Possible Process Optimization: Innovative Digital Health Implementation Models

Abstract

The digitization in healthcare faces challenges in LMICs. The success of digital tools depends on having a workforce capable of designing, implementing, and maintaining such tools. Relevant training of professional staff and familiarisation with new tasks are crucial processes for the optimization of digital tools’ performance. In the broader context, interdisciplinary and interprofessional healthcare interventions often focus on outcomes such as length of stay, readmission rates and/or mortality. However, the effects of digital health interventions on these outcomes have been inconsistent in low-and middle-income countries. The current chapter discusses identified challenges in different digital health implementation models. Addressing these challenges and conducting further research and evaluation can contribute to successfully implementing digitization and process optimization in healthcare settings, leading to improved patient outcomes and quality of care.

1 Introduction

Process optimization in healthcare refers to improving healthcare processes and systems to increase efficiency, reduce waste, and enhance patient outcomes. In healthcare, a process can be defined as a sequence of interrelated tasks or activities that aim to achieve a specific healthcare goal, such as diagnosing and treating a patient, managing a chronic condition, or providing preventive care. The digitalization of process optimization in healthcare has the potential to significantly improve healthcare outcomes by increasing efficiency, reducing errors and redundancies, and improving patient access to healthcare services. However, it is essential to ensure that digital tools are implemented to protect patient privacy and security and that healthcare providers are appropriately trained to effectively use these tools.

Optimization of limited healthcare resources utilising digital technologies might provide significant advantages to low- and middle-income countries (LMICs). Finding the optimal way to handle the sophisticated healthcare requirements of hospitalised, mostly multimorbid medical patients is a global issue. The quality of health services should be measured by their efficacy, safety, and focus on the needs of the patients. For the advantages of quality health care to be realised, health services must be timely, equitable, integrated, and efficient. On the other hand, fundamental logistic and organisational aspects of medical hospital care have been prioritised less than treating particular diseases.

Digitalization has the potential to offer substantial benefits to the optimization of healthcare processes in LMICs. Nonetheless, a number of challenges must be overcome to guarantee the successful implementation and adoption of digital tools in these settings.

Benefits of digitalization in LMICs:

• Improved access to healthcare. Digital tools such as telemedicine and mobile health apps can help bridge gaps in healthcare access in LMICs, particularly in remote or underserved areas (World Health Organization 2021).

• Increased efficiency. Digital tools can automate routine tasks and improve data management, reducing the time and resources needed to manage healthcare processes (Olu et al. 2019).

• Enhanced patient outcomes. Digital tools can improve the accuracy and completeness of patient health information, leading to better diagnosis and treatment decisions (Attia et al. 2019).

• Cost savings. Digital tools can help reduce the costs associated with healthcare processes, such as paper-based record-keeping and administrative tasks (World Health Organization 2019).

• Increased collaboration. Digital tools can facilitate communication and cooperation between healthcare providers, improving care coordination and patient outcomes (Wannheden et al. 2022).

Challenges of digitalization in LMICs (World Health Organization 2021):

• Infrastructure limitations. Many LMICs lack the infrastructure to support digital tools, such as reliable electricity and internet access.

• Limited resources. Healthcare providers in LMICs may lack the resources or expertise necessary to implement and utilise digital tools effectively.

• Limited digital literacy. Patients and healthcare providers in LMICs may have limited experience with digital tools and require additional training and support.

• Data privacy and security. Digital tools may raise concerns about data privacy and security, particularly in LMICs where regulations and infrastructure to support secure data management may be lacking.

• Integration with existing systems: Digital tools may need to be integrated with existing healthcare systems, which can be complex and require significant resources.

Overall, despite the fact that digitalization can significantly improve healthcare process optimization in LMICs, there are still a number of obstacles to be resolved before these technologies can be successfully adopted and implemented. Addressing these challenges will require collaboration between healthcare providers, policymakers, and technology providers to ensure that digital tools are tailored to the needs and context of LMICs.

Several process optimization solutions have already acquired widespread implementation in a broad spectrum of industries. Some are now being piloted on a limited basis, and others are still in the initial phases of research. Figure 1 shows several process optimization technologies in three stages of development (Chowdhury and Pick 2019).

Fig. 1

Healthcare process optimization applications on scaled, pilot and research stages

2 Electronic Health Record-Based Tools

Electronic Health Record (EHR) is a permanent electronic record of patient health information created by one or more encounters in any healthcare setting. EHR is a patient’s official health documentation maintained over time by the provider and shared among various facilities and organisations. It may include all relevant administrative and clinical information required for that person’s treatment by that provider, such as demographics, progress notes, issues, treatments, vitals, past medical histories, vaccinations, laboratory results, and radiology reports (Resource Center – All Resources | HIMSS 2023). EHR systems have been used by many healthcare institutions throughout the globe owing to their various advantages over traditional paper charts. Consequently, transitioning from paper to electronic charting has become a priority for several healthcare facilities that previously utilised paper documentation. Implementing a comprehensive healthcare data management system with a solid case has been found to enhance healthcare quality through facilitating fast data extraction (Xu et al. 2016), improving clinical research (DeShazo and Hoffman 2015; Adane et al. 2019) and clinical practice communication (Xu et al. 2016; Bookman et al. 2017; Lakin et al. 2016; Halpern et al. 2016; Weber and Kohane 2013), minimising medical errors (Adane et al. 2019), standardising medical documentation (Adane et al. 2019; Plantier et al. 2017; DesRoches et al. 2008), and raising the standard of healthcare services (Jawhari et al. 2016). Clinical decision support (CDS) tools, health information exchange (HIE) and computerised physician order entry (CPOE) systems are three specific features that show significant promise in improving the efficacy of healthcare and decreasing costs at the healthcare system level.

A CDS system assists the expert in making choices about patient care by delivering the most recent data on a therapeutic agent, cross-referencing a patient’s allergy to a medicine, and flagging probable drug interactions and patient difficulties. These aspects contribute to the provider’s ability to deliver patients the most effective care possible. As a result of the expanding medical knowledge, each of these capabilities now offers a mechanism through which maintenance may be provided far safer and more effectively. One may anticipate that some medical mistakes will be avoided as the number of CDS systems in use increases and that the patient will, on the whole, get the treatment that is both more effective and safer as a result (Sutton et al. 2020).

HIE is exchanging patient-level electronic health information across organisations, which may lead to significant improvements in healthcare delivery. HIE may eliminate expensive duplicate tests done because one provider does not have access to clinical information held at another provider’s site by allowing for the secure and possibly real-time interchange of patient information. HIE allows for the interaction of this information through EHRs, which may lead to significantly more cost-effective and high-quality care (Walker et al. 2005).

CPOE systems are intended to substitute a hospital’s paper-based ordering system by enabling consumers to electronically make the full spectrum of orders, retain an online prescription administration record, and monitor modifications made to orders by successive staff. In addition, devices provide safety notifications triggered when an unsafe order (for instance, a request for duplicate prescribed medications) is submitted, as well as clinical decision assistance to direct clinicians to less costly options or choices that better correspond to hospital guidelines (Connelly and Korvek 2022).

Despite the adoption of EHR systems by numerous medical institutions in high-income countries, implementing EHR software in healthcare facilities in LMICs remains a significant challenge due to financial constraints, lack of necessary technological infrastructure, and limited access to software training (Why sub-Saharan Africa 2023; Akhlaq et al. 2016; Ajami and Bagheri-Tadi 2013).

Nonetheless, it is crucial to implement EHR-based tools in LMICs because they can improve the quality of care, reduce medical errors, and improve overall healthcare outcomes. To introduce EHR-based instruments in LMICs, the following actions can be taken (Archer et al. 2021; Fraser et al. 2005; Ohuabunwa et al. 2016):

• 1. Evaluation of the current state of health information technology (IT) infrastructure in the target LMICs: determination of the availability of hardware, software, internet connectivity, and other necessary resources for EHR implementation.

• 2. Development of a solid understanding of the local healthcare system, including its structure, processes, and data needs and involvement of key stakeholders, such as healthcare providers, administrators, policymakers, and patients, to determine their unique needs and obstacles.

• 3. Investment in training and capacity-building programs for building local health IT expertise: train healthcare professionals, IT specialists, and other relevant personnel in EHR implementation, data management, and system administration. This contributes to the sustainability of EHR implementation and upkeep.

• 4. Adaptation of EHR systems to local requirements: numerous off-the-shelf EHR systems may not meet the specific needs of LMICs, by collaboration with vendors or developers to adapt EHR systems to local protocols, dialects, clinical guidelines, and data standards, and development or modifying EHR modules, interfaces, and reporting tools may be required.

• 5. Establishment of data governance and privacy policies, including developing robust data governance frameworks to address concerns regarding privacy, security, and confidentiality, assuring compliance with local data protection regulations and international standards and establishing data sharing, access control, and informed consent policies.

• 6. Enhancement of health information exchange, including promoting interoperability among healthcare facilities and systems to enable the seamless exchange of health data and implementing standards to facilitate data sharing and integration across various EHR platforms.

• 7. Pilot project implementation and scalability: it is recommended to start with small-scale pilot programs to evaluate the feasibility and efficacy of EHR implementation in particular settings or regions, followed by an evaluation of the outcomes, lessons learned, and difficulties encountered during the pilot phase. Based on the findings, the implementation strategy and the development of a scalable plan for expanding EHR adoption across the nation or region should be modified.

• 8. Assuring infrastructure readiness: strengthening the IT infrastructure by enhancing internet connectivity, power supply, and hardware availability and utilising mobile technologies, such as smartphones and tablets, to overcome infrastructure limitations in remote or resource-constrained areas.

• 9. Training the users and providing ongoing support by conducting comprehensive training programs for healthcare professionals to ensure they are proficient in using EHR systems effectively and providing ongoing technical support to address any problems encountered during the implementation and post-implementation phases.

• 10. Monitoring and evaluation by establishing mechanisms to monitor the impact of EHR implementation on healthcare delivery, patient outcomes, and system performance, continuously evaluating the benefits and challenges of EHR adoption and making the necessary adjustments to optimise its efficacy.

Innovative transition tools based on electronic health records are necessary to decrease unequal resource distribution and significantly improve the clinical needs of hospitalised medical patients.

Despite this, several studies indicate that businesses wishing to install EHR software at healthcare institutions in LMICs must take a highly personalised strategy due to the substantial variation in hospital and government policies.

Below are some examples of open-source EHR platforms that LMICs can use.

2.1 Smile Train Express (STX)

Smile Train Express (STX) is an EHR system developed by Smile Train, a charity focused on cleft lip and palate treatment. Smile Train, the largest cleft charity in the world with more than two decades of experience collaborating with healthcare facilities in LMICs, devised a cleft treatment EHR system and disseminated it to their partner institutions (Louis et al. 2018). It aims to overcome barriers associated with EHR implementation by minimising technological requirements and simplifying the documentation of patient’s Protected Health Information (PHI) (Louis et al. 2018). The primary purpose of STX is to track cleft surgical data, enabling Smile Train and its partner institutions to collaborate in developing quality improvement and safety plans to enhance and standardise cleft surgical care (Louis et al. 2018). To receive funding for cleft surgeries, all Smile Train-partner institutions are obligated to enter surgical cases into STX within 31 days of the procedure and actively engage in quality improvement and safety practices (Louis et al. 2018). Case entry can be completed during patient encounters or at a later date as long as the healthcare data is uploaded to the STX cloud-based patient record database on a monthly basis. In some Smile Train-partner institutions, STX has evolved from a quality improvement tool to the primary medical documentation medium (Louis et al. 2018).

The studies suggest that the implementation of STX has impacted medical documentation practices at some partnered institutions (Ferry et al. 2021; Nutley et al. 2013; Hernández-Ávila et al. 2013). However, the integration of STX into clinical workflows at most institutions has likely been limited due to regulations and guidelines established by governing bodies. The findings emphasise that organisations aiming to implement EHR software in healthcare facilities within LMICs need to adopt a highly individualised approach. This is necessary because of the considerable variability in hospital and governmental policies within LMICs. The studies highlight the importance of understanding and adapting to each institution’s specific regulatory and policy contexts to successfully implement EHR systems in LMIC settings.

2.2 OpenMRS

OpenMRS, released in 2004, has become one of the most widely used open-source EHR systems LMICs (Serda et al. 2011). It was designed for resource-constrained environments and has been implemented globally in healthcare facilities. The software is supported by an extensive network of developers and implementers contributing to its ongoing development.

OpenMRS was initially developed by researchers, developers, and public health professionals at Regenstrief Institute and Partners in Health (PIH) for the AMPATH project in Kenya (Seebregts et al. 2009). Its early implementations focused on HIV and TB patient management in Kenya, Rwanda, and South Africa (Seebregts et al. 2009). Today, OpenMRS is utilised in various use cases and care settings, including secondary and tertiary facility-based health records management, primary healthcare, telemedicine, HIV care, tuberculosis management, non-communicable disease management, maternal and child health, reproductive health, Ebola response, and cancer care (Verma et al. 2021).

The platform was designed to be scalable across multiple countries and use cases, multilingual, and capable of functioning in challenging environments with limited internet access and low technology adoption (Wolfe et al. 2006). It is an open-source application with a robust data model, basic EHR functionality, and the ability to add new features through modules (Wolfe et al. 2006).

OpenMRS has been recognized as a “Global Good,” indicating its role as a sustainable and scalable medical records solution in LMICs (Digital Square 2023). As governments and funders increasingly promote open-source technology, including Global Goods, this study aims to analyse the reach, utilisation, impact, and return on investment of OpenMRS as a Global Good (Digital Square 2023). It also seeks to identify key challenges and unmet needs to guide continued investment in the platform.

2.3 DHIS2

DHIS2 is a web-based open-source platform primarily used as a Health Management Information System (HMIS) (About DHIS2 – DHIS2 2023). It is the largest HMIS platform in the world and is presently used by 73 low- and middle-income countries, affecting approximately 2.4 billion individuals (About DHIS2 – DHIS2 2023). Moreover, DHIS2 is in use across more than 100 countries, including programs run by non-governmental organisations (NGOs) (About DHIS2 – DHIS2 2023). Globally coordinated by the HISP Centre at the University of Oslo (UiO), the development of the DHIS2 software is a global collaborative effort. HISP, which stands for Health Information Systems Programme, is a network consisting of 17 in-country and regional organisations (About DHIS2 – DHIS2 2023). These organisations provide ongoing direct support to ministries and local implementers of DHIS2, ensuring its effective utilisation and implementation (About DHIS2 – DHIS2 2023). DHIS2 is an open-source platform for health management information systems, serving numerous countries worldwide and benefiting a significant portion of the global population. The collaboration and support provided by the HISP network further contribute to its successful implementation and development (About DHIS2 – DHIS2 2023).

Some papers suggest that DHIS2 has value for Health Impact Assessment (HIA) in low-resource settings by standardising data collection processes and improving reporting rates and accuracy (DHIS2 as a tool for health 2023; Byrne and Sæbø 2022). However, there are obstacles to effectively using DHIS2 data for HIA. The key challenges identified include limitations in data quality, analysis, and access (DHIS2 as a tool for health 2023). Multiple platforms operating independently across different ministries, sectors, or organisations can hinder a comprehensive understanding of health conditions. To address the challenges, additional funding and cross-institutional collaboration are needed to integrate platforms, promote national stewardship of DHIS2, and establish shared understandings of data through data dictionaries, metadata packages, and formal processes for integrating systematic data collection into global health monitoring and evaluation frameworks (DHIS2 as a tool for health 2023). Efforts are also required to build human resource capacity for HIA, including training for data cleaning, analysis, and visualisation (DHIS2 as a tool for health 2023). Furthermore, expanding accessibility to DHIS2 data through public web portals can enhance the value of DHIS2 for HIA and evidence-based health policy, ultimately improving health outcomes (DHIS2 as a tool for health 2023).

In summary, DHIS2 offers numerous advantages for LMICs, including its cost-effectiveness, customizability, scalability, and data standardisation features. However, technical requirements, data quality, sustainability, and system complexity must be addressed to successfully implement and utilise DHIS2 in LMICs. Close collaboration with stakeholders, adequate investment in infrastructure and capacity-building, and continuous support are essential for maximising the benefits of DHIS2 in resource-constrained settings.

3 Healthcare Scheduling Optimization

Healthcare scheduling optimization refers to the process of using advanced techniques and algorithms to optimise the scheduling and allocation of resources in healthcare settings. It aims to improve operational efficiency, patient outcomes, and resource utilisation while considering various constraints and objectives.

Healthcare scheduling research is crucial for optimising costs, improving patient flow, and efficiently utilising hospital resources. In recent decades, there has been an increasing number of systems that use metaheuristic methodologies to automate the search for optimum resource management in healthcare. The focus has primarily been solving healthcare scheduling problems such as patient admission scheduling (Ceschia and Schaerf 2012), nurse organising issues, and operating room scheduling/surgical scheduling (Di Martinelly et al. 2014). These methods aim to provide timely treatment administration and maximise the utilisation of available hospital resources.

3.1 Patient Admission Scheduling Problem

Patient Admission Scheduling Problem (PASP) is a complex combinatorial problem involving scheduling patients within specific time slots to optimise management competency, patient comfort and safety, and overall medical care (Abdalkareem et al. 2021). The problem aims to allocate patients to appropriate beds in relevant departments, considering their specific medical needs and restrictions (Abdalkareem et al. 2021). In some hospitals, a centralised admission office handles the assignment of patients to beds by coordinating with different departments in advance (Abdalkareem et al. 2021). However, in other cases, the responsibility of patient admission is decentralised, leading to a lack of overall knowledge and information among departments (Abdalkareem et al. 2021). This decentralised approach can result in suboptimal occupancy of beds, with some departments facing a shortage of available beds while others have extra beds (Abdalkareem et al. 2021). Efficiently solving the patient admission scheduling problem requires developing strategies and algorithms considering various factors, including patient needs, department capacities, and medical restrictions. By addressing this challenge, hospitals can improve bed allocation, optimise resource utilisation, and ensure that patients receive appropriate care in a timely manner (Abdalkareem et al. 2021).

Several software solutions available for patient admission scheduling are designed explicitly for LMICs. Here are a few examples:

• Open Hospital: Open Hospital is an open-source hospital management system with patient admission scheduling features. It was designed to be adaptive and configurable to the requirements of LMICs (Open Hospital 2023). Open Hospital offers functionalities for managing patient appointments, bed allocation, and resource utilisation. It aims to provide affordable and efficient solutions for healthcare facilities in resource-constrained settings (Open Hospital 2023).

• MedScheduler: MedScheduler is a web-based scheduling software designed for healthcare facilities in LMICs. It offers features for managing patient appointments, tracking bed availability, and optimising schedules based on various factors (New Innovations – GME Details 2023). MedScheduler aims to improve efficiency and patient flow in healthcare facilities with limited resources (New Innovations – GME Details 2023).

• mHealth applications: Mobile health (mHealth) applications, particularly those developed for LMICs, often include features for patient appointment scheduling and management (mHealth App Development 2023). These applications can be accessed through smartphones or feature phones, allowing healthcare providers to schedule patient admissions, send appointment reminders, and track patient flow (mHealth App Development 2023).

3.2 Nurse Scheduling Problem

Generating nursing schedules is a crucial task requiring significant time and effort from managers. It involves efficiently allocating nurses to shifts, considering a range of constraints such as shift timings, holidays, leaves, and unexpected events (D’souza et al. 2021).

Nursing scheduling software, a health rota or roster software, is crucial in healthcare environments for efficiently allocating nurses to shifts while considering various constraints (D’souza et al. 2021). These software solutions help automate the process of creating and managing nursing schedules, considering factors such as shift timings, holidays, leaves, and emergencies (D’souza et al. 2021). By using nursing scheduling software, managers can optimise the allocation of nurses, ensure appropriate coverage, and improve overall workforce management (D’souza et al. 2021).

Examples of nursing scheduling software include ROTA, NurseGrid, and Schedule360 (D’souza et al. 2021; NurseGrid 2023; Schedule360 2023). These software options offer various features and functionalities to streamline the nursing scheduling process and improve overall workforce management in healthcare settings.

As an example of utilising roster software in LMIC, the Hong Kong Health Authority operates an AI-based tool developed by the City University of Hong Kong to generate staff rosters that meet various constraints (Nurse Rostering 2023). These constraints include staff availability, preferences, working hours, operational requirements, and regulations. Implemented across 40 public hospitals, the tool manages the scheduling of over 40,000 staff members. The introduction of this system has resulted in increased productivity, improved staff morale, and enhanced service quality. The tool is perceived as fair, saves managers’ time, and provides valuable insights into working patterns and resource utilisation, benefiting overall management efficiency (HA: Nurse Rostering 2023).

3.3 Operating Room Scheduling/Surgical Scheduling

The operating room theatre is a critical component of the healthcare sector, significantly influencing hospital performance. It involves a specialised combination of personnel and equipment, and each surgery requires preoperative and postoperative preparations. However, managing and scheduling the operating room theatre is challenging due to various constraints and stakeholder preferences. Furthermore, limited resources and the growing demand for surgical services have prompted the development of improved approaches for room scheduling. These approaches aim to effectively manage the operating room theatre by implementing different strategies and methodologies (Abdalkareem et al. 2021).

Several examples of operating room scheduling/surgical scheduling software are suitable for LMICs. Here are a few examples:

• SurgiDat: SurgiDat is a web-based operating room management system for resource-constrained settings. It helps hospitals and surgical centres optimise operating room scheduling, manage patient flow, track surgical instruments, and monitor performance indicators (SurgiDat 2023).

• Surgimate: Surgimate is a comprehensive surgical scheduling software that helps streamline the entire surgical workflow, from preoperative planning to postoperative follow-up. It allows hospitals and surgical centres to efficiently manage their surgical resources, schedule surgeries, track patient information, and generate reports (Surgimate 2023).

These software solutions can help healthcare facilities in LMICs overcome the challenges associated with operating room scheduling, optimise resource utilisation, and improve the efficiency of surgical services. Evaluating each software’s specific features and suitability for the local context is essential before implementation.

4 Supply Chain Management for Medicines

The healthcare supply chain (SC) has been described as the information, supplies, and finances associated with acquiring and transferring products and services from the supplier to the end user of products and services from the supplier to the end user in order to improve clinical outcomes and control costs (Schneller and Smeltzer 2006). Due to the unique nature of health services, the supply chain is not limited to physical products (drugs, pharmaceuticals, medical devices, health aides, and other products) but also includes patient flow (The management of the supply 2023). According to Turhan and Vayvay (n.d.), healthcare supply chain management (SCM) the healthcare supply chain differs from that of other industries due to its propensity for misalignment, high costs for healthcare providers, and reliance on third parties. The healthcare supply chain is decentralised, lacking (financial or contractual) coordination mechanisms between physicians, hospitals, and patients, and is subject to substantial regulatory pressure (Dobrzykowski 2019).

Digitalization plays a crucial role in transforming healthcare supply chain management. By leveraging technology and digital tools, healthcare organisations can enhance efficiency, transparency, and coordination throughout the supply chain process (Beaulieu et al. 2021). Digital platforms and software solutions are also used to automate procurement processes, streamline supplier management, and optimise distribution logistics. These tools facilitate automated ordering, invoice processing, and payment systems, reducing administrative burdens and improving operational efficiency (Beaulieu et al. 2021).

The concept of SC digitalization encompasses a range of technologies, including both traditional and advanced ones. Conventional technologies like electronic data interchange (EDI), electronic catalogues, radio frequency identification (RFID), and automated guided vehicles (AGVs) (Bechtsis et al. 2017; Morenza-Cinos et al. 2019) are now integrated into the broader concept of digitalization, alongside newer technologies such as cloud computing, IoT, big data analytics, 3D printing (Kosmol et al. 2019), blockchain (Chang et al. 2019), and artificial intelligence (Ehie and Ferreira 2019). However, there is still a need for a standard definition of terms like big data within the context of SC digitalization.

These technologies offer the potential to enhance supply chain management by enabling real-time synchronisation of material and information flows, personalised production (Büyüközkan and Göçer 2018), and improved flexibility and agility (Seyedghorban et al. 2019). However, their adoption would require restructuring the roles of actors within the supply chain and the development of necessary skills to effectively use the tools and analyse the vast amounts of data generated. To fully benefit from these new technologies, organisations should prioritise developing deployment plans and ensuring they have the required skills and capabilities before rushing into their acquisition. This approach, suggested by Hartley and Sawaya, emphasises the importance of adequate preparation to fully leverage the potential of digitalization in the supply chain domain (Hartley and Sawaya 2019).

Constructing effective SCM systems in LMICs can be complicated due to limited infrastructure, insufficient institutional frameworks, and resource constraints. Nonetheless, there are a number of strategies and approaches that can aid in enhancing supply chain administration in LMICs.

The eLMIS, which stands for Electronic Logistics Management Information System, is an innovative and cost-efficient solution for managing health data (Usaid 2015). Its implementation in Zambia and Tanzania has enhanced commodity security and improved health outcomes for the population (Strengthening Health Systems 2023). In healthcare programs, the availability of an adequate quantity and quality of health products is crucial for meeting patient needs and achieving better health outcomes. To address this challenge, Zambia and Tanzania collaborated to develop the eLMIS, a comprehensive system encompassing various major health programs in the countries. By establishing a connection between health facilities and the central store, the eLMIS enables the collection and real-time distribution of logistics data. This information plays a vital role in supply chain management by providing insights into the utilisation and demand for medicines, thereby facilitating the provision of uninterrupted supplies to patients.

Bangladesh’s Directorate General of Health Services (DGHS) faced challenges in health supply chain management, including the lack of an integrated inventory management system and tracking capabilities, mainly when COVID-19 emerged in the country, USAID Medicines, Technologies, and Pharmaceutical Services (MTaPS) collaborated with DGHS to develop a comprehensive COVID-19 eLMIS based on an existing eLIMS (Digitalization of COVID-19 Commodities 2023). MTaPS initially established a basic online reporting system and trained around 500 health workers. This enabled centralised tracking of emergency commodity stock levels at health facilities and distribution centres. By April, the DGHS, central administration, suppliers, and beneficiaries received daily updates on emergency commodity stocks, with 99% of COVID-19-dedicated health facilities reporting regularly. Recognizing the need for an expanded inventory management system, MTaPS and DGHS upgraded the reporting system into a comprehensive COVID-19 eLMIS. This enhanced system included a quantification tool for real-time stock information, which is crucial for timely procurement and distribution decisions at the central level. The eLMIS was gradually implemented in a phased rollout after user acceptance testing and facility assessments. The collaboration between MTaPS and DGHS in developing the COVID-19 eLMIS helped overcome the supply chain crisis. The system improved the visibility, tracking, and reporting of emergency commodity stocks, facilitating informed decision-making at the central level.

5 Patient Registries

Patient registries are systematic databases that collect standardised data on a specific population affected by a disease, condition, or exposure (McGettigan et al. 2019). These registries follow patients over time, gathering information on their demographics, medical history, treatment outcomes, and other relevant factors (McGettigan et al. 2019). Patient registries have the potential to provide valuable data for regulatory decision-making, particularly when evaluating treatments for rare diseases (McGettigan et al. 2019). The digitalization of patient registries refers to the process of transitioning from paper-based or manual record-keeping systems to electronic platforms for data collection, storage, and analysis (Digital Health and Patient Registries 2023). The digitalization of patient registries offers numerous advantages, including improved data quality, efficient data management, advanced analytics, and enhanced collaboration (Digital Health and Patient Registries 2023). By leveraging digital platforms, patient registries can maximise their potential to inform clinical practice, support regulatory decision-making, and contribute to research and healthcare improvement (Digital Health and Patient Registries 2023).

The digitalization of patient registries in LMICs can bring significant benefits to healthcare systems, despite their unique challenges. Here are a few examples of digitization efforts for patient registries in LMICs:

• OpenSRP (Open Smart Register Platform)—OpenSRP is an open-source mobile health platform developed by the Digital Impact Alliance (DIAL). It has been used in LMICs, such as Bangladesh, Zambia, and Kenya, to digitise patient registries and improve data collection and reporting for maternal and child health. OpenSRP enables community health workers to register patients, track vaccinations, monitor growth, and provide personalised care through mobile devices (OpenSRP—Open-source smart register platform (SRP) 2023).

• REDCap (Research Electronic Data Capture)—REDCap is a web-based platform for building and managing online surveys and databases (Harris et al. 2019). It has been used in LMICs for digitising patient registries in various research studies and clinical trials. REDCap enables data entry, storage, and analysis while ensuring data security and privacy. It can be customised to meet specific registry requirements and utilised in countries like Ethiopia and Uganda (Harris et al. 2019).

6 Biometric Systems

Biometric systems are computer-based systems that use unique physical features, such as fingerprints, facial characteristics, or iris patterns., to identify individuals. These systems have gained widespread use in recent years for various applications, including access control, border control, and identity verification (Committee NRC (US) WB et al. 2010a).

One of the main advantages of biometric systems is that they provide a higher level of security than traditional authentication methods, such as passwords or PINs. Individual biometric characteristics are difficult to duplicate or forge, making them practical for preventing unauthorised access. Biometric systems can also be more convenient for users, as they do not need to remember passwords or carry additional authentication devices (Nigam et al. 2022).

Several biometric systems exist, including fingerprint, facial recognition, and iris scanners. Each type of system has specific advantages and disadvantages and the appropriate method for a particular application will depend on its specific requirements and constraints (Guennouni et al. 2019).

Fingerprint scanners are one of the most widely used biometric systems. They capture an image of the fingerprint and analyse the unique patterns and ridges present in the fingerprint. These systems are relatively inexpensive, easy to use, and accurate, making them suitable for various applications. Fingerprint scanners can be used for multiple purposes, including access control, attendance tracking, and financial transactions (Maltoni et al. 2022).

Facial recognition systems use artificial intelligence and machine learning algorithms to analyse the unique characteristics of a person’s face, such as the shape and size of the eyes, nose, and mouth. These systems are highly accurate, but they can be affected by changes in appearance, such as facial hair or makeup, and they may be less accurate for people of certain ethnicities (Li et al. 2020). Facial recognition systems have been used for various purposes, including identifying criminals, detecting terrorists, and tracking the movement of individuals (Robbins 2021).

Iris scanners use a camera to capture an image of the iris, the coloured part of the eye surrounding the pupil. The unique patterns in the iris are then analysed to identify the individual. Iris scanners are highly accurate and resistant to changes in appearance, but they may be more expensive and complex to implement than other biometric systems. Iris scanners have been used for various purposes, including access control and identity verification (Daugman 2004).

Biometric systems can potentially revolutionise how we authenticate ourselves and access services and resources. However, there are also potential privacy and ethical concerns associated with these systems (Cooper and Yon 2019). For example, biometric data collected by these systems may be stored and used for purposes beyond the original intent. There is a risk of discrimination based on the characteristics being measured. Biometric systems may cause false positives and negatives, raising concerns regarding their reliability and accuracy (Committee NRC (US) WB et al. 2010b).

To address these concerns, organisations adopting biometric systems need to consider the following factors (Ratha et al. 2001):

• Privacy: Organisations should implement appropriate safeguards to protect the privacy of individuals and ensure that biometric data is only used for the intended purpose.

• Accuracy: Organisations should ensure that biometric systems are accurate and reliable and have processes to address false positives and negatives.

• Ethical considerations: Organisations should consider biometric systems’ potential implications and implement appropriate safeguards to prevent discrimination based on measured characteristics.

In the healthcare sector of LMICs, biometric systems are increasingly used for client registration and identification to improve healthcare service delivery, reduce fraud, and ensure accurate patient records. Here are a few examples of biometric systems used in LMICs healthcare:

• Aadhaar (India): The Aadhaar system in India is one of the most significant biometric identification projects globally. It uses a combination of fingerprint, iris, and face recognition to assign residents a unique 12-digit identification number. Aadhaar has been widely used for various government services, including social welfare programs, banking, and healthcare (Home – Unique Identification Authority of India 2023).

• Biometric Patient Identification System (Kenya): The Biometric Patient Identification System (BPIS) was implemented in Kenya to improve patient identification in healthcare facilities. The system utilises fingerprint biometrics to identify patients and link them to their medical records accurately. It helps prevent medical errors, ensures continuity of care, and reduces the risk of misidentification (Anne et al. 2020).

• Mother and Child Tracking System (India): India implemented the Mother and Child Tracking System (MCTS), which utilises biometric identification to track and monitor the health of pregnant women and children. The system captures biometric data, including fingerprints and photographs, to create unique individual identification records. This facilitates targeted healthcare interventions and ensures proper delivery of maternal and child health services (India’s Mother and Child Tracking System 2023).

In conclusion, biometric systems have the potential to increase security and convenience for a variety of applications. However, organisations must consider these systems’ potential privacy, accuracy, and ethical issues and implement appropriate safeguards to protect individuals’ rights and interests.

7 Counterfeit Drug Testing

Counterfeit drugs are a significant public health threat, as they often contain incorrect or harmful ingredients, leading to serious health consequences for those who consume them (Kon and Mikov 2011). One important factor contributing to the proliferation of counterfeit drugs is the increasing availability of online pharmacies. These pharmacies often sell drugs not approved by regulatory agencies and may be fake or of substandard quality. Enhancing online surveillance and enforcement efforts to educate consumers about the risks of purchasing drugs from unverified sources is essential to address this issue. To combat the problem, it is crucial to optimise the testing of counterfeit drugs to ensure that they are accurately identified and removed from circulation (Islam and Islam 2022).

One approach to optimising counterfeit drug testing is using advanced analytical techniques. These techniques, such as high-performance liquid chromatography (HPLC) and mass spectrometry (MS), can provide detailed chemical analyses of drugs and identify any discrepancies or deviations from the expected composition. These techniques are also susceptible, allowing for detecting even small amounts of counterfeit material (Martino et al. 2010a).

Another way to optimise counterfeit drug testing is by using reference standards. These standards, carefully calibrated and validated samples of known drugs, can be used to compare the quality and purity of tested drugs. By using these standards, laboratories can more accurately determine whether a drug is counterfeit (Martino et al. 2010b).

In addition to these analytical techniques, several approaches can be taken to optimise the overall process of counterfeit drug testing. For example, establishing a well-coordinated network of laboratories to conduct testing can help to ensure that samples are analysed efficiently and effectively. Additionally, implementing quality management systems and training programs can help ensure testing is performed according to established protocols and standards (Implementation of a Quality Management System n.d.).

Another critical factor in the fight against counterfeit drugs is the development of innovative technologies for tracking and tracing the supply chain. These technologies, such as blockchain and RFID tagging, can help ensure that medications are authentic, properly stored, and handled during transportation. Implementing these technologies can reduce the risk of counterfeit medicines entering the supply chain and reaching consumers (Zakari et al. 2022).

Several regulatory measures can be taken to prevent counterfeit drugs from entering the market. For example, strengthening intellectual property protection for branded drugs can help to reduce the profitability of counterfeiting, as it becomes more difficult for counterfeiters to pass off their products as genuine articles. Additionally, increasing penalties and enforcement efforts for those who produce or distribute counterfeit drugs can deter would-be counterfeiters (WHO Member State Mechanism 2023; 1 in 10 medical products in developing countries 2023).

Effective collaboration between government, industry, and other stakeholders is also essential in the fight against counterfeit drugs. Working together, sharing information and resources, and developing more comprehensive strategies for addressing this complex problem is achievable (WHO Member State Mechanism 2023; 1 in 10 medical products in developing countries 2023).

Combating counterfeit drugs requires a multifaceted approach that involves advanced technologies, scientific excellence and collaboration. By taking these steps, reducing the risks posed by these dangerous products and protecting public health is possible.

While specific software solutions for counterfeit drug testing may vary across different countries and regions, here are a few examples of counterfeit drug testing software used in LMICs:

• PharmaSecure is a software platform that helps combat counterfeit drugs by providing unique identification codes on drug packaging. Patients or healthcare providers can send a text message with the code to a designated number, and they receive an immediate response confirming the product’s authenticity. This solution has been implemented in several LMICs, including India, Nigeria, and Kenya (PharmaSecure 2023).

• mPedigree is a mobile-based anti-counterfeiting solution that enables patients and healthcare providers to verify the authenticity of medicines through a unique scratch-off code on the drug packaging. By sending the code via SMS, users receive an instant response indicating whether the product is genuine. The mPedigree platform has been deployed in countries like Ghana, Kenya, and Nigeria (Rasheed et al. 2018).

• Sproxil is a mobile verification solution that employs scratch-off labels with unique codes on drug packaging. Users can send the code via SMS or use a smartphone app to verify the authenticity of the medicine. Sproxil has been used in several LMICs, including Nigeria, India, and Kenya, to combat counterfeit drugs (Johnston and Holt 2014).

These examples highlight some of the software solutions that have been deployed in LMICs to address the issue of counterfeit drugs. It should be emphasised that the deployment of these technologies can differ depending on the unique requirements, regulations, and available resources of each country or region.

8 Automated Completion or Analysis of Medical Records

With the growth and global adoption of EMR systems in medical healthcare, vast quantities of information have become available, necessitating the investigation of alternative methods for maximising the utility of these massive datasets (Sun et al. 2018). Automated completion or analysis of medical records refers to the use of technology and algorithms to assist in the process of completing or analysing patient medical records. This approach aims to streamline and enhance the efficiency of healthcare documentation and data analysis (Sun et al. 2018). Automated filling and analysis of medical records possess the capability to enhance the efficiency and quality of healthcare documentation, facilitate data-informed decision-making, and enable thorough examination of patient information. However, it is essential to ensure patient data’s accuracy, privacy, and security when implementing these automated systems (Ozonze et al. 2023). The automation of medical records completion and analysis is a subject of continuous research and development, and its widespread use may not be prevalent at the current time. While there have been advancements in natural language processing, machine learning, and data analytics techniques for medical record automation and analysis, the implementation and adoption of these technologies in healthcare settings vary.

Below are presented a few examples of research studies related to the automated completion or analysis of medical records:

• Using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, which contains a diverse range of healthcare data, conducted an experimental investigation into the utilisation of machine learning techniques to create predictive models for sepsis. These models considered vital signs, laboratory test results, and demographics as predictive features for sepsis. The experiment results demonstrated that the machine learning models outperformed the commonly used Sequential Organ Failure Assessment (SOFA) and Quick SOFA (qSOFA) scoring systems at the time of sepsis onset. This suggests that the machine learning models have a higher overall performance in predicting sepsis compared to the traditional scoring systems (Camacho-Cogollo et al. 2022).

• Evaluation of automated electronic case report form (CRF) data entry from EHRs explored to assess the feasibility and benefits of automated data transfer from EHRs to electronic CRFs in a clinical trial for hospitalised COVID-19 patients. Drawing on these discoveries, the study reached the conclusion that automated EHR-to-CRF data transfer had the potential to significantly reduce the effort required by study personnel while improving the accuracy of the data in CRFs. Implementing automation in data transfer can enhance efficiency, reduce errors, and enhance the quality and safety of clinical trial data collection (Cheng et al. 2023).

These examples highlight the ongoing research efforts to develop and validate automated completion or analysis techniques for medical records. While these studies demonstrate the potential benefits of automation in healthcare, it should be acknowledged that the field is still in a state of development, and additional research is necessary to enhance and validate these approaches prior to their broad implementation in clinical settings.

9 Patient Feedback Collection and Analysis

Collecting patient feedback digitally offers numerous advantages, including leveraging existing communication channels, collecting data more efficiently, and gathering real-time insights. Digital methods, such as online surveys, QR codes, and social media, provide healthcare organisations with a broader reach and faster data collection beyond the immediate episode of care. While traditional post-visit or post-discharge surveys provide valuable information about the patient experience, digital listening strategies enhance and deepen patient insights. These strategies enable healthcare organisations to stay attuned to real-time data, capturing feedback on specific touchpoints and adventures throughout the patient journey. By utilising digital channels, healthcare providers can access a broader range of patient feedback and gain actionable insights to improve the quality of care (Von Wedel et al. 2022).

The DEPEND study research paper explores the implementation of a digital feedback intervention in the English National Health Service (NHS). The study found that healthcare staff viewed digital feedback positively, finding it attractive, quick to complete, and easy to analyse. However, patient perspectives varied depending on their familiarity with digital technology. Some patients encountered barriers such as a lack of visibility of the feedback system, concerns about privacy, limited digital literacy, and technical difficulties. The level of engagement with digital feedback varied across sites due to workload pressures, perceptions of roles and responsibilities, and ongoing organisational changes. The concentration of mental health service users with digital feedback was influenced by their relationships with staff and their health status. The study emphasised the importance of considering local contexts, different patient groups, and organisational leadership when implementing digital feedback methods. Involving patients in the change process and adaptation of the intervention was crucial for successfully integrating digital feedback into routine practices. The application of the Normalisation Process Theory (NPT) provided a comprehensive understanding of the actions and interactions of staff and patients involved in the implementation (Ong et al. 2020).

Several examples of digitalization of patient feedback collection and analysis in LMICs have emerged in recent years:

• Interactive Voice Response (IVR) Systems: IVR systems collect patient feedback via phone calls. In India, the National Rural Health Mission implemented a toll-free IVR system where patients can share their feedback on healthcare services, including cleanliness, staff behaviour, and availability of medicines (Swendeman et al. 2015).

• mHealth Apps: Mobile health applications have been developed to collect patient feedback and assess healthcare quality. For instance, in Uganda, the mTrac app allows patients to report on the availability of medicines, quality of care, and health worker performance through SMS-based surveys (Cummins 2015).

These examples highlight the diverse approaches taken to digitise patient feedback collection and analysis in LMICs. However, it is crucial to consider the specific context and infrastructure challenges of each LMIC when implementing digital patient feedback systems. Collaboration between stakeholders, including governments, healthcare providers, technology experts, and patients, is crucial for successful implementation and sustainability.

10 Quality/Performance Improvement Analytics

Quality/performance improvement analytics refers to the process of using data analysis and evaluation techniques to assess, monitor, and enhance the quality and performance of healthcare systems, processes, and outcomes. It involves collecting and analysing data related to various quality indicators and performance metrics to identify areas for improvement and implement evidence-based interventions. The digitalization and process optimization of quality/performance improvement analytics bring several benefits to healthcare organisations enabling healthcare organisations to make data-driven decisions, improve operational efficiency, enhance patient outcomes, and foster a culture of continuous quality improvement (Martin et al. 2023).

In LMICs, there is a growing interest in leveraging quality/performance improvement analytics tools to enhance healthcare delivery. The availability and adoption of specific devices may vary across different countries and settings. It should be emphasised that the accessibility and adoption of these tools in LMICs can be influenced by factors such as infrastructure, financial resources, and local capabilities. Additionally, ongoing research and innovation in LMICs continue to contribute to developing context-specific tools and approaches for quality/performance improvement analytics.

11 Conclusion

The adoption of digitalization measures in healthcare, such as electronic medical records, faces challenges in LMICs. The success of these tools depends on having a workforce capable of designing, implementing, and maintaining the systems. Limited human resources can hinder the effective management of these technologies. Additionally, if digitalization introduces a new service rather than replacing an existing system, it may strain health workers, requiring additional training and recruitment.

Data training and expertise are crucial for process optimization tools. Good data leads to good outcomes, while insufficient data can have negative consequences, such as misallocating resources. User-friendly platforms and ensuring data interoperability are essential for success. However, the increasing use of “people analytics” has been criticised for potentially dehumanising work and may not effectively increase productivity or optimise practices.

Tools that support process optimization do not provide immediate relief or diagnosis but aim to improve back-end processes, freeing up time and resources for healthcare workers. Over the medium to long term, these tools can increase capacity and support quality research through enhanced data collection.

In the broader context, interdisciplinary and interprofessional healthcare interventions often focus on outcomes such as length of stay, readmission rates, mortality, or functional status. However, the effects of such interventions on these outcomes can be inconsistent. While some improvements in interprofessional collaboration have been associated with reduced complications of care, interventions confined to the inpatient setting may not reduce readmissions or mortality rates. The generalizability of results can be limited due to differences in healthcare systems and standards of care.

There is a need for further validation and research on interventions to optimise patient flow, discharge processes, and overall patient management. Consensus on performance benchmarking data and needed interventions are lacking, hindering quality improvements. Standard sets and patient-reported outcome measurement systems can help assess the impact of diagnostic and therapeutic steps on patient outcomes. Prospective time-series analyses and randomised controlled trials can also be valuable study designs in investigating the effects of transition-changing interventions.

Additionally, the availability of anonymized patient-level data from clinical trials can enhance research and accountability and avoid duplication of trials. It is important to prevent “data dumpsters” by linking data to relevant documentation and analyses. Furthermore, the costs and benefits of novel interventions need to be carefully evaluated, considering the additional resource deviation they may require.

Addressing these challenges and conducting further research and evaluation can contribute to successfully implementing digitalization and process optimization in healthcare settings, leading to improved patient outcomes and quality of care.