Dataset Information

Evaluation of the Implementation of the Dutch Breast Cancer Surveillance Decision Aid including Personalized Risk Estimates in the SHOUT-BC Study: A Mixed Methods Approach.

ABSTRACT:

Background

To improve Shared decision-making (SDM) regarding personalized post-treatment surveillance, the Breast Cancer Surveillance Decision Aid (BCS-PtDA), integrating personalized risk information, was developed and implemented in eight hospitals. The aim of this mixed-methods study was to (1) assess the implementation and participation rates, (2) identify facilitators and barriers for use by health care professionals (HCPs), (3) quantify the observed level of SDM, and (4) evaluate risk communication and SDM application in consultations.

Methods

Implementation and participation rates and patients' BCS-PtDA use were calculated using hospital registry data and BCS-PtDA log data. HCPs' perspective on facilitators and barriers were collected using the MIDI framework. Observed SDM levels in consultation transcripts were quantified using the OPTION-5 scale. Thematic analysis was performed to assess consultation content.

Results

The average PtDA implementation and participation rates were, respectively, 26% and 61%. HCPs reported that the PtDA supported choice awareness. Reported barriers for implementation were mainly increased workload and a lack of perceived benefits. The consultation analysis (n = 64) showed patients were offered a choice, but deliberation was lacking. Risk communication was generally adequate.

Discussion

When the BCS-PtDA was used, patients were clearly given a choice regarding their post-treatment surveillance, but information provision and SDM application can be improved.

SUBMITTER: Ankersmid JW

PROVIDER: S-EPMC11010914 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evaluation of the Implementation of the Dutch Breast Cancer Surveillance Decision Aid including Personalized Risk Estimates in the SHOUT-BC Study: A Mixed Methods Approach.

Ankersmid Jet W JW Engelhardt Ellen G EG Lansink Rotgerink Fleur K FK The Regina R Strobbe Luc J A LJA Drossaert Constance H C CHC Siesling Sabine S van Uden-Kraan Cornelia F CF

Cancers 20240331 7

<h4>Background</h4>To improve Shared decision-making (SDM) regarding personalized post-treatment surveillance, the Breast Cancer Surveillance Decision Aid (BCS-PtDA), integrating personalized risk information, was developed and implemented in eight hospitals. The aim of this mixed-methods study was to (1) assess the implementation and participation rates, (2) identify facilitators and barriers for use by health care professionals (HCPs), (3) quantify the observed level of SDM, and (4) evaluate r ...[more]

PMID: 38611068

Similar Datasets

Project description:BackgroundThe American Urological Association White Paper on Implementation of Shared Decision Making (SDM) into Urological Practice suggested SDM represents the state of the art in counseling for patients who are faced with difficult or uncertain medical decisions. The Michigan Urological Surgery Improvement Collaborative (MUSIC) implemented a decision aid, Personal Patient Profile-Prostate (P3P), in 2018 to help newly diagnosed prostate cancer patients make shared decisions with their clinicians. We conducted a qualitative study to assess statewide implementation of P3P throughout MUSIC.MethodsWe recruited urologists and staff from 17 MUSIC practices (8 implementation and 9 comparator practices) to understand how practices engaged patients on treatment discussions and to assess facilitators and barriers to implementing P3P. Interview guides were developed based on the Tailored Interventions for Chronic Disease (TICD) Framework. Interviews were transcribed for analysis and coded independently by two investigators in NVivo, PRO 12. Additionally, quantitative program data were integrated into thematic analyses.ResultsWe interviewed 15 urologists and 11 staff from 16 practices. Thematic analysis of interview transcripts indicated three key themes including the following: (i) P3P is compatible as a SDM tool as over 80% of implementation urologists asked patients to complete the P3P questionnaire routinely and used P3P reports during treatment discussions; (ii) patient receptivity was demonstrated by 370 (50%) of newly diagnosed patients (n = 737) from 8 practices enrolled in P3P with 78% completion rate, which accounts for 39% of all newly diagnosed patients in these practices; and (iii) urologists' attitudes towards SDM varied. Over a third of urologists stated they did not rely on a decision aid. Comparator practices indicated habit, inertia, or concerns about clinic flow as reasons for not adopting P3P and some were unconvinced a decision aid is needed in their practice.ConclusionUrologists and staff affiliated with MUSIC implementation sites indicated that P3P focuses the treatment discussion on items that are important to patients. Experiences of implementation practices indicate that once initiated, there were no negative effects on clinic flow and urologists indicated P3P saves time during patient counseling, as patients were better prepared for focused discussions. Lack of awareness, personal habits, and inertia are reasons for not implementing P3P among the comparator practices.

Project description:BackgroundAlthough the treatment for breast cancer is highly personalized, posttreatment surveillance remains one-size-fits-all: annual imaging and physical examination for at least five years after treatment. The INFLUENCE nomogram is a prognostic model for estimating the 5-year risk for locoregional recurrences and second primary tumors after breast cancer. The use of personalized outcome data (such as risks for recurrences) can enrich the process of shared decision-making (SDM) for personalized surveillance after breast cancer.ObjectiveThis study aimed to develop a patient decision aid (PtDA), integrating personalized risk calculations on risks for recurrences, to support SDM for personalized surveillance after curative treatment for invasive breast cancer.MethodsFor the development of the PtDA, the International Patient Decision Aids Standards development process was combined with a mixed methods design inspired by the development process of previously developed PtDAs. In the development, 8 steps were distinguished: establishing a multidisciplinary steering group; definition of the end users, scope, and purpose of the PtDA; assessment of the decisional needs of end users; defining requirements for the PtDA; determining the format and implementation strategy for the PtDA; prototyping; alpha testing; and beta testing. The composed steering group convened during regular working-group sessions throughout the development process.ResultsThe "Breast Cancer Surveillance Decision Aid" consists of 3 components that support the SDM process: a handout sheet on which personalized risks for recurrences, calculated using the INFLUENCE-nomogram, can be visualized and which contains an explanation about the decision for surveillance and a login code for a web-based deliberation tool; a web-based deliberation tool, including a patient-reported outcome measure on fear of cancer recurrence; and a summary sheet summarizing patient preferences and considerations. The PtDA was assessed as usable and acceptable during alpha testing. Beta testing is currently ongoing.ConclusionsWe developed an acceptable and usable PtDA that integrates personalized risk calculations for the risk for recurrences to support SDM for surveillance after breast cancer. The implementation and effects of the use of the "Breast Cancer Surveillance Decision Aid" are being investigated in a clinical trial.

Project description:ObjectiveTo provide the details of the study protocol for an observational, case study design, implementation trial.MethodsImplementing the DEcision-Aid for Lupus (IDEAL) study will put into practice a shared decision-making (SDM) strategy, using an individualized, culturally appropriate computerized decision-aid (DA) for lupus patients in 15 geographically diverse clinics in the USA. The overarching frameworks that guide this implementation study are the Consolidated Framework for Implementation Research (CFIR) and Powell's typology of implementation strategies. All 15 clinics will receive standardized capacity-building activities for lupus DA implementation in the clinic, including education, training, technical assistance, re-training, and incorporation of a clinic champion in the core team of each site. In addition, clinics will also choose among clinic-targeted activities to integrate the DA into existing work processes and/or patient-targeted activities to raise awareness and educate patients about the DA. These activities will be chosen to stimulate participant recruitment and retention activities that support the implementation of the DA at their clinic. In study aim 1, using surveys and semi-structured interviews with clinic personnel in 15 lupus clinics, we will assess stakeholder needs and identify clinic and contextual characteristics that inform the implementation strategy component selection and influence implementation effectiveness. Study aim 2 is to implement and assess the effectiveness of the IDEAL (standardized and tailored) strategy in 15 lupus clinics by examining the changes in our primary outcome of penetration, i.e., the proportion of all eligible patients in the clinic that receive the lupus DA, and secondary outcomes include DA appropriateness, acceptability, success, permanence, and feasibility. Study aim 3 is to identify ways to sustain and disseminate our lupus DA via semi-structured debriefing interviews with key clinic personnel and patients.DiscussionThe study will enroll at least 500 patient participants with lupus across all 15 sites and assess the effectiveness in implementing the DA in various clinic settings across the USA.Trial registrationClinicalTrials.gov, NCT03735238 . Protocol version number: 15, date 6/8/2020.

Project description:ImportanceDevelopmental delays and disabilities are common in children. Research has indicated that intervention during the early years of a child's life has a positive effect on cognitive development, social skills and behavior, and subsequent school performance.ObjectiveTo determine whether a computerized clinical decision support system is an effective approach to improve standardized developmental surveillance and screening (DSS) within primary care practices.Design, setting, and participantsIn this cluster randomized clinical trial performed in 4 pediatric clinics from June 1, 2010, through December 31, 2012, children younger than 66 months seen for primary care were studied.InterventionsWe compared surveillance and screening practices after adding a DSS module to an existing computer decision support system.Main outcomes and measuresThe rates at which children were screened for developmental delay.ResultsMedical records were reviewed for 360 children (180 each in the intervention and control groups) to compare rates of developmental screening at the 9-, 18-, or 30-month well-child care visits. The DSS module led to a significant increase in the percentage of patients screened with a standardized screening tool (85.0% vs 24.4%, P < .001). An additional 120 records (60 each in the intervention and control groups) were reviewed to examine surveillance rates at visits outside the screening windows. The DSS module led to a significant increase in the percentage of patients whose parents were assessed for concerns about their child's development (71.7% vs 41.7%, P = .04).Conclusions and relevanceUsing a computerized clinical decision support system to automate the screening of children for developmental delay significantly increased the numbers of children screened at 9, 18, and 30 months of age. It also significantly improved surveillance at other visits. Moreover, it increased the number of children who ultimately were diagnosed as having developmental delay and who were referred for timely services at an earlier age.Trial registrationclinicaltrials.gov Identifier: NCT01351077.

Project description:Background:Treatment decisions commonly have to be made in intensive care units (ICUs). These decisions are difficult for surrogate decision makers and often lead to decisional conflict, psychological distress, and treatments misaligned with patient preferences. Objective:To determine whether a decision aid about prolonged mechanical ventilation improved prognostic concordance between surrogate decision makers and clinicians compared with a usual care control. Design:Multicenter, parallel, randomized, clinical trial. (ClinicalTrials.gov: NCT01751061). Setting:13 medical and surgical ICUs at 5 hospitals. Participants:Adult patients receiving prolonged mechanical ventilation and their surrogates, ICU physicians, and ICU nurses. Intervention:A Web-based decision aid provided personalized prognostic estimates, explained treatment options, and interactively clarified patient values to inform a family meeting. The control group received information according to usual care practices followed by a family meeting. Measurements:The primary outcome was improved concordance on 1-year survival estimates, measured with the clinician-surrogate concordance scale (range, 0 to 100 percentage points; higher scores indicate more discordance). Secondary and additional outcomes assessed the experiences of surrogates (psychological distress, decisional conflict, and quality of communication) and patients (length of stay and 6-month mortality). Outcomes assessors were blinded to group allocation. Results:The study enrolled 277 patients, 416 surrogates, and 427 clinicians. Concordance improvement did not differ between intervention and control groups (mean difference in score change from baseline, -1.7 percentage points [95% CI, -8.3 to 4.8 percentage points]; P = 0.60). Surrogates' postintervention estimates of patients' 1-year prognoses did not differ between intervention and control groups (median, 86.0% [interquartile range {IQR}, 50.0%] vs. 92.5% [IQR, 47.0%]; P = 0.23) and were substantially more optimistic than results of a validated prediction model (median, 56.0% [IQR, 43.0%]) and physician estimates (median, 50.0% [IQR, 55.5%]). Eighty-two intervention surrogates (43%) favored a treatment option that was more aggressive than their report of patient preferences. Although intervention surrogates had greater reduction in decisional conflict than control surrogates (mean difference in change from baseline, 0.4 points [CI, 0.0 to 0.7 points]; P = 0.041), other surrogate and patient outcomes did not differ. Limitation:Contamination among clinicians could have biased results toward the null hypothesis. Conclusion:A decision aid about prolonged mechanical ventilation did not improve prognostic concordance between clinicians and surrogates, reduce psychological distress among surrogates, or alter clinical outcomes. Decision support in acute care settings may require greater individualized attention for both the cognitive and affective challenges of decision making. Primary Funding Source:National Institutes of Health.

Project description:BackgroundMany cancer survivors struggle to choose a health insurance plan that meets their needs because of high costs, limited health insurance literacy, and lack of decision support. We developed a web-based decision aid, Improving Cancer Patients' Insurance Choices (I Can PIC), and evaluated it in a randomized trial.Materials and methodsEligible individuals (18-64 years, diagnosed with cancer for ≤5 years, English-speaking, not Medicaid or Medicare eligible) were randomized to I Can PIC or an attention control health insurance worksheet. Primary outcomes included health insurance knowledge, decisional conflict, and decision self-efficacy after completing I Can PIC or the control. Secondary outcomes included knowledge, decisional conflict, decision self-efficacy, health insurance literacy, financial toxicity, and delayed care at a 3-6-month follow-up.ResultsA total of 263 of 335 eligible participants (79%) consented and were randomized; 206 (73%) completed the initial survey (106 in I Can PIC; 100 in the control), and 180 (87%) completed a 3-6 month follow-up. After viewing I Can PIC or the control, health insurance knowledge and a health insurance literacy item assessing confidence understanding health insurance were higher in the I Can PIC group. At follow-up, the I Can PIC group retained higher knowledge than the control; confidence understanding health insurance was not reassessed. There were no significant differences between groups in other outcomes. Results did not change when controlling for health literacy and employment. Both groups reported having limited health insurance options.ConclusionI Can PIC can improve cancer survivors' health insurance knowledge and confidence using health insurance. System-level interventions are needed to lower financial toxicity and help patients manage care costs.Implications for practiceInadequate health insurance compromises cancer treatment and impacts overall and cancer-specific mortality. Uninsured or underinsured survivors report fewer recommended cancer screenings and may delay or avoid needed follow-up cancer care because of costs. Even those with adequate insurance report difficulty managing care costs. Health insurance decision support and resources to help manage care costs are thus paramount to cancer survivors' health and care management. We developed a web-based decision aid, Improving Cancer Patients' Insurance Choices (I Can PIC), and evaluated it in a randomized trial. I Can PIC provides health insurance information, supports patients through managing care costs, offers a list of financial and emotional support resources, and provides a personalized cost estimate of annual health care expenses across plan types.

Dataset Information

Evaluation of the Implementation of the Dutch Breast Cancer Surveillance Decision Aid including Personalized Risk Estimates in the SHOUT-BC Study: A Mixed Methods Approach.

Background

Methods

Results

Discussion

Publications

Evaluation of the Implementation of the Dutch Breast Cancer Surveillance Decision Aid including Personalized Risk Estimates in the SHOUT-BC Study: A Mixed Methods Approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets