Real-World Evidence—Current Developments and Perspectives

Schad, Friedemann; Thronicke, Anja

doi:10.3390/ijerph191610159

Cited by 43 publications

(25 citation statements)

References 23 publications

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“…RWE, a growing focus in medical research, comprises information produced from RWSs [14]. Causal inference, particularly causal models, is an important component of RWE [82].…”

Section: Discussionmentioning

confidence: 99%

“…[13]. RWSs use RWD to generate various levels of real-world evidence (RWE) [14]. Whereas analysis of confirmatory RCTs relies on traditional statistical methods, interest is growing in the application of machine learning (ML) to address challenges in RWD analysis, such as highdimensional, complex, and unknown data patterns, and the rapid growth of data volume [15,16].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Methods in Real-World Studies of Cardiovascular Disease

Zhou

You

Bai

et al. 2023

CVIA

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Objective: Cardiovascular disease (CVD) is one of the leading causes of death worldwide, and answers are urgently needed regarding many aspects, particularly risk identification and prognosis prediction. Real-world studies with large numbers of observations provide an important basis for CVD research but are constrained by high dimensionality, and missing or unstructured data. Machine learning (ML) methods, including a variety of supervised and unsupervised algorithms, are useful for data governance, and are effective for high dimensional data analysis and imputation in real-world studies. This article reviews the theory, strengths and limitations, and applications of several commonly used ML methods in the CVD field, to provide a reference for further application. Methods: This article introduces the origin, purpose, theory, advantages and limitations, and applications of multiple commonly used ML algorithms, including hierarchical and k-means clustering, principal component analysis, random forest, support vector machine, and neural networks. An example uses a random forest on the Systolic Blood Pressure Intervention Trial (SPRINT) data to demonstrate the process and main results of ML application in CVD. Conclusion: ML methods are effective tools for producing real-world evidence to support clinical decisions and meet clinical needs. This review explains the principles of multiple ML methods in plain language, to provide a reference for further application. Future research is warranted to develop accurate ensemble learning methods for wide application in the medical field.

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“…RWE, a growing focus in medical research, comprises information produced from RWSs [14]. Causal inference, particularly causal models, is an important component of RWE [82].…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods in Real-World Studies of Cardiovascular Disease

Zhou

You

Bai

et al. 2023

CVIA

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“…As mentioned before, overcrowding in EDs is an international problem 1,6 , and that regardless the massive quantity of works aimed to operational research there is still a lot of work to do to solve this problem 21,22 , especially in the current discussion on real-world evidence and healthcare data 19,26,75 . In the current discussion regarding data-driven healthcare in international settings, our pipeline could be interesting to implement for participatory approaches and to facilitate discussions about the problem from the perspectives of different healthcare policies.…”

Section: Discussionmentioning

confidence: 99%

How to diagnose an overcrowded emergency department from its EHRs? Enhancing opportunities and challenges of real-world data from a whole-system perspective

Marzano,

Darwich,

Jayanth

et al. 2023

Preprint

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Emergency department overcrowding is a complex problem that persists globally. Data of visits constitute an opportunity to understand it. However, the gap between the collected information and the real-life clinical processes, and the lack of a whole-system perspective, still constitutes a relevant limitation. An analytical pipeline was developed to analyse one-year of production data following the patients that came from the ED (n = 49,938) at Uppsala University Hospital (Uppsala, Sweden) by involving clinical experts in all the steps of the analysis. The key internal issues to the ED were the high volume of generic or non-specific diagnoses from non-urgent visits, and the delayed decision regarding hospital admission caused by several imaging assessments and lack of hospital beds. Furthermore, the external pressure of high frequent re-visits of geriatric, psychiatric, and patients with unspecified diagnoses dramatically contributed to the overcrowding. Our work demonstrates that through analysis of production data of the ED patient flow and participation of clinical experts in the pipeline, it was possible to identify systemic issues and directions for solutions. A critical factor was to take a whole systems perspective, as it opened the scope to the boundary effects of inflow and outflow in the whole healthcare system.

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“…The systematic use of Real World Data (RWD) in clinical practice and the real-time application of clinical epidemiology to clinical healthcare and management decision-making processes represent one of the healthcare innovations with the most significant possible impact on the sustainability of healthcare systems and the appropriateness of clinical decisions (1)(2)(3)(4). For this to be possible, the quality of the data, the analysis performed, and their availability must be adequate to answer the science question; it should be systematically recorded and compliant with the FAIR principles: findable, accessible, interoperable and reusable.…”

mentioning

confidence: 99%