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Clinical Trials: Overview

Clinical Trials: Overview

Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...

Clinical Trials

Clinical Trials

Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...

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Protocol for a Single-Arm Pilot Clinical Trial: Developing and Evaluating a Machine Learning Opioid Prediction &

Je-Won J Hong¹, Debbie L Wilson², Khoa Nguyen¹

¹Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA.

Journal of Clinical Medicine

|December 11, 2025

Summary

This summary is machine-generated.

This study evaluates a machine learning tool to predict opioid overdose risk, aiming to improve patient safety and reduce harm through clinical decision support. The Overdose Prevention Alert system guides primary care providers in managing high-risk patients.

Keywords:

clinical decision support machine learning opioid overdose primary care

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Area of Science:

Clinical Informatics
Machine Learning in Healthcare
Public Health Interventions

Background:

Opioid overdose remains a significant public health crisis.
Clinical decision support (CDS) tools can aid in risk stratification.
The DEMONSTRATE trial investigates a novel machine learning (ML) approach to predict overdose risk.

Purpose of the Study:

To assess the usability, acceptability, feasibility, and effectiveness of an ML-based CDS tool (Overdose Prevention Alert).
To identify high-risk patients for opioid overdose within three months.
To inform strategies for reducing opioid-related harm.

Main Methods:

A single-arm, pre-post implementation study in 13 primary care clinics.
Mixed-methods evaluation including quantitative metrics and qualitative interviews.
Focus on patients aged ≥18 with recent opioid prescriptions identified as high-risk by the ML algorithm.

Main Results:

Effectiveness measured by a composite of 6 favorable patient outcomes (e.g., naloxone access, absence of overdose events).
Quantitative metrics include alert penetration and clinical actions taken.
Usability and acceptability assessed via PCP questionnaires and interviews.

Conclusions:

The trial will provide real-world insights into implementing ML-driven CDS tools.
Findings will guide future strategies to mitigate opioid-related harm.
This research supports the integration of AI in proactive patient care for opioid safety.