Dataset Information

Presenting quantitative information about decision outcomes: a risk communication primer for patient decision aid developers.

ABSTRACT:

Background

Making evidence-based decisions often requires comparison of two or more options. Research-based evidence may exist which quantifies how likely the outcomes are for each option. Understanding these numeric estimates improves patients' risk perception and leads to better informed decision making. This paper summarises current "best practices" in communication of evidence-based numeric outcomes for developers of patient decision aids (PtDAs) and other health communication tools.

Method

An expert consensus group of fourteen researchers from North America, Europe, and Australasia identified eleven main issues in risk communication. Two experts for each issue wrote a "state of the art" summary of best evidence, drawing on the PtDA, health, psychological, and broader scientific literature. In addition, commonly used terms were defined and a set of guiding principles and key messages derived from the results.

Results

The eleven key components of risk communication were: 1) Presenting the chance an event will occur; 2) Presenting changes in numeric outcomes; 3) Outcome estimates for test and screening decisions; 4) Numeric estimates in context and with evaluative labels; 5) Conveying uncertainty; 6) Visual formats; 7) Tailoring estimates; 8) Formats for understanding outcomes over time; 9) Narrative methods for conveying the chance of an event; 10) Important skills for understanding numerical estimates; and 11) Interactive web-based formats. Guiding principles from the evidence summaries advise that risk communication formats should reflect the task required of the user, should always define a relevant reference class (i.e., denominator) over time, should aim to use a consistent format throughout documents, should avoid "1 in x" formats and variable denominators, consider the magnitude of numbers used and the possibility of format bias, and should take into account the numeracy and graph literacy of the audience.

Conclusion

A substantial and rapidly expanding evidence base exists for risk communication. Developers of tools to facilitate evidence-based decision making should apply these principles to improve the quality of risk communication in practice.

SUBMITTER: Trevena LJ

PROVIDER: S-EPMC4045391 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Presenting quantitative information about decision outcomes: a risk communication primer for patient decision aid developers.

Trevena Lyndal J LJ Zikmund-Fisher Brian J BJ Edwards Adrian A Gaissmaier Wolfgang W Galesic Mirta M Han Paul K J PK King John J Lawson Margaret L ML Linder Suzanne K SK Lipkus Isaac I Ozanne Elissa E Peters Ellen E Timmermans Danielle D Woloshin Steven S

BMC medical informatics and decision making 20131129

<h4>Background</h4>Making evidence-based decisions often requires comparison of two or more options. Research-based evidence may exist which quantifies how likely the outcomes are for each option. Understanding these numeric estimates improves patients' risk perception and leads to better informed decision making. This paper summarises current "best practices" in communication of evidence-based numeric outcomes for developers of patient decision aids (PtDAs) and other health communication tools. ...[more]

PMID: 24625237

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Project description:Men with localized prostate cancer often have unrealistic expectations. Practitioners are poor judges of men's preferences, contributing to preference misdiagnosis and unwarranted practice variation. Patient decision aids (PtDAs) can support men with decisions about localized prostate cancer. This is a comparative case study of two strategies for implementing PtDAs in clinical pathways for men with localized prostate cancer, evaluating (a) PtDA use; (b) impact on men, practitioners, and health system outcomes; and (c) factors influencing sustained use.Guided by the Knowledge to Action Framework, this comparative case study will be conducted using administrative data, interviews, and surveys. Cases will be bound by geographic location (one hospital in Ontario; province of Saskatchewan) and time. Eligible participants will be all men newly diagnosed with localized prostate cancer, with outcomes assessed using administrative data and interviews. Nurses, urologists, radiation oncologists, and managers will be surveyed and a smaller sample interviewed. Cases will be established for each setting with findings compared across cases. Changes in the proportions of men given the PtDA over 2 years will be determined from administrative data. Factors associated with receiving the PtDA will be explored using multivariable logistic regression analysis. To assess the impact of the PtDA, outcomes will be described using mean and standard deviation (men's decisional conflict) and frequency and proportions (practitioners consulted, uptake of treatment). To estimate the effect of the PtDA on these outcomes, adjusted mean differences and odds ratios will be calculated using exploratory multivariable general linear regression and binary or multinomial logistic regression. Factors influencing sustained PtDA use will be assessed using descriptive analysis of survey findings and thematic analysis of interview transcripts.Determining how to embed PtDAs effectively within clinical pathways for men with localized prostate cancer is essential. PtDAs have the potential to strengthen men's active role in making prostate cancer decisions, enhance uptake of shared decision-making by practitioners, and reduce practice variation. Our team of researchers and knowledge users will use findings to improve current PtDA use and consider scaling-up implementation.

Project description:INTRODUCTION:Patient decision-aids (PDAs) support patients in selecting evidence-based treatment options. PDA is useful only if the user understands the content to make personalised decisions. Cultural adaptation is a process of adjusting health messages so that the information is accurate, relevant and understandable to users from a different population. A PDA has been developed to assist Malaysian patients with secondary drug failure to initiate insulin therapy to control their type 2 diabetes mellitus (T2DM). Likewise, patients with T2DM in neighbouring Singapore face similar barriers in commencing insulin treatment, which a PDA may facilitate decision-making in selecting personalised therapy. OBJECTIVE:The study aimed to explore the views and perceptions of Singaporean primary care providers on the Malaysia PDA to initiate insulin therapy and described the cultural adaptation process used in the design and development of a new PDA, which would be trialled in a Singapore primary healthcare institution. METHOD:Qualitative research method was deployed to conduct one-to-one in-depth interviews of the healthcare providers at the trial site (SingHealth Polyclinics-SHP), including six primary care physicians and four nurses to gather their views and feedbacks on the Malaysian PDA. The interviews were transcribed, audited and analysed (standard content analysis) to identify themes relating to the content, layout, concerns of the original PDA and suggestions to the design of the new SHP PDA. RESULTS:Cultural adaptation of the new PDA includes change to the overall design, graphics (including pictograms), presentation styles, additional contextualised content (personalisation, subheadings, cost and treatment option), modified phrasing of the subtitles and concerns (choice of words) relevant to the new users. CONCLUSION:A PDA on insulin therapy underwent cultural adaptation before its implementation in another population in a neighbouring country. Its relevance and effectiveness will be evaluated in future research.

Project description:ImportanceDecision aids can help inform appropriate selection of total knee replacement (TKR) for advanced knee osteoarthritis (OA). However, few decision aids combine patient education, preference assessment, and artificial intelligence (AI) using patient-reported outcome measurement data to generate personalized estimations of outcomes to augment shared decision-making (SDM).ObjectiveTo assess the effect of an AI-enabled patient decision aid that includes education, preference assessment, and personalized outcome estimations (using patient-reported outcome measurements) on decision quality, patient experience, functional outcomes, and process-level outcomes among individuals with advanced knee OA considering TKR in comparison with education only.Design, setting, and participantsThis randomized clinical trial at a single US academic orthopedic practice included 129 new adult patients presenting for OA-related knee pain from March 2019 to January 2020. Data were analyzed from April to May 2020.InterventionPatients were randomized into a group that received a decision aid including patient education, preference assessment, and personalized outcome estimations (intervention group) or a group receiving educational material only (control group) alongside usual care.Main outcomes and measuresThe primary outcome was decision quality, measured using the Knee OA Decision Quality Instrument (K-DQI). Secondary outcomes were collaborative decision-making (assessed using the CollaboRATE survey), patient satisfaction with consultation (using a numerical rating scale), Knee Injury and Osteoarthritis Outcome Score Joint Replacement (KOOS JR) score, consultation time, TKR rate, and treatment concordance.ResultsA total of 69 patients in the intervention group (46 [67%] women) and 60 patients in the control group (37 [62%] women) were included in the analysis. The intervention group showed better decisional quality (K-DQI mean difference, 20.0%; SE, 3.02; 95% CI, 14.2%-26.1%; P?<?.001), collaborative decision-making (CollaboRATE, 8 of 69 [12%] vs 28 of 60 [47%] patients below median; P?<?.001), satisfaction (numerical rating scale, 9 of 65 [14%] vs 19 of 58 [33%] patients below median; P?=?.01), and improved functional outcomes at 4 to 6 months (mean [SE] KOOS JR, 4.9 [2.24] points higher in intervention group; 95% CI, 0.8-9.0 points; P?=?.02). The intervention did not significantly affect consultation time (mean [SE] difference, 2.23 [2.18] minutes; P?=?.31), TKR rates (16 of 69 [23%] vs 7 of 60 [12%] patients; P?=?.11), or treatment concordance (58 of 69 [84%] vs 44 of 60 [73%] patients; P?=?.19).Conclusions and relevanceIn this randomized clinical trial, an AI-enabled decision aid significantly improved decision quality, level of SDM, satisfaction, and physical limitations without significantly impacting consultation times, TKR rates, or treatment concordance in patients with knee OA considering TKR. Decision aids using a personalized, data-driven approach can enhance SDM in the management of knee OA.Trial registrationClinicalTrials.gov Identifier: NCT03956004.

Dataset Information

Presenting quantitative information about decision outcomes: a risk communication primer for patient decision aid developers.

Background

Method

Results

Conclusion

Publications

Presenting quantitative information about decision outcomes: a risk communication primer for patient decision aid developers.

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