SDM papers | ABM papers | Hybrid papers | |
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Purpose of research | Testing policies or interventions: • to relieve at-capacity healthcare services, reduce ward occupancy and patient length of stay [28, 31, 36, 43, 49, 50, 54, 58, 62]. • to reduce time to patient admission and treatment [33, 53, 61] • to reduce delayed discharges [31] • to increase the uptake of healthcare services and level of healthcare provision [60] • to target undesirable patient health outcomes (morbidity, mortality, post-treatment complications) [47, 58, 60, 63]. • to optimise performance-based incentive policies against health professional productivity, quality of care and volume of services [30, 59]. • to reduce the total cost of care [33, 47, 58, 60, 61, 63]. • to reduce deficit of health professionals [51] • to reduce generation of incineration-only health care waste [52] • to increase the number of patients who currently do not seek medical care [64] Other: • explore factors leading to undesirable emergency care system behaviour [56, 57] • simulating hospital waste management systems and predicting future waste generation [37, 48, 55]. • estimating future demand for cardiac care [44]. • exploring the impact of patient admission on health professionals stress level in an integrated care system (IC) [45]. • exploring variation in physician decision-making [32]. | Testing policies or interventions: • to decrease the time agents spent performing tasks, waiting for a service or residing in parts of the system [20, 22, 24, 67]. • to reduce undesirable patient outcomes (mortality and hospitalisation) [23, 25, 47, 67]. • to reduce the number of patients who left a health facility without being seen by a physician [22, 67]. • to reduce number of patients who are wrongly discharged [67] • to optimise utility of resources (staff, beds) [46, 66, 67]. • on bypass rate of patients accessing care at alternative facilities [23] • to reduce total cost of care [25] Other: • Create tools capable of comparing health insurance reimbursement schemes [65]. • Assessing risk, allocation of resources and identifying weaknesses in emergency care services [21]. | Testing policies or interventions: SDM-DES • to improve access to social support and care services [43]. ABM-DES • to decrease patient waiting time to be seen by a physician [24]. • to improve patient flow and length of stay through the system by optimising resource allocation [46]. SDM-ABM • to reduce undesirable patient outcomes (morbidity) [47]. Other: SDM-DES • Estimate the future demand for health care from patients with cardiac disease [44]. • Model patient flow through an integrated care system to estimate impact of patient admission on health care professional’s wellbeing [45]. |
Healthcare setting modelled | • Elderly care or LTC services [28, 31, 36, 49,50,51, 54, 61, 62] • Emergency or acute care [28, 31, 36, 50, 56,57,58, 61, 62] • Hospital waste management [37, 48, 52, 55] • ACO or health insurance schemes [63] • Orthopaedic care [63] | • Cardiology care [66] | SDM-DES • Cardiology care [44] • Elderly care or LTC services [43,44,45] • Emergency or acute care [45] ABM-DES • MNCH [46] • Orthopaedic care [24] SDM-ABM • Emergency or acute care [47] |
Rationale for using model | • Gain holistic perspective of system to investigate delays and bottlenecks in health facility processes, exploring counter-intuitive behaviour and monitoring interconnected processes between sub-systems over time [28, 30, 31, 36, 37, 48, 56, 58]. • Useful tool for predicting future health system behaviour and demand for care services, essential for health resource and capacity planning [48, 60]. • Configuration of model was not limited by data availability [28, 52, 64] and could integrate data from various sources when required [51]. • Used as a tool for health policy exploration and optimising health system interventions [33, 36, 51, 54, 57, 58, 64]. • Useful for establishing clinical and financial ramifications on multiple groups (such as patients and health care providers) [63]. • Identifying and simulating feedback, policy resistance or unintended system consequences [59, 61]. • Quantifying the impact of change to the health system before real world implementation [62]. • Visual learning environment enabled engagement with stakeholders necessary for model conception and validation [48, 50, 55, 57]. • Utilised by decision makers to develop and test alternative policies in a ‘real-world’ framework [31, 49, 58, 61]. • Suitable for quantitative analyses [53]. • Fast running simulation [54]. | • Ability to closely replicate human behaviour that exists in the real system [20,21,22, 25, 66]. • Provides deeper understanding of multiple agent decision-making [23, 67], agent networks [25] and interactions [21, 22]. • Provides flexible framework capable of conveying intricate system structures [20], where simulations captured agent capacity for learning and adaptive behaviour [20, 25]. • Could incorporate stochastic processes that mimicked agent transition between states [25]. • Took advantage of key individual level agent data [25] and integrated information from various sources [65]. • Simulation allows patients to have multiple medical problems at the same time [65]. • Model can be made generalisable to other settings [65]. • Visualization of system facilitated stakeholder understanding of tested policy impact [23], particularly those in the health industry with minimal modelling experience [67]. | SDM-DES • Enabled retention of deterministic and stochastic system variability and preservation of unique and valuable features of both methods [44]. • Capable of simulating flow of entities through system and provides rapid insight without need for large data collection [43]. • Can simulate individual variability and detailed interactions that influence system behaviour [43]. • Offered dual model functionality [44] vital for simulating human-centric activity [45], reducing the practical limitations that come with using a single simulation method to model health systems [45]. ABM-DES • Captured both patient flow through system and agent decision-making that enabled identification of health care bottlenecks and optimum resource allocation [24]. SDM-ABM • Could reproduce detailed, high granularity system elements in addition to abstract, aggregate health system variables [47]. |
Methods of validation | Behavioural validity tests: • Model output reviewed by experts [57, 60]. • Model output compared with historical data and relevant literature [31,32,33, 36, 48, 50, 54, 58, 59, 61, 62, 64]. Structural validity tests: • Model conception [28, 60], development [30, 36, 50, 53, 54, 57, 62] and formulation [54, 56, 59] validated by experts. • Extreme condition or value testing [30, 31, 52, 57, 59, 60, 64]. • Dimensional consistency checks [31, 52, 57, 59, 60]. • Model boundary accuracy checks [31]. • Mass balance checks [54]. • Integration error checks [31, 52]. Sensitivity analysis • to assess how sensitive model output was to changes in key parameters [49, 51, 57, 60, 64]. • to test the impact of parameters that had been based on expert opinion on model output [28]. • to test the robustness and effectiveness of policies [28, 30, 52, 53, 58, 63] (on the assumption of imperfect policy implementation [28]). | Behavioural validity tests: • Model output reviewed by experts [46, 66]. • Model output compared with historical data and relevant literature [20, 22,23,24,25, 46, 65, 66]. • F-test [20] and T-test [20, 24] (equivalence of variance and difference in mean tests). Structural validity tests: • Extreme condition or value testing [23, 46]. • Model framework reviewed by experts [22, 47]. Sensitivity analysis: • to determine how variations or uncertainty in key parameters (particularly where they had not been derived from historical or care data [65]) affected model outcomes [23, 25]. | Behavioural validity tests: ABM-DES • Model output reviewed by experts [46]. • Model output compared with historical data [24, 46]. • T-test (difference in mean tests) [24]. Structural validity tests: ABM-DES • Extreme condition or value testing [46]. SDM-ABM • Model framework reviewed by experts [47]. Sensitivity analysis: SDM-DES • To assess how sensitive model output was to changes in key parameters [44]. |
Study limitations | • Did not consider how future improvements in technology or service delivery may impact results [31, 44, 49, 51]. • May not have simulated all possible actions or interactions that occurred in real system [30, 61]. • Model cannot encapsulate all health sub-sector behaviour and spill-over effects [31, 53]. • Simplification of real system in model [55, 62, 63]. • Lack of facility data required for model conception, formulation and validation [32, 36, 59]. • Lack of costing or cost effectiveness analysis [58, 60]. • Simulation was over a short time scale and did not evaluate long term patient outcomes [33, 57]. • Assumptions made in model development may not be generalisable to other settings [36, 63]. • Discussion with stakeholders that contributed to model development was not performed systematically [51]. • Quantifying model uncertainty was limited [64]. | • Model parameterised with best information available, sometimes missing key data [20, 22, 25, 67]. • Did not model all real system complexity, simplifications made to agents and their attributes [20, 23, 65, 66]. • Did not consider all hospital units affected by possible spill-over effects [21]. | SDM-DES • Did not consider how future improvements in technology may impact results [44]. • Did not model all real system complexity, stable number of patients with disease per age group [44]. • Lack of technology support led to simplifications in configuration of model (how information was passed between two distinct models) [45]. ABM-DES • Need more case studies to externally validate model [24]. |
Software platform | • iThink or STELLA (same software) [33, 36, 37, 48, 50, 54, 55, 57, 60, 61]. • MATLAB and Simulink [30]. | • Java [66]. | SDM-DES • Does not state [44]. ABM-DES SDM-ABM • AnyLogic [47]. |