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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Health Information Technology and Healthcare Information System01:30

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Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
Nursing Clinical Information System01:27

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A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
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Drug Regulation

Drug regulation encompasses the management of drug usage by evaluating its safety and efficacy through assessments conducted by regulatory authorities. Regrettably, the history of drug regulation is marred by several catastrophic events. One such incident is the Elixir Sulfanilamide tragedy, in which the toxic compound diethyl glycol was included in a sweet-tasting medication, leading to numerous fatalities. This event prompted the enactment of the Food, Drug, and Cosmetic Act in 1938. Under...
Drug Control Governance: Regulatory Bodies and Their Impact01:03

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Drug control governance involves the oversight and regulation of pharmaceuticals to ensure their safety and efficacy while preventing illegal drug use and trafficking. Regulatory bodies, including the US Food and Drug Administration (FDA) and the European Union's European Medicines Agency (EMA), play a central role in this process. These agencies evaluate the safety and efficacy of drugs before they can be marketed. They fund clinical trials and assess the benefits and risks associated with a...
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PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).

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Related Experiment Video

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Algorithm-Based Clinical Decision Support: Evolving Regulatory Landscape and Best Practices for Local Oversight.

Anthony L Lin1, Amanda B Parrish2, Michael Cary3

  • 1Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.

Annual Review of Biomedical Data Science
|April 21, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI)-enabled clinical decision support (CDS) offers growing potential in healthcare. This review guides implementers and regulators on safe, effective, and equitable AI-CDS use, covering regulations, governance, and best practices.

Keywords:
artificial intelligenceclinical decision support systemsclinical governancehealthcare qualitymachine learningtrust

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Area of Science:

  • Healthcare technology
  • Clinical informatics
  • Artificial intelligence in medicine

Background:

  • Algorithm-based clinical decision support (CDS) systems are increasingly prevalent in healthcare.
  • The integration of AI-enabled CDS presents novel opportunities and challenges for clinicians, patients, and health systems.
  • Current implementation and regulatory frameworks for AI-CDS are still evolving.

Purpose of the Study:

  • To provide a comprehensive overview of the regulatory and governance landscape for AI-enabled CDS.
  • To outline best practices for the implementation, evaluation, and monitoring of CDS systems.
  • To address critical considerations regarding safety and equity in AI-CDS deployment.

Main Methods:

  • Review of key regulatory decisions impacting algorithm-based CDS.
  • Analysis of modern governance frameworks for overseeing CDS systems.
  • Exploration of evaluation and monitoring strategies across the CDS lifecycle.

Main Results:

  • Identification of critical regulatory precedents and evolving governance models.
  • Discussion of nuanced approaches to CDS evaluation and real-world implementation.
  • Highlighting of essential safety and equity considerations for AI-CDS.

Conclusions:

  • Effective, safe, and equitable implementation of AI-enabled CDS requires careful navigation of regulatory and governance structures.
  • Continuous evaluation, monitoring, and a focus on safety and equity are paramount for successful AI-CDS integration.
  • Future innovation in AI-CDS necessitates ongoing collaboration among stakeholders.