Constrained Optimization for Decision Making in Health Care Using Python: A Tutorial.
Ontology highlight
ABSTRACT: Highlights
This tutorial provides a user-friendly guide to mathematically formulating constrained optimization problems and implementing them using Python.Two examples are presented to illustrate how constrained optimization is used in health applications, with accompanying Python code provided.
SUBMITTER: Leung KHB
PROVIDER: S-EPMC10625722 | biostudies-literature | 2023 Oct-Nov
REPOSITORIES: biostudies-literature
Publications
Constrained Optimization for Decision Making in Health Care Using Python: A Tutorial.
Leung K H Benjamin KHB Yousefi Nasrin N Chan Timothy C Y TCY Bayoumi Ahmed M AM
Medical decision making : an international journal of the Society for Medical Decision Making 20230722 7-8
<h4>Highlights</h4>This tutorial provides a user-friendly guide to mathematically formulating constrained optimization problems and implementing them using Python.Two examples are presented to illustrate how constrained optimization is used in health applications, with accompanying Python code provided. ...[more]
Similar Datasets
Project description:BackgroundMeta-analysis is a central method for quality evidence generation. In particular, meta-analysis is gaining speedy momentum in the growing world of quantitative information. There are several software applications to process and output expected results. Open-source software applications generating such results are receiving more attention. This paper uses Python's capabilities to provide applicable instruction to perform a meta-analysis.MethodsWe used the PythonMeta package with several modifications to perform the meta-analysis on an open-access dataset from Cochrane. The analyses were complemented by employing Python's zEpid package capable of creating forest plots. Also, we developed Python scripts for contour-enhanced funnel plots to assess funnel plots asymmetry. Finally, we ran the analyses in R and STATA to check the cross-validity of the results.ResultsA stepwise instruction on installing the software and packages and performing meta-analysis was provided. We shared the Python codes for meta-analysts to follow and generate the standard outputs. Our results were similar to those yielded by R and STATA.ConclusionWe successfully produced standard meta-analytic outputs using Python. This programming language has several flexibilities to improve the meta-analysis results even further.
| S-EPMC9275021 | biostudies-literature
Project description:Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions.
| S-EPMC6349385 | biostudies-literature
Project description:Health economic evaluation models have traditionally been built in Microsoft Excel, but more sophisticated tools are increasingly being used as model complexity and computational requirements increase. Of all the programming languages, R is most popular amongst health economists because it has a plethora of user created packages and is highly flexible. However, even with an integrated development environment such as R Studio, R lacks a simple point and click user interface and therefore requires some programming ability. This might make the switch from Microsoft Excel to R seem daunting, and it might make it difficult to directly communicate results with decisions makers and other stakeholders. The R package Shiny has the potential to resolve this limitation. It allows programmers to embed health economic models developed in R into interactive web browser based user interfaces. Users can specify their own assumptions about model parameters and run different scenario analyses, which, in the case of regular a Markov model, can be computed within seconds. This paper provides a tutorial on how to wrap a health economic model built in R into a Shiny application. We use a four-state Markov model developed by the Decision Analysis in R for Technologies in Health (DARTH) group as a case-study to demonstrate main principles and basic functionality. A more extensive tutorial, all code, and data are provided in a GitHub repository.
| S-EPMC7459889 | biostudies-literature
Project description:BackgroundManagerial Evidence-Based Decision-Making [EBDM] in the primary is a systematic approach that directs the decision-maker in a conscientious, explicit, and judicious utilization of reliable and best evidence based on the professional experiences and preferences of stakeholders and patients from various sources. This study aimed to investigate the challenges primary healthcare managers encounter while undertaking decision-making processes.MethodA systematic review was conducted in 2022 with the aim of identifying and collecting all qualitative articles pertaining to evidence-based decision-making in the primary healthcare system. To achieve this, a meticulous search was conducted using the relevant keywords, including primary health care and evidence-based decision making, as well as their corresponding synonyms, across the databases Web of Science, Scopus, and Pubmed. Importantly, there were no limitations imposed on the timeframe for the search. To carefully analyze and consolidate the findings of this systematic review, the meta-synthesis approach was employed.ResultsA total of 22 articles were assessed in this systematic review study. The results revealed the main categories including evidence nature, EBDM barriers, utilizing evidence, decision-makers ability, organizational structure, evidence-based, EBDM support, communication for EBDM, evidence sides, EBDM skill development, public health promotion, and health system performance improvement.ConclusionThe primary healthcare system is crucial in improving health outcomes and ensuring access to healthcare services for all individuals. This study explored the utilization of evidence-based EBDM within the primary healthcare system. We identified five key dimensions: causal, contextual, and intervening conditions, strategies, and consequences of EBDM as a core phenomenon. The findings will help policymakers and administrators comprehend the importance of evidence-based decision-making, ultimately leading to enhanced decision quality, community well-being, and efficiency within the healthcare system. EBDM entails considering the best reliable evidence, and incorporating community preferences while also exploiting the professional expertise and experiences of decision-makers. This systematic review has the potential to provide guidance for future reforms and enhance the quality of decision-making at the managerial level in primary healthcare.
| S-EPMC10770934 | biostudies-literature
Project description:BackgroundThe evidence used to inform health care decision making (HCDM) is typically uncertain. In these situations, the experience of experts is essential to help decision makers reach a decision. Structured expert elicitation (referred to as elicitation) is a quantitative process to capture experts' beliefs. There is heterogeneity in the existing elicitation methodology used in HCDM, and it is not clear if existing guidelines are appropriate for use in this context. In this article, we seek to establish reference case methods for elicitation to inform HCDM.MethodsWe collated the methods available for elicitation using reviews and critique. In addition, we conducted controlled experiments to test the accuracy of alternative methods. We determined the suitability of the methods choices for use in HCDM according to a predefined set of principles for elicitation in HCDM, which we have also generated. We determined reference case methods for elicitation in HCDM for health technology assessment (HTA).ResultsIn almost all methods choices available for elicitation, we found a lack of empirical evidence supporting recommendations. Despite this, it is possible to define reference case methods for HTA. The reference methods include a focus on gathering experts with substantive knowledge of the quantities being elicited as opposed to those trained in probability and statistics, eliciting quantities that the expert might observe directly, and individual elicitation of beliefs, rather than solely consensus methods. It is likely that there are additional considerations for decision makers in health care outside of HTA.ConclusionsThe reference case developed here allows the use of different methods, depending on the decision-making setting. Further applied examples of elicitation methods would be useful. Experimental evidence comparing methods should be generated.
| S-EPMC8777312 | biostudies-literature
Project description:BackgroundEngagement in healthcare decision making has been recognized as an important, and often lacking, aspect of care, especially in the care of older adults who are major users of the healthcare system.ObjectiveWe aimed to conduct a review of available knowledge on engagement in healthcare decision making with a focus on older patients and their caregivers.MethodsWe conducted a realist synthesis focusing on strategies for engagement of older patients and their caregivers in healthcare decision making. The synthesis encompassed theoretical frameworks and both peer-reviewed and grey literature. Expert consultations included interviews (n = 2) with academics and group consultations (n = 3) with older adults and their caregivers. Abstracts that reported description, assessment, or evaluation of strategies for engagement of adult patients, families, or caregivers (i.e., that report on actual experiences of engagement) were included.ResultsThe search generated 15,683 articles, 663 of which were pertinent to healthcare decision making. Theoretical and empirical work identified a range of strategies and levels of engagement of older patients and their families in healthcare decision making. The importance of communication emerged as a key recommendation for meaningful engagement among providers and patients and their caregivers. The principles developed in this study should be implemented with consideration of the context in which care is being provided.ConclusionsWe have developed a framework that promotes the engagement of patients and their caregivers as equal partners in healthcare decision making. Future research should implement and test the framework in various clinical settings.
| S-EPMC5021754 | biostudies-literature
Project description:The main purpose of many medical studies is to estimate the effects of a treatment or exposure on an outcome. However, it is not always possible to randomize the study participants to a particular treatment, therefore observational study designs may be used. There are major challenges with observational studies; one of which is confounding. Controlling for confounding is commonly performed by direct adjustment of measured confounders; although, sometimes this approach is suboptimal due to modeling assumptions and misspecification. Recent advances in the field of causal inference have dealt with confounding by building on classical standardization methods. However, these recent advances have progressed quickly with a relative paucity of computational-oriented applied tutorials contributing to some confusion in the use of these methods among applied researchers. In this tutorial, we show the computational implementation of different causal inference estimators from a historical perspective where new estimators were developed to overcome the limitations of the previous estimators (ie, nonparametric and parametric g-formula, inverse probability weighting, double-robust, and data-adaptive estimators). We illustrate the implementation of different methods using an empirical example from the Connors study based on intensive care medicine, and most importantly, we provide reproducible and commented code in Stata, R, and Python for researchers to adapt in their own observational study. The code can be accessed at https://github.com/migariane/Tutorial_Computational_Causal_Inference_Estimators.
| S-EPMC11795351 | biostudies-literature
Project description:Evidence-informed health care decisions and recommendations need to be made systematically and transparently. Mediating technology can help manage boundaries between groups making decisions and target audiences, enhancing salience, credibility, and legitimacy for all. This article describes the development of the Evidence to Decision (EtD) framework and an interactive tool to create and use frameworks (iEtD) to support communication in decision making. Methods: Using a human-centered design approach, we created prototypes employing a broad range of methods to iteratively develop EtD framework content and iEtD tool functionality. Results: We developed tailored EtD frameworks for making evidence-informed decisions and recommendations about clinical practice interventions, diagnostic and screening tests, coverage, and health system and public health options. The iEtD tool provides functionality for preparing frameworks, using them in group discussions, and publishing output for implementation or adaption. EtD and iEtD are intuitive and useful for producers and users of frameworks, and flexible for use across different types of topics, decisions, and organizations. They bring valued structure to panel discussions and transparency to published output. Conclusion: EtD and iEtD can resolve some of the challenges inherent in multicriteria, multistakeholder decision systems. They are freely available online for all to use at https://ietd.epistemonikos.org/ and https://gradepro.org.
| S-EPMC6607226 | biostudies-literature
Project description:BackgroundWith rising healthcare costs comes an increasing demand for evidence-informed resource allocation using economic evaluations worldwide. Furthermore, standardization of costing and reporting methods both at international and national levels are imperative to make economic evaluations a valid tool for decision-making. The aim of this review is to assess the availability and consistency of costing evidence that could be used for decision-making in Austria. It describes systematically the current economic evaluation and costing studies landscape focusing on the applied costing methods and their reporting standards. Findings are discussed in terms of their likely impacts on evidence-based decision-making and potential suggestions for areas of development.MethodsA systematic literature review of English and German language peer-reviewed as well as grey literature (2004-2015) was conducted to identify Austrian economic analyses. The databases MEDLINE, EMBASE, SSCI, EconLit, NHS EED and Scopus were searched. Publication and study characteristics, costing methods, reporting standards and valuation sources were systematically synthesised and assessed.ResultsA total of 93 studies were included. 87% were journal articles, 13% were reports. 41% of all studies were full economic evaluations, mostly cost-effectiveness analyses. Based on relevant standards the most commonly observed limitations were that 60% of the studies did not clearly state an analytical perspective, 25% of the studies did not provide the year of costing, 27% did not comprehensively list all valuation sources, and 38% did not report all applied unit costs.ConclusionThere are substantial inconsistencies in the costing methods and reporting standards in economic analyses in Austria, which may contribute to a low acceptance and lack of interest in economic evaluation-informed decision making. To improve comparability and quality of future studies, national costing guidelines should be updated with more specific methodological guidance and a national reference cost library should be set up to allow harmonisation of valuation methods.
| S-EPMC5555669 | biostudies-literature
Project description: Not available
| S-EPMC1125994 | biostudies-literature