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Decision Aids Can Support Cancer Clinical Trials Decisions: Results of a Randomized Trial.

ABSTRACT: Cancer patients often do not make informed decisions regarding clinical trial participation. This study evaluated whether a web-based decision aid (DA) could support trial decisions compared with our cancer center's website.Adults diagnosed with cancer in the past 6 months who had not previously participated in a cancer clinical trial were eligible. Participants were randomized to view the DA or our cancer center's website (enhanced usual care [UC]). Controlling for whether participants had heard of cancer clinical trials and educational attainment, multivariable linear regression examined group on knowledge, self-efficacy for finding trial information, decisional conflict (values clarity and uncertainty), intent to participate, decision readiness, and trial perceptions.Two hundred patients (86%) consented between May 2014 and April 2015. One hundred were randomized to each group. Surveys were completed by 87 in the DA group and 90 in the UC group. DA group participants reported clearer values regarding trial participation than UC group participants reported (least squares [LS] mean = 15.8 vs. 32, p < .0001) and less uncertainty (LS mean = 24.3 vs. 36.4, p = .025). The DA group had higher objective knowledge than the UC group's (LS mean = 69.8 vs. 55.8, p < .0001). There were no differences between groups in intent to participate.Improvements on key decision outcomes including knowledge, self-efficacy, certainty about choice, and values clarity among participants who viewed the DA suggest web-based DAs can support informed decisions about trial participation among cancer patients facing this preference-sensitive choice. Although better informing patients before trial participation could improve retention, more work is needed to examine DA impact on enrollment and retention.This paper describes evidence regarding a decision tool to support patients' decisions about trial participation. By improving knowledge, helping patients clarify preferences for participation, and facilitating conversations about trials, decision aids could lead to decisions about participation that better match patients' preferences, promoting patient-centered care and the ethical conduct of clinical research.

SUBMITTER: Politi MC

PROVIDER: S-EPMC5153351 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Decision Aids Can Support Cancer Clinical Trials Decisions: Results of a Randomized Trial.

Politi Mary C MC Kuzemchak Marie D MD Kaphingst Kimberly A KA Perkins Hannah H Liu Jingxia J Byrne Margaret M MM

The oncologist 20160810 12

<h4>Background</h4>Cancer patients often do not make informed decisions regarding clinical trial participation. This study evaluated whether a web-based decision aid (DA) could support trial decisions compared with our cancer center's website.<h4>Methods</h4>Adults diagnosed with cancer in the past 6 months who had not previously participated in a cancer clinical trial were eligible. Participants were randomized to view the DA or our cancer center's website (enhanced usual care [UC]). Controllin ...[more]

PMID: 27511904

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Project description:The conflicting recommendations for prostate cancer (PCa) screening and the mixed messages communicated to the public about screening effectiveness make it critical to assist men in making informed decisions.To assess the effectiveness of 2 decision aids in helping men make informed PCa screening decisions.A racially diverse group of male outpatients aged 45 to 70 years from 3 sites were interviewed by telephone at baseline, 1 month, and 13 months, from 2007 through 2011. We conducted intention-to-treat univariate analyses and multivariable linear and logistic regression analyses, adjusting for baseline outcome measures.Random assignment to print-based decision aid (n?=?628), web-based interactive decision aid (n?=?625), or usual care (UC) (n?=?626).Prostate cancer knowledge, decisional conflict, decisional satisfaction, and whether participants underwent PCa screening.Of 4794 eligible men approached, 1893 were randomized. At each follow-up assessment, univariate and multivariable analyses indicated that both decision aids resulted in significantly improved PCa knowledge and reduced decisional conflict compared with UC (all P <.001). At 1 month, the standardized mean difference (Cohen’s d) in knowledge for the web group vs UC was 0.74, and in the print group vs UC, 0.73. Decisional conflict was significantly lower for web vs UC (d?=?0.33) and print vs UC (d?=?0.36). At 13 months, these differences were smaller but remained significant. At 1 month, high satisfaction was reported by significantly more print (60.4%) than web participants (52.2%; P?=?.009) and significantly more web (P?=?.001) and print (P?=?.03) than UC participants (45.5%). At 13 months, differences in the proportion reporting high satisfaction among print (55.7%) compared with UC (49.8%; P?=?.06) and web participants (50.4%; P?=?.10) were not significant. Screening rates at 13 months did not differ significantly among groups.Both decision aids improved participants’ informed decision making about PCa screening up to 13 months later but did not affect actual screening rates. Dissemination of these decision aids may be a valuable public health tool.clinicaltrials.gov Identifier: NCT00196807.

Project description:BackgroundClinical decision support (CDS) is a valuable feature of electronic health records (EHRs) designed to improve quality and safety. However, due to the complexities of system design and inconsistent results, CDS tools may inadvertently increase alert fatigue and contribute to physician burnout. A/B testing, or rapid-cycle randomized tests, is a useful method that can be applied to the EHR in order to rapidly understand and iteratively improve design choices embedded within CDS tools.ObjectiveThis paper describes how rapid randomized controlled trials (RCTs) embedded within EHRs can be used to quickly ascertain the superiority of potential CDS design changes to improve their usability, reduce alert fatigue, and promote quality of care.MethodsA multistep process combining tools from user-centered design, A/B testing, and implementation science was used to understand, ideate, prototype, test, analyze, and improve each candidate CDS. CDS engagement metrics (alert views, acceptance rates) were used to evaluate which CDS version is superior.ResultsTo demonstrate the impact of the process, 2 experiments are highlighted. First, after multiple rounds of usability testing, a revised CDS influenza alert was tested against usual care CDS in a rapid (~6 weeks) RCT. The new alert text resulted in minimal impact on reducing firings per patients per day, but this failure triggered another round of review that identified key technical improvements (ie, removal of dismissal button and firings in procedural areas) that led to a dramatic decrease in firings per patient per day (23.1 to 7.3). In the second experiment, the process was used to test 3 versions (financial, quality, regulatory) of text supporting tobacco cessation alerts as well as 3 supporting images. Based on 3 rounds of RCTs, there was no significant difference in acceptance rates based on the framing of the messages or addition of images.ConclusionsThese experiments support the potential for this new process to rapidly develop, deploy, and rigorously evaluate CDS within an EHR. We also identified important considerations in applying these methods. This approach may be an important tool for improving the impact of and experience with CDS.Trial registrationFlu alert trial: ClinicalTrials.gov NCT03415425; https://clinicaltrials.gov/ct2/show/NCT03415425. Tobacco alert trial: ClinicalTrials.gov NCT03714191; https://clinicaltrials.gov/ct2/show/NCT03714191.

Project description:BackgroundInnovative interventions are needed to address gaps in preventive cancer care, especially in rural areas. This study evaluated the impact of clinical decision support (CDS) with and without shared decision making (SDM) on cancer-screening completion.MethodsIn this 3-arm, parallel-group, cluster-randomized trial conducted at a predominantly rural medical group, 34 primary care clinics were randomized to clinical decision support (CDS), CDS plus shared decision making (CDS+SDM), or usual care (UC). The CDS applied web-based clinical algorithms identifying patients overdue for United States Preventive Services Task Force-recommended preventive cancer care and presented evidence-based recommendations to patients and providers on printouts and on the electronic health record interface. Patients in the CDS+SDM clinic also received shared decision-making tools (SDMTs). The primary outcome was a composite indicator of the proportion of patients overdue for breast, cervical, or colorectal cancer screening at index who were up to date on these 1 y later.ResultsFrom August 1, 2018, to March 15, 2019, 69,405 patients aged 21 to 74 y had visits at study clinics and 25,198 were overdue for 1 or more cancer screening tests at an index visit. At 12-mo follow-up, 9,543 of these (37.9%) were up to date on the composite endpoint. The adjusted, model-derived percentage of patients up to date was 36.5% (95% confidence interval [CI]: 34.0-39.1) in the UC group, 38.1% (95% CI: 35.5-40.9) in the CDS group, and 34.4% (95% CI: 31.8-37.2) in the CDS+SDM group. For all comparisons, the screening rates were higher than UC in the CDS group and lower than UC in the CDS+SDM group, although these differences did not reach statistical significance.ConclusionThe CDS did not significantly increase cancer-screening rates. Exploratory analyses suggest a deeper understanding of how SDM and CDS interact to affect cancer prevention decisions is needed. Trial registration: ClinicalTrials.gov ID: NCT02986230, December 6, 2016.

Project description:BACKGROUND:Smokers usually abstain from tobacco while hospitalized but relapse after discharge. Inpatient interventions may encourage sustained quitting. We previously demonstrated that a decision support tool embedded in an electronic health record (EHR) improved physicians' treatment of hospitalized smokers. This report describes the effect on quit rates of this decision support tool and order set for hospitalized smokers. METHODS:In a single hospital system, 254 physicians were randomized 1:1 to receive a decision support tool and order set, embedded in the EHR. When an adult patient was admitted to a medical service, an electronic alert appeared if current smoking was recorded in the EHR. For physicians receiving the intervention, the alert linked to an order set for tobacco treatment medications and electronic referral to the state tobacco quitline. Additionally, "Tobacco Use Disorder" was added to the patient's problem list, and a secure message was sent to the patient's primary care provider (PCP). In the control arm, no alert appeared. Patients were contacted by phone at 1, 6, and 12?months; those reporting tobacco abstinence at 12?months were asked to return to measure exhaled carbon monoxide. Generalized estimating equations were used to model the data. RESULTS:From 2013 to 2016, the alert fired for 10,939 patients (5391 intervention, 5548 control). Compared to control physicians, intervention physicians were more likely to order tobacco treatment medication, populate the problem list with tobacco use disorder, refer to the quitline, and notify the patient's PCP. In a subset of 1044 patients recruited for intensive follow-up, one-year quit rates for intervention and control patients were, respectively, 11.5% and 11.6%, (p?=?0.94), after controlling for age, sex, race, ethnicity, and insurance. Similarly, there were no differences in 1- and 6-month quit rates. CONCLUSIONS:Although we were able to improve processes of care, long-term tobacco quit rates were unchanged. This likely reflects, in part, the need for sustained quitting interventions, and higher-than-expected quit rates in controls. Future enhancements should improve prescription of medications for smoking cessation at discharge, engagement of primary care providers, and perhaps direct engagement of patients in a more longitudinal approach. TRIAL REGISTRATION:ClinicalTrials.gov, NCT01691105 . Registered on September 12, 2012.

Project description:Importance:Patients with serious illnesses are often encouraged to actively deliberate about the desirability of life support. Yet it is unknown whether deliberation changes the substance or quality of such decisions. Objective:To identify differences in decisions about life support interventions and goals of care made intuitively vs deliberatively by patients with serious illnesses. Design, Setting, and Participants:Randomized clinical trial in which patients were asked to express treatment preferences in a series of clinical scenarios. Participants were 199 hospitalized patients aged 60 years and older with serious oncologic, cardiac, and pulmonary illnesses treated in a large, urban academic hospital from July 1, 2015, through March 15, 2016. Interventions:Patients in the intuitive group were subjected to a cognitive load and instructed to answer each question immediately based on gut instinct. Patients in the deliberative group were not cognitively loaded, were instructed to think carefully about their answers, and were required to explain their answers. Main Outcomes and Measures:Choices regarding life support (4 scenarios) and goals of care (1 scenario), concordance of these choices with patients' valuations of health states that could follow from them, and decisional uncertainty. Results:Of 199 patients, 132 (66%) were male and the mean (SD) age was 67.2 (5.0) years. Similar proportions of patients in the intuitive group (n = 97) and the deliberative group (n = 102) said they would accept a feeding tube for chronic aspiration (42% vs 44%, respectively; difference, -2%; 95% CI, -16% to 12%; P = .79), antibiotics for life-threatening infection in the event of terminal illness (39% vs 43%, respectively; difference, -4%; 95% CI, -18% to 10%; P = .57), a trial of mechanical ventilation (59% vs 60%, respectively; difference,-1%; 95% CI, -15% to 13%; P = .88), and a tracheostomy tube (37% vs 41%, respectively; difference, -4%; 95% CI, -22% to 13%; P = .64). Patients in the deliberative group were slightly more likely than patients in the intuitive group to choose a palliative approach to treatment in the event of serious illness (45% vs 30%, respectively; difference, 15%; 95% CI, 1%-29%; P = .04). Across scenarios, decisional uncertainty was similar between the 2 groups (all P > .05), and intuitive decisions were either equally or more closely aligned with patients' health state valuations than deliberative decisions. Conclusions and Relevance:In this study, encouraging hospitalized patients with serious illnesses to deliberate on end-of-life decisions did not change the content or improve the quality of these decisions. It is important to evaluate whether decision aids and structured communication interventions improve seriously ill patients' choices. Trial Registration:ClinicalTrials.gov Identifier: NCT02487810.

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Decision Aids Can Support Cancer Clinical Trials Decisions: Results of a Randomized Trial.

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Decision Aids Can Support Cancer Clinical Trials Decisions: Results of a Randomized Trial.

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