The Ethiopian healthcare delivery system and the challenges and opportunities of digital cloud computing in  the delivery of healthcare services

Negussie Tilahun, Ph.D., MA, MS, MSc.

Abstract

This study seeks to examine the existing landscape of cloud computing within Ethiopia. The analysis emphasizes three key factors influencing its implementation: organizational factors (including leadership strategies and hospital preparedness), environmental factors (such as regulations and standards), and technological factors (noting aspects such as complexity, perceived benefits, and compatibility). This examination is built upon a systematic review of high-quality peer-reviewed articles, conference papers, online resources, and a case study conducted at Finote Selam Hospital in Bahir Dar, roughly 210 km north of the capital, during the first week of January 2022. The research employs organizational systems theory to depict the Ethiopian healthcare delivery framework, which features both manual and electronic methods for the collection, processing, and dissemination of health data. Th ongoing civil war in Ethiopia since PM Abiy Ahemed came to power obliterated the already fragile healthcare infrastructures in Ethiopia  particularly in the Amhara and Tigri regions. Cloud computing can be considered as viable alternative in providing healthcare services with limited infrastructures. The introduction of cloud computing technology can be viewed as a potential means to enhance the Ethiopian healthcare delivery system, particularly following the extensive destruction of healthcare infrastructure over the past six years. Successful integration of cloud computing necessitates an adaptive management strategy aimed at maximizing value for both healthcare providers and patients, referred to as digital transformative leadership (Schiuma et al., 2024). The paper present the conceptual framework of cloud computing and the barriers of implementing the technology in low-income economies like Ethiopia.

Introduction

According to the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), cloud computing is defined as “a paradigm for enabling network access to a scalable and elastic pool of sharable physical or virtual resources with self-service provisioning and administration on demand” (Miyachi, 2018, p. 8). Cloud computing addresses two critical challenges in healthcare delivery: improving performance and lowering data storage costs by providing on-demand services. It allows hospitals to offer healthcare services more affordably by optimizing administrative operations, decreasing paperwork, and automating processes (Ciarli et al., 2021). Cost reduction occurs in two ways: hospitals avoid the need for extensive data infrastructure for storage and maintenance, and they typically pay only for the services utilized on a pay-as-you-go basis (Mujinga & Chipangura, 2011). Cloud computing can be implemented through various models, including public, community, private, or hybrid setups, with private models offering more control to hospitals or healthcare providers. Deployment options include Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), or Infrastructure-as-a-Service (IaaS), with IaaS providing greater control over hardware and software. The adoption of cloud computing technologies has notably increased in developing nations following the COVID-19 pandemic (Alashhab et al., 2021). However, many hospitals in Africa face challenges in organizational and managerial structures that hinder the implementation of cloud computing (Matchaba, 2019). Furthermore, Cloud computing facilitates communication between patients and doctors, enhancing the comprehensive treatment approach provided by physicians. It also supports the establishment of effective clinical decision support systems (CDSS), enabling a variety of activities, including monitoring vaccination coverage and following up on treatment outcomes. Overall, a CDSS can evaluate the effectiveness of a healthcare system by analyzing the repercussions of various health policies and practices (Antonio, 2014). Figure 1 illustrates the conceptual framework of cloud computing in the healthcare sector.

Figure 1: Conceptual framework of cloud computing in healthcare

The Current State of Ethiopia’s Healthcare Delivery System

This analysis apply organizational systems theory to explore the Ethiopian healthcare delivery system. This theory focuses on understanding the system as a whole, including the interconnections among its components, rather than simply examining isolated parts (Kast & Rosenzweig, 1972). It highlights the importance of recognizing how the components interact to assess the potential for technological innovations and their anticipated effects. Moreover, this theoretical framework aids in understanding processes, pinpointing inefficiencies, enhancing care quality, evaluating policy alternatives, and improving decision-making capabilities (Flood & Carson, 2013; Clarkson et al., 2018).

The Ethiopian healthcare system aims “to promote the health and well-being of society by equitably providing and regulating a comprehensive package of high-quality health services” (MOH, n.d.). It integrates manual and electronic data collection systems, utilizing paper-based and electronic tools to optimize workflows, improve communication, and enhance patient care. The structure is hierarchical, with the Ministry of Health (MOH) at the top, responsible for policy development and guideline provision. In 2015, the MOH initiated the Health Sector Transformation Plan, which introduced the Information Revolution (IR) aimed at creating an evidence-based decision-making framework by enhancing clinical data collection, analysis, and dissemination (PATH, 2019). The IR also emphasizes the necessity for cultural and attitudinal shifts regarding data management and dissemination (FMOH, 2016). In the same year, the Ethiopian Data Use Partnership (DUP) was established to standardize the Health Management Information System (HMIS), establish eHealth architecture, and improve data utilization (PATH, 2019). Additionally, the MOH created an HMIS unit responsible for managing health data, decentralizing public health system management to regional health bureaus, and implementing a mobile-based national digital Community Health Information System (eCHIS) to capture data on health extension programs and community-level services (EMH, n.d.). Other agencies also collect health data in Ethiopia. These include the Central Statistics Agency (CSA) and the Ethiopian Nutrition and Health Research Institute (NHRI), a specialized agency within the Ministry of Health responsible for public health emergency data. However, there is no coordination among government agencies, and the country lacks a national health IT policy. Consequently, there are no established guidelines to ensure the quality of collected data, and the absence of national policies governing electronic health records (EHRs) risks the interoperability of systems from different vendors. Figure 2 illustrates the Ethiopian healthcare delivery system.

Figure 2: Illustration of Ethiopia’s dual health informatics system

As noted previously, Ethiopia’s healthcare delivery system is hierarchical, with the lowest operational units being CHIS. CHIS are level I hospitals or clinics located in Woredas. These facilities gather diverse data inputs and generate daily, monthly, and annual reports, specifically longitudinal data across five key categories: (1) family planning, antenatal care, delivery, safe abortion, PMTCT; (2) EPI, vaccine wastage, child health, and illness management; (3) low birth weight (LBW), growth monitoring and promotion (GMP), severe acute malnutrition (SAM) screening, vitamin A supplementation (VAS), deworming, and iron and folic acid (IFA) supplementation; (4) antiretroviral therapy (ART) by regimen, initiation rates, and retention; and (5) malaria, visual inspection with acetic acid (VIA), diabetes, hypertension, and cardiovascular issues. Hospitals also collect socio-demographic information. These data will be reported to  level II regional or zone and Killil health centers. Health centers in Level II collect CHIS healthcare data and process data collected from Level I and send monthly and annual data to MoH with DHIS (District Health Information System). DHIS is a health information management system developed through a global collaboration led by the University of Oslo, offered free of charge as a public good (Hlaing & Zin, 2019).  Recently, the DHIS-2 Tracker, an extension of the DHIS-2 platform, was introduced in some Level II hospitals. The Tracker shares the same design principles as the overall DHIS-2, serving as a robust HMIS tool to monitor health programs and share vital clinical health data across multiple health facilities (Hlaing & Zin, 2020, p.15). The data collected manually is transformed into a digital record system, often requiring transcription into Excel or DHIS-2. The system generates medical reports for decision-making by physicians, Dar medical staff, and administrators. The healthcare facilities at the local level generate feedback to higher-level authorities, helping to identify problems and improve the process. A case study was conducted at Bahir-Dar Finote Selam Hospital to fully understand how Ethiopia’s healthcare system captures, processes, and analyzes data.

Case Study

Finote Selam Hospital in Bahir-Dar employs electronic health records (EHR) for storing patient data, scheduling appointments, and accessing clinical information while relying on manual methods for patient registration, scheduling, referrals, and other tasks better suited for manual operation. The hospital utilizes an EMR system known as Abay CHR (Connected Health Record), a standalone data collection system that does not interface with external systems. Data collection processes at both Finote Selam and Debre Birhan hospitals are dual incorporating manual and electronic methods. A patient’s initial interaction occurs at the triage unit, where the urgency of treatment is assessed based on illness severity. Critical cases, such as those involving severe bleeding or car accidents, are prioritized for immediate care, while non-urgent cases are directed to the registration unit for processing and treatment. The registration unit collects socio-demographic information, and treatment dates, and assigns a unique patient identifier for each individual. This identifier comprises a 16-digit code: the first three digits indicate location, the next three represent the institution (clinic, hospital, etc.), followed by three digits for ownership (private or government), and the final four digits uniquely identify the patient. Notably, this identifier is specific to each hospital and holds no significance outside that context. Clinical data is then recorded by healthcare providers (nurses and doctors) after patient treatment, including service dates, primary and secondary diagnoses using ICD-10 codes, discharge types (alive, deceased, etc.), and insurance status. This information is entered into Excel before being transferred to an electronic system such as DHIS. In general, electronic medical records are accessible to all medical staff, with daily departmental meetings to review the previous day’s activities based on the EMR dashboard. The medical director at Finote Selam emphasizes that the primary goal of these meetings is to assess clinical activities and prioritize daily tasks. Further analysis is necessary to ensure data integrity throughout the process. In terms of organizational structure, both hospitals studied are overseen by a board of directors responsible for strategic decision-making and operational oversight. This board appoints a Medical Director who supervises medical staff and ensures quality healthcare delivery. Medical Directors at both institutions play a crucial role in shaping medical policies and procedures, and overseeing departmental operations such as maternity and internal medicine. Each department is led by a department head responsible for managing its operations, supervising staff, and maintaining care quality. The administrative and support staff report directly to the board of directors, including the IT department, which manages the EMR and supports admissions and billing.

The Challenges of Cloud Computing in the Context of the Ethiopian Healthcare System

Over the years, Ethiopia has adopted several eHealth technologies, including SmartCare, mobile ENAT messenger, maternal interactive voice record (IVR), and the health management information system (HMIS) to align with WHO guidelines. WHO’s 2020 vision aimed to improve healthcare access globally by promoting the development and adoption of effective, accessible, affordable, scalable, and sustainable person-centered digital health solutions (Canton, 2021; WHO, 2021). Historically, Ethiopia’s healthcare infrastructure lagged significantly behind WHO recommendations for the optimal number of healthcare professionals and facilities required to serve the population (WHO, 2016). Also,  “the implementation and diffusion of eHealth technology in Ethiopia is still in its infancy” (EMH, 2019, p.10).

The Ethiopian healthcare infrastructure has severely deteriorated during the past six years  due to governmental neglect and escalating social conflict in various regions of the country. The ongoing civil war and economic turmoil across various regions of the country have further weakened the already fragile healthcare system. Since 2020, civil conflict has ravaged the country, with international organizations such as the Red Cross reporting extensive damage to the limited health infrastructure (ICRC, 2023; GanaWeb, 2023). The government has imposed internet blackouts, rendering cloud computing unfeasible without internet access. The conflict has decimated health facilities, particularly in Amhara, Tigray, and western Oromia. The resulting insecurity and transportation difficulties have severely impeded healthcare service provision and utilization. As cloud computing relies entirely on internet connectivity, the frequent interruptions in service caused by civil unrest represent a significant challenge in Ethiopia. Manyazewal et al. (2021) examined the factors influencing the adoption of digital health technologies in Africa, identifying Ethiopia as a critical case. They noted a lack of coordination among essential stakeholders and the absence of a supportive environment, particularly regarding electricity and reliable internet access, as significant barriers to implementing health informatics technologies.

Strategies for Successful Implementation of Cloud Computing

Literature on cloud computing emphasizes (i) the importance of senior management support and (ii) an effective organizational structure as critical determinants for adopting this technology (Aceto & Pescape, 2020; Amron et al., 2019). Health organizations need to adopt a technology-centric management approach that anticipates future demand and scalability to facilitate the transition to cloud computing (GeeksforGeeks, 2021). This necessitates the development of dynamic decision-making capabilities, enabled by enhanced access to real-time data, which fosters efficient resource management and cost reduction. The Ethiopian healthcare system operates under a hierarchical structure where decision-making is centralized, requiring multiple levels of approval for cloud computing adoption and implementation. This hierarchical approach often displays resistance to technological changes (McGrath & McManus, 2021). The existing hierarchical framework may hinder the necessary agility for a technology-focused management strategy, which demands swift adaptation to evolving market conditions (McGrath & McManus, 2021). Senior management is responsible for determining the development model (public, private, or hybrid) and the delivery model (hardware/software – IaaS, PaaS, or SaaS). Implementing these decisions necessitates restructuring the current hierarchical management to form an interdisciplinary, agile, and adaptive team. Achieving agility within a hierarchical structure presents challenges, primarily due to centralized decision-making at higher management levels, where technical and administrative staff may lack the authority to respond swiftly to changing circumstances. Preserving established processes and power dynamics can often take precedence. Nonetheless, Ethiopian health institutions can navigate these hierarchical challenges by adopting effective management strategies.

Some researchers argue that strong senior leadership and management support, which embrace digital management concepts and organizational structures, are vital for the successful adoption of cloud computing (Kohnke, 2017). Administrators and managers must blend traditional and dynamic leadership styles to effectively implement cloud computing within a hierarchical system. Leaders should combine agile, flexible, and adaptive decision-making capabilities while adhering to traditional leadership principles, such as supporting team members, removing barriers, identifying performance gaps, addressing shortcomings, and leveraging lessons learned (Graffeo, 2019). Effective leaders demonstrate humility, acknowledge their limitations, and balance guiding their teams with making significant decisions. Digital transformative leaders must be adept at technology and possess digital expertise. Additionally, healthcare managers are tasked with implementing cloud technology amidst technological challenges and the legal, cultural, and organizational barriers present. If not managed positively, the introduction of digital technologies may exacerbate disparities in healthcare services. A crucial element of a technology-focused management strategy is understanding a hospital’s capacity regarding patient volume, bed availability, patient-to-doctor ratios, and scaling capabilities (GeeksforGeeks, 2021). Furthermore, it is advisable to implement cloud computing in a limited number of hospitals initially, gather insights from those experiences, and then scale to full implementation. The following steps are recommended to create a conducive organizational structure for successful cloud computing implementation:

Promote training and continuous learning as essential tools for managing cloud computing technologies in an era of rapid change. Leaders in healthcare organizations adopting cloud computing must view themselves as lifelong learners. Cloud computing is anticipated to enhance operational efficiency by minimizing medical and administrative errors, streamlining patient information management, improving communication between providers, increasing access to care, decreasing readmission rates, and enhancing patient engagement and satisfaction. It also holds the potential to improve financial outcomes through cost reductions and revenue increases. Management should establish indicators to assess the impact of cloud computing. The following management and quality metrics can be adopted:

Identify the appropriate type of cloud computing that aligns with the healthcare organization’s needs and evaluate its associated benefits and costs.

Execute a management strategy that restructures the organization to enhance agility and adaptability for digital transformation. This can involve reducing hierarchical levels, decentralizing decision-making, and fostering greater collaboration among staff.

Explicitly prioritize expanding healthcare access and mitigating disparities in service delivery as management objectives, while consistently monitoring, evaluating, and addressing obstacles. Successful cloud computing management necessitates leadership that cultivates a shared vision, engaging all levels of upper leadership, not just a select few.

Promote training and continuous learning as essential tools for managing cloud computing technologies in an era of rapid change. Leaders in healthcare organizations adopting cloud computing must view themselves as lifelong learners. Cloud computing is anticipated to enhance operational efficiency by minimizing medical and administrative errors, streamlining patient information management, improving communication between providers, increasing access to care, decreasing readmission rates, and enhancing patient engagement and satisfaction. It also holds the potential to improve financial outcomes through cost reductions and revenue increases. Management should establish indicators to assess the impact of cloud computing. The following management and quality metrics can be adopted:

Management Metrics

Employee Engagement: Metrics such as turnover rate and employee productivity evaluate engagement, motivation, and commitment within the hospital.

Quality and Performance: Metrics like defect rate, error rate, and customer satisfaction, often measured through surveys, assess the quality and performance of services provided.

Innovation and Adaptability: Metrics related to research and development investments, new product launches, and market share gauge the organization’s capacity for innovation and its ability to adapt to market shifts while minimizing waste.

Employee Development and Succession Planning: Metrics such as training hours, promotion rates, and talent retention indicate the organization’s commitment to employee development and succession planning. Continuous learning is vital in managing cloud computing technologies in a fast-evolving landscape.

Ethics and Equity: Metrics assess the reduction of disparities in healthcare services between urban and rural communities or among different socioeconomic groups.

Quality Metrics

Quality metrics are also crucial for the successful implementation of cloud computing. Hospitals with established quality management plans are better positioned to initiate quality improvement initiatives, and there is a positive correlation between quality management practices and organizational performance (Nguyen et al., 2018). Transitioning to cloud computing may necessitate the development of quality metrics aligned with priorities and quality management frameworks. Some general metrics that healthcare organizations may consider include:

First-time Resolutions: Measurement of the number of issues resolved during the initial interaction with patients.

Error or Defect Rate: Tracking errors or defects in cloud computing processes.

Response Time: Monitoring the time taken to resolve issues raised by patients or clients.

Compliance Rate: Assessing non-compliance rates, as transitioning to cloud computing can be challenging, particularly in adhering to government regulations.

Conclusion

This study evaluated the primary barriers to implementing cloud computing in Ethiopia and proposed a management strategy to tackle some of the challenges associated with this technology’s adoption. Organizational systems theory was utilized to elucidate the Ethiopian healthcare delivery system, characterized by its dual methods of data collection and processing. The nation’s healthcare infrastructure remains significantly below the WHO-recommended levels regarding healthcare personnel and facilities. The analysis identified organizational, environmental, and technological barriers that obstruct the implementation of cloud computing. The ongoing civil war has further dismantled the already fragile healthcare delivery system, with health facilities in Amhara, Oromia, Tigray, and Afar regions that makes up about 3/4^th of the country. Cloud computing can offer an alternative solution in mitigating a fraction of the infrastructure devastated by the civil war. It has the potential to deliver telemedicine and digital health solutions to areas severely impacted by conflict, thereby leapfrogging healthcare services in regions lacking complete infrastructure (Zeadally & Bello, 2021). The key to successful digital transformation lies in building a capacity to adapt to continuously evolving health needs. Management must shift paradigms to address current health requirements while identifying emerging trends. A management strategy that accounts for future demand and scaling capabilities is essential for a successful implementation of cloud computing, accompanied by a series of management and quality metrics to evaluate progress.

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