Evaluating analytics DSS for the COVID-19 pandemic through WHO-INTEGRATE EtD for health policy

Mora, Manuel

doi:10.1080/12460125.2021.1914292

Cited by 3 publications

(3 citation statements)

References 46 publications

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“…Delivery of a modern healthcare is an increasingly multidimensional undertaking that requires optimization of the provision of health services, be it in the context of emergency departments ( Cabrera et al, 2011 ), or prescription of medicine ( Sintchenko et al, 2008 ), or managing a Covid-19 pandemic ( Mora et al, 2021 ). This increase in dimensionality of the problem results in the evolution of DSS used in healthcare in terms of their complexity ( Safwan et al, 2016 ), thus requiring their reliance on increasingly more complex components (e.g., data mining and machine learning) ( Shailaja et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

“…This increase in dimensionality of the problem results in the evolution of DSS used in healthcare in terms of their complexity ( Safwan et al, 2016 ), thus requiring their reliance on increasingly more complex components (e.g., data mining and machine learning) ( Shailaja et al, 2018 ). At this point, it has been reported that applications of DSS to optimize provision of healthcare in the context of Covid-19 was very limited and without incorporating such fundamental aspects as feasibility and health system considerations and consideration of the advanced methods of data analysis ( Mora et al, 2021 ). For example, there were efforts to contribute via creating DSS targeting physical distancing (Adam et al, 2021) and available food supply ( Blackmon et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

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Design of a modular DSS for public health decision-making in the context of a COVID-19 pandemic landscape

Samoilenko

Osei-Bryson

2022

Expert Systems with Applications

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The awareness of the occurrence of a new disease involves much uncertainty and the search for answers and also appropriate questions. In this paper we focus on the perspective of public health decision-makers. Typically, they would have a standard set of questions and supporting metrics that have been found in previous disease outbreaks to be useful in assessing the effectiveness of various ‘solution’ methods on the trajectory of the disease. There may be other relevant questions with which such public health domain experts may not be familiar and/or for which they are familiar but are not aware of methods for addressing such questions when there is limited data. Decision Support Systems (DSS) can be used to facilitate the exploration of established questions and some other relevant questions. Given an initial set of questions, the DSS designer should consider which sets of data analytic methods have the capabilities to adequately address. Some of these data analytic methods may also have the capability of addressing questions that could be of interest to the public health decision makers including researchers. In this paper we present a conceptual design for a relevant easy-to-construct DSS and an example of a multi-method DSS that is based on this conceptual design. Using publicly available data on the CoViD-19 pandemic, we illustrate benefits of the multi-method DSS in action.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Design of a modular DSS for public health decision-making in the context of a COVID-19 pandemic landscape

Samoilenko

Osei-Bryson

2022

Expert Systems with Applications

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“…Future research is required to use techniques that formulate more in-depth, longitudinal studies of policy formulation (PF) and implementation (Marani et al , 2021). To mitigate the effects of a global pandemic, policymakers were forced to make safe, accurate and ethical decisions (Mora et al , 2021). Therefore, the combination of the importance and need of CCDSS, challenges, gaps and the need for future research in the context of health-care policymaking justifies the motivation of this research.…”

Section: Introductionmentioning

confidence: 99%

Adoption of cognitive computing decision support system in the assessment of health-care policymaking

Behera,

Bala,

Panigrahi

et al. 2023

JSIT

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Purpose Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model. Design/methodology/approach A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data. Findings The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly. Research limitations/implications Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking. Originality/value This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy.

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Role of Executive Sponsors in business analytics success – Understanding their influence domains using Deductive Thematic Analysis

Srinivasan

Agrahari

Kumar

2022

Journal of Decision Systems

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Evaluating analytics DSS for the COVID-19 pandemic through WHO-INTEGRATE EtD for health policy

Cited by 3 publications

References 46 publications

Design of a modular DSS for public health decision-making in the context of a COVID-19 pandemic landscape

Design of a modular DSS for public health decision-making in the context of a COVID-19 pandemic landscape

Adoption of cognitive computing decision support system in the assessment of health-care policymaking

Role of Executive Sponsors in business analytics success – Understanding their influence domains using Deductive Thematic Analysis

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