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Background: This project aimed to optimize communication strategies to support family communication about familial hypercholesterolemia (FH) and improve cascade testing uptake among at-risk relatives. Individuals and families with FH provided feedback on multiple strategies including: a family letter, digital tools, and direct contact., Methods: Feedback from participants was collected via dyadic interviews (n = 11) and surveys (n = 98) on communication strategies and their proposed implementation to improve cascade testing uptake. We conducted a thematic analysis to identify how to optimize each strategy. We categorized optimizations and their implementation within the project's healthcare system using a Traffic Light approach., Results: Thematic analysis resulted in four distinct suggested optimizations for each communication strategy and seven suggested optimizations that were suitable across all strategies. Four suggestions for developing a comprehensive cascade testing program, which would offer all optimized communication strategies also emerged. All optimized suggestions coded green (n = 21) were incorporated. Suggestions coded yellow (n = 12) were partially incorporated. Only two suggestions were coded red and could not be incorporated., Conclusions: This project demonstrates how to collect and analyze stakeholder feedback for program design. We identified feasible suggested optimizations, resulting in communication strategies that are patient-informed and patient-centered. Optimized strategies were implemented in a comprehensive cascade testing program., (© 2023. The Author(s).)

Background: Processes such as prior authorization (PA) for medications, implemented by health insurance companies to ensure that safe, appropriate, cost-effective, and evidence-based care is provided to all members, have created inefficiencies within healthcare systems. Thus, healthcare systems have implemented supplemental processes to reduce burden and ensure efficiency, timeliness, and appropriate care., Objective: Evaluate implementation outcomes of two initiatives related to PA for medications: a common record that records all PA-related information that was integrated into the health record and an auto-routing of specialty prescriptions to a hospital-owned specialty pharmacy., Methods: We conducted semi-structured interviews with medical staff to understand their experience, acceptability, adoption, and feasibility of these initiatives guided by Proctor's Framework for Implementation Outcomes. Transcripts were analyzed using consensus coding., Results: Eleven medical staff participated in semi-structured interviews. The two initiatives were analyzed together because the findings were similar across both for our outcomes of acceptability, adoption, and feasibility. Participants found the implemented initiatives to be acceptable and beneficial but felt there were still challenges with the new workflow. The initiatives were fully adopted by only one clinic site within the healthcare system, but limitations arose when adopting to another site. Individuals felt the initiatives were feasible and improved workflow, communication, and transparency. However, participants described future adaptations that would help improve this process including improved standardization, automation, and transparency., Conclusion: The acceptability, adoption, and feasibility of two initiatives to improve the PA process within the one clinical site were well received but issues of generalizability limited the initiatives adoption system wide., (© 2021. The Author(s).)

Background: Numerous implementation strategies to improve utilization of statins in patients with hypercholesterolemia have been utilized, with varying degrees of success. The aim of this systematic review is to determine the state of evidence of implementation strategies on the uptake of statins., Methods and Results: This systematic review identified and categorized implementation strategies, according to the Expert Recommendations for Implementing Change (ERIC) compilation, used in studies to improve statin use. We searched Ovid MEDLINE, Embase, Scopus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and Clinicaltrials.gov from inception to October 2018. All included studies were reported in English and had at least one strategy to promote statin uptake that could be categorized using the ERIC compilation. Data extraction was completed independently, in duplicate, and disagreements were resolved by consensus. We extracted LDL-C (concentration and target achievement), statin prescribing, and statin adherence (percentage and target achievement). A total of 258 strategies were used across 86 trials. The median number of strategies used was 3 (SD 2.2, range 1-13). Implementation strategy descriptions often did not include key defining characteristics: temporality was reported in 59%, dose in 52%, affected outcome in 9%, and justification in 6%. Thirty-one trials reported at least 1 of the 3 outcomes of interest: significantly reduced LDL-C (standardized mean difference [SMD] - 0.17, 95% CI - 0.27 to - 0.07, p = 0.0006; odds ratio [OR] 1.33, 95% CI 1.13 to 1.58, p = 0.0008), increased rates of statin prescribing (OR 2.21, 95% CI 1.60 to 3.06, p < 0.0001), and improved statin adherence (SMD 0.13, 95% CI 0.06 to 0.19; p = 0.0002; OR 1.30, 95% CI 1.04 to 1.63, p = 0.023). The number of implementation strategies used per study positively influenced the efficacy outcomes., Conclusion: Although studies demonstrated improved statin prescribing, statin adherence, and reduced LDL-C, no single strategy or group of strategies consistently improved outcomes., Trial Registration: PROSPERO CRD42018114952 .

Background: Individuals with complex dyslipidemia, or those with medication intolerance, are often difficult to manage in primary care. They require the additional attention, expertise, and adherence counseling that occurs in multidisciplinary lipid clinics (MDLCs). We conducted a program evaluation of the first year of a newly implemented MDLC utilizing the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework to provide empirical data not only on program effectiveness, but also on components important to local sustainability and future generalizability., Methods: The purpose of the MDLC is to increase the uptake of guideline-based care for lipid conditions. Established in 2019, the MDLC provides care via a centralized clinic location within the healthcare system. Primary care providers and cardiologists were invited to refer individuals with lipid conditions. Using a pre/post-study design, we evaluated the implementation outcomes from the MDLC using the RE-AIM framework., Results: In 2019, 420 referrals were made to the MDLC (reach). Referrals were made by 19% (148) of the 796 active cardiology and primary care providers, with an average of 35 patient referrals per month in 2019 (SD 12) (adoption). The MDLC saw 83 patients in 2019 (reach). Additionally, 50% (41/82) had at least one follow-up MDLC visit, and 12% (10/82) had two or more follow-up visits in 2019 (implementation). In patients seen by the MDLC, we found an improved diagnosis of specific lipid conditions (FH (familial hypercholesterolemia), hypertriglyceridemia, and dyslipidemia), increased prescribing of evidence-based therapies, high rates of medication prior authorization approvals, and significant reductions in lipid levels by lipid condition subgroup (effectiveness). Over time, the operations team decided to transition from in-person follow-up to telehealth appointments to increase capacity and sustain the clinic (maintenance)., Conclusions: Despite limited reach and adoption of the MDLC, we found a large intervention effect that included improved diagnosis, increased prescribing of guideline-recommended treatments, and clinically significant reduction of lipid levels. Attention to factors including solutions to decrease the large burden of unseen referrals, discussion of the appropriate number and duration of visits, and sustainability of the clinic model could aid in enhancing the success of the MDLC and improving outcomes for more patients throughout the system.

Background: Molecular classification of tumors can be achieved by global gene expression profiling. Most machine learning classification algorithms furnish global error rates for the entire population. A few algorithms provide an estimate of probability of malignancy for each queried patient but the degree of accuracy of these estimates is unknown. On the other hand local minimax learning provides such probability estimates with best finite sample bounds on expected mean squared error on an individual basis for each queried patient. This allows a significant percentage of the patients to be identified as confidently predictable, a condition that ensures that the machine learning algorithm possesses an error rate below the tolerable level when applied to the confidently predictable patients., Results: We devise a new learning method that implements: (i) feature selection using the k-TSP algorithm and (ii) classifier construction by local minimax kernel learning. We test our method on three publicly available gene expression datasets and achieve significantly lower error rate for a substantial identifiable subset of patients. Our final classifiers are simple to interpret and they can make prediction on an individual basis with an individualized confidence level., Conclusions: Patients that were predicted confidently by the classifiers as cancer can receive immediate and appropriate treatment whilst patients that were predicted confidently as healthy will be spared from unnecessary treatment. We believe that our method can be a useful tool to translate the gene expression signatures into clinical practice for personalized medicine.