Background: A functionally effective referral system that links district level hospitals (DLHs) with referral hospitals (RHs) facilitates surgical patients getting timely access to specialist surgical expertise not available locally. Most published studies from low-and middle-income countries (LMICs) have examined only selected aspects of such referral systems, which are often fragmented. Inadequate understanding of their functionality leads to missed opportunities for improvements. This research aimed to investigate the functionality of the referral system for surgical patients in Malawi, a low-income country. Methods: This study, conducted in 2017-2019, integrated principles from two theories. We used network theory to explore interprofessional relationships between DLHs and RHs at referral network, member (hospital) and community levels; and used principles from complex adaptive systems (CAS) theory to unpack the mechanisms of network dynamics. The study employed mixed-methods, specifically surveys (n = 22 DLHs), interviews with clinicians (n = 20), and a database of incoming referrals at two sentinel RHs over a six-month period. Results: Obstacles to referral system functionality in Malawi included weaknesses in formal coordination structures, notably: unclear scope of practice of district surgical teams; lack of referral protocols; lack of referral communication standards; and misaligned organisational practices. Deficiencies in informal relationships included mistrust and uncollaborative operating environments, undermining coordination between DLHs and RHs. Poor system functionality adversely impacted the quality, efficiency and safety of patient referral-related care. Respondents identified aspects of the district-RH relationships, which could be leveraged to build more collaborative and productive inter-professional relationships in the future. Conclusion: Multi-level interventions are needed to address failures at both ends of the referral pathway. This study captured new insights into longstanding problems in referral systems in resource-limited settings, contributing to a better understanding of how to build more functional systems to optimise the continuum and quality of surgical care for rural populations in similar settings.
Through the referral system, healthcare organisations work interdependently in a network of multidimensional relationships to deliver a continuum of care to patients. 7 The capabilities of the system rest on many interacting elements, consisting of 17 : the hardware of available resources (eg, infrastructure, staffing, equipment, funding); the tangible software of knowledge, skills and processes of decision making; and the intangible software of relationships, communication and values. The intangible features, in particular, are important in shaping the behaviours of providers in the system and its overall “power to perform.” 17 Hence, the focus of this study is not just on the hardware, but rather on the interactions within and across hospitals, and how whole-system outcomes, such as the continuum of care, are generated collectively (ie, by service providers at DLHs and RHs). 7 Measuring these dimensions required us to develop an analytical framework able to capture the complexity of these relationships. We integrated concepts from two systems-thinking approaches 18,19 : network theory, concerned with examining how elements within a system interact, 19 and complexity theory, defined as ‘a perspective that conceptualises relationships of components within a system as the foundation from which the properties of a system emerge.’ 20 Our analytical approach involved three steps. Firstly, we employed network theory to depict the structure upon which the network of relationships among hospitals in the Malawi surgical referral system is built. 18 We adapted the model proposed by Cunningham et al, 21 which investigates interprofessional relationships in health service networks at three levels – community, network and member levels – within the context of network characteristics and operating environment. The first dimension analyses the joint delivery of servicesby network members to the community, defined as the population served by the network. 22 In our study we interpreted this as those surgical patients who benefit (or are expected to benefit) from the referral network. We aimed to capture the services normally expected to be provided through the referral network (ie, facilitating access to specialist care not available locally), as well as any other additional services hospitals can provide by being part of the network as opposed to working in isolation. The second dimension examines whether the network operates as a viable entity, in terms of connectedness between district and specialised hospital services and coordination. We considered the following aspects: extent of referral communication and consultation between district and RHs; continuous exchange of information and knowledge (including feedback); and management of patient transfer across facilities. The third dimension assesses whether being involved in the network is beneficial to its members (ie, district and RHs, and clinicians within them). In particular, we investigated any convergent and divergent behaviours among network members and examined how these affect every-day practice. The adapted Cunningham et al model 21 used in our study, and the list of parameters considered under each domain, is illustrated in Figure 1. Conceptual Framework. Adapted from Cunningham et al. 21 The multi-level relationships are not static, they change and evolve over time. 18 Hence our second step (bottom of Figure 1) was to integrate the Cunningham et al model 21 with key principles from the theory of complex adaptive systems (CAS) as a conceptual approach to unpack the mechanisms of network dynamics 19 and deviations from the intended way of working. Many properties of CAS in healthcare have been described in the literature. 19 For the purpose of our study we considered the following properties of CAS particularly relevant to understand the surgical referral system in Malawi: In the light of the considerable resource and operational challenges faced by the health sector in Malawi, these theoretic concepts are instrumental to understanding how the referral network self-organises to find the best fit to its environment and to determine its resilience. 19,24 How agents connect and relate to one another is critical to the survival of the system. 23 The third step (right side of Figure 1) was to determine how these multi-level sets of relationships, and the influence of external and contextual factors, affect the performance of the surgical referral system in terms of meeting the needs of the patients and their families, efficiency in utilisation of public resources and contribution to health outcomes. 21 Malawi is a low-income country in SSA, with a population of about 17.5 million people in 2018 25 and high levels of poverty (71% of the population live below the poverty line). 26 The country is comprised of three administrative areas, the Northern, Central and Southern regions, and is densely populated, with 84% of the population based in rural areas. 25 The public healthcare system has three tiers, linked to each other through an established vertical referral system. 27 At the lowest level are primary level facilities, mostly responsible for promotive, preventive and basic curative healthcare. At secondary level are 24 public DLHs, each with a catchment area of between 140 000 and 1 400 000 people. Approximately 29% of all primary and secondary health services, especially maternal health, are provided by religious institutions organised under the Christian Health Association of Malawi, through a service agreement with the Ministry of Health. At the tertiary level there are four central hospitals (CHs), each with a catchment population of several millions. 27 These facilities are located in the four largest cities and host all of the specialist surgical workforce in the country, which at 0.43 per 100 000 population is an extremely low density compared to international standards. 28 The hospital system is hindered by resource scarcity, poorly developed financing mechanisms and weak governance, 27 leaving a large unmet need for surgical care in the country. 29 Financing and management of health resources at district level are controlled by a dedicated administrative authority, the District Health Office. DLHs have limited autonomy and decision-making space, with financial allocations that are often below needs. 30 For patients, essential healthcare is free of charge at the point of entry but bypass fees have been introduced at the RHs to discourage patients circumventing lower facilities to seek treatment directly at higher levels. 27 DLHs are meant to provide for the basic and essential surgical needs of the population, and to refer more complex cases to RHs. Despite the existence of surgical graduate programmes, surgical services at DLHs are predominantly provided by generalists – Medical Officers and/or general non-physician clinicians. Good integration with specialist services at RHs is important for supporting these frontline health workers, who often deliver care in isolated rural settings with limited surgical training and supervision. This research, conducted in 2017-2019, employed mixed-methods and iterative data collection, reflecting the nature of inquiry in CAS research. 31 The first round (2017) was undertaken as part of a situation analysis for the SURG-Africa research project. 32 Data were collected to map the surgical referral network, and to conduct an initial assessment of referral practices and resource availability at DLHs. Information on the mandate, resources and governance of the referral network, as well as the intended role of its members, was gathered through a review of national health policies and other background documentation. A survey was administered in 22 of the 24 surgically active public DLHs country-wide (two DLHs were omitted due to inaccessibility at the time of data collection), and semi-structured interviews were carried out with members of the surgical team at a sample of nine DLHs (see Table 1). Abbreviations: DLH, district level hospital; SD, standard deviation. aThis includes general nurses and specialised nurses. bThis refers to any vehicle available for the transport of patients, with or without medical equipment. Once key referral links were determined, we established a data collection system to capture patient flows across hospitals. Given resource limitations, we focused on the Southern region where SURG-Africa project coordination was based. Trained data collectors were stationed at the surgical units of the two main RHs for this region: Queen Elizabeth Central Hospital (QECH), the largest tertiary facility in the country, and Zomba Central Hospital (Zomba CH). Incoming surgical referrals at these two sentinel RHs were tracked during a six-month period (November 2017-April 2018 in QECH and December 2017-May 2018 in Zomba). Information collected concerned patient demographics and clinical information, referral documentation, details of referral and hospital of origin. This initial evidence informed the subsequent data collection. In early 2019 we repeated the survey of DLHs to gather further information on referral communication practices, including sharing of feedback. We then interviewed staff at RHs to gather their perspectives on the state of the surgical referral network, key obstacles, impact on their work and suggestions for improvements. At RHs, we purposively selected and interviewed personnel who were the first-point of contact for incoming referrals, identified through staff lists where available and through snowball sampling. Ethical approvals for the study were obtained from all relevant authorities. All data collection was conducted in English. Surveys and interviews were administered in person by project researchers. Details of the tools used are described elsewhere. 32 A summary of data sources and participants is provided in Table 2. Abbreviations: QECH, Queen Elizabeth Central Hospital; Zomba CH, Zomba Central Hospital; DLHs, district level hospitals; RH, referral hospital. Quantitative data were analysed using IBM SPSS v26. Where appropriate quantitative data sources were triangulated with each other and with qualitative data. All interviews with key informants were audio recorded following informed consent from the participants and then transcribed. A thematic analysis of the interview data was conducted in NVivo v12, following a mixed top-down and bottom-up approach, 33 guided by the framework in Figure 1. A first coding framework and structure of the analysis were drafted by the lead author and discussed with the wider team of researchers. These were then revised and finalised in a collaborative manner. The qualitative data analysis aimed to gather further details about the context in which the network operates and to provide insights into network dynamics from the perspective of its members and potential areas of improvement. Further details on the integration of data sources are provided in Supplementary file 1, Table S1.
DIMA AI Care
