Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models

Is Economic Evaluation and Care Commissioning Focused on Achieving the Same Outcomes? Resource-Allocation Considerations and Challenges Using England as a Case Study

Commissioning describes the process of contracting appropriate care services to address pre-identified needs through pre-agreed payment structures. Outcomes-based commissioning (i.e., paying services for pre-agreed outcomes) shares a common goal with economic evaluation: achieving value for money for relevant outcomes (e.g., health) achieved from a finite budget. We describe considerations and challenges as to the practical role of relevant outcomes for evaluation and commissioning, seeking to bridge a gap between economic evaluation evidence and care commissioning. We describe conceptual (e.g., what are 'relevant' outcomes) alongside practical considerations (e.g., quantifying and using relevant endpoint or surrogate outcomes) and pertinent issues when linking outcomes to commissioning-based payment mechanisms, using England as a case study. Economic evaluation often focuses on a single endpoint health-focused maximand, e.g., quality-adjusted life-years (QALYs), whereas commissioning often focuses on activity-based surrogate outcomes (e.g., health monitoring), as easier-to-measure key performance indicators that are more acceptable (e.g., by clinicians) and amenable to being linked with payment structures. However, payments linked to endpoint and/or surrogate outcomes can lead to market inefficiencies; for example, when surrogates do not have the intended causal effect on endpoint outcomes or when service activity focuses on only people who can achieve prespecified payment-linked outcomes. Accounting for and explaining direct links from commissioners' payment structures to surrogate and then endpoint economic outcomes is a vital step to bridging a gap between economic evaluation approaches and commissioning. Decision-analytic models could aid this but they must be designed to account for relevant surrogate and endpoint outcomes, the payments assigned to such outcomes, and their interaction with the system commissioners purport to influence.

The relationship between compartment models and their stochastic counterparts: A comparative study with examples of the COVID-19 epidemic modeling

Deterministic compartment models (CMs) and stochastic models, including stochastic CMs and agent-based models, are widely utilized in epidemic modeling. However, the relationship between CMs and their corresponding stochastic models is not well understood. The present study aimed to address this gap by conducting a comparative study using the susceptible, exposed, infectious, and recovered (SEIR) model and its extended CMs from the coronavirus disease 2019 modeling literature. We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations. Based on this equivalence, we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment. The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics. However, it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs. Additionally, we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents. This model offered a balance between computational efficiency and accuracy. The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling. Furthermore, the results had implications for the development of hybrid models that integrated the strengths of both frameworks. Overall, the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

Contributing to health system resilience during pandemics via purchasing and supply strategies: an exploratory system dynamics approach

BACKGROUND

Health systems worldwide struggled to obtain sufficient personal protective equipment (PPE) and ventilators during the COVID-19 pandemic due to global supply chain disruptions. Our study's aim was to create a proof-of-concept model that would simulate the effects of supply strategies under various scenarios, to ultimately help decision-makers decide on alternative supply strategies for future similar health system related crises.

METHODS

We developed a system dynamics model that linked a disease transmission model structure (susceptible, exposed, infectious, recovered (SEIR)) with a model for the availability of critical supplies in hospitals; thereby connecting care demand (patients' critical care in hospitals), with care supply (available critical equipment and supplies). To inform the model structure, we used data on critical decisions and events taking place surrounding purchase, supply, and availability of PPE and ventilators during the first phase of the COVID-19 pandemic within the English national health system. We used exploratory modelling and analysis to assess the effects of uncertainties on different supply strategies in the English health system under different scenarios. Strategies analysed were: (i) purchasing from the world market or (ii) through direct tender, (iii) stockpiling, (iv) domestic production, (v) supporting innovative supply strategies, or (vi) loaning ventilators from the private sector.

RESULTS

We found through our exploratory analysis that a long-lasting shortage in PPE and ventilators is likely to be apparent in various scenarios. When considering the worst-case scenario, our proof-of-concept model shows that purchasing PPE and ventilators from the world market or through direct tender have the greatest influence on reducing supply shortages, compared to producing domestically or through supporting innovative supply strategies. However, these supply strategies are affected most by delays in their shipment time or set-up.

CONCLUSION

We demonstrated that using a system dynamics and exploratory modelling approach can be helpful in identifying the purchasing and supply chain strategies that contribute to the preparedness and responsiveness of health systems during crises. Our results suggest that to improve health systems' resilience during pandemics or similar resource-constrained situations, purchasing and supply chain decision-makers can develop crisis frameworks that propose a plan of action and consequently accelerate and improve procurement processes and other governance processes during health-related crises; implement diverse supplier frameworks; and (re)consider stockpiling. This proof-of-concept model demonstrates the importance of including critical supply chain strategies as part of the preparedness and response activities to contribute to health system resilience.

Review: The prevailing mathematical modeling classifications and paradigms to support the advancement of sustainable animal production

Mathematical modeling is typically framed as the art of reductionism of scientific knowledge into an arithmetical layout. However, most untrained people get the art of modeling wrong and end up neglecting it because modeling is not simply about writing equations and generating numbers through simulations. Models tell not only about a story; they are spoken to by the circumstances under which they are envisioned. They guide apprentice and experienced modelers to build better models by preventing known pitfalls and invalid assumptions in the virtual world and, most importantly, learn from them through simulation and identify gaps in pushing scientific knowledge further. The power of the human mind is well-documented for idealizing concepts and creating virtual reality models, and as our hypotheses grow more complicated and more complex data become available, modeling earns more noticeable footing in biological sciences. The fundamental modeling paradigms include discrete-events, dynamic systems, agent-based (AB), and system dynamics (SD). The source of knowledge is the most critical step in the model-building process regardless of the paradigm, and the necessary expertise includes (a) clear and concise mental concepts acquired through different ways that provide the fundamental structure and expected behaviors of the model and (b) numerical data necessary for statistical analysis, not for building the model. The unreasonable effectiveness of models to grow scientific learning and knowledge in sciences arise because different researchers would model the same problem differently, given their knowledge and experiential background, leading to choosing different variables and model structures. Secondly, different researchers might use different paradigms and even unalike mathematics to resolve the same problem; thus, model needs are intrinsic to their perceived assumptions and structures. Thirdly, models evolve as the scientific community knowledge accumulates and matures over time, hopefully resulting in improved modeling efforts; thus, the perfect model is fictional. Some paradigms are most appropriate for macro, high abstraction with less detailed-oriented scenarios, while others are most suitable for micro, low abstraction with higher detailed-oriented strategies. Modern hybridization aggregating artificial intelligence (AI) to mathematical models can become the next technological wave in modeling. AI can be an integral part of the SD/AB models and, before long, write the model code by itself. Success and failures in model building are more related to the ability of the researcher to interpret the data and understand the underlying principles and mechanisms to formulate the correct relationship among variables rather than profound mathematical knowledge.

Mental health systems modelling for evidence-informed service reform in Australia

Australia's Fifth National Mental Health Plan required governments to report, not only on the progress of changes to mental health service delivery, but to also plan for services that should be provided. Future population demand for treatment and care is challenging to predict and one solution involves modelling the uncertain demands on the system. Modelling can help decision-makers understand likely future changes in mental health service demand and more intelligently choose appropriate responses. It can also support greater scrutiny, accountability and transparency of these processes. Australia has an emerging national capacity for systems modelling in mental health which can enhance the next phase of mental health reform. This paper introduces concepts useful for understanding mental health modelling and identifies where modelling approaches can support health service planners to make evidence-informed decisions regarding planning and investment for the Australian population.

Mathematical Modeling for the Assessment of Public Policies in the Cancer Health-Care System Implemented for the Colombian Case

The incidence of cancer has been constantly growing worldwide, placing pressure on health systems and increasing the costs associated with the treatment of cancer. In particular, low- and middle-income countries are expected to face serious challenges related to caring for the majority of the world's new cancer cases in the next 10 years. In this study, we propose a mathematical model that allows for the simulation of different strategies focused on public policies by combining spending and epidemiological indicators. In this way, strategies aimed at efficient spending management with better epidemiological indicators can be determined. For validation and calibration of the model, we use data from Colombia-which, according to the World Bank, is an upper-middle-income country. The results of the simulations using the proposed model, calibrated and validated for Colombia, indicate that the most effective strategy for reducing mortality and financial burden consists of a combination of early detection and greater efficiency of treatment in the early stages of cancer. This approach is found to present a 38% reduction in mortality rate and a 20% reduction in costs (% GDP) when compared to the baseline scenario. Hence, Colombia should prioritize comprehensive care models that focus on patient-centered care, prevention, and early detection.

Towards the Development of a System Dynamics Model for the Prediction of Lower Extremity Injuries

Acute noncontact Lower Extremity (LE) injuries constitute a significant problem in team sports. Despite extensive research, current knowledge on the risk factors of LE injuries is limited to static simplistic models of instantaneous cause and effect relationships ignoring the time dimension and the embedded complexity of LE injuries. Even though complex systems approaches have been used in various cases to improve policy and intervention effectiveness, there is limited research on predicting and managing LE injuries. This creates an opportunity to fill the gap in the current literature by applying the System Dynamics (SD) methodology to model LE injuries. The proposed approach allows for synthesizing risk factors and examining their interaction. This paper makes the first step towards such an approach by developing a causal loop model revealing the etiology of LE injuries. A causal loop model for LE injuries is developed via an extensive literature review and brainstorming with experts. In contrast to the traditional static approaches, the proposed model reveals some of the complexity and nonlinear relationships of the various sports injury risk factors. The derived causal loop model may then be used to quantify these interactions and develop a simulation model. This will be achieved by operationalizing and incorporating the main risk factors that impact LE injuries in an integrated sports injury prediction model. In this way, plausible strategies for preventing LE injuries can be tested prior implementation and thereby achieve optimization of intervention programs.

Process modelling of NHS cardiovascular waiting lists in response to the COVID-19 pandemic

OBJECTIVE

To model the referral, diagnostic and treatment pathway for cardiovascular disease (CVD) in the English National Health Service (NHS) to provide commissioners and managers with a methodology to optimise patient flow and reduce waiting lists.

STUDY DESIGN

A systems dynamics approach modelling the CVD healthcare system in England. The model is designed to capture current and predict future states of waiting lists.

SETTING

Routinely collected, publicly available data streams of primary and secondary care, sourced from NHS Digital, NHS England, the Office of National Statistics and StatsWales.

DATA COLLECTION AND EXTRACTION METHODS

The data used to train and validate the model were routinely collected and publicly available data. It was extracted and implemented in the model using the PySD package in python.

RESULTS

NHS cardiovascular waiting lists in England have increased by over 40% compared with pre- COVID-19 levels. The rise in waiting lists was primarily due to restrictions in referrals from primary care, creating a bottleneck postpandemic. Predictive models show increasing point capacities within the system may paradoxically worsen downstream flow. While there is no simple rate-limiting step, the intervention that would most improve patient flow would be to increase consultant outpatient appointments.

CONCLUSIONS

The increase in NHS CVD waiting lists in England can be captured using a systems dynamics approach, as can the future state of waiting lists in the presence of further shocks/interventions. It is important for those planning services to use such a systems-oriented approach because the feed-forward and feedback nature of patient flow through referral, diagnostics and treatment leads to counterintuitive effects of interventions designed to reduce waiting lists.

Investigating the Connections Between Delivery of Care, Reablement, Workload, and Organizational Factors in Home Care Services: Mixed Methods Study

BACKGROUND

Home care is facing increasing demand due to an aging population. Several challenges have been identified in the provision of home care, such as the need for support and tailoring support to individual needs. Goal-oriented interventions, such as reablement, may provide a solution to some of these challenges. The reablement approach targets adaptation to disease and relearning of everyday life skills and has been found to improve health-related quality of life while reducing service use.

OBJECTIVE

The objective of this study is to characterize home care system variables (elements) and their relationships (connections) relevant to home care staff workload, home care user needs and satisfaction, and the reablement approach. This is to examine the effects of improvement and interventions, such as the person-centered reablement approach, on the delivery of home care services, workload, work-related stress, home care user experience, and other organizational factors. The main focus was on Swedish home care and tax-funded universal welfare systems.

METHODS

The study used a mixed methods approach where a causal loop diagram was developed grounded in participatory methods with academic health care science research experts in nursing, occupational therapy, aging, and the reablement approach. The approach was supplemented with theoretical models and the scientific literature. The developed model was verified by the same group of experts and empirical evidence. Finally, the model was analyzed qualitatively and through simulation methods.

RESULTS

The final causal loop diagram included elements and connections across the categories: stress, home care staff, home care user, organization, social support network of the home care user, and societal level. The model was able to qualitatively describe observed intervention outcomes from the literature. The analysis suggested elements to target for improvement and the potential impact of relevant studied interventions. For example, the elements "workload" and "distress" were important determinants of home care staff health, provision, and quality of care.

CONCLUSIONS

The developed model may be of value for informing hypothesis formulation, study design, and discourse within the context of improvement in home care. Further work will include a broader group of stakeholders to reduce the risk of bias. Translation into a quantitative model will be explored.

System dynamics models of depression at the population level: a scoping review

AIMS

Depression is a disease driven by dynamic processes both at the individual- and system-level. System dynamics (SD) models are a useful tool to capture this complexity, project the future prevalence of depression and understand the potential impact of interventions and policies. SD models have been used to model infectious and chronic disease, but rarely applied to mental health. This scoping review aimed to identify population-based SD models of depression and report on their modelling strategies and applications to policy and decision-making to inform research in this emergent field.

METHODS

We searched articles in MEDLINE, Embase, PsychInfo, Scopus, MedXriv, and abstracts from the System Dynamics Society from inception to October 20, 2021 for studies of population-level SD models of depression. We extracted data on model purpose, elements of SD models, results, and interventions, and assessed the quality of reporting.

RESULTS

We identified 1899 records and found four studies that met the inclusion criteria. Studies used SD models to assess various system-level processes and interventions, including the impact of antidepressant use on population-level depression in Canada; the impact of recall error on lifetime estimates of depression in the USA; smoking-related outcomes among adults with and without depression in the USA; and the impact of increasing depression incidence and counselling rates on depression in Zimbabwe. Studies included diverse stocks and flows for depression severity, recurrence, and remittance, but all models included flows for incidence and recurrence of depression. Feedback loops were also present in all models. Three studies provided sufficient information for replicability.

CONCLUSIONS

The review highlights the usefulness of SD models to model the dynamics of population-level depression and inform policy and decision-making. These results can help guide future applications of SD models to depression at the population-level.

A Systematic Review of Systems Science Approaches to Understand and Address Domestic and Gender-Based Violence

Purpose

We aimed to synthesize insights from systems science approaches applied to domestic and gender-based violence.

Methods

We conducted a systematic review of systems science studies (systems thinking, group model-building, agent-based modeling [ABM], system dynamics [SD] modeling, social network analysis [SNA], and network analysis [NA]) applied to domestic or gender-based violence, including victimization, perpetration, prevention, and community responses. We used blinded review to identify papers meeting our inclusion criteria (i.e., peer-reviewed journal article or published book chapter that described a systems science approach to domestic or gender-based violence, broadly defined) and assessed the quality and transparency of each study.

Results

Our search yielded 1,841 studies, and 74 studies met our inclusion criteria (45 SNA, 12 NA, 8 ABM, and 3 SD). Although research aims varied across study types, the included studies highlighted social network influences on risks for domestic violence, clustering of risk factors and violence experiences, and potential targets for intervention. We assessed the quality of the included studies as moderate, though only a minority adhered to best practices in model development and dissemination, including stakeholder engagement and sharing of model code.

Conclusions

Systems science approaches for the study of domestic and gender-based violence have shed light on the complex processes that characterize domestic violence and its broader context. Future research in this area should include greater dialogue between different types of systems science approaches, consideration of peer and family influences in the same models, and expanded use of best practices, including continued engagement of community stakeholders.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10896-023-00578-8.

Human Decision-making in an Artificial Intelligence-Driven Future in Health: Protocol for Comparative Analysis and Simulation

BACKGROUND

Health care can broadly be divided into two domains: clinical health services and complex health services (ie, nonclinical health services, eg, health policy and health regulation). Artificial intelligence (AI) is transforming both of these areas. Currently, humans are leaders, managers, and decision makers in complex health services. However, with the rise of AI, the time has come to ask whether humans will continue to have meaningful decision-making roles in this domain. Further, rationality has long dominated this space. What role will intuition play?

OBJECTIVE

The aim is to establish a protocol of protocols to be used in the proposed research, which aims to explore whether humans will continue in meaningful decision-making roles in complex health services in an AI-driven future.

METHODS

This paper describes a set of protocols for the proposed research, which is designed as a 4-step project across two phases. This paper describes the protocols for each step. The first step is a scoping review to identify and map human attributes that influence decision-making in complex health services. The research question focuses on the attributes that influence human decision-making in this context as reported in the literature. The second step is a scoping review to identify and map AI attributes that influence decision-making in complex health services. The research question focuses on attributes that influence AI decision-making in this context as reported in the literature. The third step is a comparative analysis: a narrative comparison followed by a mathematical comparison of the two sets of attributes-human and AI. This analysis will investigate whether humans have one or more unique attributes that could influence decision-making for the better. The fourth step is a simulation of a nonclinical environment in health regulation and policy into which virtual human and AI decision makers (agents) are introduced. The virtual human and AI will be based on the human and AI attributes identified in the scoping reviews. The simulation will explore, observe, and document how humans interact with AI, and whether humans are likely to compete, cooperate, or converge with AI.

RESULTS

The results will be presented in tabular form, visually intuitive formats, and-in the case of the simulation-multimedia formats.

CONCLUSIONS

This paper provides a road map for the proposed research. It also provides an example of a protocol of protocols for methods used in complex health research. While there are established guidelines for a priori protocols for scoping reviews, there is a paucity of guidance on establishing a protocol of protocols. This paper takes the first step toward building a scaffolding for future guidelines in this regard.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/42353.

Stress-testing the resilience of the Austrian healthcare system using agent-based simulation

Patients do not access physicians at random but rather via naturally emerging networks of patient flows between them. As mass quarantines, absences due to sickness, or other shocks thin out these networks, the system might be pushed to a tipping point where it loses its ability to deliver care. Here, we propose a data-driven framework to quantify regional resilience to such shocks via an agent-based model. For each region and medical specialty we construct patient-sharing networks and stress-test these by removing physicians. This allows us to measure regional resilience indicators describing how many physicians can be removed before patients will not be treated anymore. Our model could therefore enable health authorities to rapidly identify bottlenecks in access to care. Here, we show that regions and medical specialties differ substantially in their resilience and that these systemic differences can be related to indicators for individual physicians by quantifying their risk and benefit to the system.

Exploring the link between cancer policies and cancer survival: a comparison of International Cancer Benchmarking Partnership countries

Cancer policy differences might help to explain international variation in cancer survival, but empirical evidence is scarce. We reviewed cancer policies in 20 International Cancer Benchmarking Partnership jurisdictions in seven countries and did exploratory analyses linking an index of cancer policy consistency over time, with monitoring and implementation mechanisms, to survival from seven cancers in a subset of ten jurisdictions from 1995 to 2014. All ten jurisdictions had structures in place to oversee or deliver cancer control policies and had published at least one major cancer plan. Few cancer plans had explicit budgets for implementation or mandated external evaluation. Cancer policy consistency was positively correlated with improvements in survival over time for six of the seven cancer sites. Jurisdictions that scored the highest on policy consistency had large improvements in survival for most sites. Our analysis provides an important first step to systematically capture and evaluate what are inherently complex policy processes. The findings can help guide policy makers seeking approaches and frameworks to improve cancer services and, ultimately, cancer outcomes.

Viewing the global health system as a complex adaptive system - implications for research and practice

The global health system (GHS) is ill-equipped to deal with the increasing number of transnational challenges. The GHS needs reform to enhance global resilience to future risks to health. In this article we argue that the starting point for any reform must be conceptualizing and studying the GHS as a complex adaptive system (CAS) with a large and escalating number of interconnected global health actors that learn and adapt their behaviours in response to each other and changes in their environment. The GHS can be viewed as a multi-scalar, nested health system comprising all national health systems together with the global health architecture, in which behaviours are influenced by cross-scale interactions. However, current methods cannot adequately capture the dynamism or complexity of the GHS or quantify the effects of challenges or potential reform options. We provide an overview of a selection of systems thinking and complexity science methods available to researchers and highlight the numerous policy insights their application could yield.   We also discuss the challenges for researchers of applying these methods and for policy makers of digesting and acting upon them. We encourage application of a CAS approach to GHS research and policy making to help bolster resilience to future risks that transcend national boundaries and system scales.

How to do (or not to do)…Using Causal Loop Diagrams for Health System Research in Low- and Middle-Income Settings

Causal loop diagrams (CLDs) are a systems thinking method that can be used to visualize and unpack complex health system behaviour. They can be employed prospectively or retrospectively to identify the mechanisms and consequences of policies or interventions designed to strengthen health systems and inform discussion with policymakers and stakeholders on actions that may alleviate sub-optimal outcomes. Whilst the use of CLDs in health systems research has generally increased, there is still limited use in low- and middle-income settings. In addition to their suitability for evaluating complex systems, CLDs can be developed where opportunities for primary data collection may be limited (such as in humanitarian or conflict settings) and instead be formulated using secondary data, published or grey literature, health surveys/reports and policy documents. The purpose of this paper is to provide a step-by-step guide for designing a health system research study that uses CLDs as their chosen research method, with particular attention to issues of relevance to research in low- and middle-income countries (LMICs). The guidance draws on examples from the LMIC literature and authors’ own experience of using CLDs in this research area. This paper guides researchers in addressing the following four questions in the study design process; (1) What is the scope of this research? (2) What data do I need to collect or source? (3) What is my chosen method for CLD development? (4) How will I validate the CLD? In providing supporting information to readers on avenues for addressing these key design questions, authors hope to promote CLDs for wider use by health system researchers working in LMICs.

Using Group Model Building to Capture the Complex Dynamics of Scaling Up District-Level Surgery in Arusha Region, Tanzania

BACKGROUND

Scaling up surgery at district hospitals (DHs) is the critical challenge if the Tanzanian national Surgical, Obstetric, and Anesthesia Plan (NSOAP) objectives are to be achieved. Our study aims to address this challenge by taking a dynamic view of surgical scale-up at the district level using a participatory research approach.

METHODS

A group model building (GMB) workshop was held with 18 professionals from three hospitals in the Arusha region. They built a graphical representation of the local system of surgical services delivery through a facilitated discussion that employed the nominal group technique. This resulted in a causal loop diagram (CLD) from which the participants identified the requirements for scaling-up surgery and the stakeholders who could satisfy these. After the GMB sessions, we identified clusters of related variables using inductive thematic analysis and the main feedback loops driving the model.

RESULTS

The CLD consists of 57 variables. These include the 48 variables that were obtained through the nominal group technique and those that participants added later. We identified 6 themes: patient benefits, financing of surgery, cost sharing, staff motivation, communication, and effects on referral hospital. There are 5 self-reinforcing feedback loops: training, learning, meeting demand, revenues, and willingness to work in a good hospital. There are four self-correcting feedback loops or 'resistors to change:' recurrent costs, income lost, staff stress, and brain drain.

CONCLUSION

This study provides a systems view on the scaling up of surgery from a district level perspective. Its results enable a critical appraisal of the feasibility of implementing the NSOAP. Our results suggest that policy-makers should be wary of 'quick fixes' that have short term gains only. Long term policy that considers the complex dynamics of surgical systems and that allows for periodic evaluation and adaption is needed to scale up surgery in a sustainable manner.

Knowledge Management as a Domain, System Dynamics as a Methodology

For decades, system dynamics has been utilised as a framework for evaluating and interpreting various types of systems with varying degrees of complexity and knowledge demands. Knowledge management is strongly related to system dynamics on a thematic level. We did a thorough review to identify potential applications and analysed system dynamics and knowledge management domains. The systematic review followed the PRISMA method. We identified two major groups and one subgroup of the combination of system dynamics and knowledge management after examining and categorising 45 papers. Articles were searched for on Web of Science, Scopus, and LENS. We then concentrated on the categorisation of articles by theme. We discovered that system dynamics models were used as a component of a decision support tool or a knowledge management system in some instances, or the integration of knowledge management processes into specific systems. This study contributes to the growth of system dynamics as a methodology capable of generating novel ideas, highlighting limitations, and providing analogies for future research in a variety of academic areas.

An Agile Systems Modeling Framework for Bed Resource Planning During COVID-19 Pandemic in Singapore

Background

The COVID-19 pandemic has had a major impact on health systems globally. The sufficiency of hospitals' bed resource is a cornerstone for access to care which can significantly impact the public health outcomes.

Objective

We describe the development of a dynamic simulation framework to support agile resource planning during the COVID-19 pandemic in Singapore.

Materials and Methods

The study data were derived from the Singapore General Hospital and public domain sources over the period from 1 January 2020 till 31 May 2020 covering the period when the initial outbreak and surge of COVID-19 cases in Singapore happened. The simulation models and its variants take into consideration the dynamic evolution of the pandemic and the rapidly evolving policies and processes in Singapore.

Results

The models were calibrated against historical data for the Singapore COVID-19 situation. Several variants of the resource planning model were rapidly developed to adapt to the fast-changing COVID-19 situation in Singapore.

Conclusion

The agility in adaptable models and robust collaborative management structure enabled the quick deployment of human and capital resources to sustain the high level of health services delivery during the COVID-19 surge.

A configurable computer simulation model for reducing patient waiting time in oncology departments

Nowadays, the increase in patient demand and the decline in resources are lengthening patient waiting times in many chemotherapy oncology departments. Therefore, enhancing healthcare services is necessary to reduce patient complaints. Reducing the patient waiting times in the oncology departments represents one of the main goals of healthcare managers. Simulation models are considered an effective tool for identifying potential ways to improve patient flow in oncology departments. This paper presents a new agent-based simulation model designed to be configurable and adaptable to the needs of oncology departments which have to interact with an external pharmacy. When external pharmacies are utilised, a courier service is needed to deliver the individual therapies from the pharmacy to the oncology department. An oncology department located in southern Italy was studied through the simulation model and different scenarios were compared with the aim of selecting the department configuration capable of reducing the patient waiting times.

Using system dynamics modelling to estimate the costs of relaxing health system constraints: a case study of tuberculosis prevention and control interventions in South Africa

Health system constraints are increasingly recognized as an important addition to model-based analyses of disease control interventions, as they affect achievable impact and scale. Enabling activities implemented alongside interventions to relax constraints and reach the intended coverage may incur additional costs, which should be considered in priority setting decisions. We explore the use of group model building, a participatory system dynamics modelling technique, for eliciting information from key stakeholders on the constraints that apply to tuberculosis infection prevention and control processes within primary healthcare clinics in South Africa. This information was used to design feasible interventions, including the necessary enablers to relax existing constraints. Intervention and enabler costs were then calculated at two clinics in KwaZulu-Natal using input prices and quantities from the published literature and local suppliers. Among the proposed interventions, the most inexpensive was retrofitting buildings to improve ventilation (US$1644 per year), followed by maximizing the use of community sites for medication collection among stable patients on antiretroviral therapy (ART; US$3753) and introducing appointments systems to reduce crowding (US$9302). Enablers identified included enhanced staff training, supervision and patient engagement activities to support behaviour change and local ownership. Several of the enablers identified by the stakeholders, such as obtaining building permissions or improving information flow between levels of the health systems, were not amenable to costing. Despite this limitation, an approach to costing rooted in system dynamics modelling can be successfully applied in economic evaluations to more accurately estimate the 'real world' opportunity cost of intervention options. Further empirical research applying this approach to different intervention types (e.g. new preventive technologies or diagnostics) may identify interventions that are not cost-effective in specific contexts based on the size of the required investment in enablers.

Long-term effects of payment for performance on maternal and child health outcomes: evidence from Tanzania

BACKGROUND

The success of payment for performance (P4P) schemes relies on their ability to generate sustainable changes in the behaviour of healthcare providers. This paper examines short-term and longer-term effects of P4P in Tanzania and the reasons for these changes.

METHODS

We conducted a controlled before and after study and an embedded process evaluation. Three rounds of facility, patient and household survey data (at baseline, after 13 months and at 36 months) measured programme effects in seven intervention districts and four comparison districts. We used linear difference-in-difference regression analysis to determine programme effects, and differential effects over time. Four rounds of qualitative data examined evolution in programme design, implementation and mechanisms of change.

RESULTS

Programme effects on the rate of institutional deliveries and antimalarial treatment during antenatal care reduced overtime, with stock out rates of antimalarials increasing over time to baseline levels. P4P led to sustained improvements in kindness during deliveries, with a wider set of improvements in patient experience of care in the longer term. A change in programme management and funding delayed incentive payments affecting performance on some indicators. The verification system became more integrated within routine systems over time, reducing the time burden on managers and health workers. Ongoing financial autonomy and supervision sustained motivational effects in those aspects of care giving not reliant on funding.

CONCLUSION

Our study adds to limited and mixed evidence documenting how P4P effects evolve over time. Our findings highlight the importance of undertaking ongoing assessment of effects over time.

Advancing sustainable development goals through immunization: a literature review

BACKGROUND

Immunization directly impacts health (SDG3) and brings a contribution to 14 out of the 17 Sustainable Development Goals (SDGs), such as ending poverty, reducing hunger, and reducing inequalities. Therefore, immunization is recognized to play a central role in reaching the SDGs, especially in low- and middle-income countries (LMICs). Despite continuous interventions to strengthen immunization systems and to adequately respond to emergency immunization during epidemics, the immunization-related indicators for SDG3 lag behind in sub-Saharan Africa. Especially taking into account the current Covid19 pandemic, the current performance on the connected SDGs is both a cause and a result of this.

METHODS

We conduct a literature review through a keyword search strategy complemented with handpicking and snowballing from earlier reviews. After title and abstract screening, we conducted a qualitative analysis of key insights and categorized them according to showing the impact of immunization on SDGs, sustainability challenges, and model-based solutions to these challenges.

RESULTS

We reveal the leveraging mechanisms triggered by immunization and position them vis-à-vis the SDGs, within the framework of Public Health and Planetary Health. Several challenges for sustainable control of vaccine-preventable diseases are identified: access to immunization services, global vaccine availability to LMICs, context-dependent vaccine effectiveness, safe and affordable vaccines, local/regional vaccine production, public-private partnerships, and immunization capacity/capability building. Model-based approaches that support SDG-promoting interventions concerning immunization systems are analyzed in light of the strategic priorities of the Immunization Agenda 2030.

CONCLUSIONS

In general terms, it can be concluded that relevant future research requires (i) design for system resilience, (ii) transdisciplinary modeling, (iii) connecting interventions in immunization with SDG outcomes, (iv) designing interventions and their implementation simultaneously, (v) offering tailored solutions, and (vi) model coordination and integration of services and partnerships. The research and health community is called upon to join forces to activate existing knowledge, generate new insights and develop decision-supporting tools for Low-and Middle-Income Countries' health authorities and communities to leverage immunization in its transformational role toward successfully meeting the SDGs in 2030.

Program Evaluation of Population- and System-Level Policies: Evidence for Decision Making

BACKGROUND

Policy evaluations often focus on ex post estimation of causal effects on short-term surrogate outcomes. The value of such information is limited for decision making, as the failure to reflect policy-relevant outcomes and disregard for opportunity costs prohibits the assessment of value for money. Further, these evaluations do not always consider all relevant evidence, other courses of action, or decision uncertainty.

METHODS

In this article, we explore how policy evaluation could better meet the needs of decision making. We begin by defining the evidence required to inform decision making. We then conduct a literature review of challenges in evaluating policies. Finally, we highlight potential methods available to help address these challenges.

RESULTS

The evidence required to inform decision making includes the impacts on the policy-relevant outcomes, the costs and associated opportunity costs, and the consequences of uncertainty. Challenges in evaluating health policies are described using 8 categories: 1) valuation space; 2) comparators; 3) time of evaluation; 4) mechanisms of action; 5) effects; 6) resources, constraints, and opportunity costs; 7) fidelity, adaptation, and level of implementation; and 8) generalizability and external validity. Methods from a broad set of disciplines are available to improve policy evaluation, relating to causal inference, decision-analytic modeling, theory of change, realist evaluation, and structured expert elicitation.

LIMITATIONS

The targeted review may not identify all possible challenges, and the methods covered are not exhaustive.

CONCLUSIONS

Evaluations should provide appropriate evidence to inform decision making. There are challenges in evaluating policies, but methods from multiple disciplines are available to address these challenges.

IMPLICATIONS

Evaluators need to carefully consider the decision being informed, the necessary evidence to inform it, and the appropriate methods.[Box: see text].

Optimization of Patient Flow in Urgent Care Centers Using a Digital Tool for Recording Patient Symptoms and History: Simulation Study

Background

Crowding can negatively affect patient and staff experience, and consequently the performance of health care facilities. Crowding can potentially be eased through streamlining and the reduction of duplication in patient history-taking through the use of a digital symptom-taking app.

Objective

We simulated the introduction of a digital symptom-taking app on patient flow. We hypothesized that waiting times and crowding in an urgent care center (UCC) could be reduced, and that this would be more efficient than simply adding more staff.

Methods

A discrete-event approach was used to simulate patient flow in a UCC during a 4-hour time frame. The baseline scenario was a small UCC with 2 triage nurses, 2 doctors, 1 treatment/examination nurse, and 1 discharge administrator in service. We simulated 33 scenarios with different staff numbers or different potential time savings through the app. We explored average queue length, waiting time, idle time, and staff utilization for each scenario.

Results

Discrete-event simulation showed that even a few minutes saved through patient app-based self-history recording during triage could result in significantly increased efficiency. A modest estimated time saving per patient of 2.5 minutes decreased the average patient wait time for triage by 26.17%, whereas a time saving of 5 minutes led to a 54.88% reduction in patient wait times. Alternatively, adding an additional triage nurse was less efficient, as the additional staff were only required at the busiest times.

Conclusions

Small time savings in the history-taking process have potential to result in substantial reductions in total patient waiting time for triage nurses, with likely effects of reduced patient anxiety, staff anxiety, and improved patient care. Patient self-history recording could be carried out at home or in the waiting room via a check-in kiosk or a portable tablet computer. This formative simulation study has potential to impact service provision and approaches to digitalization at scale.

Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies

Background

Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health.

Objective

The aim of this study was to assess the impact of the use of big data analytics on people’s health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2–related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people’s health.

Methods

Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist.

Results

The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. “Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease” and “suicide mortality rate” were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as “critically low” for 25 reviews, as “low” for 7 reviews, and as “moderate” for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data.

Conclusions

Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes.

Trial Registration

International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048

A realist review to assess for whom, under what conditions and how pay for performance programmes work in low- and middle-income countries

Pay for performance (P4P) programmes are popular health system-focused interventions aiming to improve health outcomes in low-and middle-income countries (LMICs). This realist review aims to understand how, why and under what circumstance P4P works in LMICs.We systematically searched peer-reviewed and grey literature databases, and examined the mechanisms underpinning P4P effects on: utilisation of services, patient satisfaction, provider productivity and broader health system, and contextual factors moderating these. This evidence was then used to construct a causal loop diagram.We included 112 records (19 grey literature; 93 peer-reviewed articles) assessing P4P schemes in 36 countries. Although we found mixed evidence of P4P's effects on identified outcomes, common pathways to improved outcomes include: community outreach; adherence to clinical guidelines, patient-provider interactions, patient trust, facility improvements, access to drugs and equipment, facility autonomy, and lower user fees. Contextual factors shaping the system response to P4P include: degree of facility autonomy, efficiency of banking, role of user charges in financing public services; staffing levels; staff training and motivation, quality of facility infrastructure and community social norms. Programme design features supporting or impeding health system effects of P4P included: scope of incentivised indicators, fairness and reach of incentives, timely payments and a supportive, robust verification system that does not overburden staff. Facility bonuses are a key element of P4P, but rely on provider autonomy for maximum effect. If health system inputs are vastly underperforming pre-P4P, they are unlikely to improve only due to P4P. This is the first realist review describing how and why P4P initiatives work (or fail) in different LMIC contexts by exploring the underlying mechanisms and contextual and programme design moderators. Future studies should systematically examine health system pathways to outcomes for P4P and other health system strengthening initiatives, and offer more understanding of how programme design shapes mechanisms and effects.