Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

382
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
382
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

538
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
538
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

385
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
385
Hazard Ratio01:12

Hazard Ratio

549
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
549
Longitudinal Studies01:26

Longitudinal Studies

449
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
449
Cancer Survival Analysis01:21

Cancer Survival Analysis

630
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
630

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Improving medicines use in people with polypharmacy in primary care: a synopsis from the IMPPP cluster-RCT with pilot-feasibility study.

Health and social care delivery research·2026
Same author

Preserving a Functional Foot: Limb-Salvage Surgery with Brachy Mono-Therapy for Soft Tissue Sarcoma.

Indian journal of surgical oncology·2026
Same author

Alpha2 agonists for sedation to produce better outcomes for adults with critical illness: a synopsis of the A2B RCT with cost-effectiveness and process evaluation.

Health technology assessment (Winchester, England)·2026
Same author

Correction to: From Dry Cavities to Healing Pathways: Innovations in Managing Empty Nose Syndrome.

Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India·2026
Same author

Temporal trends in time to asystole in patients considered for organ donation after circulatory death.

Journal of the Intensive Care Society·2026
Same author

Accelerated deficit accumulation in frailty and associations with adverse outcomes: a longitudinal population data analysis.

The lancet. Healthy longevity·2026

Related Experiment Video

Updated: Jan 9, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.5K

Risk prediction tools in multiple long-term conditions management: a qualitative study.

Stella Arakelyan1, Atul Anand2,3, Stewart W Mercer4

  • 1Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK stella.arakelyan@ed.ac.uk.

The British Journal of General Practice : the Journal of the Royal College of General Practitioners
|December 4, 2025
PubMed
Summary

Risk prediction tools for multiple long-term conditions (MLTC) show promise but require careful implementation. Healthcare professionals and patients highlight the need for tools that integrate clinical judgment, consider psychosocial factors, and align with patient priorities.

Keywords:
decision support techniquesmultimorbiditymultiple long-term conditionspatient-centred carerisk prediction toolrisk stratification

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K

Related Experiment Videos

Last Updated: Jan 9, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K

Area of Science:

  • Health Services Research
  • Clinical Informatics
  • Patient-Centered Care

Background:

  • Risk stratification is crucial for effective healthcare delivery.
  • Its application in managing patients with multiple long-term conditions (MLTC) requires further investigation.

Purpose of the Study:

  • To explore healthcare professionals', patients', and carers' views on the benefits and challenges of using risk prediction tools in MLTC management.

Main Methods:

  • The study involved thematic analysis of interviews with 30 healthcare professionals and six focus groups with 28 patients with MLTC and their carers.
  • Data collection occurred between May 2023 and May 2024 across four Scottish integrated Health and Social Care Partnerships.

Main Results:

  • Healthcare professionals raised concerns about the clinical utility and algorithmic bias of current risk prediction tools, emphasizing the need for integration with clinical judgment and psychosocial factors.
  • Patients and carers expressed apprehension regarding potential anxiety and loss of autonomy from risk communication, stressing the importance of contextual relevance and patient priorities.
  • Artificial intelligence (AI) and routine data hold potential for enhancing predictive accuracy, but require robust IT infrastructure, training, and human oversight.

Conclusions:

  • AI-informed risk stratification tools may be beneficial for MLTC management if they do not increase workload, support clinical decision-making, and incorporate patient complexity and preferences.
  • Effective implementation necessitates addressing concerns about clinical utility, workload, and patient-centered communication.