Systems Thinking and Models in Public Health

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 30872

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Special Issue Editors


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Guest Editor
Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA
Interests: health behaviors; machine learning; modeling; simulation
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Guest Editor
Translational Health Research Institute, Campbelltown Campus, Western Sydney University, Locked Bag 1797, Penrith, NSW 2751, Australia
Interests: epidemiology; mental health; psychology and public health; social determinants of health

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue on “Systems Thinking and Models in Public Health”. Systems Thinking and Modeling have been essential approaches to identifying drivers of health outcomes and analyzing their complex interrelationships, hence paving the way to find interventions that improve outcomes while minimizing unintended consequences. These methods support interdisciplinary teams in representing and navigating complex systems, thus asking essential ‘what-if’ questions and serving numerous other goals, from guiding data collection efforts to comparing the perspectives of stakeholders or validating theories. This Special Issue seeks methodological innovations and applications for several determinants and key population global health priority areas.

Priority determinants include, but are not limited to, social determinants (e.g., with respect to health inequalities), climate change (through its impact on health), behavioral factors, and facets of the healthcare system ranging from primary services to secondary prevention (i.e., screening and case-finding) and tertiary case (e.g., treatment optimization, resource allocation). Systems thinking and modeling can shed light on how such determinants ultimately shape health outcomes and/or the cost-effectiveness of an intervention. Although the reduction of any disease burden is of interest, we highlight the importance of seven categories as global health priority areas: cardiovascular diseases, cancer, mental health, injury, diabetes, respiratory diseases, and maternal and neonatal disorders. As a Special Issue showcasing the latest developments in research, we also welcome contributions on novel topics. While COVID-19 models are already aptly handled by other Special Issues, note that long COVID would exemplify emerging applications of interest for global health.   

All submissions should strive to justify the selection of their methods and promote transparency in their application. For example, best practices in participatory modeling suggest that there are clear processes to identify and engage stakeholders (e.g., community members, public health experts, clinicians, decision-makers). Cases that either produce systems maps (e.g., causal loop diagrams, mind maps) and models (e.g., system dynamics, agent-based models, network models, fuzzy cognitive maps) or analyze them to prioritize interventions should thus be explicit in their participatory design and analytical approach.

We note that there are several methodological open problems in the application of systems science and modeling to public health, hence submissions may contribute to addressing these gaps through their case studies or methodological novelty. Three aspects are of particular interest when submissions emphasize methodological innovations. First, we acknowledge that systems thinking and models are essential tools to identify interventions, but their value is much broader than providing predictions. Indeed, models also serve to learn about a phenomenon and to guide productive discussions that are not limited by disciplinary boundaries. Innovations can thus contribute to the study of models as vehicles to facilitate knowledge translation. Second, models can serve the dual role of identifying and evaluating interventions. For example, results from an intervention can be compared with expectations from the model, thus revealing potential discrepancies and bottlenecks. We thus encourage research that embeds models in practices, for instance as part of program evaluation. Finally, we note that data science has permeated virtually every field of research, and public health is no exception. Opportunities (e.g., for calibration, verification and validation) and challenges (e.g., uncertainty) in the use of data can thus contribute to advancing the field.

We solicit papers for this Special Issue that broadly deal with such challenges by addressing open questions, providing novel case studies, or leading to interesting and challenging debates. Papers can be reviews, syntheses, viewpoints, meta-analyses, or original research articles.

Dr. Philippe J. Giabbanelli
Prof. Dr. Andrew Page
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • agent-based models
  • computational epidemiology
  • modeling and simulation
  • participatory modeling
  • population health
  • public health informatics
  • systems maps

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Published Papers (12 papers)

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Editorial

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3 pages, 246 KiB  
Editorial
Systems Thinking and Models in Public Health
by Philippe J. Giabbanelli and Andrew Page
Systems 2024, 12(3), 101; https://doi.org/10.3390/systems12030101 - 16 Mar 2024
Viewed by 1627
Abstract
In responding to population health challenges, epidemiologists want to identify causal associations between an exposure (e [...] Full article
(This article belongs to the Special Issue Systems Thinking and Models in Public Health)

Research

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19 pages, 2583 KiB  
Article
Simulation and Goal Programming Approach to Improve Public Hospital Emergency Department Resource Allocation
by Ateekh Ur Rehman, Yusuf Siraj Usmani, Syed Hammad Mian, Mustufa Haider Abidi and Hisham Alkhalefah
Systems 2023, 11(9), 467; https://doi.org/10.3390/systems11090467 - 8 Sep 2023
Cited by 1 | Viewed by 2907
Abstract
Efficient and effective operation of an emergency department is necessary. Since patients can visit the emergency department without making an appointment, the emergency department always treats a lot of critical patients. Moreover, the severity of the ailment determines which patients should be prioritized. [...] Read more.
Efficient and effective operation of an emergency department is necessary. Since patients can visit the emergency department without making an appointment, the emergency department always treats a lot of critical patients. Moreover, the severity of the ailment determines which patients should be prioritized. Therefore, the patients are greatly impacted as a consequence of longer waiting times caused primarily by incorrect resource allocation. It frequently happens that patients leave the hospital or waiting area without treatment. Certainly, the emergency department’s operation can be made more effective and efficient by examining its work and making modifications to the number of resources and their allocation. This study, therefore, investigates the emergency department of a public hospital to improve its functioning. The goal of this research is to model and simulate an emergency department to minimize patient wait times and also minimize the number of patients leaving the hospital without service. A comprehensive simulation model is developed using the Arena simulation platform and goal programming is undertaken to conduct simulation optimization and resource allocation analysis. Hospital management should realize that all resources must be prioritized rather than just focusing on one or two of them. The case scenario (S3) in this study that implements goal programming with variable weights yields the most favorable results. For example, it is observed in this instance that the number of patients leaving the system without service drops by 61.7%, and there is also a substantial drop in waiting times for various types of patients. Full article
(This article belongs to the Special Issue Systems Thinking and Models in Public Health)
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17 pages, 1702 KiB  
Article
Evaluating the Impact of Increased Dispensing of Opioid Agonist Therapy Take-Home Doses on Treatment Retention and Opioid-Related Harm among Opioid Agonist Therapy Recipients: A Simulation Study
by Narjes Shojaati and Nathaniel D. Osgood
Systems 2023, 11(8), 391; https://doi.org/10.3390/systems11080391 - 1 Aug 2023
Cited by 1 | Viewed by 1373
Abstract
Modified opioid agonist therapy (OAT) guidelines that were initially introduced during the COVID-19 pandemic allow prescribers to increase the number of take-home doses to fulfill their need for physical distancing and prevent treatment discontinuation. It is crucial to evaluate the consequence of administering [...] Read more.
Modified opioid agonist therapy (OAT) guidelines that were initially introduced during the COVID-19 pandemic allow prescribers to increase the number of take-home doses to fulfill their need for physical distancing and prevent treatment discontinuation. It is crucial to evaluate the consequence of administering higher take-home doses of OAT on treatment retention and opioid-related harms among OAT recipients to decide whether the new recommendations should be retained post-pandemic. This study used an agent-based model to simulate individuals dispensed daily or weekly OAT (methadone or buprenorphine/naloxone) with a prescription over a six-month treatment period. Within the model simulation, a subset of OAT recipients was deemed eligible for receiving increased take-home doses of OAT at varying points during their treatment time course. Model results demonstrated that the earlier dispensing of increased take-home doses of OAT were effective in achieving a slightly higher treatment retention among OAT recipients. Extended take-home doses also increased opioid-related harms among buprenorphine/naloxone-treated individuals. The model results also illustrated that expanding naloxone availability within OAT patients’ networks could prevent these possible side effects. Therefore, policymakers may need to strike a balance between expanding access to OAT through longer-duration take-home doses and managing the potential risks associated with increased opioid-related harms. Full article
(This article belongs to the Special Issue Systems Thinking and Models in Public Health)
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31 pages, 7822 KiB  
Article
Participatory Modeling with Discrete-Event Simulation: A Hybrid Approach to Inform Policy Development to Reduce Emergency Department Wait Times
by Yuan Tian, Jenny Basran, James Stempien, Adrienne Danyliw, Graham Fast, Patrick Falastein and Nathaniel D. Osgood
Systems 2023, 11(7), 362; https://doi.org/10.3390/systems11070362 - 17 Jul 2023
Viewed by 2355
Abstract
We detail a case study using a participatory modeling approach in the development and use of discrete-event simulations to identify intervention strategies aimed at reducing emergency department (ED) wait times in a Canadian health policy setting. A four-stage participatory modeling approach specifically adapted [...] Read more.
We detail a case study using a participatory modeling approach in the development and use of discrete-event simulations to identify intervention strategies aimed at reducing emergency department (ED) wait times in a Canadian health policy setting. A four-stage participatory modeling approach specifically adapted to the local policy environment was developed to engage stakeholders throughout the modeling processes. The participatory approach enabled a provincial team to engage a broad range of stakeholders to examine and identify the causes and solutions to lengthy ED wait times in the studied hospitals from a whole-system perspective. Each stage of the approach was demonstrated through its application in the case study. A novel and key feature of the participatory modeling approach was the development and use of a multi-criteria framework to identify and prioritize interventions to reduce ED wait times. We conclude with a discussion on lessons learned, which provide insights into future development and applications of participatory modeling methods to facilitate policy development and build multi-stakeholder consensus. Full article
(This article belongs to the Special Issue Systems Thinking and Models in Public Health)
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20 pages, 1757 KiB  
Article
Dynamic Optimization of Emergency Logistics for Major Epidemic Considering Demand Urgency
by Jianjun Zhang, Jingru Huang, Tianhao Wang and Jin Zhao
Systems 2023, 11(6), 303; https://doi.org/10.3390/systems11060303 - 13 Jun 2023
Cited by 2 | Viewed by 1911
Abstract
In recent years, epidemic disasters broke through frequently around the world, posing a huge threat to economic and social development, as well as human health. A fair and accurate distribution of emergency supplies during an epidemic is vital for improving emergency rescue efficiency [...] Read more.
In recent years, epidemic disasters broke through frequently around the world, posing a huge threat to economic and social development, as well as human health. A fair and accurate distribution of emergency supplies during an epidemic is vital for improving emergency rescue efficiency and reducing economic losses. However, traditional emergency material allocation models often focus on meeting the amount of materials requested, and ignore the differences in the importance of different emergency materials and the subjective urgency demand of the disaster victims. As a result, it is difficult for the system to fairly and reasonably match different scarce materials to the corresponding areas of greatest need. Consequently, this paper proposes a material shortage adjustment coefficient based on the entropy weight method, which includes indicators such as material consumption rate, material reproduction rate, durability, degree of danger to life, and degree of irreplaceability, to enlarge and narrow the actual shortage of material supply according to the demand urgency. Due to the fact that emergency materials are not dispatched in one go during epidemic periods, a multi-period integer programming model was established to minimize the adjusted total material shortage based on the above function. Taking the cases of Wuhan and Shanghai during the lockdown and static management period, the quantitative analysis based on material distribution reflected that the model established in this paper was effective in different scenarios where there were significant differences in the quantity and structure of material demand. At the same time, the model could significantly adjust the shortage of emergency materials with higher importance and improve the satisfaction rate. Full article
(This article belongs to the Special Issue Systems Thinking and Models in Public Health)
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21 pages, 2156 KiB  
Article
Research on Intelligent Emergency Resource Allocation Mechanism for Public Health Emergencies: A Case Study on the Prevention and Control of COVID-19 in China
by Ruhao Ma, Fansheng Meng and Haiwen Du
Systems 2023, 11(6), 300; https://doi.org/10.3390/systems11060300 - 11 Jun 2023
Cited by 3 | Viewed by 2679
Abstract
The outbreak of COVID-19 posed a significant challenge to the emergency management system for public health emergencies, especially in China, where the epidemic began. As intelligent technology has injected new vitality into emergency management, applying intelligent technology to optimize emergency resource allocation (ERA) [...] Read more.
The outbreak of COVID-19 posed a significant challenge to the emergency management system for public health emergencies, especially in China, where the epidemic began. As intelligent technology has injected new vitality into emergency management, applying intelligent technology to optimize emergency resource allocation (ERA) has become a focus of research in the post-epidemic era. Based on China’s experience in preventing and controlling COVID-19, this paper first analyzes the characteristics and process of ERA in public health emergencies, and then synthesizes the relevant Chinese studies in recent years to identify the intelligent technologies affecting ERA in China using word frequency analysis technology. We also construct an intelligent emergency resource allocation mechanism in four areas: medical intelligence, management intelligence, decision-making intelligence, and supervision intelligence. Finally, we use the entropy-TOPSIS method to evaluate the impact of intelligent technologies on ERA, and we rank the criticality of intelligent technologies. The experimental results show that (i.) medical intelligence and management intelligence are the keys to developing intelligent ERA, and (ii.) among the identified essential intelligent technologies, artificial intelligence (AI), and big data technology have a more significant and critical role in emergency resource intelligence allocation. Full article
(This article belongs to the Special Issue Systems Thinking and Models in Public Health)
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11 pages, 2020 KiB  
Article
Strategies to Prevent Suicide and Attempted Suicide in New South Wales (Australia): Community-Based Outreach, Alternatives to Emergency Department Care, and Early Intervention
by Eileen Goldberg, Cindy Peng, Andrew Page, Piumee Bandara and Danielle Currie
Systems 2023, 11(6), 275; https://doi.org/10.3390/systems11060275 - 31 May 2023
Viewed by 2822
Abstract
Background: This study describes the development of a system dynamics model to project the potential impact of a series of proposed suicide prevention interventions in New South Wales (NSW, Australia) over the period 2016 to 2031. Methods: A system dynamics model for the [...] Read more.
Background: This study describes the development of a system dynamics model to project the potential impact of a series of proposed suicide prevention interventions in New South Wales (NSW, Australia) over the period 2016 to 2031. Methods: A system dynamics model for the NSW population aged ≥ 20 years which represented the current incidence of suicide and attempted suicide in NSW was developed in partnership with a consortium of stakeholders, subject matter experts, and consumers with lived experience. Scenarios relating to current suicide prevention initiatives were investigated to identify the combination of interventions associated with the largest reductions in the projected number of attempted suicide and suicide cases for a 5-year follow-up period (2019–2023). Results: The largest proportion of cases averted for both suicide and attempted suicide over the intervention period was associated with community-based suicide prevention outreach teams and peer-led drop-in facilities (6.8% for attempted suicide, 6.4% for suicide). A similar proportion of potential cases averted of both attempted suicide and suicide (6.4%) was evident for targeted interventions focusing only on those in the population with suicidal thoughts and a previous history of attempted suicide. Conclusion: Initiatives that are characterised by the short-term stabilisation of suicidal distress at the point of crisis, averting the need for a hospital encounter, and the referral of individuals to non-acute community-based care were associated with the largest potential reductions in suicidal behaviour in NSW. Full article
(This article belongs to the Special Issue Systems Thinking and Models in Public Health)
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20 pages, 7785 KiB  
Article
An Agent-Based Social Impact Theory Model to Study the Impact of In-Person School Closures on Nonmedical Prescription Opioid Use among Youth
by Narjes Shojaati and Nathaniel D. Osgood
Systems 2023, 11(2), 72; https://doi.org/10.3390/systems11020072 - 1 Feb 2023
Cited by 1 | Viewed by 2176
Abstract
Substance use behavior among youth is a complex peer-group phenomenon shaped by many factors. Peer influence, easily accessible prescription opioids, and a youth’s socio-cultural environment play recognized roles in the initiation and persistence of youth nonmedical prescription opioid use. By altering the physical [...] Read more.
Substance use behavior among youth is a complex peer-group phenomenon shaped by many factors. Peer influence, easily accessible prescription opioids, and a youth’s socio-cultural environment play recognized roles in the initiation and persistence of youth nonmedical prescription opioid use. By altering the physical surroundings and social environment of youth, in-person school closures may change risk factors for youth drug use. Acknowledging past research on the importance of the presence of peers in youth substance use risk behavior, this paper reports the findings from the use of an agent-based simulation grounded in social impact theory to investigate possible impacts of in-person school closures due to COVID-19 on the prevalence of nonmedical prescription opioid use among youth. The presented model integrates data from the Ontario Student Drug Use and Health Survey and characterizes the accessibility of within-home prescription opioids. Under the status quo, the lifting of in-person school closures reliably entails an increase in the prevalence of youth with nonmedical prescription opioid use, but this effect is ameliorated if the prescription opioids are securely stored during the in-person school closures period. Full article
(This article belongs to the Special Issue Systems Thinking and Models in Public Health)
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20 pages, 4747 KiB  
Article
Simulation-Based Assessment of Cholera Epidemic Response: A Case Study of Al-Hudaydah, Yemen
by Pei Shan Loo, Anaely Aguiar and Birgit Kopainsky
Systems 2023, 11(1), 3; https://doi.org/10.3390/systems11010003 - 21 Dec 2022
Cited by 3 | Viewed by 3724
Abstract
Cholera kills between 21,000 and 143,000 people globally each year. It is often fatal, killing up to 50% of the severely symptomatic patients; but death by cholera is preventable with timely treatment, so that the fatality rate can drop to less than 1%. [...] Read more.
Cholera kills between 21,000 and 143,000 people globally each year. It is often fatal, killing up to 50% of the severely symptomatic patients; but death by cholera is preventable with timely treatment, so that the fatality rate can drop to less than 1%. Due to cholera’s multi-pathway transmission, a multifaceted and multi-sectoral approach to combat this disease is needed. Such complexity gives rise to uncertainty about where it is best to intervene, as stakeholders have to balance prevention and treatment under highly constrained resources. Using Al-Hudaydah, Yemen as a case study, this paper demonstrates how a system dynamics model can be built using a classic infection structure with empirically grounded operational structures: health treatment, water, sanitation, and hygiene (WASH), vaccination, and a data surveillance system. The model explores the implications of the joint interventions with different start times. The model analysis revealed that the historical interventions likely prevented 55% more deaths in 2017 as compared to a counterfactual business-as-usual scenario with no interventions in the past. At the same time, some 40% of deaths could potentially have been prevented if interventions (with the same resources as historical data) had been initiated earlier in April 2017. Further research will explore each intervention impact for more detailed policy analysis and simulations into the future. Full article
(This article belongs to the Special Issue Systems Thinking and Models in Public Health)
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25 pages, 1537 KiB  
Article
On the Relationships among Nurse Staffing, Inpatient Care Quality, and Hospital Competition under the Global Budget Payment Scheme of Taiwan’s National Health Insurance System: Mixed Frequency VAR Analyses
by Wen-Yi Chen
Systems 2022, 10(5), 187; https://doi.org/10.3390/systems10050187 - 14 Oct 2022
Cited by 2 | Viewed by 2172
Abstract
Background: Time series analyses on the relationship between nurse staffing and inpatient care quality are rare due to inconsistent frequencies of data between common observations of nurse-staffing (e.g., monthly) and inpatient care quality indicators (e.g., quarterly). Methods: In order to deal with the [...] Read more.
Background: Time series analyses on the relationship between nurse staffing and inpatient care quality are rare due to inconsistent frequencies of data between common observations of nurse-staffing (e.g., monthly) and inpatient care quality indicators (e.g., quarterly). Methods: In order to deal with the issue of mixed frequency data, this research adopted the MF-VAR model to explore causal relationships among nurse staffing, inpatient care quality, and hospital competition under the global budget payment scheme of Taiwan’s healthcare system. Results: Our results identified bi-directional causation between nurse staffing and patient outcomes and one-way Granger causality running between nurse staffing and reimbursement payments for inpatient care services. Impulse-response analyses found positive (negative) effects of the patient-to-nurse ratio on adverse patient outcomes (reimbursement payments) in all types of hospitals and detrimental effects of adverse patient outcomes on the patient-to-nurse ratio in medical centers and regional hospitals across a 12-month period. Conclusions: These findings suggest that nurse staffing is an essential determinant of both patient outcomes and reimbursement payments. Strategic policies such as direct subsidy and hospital accreditation for appropriate nurse staffing levels should be implemented for medical centers and regional hospitals to mitigate the harmful effects of adverse patient outcomes on nurse staffing. Full article
(This article belongs to the Special Issue Systems Thinking and Models in Public Health)
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Review

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17 pages, 614 KiB  
Review
Systems-Oriented Modelling Methods in Preventing and Controlling Emerging Infectious Diseases in the Context of Healthcare Policy: A Scoping Review
by Mariam Abdulmonem Mansouri, Leandro Garcia, Frank Kee and Declan Terence Bradley
Systems 2022, 10(5), 182; https://doi.org/10.3390/systems10050182 - 9 Oct 2022
Cited by 2 | Viewed by 2476
Abstract
Background: Emerging infectious diseases (EIDs) arise and affect society in complex ways. We conducted a scoping review to explore how systems-oriented methods have been used to prevent and control EIDs. Methods: We used the Joanna Briggs Institute framework for scoping reviews in this [...] Read more.
Background: Emerging infectious diseases (EIDs) arise and affect society in complex ways. We conducted a scoping review to explore how systems-oriented methods have been used to prevent and control EIDs. Methods: We used the Joanna Briggs Institute framework for scoping reviews in this study. We included peer-reviewed articles about health care systems preparedness and response, published from 1 January 2000. We considered the World Health Organisation’s (WHO) list of prioritised diseases for research and development when choosing the pathogens and only included studies that considered the dynamics between the system’s elements. Results: Our initial search yielded 9985 studies. After screening, 177 studies were considered for inclusion in this review. After assessment by two independent reviewers, seven studies were included. The studies were published between 2009 and 2021. Most focused on sarbecoviruses and targeted healthcare policymakers and governments. System dynamics approaches were the most used methods. Most of the studies incorporated the classical epidemiological models alongside systems-oriented methods. The studies were conducted in context of diseases dynamics and its burden on human health, the economy and healthcare systems. The most reported challenge was epidemiological and geographical data timeliness and quality. Conclusions: Systems dynamics approaches can help policy makers understand the elements of a complex system and thus offer potential solutions for preventing and controlling EIDs. Full article
(This article belongs to the Special Issue Systems Thinking and Models in Public Health)
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Other

10 pages, 850 KiB  
Hypothesis
Identifying Policy Gaps in a COVID-19 Online Tool Using the Five-Factor Framework
by Janet Michel, David Evans, Marcel Tanner and Thomas C. Sauter
Systems 2022, 10(6), 257; https://doi.org/10.3390/systems10060257 - 15 Dec 2022
Viewed by 1925
Abstract
Introduction: Worldwide health systems are being faced with unprecedented COVID-19-related challenges, ranging from the problems of a novel condition and a shortage of personal protective equipment to frequently changing medical guidelines. Many institutions were forced to innovate and many hospitals, as well as [...] Read more.
Introduction: Worldwide health systems are being faced with unprecedented COVID-19-related challenges, ranging from the problems of a novel condition and a shortage of personal protective equipment to frequently changing medical guidelines. Many institutions were forced to innovate and many hospitals, as well as telehealth providers, set up online forward triage tools (OFTTs). Using an OFTT before visiting the emergency department or a doctor’s practice became common practice. A policy can be defined as what an institution or government chooses to do or not to do. An OFTT, in this case, has become both a policy and a practice. Methods: The study was part of a broader multiphase sequential explanatory design. First, an online survey was carried out using a questionnaire to n = 176 patients who consented during OFTT usage. Descriptive analysis was carried out to identify who used the tool, for what purpose, and if the participant followed the recommendations. The quantitative results shaped the interview guide’s development. Second, in-depth interviews were held with a purposeful sample of n = 19, selected from the OFTT users who had consented to a further qualitative study. The qualitative findings were meant to explain the quantitative results. Third, in-depth interviews were held with healthcare providers and authorities (n = 5) that were privy to the tool. Framework analysis was adopted using the five-factor framework as a lens with which to analyze the qualitative data only. Results: The five-factor framework proved useful in identifying gaps that affected the utility of the COVID-19 OFTT. The identified gaps could fit and be represented by five factors: primary, secondary, tertiary, and extraneous factors, along with a lack of systems thinking. Conclusion: A theory or framework provides a road map to systematically identify those factors affecting policy implementation. Knowing how and why policy practice gaps come about in a COVID-19 OFFT context facilitates better future OFTTs. The framework in this study, although developed in a universal health coverage (UHC) context in South Africa, proved useful in a telehealth context in Switzerland, in Europe. The importance of systems thinking in developing digital tools cannot be overemphasized. Full article
(This article belongs to the Special Issue Systems Thinking and Models in Public Health)
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