Skip to main content
Download PDF

Development and implementation of a digital clinical decision support system to increase the quality of primary healthcare delivery in a refugee setting in Chad

BMC Primary Care volume 26, Article number: 113 (2025) Cite this article

Abstract

Background

Digital clinical decision support systems (CDSS) enhance the quality of primary healthcare service delivery for vulnerable populations in resource-limited settings. This improvement occurs by strengthening healthcare providers’ clinical skills and enabling them to operate more independently while adhering to standard treatment guidelines. From January 2019 to June 2023, we developed and implemented a digital, tablet-based CDSS for children aged 2–59 months. The phases of development, validation, and implementation, as well as lessons learnt and bottlenecks requiring attention, are analysed.

Methods

The project was carried out in three primary healthcare facilities within a health district in southern Chad, covering a population of 48,000, which includes a significant number of refugees from the Central African Republic. The intended end users were nurses, nurse assistants, and midwives, with supervision provided by health district teams.

Results

The CDSS, based on the WHO’s Integrated Management of Childhood Illness (IMCI) and national guidelines, was tailored to the context of available resources and epidemiological patterns. From the outset, the active involvement of a diverse group of local, national, and international technical stakeholders (clinicians, information and communication technology (ICT) specialists, health workers, and district health authorities) facilitated mutual knowledge sharing and product co-creation processes. The CDSS was adapted to the local context, which enhanced local ownership. However, its complexity requires significant effort from clinicians and ICT specialists for development and validation. Additionally, health centres must rely on a technical infrastructure (electricity, internet connection, and server solutions).

Conclusions

From the outset, a participatory approach involving key stakeholders from the local to the national level of the health system significantly contributed to the successful development and implementation of the CDSS. The sustainability of such an intervention necessitates ongoing long-term commitment. This includes establishing and maintaining the infrastructure, ensuring continuous human resources and technical expertise for implementation and quality assurance, and updating content to reflect advancements in clinical medicine.

Peer Review reports

Background

Healthcare in resource-constrained settings with a humanitarian context

The quality of healthcare and service delivery in resource-constrained settings is challenging due to a paucity of skilled health workers coupled with poor medical infrastructure and limited essential equipment, diagnostic capacity, and supply issues [1]. Conflicts marked by an influx of refugees and insufficient functionality of primary healthcare (PHC) services pose additional constraints requiring innovative and agile solutions to support healthcare delivery and ensure equity between the refugee and host area populations [2]. Despite a common vision of providing access to quality healthcare services to all populations in need, intervention principles differ between national health systems and humanitarian care providers in terms of policies, implementation modalities, and duration [2, 3]. Standard healthcare services and programs for the control and elimination of diseases (e.g., vaccination and deworming) in areas of conflict may be disrupted or inaccessible for refugee populations, which negatively affects access and ultimately health outcomes. The provision of quality healthcare delivery to large numbers of people in need calls for integrated approaches and strategies supporting and strengthening healthcare workers’ capacity to detect, manage, and prevent health issues in primary healthcare [4].

Clinical decision-making

A series of WHO guidelines on the integrated management of childhood illness (IMCI) [5], maternal and new-born care [6, 7], and children and adolescents in health facilities [8] were produced to improve the quality of care at the PHC level. However, adherence to IMCI guidelines by health workers may be rather poor in practice [9,10,11,12].

Clinical decision support systems (CDSS) are “digitised job aids that combine an individual’s health information with the health worker’s knowledge and clinical protocols to assist health workers in making diagnosis and treatment decisions” [13]. CDSS offer promising agile solutions that play an increasingly significant role in improving the quality and comprehensiveness of primary healthcare service delivery and health outcomes for underserved populations [14]. The “Practical Approach to Care Kit (PACK)” is an example of a simplified and streamlined paper-based clinical decision support tool to support PHC providers. The tool was developed, implemented, and evaluated in South Africa and scaled up to various other countries. It is tailored to end users with different levels of training (clinicians, nurses, and nurse assistants) and is updated annually [15]. When provided on a hand-held mobile device, the CDSS can increase health workers’ adherence to clinical guidelines [16]. Moreover, they hold potential for healthcare providers’ capacity strengthening by building upon clinical skills, increasing knowledge, and raising awareness of lesser-known conditions. This empowers them to work more independently, which is key for maintaining basic quality services in resource-limited settings [17,18,19]. Studies from Tanzania using electronic IMCI algorithms demonstrated improved clinical assessment and management, resulting in improved health outcomes and quality of care [20] and reduced antibiotic treatment [21,22,23,24]. The processes of translating clinical guidelines into a digital decision support system follow a standardised nomenclature that builds on a five-level conversion system for the digitisation of SMART guidelines developed by the WHO [25]. Level 1 refers to available clinical guidelines in a narrative format (disease-specific or other written guidelines). Level 2 includes the semi-structured stage, which refers to the transformation of Level 1 guidelines to a decision tree format that is still human-readable. Level 3 presents the machine readability stage (with code, terminology and interoperability standards, and software-neutral specifications). Level 4 concerns the reference software used to execute the algorithms and interoperable digital components in a local execution environment. Level 5 encompasses trained and optimised dynamic executable clinical algorithms for prioritised outcomes.

The Chadian context

The Chadian health system is notoriously underfunded. The coverage and quality of healthcare services are poor because of a lack of qualified health staff and equipment, poor infrastructure (absence of electricity and clean water), and an unreliable supply of medicines and consumables [26,27,28,29]. Domestic governmental health expenditures as a percentage of government expenditures were 5.2% in Chad in 2019 [30], whereas the Abuja declaration of the African Head of State recommended a benchmark of 15% [31]. The density of medical doctors per 10,000 people was 0.6, and that of nursing and midwives was 2.0. Chad had one of the highest infant and maternal mortality rates worldwide, with a value of 1,140 per 100,000 live births in 2017 [32]. Access to PHC services is low, the ‘continuum of care’ is dysfunctional (poor connection from the PHC level to a higher level of care for severe cases), and health management information systems in place are insufficient to produce quality data [33]. The humanitarian crises leading to the influx of refugees from neighbouring countries are overburdening the under-resourced national healthcare system, even if basic health services are partially covered by the humanitarian community [34]. Domestic insecurity results in more than 1 million internally displaced persons and returnees [35]. Refugees from the neighbouring countries of Sudan, the Central African Republic, Nigeria, and Cameroon numbered over 1.15 million in early 2024 [36]. More than 1.8 million children under 5 years of age are estimated to be acutely malnourished [37] as a result of food insecurity, childhood illnesses, displacement, limited access to healthcare, and poor sanitary conditions [35].

In acknowledging the challenges described above, we developed and implemented a digital CDSS in collaboration with the National Ministry of Health (MoH) and the UNHCR to improve the quality of PHC services for refugees in Southern Chad. The CDSS was based on IMCI guidelines and targeted children aged 2–59 months, and [38] end users at the PHC level included nurses, nurse assistants, and midwives. We first describe the phases and processes of developing, validating, and implementing the tablet-based CDSS tool, as well as the lessons learnt from development and testing. Second, we discuss encountered issues and bottlenecks within the project warranting attention by future similar initiatives.

Methods

Project zone

The health district of Goré in the Logone Oriental province in southern Chad was chosen based on a highly dynamic influx of refugees from the Central African Republic since 2003 and a high population density compared with other regions (Fig. 1). The project was conducted from January 2019 until June 2023 and covered 3 PHC facilities serving a host population of approximately 48,000 in 2019 [39]. The villages of the host area and the refugee camps were physically separated. PHC services were accessible to all in the catchment area but were free of charge only for the refugees. The health facilities and services were supported by the UNHCR in terms of the health workforce, drug supply, and consumables. The facilities have no internet connection, but antennas were installed by the UNHCR in the refugee camps in 2019.

Fig. 1
figure 1

Project zone including the health centres and refugee population in the district of Goré, Chad

Full size image

Framework for the CDSS development and implementation processes

The project encompassed the three phases of “situational analysis, organisation and infrastructure set up” from January-June 2019, “CDSS development and validation” from July 2019 to June 2021, and “CDSS implementation and evaluation” from July 2021 to June 2023. The phases and the CDSS product are illustrated in Fig. 2 and summarised below.

Fig. 2
figure 2

Approach of CDSS development and implementation processes and product

Full size image

Situational analysis

The idea of a CDSS for better quality healthcare provision to refugees was introduced by the project team to national and local health authorities and other relevant stakeholders (the UNHCR and nongovernmental organisations) in Chad. The project team then conducted a situational analysis of the local context by assessing the infrastructure, workforce, capacity and skills, and local population health needs inspired by the WHO Service Availability and Readiness (SARA) tool [40, 41]. Within the health system, clinical priorities and resource gaps that affect the project’s design, scale, and content of the CDSS were identified. These findings guided the selection of health centres, the identification of end users, and priority medical conditions along with requirements for technical and medical equipment.

Set up of partners, project team, and infrastructure

Cooperation agreements were signed with the key partners, i.e., the MoH, the UNHCR, and the national non-governmental organisation “Centre de Support en Santé Internationale” (CSSI). With support from the CSSI, we recruited a local project team, the local office infrastructure was set up, and solar panels and indoor lighting were installed in the selected health centres.

Setup of the national expert group

An initial workshop served to identify and endorse a national expert panel consisting of clinical specialists from the primary to the tertiary and central level of the MoH, national vertical programmes (IMCI, vaccination, nutrition, and malaria), international humanitarian organisations (UNHCR), NGOs and public health institutions (WHO), and the future end users of the CDSS. We also engaged with national specialists in paediatrics, internal medicine, tropical medicine, infectious diseases, and obstetrics and gynaecology to review and validate the content of the clinical algorithms for a PHC setting.

Mapping, selection, and approval of clinical guidelines by national experts

We collected and reviewed available clinical guidelines and protocols used at the local, national, and regional levels. These included, for example, recommendations and treatment guidelines from national programmes for malaria, immunisation, and neglected tropical diseases.

The algorithm design was based on the main source documents of the IMCI guidelines and the national standard guideline “ordinogram”, which uses a symptom-based approach. The intended end users were expected to use the “ordinogram” and, thus be familiar with the design. Adaptations were made to reflect the local epidemiology and account for identified priority conditions. If relevant guidelines were absent or outdated, we sourced complementary topic-specific publications and recommendations from international public health and humanitarian organisations. For example, the WHO guidelines for skin-related diseases, as well as neglected tropical diseases and Médecins sans Frontiers (MSF) guidelines, were included, because they address a variety of options for clinical management under limited resources and logistical challenges [42, 43]. The national expert panel reviewed and formally approved the scope of medical conditions, including their diagnosis and management.

Design, external review, and approval of draft algorithms

The clinical algorithms were designed with open-source diagram software [44]. In the frame of an external review process of the draft algorithms, the national expert panel members were expected to provide feedback on language, local terms and content, and discrepancies and gaps in the L2 flow diagrams. This included critically reviewing diagnoses, management, treatment, and language before consensus and formal approval.

Digitisation of algorithms and prototype piloting

The designed clinical algorithms [44] were mapped on an open-source format (XLSForm/xlm) by the ICT specialists. Early versions of the approved clinical algorithms were manually digitised. The mapping process was later accelerated by introducing a “tool for rapid implementation of clinical content” (TRICC) that automatically converts the drawings into the XLSForm/xlm. TRICC was developed in-house and facilitates automatic updates of the digitised clinical algorithms. The XLSForm is then uploaded to an application of choice and presented on the electronic device as a questionnaire. To evaluate the CDSS’s functionality, the project team, together with the health workers, tested an early prototype in the project health centres.

Testing and validation

The approval of the digitised clinical algorithms was preceded by an extensive iterative process, including repeated testing, reviewing, and refining by clinicians and ICT specialists. IT testing consisted of defining and executing an IT-related system and integration tests to validate the IT logic (order, structure, content, and form of questions and instructions, such as “observe”, “determine”, and “ask”). The consistency between each decision tree and the questions in the app was verified.

Owing to the complexity of the algorithms, it was not possible to manually verify each pathway. Hence, for the clinical content testing and validation, we used a priority matrix for key health conditions based on defined criteria concerning occurrence, severity, and needs for knowledge and management. Diagnoses with an overall score above a threshold were systematically tested. National clinicians with experience in primary healthcare developed clinical vignettes and tested the CDSS with these for a hypothetical consultation. We defined a clinical vignette as a test case to simulate a real-life scenario, which consisted of a variety of clinical symptoms and signs, followed by steps in clinical assessment leading to a specific diagnosis based on medical standards. The clinicians and ICT specialists discussed and resolved all apparent clinical and technical issues. A report was created for each test executed and later used for the validation of the clinical algorithm.

End-user training

We employed a 3-day training of trainers approach involving national clinicians at the central and district levels. The training material was prepared mainly by local project clinicians and ICT specialists. The content was tailored to clinical skills for diagnoses covered by the CDSS and included additional topics, such as patient management, pre-referral, referral, medical triage, principles of consultation and communication, antibiotic stewardship, and technical skills to manipulate the electronic device. The participants’ skills were assessed during each training session through pre- and post-tests via a questionnaire containing multiple-choice questions.

Quality assurance

Quality assurance measures were employed throughout all project phases. The national expert panel reviewed and approved the source documents and clinical algorithm design. The content and terminology were adapted to the local setting to minimise misunderstanding and misinterpretation. Testing and validation of the CDSS involved external national clinicians. The project team, together with central, provincial, and district health authorities and other partners, provided formative supervision and repeated refresher training to end users, including topics suggested by them, to strengthen their clinical and patient management skills.

Results

CDSS

The CDSS was developed as an offline-compatible tablet application for primary healthcare providers. It included a structured decision-support system to improve the diagnosis and management of common paediatric conditions. The CDSS was tailored to local priority health conditions, disease patterns, locally available resources, and sociocultural factors about the resident and refugee populations. The design integrated local terms, language, and images for appropriate clinical assessment. It was built on IMCI guidelines, which were adapted from the national standard guideline “ordinogram” [45], applying a symptom- and syndromic-based approach to facilitate the detection and management of lesser-known conditions. Where available, we integrated low-cost point-of-care diagnostics to support and/or confirm clinical diagnosis. We also included clinical algorithms to support the healthcare provider in the evaluation, case detection, and management of febrile conditions about emerging or re-emerging infectious diseases causing recent regional outbreaks, such as dengue and chikungunya [46, 47]. The project team developed 33 paediatric clinical algorithms and tested 40 priority diagnoses via self-developed clinical vignettes. Six workshops with the clinical expert panel were organised to review and validate the clinical guidelines and algorithms. Advantages of the CDSS with expected impact in terms of design and clinical and therapeutic management and advice, as well as issues encountered, are summarised in Table 1.

Table 1 Advantages of the CDSS with expected impact
Full size table

IT architecture

Figure 3 outlines the CDSS’ architecture. The CDSS ran on the initially chosen application CommCare [48] and was later migrated to the Community Health Toolkit [49]. The health facilities had no internet; therefore, the application stored the case recorded by the health worker offline on the electronic tablet. The data were regularly synchronised by the health centre manager with an online data server via a mobile router.

Fig. 3
figure 3

Architecture of the CDSS

Full size image

IT governance (data management, protection, and security)

The patient data were de-identified, and the information was encrypted and password-protected [50]. A unique code was assigned to each patient. The code was drawn from a list of automatically created codes continuously distributed to the health facilities. It was recorded in the health booklet at the patient registration service and then entered into the electronic tablet by the consulting health worker for the first visit of a case and used for follow-up visits. To secure electronic devices, a liability contract was signed by the involved parties (the project coordinator, the district medical officer, and the health centre manager). The devices, accessories, and their conditions were recorded in a handover book at every shift change to produce traceability. The devices were tracked by geolocation and locked in cupboards when not in use. User access and rights were restricted by creating individual user accounts according to the type of utilisation. New account and access requests were analysed and approved by the project coordinator. A guideline on the project’s data governance was developed.

Implementation of the CDSS

The implementation phase included end-user training, a CDSS launch, a user satisfaction study, and content update and adaptation. The CDSS was launched in June 2021. The end users were trained before and received repeated refresher training in October 2021, April 2022, and January 2023. They were also closely accompanied and supervised jointly by the project and health district teams. A total of 77,214 consultations eligible for use of the CDSS were performed, and the CDSS was effectively used for 25,621 consultations (33.2%) between July 2021 and April 2023. To sustain the intervention, all the project equipment was retained by the implementers at the end of the project. The health district team and a focal point in each of the health centres were designated by the MoH to continue providing technical supervision to the CDSS end users in the health centres. Moreover, they were supported remotely by the project’s former clinician and ICT specialist [51]. Figure 4 shows a medical consultation of a child in the health district of Goré, Chad.

Fig. 4
figure 4

Medical consultation of a child in the health district of Goré, Chad, January 2023. Picture credit: Salomon Djekorgee Dainyoo/Swiss Tropical and Public Health Institute/Stanley Thomas Johnson Foundation/Fairpicture

Full size image

Update and content adaptation

The end users were encouraged to provide feedback on the CDSS during piloting, validation, and implementation, and their inputs were collected mainly through supervision visits, analysed, and addressed as appropriate.

Evaluation of use

Five evaluations were carried out by the national MoH and the "Ministry of Economic Forecasting and International Partners". In addition, the Foundation commissioned an evaluation given its funding programme’s future. We conducted a user satisfaction study with end users of the CDSS and auxiliary personnel who did not directly use the CDSS but were impacted by changes in the CDSS implementation in their daily work. The study is detailed elsewhere [52]. The study design and questionnaire were adapted from a previous study that assessed key elements of successful CDSS implementation, including adaptation, adoption, feasibility, acceptability, and sustainability [53]. Two-point data collection 6 and 16 months after implementation allowed the assessment of satisfaction over time. Using a mixed methods approach combining semi-quantitative and qualitative data allowed the authors to quantify and compare satisfaction between the two study time points and find explanations for the observed changes in satisfaction.

The phases and related activities, methodological approaches, and outcomes of the CDSS development and implementation are summarised in Table 2.

Table 2 Phases and activities, approaches and outcomes of the CDSS development and implementation
Full size table

Discussion

We developed and implemented a digital CDSS targeting children aged 2–59 months. The design and clinical content were tailored to the end users’ competencies and resources available to them, thereby taking into account the context. Key achievements and issues encountered and limitations identified in the different phases of the project, are discussed here and structured along ‘impact', ‘potential', ‘adoption', and ‘challenges and weaknesses'.

Impact

Even though the project’s approach to outcome and impact monitoring did not allow to establish evidence in respect to quality of care changes or other clinical outcomes, the CDSS contributed to strengthening the end user’s clinical knowledge and skills, and their awareness of less well-known diseases. The provision of repeated refresher training and formative supervision to end users to build and maintain their knowledge and skills in clinical and patient management and to use the CDSS was crucial to sustaining such an intervention, as also mentioned in other sub-Saharan settings [10, 11, 54]. The CDSS is currently replicated in an additional selected health district within the national health system. The involved stakeholders and end users expressed their strong interest in the continuation and expansion of CDSS use, which would require the involvement of key stakeholders at all levels of the MoH.

Potential

An integrated disease management approach beyond IMCI, including priority and less well-known conditions, may consider integrating adapted algorithms for syndromic disease management, such as sexually transmitted infections. This approach would allow accounting for endemic parasitic diseases with confounding clinical symptoms (e.g. female genital schistosomiasis), to guide assessment and management in girls as a vulnerable group. Coupled with a reliable electronic recording and reporting system, the CDSS has the potential for health facility-based disease surveillance. CDSS-based real-time data generated can be fed into dashboards for case detection, support basic mapping and monitoring of potential outbreaks, and identify trends of notifiable, epidemic-prone, and emerging infectious diseases. Health facility-based electronic integrated disease surveillance and response (eIDSR) systems are increasingly implemented, e.g., in Nigeria [55], Sierra Leone [56,57,58], and Tanzania [59]. Refugee settlements are particularly at risk of endemic disease outbreaks because of poor sanitary infrastructure and high population density. Population and cross-border movements and alterations in local vector abundance can facilitate the emergence or re-emergence of communicable and vector-borne diseases, such as dengue, chikungunya, and zika, which can then spread among formerly disease-naïve populations [60, 61].

Adoption

Partner commitment benefitted from an established long-term network in Chad through previous projects and programmes. The MoH’s interest and commitment throughout all project phases were crucial. The national expert panel established by the MoH’s Department of Health Services Organisation and Quality of Care brought different stakeholders from the ministry, national vertical programmes, NGOs, and academia together and substantially contributed to strengthening the technical capacity of digital health. The organisation of national workshops on the capitalisation of the project and digital health contributed to advocating the CDSS at the national and regional levels. The active involvement of the national expert panel that also included end users in the development and validation processes and provision of feedback on the design of the clinical algorithms helped to refine and adapt the CDSS and enhanced co-creation and local ownership of the CDSS. Being aware of the need to keep exchanges for regular content updates to sustain the intervention, the national clinicians from the expert panel formed a technical working group on their initiative. The content update consists of reviewing and updating the clinical content of the CDSS based on novel drugs, diagnostics, clinical guidelines, and changing patterns of disease epidemiology. Access by health professionals from resource-constrained settings to communities of practice is crucial to strengthen the collaboration and regional and international networks [62] and can be a well-accepted resource for clinical decision-making by peers [63].

The findings from the user satisfaction study indicated high acceptance of the CDSS by the end users. Adapting the CDSS to the local context, building a co-creation and constant feedback process, and providing continuous technical support to the end users was key to a successful implementation of the CDSS [52].

From a gender perspective, female health professionals at the local and national levels were represented in the expert panel. The national clinical consultants to develop locally adapted clinical vignettes were men, as no female clinical consultants could be engaged in this task.

The situational analysis allowed the identification of significant pre-existing resource limitations, namely, poor medico-technical equipment and stock shortages of medications and rapid diagnostic tests. The project thus provided health centres with medico technical equipment, and the CDSS was adapted to suggest second and third-line medications as well as offer decision points with and without test availability.

The project team and national clinical specialists involved underwent a mutual learning process during the development and validation phases, from reaching a common understanding of the concept and scope of the CDSS to thorough testing, reconsidering, and adapting different approaches, which required agile management. The complexity of the CDSS is an approximate reflection of clinical decision-making. It is thus essential to what, how, and in which order information is presented. The information chosen out of a diagnostic process can be as important as the information chosen to be included. The clinical content must be up-to-date and evidence-based, incorporate and balance national and international standards and guidelines, and be appropriate and useful to end users. Therefore, the development of clinical content for a decision tool is completely different from translating a pre-existing guideline into a digital format and requires a high investment of reflection, focus, and attention at various levels. The CDSS provides clinical decision support and is not intended to provide the user with a diagnosis. The user has the freedom to rely on her or his judgment and retains ultimate responsibility for the decision that has been made or the medication that has been prescribed. The end users come with varying levels of training, experience, and baseline knowledge, and the tool might replace individual judgments or assert a cognitive bias. For accuracy and clinical safety, therefore, the tool should, as far as possible, reflect the decision logic that a majority of clinicians would most likely make if presented with the same set of parameters and background information. A comparative analysis of four IMCI-related CDSS showed that conversion of narrative guidelines in a decision logic requires interpretation, which calls for CDSS development standards to ensure health and quality of care outcomes [64].

The testing and validation of digitised clinical algorithms are crucial for providing end users with a safe digital tool and ensuring sufficient adaptation by minimising the risk of providing erroneous advice, which is more harmful than beneficial. Every conceivable scenario should be considered; therefore, various clinical tests (clinical vignettes) were developed and carried out. The approval of the digitised clinical algorithms is thus preceded by an extensive iterative process of testing, reviewing, and refining each of the prioritised clinical diagnoses, involving close interactions between clinicians and ICT specialists. End users should be actively involved in this process from the beginning to foster their understanding. Early testing focused more on the IT logic and clinical vignettes covering diagnoses of common childhood illnesses, whereas later testing was built on lessons learnt and included a broad range of additional clinical vignettes developed by national clinicians. To respond to new evidence and updated guidelines, a process of regularly refreshing the clinical content (by periodical revisits of the clinical algorithms and adaptation if required), is needed to ensure that it remains relevant. To sustain certain agility of the CDSS over time, processes should be established to clinically and technically reassess and test it after each adjustment. This should be addressed at both the planning stage and during the initial development.

The national expert panel reviewed and approved the source documents and algorithm design during repeated validation workshops. However, a critical review of inputs questioning the content and quality of the algorithm and feedback on discrepancies and gaps was rather poor for the paediatric algorithms. This lesson was taken into account during the development of adult clinical algorithms (which are not further discussed here). We adapted the review approach by building small working groups of national clinicians and assigned them several clinical algorithms for a thorough review and a later discussion in the plenum. This approach provoked lively debate among the panel members and encouraged critical input. Moreover, the repeated workshops likely allowed gradual familiarity with the project team, encouraging the expert panel members to question the clinical algorithms.

Challenges and weaknesses

The major challenge of the development, validation, and deployment of a CDSS to a refugee context in southern Chad was its complexity. It required a strong commitment and a substantial effort, particularly from the clinicians and ICT specialists, during the development and validation phases, which resulted in an unexpectedly high workload. At the beginning, the project team had to reach a common understanding and consensus of the concept and procedures. The decision on the algorithm’s design had to be weighted between its complexity and the usability of the end product (CDSS) in a resource-constrained local context. Some of the algorithms were thus decided to be slightly reduced to keep the number of items to answer per consultation as short as possible. We believe that work overload may affect health professionals’ ability to perform their duties at a high quality, including consistently using the tools and tests provided, which may result in inaccurate diagnoses and inappropriate treatment. These compromises have also been reported in other settings of low- and middle-income countries [65]. Importantly, the development process is not complete with the launch of a CDSS; its use needs to be solidly embedded into working routines and local training curricula.

The local setup and sustainment of a CDSS involves the continuous maintenance of infrastructure resources, including technology and equipment, by local clinical and ICT expertise, which requires solid long-term commitment, including funding, as it has also been pointed out in comparable settings, such as in Burkina Faso [66], Cameroon [67], and in Tanzania [68]. The setup of the current project’s IT infrastructure faced several bottlenecks. For example, health centres had to be equipped with solar panels for electricity provision for room lighting and charging electronic tablets. Mobile routers had to be installed for data synchronisation between the online server and the electronic devices. Regarding data hosting, storage, and transfer, the project team made extensive inquiries about a host server solution in Chad. The governmental infrastructure and technology were insufficient at that time to host a local server, so the team had to opt for a cloud server subscription.

A limitation of the project design was the evaluation framework, which lacked a robust research design to evaluate the clinical outcome, efficacy, safety, and quality of care of the intervention. This would however require substantial financial resources for the conduct of a clinical trial or a pre-post study which was unfortunately not available. The assessment and analysis of the target population’s health priorities should have been based on different sources instead of mainly routine health data. This would have allowed the project team to gain a deeper understanding of key issues and gaps and to set a reliable baseline benchmark of the target population’s health situation to design project indicators adapted to this specific local context.

An important health workforce-related barrier to embedding the CDSS into routine healthcare delivery was the mobility of health workers. Their observed monthly patterns influenced the use of the CDSS, explaining the comparatively low use of the CDSS. Towards the end of the project, the health staff in the facilities was drastically reduced due to funding issues. The user satisfaction survey indicated that nurses organised themselves into a team of two, whereby one performed the patient consultation, and the other applied the CDSS and recorded the information in the patient registry. When the patient consultation was performed by one nurse, the motivation to use the CDSS decreased because of the additional workload and increased consultation time [52]. A scoping review investigating the use of the CDSS by health workers and the associated effects on workload and workflow indicated that using the CDSS did not necessarily lead to increased or decreased consultation duration. However, perceived additional time taken for consultation, increased administrative workload, and disrupted workflow patterns were identified as barriers to the use of the CDSS and point-of-care diagnostic testing in daily practice [69,70,71,72]. Besides that, incoming health workers were entitled to use the CDSS only after successfully completing a CDSS user training, which may have influenced the share of consultations using the CDSS as a support tool. The project team could mitigate the consequences of this dynamic only to a limited extent, for example, through regular supervision and the design of the CDSS. Various clinical topics were linked so that the questionnaire did not need to be restarted in the event of another health problem.

Conclusions

We described here the development and implementation of a CDSS in Southern Chad, taking into account the context, such as high constraints regarding resources addressing priority needs of vulnerable and mobile refugee population groups. A participatory approach engaging key stakeholders from the local to the national level of the health system in the process from the beginning considerably contributed to successful development and implementation. The sustainment of such an intervention requires a solid commitment to establish and maintain the technical and infrastructural setting (electricity, internet connection, and server solutions), continuous human resources, and technical support (medical professionals and ICT specialists). The CDSS reflects realities on the ground and thus requires continuous content updates to integrate the evolving clinical medicine (refreshed standard guidelines and new medicines and diagnostics) and refine for sustainability. A clinical trial or pre-post study allowing measuring clinical outcome, efficiency, and safety of the CDSS and quality of care aspects would contribute to generating evidence of its benefit and the value of the approach applied for its development and implementation.

Data availability

Aggregated data and materials are available on request.

Abbreviations

CDSS:

Clinical decision support system

CSSI:

Centre de Support en Santé Internationale

ICT:

Information, communication and technology

IMCI:

Integrated management of childhood illnesses

MoH:

Ministry of Health

MSF:

Médecins sans Frontières

PHC:

Primary healthcare

References

  1. Jordan K, Lewis TP, Roberts B. Quality in crisis: a systematic review of the quality of health systems in humanitarian settings. Confl Health. 2021;15(1):7.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Singh NS, Ataullahjan A, Ndiaye K, Das JK, Wise PH, Altare C, et al. Delivering health interventions to women, children, and adolescents in conflict settings: what have we learned from ten country case studies? Lancet. 2021;397(10273):533–42.

    Article  PubMed  Google Scholar 

  3. Iqbal PM, Walpola R, Harris-Roxas B, Li J, Mears S, Hall J, et. al. Improving primary health care quality for refugees and asylum seekers: A systematic review of interventional approaches. Health Expect. 2022;25(5):2065–94.

  4. Ekezie W, Adaji EE, Murray RL. Essential healthcare services provided to conflict-affected internally displaced populations in low and middle-income countries: A systematic review. Health Promot Perspect. 2020;10(1):24–37.

    Article  PubMed  PubMed Central  Google Scholar 

  5. WHO. Integrated management of childhood illness: distance learning course. Geneva: World Health Organization; 2014.

  6. WHO. Standards for improving quality of maternal and newborn care in health facilities. Geneva: World Health Organization; 2016.

  7. WHO. Standards for improving quality of care for small and sick newborns in health facilities. Geneva: World Health Organization; 2020.

  8. WHO. Standards for improving the quality of care for children and young adolescents in health facilities. Geneva: World Health Organization; 2018.

  9. Walter ND, Lyimo T, Skarbinski J, Metta E, Kahigwa E, Flannery B, et al. Why first-level health workers fail to follow guidelines for managing severe disease in children in the Coast Region, the United Republic of Tanzania. Bull World Health Organ. 2009;87(2):99–107.

    Article  PubMed  Google Scholar 

  10. Kruger C, Heinzel-Gutenbrunner M, Ali M. Adherence to the integrated management of childhood illness guidelines in Namibia, Kenya, Tanzania and Uganda: evidence from the national service provision assessment surveys. BMC Health Serv Res. 2017;17(1):822.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Horwood C, Vermaak K, Rollins N, Haskins L, Nkosi P, Qazi S. An evaluation of the quality of IMCI assessments among IMCI trained health workers in South Africa. PLoS ONE. 2009;4(6):e5937.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Bjornstad E, Preidis GA, Lufesi N, Olson D, Kamthunzi P, Hosseinipour MC, et al. Determining the quality of IMCI pneumonia care in Malawian children. Paediatr Int Child Health. 2014;34(1):29–36.

    Article  PubMed  Google Scholar 

  13. WHO. WHO guideline: recommendations on digital interventions for health system strengthening. Geneva: World Health Organization; 2019.

  14. Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digital Medicine. 2020;3:17.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Cornick R, Picken S, Wattrus C, Awotiwon A, Carkeek E, Hannington J, et al. The Practical Approach to Care Kit (PACK) guide: developing a clinical decision support tool to simplify, standardise and strengthen primary healthcare delivery. BMJ Glob Health. 2018;3(Suppl 5):e000962.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Mitchell M, Hedt-Gauthier BL, Msellemu D, Nkaka M, Lesh N. Using electronic technology to improve clinical care - results from a before-after cluster trial to evaluate assessment and classification of sick children according to Integrated Management of Childhood Illness (IMCI) protocol in Tanzania. BMC Med Inform Decis Mak. 2013;13:95.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Orton M, Agarwal S, Muhoza P, Vasudevan L, Vu A. Strengthening Delivery of Health Services Using Digital Devices. Glob health sci pract. 2018;6(Suppl 1):S61–71.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Asi YM, Williams C. The role of digital health in making progress toward Sustainable Development Goal (SDG) 3 in conflict-affected populations. Int J Med Inform. 2018;114:114–20.

    Article  PubMed  Google Scholar 

  19. Bessat C, Zonon NA, D’Acremont V. Large-scale implementation of electronic Integrated Management of Childhood Illness (eIMCI) at the primary care level in Burkina Faso: a qualitative study on health worker perception of its medical content, usability and impact on antibiotic prescription and resistance. BMC Public Health. 2019;19(1):449.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Schmitz T, Beynon F, Musard C, Kwiatkowski M, Landi M, Ishaya D, Zira J, et al. Effectiveness of an electronic clinical decision support system in improving the management of childhood illness in primary care in rural Nigeria: an observational study. BMJ Open. 2022;12(7):e055315.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Keitel K, Kagoro F, Samaka J, Masimba J, Said Z, Temba H, et al. A novel electronic algorithm using host biomarker point-of-care tests for the management of febrile illnesses in Tanzanian children (e-POCT): A randomized, controlled non-inferiority trial. PLoS Med. 2017;14(10):e1002411.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Tan R, Kavishe G, Luwanda LB, Kulinkina AV, Renggli S, Mangu C, et al. A digital health algorithm to guide antibiotic prescription in pediatric outpatient care: a cluster randomized controlled trial. Nat Med. 2024;30(1):76–84.

    Article  CAS  PubMed  Google Scholar 

  23. Shao AF, Rambaud-Althaus C, Samaka J, Faustine AF, Perri-Moore S, Swai N, et al. New algorithm for managing childhood illness using mobile technology (ALMANACH): A controlled non-Inferiority study on clinical outcome and antibiotic use in Tanzania. PLoS ONE. 2015;10(7):e0132316.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Bernasconi A, Crabbe F, Adedeji AM, Bello A, Schmitz T, Landi M, et al. Results from one-year use of an electronic clinical secision aupport aystem in a post-conflict context: An implementation research. PLoS ONE. 2019;14(12):e0225634.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. WHO. SMART guidelines: World Health Organization; 2023. Available from: https://www.who.int/teams/digital-health-and-innovation/smart-guidelines.

  26. Erismann S, Gami JP, Ouedraogo B, Revault D, Prytherch H, Lechthaler F. Effects of a four-year health systems intervention on the use of maternal and infant health services: results from a programme evaluation in two districts of rural Chad. BMC Public Health. 2021;21(1):2304.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Lechthaler F, Abakar MF, Schelling E, Hattendorf J, Ouedraogo B, Moto DD, et al. Bottlenecks in the provision of antenatal care: rural settled and mobile pastoralist communities in Chad. Trop Med Int Health. 2018;23(9):1033–44.

    Article  PubMed  Google Scholar 

  28. Dansereau E, Miangotar Y, Squires E, Mimche H, group UND, group I, et al. Challenges to implementing Gavi's health system strengthening support in Chad and Cameroon: results from a mixed-methods evaluation. Global Health. 2017;13(1):83.

  29. Azétsop J, Ochieng M. The right to health, health systems development and public health policy challenges in Chad. Philos Ethics Humanit Med. 2015;10:1.

    Article  PubMed  PubMed Central  Google Scholar 

  30. WHO. Global Health Observatory data repository. Domestic general government health expenditure (GGHE-D) as percentage of general government expenditure (GGE) (%). Data by country.: World Health Organization; 2022.

  31. Organisation of African Unity. Abuja declaration on HIV7AIDS, Tuberculosis and other related infectious diseases. In: African summit on HIV/Aids Taoridf-A, editor. Abuja, Nigeria: Federal Republic of Nigeria; 2001.

  32. WHO. World health statistics 2022: monitoring health for the SDGs, sustainable development goals. Geneva; 2022. Report No.: CC BY-NC-SA 3.0 IGO.

  33. Moukénet A, de Cola MA, Ward C, Beakgoube H, Baker K, Donovan L, et al. Health management information system (HMIS) data quality and associated factors in Massaguet district, Chad. BMC Med Inform Decis Mak. 2021;21(1):326.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Samarasekera U. Huge health needs for refugees in Chad. Lancet. 2024;403(10427):603.

    Article  PubMed  Google Scholar 

  35. OCHA. Global humanitarian overview 2022: OCHA; 2022 [Available from: https://gho.unocha.org/chad. Accessed Sept 2022.

  36. UNHCR. Rapport opérationnel Chad du 29 mars 2024. N'Djamena, Chad: UNHCR; 2024.

  37. IPC. Chad: acute malnutrition snapshot October 2022 - September 2023. N'Djamena, Chad: Integrated Food Security Phase Classification; 2023.

  38. Gove S. Integrated management of childhood illness by outpatient health workers: technical basis and overview. The WHO working group on guidelines for integrated management of the sick child. Bull World Health Organ. 1997;75 Suppl 1(Suppl 1):7–24.

  39. UNHCR. Population réfugiée dans le district sanitaire de Goré, Tchad en 2019. In: UNHCR, editor. Goré, Chad: UNHCR; 2023.

  40. WHO. Service availability and readiness assessment (SARA). Annual monitoring system for service delivery. Implementation guide. Geneva, Switzerland: World Health Organization; 2015.

  41. WHO. Service availability and readiness assessment (SARA). Annual monitoring system for service delivery. Reference manual. Geneva, Switzerland: World Health Organization; 2015.

  42. MSF. Guide clinique et thérapeutique. Pour les programmes curatifs des hôpitaux et des dispensaires. A l'usage des prescripteurs.: Médecin sans frontière; 2019.

  43. OMS. Comment reconnaître les maladies tropicales négligées selon les altérations de la peau : guide de formation à l’usage du personnel sanitaire de première lignes. Genève: Organisation mondiale de la Santé; 2018.

  44. draw.io. The easiest way for confluence teams to collaborate using diagrams. Available from: https://drawio-app.com/.

  45. Ministère de la Santé Publique. Manuel d'ordinogrammes à l'usage des agents de santé de base. N'Djamena, Tchad: Ministère de la Santé Publique, Délégation sanitaire du Logone occidental, CIDR/CPG; 2006.

  46. WHO. Disease outbreak news. Dengue - Chad: World Health Organization; 2023 [16 October 2023]. Available from: https://www.who.int/emergencies/disease-outbreak-news/item/2023-DON491.

  47. WHO. Disease outbreak news. Chikungunya - Chad.: World Health Organization; 2020 [24 September 2020]. Available from: https://www.who.int/emergencies/disease-outbreak-news/item/chikungunya-chad.

  48. Dimagi. CommCare 2024. Available from: https://dimagi.com/commcare/.

  49. Community Health Toolkit. The Community Health Toolkit 2024 [cited 2024 July]. Available from: https://communityhealthtoolkit.org/.

  50. WCG. WCG whitepapers [Internet]. Princeton, USA: WCG. 2021. [cited 2021 28 October 2024]. Available from: https://www.wcgclinical.com/insights/do-you-know-me-the-subtle-distinction-between-anonymous-and-de-identified-data-in-clinical-research/.

  51. SPONGAH. Rapport d'évaluation finale. Projet : Système Numérique pour une meilleure gestion de la santé des réfugies » (SysRef) dans le District Sanitaire de Goré, Délégation Provinciale de la Santé et de la Prévention du Logone Oriental. N'Djamena, Chad: Secretariat permanent des organisations non gouvernementales et des affaires humanitaires au Ministère de la prospective economique et des partenariats internationaux du Tchad (SPONGAH); 2023 August 2023.

  52. Léchenne M, Montolnan A, Revault D. Digital system for better management of refugee health care (SysRef). Summary of the results of the user satisfaction study. N'Djamena, Chad: Swiss Tropical and Public Health Institute; 2023.

  53. Muhindo M, Bress J, Kalanda R, Armas J, Danziger E, Kamya MR, et al. Implementation of a Newborn Clinical Decision Support Software (NoviGuide) in a Rural District Hospital in Eastern Uganda: Feasibility and Acceptability Study. JMIR Mhealth Uhealth. 2021;9(2):e23737.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Bucher SL, Cardellichio P, Muinga N, Patterson JK, Thukral A, Deorari AK, et al. Digital Health Innovations, Tools, and Resources to Support Helping Babies Survive Programs. Pediatrics. 2020;146(Suppl 2):S165–82.

    Article  PubMed  Google Scholar 

  55. Ibrahim LM, Okudo I, Stephen M, Ogundiran O, Pantuvo JS, Oyaole DR, et al. Electronic reporting of integrated disease surveillance and response: lessons learned from northeast, Nigeria, 2019. BMC Public Health. 2021;21(1):916.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Martin DW, Sloan ML, Gleason BL, de Wit L, Vandi MA, Kargbo DK, et al. Implementing nationwide facility-based electronic disease surveillance in Sierra Leone: lessons learned. Health Secur. 2020;18(S1):S72–80.

    Article  PubMed  Google Scholar 

  57. Njuguna C, Jambai A, Chimbaru A, Nordstrom A, Conteh R, Latt A, et al. Revitalization of integrated disease surveillance and response in Sierra Leone post Ebola virus disease outbreak. BMC Public Health. 2019;19(1):364.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Njuguna C, Vandi M, Squire JS, Kanu JS, Gachari W, Liyosi E, et al. Innovative approach to monitor performance of integrated disease surveillance and response after the Ebola outbreak in Sierra Leone: lessons from the field. BMC Health Serv Res. 2022;22(1):1270.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Joseph JJ, Mkali HR, Reaves EJ, Mwaipape OS, Mohamed A, Lazaro SN, et al. Improvements in malaria surveillance through the electronic Integrated Disease Surveillance and Response (eIDSR) system in mainland Tanzania, 2013–2021. Malar J. 2022;21(1):321.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Mordecai EA, Ryan SJ, Caldwell JM, Shah MM, LaBeaud AD. Climate change could shift disease burden from malaria to arboviruses in Africa. Lancet Planet Health. 2020;4(9):e416–23.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Ahmed A, Ali Y, Mohamed NS. Arboviral diseases: the emergence of a major yet ignored public health threat in Africa. Lancet Planet Health. 2020;4(12):e555.

    Article  PubMed  Google Scholar 

  62. Beynon F, Salzmann T, Faye PM, Thiongane A, Ndiaye O, Luwanda LB, et al. Systèmes d’aide à la décision clinique pour la pédiatrie en santé globale. Rev Med Suisse. 2023;19(836):1398–403.

    PubMed  Google Scholar 

  63. Rodríguez-Ortega M, Ortega-Latorre Y, Montano-Navarro E, Casado-Collado AJ, Nguepy FR, Damou F. Telemedicine as a counselling tool for nurses in Central Africa. J Adv Nurs. 2025;81(5):2477–86.

  64. Beynon F, Guérin F, Lampariello R, Schmitz T, Tan R, Ratanaprayul N, et. al. Digitalizing clinical guidelines: experiences in the development of clinical decision support algorithms for management of childhood illness in resource-constrained settings. Glob Health Sci Pract. 2023;28;11(4):e2200439.

  65. Tan R, Cobuccio L, Beynon F, Levine GA, Vaezipour N, Luwanda LB, et al. ePOCT+ and the medAL-suite: Development of an electronic clinical decision support algorithm and digital platform for pediatric outpatients in low- and middle-income countries. PLOS digit health. 2023;2(1):e0000170.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Blanchet K, Sanon VP, Sarrassat S, Some AS. Realistic evaluation of the integrated electronic diagnosis approach (IeDA) for the management of childhood illnesses at primary health facilities in Burkina Faso. Int J Health Policy Manag. 2023;12:6073.

    PubMed  PubMed Central  Google Scholar 

  67. Jonnalagedda-Cattin M, Moukam Datchoua AM, Yakam VF, Kenfack B, Petignat P, Thiran JP, et al. Barriers and facilitators to the preadoption of a computer-aided diagnosis tool for cervical cancer: qualitative study on health care providers’ perspectives in western Cameroon. JMIR Cancer. 2025;11:e50124.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Mwogosi A, Kibusi S. Effectiveness of EHR systems on decision support in primary healthcare: a technology acceptance model 3 perspective. J Health Organ Manag. 2024. Ahead-of-print.

  69. Fletcher E, Burns A, Wiering B, Lavu D, Shephard E, Hamilton W, et al. Workload and workflow implications associated with the use of electronic clinical decision support tools used by health professionals in general practice: a scoping review. BMC Prim Care. 2023;24(1):23.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Rosenberg J, Miller K, Pickard O, Henrich N, Karlage A, Weintraub R. Barriers and facilitators to use of a digital clinical decision support tool: a cohort study combining clickstream and survey data. BMJ Open. 2022;12(11):e064952.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Ansu-Mensah M, Bawontuo V, Kuupiel D, Ginindza TG. Sustainable solutions to barriers of point-of-care diagnostic testing services in health facilities without laboratories in the bono region, Ghana: a qualitative study. BMC Prim Care. 2024;25(1):179.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Horwood C, Luthuli S, Mapumulo S, Haskins L, Jensen C, Pansegrouw D, et al. Challenges of using e-health technologies to support clinical care in rural Africa: a longitudinal mixed methods study exploring primary health care nurses’ experiences of using an electronic clinical decision support system (CDSS) in South Africa. BMC Health Serv Res. 2023;23(1):30.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are grateful to the children and caregivers for their participation in the project and to healthcare providers from the selected health centres for their commitment in all project phases, allowing us to improve the CDSS. The local authorities of the Goré health district and the UNHCR ensured a welcoming and safe working environment. The MoH, through the MoH’s Department of Health Services Organisation and Quality of Care (DOSSQS), was actively involved in all project phases and strongly supported the project. The CSSI provided administrative and logistical support to the project and staff. We thank Peter Steinmann, and Talia Salzmann, and the external reviewers for reviewing the manuscript. We are grateful to the Stanley Thomas Johnson Foundation for their strong support of the project.

Funding

Open access funding provided by University of Basel The project was funded by the Stanley Thomas Johnson Foundation based in Switzerland.

Author information

Authors and Affiliations

  1. Swiss Tropical and Public Health Institute, Allschwil, Switzerland

    B. Matthys, N. Monnier, P. Delcroix, M. Pereira, T. Schmitz, M. Zahorka, M. Léchenne, D. Revault, K. Wyss & A. Montolnan

  2. Centre de Support en Santé Internationale, N’Djamena, Chad

    M. Ngaradoumadji & Y. Toubangue

  3. Lewisham and Greenwich NHS Trust, London, UK

    J. Armour-Marshall

  4. Cantonal Hospital Winterthur, Winterthur, Switzerland

    K. Sugimoto

  5. University of Basel, Canton of Basel Stadt, Basel, Switzerland

    B. Matthys, N. Monnier, P. Delcroix, M. Pereira, T. Schmitz, M. Zahorka, M. Léchenne, D. Revault, K. Wyss & A. Montolnan

Authors
  1. B. Matthys
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. N. Monnier
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. M. Ngaradoumadji
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Y. Toubangue
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. P. Delcroix
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. M. Pereira
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. T. Schmitz
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. J. Armour-Marshall
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. M. Zahorka
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. K. Sugimoto
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. M. Léchenne
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. D. Revault
    View author publications

    You can also search for this author inPubMed Google Scholar

  13. K. Wyss
    View author publications

    You can also search for this author inPubMed Google Scholar

  14. A. Montolnan
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

BM drafted the manuscript and led its development process. AM, NM, DN, YT, PD, MP, TS, JA, MZ, and KS contributed to the content development of the CDSS. AM, DN, and YT were in charge of the implementation of the tool, and ML and BM were in charge of its monitoring and evaluation. DR and KW led the project. All co-authors contributed intellectual content, and reviewed and approved the final manuscript. The manuscript underwent a language check and edit suggestions by the AI-supported software “Curie” from AJE (https://beta.springernature.com/pre-submission/writing-quality?utm_source=Website_BMC&utm_medium=Digital_Edit&utm_campaign=Free+Digital+Edit+Referral+2023&utm_id=Curie2023).

Corresponding author

Correspondence to B. Matthys.

Ethics declarations

Ethics approval and consent to participate

The project was approved by the Government of the Republic of Chad (letter of approval no. 0230/MEPD/SE/DG/0004/DONGAH/2019, project agreement no. 0668/DSAONGOC/2019). The study protocol for the situational analysis was approved by the Chadian National Bioethics Committee (authorisation no. 155/PR/MESRI/SG/CNBT/2019). The research protocol for the user satisfaction study was approved by the Chad National Bioethics Committee (CNBT) (authorisation no. 11/PR/MESRSI/SE/DG/CNBT/SG/2021). The North-Western and Central Switzerland Ethics Commission (Ethikkommission Nordwest- und Zentralschweiz; EKNZ) confirmed that the study met all the requirements of a Swiss research project (AO_2021 - 00068), implying accordance with the protocol, the Declaration of Helsinki, the principles of Good Clinical Practice, the Human Research Act and the Human Research Ordinance. Written informed consent was obtained from all interviewees who agreed to participate in the studies.

Consent for publication

Consent for publication for Figure 4 from the health care provider and caregiver of the child is available.

Competing interests

The authors are members of the project team, contributing to the design and implementation of the CDSS examined in this study. They hold a financial and employment relationship with the project under review.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Matthys, B., Monnier, N., Ngaradoumadji, M. et al. Development and implementation of a digital clinical decision support system to increase the quality of primary healthcare delivery in a refugee setting in Chad. BMC Prim. Care 26, 113 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12875-025-02818-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12875-025-02818-w

Keywords

BMC Primary Care

ISSN: 2731-4553

Contact us