Decision Support Tool For Patients Research Articles

Implementation of digital patient decision support tools for Atrial Fibrillation treatment: systematic review

Abstract Background/ Introduction Treating atrial fibrillation (AF) involves complex decisions. Digital patient decision-support tools can facilitate shared decision making by significantly reducing decisional conflict and improving patient knowledge [1,2]. Yet, implementation in clinical practice is lagging [3]. Purpose To systematically review trials of digital patient decision-support tools for AF treatment decisions, focusing on factors influencing their implementation in clinical practice. Methods We conducted a systematic review following Cochrane methods. EMBASE, MEDLINE and Scopus were searched from 2005 to 2023. Eligible randomised controlled trials and quasi-experimental trials that evaluated digital patient decision-support tools for AF treatment decisions were included. Two independent reviewers screened title and abstract, extracted data and assessed risk of bias. The implementation outcomes included a description of when and how the tool was used, as well as its characteristics and user satisfaction. Results Thirteen articles reporting on eleven studies (5 RCTs and 6 quasi-experimental trials) met the inclusion criteria (2,714 participants; mean age 71±4.2 years; 26% women; 92% with non-valvular AF, remaining not reported). Risk of bias was low for 2 studies, moderate for one and high for seven studies. All tools (n=11) focused on anticoagulation choice (1: warfarin vs no warfarin; 1: aspirin vs warfarin; 7: Direct Oral Anticoagulants (DOAC) vs antiplatelet or warfarin; 2: unspecified choice). The tools were used either pre-visit (at home or in the waiting room) or during the consultation (n=5). Tools were delivered using a mobile application (n=4), web-based application (n=5), or a desktop (n=2). Of the eleven tools, 2 could automatically calculate individualized stroke risk using data from electronic health records. Most articles reported favourable user feedback regarding use of the digital patient decision-support tools, such as high perceived usefulness, user-friendliness, and overall satisfaction. No significant difference was found in encounter times between intervention and control arms (2 RCTs). Four tools were publicly available and 3 were implemented in healthcare delivery beyond research. Conclusion Despite demonstrated efficacy in reducing decisional conflict and improving patient knowledge, as well as positive clinician and patient experience, only 3 tools were implemented in clinical practice beyond their trial. Future tools could better leverage digital capabilities to integrate with Electronic Health Records, automate stroke and bleeding risk calculations, and personalise content based on patient values and preferences. Additional robust trials and implementation studies are warranted to understand barriers and enablers to the use of these tools.

Examining Health Equity in a Systematic Review of Digital Patient Decision Support Tools for Atrial Fibrillation (AF) Treatment

Examining Health Equity in a Systematic Review of Digital Patient Decision Support Tools for Atrial Fibrillation (AF) Treatment

DCIS knowledge of women choosing between active surveillance and surgery for low-risk DCIS

BackgroundDuctal carcinoma in situ (DCIS) can progress to invasive breast cancer (IBC), but often never will. As we cannot predict accurately which DCIS-lesions will or will not progress to IBC, almost all women with DCIS undergo breast-conserving surgery supplemented with radiotherapy, or even mastectomy. In some countries, endocrine treatment is prescribed as well. This implies many women with non-progressive DCIS undergo overtreatment. To reduce this, the LORD patient preference trial (LORD-PPT) tests whether mammographic active surveillance (AS) is safe by giving women with low-risk DCIS a choice between treatment and AS. For this, sufficient knowledge about DCIS is crucial. Therefore, we assessed women's DCIS knowledge in association with socio-demographic and clinical characteristics. MethodsLORD-PPT participants (N = 376) completed a questionnaire assessing socio-demographic and clinical characteristics, risk perception, treatment choice and DCIS knowledge after being informed about their diagnosis and treatment options. Results66 % of participants had poor knowledge (i.e., answered ≤3 out of 7 knowledge items correctly). Most incorrect answers involved overestimating the safety of AS and misunderstanding of DCIS prognostic risks. Overall, women with higher DCIS knowledge score perceived their risk of developing IBC as being somewhat higher than women with poorer knowledge (p = 0.049). Women with better DCIS knowledge more often chose surgery whilst most women with poorer knowledge chose active surveillance (p = 0.049). DiscussionOur findings show that there is room for improvement of information provision to patients. Decision support tools for patients and clinicians could help to stimulate effective shared decision-making about DCIS management.

Designing an Implementable Clinical Prediction Model for Near-Term Mortality and Long-Term Survival in Patients on Maintenance Hemodialysis

The life expectancy of patients treated with maintenance hemodialysis (MHD) is heterogeneous. Knowledge of life-expectancy may focus care decisions on near-term versus long-term goals. The current tools are limited and focus on near-term mortality. Here, we develop and assess potential utility for predicting near-term mortality and long-term survival on MHD. Predictive modeling study. 42,351 patients contributing 997,381 patient months over 11 years, abstracted from the electronic health record (EHR) system of midsize, nonprofit dialysis providers. Demographics, laboratory results, vital signs, and service utilization data available within dialysis EHR. For each patient month, we ascertained death within the next 6 months (ie, near-term mortality) and survival over more than 5 years during receipt of MHD or after kidney transplantation (ie, long-term survival). We used least absolute shrinkage and selection operator logistic regression and gradient-boosting machines to predict each outcome. We compared these to time-to-event models spanning both time horizons. We explored the performance of decision rules at different cut points. All models achieved an area under the receiver operator characteristic curve of≥0.80 and optimal calibration metrics in the test set. The long-term survival models had significantly better performance than the near-term mortality models. The time-to-event models performed similarly to binary models. Applying different cut points spanning from the 1st to 90th percentile of the predictions, a positive predictive value (PPV) of 54% could be achieved for near-term mortality, but with poor sensitivity of 6%. A PPV of 71% could be achieved for long-term survival with a sensitivity of 67%. The retrospective models would need to be prospectively validated before they could be appropriately used as clinical decision aids. A model built with readily available clinical variables to support easy implementation can predict clinically important life expectancy thresholds and shows promise as a clinical decision support tool for patients on MHD. Predicting long-term survival has better decision rule performance than predicting near-term mortality. Clinical prediction models (CPMs) are not widely used for patients undergoing maintenance hemodialysis (MHD). Although a variety of CPMs have been reported in the literature, many of these were not well-designed to be easily implementable. We consider the performance of an implementable CPM for both near-term mortality and long-term survival for patients undergoing MHD. Both near-term and long-term models have similar predictive performance, but the long-term models have greater clinical utility. We further consider how the differential performance of predicting over different time horizons may be used to impact clinical decision making. Although predictive modeling is not regularly used for MHD patients, such tools may help promote individualized care planning and foster shared decision making.

Taking Shared Decision Making from Concept to Clinical Practice: What Do Sickle Cell Healthcare Providers Need?

Introduction:The landscape of therapies for sickle cell disease (SCD) is rapidly evolving. While hydroxyurea remains the gold standard treatment, since 2017 the FDA has approved three additional drugs (i.e., L-glutamine, voxelotor, crizanlizumab), and many others are under investigation. Thus, new therapies and multi-agent treatment approaches for SCD are on the horizon, along with increasingly complex treatment decisions for patients, caregivers, and clinicians. Best practice SCD guidelines recommend shared decision making (SDM; NHLBI, 2014) and SCD patients and caregivers strongly prefer a SDM approach when considering disease-modifying therapies (Jabour et al., 2019; Ross et al., 2016). SDM is a collaborative process in which patients, caregivers, and clinicians work in partnership to reach healthcare decisions based on scientific evidence and the patient's/caregiver's values, preferences, and treatment goals (Stiggelbout et al., 2012). SDM enhances patient and parent knowledge, engagement in the decision making process, and satisfaction with care (Stacey et al., 2014; Wyatt et al., 2015) and is particularly beneficial for marginalized groups (Durand et al., 2014). Evidence also suggests positive impacts of SDM on more distal outcomes, including adherence to the selected treatment (Hauser et al., 2015; Joosten et al., 2008) and health outcomes (Hauser et al., 2015; Tapp et al., 2017). However, few tools are available to facilitate SDM for SCD therapies (Crosby et al., 2019), and most healthcare providers who care for youth with SCD have not had specific training in SDM (Yawn et al., 2014). This study examined clinician perspectives on resources needed to foster SDM in the context of disease-modifying treatment decisions for SCD. Methods:SCD providers were recruited from across the United States using maximum variation purposeful sampling to ensure diversity in provider characteristics (age, sex, race, discipline, years in profession) and clinics (region, patient volume). After electronic consent, providers completed a demographics survey and joined a private online “crowd” on Slack. There were five waves of data collection; during each wave, a series of 4-5 questions were posed to the crowd. Providers were paid for each question they answered and encouraged to view and respond to others' posts. Thematic content analysis was used to summarize perspectives on resources needed to help healthcare providers better engage patients and caregivers in SDM for sickle cell therapies. Two raters independently coded each provider's responses; coding discrepancies were resolved via group discussion to consensus. Results:Of the 59 SCD providers who enrolled, approximately two-thirds were female (67%) and 61% were White; the remainder of the sample identified as Asian (20%), Black or African American (10%), or Middle Eastern (3%). Most were non-Hispanic or Latino (95%). Providers included physicians (73%), nurse practitioners or advanced practice nurses (22%), and nurses (5%) with a wide range of years in practice (2 - 57, M=12.8, SD=10.6). Providers were employed at care centers serving pediatric patients (37%), adult patients (29%), or both pediatric and adult patients with SCD (34%), with annual patient volume ranging from 16 to 2200 (M=420.5, SD=405.1). Across the waves of data collection, response rates ranged from 53-59%. These clinicians identified five resources necessary to optimize SDM for disease-modifying therapies: 1) well-staffed multidisciplinary care teams; 2) high quality patient education materials; 3) billing and reimbursement policies that incentivize SDM; 4) resources to address social determinants of health barriers; and, 5) peer mentoring and support programs for patients. See Table 1 for illustrative quotes. Conclusions:Providers identified several supports and resources to facilitate SDM with individuals with SCD. Findings from this study will inform the development of a multi-component SDM intervention, including decision support tools for patients, caregivers, and healthcare providers and clinic-based implementation tools. Acknowledgements: This research was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number P20GM109021. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Development of an Electronic Health Record-Based Clinical Decision Support Tool for Patients With Lynch Syndrome.

To develop an electronic health record (EHR)-based clinical decision support (CDS) tool to promote guideline-recommended cancer risk management among patients with Lynch syndrome (LS), an inherited cancer syndrome that confers an increased risk of colorectal and other cancer types. We conducted a cross-sectional study to determine the baseline prevalence and predictors of guideline-recommended colonic surveillance and annual genetics program visits among patients with LS. Multivariable log-binomial regressions estimated prevalence ratios (PRs) of cancer risk management adherence by baseline sociodemographic and clinical characteristics. These analyses provided rationale for the development of an EHR-based CDS tool to support patients and clinicians with LS-related endoscopic surveillance and annual genetics program visits. The CDS leverages an EHR platform linking discrete genetic data to LS Genomic Indicators, in turn driving downstream clinician- and patient-facing CDS. Among 323 patients with LS, cross-sectional adherence to colonic surveillance and annual genetics program visits was 69.3% and 55.4%, respectively. Patients with recent electronic patient portal use were more likely to be adherent to colonic surveillance (PR, 1.67; 95% CI, 1.11 to 2.52). Patients more recently diagnosed with LS were more likely to be adherent to annual genetics program visits (PR, 0.58; 95% CI, 0.44 to 0.76 for 2-4 years; PR, 0.62; 95% CI, 0.51 to 0.75 for ≥4 compared with <2 years). Our EHR-based CDS tool is now active for 421 patients with LS throughout our health system. We have successfully developed an EHR-based CDS tool to promote guideline-recommended cancer risk management among patients with LS.

Patient experiences through head and neck cancer: Information delivery combatting psychological distress

As part of a larger study investigating the utility of electronic decision support tools for patients with head and neck cancer (HNC), this article describes the patient experience of receiving cancer treatment in British Columbia, Canada. It aims to give voice to the patient experience and recommend a model of psychological adjustment for clinicians and clinical service management to consider to refine patient centric care pathways for HNC. Based in phenomenology, semi-structured interviews were conducted with 12 survivors of HNC, audio-video recorded, and thematically analyzed. Three themes were identified: (1) patients have high, though varying information needs; (2) an emotional experience; and (3) coping, strength, and resiliency. These themes arose from six concepts: (1) information needs; (2) fear of the unknown; (3) desire for personalized information; (4) varying degrees of information needs; (5) fear as a motivator versus stressor; and (6) high information needs on life after treatment. Conclusions: The patient experience must be considered through the full care trajectory and into survivorship to provide the right information to the correct patient at the optimal time. Patient journey mapping may be a novel approach to exploring the temporal relationship between information needs and the patient experience along the cancer continuum to uncover opportune moments, from the patient perspective, for knowledge and supportive care intervention. The model of psychological adjustment by Calver et al. (2019) can be considered to inform the delivery of cancer care information in a method recognizing the patient as the ultimate knowledge holder.

A shared decision-making tool to support the decision of patients with Lumbar Disc Herniation on whether to undergo surgery: a pilot study

This study aims to develop a decision support tool for patients and surgeons dealing with the uncertainty of surgical outcomes and the expectations of both parties for treating symptomatic lumbar disc herniation (LDH). The study uses a mixed-methods approach with Markov Chains (MC) and Analytic Hierarchy Process (AHP) to predict future health states after surgery for LDH, based on patient-reported outcomes measures (PROMs) and custom weights to each individual PROM and elicited priorities. A case-based analysis of two patients is presented to demonstrate the utility of the model in providing a likely trajectory that priority PROMs will follow over time. The study was conducted under the STROBE guidelines as a post hoc analysis of a large spine outcomes research study conducted in southern Brazil. Data were collected from patients operated between 2006 and 2017 to assess pain, disability, mood, and general health status from the preoperative time point to 1 year after surgery using patient-reported outcome questionnaires. The output of the algorithm represented the chances of surgery fulfilling the expectations of Patients A and B. The results presented by the tool suggest that Patient A will have a considerably higher probability of satisfaction and/or not meeting expectations with surgery than Patient B. The study demonstrates the feasibility and utility of a data-driven decision support tool that takes into account patient preferences and beliefs in generating high-quality decision-making and more utility for long-term outcomes in treating symptomatic lumbar disc herniation.

P085Clinicians’ perspectives on improving shared decision making during contraceptive counseling: A qualitative study in the US

<h3>Objectives</h3> To explore clinicians' perspectives, challenges, needs, and ideas regarding contraceptive shared decision making and the design of patient decision support tools. <h3>Methods</h3> We conducted 45-minute semi-structured interviews with 15 family planning–focused clinicians in the US recruited from the Society of Family Planning listserv. We used an inductive content analysis approach to reveal themes within the transcripts. All transcripts were coded with an evolving codebook, and the first five transcripts were recoded with the finalized codebook. <h3>Results</h3> Twelve medical doctors and three nurse practitioners with an average of 19 years of experience participated. Thematic analysis indicated that clinicians support the ideology and practice of shared decision making in contraceptive counseling but may have trouble implementing it effectively due to time constraints, clinician cultural biases, or low reproductive health literacy among some patient groups. Suggested improvements to contraceptive counseling included incorporating clinical team members with health education, and increasing access to team members for continuous health education needs. Clinicians noted that some effective tools exist, but systems-level issues (eg, healthcare coverage and telemedicine access) play into the complexity of providing equitable decision support. <h3>Conclusions</h3> Based on these qualitative findings, shared decision-making tools used in contraceptive counseling should be designed to alleviate time constraints, mitigate cultural biases, and create a foundation of reproductive health knowledge. While effective tools exist, policies and system-level support are needed to ensure implementation addresses known gaps and that all patients have access to high-quality contraceptive counseling and shared decision making.

A Scoping Review of Integrated Medical Devices and Clinical Decision Support in the Acute Care Setting.

Seamless data integration between point-of-care medical devices and the electronic health record (EHR) can be central to clinical decision support systems (CDSS). The objective of this scoping review is to (1) examine the existing evidence related to integrated medical devices, primarily medication pump devices, and associated clinical decision support (CDS) in acute care settings and (2) to identify how acute care clinicians may use device CDS in clinical decision-making. The rationale for this review is that integrated devices are ubiquitous in the acute care setting, and they generate data that may help to contribute to the situational awareness of the clinical team necessary to provide individualized patient care. This scoping review was conducted using the Joanna Briggs Institute Manual for Evidence Synthesis and the Preferred Reporting Items for Systematic Reviews and Meta-analyses Extensions for Scoping Review guidelines. PubMed, CINAHL, IEEE Xplore, and Scopus databases were searched for scholarly, peer-reviewed journals indexed between January 1, 2010 and December 31, 2020. A priori inclusion criteria were established. Of the 1,924 articles screened, 18 were ultimately included for synthesis, and primarily included articles on devices such as intravenous medication pumps and vital signs machines. Clinical alarm burden was mentioned in most of the articles, and despite not including the term "medication" there were many articles about smart pumps being integrated with the EHR. The Revised Technology, Nursing & Patient Safety Conceptual Model provided the organizational framework. Ten articles described patient assessment, monitoring, or surveillance use. Three articles described patient protection from harm. Four articles described direct care use scenarios, all of which described insulin administration. One article described a hybrid situation of patient communication and monitoring. Most of the articles described devices and decision support primarily used by registered nurses (RNs). The articles in this review discussed devices and the associated CDSS that are used by clinicians, primarily RNs, in the daily provision of care for patients. Integrated device data provide insight into user-device interactions and help to illustrate health care processes, especially the activities when providing direct care to patients in an acute care setting. While there are CDSS designed to support the clinician while working with devices, RNs and providers may disregard this guidance, and defer to their own expertise. Additionally, if clinicians perceive CDSS as intrusive, they are at risk for alarm and alert fatigue if CDSS are not tailored to sync with the workflow of the end-user. Areas for future research include refining inclusion criteria to examine the evidence for devices and their CDS that are most likely used by other groups' health care professionals (i.e., doctors and therapists), using integrated device metadata and deep learning analytics to identify patterns in care delivery, and decision support tools for patients using their own personal data.

A smart, practical, deep learning-based clinical decision support tool for patients in the prostate-specific antigen gray zone: model development and validation.

Despite efforts to improve screening and early detection of prostate cancer (PC), no available biomarker has shown acceptable performance in patients with prostate-specific antigen (PSA) gray zones. We aimed to develop a deep learning-based prediction model with minimized parameters and missing value handling algorithms for PC and clinically significant PC (CSPC). We retrospectively analyzed data from 18 824 prostate biopsies collected between March 2003 and December 2020 from 2 databases, resulting in 12 739 cases in the PSA gray zone of 2.0-10.0 ng/mL. Dense neural network (DNN) and extreme gradient boosting (XGBoost) models for PC and CSPC were developed with 5-fold cross-validation. The area under the curve of the receiver operating characteristic (AUROC) was compared with that of serum PSA, PSA density, free PSA (fPSA) portion, and prostate health index (PHI). The AUROC values in the DNN model with the imputation of missing values were 0.739 and 0.708 (PC) and 0.769 and 0.742 (CSPC) in internal and external validation, whereas those of the non-imputed dataset were 0.740 and 0.771 (PC) and 0.807 and 0.771 (CSPC), respectively. The performance of the DNN model was like that of the XGBoost model, but better than all tested clinical biomarkers for both PC and CSPC. The developed DNN model outperformed PHI, serum PSA, and percent-fPSA with or without missing value imputation. DNN models for missing value imputation can be used to predict PC and CSPC. Further validation in real-life scenarios are need to recommend for actual implementation, but the results from our study support the increasing role of deep learning analytics in the clinical setting. A deep learning model for PC and CSPC in PSA gray zones using minimal, routinely used clinical parameter variables and data imputation of missing values was successfully developed and validated.

Machine learning as a clinical decision support tool for patients with acromegaly

To develop machine learning (ML) models that predict postoperative remission, remission at last visit, and resistance to somatostatin receptor ligands (SRL) in patients with acromegaly and to determine the clinical features associated with the prognosis. We studied outcomes using the area under the receiver operating characteristics (AUROC) values, which were reported as the performance metric. To determine the importance of each feature and easy interpretation, Shapley Additive explanations (SHAP) values, which help explain the outputs of ML models, are used. One-hundred fifty-two patients with acromegaly were included in the final analysis. The mean AUROC values resulting from 100 independent replications were 0.728 for postoperative 3 months remission status classification, 0.879 for remission at last visit classification, and 0.753 for SRL resistance status classification. Extreme gradient boosting model demonstrated that preoperative growth hormone (GH) level, age at operation, and preoperative tumor size were the most important predictors for early remission; resistance to SRL and preoperative tumor size represented the most important predictors of remission at last visit, and postoperative 3-month insulin-like growth factor 1 (IGF1) and GH levels (random and nadir) together with the sparsely granulated somatotroph adenoma subtype served as the most important predictors of SRL resistance. ML models may serve as valuable tools in the prediction of remission and SRL resistance.

A web-based personalized decision support tool for patients diagnosed with ductal carcinoma in situ: development, content evaluation, and usability testing.

Patients diagnosed with ductal carcinoma in situ (DCIS) face trade-offs when deciding among different treatments, including surgery, radiation, and endocrine therapy. A less chosen option is active monitoring. While evidence from clinical trials is not yet available, observational studies show comparable results for active monitoring and immediate treatment on cancer outcomes in select subgroups of patients. We developed and tested a web-based decision support tool (DST) to help patients explore current knowledge about DCIS and make an informed choice. The DST, an interactive web application, was informed by literature reviews and formative work with patients, breast surgeons, and health communication experts. We conducted iterative interviews to evaluate the DST content among women with and without a history of breast cancer, as well as breast cancer experts. For usability testing, we conducted an online survey among women with and without a history of breast cancer. For content evaluation, 5 women with and 10 women without a history of DCIS were interviewed. The sample included 11 White and 4 non-White women, with a mean age of 64years. The expert sample consisted of 5 attendings and a physician assistant. The feedback was used to add, clarify, or reorganize information in the DST. For usability testing, 22 participants with a mean age of 61years were recruited including 15 White and 7 Black women and 6 women with a history of DCIS. The mean usability score was 3.7 out of 5. Most participants (86%) found that the DST provided unbiased information about treatments. To improve usability, we reduced the per-page content and added navigation cues. Content and usability evaluation showed that the DST helps patients explore trade-offs of active monitoring and immediate treatment. By adopting a personalized approach, the tool will enable informed decisions aligned with patients' values and expectations.

97856 Implementation of DPYD and UGT1A1 pharmacogenetic testing to guide chemotherapy dosing

ABSTRACT IMPACT: The implementation of DPYD and UGT1A1 pharmacogenetic testing, a promising tool of precision medicine, translates evidence-based research into clinical oncology practice with personalized dosing to better predict interpatient variability in chemotherapy tolerability. OBJECTIVES/GOALS: Patients with DPYD and UGT1A1 genetic variants are at risk for severe toxicity from fluoropyrimidines and irinotecan, respectively. We propose that providing clinicians with the option to order a pharmacogenetic (PGx) test with relevant dose recommendations will increase test uptake to guide pharmacotherapy decisions and improve safety outcomes. METHODS/STUDY POPULATION: We plan to conduct a non-randomized, pragmatic, open-label study in 600 adult patients with gastrointestinal (GI) cancers initiating a fluoropyrimidine- and/or irinotecan-based regimen at three cancer centers within a health system. Implementation metrics of a new, in-house laboratory developed PGx test will be measured, including feasibility of returning results within one week, fidelity of providers following dose recommendations, and penetrance via test ordering rates. Clinical aims will include assessing severe toxicity during the first six months of chemotherapy. Outcomes will be compared to a historical control of GI cancer patients enrolled in a biobank and treated with standard dose chemotherapy. RESULTS/ANTICIPATED RESULTS: We anticipate that there will be an increase in PGx test uptake given its shorter turnaround time to facilitate clinical decision-making prior to the first dose of chemotherapy. Through integration of test results in the electronic health record (EHR) and clinical decision support tools for patients with actionable genotypes, we also expect that providers will have a high level of agreement to the recommended dose adjustments. We anticipate a decreased incidence of severe (Grade &gt;3) toxicity among prospectively genotyped patients in the first six months of chemotherapy compared to DPYD and UGT1A1 variant carriers in the historical control group. Exploratory clinical utility data on costs of hospitalizations, chemotherapy treatment, PGx test, and medical services will also be reported. DISCUSSION/SIGNIFICANCE OF FINDINGS: This study aims to address barriers identified by key stakeholders to implementing PGx testing to better tailor chemotherapy dosing to the genetic profiles to patients. This may prevent adverse event-related hospitalizations, improve quality of life for patients, and reduce health system resource utilization costs.

Abstract PS8-05: Randomized controlled trial of decision support tools for patients and providers to increase breast cancer chemoprevention

Abstract Background: Breast cancer chemoprevention with selective estrogen receptor modulators (SERMs) and aromatase inhibitors (AIs) have been shown in randomized controlled trials to reduce breast cancer incidence by 50-65% among high-risk women. However, chemoprevention is still underutilized with fewer than 5% of high-risk women offered chemoprevention agreeing to take the medications. Reasons for low chemoprevention uptake include inadequate knowledge about chemoprevention among patients and healthcare providers, concerns about side effects, competing comorbidities, and time constraints during the clinical encounter. To address these barriers to chemoprevention uptake, we developed decision support tools for patients and providers. Methods: We conducted a randomized controlled trial of standard educational materials alone or combined with web-based decision support tools, RealRisks and BNAV, among 300 high-risk women and 50 healthcare providers, respectively. Patient eligibility criteria included: 1) women, age 35-75 years; 2) high-risk for breast cancer, defined as a 5-year invasive breast cancer risk ≥1.67% according to the Gail model or history of lobular carcinoma in situ (LCIS); 3) no prior use of a SERM or AI; 4) No personal history of breast cancer; 5) Access to the internet. Providers included primary care providers, such as internists, family practitioners, gynecologists, and nurse practitioners, and specialists, such as breast surgeons and medical oncologists. The RealRisks decision aid (DA) is available in English and Spanish and includes interactive modules to communicate personalized breast cancer risk and the risks and benefits of chemoprevention. The BNAV tool includes self-paced learning modules on breast cancer risk factors and chemoprevention and was made available to all providers through the electronic health record (EHR) dashboard. The primary endpoint was chemoprevention uptake at 6 months as assessed via the EHR. Secondary outcomes included validated measures of perceived breast cancer risk, breast cancer worry, chemoprevention knowledge, self-efficacy, decision conflict, chemoprevention attitudes, intention, and informed choice, which were administered to patients at baseline, 1 month, and 6 months after randomization. Results: Among 282 evaluable high-risk women enrolled from November 2016 to March 2020, mean age was 57 years (SD, 9.9), including 59% white, 14% black, 21% Hispanic, and 6% other. Mean 5-year invasive breast cancer risk was 2.98% (SD, 1.42), including 67% of women with benign breast disease and 64% with a first-degree family history of breast cancer. Comparing the intervention and control arms at 1 month, there were significant differences among high-risk women in accurate breast cancer risk perceptions (56% vs. 39%, p=0.017), adequate chemoprevention knowledge (49% vs. 27%, p&amp;lt;0.001), informed choice (41% vs. 23%, p=0.003), and mean decision conflict (34.0 vs. 47.0, p&amp;lt;0.001). There were no significant differences between the two groups in other patient-reported outcomes. Among patients who completed their 6-month follow-up, only 2.3% of high-risk women in the intervention arm and 3.8% in the control arm initiated a SERM or AI for breast cancer chemoprevention. Conclusions: With short-term follow-up, exposure to our decision support tools was associated with improvement in decision antecedents (accurate breast cancer risk perceptions, adequate chemoprevention knowledge) and decision quality (increased informed choice, reduced decision conflict). However, actual chemoprevention uptake in both arms remained low at less than 5%. Future research should focus on targeting high-risk women with LCIS or atypical hyperplasia and better incorporating decision support into clinic workflow through EHRs and patient portals. Citation Format: Katherine D Crew, Gauri Bhatkhande, Thomas Silverman, Tarsha Jones, Jennie Mata, Ashlee Guzman, Ting He, Jill Dimond, Rita Kukafka. Randomized controlled trial of decision support tools for patients and providers to increase breast cancer chemoprevention [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS8-05.

Patient preferences for provider choice: a discrete choice experiment.

There is an ongoing policy discussion regarding an adequate breadth of provider networks. Health plans with "restricted networks" of providers have proved surprisingly popular on the Affordable Care Act health insurance exchanges because of a substantial gap in premiums between plans with open networks and closed networks. The objective of this paper is to assess which other attributes of the provider network matter to patients when choosing health insurance. We used a discrete choice experiment to analyze the effect of previously unobserved characteristics regarding provider networks on plan choice, including wait time, breadth, travel time, whether the plan covers care for their personal doctor, and monthly premium. Hypothetical plan options were offered to respondents of an online survey using Qualtrics software. We used mixed multinomial logit models to estimate preference-based utilities for attributes of primary care provider networks and willingness to pay. Coverage of a personal doctor was the most important attribute, followed by premium, wait time to see a primary care provider, the breadth of the network, and travel time to the closest doctor covered by the plan. Respondents were willing to pay $95 per month to have a plan that covers care for their personal doctor, and they were willing to wait 6 days for an appointment to have a plan covering care for their personal doctor. The results of this study provide new insights to federal and state legislators developing new models or standards on network adequacy and patient decision support tools.

Core Elements of Shared Decision-making for Women Considering Breast Cancer Screening: Results of a Modified Delphi Survey.

The United States Preventive Services Task Force recommends individualized breast cancer screening for average-risk women before age 50, advised by risk assessment and shared decision-making (SDM). However, the foundational principles of this recommendation that would inform decision support tools for patients and primary care physicians at the point of care have not been codified. Determining the core elements of SDM for breast cancer screening as valued by patients and primary care providers (PCPs) is necessary for implementing effective SDM tools. The aim of this study is to affirm core elements of SDM in the context of clinical interactions, through a Delphi consensus process. A Delphi was conducted with 30 participants (10 women aged 40-49, 10 PCPs, and 10 healthcare decision scientists), to codify core elements of breast cancer screening SDM. The criterion for establishing consensus was a threshold of 80% agreement. The Delphi concluded with an 83% response rate. Of 48 items fielded, 44 met the threshold on the high-importance end of the response scale and were accepted as core elements. Core elements across three thematic categories-information delivery and patient education, interpersonal clinician-patient communication, and framework of the decision-received panelists' support in nearly equal measure. Panelists unanimously agreed that SDM should include provision of clearly understandable information, including that of personal breast cancer risk factors, and benefits and harms of mammography screening, and that PCPs should convey they are listening, knowledgeable, and demonstrate cultural sensitivity. This research codifies the core elements of SDM for mammography in women 40-49, augmenting the evidence to inform discussions between patients and physicians. These core elements of SDM have the potential to operationalize SDM for breast cancer screening in an effort to improve public health outcomes.

Logic model framework for considering the inputs, processes and outcomes of a healthcare organisation–research partnership

BackgroundThe published literature provides few insights regarding how to develop or consider the effects of knowledge co-production partnerships in the context of delivery system science.ObjectiveTo describe how a healthcare organisation–university-based...

National Comprehensive Cancer Network (NCCN) Decision Support Tool for Patients With NSCLC

National Comprehensive Cancer Network (NCCN) Decision Support Tool for Patients With NSCLC

Implementation, participation and satisfaction rates of a web-based decision support tool for patients with metastatic colorectal cancer

ObjectiveTo examine implementation and patients’ and providers’ participation and satisfaction of a newly developed decision support tool (DST) for patients with metastatic colorectal cancer (mCRC) in palliative setting. MethodsOur DST consisted of a consultation sheet and web-based tailored information for mCRC treatment options. We conducted an implementation trajectory in 11 Dutch hospitals and evaluated implementation, participation and satisfaction rates. ResultsImplementation rates fluctuated between 3 and 72 handed out (median:23) consultation sheets per hospital with patients’ login rates between 36% and 83% (median:57%). The majority of patients (68%) had (intermediate)-high participation scores. The median time spent using the DST was 38 min (IQR:18–56) and was highest for questions concerning patients’ perspective (5 min). Seventy-six% of patients were (very) satisfied. The provider DST rating was 7.8 (scale 1–10) and participation ranged between 25 and 100%. Remaining implementation thresholds included providers’ treatment preferences, resistance against shared decision-making and (over)confidence in shared decision-making concepts already in use. ConclusionWe implemented a DST with sufficient patient and oncologist satisfaction and high patient participation, but participation differed considerably between hospitals suggesting unequal adoption of our tool. Practice implicationsRequirements for structural implementation are to overcome remaining thresholds and increase awareness for additional decision support.