Dataset Information

Development of a resource modelling tool to support decision makers in pandemic influenza preparedness: The AsiaFluCap Simulator.

ABSTRACT: BACKGROUND:Health care planning for pandemic influenza is a challenging task which requires predictive models by which the impact of different response strategies can be evaluated. However, current preparedness plans and simulations exercises, as well as freely available simulation models previously made for policy makers, do not explicitly address the availability of health care resources or determine the impact of shortages on public health. Nevertheless, the feasibility of health systems to implement response measures or interventions described in plans and trained in exercises depends on the available resource capacity. As part of the AsiaFluCap project, we developed a comprehensive and flexible resource modelling tool to support public health officials in understanding and preparing for surges in resource demand during future pandemics. RESULTS:The AsiaFluCap Simulator is a combination of a resource model containing 28 health care resources and an epidemiological model. The tool was built in MS Excel© and contains a user-friendly interface which allows users to select mild or severe pandemic scenarios, change resource parameters and run simulations for one or multiple regions. Besides epidemiological estimations, the simulator provides indications on resource gaps or surpluses, and the impact of shortages on public health for each selected region. It allows for a comparative analysis of the effects of resource availability and consequences of different strategies of resource use, which can provide guidance on resource prioritising and/or mobilisation. Simulation results are displayed in various tables and graphs, and can also be easily exported to GIS software to create maps for geographical analysis of the distribution of resources. CONCLUSIONS:The AsiaFluCap Simulator is freely available software (http://www.cdprg.org) which can be used by policy makers, policy advisors, donors and other stakeholders involved in preparedness for providing evidence based and illustrative information on health care resource capacities during future pandemics. The tool can inform both preparedness plans and simulation exercises and can help increase the general understanding of dynamics in resource capacities during a pandemic. The combination of a mathematical model with multiple resources and the linkage to GIS for creating maps makes the tool unique compared to other available software.

SUBMITTER: Stein ML

PROVIDER: S-EPMC3509032 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Development of a resource modelling tool to support decision makers in pandemic influenza preparedness: The AsiaFluCap Simulator.

Stein Mart Lambertus ML Rudge James W JW Coker Richard R van der Weijden Charlie C Krumkamp Ralf R Hanvoravongchai Piya P Chavez Irwin I Putthasri Weerasak W Phommasack Bounlay B Adisasmito Wiku W Touch Sok S Sat Le Minh le M Hsu Yu-Chen YC Kretzschmar Mirjam M Timen Aura A

BMC public health 20121012

<h4>Background</h4>Health care planning for pandemic influenza is a challenging task which requires predictive models by which the impact of different response strategies can be evaluated. However, current preparedness plans and simulations exercises, as well as freely available simulation models previously made for policy makers, do not explicitly address the availability of health care resources or determine the impact of shortages on public health. Nevertheless, the feasibility of health syst ...[more]

PMID: 23061807

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Project description:BackgroundPromising health interventions tested in pilot studies will only achieve population-wide impact if they are implemented at scale across communities and health systems. Scaling up effective health interventions is vital as not doing so denies the community the most effective services and programmes. However, there remains a paucity of practical tools to assess the suitability of health interventions for scale-up. The Intervention Scalability Assessment Tool (ISAT) was developed to support policy-makers and practitioners to make systematic assessments of the suitability of health interventions for scale-up.MethodsThe ISAT was developed over three stages; the first stage involved a literature review to identify similar tools and frameworks that could be used to guide scalability assessments, and expert input to develop draft ISAT content. In the second stage, the draft ISAT tool was tested with end users. The third stage involved revising and re-testing the ISAT with end users to further refine the language and structure of the final ISAT.ResultsA variety of information and sources of evidence should be used to complete the ISAT. The ISAT consists of three parts. Part A: 'setting the scene' requires consideration of the context in which the intervention is being considered for scale-up and consists of five domains, as follows: (1) the problem; (2) the intervention; (3) strategic/political context; (4) evidence of effectiveness; and (5) intervention costs and benefits. Part B asks users to assess the potential implementation and scale-up requirements within five domains, namely (1) fidelity and adaptation; (2) reach and acceptability; (3) delivery setting and workforce; (4) implementation infrastructure; and (5) sustainability. Part C generates a graphical representation of the strengths and weaknesses of the readiness of the proposed intervention for scale-up. Users are also prompted for a recommendation as to whether the intervention (1) is recommended for scale-up, (2) is promising but needs further information before scaling up, or (3) does not yet merit scale-up.ConclusionThe ISAT fills an important gap in applied scalability assessment and can become a critical decision support tool for policy-makers and practitioners when selecting health interventions for scale-up. Although the ISAT is designed to be a health policy and practitioner tool, it can also be used by researchers in the design of research to fill important evidence gaps.

Project description:BackgroundSurrogate decision makers of stroke patients are often unprepared to make critical decisions on life-sustaining treatments. We describe the development process and key features for the Understanding Stroke web-based decision support tool.MethodsWe used multiple strategies to develop a patient-centered, tailored decision aid. We began by forming a Patient and Family Advisory Council to provide continuous input to our multidisciplinary team on the development of the tool. Additionally, focus groups consisting of nurses, therapists, social workers, physicians, stroke survivors, and family members reviewed key elements of the tool, including prognostic information, graphical displays, and values clarification exercise. To design the values clarification exercise, we asked focus groups to provide feedback on a list of important activities of daily living. An ordinal prognostic model was developed for ischemic stroke and intracerebral hemorrhage using data taken from the Virtual International Stroke Trials Archive Plus, and incorporated into the tool.ResultsFocus group participants recommended making numeric prognostic information optional due to possible emotional distress. Pie charts were generally favored by participants for graphical presentation of prognostic information, though a horizontal stacked bar chart was also added due to its prevalence in stroke literature. Plain language descriptions of the modified Rankin Scale were created to accompany the prognostic information. A values clarification exercise was developed consisting of a list of 13 situations that may make an individual consider comfort measures only. The final version of the web based tool (which can be viewed on tablets) included the following sections: general introduction to stroke, outcomes (prognostic information and recovery), in-hospital and life-sustaining treatments, decision making and values clarification, post-hospital care, tips for talking to the health care team, and a summary report. Preliminary usability testing received generally favorable feedback.ConclusionWe developed Understanding Stroke, a tailored decision support tool for surrogate decision makers of stroke patients. The tool was well received and will be formally pilot tested in a group of stroke surrogate decision makers.Trial registrationClinicalTrials.gov ( NCT03427645 ).

Project description:Rationale: Breakdowns in clinician-family communication in intensive care units (ICUs) are common, yet there are no easily scaled interventions to prevent this problem.Objectives: To assess the feasibility, usability, acceptability, and perceived effectiveness of a communication intervention that pairs proactive family meetings with an interactive, web-based tool to help surrogates prepare for clinician-family meetings.Methods: We conducted a two-arm, single-blind, patient-level randomized trial comparing the Family Support Tool with enhanced usual care in two ICUs in a tertiary-care hospital. Eligible participants included surrogates of incapacitated patients judged by their physicians to have ≥40% risk of death or severe long-term functional impairment. The intervention group received unlimited tool access, with prompts to complete specific content upon enrollment and before two scheduled family meetings. Before family meetings, research staff shared with clinicians a one-page summary of surrogates' main questions, prognostic expectations, beliefs about the patient's values, and attitudes about goals of care. The comparator group received usual care enhanced with scheduled family meetings. Feasibility outcomes included the proportion of participants who accessed the tool before the first family meeting, mean number of logins, and average tool engagement time. We assessed tool usability with the System Usability Scale, assessed tool acceptability and perceived effectiveness with internally developed questionnaires, and assessed quality of communication and shared decision-making using the Quality of Communication questionnaire.Results: Of 182 screened patients, 77 were eligible. We enrolled 52 (67.5%) patients and their primary surrogate. Ninety-six percent of intervention surrogates (24/25) accessed the tool before the first family meeting (mean engagement time, 62 min ± 27.7) and logged in 4.2 times (±2.1) on average throughout the hospitalization. Surrogates reported that the tool was highly usable (mean, 82.4/100), acceptable (mean, 4.5/5 ± 0.9), and effective (mean, 4.4/5 ± 0.2). Compared with the control group, surrogates who used the tool reported higher overall quality of communication (mean, 8.9/10 ± 1.6 vs. 8.0/10 ± 2.4) and higher quality in shared decision-making (mean, 8.7/10 ± 1.5 vs. 8.0/10 ± 2.4), but the difference did not reach statistical significance.Conclusions: It is feasible to deploy an interactive web-based tool to support communication and shared decision-making for surrogates in ICUs. Surrogates and clinicians rated the tool as highly usable, acceptable, and effective.

Project description:BackgroundSystems science methods such as dynamic simulation modelling are well suited to address questions about public health policy as they consider the complexity, context and dynamic nature of system-wide behaviours. Advances in technology have led to increased accessibility and interest in systems methods to address complex health policy issues. However, the involvement of policy decision makers in health-related simulation model development has been lacking. Where end-users have been included, there has been limited examination of their experience of the participatory modelling process and their views about the utility of the findings. This paper reports the experience of end-user decision makers, including senior public health policy makers and health service providers, who participated in three participatory simulation modelling for health policy case studies (alcohol related harm, childhood obesity prevention, diabetes in pregnancy), and their perceptions of the value and efficacy of this method in an applied health sector context.MethodsSemi-structured interviews were conducted with end-user participants from three participatory simulation modelling case studies in Australian real-world policy settings. Interviewees were employees of government agencies with jurisdiction over policy and program decisions and were purposively selected to include perspectives at different stages of model development.ResultsThe 'co-production' aspect of the participatory approach was highly valued. It was reported as an essential component of building understanding of the modelling process, and thus trust in the model and its outputs as a decision-support tool. The unique benefits of simulation modelling included its capacity to explore interactions of risk factors and combined interventions, and the impact of scaling up interventions. Participants also valued simulating new interventions prior to implementation in the real world, and the comprehensive mapping of evidence and its gaps to prioritise future research. The participatory aspect of simulation modelling was time and resource intensive and therefore most suited to high priority complex topics with contested options for intervening.ConclusionThese findings highlight the value of a participatory approach to dynamic simulation modelling to support its utility in applied health policy settings.

Project description:Background: Advance care planning (ACP) is intended to help patients and their spokespersons prepare for end-of-life decision making, yet little is known about what factors influence the extent to which spokespersons feel prepared for that role. Objective: To examine spokespersons' perceived preparedness for surrogate decision making after engaging in ACP. Design: Mixed methods experimental design with qualitative thematic analysis and data transformation (creating categorical data from rich qualitative data) of interviews collected during a randomized controlled trial (2012-2017). Setting/Participants: Two tertiary care medical centers (Hershey, PA and Boston, MA). Of 285 dyads (patients with advanced illness and their spokespersons) enrolled in the trial, 200 spokesperson interviews were purposively sampled and 198 included in the analyses. Main Outcomes and Measures: Interviews with spokespersons (four weeks post-intervention) explored spokespersons' perceived preparedness for surrogate decision making, occurrence of ACP conversations, and spokespersons' intentions regarding future surrogate decisions. Data transformation was used to categorize participants' responses into three categories: Very Prepared, Very Unprepared, or In Between Prepared and Unprepared. Themes and categories were compared across arms. Results: About 72.72% of spokespersons (144/198) reported being Very Prepared and 27.28% (54/198) reported being Very Unprepared or In Between with no differences in preparedness across study arms. Occurrence of post-intervention ACP conversations did not influence perceived preparedness; however, spokespersons who used an ACP decision aid reported more conversations. Four themes emerged to explain spokespersons' perceived preparedness: (1) perceptions about ACP; (2) level of comfort with uncertainty; (3) relational issues; and (4) personal characteristics. Regarding future intentions, it emerged that spokespersons believed their knowledge of patient wishes, as well as other personal, relational, situational, and emotional factors would influence their surrogate decisions. Conclusions: Factors extrinsic to specific ACP interventions influence how prepared spokespersons feel to act as spokespersons. Understanding these factors is important for understanding how to improve concordance between patients' stated end-of-life wishes and surrogate decisions. Trial Registration: NCT02429479.

Project description:ImportanceHospitals ceased most elective procedures during the height of coronavirus disease 2019 (COVID-19) infections. As hospitals begin to recommence elective procedures, it is necessary to have a means to assess how resource intensive a given case may be.ObjectiveTo evaluate the development and performance of a clinical decision support tool to inform resource utilization for elective procedures.Design, setting, and participantsIn this prognostic study, predictive modeling was used on retrospective electronic health records data from a large academic health system comprising 1 tertiary care hospital and 2 community hospitals of patients undergoing scheduled elective procedures from January 1, 2017, to March 1, 2020. Electronic health records data on case type, patient demographic characteristics, service utilization history, comorbidities, and medications were and abstracted and analyzed. Data were analyzed from April to June 2020.Main outcomes and measuresPredicitons of hospital length of stay, intensive care unit length of stay, need for mechanical ventilation, and need to be discharged to a skilled nursing facility. These predictions were generated using the random forests algorithm. Predicted probabilities were turned into risk classifications designed to give assessments of resource utilization risk.ResultsData from the electronic health records of 42 199 patients from 3 hospitals were abstracted for analysis. The median length of stay was 2.3 days (range, 1.3-4.2 days), 6416 patients (15.2%) were admitted to the intensive care unit, 1624 (3.8%) received mechanical ventilation, and 2843 (6.7%) were discharged to a skilled nursing facility. Predictive performance was strong with an area under the receiver operator characteristic ranging from 0.76 to 0.93. Sensitivity of the high-risk and medium-risk groupings was set at 95%. The negative predictive value of the low-risk grouping was 99%. We integrated the models into a daily refreshing Tableau dashboard to guide decision-making.Conclusions and relevanceThe clinical decision support tool is currently being used by surgical leadership to inform case scheduling. This work shows the importance of a learning health care environment in surgical care, using quantitative modeling to guide decision-making.

Dataset Information

Development of a resource modelling tool to support decision makers in pandemic influenza preparedness: The AsiaFluCap Simulator.

Publications

Development of a resource modelling tool to support decision makers in pandemic influenza preparedness: The AsiaFluCap Simulator.

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