compartments; a tray to provide a firm resuscitation surface, and a base to store resuscitation equipment. The design was formed while considering contextual factors, using the views of individual women from the community served by the local hospitals, medical staff, and skilled birth attendants in both Uganda and the UK. Sub-Saharan Africa is reported to have 1 million newborn deaths annually, accounting for 42% of the world’s total newborn deaths . The neeProviding neonatal resuscitation at the mother’s bedside with an intact umbilical cord is potentially a high benefit practice with major global benefits; it enables physiological benefits for the baby, keeps the midwife with the mother in the vital few minutes after birth, and allows the mother to stay with her newborn, preventing any suspicion of malpractice ,10,11.In the United Kingdom, Hutchon and colleagues explored ways of achieving resuscitation with an intact cord. This resulted in the development of a small mobile bedside resuscitation trolley, later commercialised as the LifeStart trolley  ,13,14,15The BabySaver is a simple mobile vacuum-moulded, oval plastic assembly resuscitation unit developed by a team of designers at the Royal Liverpool University Hospital, researchers at SAfRI, and clinicians at Liverpool Women’s Hospital (LWH) and Mbale Regional Referral Hospital. It is the first medical device designed to promote neonatal resuscitation with an intact cord in low-resource settings. The final device prototype has since undergone phase I and II clinical testing studies in Uganda, reported elsewhere.This paper discusses the rationale and design of a medical device, including the nature and effect of contextual factors on its final design.provides a stable, flat but firm surface for the baby  and enabfacilitates resuscitation of depressed neonates at birth whilst keeping the umbilical cord intact ,10,11allows resuscitation of the babies by the mother’s side ,10,11prevents separation of the skilled birth attendant and mother, so that the care of the mother is not interruptedallows the mother to remain with the baby during its initial support, instead of removing it from her sight during these vital momentsfacilitates newborn resuscitation when only one skilled birth attendant is present.The primary objective for developing the BabySaver device was to reduce intrapartum-related deaths in low-resource delivery environments. We aimed to develop a device that:The BabySaver newborn resuscitation device was designed from simple plastics to maximise sustainability in Uganda’s public health facilities and replicability in low-resource settings across the world.The design and development of the BabySaver device were coordinated by a small team of academics working within the Sanyu Research Unit, the Royal Liverpool University Hospital Department of Physics and Engineering, in collaboration with SAfRI. SAfRI is a not-for-profit non-government organisation, based at Mbale Regional Referral Hospital in Eastern Uganda; it and the Sanyu Research Unit in Liverpool were set up to research low-cost innovations and improve the care of mothers and their newborns. The team includes those who were responsible for the development of a high-end bedside resuscitation trolley for the US and European market, sold as the “Lifestart” trolley .The BabySaver underwent systematic design using the framework of engineering design . The desThe development of the device was informed by the best practice reviews that recommend delayed cord clamping at birth ,24,25, tThough local guidelines in Uganda still recommend immediate cord clamping , many skIn February 2015, a design team was formed, composed of Chris Dewhurst (consultant neonatologist), Julius Wandabwa (professor of obstetrics), Peter Watt (design engineer), James Ditai (research fellow), Julian Abeso (paediatrician), Bill Yoxall (consultant neonatologist), Sam Ononge (consultant obstetrician), Lelia Duley  and Andrew Weeks . A face-to-face meeting was held in the department of women’s and children’s health with some members of the design team, whilst others contributed virtually or through emailed comments. The meetings discussed resuscitation and cord clamping at birth in the delivery rooms of Uganda, design idea, initial design features, requirements, and constraints.The design team proposed design specifications at this stage based on their experience, observation of the delivery environment, and informal consultation with staff of Mbale regional referral hospital in Uganda and LWH in the United Kingdom. Peter Watt and Aisling Barry, an MSc Engineering student, worked on the design process with further input from Nick Bettles , Tony Fisher , Kathy Burgoine , and Dot Lambert . The design team enlisted the assistance of stakeholders who might interact with the device at different design phases in Uganda and the United Kingdom. These included end-users , developers (design and production engineers), regulatory authorities , policymakers , and funders .The problems with the current method of resuscitation at birth were established through the personal experiences of the design team, ongoing consultation with relevant stakeholders, and observation of the delivery room facilities of Mulago National Referral Hospital and Mbale Regional Referral Hospital. The first concepts had a slatted surface to place the baby on, a solar-powered light, and a mechanical timer. The design was to be stored in the sun when not in use, and the resulting heat was stored in a solar-heated thermal capacitor  gel pack was exposed to the sunshine in Uganda to assess the time and ambient temperature required to melt it. Three hours of sun exposure at a maximum air temperature of 26.2 °C caused partial melting of the gel, but not to state where it could be reactivated. A second used gel pack was boiled in an autoclave at 103 °C for less than 5 min, wrapped in a linen cloth to prevent melting of the plastic shell of the gel pack against the metal. This led to the complete melting of the crystals.Finally, questions about the use of the gel pack were raised in subsequent feedback meetings. Though both boiling and autoclaving could regenerate the gel pack, some users wanted to know if just pouring boiling water from a kettle over the pack would work instead, due to the common availability of kettles in the delivery suites. This indeed would melt the pack by direct boiling of the pack in the kettle with water.We explored two methods to produce the design; injection moulding (which was expensive and rejected) and thermoforming. Thermoforming is cheap, achieves less complicated shapes, and was subsequently chosen for mass production of the finished design .Materials that were suitable for thermoforming were compared with the requirements for the design. The design needed to be made of a material that is strong, heat resistant, and that does not degrade when treated with bleach. The material of the device also needed to be biologically compatible. Consultation with a local plastics company ended in the recommendation to use Polyethylene terephthalate glycol-modified (PETG). PETG is a copolymerization of PET, which is a semicrystalline plastic. The addition of glycol prevents crystallization and lowers the melting temperature of the plastic.PW made the first rough model out of hard paper and glue for the chosen design concept in 2015  on elective placement designed the initial pictorial instructions, which were subsequently revised.The stakeholders agreed with the purpose, content, scope, dimensions, and function of the design.(i)The rough model was not long enough for the baby and changes to the dimensions were proposed.(ii)The design required neck support as an add-in, to promote a natural neutral position during resuscitation.(iii)There were concerns that the timer could not sustain the bleaching effects of Sodium hypochlorite (Jik) solution following prolonged and frequent cleaning.(iv)They agreed with the need to include the storage tray for the pieces of equipment for resuscitation.(v)They proposed to add a valley on the top surface of the design as a slot for the gel pack. This would allow easier inserting and cleaning of the gel pack.(vi)Incorporating a light source in the tray would be incompatible with several specifications, especially “low cost and easy to sterilise using local methods”.(vii)Light and timer were henceforth decided to be removed from the list and made as separate components to store in the design when needed.We sought feedback about the paper-based rough model from midwives in Mbale via group discussions in April 2015. The feedback included:We presented the modified design, alongside the rough design model, to the Commonwealth and FIGO Wellbeing Fellows  in a meeting at the University Liverpool. They agreed with the proposed changes, one participant emphasizing that “even if the light and timer were separate or away for repair, the current design device could still be used”. However, there was an argument against the neck support due to the different sizes of the baby and hence the need for different sizes of neck supports.Initially, the design was planned to be used on either the mother’s abdomen or delivery bed. Stakeholders were concerned about the position of the maternal abdomen for the design at the time of resuscitation due to its weight and that of equipment. Further, the position of the abdomen and the side of the mother would not allow efficient blood transfer to the newborn by gravity. However, positioning the design in between the mother’s legs on the delivery bed was considered appropriate to allow placental circulation with the umbilical cord intact. This generated an add-on design specification that ensures the baby is as close as possible to the mother. The design further had to assume a shape that fits in between the mother’s legs in the lithotomy position. This formed the design for the preliminary layout.We took random measurements of the differently sized abdomen of gravid women and took the largest length and width for the design. Four pieces of timber were assembled to construct the design with narrow and broad ends. The team created a cardboard model showing the curve and dimensions of the preliminary layout, which was presented to midwives and obstetricians via face-to-face and international meetings. In these meetings, we sought feedback about the design and the process of use in a simulated labour environment from a lone midwife to a hospital team.The feedback about the model in practical use helped identify the main areas of the design for remodelling.We constructed a prototype following approval of the preliminary layout and materials. This was not a fully functional prototype, but a scale model made to demonstrate the functions and dimensions of the finished design.The model was made from cardboard. The prototype was presented to 37 end users of Mulago national referral hospital and Mbale regional referral hospital, and two health centres in December 2015. These viewed the design as a device that could provide an additional location to resuscitate the baby without taking the midwife away from the delivery suite. However, the negative responses included the design’s resemblance to a coffin, the unlikelihood of it fitting onto current beds, and dissatisfaction with current resuscitation practices. They recommended making the design’s shape friendlier (less coffin-like), broadening one end and narrowing the curve at the other end. All agreed to the need for neck support but differed in opinion on its height. They proposed excluding the timer and providing multiple trays or coverings for sterile purposes. They suggested a clear tray to allow visibility of the pieces of equipment for resuscitation inside, child-friendly stickers, and more curves to the design. Several people requested that the tray be more ergonomically designed for resuscitation with a bag and mask, mostly centred on the rounding of the sharp edge where the curved section of the tray meets the instruction section. They also proposed making the base of the design flatter to look more like the weighing scales which are widely used. This was seen to have the added benefit of providing more room for the babies’ shoulders.We sought feedback on the desired contents in the design. While most users were satisfied with the proposed auxiliary components, some wanted additional equipment. This ranged from thermometers to caps for babies, drugs, and cannulas. Though most people interviewed desired a timer to be included in the tray, they were happy with the current use of a wall clock for time.However, it was subsequently argued that additional components would take focus away from the main key steps of resuscitation. Further, including consumables in the design would discourage use when they were gone or out of stock. We hence agreed to keep the main contents for resuscitation in the design. We hence classified contents into the design as essential and optional .The definitive layout was proposed by Peter Watt and developed as part of the MSc engineering project for Aisling Barry. To escape the coffin shape, an egg shape was proposed, designed with more complex intersecting curves using Pro/Engineer 3D solid modelling software , who provided written feedback. The final layout was achieved by December 2017.We produced the technical drawings of the version of the design to be made into the first functional prototype.The revised design was drawn up using Autodesk Inventor, a computer-aided design software package. Both the tray  . These rThe base is a non slip white plastic compartment. It has space for all the essential pieces of equipment for resuscitation at birth recommended for HBB  and the The base is specifically in white to easily detect any stain and is suitable for use on the resuscitation table, a delivery bed, an operation table, or any other available surface.The weight of the base alone is 900 g. It is 103 mm deep with the widest broad end measuring 455 mm and its length is 685 mm. It is 3 mm thick, Providing neonatal resuscitation at the bedside with an intact umbilical cord is potentially a high benefit practice with major global benefits ,10,11. TThe BabySaver has the potential to be used in any other low-resource settings outside Uganda, and outside hospital settings by trained birth attendants. The midwives could have the BabySaver readily available with them for use in emergency and or unplanned births places such as home, the roadside, en route to the hospital, etc.Every effort has been made to make the design user-friendly. The shape of the design follows nature; for us in Uganda, the egg gives life, the egg-shape of a BabySaver can remind us that its purpose is to enable the life of a baby. The design is more eco and user-friendly, simpler and easier to use than the designs, such as LifeStart trolley ,13,14,15The initial design was different from the final design. Although medical devices need to satisfy their intended purpose, considering the context for the device to operate efficiently is primary when designing medical devices for low resource settings . We hencThe timer was removed from the final design based on the recommendation of the end-users. Various methods for time function are already in place during resuscitation. Midwives usually use the second hand on the wall clock in the labour suite, nurses’ watches, and mobile smartphones with a time function.The instructions displayed on the design were in line with best practice resuscitation recommendations and the local guidelines for helping babies breathe (HBB) ,31,38. TLess than two hours of training are required before the BabySaver can be used in practice, which can easily be integrated into the HBB training curriculum ,39.The position of the device at the time of resuscitation is in between the mother’s legs. This compares favourably with the mother’s side position of the LifeStart trolley and Concord trolley . This poThe device is currently suitable for use on a resuscitation table, a delivery bed, an operation table, or any other available surface. However, we do not recommend its use on the mother’s chest/abdomen due to its inability to provide blood transfer by gravity. While the device can be introduced and used in the operating theatre, this is only recommended after ensuring sterility will be maintained. We propose sewing linen in a pocket fashion, following the shape of the BabySaver tray, sterilizing it, and having the tray dressed in sterile linen.It took about two years to achieve the definitive design. This included a delay of about 8 months while seeking funds. We would expect designers of any related device to need less time, approximately 6 months if funding is secured.In our design process, the outcome was a design that responded to the local needs of the users and the delivery environment . We iterThe current prototype has undergone phase I/II usability clinical testing. There are plans to refine the device based on feedback from the usability testing before checking for its clinical effectiveness in community health facilities in Uganda. The clinical effectiveness data will inform the scale of the device in Uganda, across other low-resource countries, Islamic Development Bank member countries, and any other interested high-resources settings.This is the first mobile resuscitation device developed to facilitate the resuscitation of newborns in between the mother’s legs with placental transfusion at birth. The name BabySaver implies a commitment to saving neonates at birth. Further studies will assess its feasibility, efficacy, safety, and acceptability in the delivery rooms in Uganda. An effectiveness trial will be conducted after the results of the feasibility study.