Community Maternal Danger Score (CMDS) App

The problem to be addressed is the extremely high maternal mortality in Nigeria. According to the United Nations Economic Commission for Africa about 95 per cent of deaths during childbirth are preventable. The World Health Organization  (WHO) associates the high prevalence of maternal death in Nigeria to inequalities in access to health services. Women in resource-poor settings are the least likely to receive adequate, timely and affordable health services by skilled personnel.  Maternal mortality is a huge problem in many  low and middle-income countries but of note is that  one in seven global maternal deaths occur in Nigeria.  Each year in Nigeria more than 50,000 women die a maternal death.

 The standardized measurement used to evaluate and compare maternal death is the maternal mortality ratio (MMR). The MMR in Canada is 8 deaths per 100,000 live births (1);  whereas the MMR in Nigeria is more than 100 times higher  with 917 deaths' per 100,000 (2) nationally. Our Canadian Network for International Surgery(CNIS) studies in Benue State, Nigeria calculated a ratio marginally higher than the national ratio with an  MMR of 1189 deaths per 100,000 live births.  These numbers indicate that in Nigeria, 1 woman dies  for every 100 babies that are born. It is so common that everyone knows someone who has died a maternal cause.

 A very important cause of high maternal mortality is the failure to access skilled care at the time of delivery. Half of all pregnant women in Benue state  do not seek care. Therefore during pregnancy there  is no determination of their risk or action taken to mitigate risk. CNIS analysis  has shown that 22% of women in Benue state who are among the 50% who do seek care, have high-risk pregnancies.  Even among these women who do seek care the identification of high-risk pregnancy is often delayed and sometimes missed altogether.  None of the high-risk women in the other 50%  who do not seek care at all are identified until they develop life-threating complications and some only identified by their demise. The women who deliver at home unattended  and those at high risk  who come to health centers and are not identified suffer unnecessary morbidity or death.  The high mortality of newborns in Nigeria which is linked with maternal care and obstetrical complications (4) would be reduced with  early referral of their high-risk mothers.  

CNIS has been commissioned by the WHO to study  obstetrical data collection in Benue State Nigeria. This study evaluated gaps, lack of  harmonized data collection, risk assessment and referral criteria. There is no standardized data collection, minimal data set,  determination of maternal risk or standardized methodology for patient referral. This puts the front-line health professionals at extreme disadvantage in making informed decisions about their patients. Assessment of risk is informal and  left to the acumen of the  midwife or community health worker.  The deficiencies in data collection, risk assessment and standardized referral criteria contributes to the high MMR in Benue State.  

In Benue state, the high-risk level of the 50% of  pregnant women who seek care is about 1 in 5.  But outside of the CMDS pilot there has been no methodology to identify them. The  proportion of women at high risk who do not seek care is at least as high as those who do seek care but  likely is higher. Thus in Benue state where maternal mortality is high, many  of the  women at high risk who attend primary health care centers  are not identified and at least 20% of the pregnant women who do not seek care are unaware of their risk and often do not access the health-care system and if they do access the system they arrive too late.     

1.  Hirshberg  A, Srinivas  SK.  Epidemiology of maternal morbidity and mortality.  Seminars in Perinatology. 2017;41(6):332-337.

2. WHO, UNICEF, UNFPA, World Bank Group & United Nations Population Division. Trends in maternal mortality: 2000 to 2017. WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division. 2017. https://data.unicef.org/resources/trends-maternal-mortality-2000- 2017/

3.  Bola R, Ujoh F, Ukah UV, Lett R. Assessment and validation of the Community Maternal Danger Score algorithm. Global health research and policy. 2022;7:6-6.

4. Ugochukwu Nwokoro et al. Determinants of perinatal mortailty in public secondary health facilties Abuja, Municipal Area Counil Federal Capitol Territoty Abuja Nigeria Pan African Medical Journal 2020; 37(114) 10,11604/pamj.2020.37.114.17108

 Our solution to the high maternal mortality is data-driven, and that data is derived from the patient in need, that is the pregnant woman. The CNIS has developed an evidence-based algorithm called the Community Maternal Danger Score (CMDS). The variables chosen for inclusion in  the algorithm had scientific evidence of association with maternal mortality or the  need for clinical care.   The CMDS algorithm based on evidence of risk defined in the literature was validated  as an indicator of maternal risk in Makurdi Benue state.(3)  In the Makurdi study the CMDS algorithm was 85% accurate in the prediction of maternal mortality.

The prenatal CMDS score is assessed through the domains of age, parity, patient body mass index (calculated by the app using height & weight), and previous obstetrical history. This provides a prenatal score out of 5 and informs the  pregnant woman and her midwife about her potential risks on her initial presentation.  In the third trimester perinatal score  is based on the fetal  size (calculated by the app with fundal height and gestational age), signs and symptoms of pre-eclampsia, and coexisting conditions that the mother  developed. This provides a perinatal score out of 5, with a summated pregnancy risk score out of 10.  CNIS has shown that full algorithm is more than 85% accurate in predicting maternal mortality.

In our study of obstetrical data collection for the WHO mentioned above,  a  search of the literature  failed to identify a minimal obstetrical data set to use as a standard for evaluation. A minimal data set for obstetrics can be defined as a set of standardized measures used to index the minimum amount of data that is crucial for obtaining a global image of pregnant women across all healthcare sectors and disciplines and in every stage of the pregnancy.(4). As there is no minimal data set defined in the literature,  we used the 7 indices of the CMDS as a minimal data set, as the variables are evidence-based and their validity confirmed across different stages of pregnancy.   We currently are  proposing to the WHO  the introduction of the variables in the  CMDS Algorithm as the minimal data set for obstetrical patients.

To make the utilization of the  CDMS feasible in the primary health care center and the community.  CNIS placed the  algorithm on a mobile phone app and conducted  a field proof of concept evaluation  of this app in Gboko, LGA  Benue State. The CMDS algorithm estimated the risk of individual women and the  prevalence of high-risk pregnancies in Gboko.(2) in patients who came to the primary health care center. It provided an assessment of patient risk to the midwives in real-time.  The score was automatically texted from the midwives" smart phone to the patients phone (simple or smart) together with a recommendations for continuing  care and obstetrical delivery. This score and advice in writing strengthened  the verbal advice given to the patient and was available to the patients" family.   

 The estimate that 22% of women in Gboko at high risk was made by the CMDS app which was higher and more precise than the 14% estimate we calculated from existing prenatal data. In this field test,  the CMDS app could identify the patients at high risk in real-time.  It is important to have the risk information in real-time so that  midwives can  make referrals,  and they can inform their  patients about their risk and the need for skilled birth attendance. It is important that the patients have the score and advice texted to them in their own language so that they have a written record which they can share with their family.  

For logistic reasons due to  the COVID-19 pandemic the CMDS app was not field tested by Community Health Extension Workers on women in the village.  It was not used on women who had not  sought pre-natal care or skilled birth attendants.  This is the category of women who would most benefit from the CMDS and most likely have high risk pregnancy.  

To address maternal mortality  we recommend that the 7 data domains of the CMDS algorithm be defined as a minimal data set and that the CMDS app be implement at the health center level by the midwives and  at the village level by the Community Health Extension workers.  Our solution addressed the failure of identification of women at risk and the failure for their mobilization of women to seek skilled birth attendance. This is the issue our solution to maternal mortality addresses. If women are identified, they will be systematically mobilized and so will  receive skilled  care which is a matter that is crucial to the survival of the mother and child.

This solution targets pregnant women in communities with high maternal mortality. Those  who come to the Primary Health Care Centers for prenatal care and pregnant village women who do not attend PHCs.  The app will  also supports the midwives at the health centers  and community health workers in the villages in the identification of women at high-risk which will facilitate successful conduct of their responsibilities in patient care   

The study  will be conducted in Benue State, Nigeria in the communities among pregnant women who do not attend prenatal clinics.  These women are not underserved by the health care system. they are not served at all.  The patients and their families  do not understand the level of their personal risk nor that this risk can be mitigated by skilled attendance at the time of delivery.   Women in rural and impoverished contexts are unempowered.  They do not make decisions crucial to their survival, in particular choosing to seek skilled birth attendance and do not have the information needed to advocate for care.  The text messages that are sent to any mobile phone including the contract numbers provided by the patient informs them of the level of their risk and instructs them about the appropriate management of their pregnancy and care at the time of delivery. 

Midwives need to make referral decisions. This requires that they identify patients at risk and that when they do refer a patient they are able to justify this decisions to the pregnant woman and her family as well as the receiving hospital. 

CNIS has successfully  conducted 3 development projects in Benue State Nigeria since 2018  They have all depended on Nigerian partnership which was critical for success.   The initial project (2018-2020) was  conducted in Otukpo and Gboko Local  Government Areas (LGAs)  and Makurdi  Municipality  with the  support from Grand Challenges Canada. Baseline obstetrical studies were performed at the primary health care centers in Gboko and Otukpo  and the CMDS algorithm was validated at the Makurdi Federal Medical Center.  The CMDS app was  field-tested with midwives in  Gboko LGA and the the app was demonstrated to students at the Makurdi and Mkar Midwifery Training Colleges as a teaching aid. The work from this initial project has been disseminated through peer reviewed scientific publications.  (1,2,3)

Subsequently (2019-2021)  CNIS conducted a randomized controlled trial of digital vs small group teaching of its Fundamental Interventions Referral and Safe Transfer (FIRST) for Midwives course at the midwifery colleges at Makurdi and Mkar. This project was conducted with the support for the Fund for Innovation and Transformation. Over 130 second-year students benefited from the teaching that was conducted as part of the trial. Two scientific papers are currently being prepared for submission. (4, 5) 

 Most recently (late 2021-2022)  CNIS conducted a consultancy critiquing obstetrical data collection and risk assessment  in 4 primary health care centers and one referral hospital in Benue State  for the Science Division of the Product Design and Impact, Quality Assurance, Norms and Standards Department, World Health Organization Geneva.  A report for the WHO is being finalized and a scientific paper will be prepared. (6)

These activities have been done under the auspices  of the Benue State Ministry of Health.  CNIS has  has developed  relationships with health professionals from the state ministry of health to the local government areas. We know and have collaborated with the nursing, midwifery and primary health care  leadership at the ministry and are  well known to the Honorable Commissioner for Health.  We have worked with medical doctors at Makurdi Federal Medical Center, midwives at the local government areas and the principals and instructors at the midwifery training colleges.  Of particular importance linking us to the community and the women of Benue state are the midwifery school principals: Victoria Gusa, Rosemary Apeaii.

During these projects zoom meetings were held weekly with full engagement of ministry of health personnel.  In addition to the partners from the Benue Health and Educational institutions  CNIS has a project coordinator in Abjua  the National Capitol who is from Benue State and two project assistants one in Makurdi the other in Gboko.  This team was particularly important from April 2020 to August 2021 when Canadians were not permitted to travel to Nigeria because of the pandemic.  

The CNIS track record shows we  have the skill and expertise to conduct development projects in Benue state both remotely and in person.  Dr Fanan Ujoh a PhD geographer the project coordinator is involved in scientific design, authorship of publications, has contacts throughout the state and has the on-ground expertise as a problem solver.  From internet routers to police protection, he knows what to do.  Dr Ronald Lett worked in Benue State as a junior doctor, is able to stay safe and is very comfortable in Nigeria.  CNIS expertise includes Dr Lett as the director of curriculum development and Dr Jan Christilaw as the Director of Women's Health. Both Dr Lett are faculty at the University of British Columbia. CNIS has digital development expertise from Issacs Ohene Gyan internally and externally from Rigel Software Inc. of Vancouver.   Senior academic support for these projects was provided by Dr Stephen Hodges Public Health Professor from the University of Alberta. Two U of A, graduate students are working with CNIS under Dr Hodges supervision Anja Džunić and Rafat Noor. Another graduate student from the University of British Columbia Rajan Bola works under the supervision of Dr Lett.  For this project CNIS has the scientific support from two Nigerians post-doctoral fellows in Canada,  Dr Ugochinyere Vivian Ukah an expert on maternal care at McGill University who co-authored our paper on the CMDS Makurdi Validation and Dr Segun Ogundele our newest participant an expert on implementation science at the University of Toronto. CNIS has a board of directors that includes academic health professionals from across Canada.  The board is not only a means of governance but provide formal links to universities from across Canada which are an important source of volunteers.

CNIS with expertise from Canada and Nigeria and its strong partners and staff in Benue State, Nigeria is well-positioned to successfully  complete this project.

NOTE: I tried to have my team members listed on the dashboard of this application but several had technical issues posting their photos and entering the platform so rather than present those who accidently succeeded, I  deleted everyone.

1. Bola, R., Ujoh, F., & Lett, R. (2021). Stillbirths among pregnant women in Otukpo Local Government Area, Benue State, Nigeria. The Nigerian Health Journal, 21(3), 123-134.

2.Bola R, Ujoh F, Ukah UV, Lett R. Assessment and validation of the Community Maternal Danger Score algorithm. Global health research and policy. 2022;7:6-6.

3.Bola, R., Ujoh, F., & Lett, R. (2021). Identification and Mitigation of High-Risk Pregnancy with the Community Maternal Danger Score Mobile Application in Gboko, Nigeria. Submitted to: PLOS One.

4. Anja Džunić, Fanan Ujoh, Victoria Gusa, Rosemary Apeaii, Rafat Noor, Rajan Bola, Isaac Ohene Guyan, Jan Christilaw, Stephen Hodgins, Ronald Lett Abstract submitted to the Bethune Round Table 2022. Manuscript in preparation.

5.Rafat Noor, Anja Džunić, Fanan Ujoh, Victoria Gusa, Rosemary, Rajan Bola, Isaac Ohene Guyan, Jan Christilaw, Ronald Lett, Stephen Hodgins, Learnings on implementation of a Randomized Clinical Training Trial in Nigeria, Abstract submitted to the Bethune Round Table 2022. Manuscript in preparation Abstract submitted to the Bethune Round Table 2022.  Manuscript in Preparation.

6. Bola, R., Kihumuro, RB., Ujoh, F Ngonzi, J., & Lett,  Obstetrical Data Collection in Uganda and Nigeria: An Evaluation of Representative Health Institutions in Mbarara District and Benue State Abstract submitted to the Bethune Round Table 2022.  Manuscript in Preparation

CNIS is applying to SOLVE as a step to obtain transition to scale funding from Grand Challenges Canada (GCC).  In the preceding section of this proposal we have categorized this project as pilot however a substantive pilot has been conducted and the purpose is to complete that pilot. Continuation of this project will also lead to growth as we focus on the implementation of the CMDS app with women outside of the health care system.

The CNIS and its investor had requested that GCC invite us to apply for a transition to scale grant where  they  match up to 1 million Canadian dollars in funds to address the issues needed to take our project to scale.  The outcome of that presentation was a request from GCC for more proof that the CMDS would reduce maternal mortality. We agreed that we would provide more evidence and asked GCC to provide further funds.  They told us to seek funds elsewhere.  

We continued in discussion with GCC to determine what evidence they would accept, as demonstrating reduced maternal mortality would be expensive.  Despite the very high maternal mortality we calculated that the sample size for such a study was 50,000 pregnant women, the cost of such a study would be similar to the 2 million we hope to obtain with the transition to scale project.  

In further discussion with GCC, they agreed that demonstration of a behavior change would be acceptable.  That is an increased number of women who would seek skilled birth attendance would be evidence that the app mobilizes women to seek skilled health care during pregnancy and for delivery.  To show this result we would target women in the village who were not getting prenatal care.  Some women who get prenatal care do not seek skilled birth attendance, but a high proportion also seek skilled birth attendance. Thus, our previous pilot conducted with women who had come to the health center would be too inefficient to prove a behavior change. We are applying for this grant to prove that it changes behavior in a subset of high-risk women, those who do not seek prenatal care or skilled birth attendance for delivery. 

We need the transition to scale funding as we know there are obstacles to use of maternal health care services, other than the women's understanding of risk, to overcome.  We will need to learn more about very important family and economic obstacles and learn how to promote the app in a low-income community.  With the transition to scale funds we will understand obstacles to policy change and determine the best business model for the implementations of the app.

Improving clinical facilities and providing skill training for health professionals is important but insufficient to have an impact on maternal mortality.  Our solution is innovative because it addressed the information gaps which are obstacles to reduced mortality and is particularly innovative as the data system is based on the patient’s risk assessment which informs the professionals and subsequently the health care system about their needs rather than a top-down approach.   

The application of the CMDS app and variables in its algorithm which we recommend as minimal data set are innovative at 3 levels. Community health, primary health  and the secondary/tertiary level health facility referral. [OO1] 

1.  The provision of the CMDS score provides the pregnant patient in the village with an assessment of her own risk so that she can make an informed decision about her own care. The score provides a  description of the risk to her life and a recommendation for action. This result is provided verbally and in written form through an SMS text which will be sent to her contact phone.  Thus the patient is empowered, they have agency or the authority to control their own action in regard to seeking skilled birth attendance. 

2. The CMDS score provides the frontline health worker, the midwife or community health extension worker criteria for referral. The health worker  can explain to the patient and her family why a referral to another center is needed and justly to the referral center and higher-level health professionals why the patient was considered to be at risk and needs health care.

3. The variables that make up the 7 domains of the CMDS are currently  incompletely collected in Benue State. The incomplete collection results from failure to include the variables in the logbooks and forms and even when present failure to collect those variables.  These variables were chosen because there they are evidence-based and have been validated to predict maternal mortality.  CNIS has proposed to the WHO that the variables for these 7 domains be considered a minimal data set.  This minimal data set will provide the innovation of consistent data collection  at all levels of the health care system  and of great interest that they can be used to assess patient risk in real-time.  Currently, even the data that is collected at obstetrical centers is of little value to the patient as no use is made of it in real-time.   In addition this data is not easily accessible by the ministry of health.  Consistent collection of the CMDS variables as a minimal data set via digital means and stored on a centralized server would make data instantly accessible to the ministry of health.  We can pilot such a system with propriety software however in the long term this would require a stand-alone system.  A state ministry of health having such as system would be highly innovative and powerful.

Thus in Benue state together with our Nigerian partners we would start with  innovation for the patient in the village,  proceed to innovations for the health professionals at primary health care center, district hospitals and referral centers and this would  lead to  innovations  for the policy makers at the ministry level.

The long-term goal is to reduce maternal & neonatal mortality and morbidity in in Benue State and potentially all of Nigeria. This objective is to be achieved through provision of data from the patient to the health care  facilities and policy makers.   

Data will  provide each women with information on their personal risk which can be shared with her family.  This will empower women with the agency they need to obtain skilled birth attendance. Our short-term goal is to increase skill birth attendance to 25%. Within their family and thier community we will need to  promote the  understanding that  will provide the social and economic  support for their mobilization.  This is the goal of the SOLVE project which we expect will lead to a transition to scale funding.

 Data will inform the frontline health workers midwives and community health extension workers. The transion to scale project will address social obstacles and develop  marketing and  business strategies.  Standardized patient risk assessment will result in timely referral to hospitals where their decision will be respected this wll lead to the  next objective which is the the  provision of care will be augmented.  Women with sepsis will receive antibiotics, those with blood loss will receive intravenous fluids and those with obstructed labor assisted vaginal delivery and caesarean section. 

The final object is scaling up of the data intervention throughout Benue State.  Data provided  digitally and systematically  to the state will promote  the geographic  allocation of the necessary  human and material resources based on need. The objective  and the impact to be  achieved  long-term is the  reduce maternal & neonatal mortality and morbidity in in Benue State. If this is achieve in Benue State it will be replicated throughout Nigeria

The primary indicator of effectiveness of the app within the context of this project  will be mobilization of pregnant women to seek skilled birth attendance. This would mean a 25% increase in delivery at maternity or primary health care centers of women in the community  who have a risk determination using the CMDS app as compared to a women from similar communities  who do not have a risk determination.  

The next level of indication will be an increased access of women to  antibiotics, antihypertensives, intravenous fluids and operative vaginal and surgical deliveries. Reduction in the  prevalence of adverse birth outcome's including still births and premature delivers and neonatal mortality will also be expected. 

The  long term will be a  comprehensive obstetrical data system which demonstrates a  reduction in maternal mortality by at least 50%.    

Women die from preventable complications of pregnancy and delivery.  In low- and middle-income countries these deaths often come as a surprise to the family, the community and the health professionals.  The ministries of health are aware of the carnage in retrospect but the women die without diagnosis  and the health care system remain unchanged.

The theory of change is simple, if women know they are at risk and their families support them as they seek skilled birth attendance mortality will be reduced.  By identifying the mother's risk the families will not be able to deny they have been informed. Community education in addition to providing the mother with her risk score will be necessary so that the family will spend the funds needed to access skills birth attendance.

Midwives and Community health extension workers will be able to identify women at risk and become advocates for their care both to the family and to the health professionals at the maternity centers and referral hospitals.

Data that is generated will be centralized at the ministry of health. This will provide policymakers the information they need to effect systematic change in funding, human resource allocation and public health education.   

The CMDS application is composed of two parts: a mobile application written in React Native (platform-independent code) and a web server written in Typescript. The data is stored in a Google Firebase table which is used to store patient information. This data includes patient demographic information,  mobile phone numbers,  Prenatal variables which make up four  domains of age, parity, mother size past history,  perinatal variables make up the domains of pre-eclampsia, underlying health and pregnancy, fetal growth and a postpartum survey or maternal and neonatal survival and location of delivery.

Improvements are necessary and feasible during this project. They will result in  better adoption including :

• Improved security and functionality to ensure that personal medical information is kept secure and compliant with GDPR, CCPA and other regulations.
  • at rest encryption for personal data on firebase
  • improved authentication and authorization practices
• Localisation
  • We would need to translate the messages and have the option of messaging in at several more Benue state languages.
• Other features to be added would
  • near field links of data to a patient wrist band so that their data could be accessed at primary health, maternity, district hospital and referral centers. 
    • notification features, for example users could be notified when information changes
    • report generation: aggregated information across users, statistical analyses, and other data that may be visualized for an individual or collection of records
• Improved user experience based on initial prototype feedback

The CMDS team is made up of CNIS Contractors,  Canadian and Nigerian Academics and Nigerian Health Professionals and social scientist.  Gender balance is 7to 7  CNIS reflect Vancouver with Caucasian, Asian and African ethnicity. Our Academics include students  from McGill, University of Alberta and University of Toronto. These students  include Nigerians, South Asians, Europeans and Canadian Caucasians. In Nigeria are staff and colleagues are representee of the 3 major tribes of Benue State.  CNIS has no policy on gender self  identification. We don't know if this would promote more diversity or inclusiveness and would be interested on MITs advice on this issue.  CNIS does have policies on harassment and  exploitations and  follows the the laws of British Columbia which  are quite rigorous on workplace safety including a respectful environment.    

1. The CNIS business model is that of a non-governmental non profit organization which  serves  population who do not have access to safe obstetrical and surgical care.  Our revenue comes from  grants, funded proposals, membership fees,  individual donations  as well as the volunteer time contributions of health professionals and other with relevant skills.  We are in our 27th year of operation and have maintained funding support by always being innovative and setting or being a head or trends in clinical education. 

2. The  CNIS  is committed to empowering low-income countries to create an environment where the risk from injuries is minimal and that all people receive adequate healthcare.  The CNIS committeemen to this mandate has resulted in funding support for 27 years. CNIS believes in sharing knowledge, expertise and experience to promote lasting and sustainable improvements in health and safety in the developing world. CNIS busines  model is one of agility. Over the last 7 years we have been progressivity increased the use of digital means to achieve our mandate.   The  specific beneficiaries in Benue State are the pregnant mothers and their children. We have studied their situation taught midwives and midwifery students and are ready to introduce an information system to save their lives.   

3. The CNIS  product the Community Danger Score app is the result of scientific  and development activities conducted in Benue State Nigeria over the last 4 years. The objective is to reduce the risk of mortality and mortality of pregnant  women and their children by identifying  women at risk and providing  recommendations about their obstetrical care  We see the promotion of survival of both women and children in Benue State and beyond  with the CMDS will need a change to a new business model. We will need to pivot  to a social enterprise model. 

4. We provide the CMDS  app to health care providers through google play store and will expand this to include the apple store, This will be free  at  to the health care professional but we will offset the cost of provision of the apps with in app advertising by companies that provide services and product to mothers and their babies.  Vitamins, baby food, diapers. over the counter medications.

5.   The larger  source of revenue that we foresee from the CMDS minimal data system is  through the comprehensive  information system that would be established from the pregnant women and the primary health centers to the maternity and district hospitals and referral centers.   In the scaling  up of the CMDS, the algorithm  would  be introduced as a minimal data set to all levels of obstetrical care.  This data  will be stored in a server which will provide the Benue  minister of health data for management of maternal and neonatal morbidity and mortality.  This data system would be of interest to telecommunications companies who druing the pandemic have become providers of remote healthcare. This data will have commercial and scientific and  economic value: we expect to sell licenses for its use. 

6.  One of the largest Canadian Corporations  the telecommunication giant TELUS expanded into  remote health care in Canada  and is interested in the African market. TELUS health has indicated to CNIS that they would match the 1 million dollars we are targeting as a transition to scale project funded by Grand Challenges Canada.     

7.  Because data on underlying  health is collected by the CMDS  other heath agencies like the Nigeria Center for Disease Control and the WHO would be expected to provide funding as the maternal health data would be unique and useful. The collection of data on  underlying disease collected as part of prenatal risk  assessment would also be a means of pandemic alert. After the recent experience pandemic surveillance will be taken seriously and Africa in particular populous countries like Nigeria would be likely sources and appropriate alert sites.

8.  The data from such a system  could also be licensed or sold to  pharmaceutical companies and other corporate entities for analysis for their research, development and marketing activities. CNIS sees the  need for the pivot to a social enterprise and  seek MIT support in this regard.

1.   The first step in our plan has been accomplished. The CMDS algorytym has been validated and the app has been field test in rural Nigeria.   Midwives can risk score the patients and the existing telecommunications network in Nigeria can text the score and a message with the advice to the mother.

2.  Out  next  step is to prove that the CMDS app  mobilizes village women to seek skilled birth attendance. With that  proof which is the objective of the MIT SOLVE proposal CNIS would be eligible for a Grand Challenges Canada Transition to scale project.  This grant is up to 1 million Canadian dollars depending on the match of a corporate partner. TELUS Health has committed to provide a match of 1 million dollars. These funds will sustain us for 2 to e years.

3. The transition to scale would address the obstacles to implementation of the CMDS app which include societal, family and economic obstacles.  We will need to  establish the CMDS algorithm  as minimal data set throughout Benue State. We would espect  the WHO to be support CNIS who has been studying the issues of minimal data sets and  risk analysis in real time  during  pregnancy with WHO funds.   The translation to scale would also address the establishment of a  social enterprise which might be a joint venture between CNIS, TELUS Health and teh Ministry of Health of Benue State. 

CNIS has received grants from Grand Challenges Canada to develop the CMDS algorithm and the CMDS app. CNIS was funded to conduct a randomized controlled trial of digital vs small group teaching of midwifery in Benue State. Nested within this project was the testing of the CMDS app.  CNIS has extricated this module and is developing the teaching of CMDS as a stand-alone learning management  system training course which can be accessed on a mobile phone app.   During our attempt to obtain a transltion to scale grant from Grand Challenges Canada TELUS health committed to matching funds of 1 Million dollars.