Caraballo, Pedro J.

Introduction: The main goal of Clinical Decision Support (CDS) is to provide timely and useful information for providers to deliver high-quality and safe care. Issues, like alert fatigue and provider burnout are commonly cited as unintended consequences of the CDS systems. Herein, we describe leveraging electronic health record (EHR) self-service reporting tools to gain insights on a CDS rule designed to avoid providers from ordering QT-prolonging medication in patients with history of QT-prolongation. Methods: Our EHR (Epic) SlicerDicer reporting tool was used to identify the frequency of QT-prolongation Best Practice Advisory (BPA) triggerings from 1/1/2020 to 12/31/2020 across Mayo Clinic sites. Additionally, associated patient encounter and BPA-specific data were queried. Text from acknowledgement comments to the BPA were reviewed and central themes and comments were categorized. Epic Clarity Data Warehouse was queried to identify the BPA-triggering medications and frequencies. Comparisons of percent action taken on the BPA were compared internally to Mayo Clinic’s BPA benchmark, and with national benchmarks using Epic BestPractice Pulse dashboard. Results: A total of 17,538 BPAs of interest were triggered across the Mayo Clinic sites in 2020, of which 3,696 (20.1%) were acted upon through either monitoring or medication discontinuation. This is below Mayo Clinic’s median of 29.2% for inpatient BPAs and 45.6% BPAs for outpatient, but above the national median of 10% (IQR 6.1-13.1%) for inpatient and 10% (IQR 4.3-19.5%) for outpatient BPAs actioned upon. A total of 30 different medications triggered the BPA, with the most common being ondansetron (41.5%), amiodarone (11.3%), and haloperidol (10.1%). User comments were left on 2,382 orders (13.5%) that acknowledged the BPA, but provided argument to prescribe drug. Three most common themes identified from comments on overridden BPAs: patient previously tolerated medication (19.0%), QT interval not prolonged on recent ECG (14.2%), and patient on hospice care (5.2%). Conclusion: Analysis of free text comments of CDS can be and should be performed to reveal opportunities for further improvement of CDS. Providers should be encouraged to provide comments when encountering CDS interventions.

Abstract Modern AI-based clinical decision support models owe their success in part to the very large number of predictors they use. Safe and robust decision support, especially for intervention planning, requires causal, not associative, relationships. Traditional methods of causal discovery, clinical trials and extracting biochemical pathways, are resource intensive and may not scale up to the number and complexity of relationships sufficient for precision treatment planning. Computational causal structure discovery (CSD) from electronic health records (EHR) data can represent a solution, however, current CSD methods fall short on EHR data. This paper presents a CSD method tailored to the EHR data. The application of the proposed methodology was demonstrated on type-2 diabetes mellitus. A large EHR dataset from Mayo Clinic was used as development cohort, and another large dataset from an independent health system, M Health Fairview, as external validation cohort. The proposed method achieved very high recall (.95) and substantially higher precision than the general-purpose methods (.84 versus .29, and .55). The causal relationships extracted from the development and external validation cohorts had a high (81%) overlap. Due to the adaptations to EHR data, the proposed method is more suitable for use in clinical decision support than the general-purpose methods.

There is a need for multimodal strategies to keep research participants informed about study results. Our aim was to characterize preferences of genomic research participants from two institutions along four dimensions of general research result updates: content, timing, mechanism, and frequency. Methods: We conducted a web-based cross-sectional survey that was administered from 25 June 2018 to 5 December 2018. Results: 397 participants completed the survey, most of whom (96%) expressed a desire to receive research updates. Preferences with high endorsement included: update content (brief descriptions of major findings, descriptions of purpose and goals, and educational material); update timing (when the research is completed, when findings are reviewed, when findings are published, and when the study status changes); update mechanism (email with updates, and email newsletter); and update frequency (every three months). Hierarchical cluster analyses based on the four update preferences identified four profiles of participants with similar preference patterns. Very few participants in the largest profile were comfortable with budgeting less money for research activities so that researchers have money to set up services to send research result updates to study participants. Conclusion: Future studies may benefit from exploring preferences for research result updates, as we have in our study. In addition, this work provides evidence of a need for funders to incentivize researchers to communicate results to participants.

Abstract Objective: Transgender and gender-diverse individuals are at risk of insufficient treatment of endocrine diseases due to a number of barriers to care. There are currently no data evaluating the prevalence of thyroid disease in this population, and it is unknown if gender-affirming hormone therapy affects treatment of thyroid disease in these individuals. Methods: Utilizing a registry of 676 patients over 18 years of age who were seen in our Transgender and Intersex Specialty Care Clinic from 2015-2019, we identified 554 individuals seeking medical therapy for gender dysphoria/incongruence. Of these, 56 patients were taking thyroid hormone or antithyroid medications or had a coded diagnosis of thyroid disease; 2 were excluded as they were receiving thyroid hormone as adjunctive therapy for refractory depression. 54 patients were therefore analyzed with respect to thyroid disease (TD+), and compared to those 498 patients without thyroid disease (TD-) with regard to average vital signs, demographics, and presence of common comorbidities. Results: Fifty percent of our TD+ patients were recorded female sex at birth, and 98% were Caucasian. TD+ patients were much more likely to have essential hypertension (OR 5.99), to use tobacco (OR 2.23), and to be overweight or obese (OR 2.16) than TD- patients. Due to the evolving natural history of disease, some TD+ patients belonged to multiple categories: 48 patients, 26 trans feminine and 21 trans masculine, had hypothyroidism, of whom 41 had overt hypothyroidism and 2 patients had subacute thyroiditis that progressed to the hypothyroid phase of illness. 5 patients (3 trans masculine and 2 trans feminine) demonstrated hyperthyroidism, 4 of whom had Graves’ Disease. 2 patients had multinodular goiter, both trans masculine. 2 patients had thyroid malignancies, both trans feminine, of whom one had proven follicular thyroid carcinoma and the other had unspecified thyroid malignancy. Among hypothyroid patients, there was a slight non-significant trend toward increased thyroid hormone requirements while receiving treatment with estradiol or testosterone. Conclusion: To our knowledge, this represents the first attempt to characterize the prevalence of various thyroid disease states in the gender diverse population. Our data corresponded to a prevalence of hypothyroidism of approximately 8.7%, which is higher than previously published estimates of 3-5% in the general population. Whether this represents actual increased prevalence or assessment bias is uncertain. It is important for all health professionals who care for gender diverse people to identify and appropriately treat thyroid disease, and to monitor thyroid function closely if gender-affirming hormone therapy is initiated.

AbstractObjective Familial hypercholesterolemia (FH), a prevalent genomic disorder that increases risk of coronary heart disease, remains significantly underdiagnosed. Clinical decision support (CDS) tools have the potential to increase FH detection. We describe our experience in the development and implementation of a genomic CDS for FH at a large academic medical center.Methods CDS development and implementation were conducted in four phases: (1) development and validation of an algorithm to identify “possible FH”; (2) obtaining approvals from institutional committees to develop the CDS; (3) development of the initial prototype; and (4) use of an implementation science framework to evaluate the CDS.Results The timeline for this work was approximately 4 years; algorithm development and validation occurred from August 2018 to February 2020. During this 4-year period, we engaged with 15 stakeholder groups to build and integrate the CDS, including health care providers who gave feedback at each stage of development. During CDS implementation six main challenges were identified: (1) need for multiple institutional committee approvals; (2) need to align the CDS with institutional knowledge resources; (3) need to adapt the CDS to differing workflows; (4) lack of institutional guidelines for CDS implementation; (5) transition to a new institutional electronic health record (EHR) system; and (6) limitations of the EHR related to genomic medicine.Conclusion We identified multiple challenges in different domains while developing CDS for FH and integrating it with the EHR. The lessons learned herein may be helpful in streamlining the development and deployment of CDS to facilitate genomic medicine implementation.

Electronic health record (EHR)-based clinical decision support (CDS) can address the low awareness and undertreatment of familial hypercholesterolemia (FH), a disorder associated with a markedly increased risk of coronary heart disease. We aimed to incorporate provider perspectives into the development and implementation of a CDS tool for FH. An implementation science framework and a user-centered design process were used to create a CDS tool for FH. Primary care physicians and specialist physicians participated in qualitative interviews, usability testing and an implementation survey. The CDS was configured in two formats—a best practice alert (BPA) and an in-basket message and subsequently deployed in the EHR in silent mode. The key themes that emerged from the analysis of interview transcripts included understanding and awareness of FH, clinical workflow, physician preferences and value of CDS tools, perspectives on patient needs and values and dissemination and implementation. Recommendations related to usability included preferred CDS format and placement, content, timing and frequency, and level of alert urgency/prioritization. In response to the survey, 84.6% of physicians agreed that the CDS would improve early FH diagnosis and 92.3% agreed that it would help them identify and manage FH patients. Physician feedback led to iterative CDS refinement. In summary, we developed a CDS tool for FH using an implementation science framework and physician feedback. Initial deployment revealed a significant burden of FH and the potential for the CDS tool to have a large impact.