Real-World Evidence in a Patient-Centric Digital Era

Zou, Kelly H.; Salem, Lobna A.

doi:10.1201/9781003017523

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Patient health outcomes serve as a benchmark for the success of pharmacy practices, emphasizing the need for innovative approaches, such as predictive analytics, to optimize pharmaceutical care. This paper aims to underscore the pivotal role of predictive analytics in pharmacy practice, recognizing it as a catalyst for advancing patient-centric care (Zou et al, 2022). By scrutinizing historical data and identifying patterns, predictive analytics empowers pharmacists to proactively address health concerns, optimize medication regimens, and contribute to more favorable patient outcomes.…”

Section: Introductionmentioning

confidence: 99%

A review of the use of machine learning in predictive analytics for patient health outcomes in pharmacy practice

Ehizogie Paul Adeghe,

Chioma Anthonia Okolo,

Olumuyiwa Tolulope Ojeyinka

2024

Open Access Res. J. Life Sci.

View full text Add to dashboard Cite

Predictive analytics, empowered by machine learning, has emerged as a transformative force in healthcare, offering unparalleled opportunities for enhancing patient outcomes. The primary focus is on understanding the implications, applications, and challenges associated with the use of machine learning algorithms in predicting patient health outcomes. The paper begins by establishing the context with an overview of predictive analytics in healthcare and its evolution. Emphasis is placed on the critical role of patient health outcomes in pharmacy practice. The review explores the current landscape of predictive analytics in pharmacy practice, detailing traditional approaches, their limitations, and the advantages that machine learning brings to the forefront. An in-depth examination of applications follows, focusing on areas such as medication adherence prediction, disease progression modeling, and personalized medication regimens. Real-world case studies and success stories illustrate the practical impact of machine learning on patient outcomes. Addressing the importance of data sources, the paper discusses the diverse types of data employed in predictive analytics, ranging from electronic health records to patient-generated data and wearables. Ethical and privacy concerns are thoroughly explored, emphasizing the need for responsible data usage. The implications for pharmacists and healthcare providers are discussed, highlighting the evolving role of pharmacists in predictive analytics and the potential benefits and challenges for healthcare providers. The conclusion summarizes key findings and issues a call to action, encouraging further research and adoption of machine learning in pharmacy practice to harness its potential for improving patient outcomes.

show abstract

Section: Introductionmentioning

confidence: 99%

A review of the use of machine learning in predictive analytics for patient health outcomes in pharmacy practice

Ehizogie Paul Adeghe,

Chioma Anthonia Okolo,

Olumuyiwa Tolulope Ojeyinka

2024

Open Access Res. J. Life Sci.

View full text Add to dashboard Cite

show abstract

Ensuring safety and efficacy in combination products: regulatory challenges and best practices

Gupta,

Tiwari,

Yadav

et al. 2024

Front. Med. Technol.

View full text Add to dashboard Cite

Combination products, amalgamating drugs, biologics, and medical devices, have revolutionized the healthcare landscape with their potential for innovative therapies. However, the intersection of diverse components within these products presents a complex regulatory environment, demanding rigorous attention to safety and efficacy. This article delves into the intricate landscape of regulatory considerations, safety, and efficacy assessments pertaining to combination products—a category at the intersection of drugs, devices, and biologics. The regulatory framework, primarily governed by the U.S. Food and Drug Administration (FDA), necessitates a nuanced classification determining the regulatory pathway. Collaboration between diverse regulatory centers, such as the Center for Drug Evaluation and Research (CDER) and the Center for Devices and Radiological Health (CDRH), underscores the integrated approach required for these innovative healthcare solutions. Safety considerations unravel the potential risks and adverse events associated with combining diverse components, emphasizing the need for robust risk assessment and mitigation strategies. The evaluation of efficacy involves sophisticated methodologies, clinical trials, and post-market surveillance, with recent advancements incorporating digital technologies. This comprehensive exploration aims to contribute to the evolving understanding and best practices in the regulatory and scientific realms, fostering collaboration and innovation in the development and assessment of combination products.

show abstract

Real-World Data and Real-World Evidence in Healthcare in the United States and Europe Union

Zou,

Berger

2024

Bioengineering

View full text Add to dashboard Cite

The use of real-world data (RWD) for healthcare decision-making is complicated by concerns regarding whether RWD is fit-for-purpose or is of sufficient validity to support the creation of credible RWE. An efficient mechanism for screening the quality of RWD is needed as regulatory agencies begin to use real-world evidence (RWE) to inform decisions about treatment effectiveness and safety. First, we provide an overview of RWD and RWE. Data quality frameworks (DQFs) in the US and EU were examined, including their dimensions and subdimensions. There is some convergence of the conceptual DQFs on specific assessment criteria. Second, we describe a list of screening criteria for assessing the quality of RWD sources. The curation and analysis of RWD will continue to evolve in light of developments in digital health and artificial intelligence (AI). In conclusion, this paper provides a perspective on the utilization of RWD and RWE in healthcare decision-making. It covers the types and uses of RWD, data quality frameworks (DQFs), regulatory landscapes, and the potential impact of RWE, as well as the challenges and opportunities for the greater leveraging of RWD to create credible RWE.

show abstract

Real-World Evidence in a Patient-Centric Digital Era

Cited by 4 publications

References 0 publications

A review of the use of machine learning in predictive analytics for patient health outcomes in pharmacy practice

A review of the use of machine learning in predictive analytics for patient health outcomes in pharmacy practice

Ensuring safety and efficacy in combination products: regulatory challenges and best practices

Real-World Data and Real-World Evidence in Healthcare in the United States and Europe Union

Contact Info

Product

Resources

About