Next Generation Healthcare: Combining AI and Telehealth

Congenital syndromes are structural anomalies, these anomalies often originate during intrauterine life and are at birth. The worldwide prevalence is 2-3%. Congenital syndromes due to chromosomal abnormalities are seen in 1/170 live births. Though reliable local data in Nigeria are not available, worldwide, about 240 000 newborns die within the first month of birth annually due to malformations seen in congenital syndromes. An additional 170 000 pediatric mortalities occur between the ages of 1 month and 5 years due to congenital malformations, 90% of children with serious congenital malformations are born in in low- and middle-income countries. The decline in neonatal and under-5 mortality rates thus means that malformations seen in congenital syndromes will become a larger proportion of the cause of neonatal and under-5 deaths. Moreover, congenital malformations can cause long-term disability, with subsequent consequences on individuals, families, health care systems and societies.

Screening is an important step in the detection of malformations seen in congenital syndromes. Newborn screening started about six decades ago and currently, there are established public health programs screening newborns for various congenital disorders in the Northern hemisphere. The early identification enables prompt management thereby preventing severe complications, handicaps and premature death. Despite the obvious advantages of newborn screening, it is not widely adopted, (in fact, it is absent) in many African countries. This constitutes a lost opportunity to improve health care and reduce disabilities.

Accurate diagnosis of congenital syndromes is the next crucial step in patient management. The genetic epidemiology and phenotypic manifestation of congenital syndromes varies among different populations so it is important to define the phenotypic footprint among individual ethno-linguistic groups in order to ensure accurate diagnosis and devise appropriate management procedures. Defining the variants related to congenital syndromes among Africans is even more intriguing because of the central role of Africans in human evolution. Great heterogeneity is expected in congenital syndromes among sub-Saharan Africans because of the age of the population, the ancient population admixtures and the relatively recent back-migration of non-African populations into Africa. 

Unfortunately, the average medical textbooks are filled with descriptions of congenital syndromes from Caucasian populations, thus the typical textbook description of congenital syndromes seen among the Caucasian population may not necessarily be an accurate reflection of similar syndromes in sub–Saharan Africans. Moreover, the paucity of clinical data from sub–Saharan Africa reveals a wide gap in knowledge. Therefore, detailed clinical characterizations congenital malformations will provide a foundation for appropriate management in Africa. 

Despite the huge demand, there is absolute lack of clinical geneticists in many sub-Saharan African countries. This situation hampers accurate diagnosis of rare congenital syndromes and syndromes presenting in forms dissimilar to Caucasian populations.

Our solution enables remote diagnosis of congenital syndromes in under-served communities in Africa. This remote diagnosis and management support is based on the use of Artificial Intelligence and Telehealth. This solution is based on the belief that accurate diagnosis of congenital syndromes in under-served communities can be achieved effectively and cheaply using Artificial Intelligence and Telehealth capabilities. Our solution will deploy facial recognition software and compare captured images and clinical information to existing databases. The Artificial Intelligence will account for differences in phenotypic appearances between Africans and other ethnolinguistic groups that have provided prior information on congenital syndromes. 

The solution will provide the most likely diagnosis and offer best line of management. All these will be done without the need for costly travel to urban centers for clinical diagnosis and will help to overcome the deficit of clinical geneticists in African countries. For example, Nigeria with a population of about 200 million people does not have a single clinical geneticist.  Training of all participating health workers will be done using specially developed teaching tools including identification of signs suggesting the presence of a congenital malformation. After each delivery, where a congenital syndromes is suspected, the nurse/health worker will take clinical photographs – after securing informed consent from the parents – using the facial recognition software. The images will be uploaded into a secure cloud server (via existing telecommunication back bone) for remote review by the Artificial Intelligence software. Detailed characterization of the phenotype will be completed as required and a diagnosis and plan of management offered. 

Many African countries including Nigeria lack clinical geneticist, we aim to change the status quo by implementing remote diagnosis of congenital syndromes within existing healthcare infrastructure and provide clinical templates to improve their management. This will substitute for the lack of clinical geneticists in many parts of Africa. Our solution is aimed at under-served communities in Africa where specialist healthcare is often unavailable. By deploying smart devices and utilizing Telehealth capabilities based on existing telecommunication infrastructure we can provide diagnosis and management guide for hard to diagnose congenital syndromes. 

Precision medicine has huge potential for Africans, replication of this solution across Africa will enable building blocks for libraries of phenotypic materials that are crucial in accurate clinical diagnosis of congenital syndromes. 

The team is made up of Nigerians resident in Nigeria. The Team Lead is a physician involved in management of children with congenital syndromes. This afforded a first hand experience of the operational environment and circumstances. The other members of the team also live or work in the target communities, thus the entire team have a close connection with the beneficiary population. 

1. The current medical textbooks are poorly representative of rare diseases including congenital syndromes among African populations. The textbooks do not have sufficient clinical data on the presentation spectrum of these disorders in the African population.

2. The African population possesses great genetic heterogeneity (due to multiple factors) thus there could be marked variation in clinical presentation of congenital syndromes (especially rare ones).

3. There is great lack of clinical geneticist in many sub-Saharan African countries. This has created a barrier to adequate diagnosis and management of congenital syndromes.

4. The financial handicap to travel from under-served communities to centers where medical care can be accessed is a huge barrier to families of children with congenital syndromes 

1. Newborn screening is absent in many African countries, we are challenging the status quo. An offshoot of this solution is the establishment of a newborn screening program within existing healthcare infrastructure. 

2. This solution deploys a facial recognition software  powered by Artificial Intelligence to identify rare cases of congenital syndromes. This may be the first time such technology is deployed in patient care in Africa, especially in addressing obvious lack of skilled manpower. The technology will help to overcome the deficit of clinical geneticists in African countries.

3. Organized street address system is often limited to certain parts of capital cities and larger towns in Africa, this makes accurate tracking of living quarters an herculean or impossible task in many parts of Africa, especially in rural communities. Our solution will overcome this by using what3words software to map every living quarter in the under-served communities we will operate in, this will facilitate accurate tracking. 

4.  Health insurance coverage is still a novelty in many parts of Africa. We aim to ensure the sustainability of our solution through inclusion of the service provision into health insurance plans via partnership with State Government Health Insurance Agency.

5. Pre-recorded audio and video in local languages providing post screening/diagnosis counseling will be made available to parents of the newborn children. These recordings will be updated regularly based on feedback from the target audience to broaden the information database. Telehealth consultations will be done to facilitate adequate counseling for parents.

1. Improve the accuracy of diagnosis, management and prognosis of clinical syndromes in sub-Saharan Africa.

Apart from using Artificial Intelligence to make diagnosis based on the wide array of available medical information, we will operate a feedback system on the subsequent clinical outcome of each index case in order to improve the performance of the AI software.

Speedy diagnosis and management plan suggestion will enable immediate commencement of management using available resources before subsequent transfer to more specialized centers (as required). This hopefully will improve the prognosis of the cases.

2. Provide clinical information to the medical world on the phenotypes of congenital syndromes among Africans.

Inadequate clinical data collection and publication is the bane of accurate diagnosis of congenital syndromes arising in Africa. We intend to change the situation via the pictorial documentation and compilation of the relevant clinical data on congenital syndromes in Africa. This solution will also enable a comprehensive data collection by incorporating nearly all players involved in healthcare delivery (especially nurses, traditional birth attendant, community health officers and others operating in under-served communities). The publication of these data will increase the worldwide knowledge base of these disease conditions.

3. Improve the management of congenital syndromes in under-served communities in sub-Saharan Africa.

Speedy diagnosis and suggestion of medical management by the Artificial Intelligence software, achieved via Telehealth capabilities has the potential to improve survival of patients. 

4. Establish a newborn screening program in under-served communities in sub-Saharan Africa using existing healthcare infrastructure. 

A default offshoot of this solution is the establishment of newborn screening program in under-served communities. Since this is achieved through existing healthcare infrastructure it will serve as a template for the establishment more formal newborn screening programs 

5. Provide responsible and adequate counseling to parents (and relations) of children with congenital syndromes to alleviate their anxiety and stress.

Anxiety, emotional stress and trauma of parents and relations of children with congenital syndromes is an overlooked distressing situation. The situation becomes more distressful in the absence of adequate counseling. The solution will use Telehealth to provide counseling services to relieve the anxiety of patient relations.

6. Deepen the coverage of Telehealth facilities in under-served communities in sub-Saharan Africa.

Even though various forms of Telehealth offerings exists currently, incorporating the technology into this solution will help to deepen the reach and acceptability of the technology in sub-Saharan Africa.

7. Maintain sustainability of this solution through adaptation into health insurance plans. 

Adapting the solution into existing health insurance and partnering with government agencies providing health insurance will ensure sustainability of the solution and prevent expensive out-of-pocket fees that could discourage maximal benefits of the solution.

1. Number of healthcare providers (especially in under-served communities) who sign up to be part of the program annually.

2. Number of families who consent to be part of the program annually.

3. Number of cases of congenital syndromes analyzed by the solution annually (including the accuracy of diagnosis and the outcomes of the cases).

4. Feedback by parents on the adequacy and usefulness of counseling programs

5. Feedback from the medical community (both local and international) on the phenotypic information made available through the solution.

The theory of change flows from Stages A - B - C - D - E - F

Stage A:

What is the problem you are trying to solve?

Accurate diagnosis of congenital syndromes is inadequate in many parts of Africa constituting a lost opportunity to improve health care and reduce disability.

Establishing a framework for speedy and accurate diagnosis will build a model that can supply key missing component – phenotypic data – in congenital syndromes among Africans as well as providing management plans for newborn children with congenital syndromes.

Stage B:

Who is your key audience?

Newborn children with hard to diagnose congenital malformations in under-served communities in Africa 

Stage C:

What is your entry point to reaching your audience?

Community entry into will be done through community and opinion leaders, leaders of faith-based groups and other social groups. The community entry will also be done in partnership with the officials of the State Health Insurance Agency to introduce available insurance plans.

Stage D:

What steps are needed to bring about change?

Appropriately designed edutainment tools will be deployed at maternities, clinics, delivery homes and other non-health related social gatherings. Individual health workers will be encouraged to participate in the program. Participating health workers will be trained on how to use the solution. 

Stage E:

What are the wider benefits of your work?

The community will have access to modern healthcare solutions.

There will be up to date training resources and tools for all participating health workers including traditional birth attendants, nurses, and other allied health personnel. Accurate diagnosis of congenital syndromes in under-served communities, along with suggested management plans for the patients.

Stage F:

What is the long- term change you see as your goal?

It would be possible to offer accurate diagnosis for uncommon congenital syndromes in under-served African communities. This will lead to improvement in management of cases.

Availability of an effective alternative to the paucity of clinical geneticists in Africa.   

The core technologies includes:

1. Facial (and other body parts) recognition software. This software will scan images of patients and compare the images to know images of patients with similar clinical manifestation to arrive at a likely diagnosis.

2. Artificial Intelligence that will account for known phenotypic variations in Africans and integrate the knowledge to index images to suggest best possible diagnosis. The same AI will search available databases to provide management plan for these diagnosis.

We are committed to maintaining diversity, equity and inclusion in our team.

By partnering with government health agencies to incorporate our solution into existing health plans we aim to achieve sustainability. The insurance agency pays for the services rendered. This eliminates out-pocket-expenses for main clientele who are people living primarily in under-served communities