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An integrated data framework for policy guidance during the coronavirus pandemic: Towards real-time decision support for economic policymakers.

ABSTRACT: Usually, official and survey-based statistics guide policymakers in their choice of response instruments to economic crises. However, in an early phase, after a sudden and unforeseen shock has caused unexpected and fast-changing dynamics, data from traditional statistics are only available with non-negligible time delays. This leaves policymakers uncertain about how to most effectively manage their economic countermeasures to support businesses, especially when they need to respond quickly, as in the COVID-19 pandemic. Given this information deficit, we propose a framework that guided policymakers throughout all stages of this unforeseen economic shock by providing timely and reliable sources of firm-level data as a basis to make informed policy decisions. We do so by combining early stage 'ad hoc' web analyses, 'follow-up' business surveys, and 'retrospective' analyses of firm outcomes. A particular focus of our framework is on assessing the early effects of the pandemic, using highly dynamic and large-scale data from corporate websites. Most notably, we show that textual references to the coronavirus pandemic published on a large sample of company websites and state-of-the-art text analysis methods allowed to capture the heterogeneity of the pandemic's effects at a very early stage and entailed a leading indication on later movements in firm credit ratings. While the proposed framework is specific to the COVID-19 pandemic, the integration of results obtained from real-time online sources in the design of subsequent surveys and their value in forecasting firm-level outcomes typically targeted by policy measures, is a first step towards a more timely and holistic approach for policy guidance in times of economic shocks.

SUBMITTER: Dorr JO

PROVIDER: S-EPMC8843231 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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An integrated data framework for policy guidance during the coronavirus pandemic: Towards real-time decision support for economic policymakers.

Dörr Julian Oliver JO Kinne Jan J Lenz David D Licht Georg G Winker Peter P

PloS one 20220214 2

Usually, official and survey-based statistics guide policymakers in their choice of response instruments to economic crises. However, in an early phase, after a sudden and unforeseen shock has caused unexpected and fast-changing dynamics, data from traditional statistics are only available with non-negligible time delays. This leaves policymakers uncertain about how to most effectively manage their economic countermeasures to support businesses, especially when they need to respond quickly, as i ...[more]

PMID: 35157731

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Project description:BackgroundThe COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting daily management of COVID-19 pandemic emergency, by creating a strong retrospective knowledge base, a real time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level. This framework is also used as an informative, continuously enriched data lake, which is a base for several on-going predictive studies.MethodsThe information technology framework for clinical practice and research was described. It was developed using SAS Institute software analytics tool and SAS® Vyia® environment and Open-Source environment R ® and Python ® for fast prototyping and modeling. The included variables and the source extraction procedures were presented.ResultsThe Data Mart covers a retrospective cohort of 5528 patients with SARS-CoV-2 infection. People who died were older, had more comorbidities, reported more frequently dyspnea at onset, had higher d-dimer, C-reactive protein and urea nitrogen. The dashboard was developed to support the management of COVID-19 patients at three levels: hospital, single ward and individual care level.InterpretationThe COVID-19 Data Mart based on integration of a large collection of clinical data and an AI-based integrated framework has been developed, based on a set of automated procedures for data mining and retrieval, transformation and integration, and has been embedded in the clinical practice to help managing daily care. Benefits from the availability of a Data Mart include the opportunity to build predictive models with a machine learning approach to identify undescribed clinical phenotypes and to foster hospital networks. A real-time updated dashboard built from the Data Mart may represent a valid tool for a better knowledge of epidemiological and clinical features of COVID-19, especially when multiple waves are observed, as well as for epidemic and pandemic events of the same nature (e. g. with critical clinical conditions leading to severe pulmonary inflammation). Therefore, we believe the approach presented in this paper may find several applications in comparable situations even at region or state levels. Finally, models predicting the course of future waves or new pandemics could largely benefit from network of DataMarts.

Project description:BackgroundThe online offer of commercial genetic tests, also called direct-to-consumer genetic tests (DTC-GTs), enables citizens to gain insight into their health and disease risk based on their genetic profiles. DTC-GT offers often consist of a combination of services or aspects, including advertisements, information, DNA analysis, and medical or lifestyle advice. The risks and benefits of DTC-GT services have been debated and studied extensively, but instruments that assess DTC-GT services and aid policy are lacking. This leads to uncertainty among policy makers, law enforcers, and regulators on how to ensure and balance both public safety and autonomy and about the responsibilities these 3 parties have toward the public.ObjectiveThis study aimed to develop a framework that outlines aspects of DTC-GTs that lead to policy issues and to help provide policy guidance regarding DTC-GT services.MethodsWe performed 3 steps: (1) an integrative literature review to identify risks and benefits of DTC-GT services for consumers and society in Embase and Medline (January 2014-June 2022), (2) structuring benefits and risks in different steps of the consumer journey, and (3) development of a checklist for policy guidance.ResultsPotential risks and benefits of DTC-GT services were mapped from 134 papers and structured into 6 phases. In summary, these phases were called the consumer journey: (1) exposure, (2) pretest information, (3) DNA analysis, (4) data management, (5) posttest information, and (6) individual and societal impact. The checklist for evaluation of DTC-GT services consisted of 8 themes, covering 38 items that may raise policy issues in DTC-GT services. The themes included the following aspects: general service content, validity and quality assurance, potential data and privacy risks, scientific evidence and robustness, and quality of the provided information.ConclusionsBoth the consumer journey and the checklist break the DTC-GT offer down into key aspects that may impact and compromise individual and public health, safety, and autonomy. This framework helps policy makers, regulators, and law enforcers develop methods to interpret, assess, and act in the DTC-GT service market.

Project description:BackgroundClinical Decision Support Systems (CDSS) are used to assist the decision-making process in the healthcare field. Developing an effective CDSS is an arduous task that can take advantage from prior assessment of the most promising theories, techniques and methods used at the present time.ObjectiveTo identify the features of Clinical Decision Support Systems and provide an analysis of their effectiveness. Thus, two research questions were formulated: RQ1-What are the most common trend characteristics in a CDSS? RQ2-What is the maturity level of the CDSS based on the decision-making theory proposed by Simon?MethodsAIS e-library, Decision Support Systems journal, Nature, PlosOne and PubMed were selected as information sources to conduct this systematic literature review. Studies from 2000 to 2020 were chosen covering search terms in CDSS, selected according to defined eligibility criteria. The data were extracted and managed in a worksheet, based on the defined criteria. PRISMA statements were used to report the systematic review.ResultsThe outcomes showed that rule-based module was the most used approach regarding knowledge management and representation. The most common technological feature adopted by the CDSS were the recommendations and suggestions. 19,23% of studies adopt the type of system as a web-based application, and 51,92% are standalone CDSS. Temporal evolution was also possible to visualize. This study contributed to the development of a Maturity Staging Model, where it was possible to verify that most CDSS do not exceed level 2 of maturity.ConclusionThe trend characteristics addressed in the revised CDSS were identified, compared to the four predefined groups. A maturity stage model was developed based on Simon's decision-making theory, allowing to assess the level of maturity of the most common features of the CDSS. With the application of the model, it was noticed that the phases of choice and implementation are underrepresented. This constitutes the main gap in the development of an effective CDSS.

Dataset Information

An integrated data framework for policy guidance during the coronavirus pandemic: Towards real-time decision support for economic policymakers.

Publications

An integrated data framework for policy guidance during the coronavirus pandemic: Towards real-time decision support for economic policymakers.

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