Literature DB >> 25133362

Evidence-based risk communication: a systematic review.

Daniella A Zipkin, Craig A Umscheid, Nancy L Keating, Elizabeth Allen, KoKo Aung, Rebecca Beyth, Scott Kaatz, Devin M Mann, Jeremy B Sussman, Deborah Korenstein, Connie Schardt, Avishek Nagi, Richard Sloane, David A Feldstein.   

Abstract

BACKGROUND: Effective communication of risks and benefits to patients is critical for shared decision making.
PURPOSE: To review the comparative effectiveness of methods of communicating probabilistic information to patients that maximize their cognitive and behavioral outcomes. DATA SOURCES: PubMed (1966 to March 2014) and CINAHL, EMBASE, and the Cochrane Central Register of Controlled Trials (1966 to December 2011) using several keywords and structured terms. STUDY SELECTION: Prospective or cross-sectional studies that recruited patients or healthy volunteers and compared any method of communicating probabilistic information with another method. DATA EXTRACTION: Two independent reviewers extracted study characteristics and assessed risk of bias. DATA SYNTHESIS: Eighty-four articles, representing 91 unique studies, evaluated various methods of numerical and visual risk display across several risk scenarios and with diverse outcome measures. Studies showed that visual aids (icon arrays and bar graphs) improved patients' understanding and satisfaction. Presentations including absolute risk reductions were better than those including relative risk reductions for maximizing accuracy and seemed less likely than presentations with relative risk reductions to influence decisions to accept therapy. The presentation of numbers needed to treat reduced understanding. Comparative effects of presentations of frequencies (such as 1 in 5) versus event rates (percentages, such as 20%) were inconclusive. LIMITATION: Most studies were small and highly variable in terms of setting, context, and methods of administering interventions.
CONCLUSION: Visual aids and absolute risk formats can improve patients' understanding of probabilistic information, whereas numbers needed to treat can lessen their understanding. Due to study heterogeneity, the superiority of any single method for conveying probabilistic information is not established, but there are several good options to help clinicians communicate with patients. PRIMARY FUNDING SOURCE: None.

Entities:  

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Year:  2014        PMID: 25133362     DOI: 10.7326/M14-0295

Source DB:  PubMed          Journal:  Ann Intern Med        ISSN: 0003-4819            Impact factor:   25.391


  104 in total

1.  Do clinicians understand the size of treatment effects? A randomized survey across 8 countries.

Authors:  Bradley C Johnston; Pablo Alonso-Coello; Jan O Friedrich; Reem A Mustafa; Kari A O Tikkinen; Ignacio Neumann; Per O Vandvik; Elie A Akl; Bruno R da Costa; Neill K Adhikari; Gemma Mas Dalmau; Elise Kosunen; Jukka Mustonen; Mark W Crawford; Lehana Thabane; Gordon H Guyatt
Journal:  CMAJ       Date:  2015-10-26       Impact factor: 8.262

Review 2.  Clinician-patient risk discussion for atherosclerotic cardiovascular disease prevention: importance to implementation of the 2013 ACC/AHA Guidelines.

Authors:  Seth S Martin; Laurence S Sperling; Michael J Blaha; Peter W F Wilson; Ty J Gluckman; Roger S Blumenthal; Neil J Stone
Journal:  J Am Coll Cardiol       Date:  2015-04-07       Impact factor: 24.094

3.  Home Time as a Patient-Centered Outcome in Administrative Claims Data.

Authors:  Hemin Lee; Sandra M Shi; Dae Hyun Kim
Journal:  J Am Geriatr Soc       Date:  2018-12-21       Impact factor: 5.562

Review 4.  Communicating Uncertainty: a Narrative Review and Framework for Future Research.

Authors:  Arabella L Simpkin; Katrina A Armstrong
Journal:  J Gen Intern Med       Date:  2019-06-13       Impact factor: 5.128

5.  Predicting Outcomes on the Liver Transplant Waiting List in the United States: Accounting for Large Regional Variation in Organ Availability and Priority Allocation Points.

Authors:  Allyson Hart; David P Schladt; Jessica Zeglin; Joshua Pyke; W Ray Kim; John R Lake; John P Roberts; Ryutaro Hirose; David C Mulligan; Bertram L Kasiske; Jon J Snyder; Ajay K Israni
Journal:  Transplantation       Date:  2016-10       Impact factor: 4.939

Review 6.  An Evidence-Based Medicine Approach to Antihyperglycemic Therapy in Diabetes Mellitus to Overcome Overtreatment.

Authors:  Anil N Makam; Oanh K Nguyen
Journal:  Circulation       Date:  2017-01-10       Impact factor: 29.690

7.  Do invitations for cervical screening provide sufficient information to enable informed choice? A cross-sectional study of invitations for publicly funded cervical screening.

Authors:  Sie Karen Kolthoff; Mie Sara Hestbech; Karsten Juhl Jørgensen; John Brodersen
Journal:  J R Soc Med       Date:  2016-04-26       Impact factor: 5.344

8.  Quantitative Information on Oncology Prescription Drug Websites.

Authors:  Helen W Sullivan; Kathryn J Aikin; Linda B Squiers
Journal:  J Cancer Educ       Date:  2018-04       Impact factor: 2.037

9.  Improving the Understanding of Publicly Reported Healthcare-Associated Infection (HAI) Data.

Authors:  Max Masnick; Daniel J Morgan; Mark D Macek; John D Sorkin; Jessica P Brown; Penny Rheingans; Anthony D Harris
Journal:  Infect Control Hosp Epidemiol       Date:  2016-08-30       Impact factor: 3.254

10.  Association of Preferences for Papillary Thyroid Cancer Treatment With Disease Terminology: A Discrete Choice Experiment.

Authors:  Brooke Nickel; Kirsten Howard; Juan P Brito; Alexandra Barratt; Ray Moynihan; Kirsten McCaffery
Journal:  JAMA Otolaryngol Head Neck Surg       Date:  2018-10-01       Impact factor: 6.223

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