Randomized Evaluation of a Remote Management Program to Improve Guideline-Directed Medical Therapy: The DRIVE Trial.

BACKGROUND: Several SGLT2i (sodium-glucose transport protein 2 inhibitors) and GLP1-RA (glucagon-like peptide-1 receptor agonists) reduce cardiovascular events and improve kidney outcomes in patients with type 2 diabetes; however, utilization remains low despite guideline recommendations. METHODS: A randomized, remote implementation trial in the Mass General Brigham network enrolled patients with type 2 diabetes with increased cardiovascular or kidney risk. Patients eligible for, but not prescribed, SGLT2i or GLP1-RA were randomly assigned to simultaneous virtual patient education with concurrent prescription of SGLT2i or GLP1-RA (ie, Simultaneous) or 2 months of virtual education followed by medication prescription (ie, Education-First) delivered by a multidisciplinary team driven by nonlicensed navigators and clinical pharmacists who prescribed SGLT2i or GLP1-RA using a standardized treatment algorithm. The primary outcome was the proportion of patients with prescriptions for either SGLT2i or GLP1-RA by 6 months. RESULTS: Between March 2021 and December 2022, 200 patients were randomized. The mean age was 66.5 years; 36.5% were female, and 22.0% were non-White. Overall, 30.0% had cardiovascular disease, 5.0% had cerebrovascular disease, and 1.5% had both. Mean estimated glomerular filtration rate was 77.9 mL/(min‧1.73 m2), and mean urine/albumin creatinine ratio was 88.6 mg/g. After 2 months, 69 of 200 (34.5%) patients received a new prescription for either SGLT2i or GLP1-RA: 53.4% of patients in the Simultaneous arm and 8.3% of patients in the Education-First arm (P<0.001). After 6 months, 128 of 200 (64.0%) received a new prescription: 69.8% of patients in the Simultaneous arm and 56.0% of patients in Education-First (P<0.001). Patient self-report of taking SGLT2i or GLP1-RA within 6 months of trial entry was similarly greater in the Simultaneous versus Education-First arm (69 of 116 [59.5%] versus 37 of 84 [44.0%]; P<0.001) Median time to first prescription was 24 (interquartile range [IQR], 13-50) versus 85 days (IQR, 65-106), respectively (P<0.001). CONCLUSIONS: In this randomized trial, a remote, team-based program identifies patients with type 2 diabetes and high cardiovascular or kidney risk, provides virtual education, prescribes SGLT2i or GLP1-RA, and improves guideline-directed medical therapy. These findings support greater utilization of virtual team-based approaches to optimize chronic disease management. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT06046560.

I2b2-etl: Python application for importing electronic health data into the informatics for integrating biology and the bedside platform.

MOTIVATION: The i2b2 platform is used at major academic health institutions and research consortia for querying for electronic health data. However, a major obstacle for wider utilization of the platform is the complexity of data loading that entails a steep curve of learning the platform's complex data schemas. To address this problem, we have developed the i2b2-etl package that simplifies the data loading process, which will facilitate wider deployment and utilization of the platform. RESULTS: We have implemented i2b2-etl as a Python application that imports ontology and patient data using simplified input file schemas and provides inbuilt record number de-identification and data validation. We describe a real-world deployment of i2b2-etl for a population-management initiative at MassGeneral Brigham. AVAILABILITY AND IMPLEMENTATION: i2b2-etl is a free, open-source application implemented in Python available under the Mozilla 2 license. The application can be downloaded as compiled docker images. A live demo is available at https://i2b2clinical.org/demo-i2b2etl/ (username: demo, password: Etl@2021). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Correlated Double-Electron Additions at the Edge of a Two-Dimensional Electronic System.

We create laterally large and low-disorder GaAs quantum-well-based quantum dots that act as small two-dimensional electron systems. We monitor tunneling of single electrons to the dots by means of capacitance measurements and identify single-electron capacitance peaks in the addition spectrum from occupancies of one up to thousands of electrons. The data show two remarkable phenomena in the Landau level filling factor range ν=2 to ν=5 in selective probing of the edge states of the dot: (i) Coulomb blockade peaks arise from the entrance of two electrons rather than one; (ii) at and near ν=5/2 and at fixed gate voltage, these double-height peaks appear uniformly in a magnetic field with a flux periodicity of h/2e, but they group into pairs at other filling factors.

Genomic considerations for FHIR®; eMERGE implementation lessons.

Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Network's Phase III program initially used a commercially developed XML message format for standardized and structured representation of genetic results for electronic health record (EHR) integration. In a desire to move towards a standard representation, the network created a new standardized format based upon Health Level Seven Fast Healthcare Interoperability Resources (HL7® FHIR®), to represent clinical genomics results. These new standards improve the utility of HL7® FHIR® as an international healthcare interoperability standard for management of genetic data from patients. This work advances the establishment of standards that are being designed for broad adoption in the current health information technology landscape.

Building the foundation for genomics in precision medicine.

Precision medicine has the potential to profoundly improve the practice of medicine. However, the advances required will take time to implement. Genetics is already being used to direct clinical decision-making and its contribution is likely to increase. To accelerate these advances, fundamental changes are needed in the infrastructure and mechanisms for data collection, storage and sharing. This will create a continuously learning health-care system with seamless cycling between clinical care and research. Patients must be educated about the benefits of sharing data. The building blocks for such a system are already forming and they will accelerate the adoption of precision medicine.

Multi-step phase-cycling in a free-electron laser-powered pulsed electron paramagnetic resonance spectrometer.

Electron paramagnetic resonance (EPR) is a powerful tool for research in chemistry, biology, physics and materials science, which can benefit significantly from moving to frequencies above 100 GHz. In pulsed EPR spectrometers driven by powerful sub-THz oscillators, such as the free electron laser (FEL)-powered EPR spectrometer at UCSB, control of the duration, power and relative phases of the pulses in a sequence must be performed at the frequency and power level of the oscillator. Here we report on the implementation of an all-quasioptical four-step phase cycling procedure carried out directly at the kW power level of the 240 GHz pulses used in the FEL-powered EPR spectrometer. Phase shifts are introduced by modifying the optical path length of a 240 GHz pulse with precision-machined dielectric plates in a procedure we call phase cycling with optomechanical phase shifters (POPS), while numerical receiver phase cycling is implemented in post-processing. The POPS scheme was successfully used to reduce experimental dead times, enabling pulsed EPR of fast-relaxing spin systems such as gadolinium complexes at temperatures above 190 K. Coherence transfer pathway selection with POPS was used to perform spin echo relaxation experiments to measure the phase memory time of P1 centers in diamond in the presence of a strong unwanted FID signal in the background. The large excitation bandwidth of FEL-EPR, together with phase cycling, enabled the quantitative measurement of instantaneous electron spectral diffusion, from which the P1 center concentration was estimated to within 10%. Finally, phase cycling enabled saturation-recovery measurements of T1 in a trityl-water solution at room temperature - the first FEL-EPR measurement of electron T1.

Accuracy of cognitive MRI-targeted biopsy in hitting prostate cancer-positive regions of interest.

PURPOSE: Prostate cancer (PCa) diagnosis relies on clinical suspicion leading to systematic transrectal ultrasound-guided biopsy (TRUSGB). Multiparametric magnetic resonance imaging (mpMRI) allows for targeted biopsy of suspicious areas of the prostate instead of random 12-core biopsy. This method has been shown to be more accurate in detecting significant PCa. However, the precise spatial accuracy of cognitive targeting is unknown. METHODS: Consecutive patients undergoing mpMRI-targeted TRUSGB with cognitive registration (MRTB-COG) followed by robot-assisted radical prostatectomy were included in the present analysis. The regions of interest (ROIs) involved by the index lesion reported on mpMRI were subsequently targeted by two experienced urologists using the cognitive approach. The 27 ROIs were used as spatial reference. Mapping on radical prostatectomy specimen was used as reference to determine true-positive mpMRI findings. Per core correlation analysis was performed. RESULTS: Forty patients were included. Overall, 40 index lesions involving 137 ROIs (mean ROIs per index lesion 3.43) were identified on MRI. After correlating these findings with final pathology, 117 ROIs (85 %) were considered as true-positive lesions. A total of 102 biopsy cores directed toward such true-positive ROIs were available for final analysis. Cognitive targeted biopsy hit the target in 82 % of the cases (84/102). The only identified risk factor for missing the target was an anterior situated ROI (p = 0.01). CONCLUSION: In experienced hands, cognitive MRTB-COG allows for an accuracy of 82 % in hitting the correct target, given that it is a true-positive lesion. Anterior tumors are less likely to be successfully targeted.

Impact of the type of ultrasound probe on prostate cancer detection rate and characterization in patients undergoing MRI-targeted prostate biopsies using cognitive fusion.

PURPOSE: To assess the impact of ultrasound probe (end fire vs. side fire) during MRI-targeted prostate biopsy using cognitive fusion. METHODS: Inclusion criteria were as follows: consecutive patients undergoing prostate biopsies after multiparametric MRI; no PSA above 10 ng/ml; no clinical bulking disease; MRI areas suspicious for malignancy. From January 2011 to December 2012, 91 patients were included. A standard 10 TRUS-guided biopsy protocol plus 2 targeted biopsies at any MRI lesion was used. Patient's characteristics, MRI findings, and pathology evaluations were compared between the two groups. RESULTS: Mean patient age and PSA were 63 years and 5.95 ng/ml, respectively. The median number of MRI lesions was 2, and the mean volume of the index lesion was 0.64 cc. The overall PCa detection rate was 58.2 %. The MRI scoring system was significantly predictive for PCa detection and aggressiveness (p < 0.001). There was a not statistically significant trend toward greater PCa detection rate (+23 %) in the end-fire cohort (p = 0.235). The PCa detection rate is significantly improved by 1.7-fold in case of MRI score 4-5 lesion as compared to MRI score 3 lesion (p = 0.031) when using the end-fire probe. Conversely, the MRI score does not significantly influence the detection rate in the side-fire group (p = 0.250). The improvement in the PCa detection rate by the end-fire probe was predominantly reported in anterior and of apical peripheral MRI lesions. CONCLUSION: In case of high MRI score lesions, the PCa detection rate is significantly improved when using end-firing, particularly in case of anterior and apical peripheral lesions.

Using multiplexed assays of oncogenic drivers in lung cancers to select targeted drugs.

IMPORTANCE: Targeting oncogenic drivers (genomic alterations critical to cancer development and maintenance) has transformed the care of patients with lung adenocarcinomas. The Lung Cancer Mutation Consortium was formed to perform multiplexed assays testing adenocarcinomas of the lung for drivers in 10 genes to enable clinicians to select targeted treatments and enroll patients into clinical trials. OBJECTIVES: To determine the frequency of oncogenic drivers in patients with lung adenocarcinomas and to use the data to select treatments targeting the identified driver(s) and measure survival. DESIGN, SETTING, AND PARTICIPANTS: From 2009 through 2012, 14 sites in the United States enrolled patients with metastatic lung adenocarcinomas and a performance status of 0 through 2 and tested their tumors for 10 drivers. Information was collected on patients, therapies, and survival. INTERVENTIONS: Tumors were tested for 10 oncogenic drivers, and results were used to select matched targeted therapies. MAIN OUTCOMES AND MEASURES: Determination of the frequency of oncogenic drivers, the proportion of patients treated with genotype-directed therapy, and survival. RESULTS: From 2009 through 2012, tumors from 1007 patients were tested for at least 1 gene and 733 for 10 genes (patients with full genotyping). An oncogenic driver was found in 466 of 733 patients (64%). Among these 733 tumors, 182 tumors (25%) had the KRAS driver; sensitizing EGFR, 122 (17%); ALK rearrangements, 57 (8%); other EGFR, 29 (4%); 2 or more genes, 24 (3%); ERBB2 (formerly HER2), 19 (3%); BRAF, 16 (2%); PIK3CA, 6 (<1%); MET amplification, 5 (<1%); NRAS, 5 (<1%); MEK1, 1 (<1%); AKT1, 0. Results were used to select a targeted therapy or trial in 275 of 1007 patients (28%). The median survival was 3.5 years (interquartile range [IQR], 1.96-7.70) for the 260 patients with an oncogenic driver and genotype-directed therapy compared with 2.4 years (IQR, 0.88-6.20) for the 318 patients with any oncogenic driver(s) who did not receive genotype-directed therapy (propensity score-adjusted hazard ratio, 0.69 [95% CI, 0.53-0.9], P = .006). CONCLUSIONS AND RELEVANCE: Actionable drivers were detected in 64% of lung adenocarcinomas. Multiplexed testing aided physicians in selecting therapies. Although individuals with drivers receiving a matched targeted agent lived longer, randomized trials are required to determine if targeting therapy based on oncogenic drivers improves survival. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01014286.

Deep Learning-Based Psoriasis Assessment: Harnessing Clinical Trial Imaging for Accurate Psoriasis Area Severity Index Prediction.

Introduction: Image-based machine learning holds great promise for facilitating clinical care; however, the datasets often used for model training differ from the interventional clinical trial-based findings frequently used to inform treatment guidelines. Here, we draw on longitudinal imaging of psoriasis patients undergoing treatment in the Ultima 2 clinical trial (NCT02684357), including 2,700 body images with psoriasis area severity index (PASI) annotations by uniformly trained dermatologists. Methods: An image-processing workflow integrating clinical photos of multiple body regions into one model pipeline was developed, which we refer to as the "One-Step PASI" framework due to its simultaneous body detection, lesion detection, and lesion severity classification. Group-stratified cross-validation was performed with 145 deep convolutional neural network models combined in an ensemble learning architecture. Results: The highest-performing model demonstrated a mean absolute error of 3.3, Lin's concordance correlation coefficient of 0.86, and Pearson correlation coefficient of 0.90 across a wide range of PASI scores comprising disease classifications of clear skin, mild, and moderate-to-severe disease. Within-person, time-series analysis of model performance demonstrated that PASI predictions closely tracked the trajectory of physician scores from severe to clear skin without systematically over- or underestimating PASI scores or percent changes from baseline. Conclusion: This study demonstrates the potential of image processing and deep learning to translate otherwise inaccessible clinical trial data into accurate, extensible machine learning models to assess therapeutic efficacy.

Retrieval Augmented Generation Enabled Generative Pre-Trained Transformer 4 (GPT-4) Performance for Clinical Trial Screening.

Background: Subject screening is a key aspect of all clinical trials; however, traditionally, it is a labor-intensive and error-prone task, demanding significant time and resources. With the advent of large language models (LLMs) and related technologies, a paradigm shift in natural language processing capabilities offers a promising avenue for increasing both quality and efficiency of screening efforts. This study aimed to test the Retrieval-Augmented Generation (RAG) process enabled Generative Pretrained Transformer Version 4 (GPT-4) to accurately identify and report on inclusion and exclusion criteria for a clinical trial. Methods: The Co-Operative Program for Implementation of Optimal Therapy in Heart Failure (COPILOT-HF) trial aims to recruit patients with symptomatic heart failure. As part of the screening process, a list of potentially eligible patients is created through an electronic health record (EHR) query. Currently, structured data in the EHR can only be used to determine 5 out of 6 inclusion and 5 out of 17 exclusion criteria. Trained, but non-licensed, study staff complete manual chart review to determine patient eligibility and record their assessment of the inclusion and exclusion criteria. We obtained the structured assessments completed by the study staff and clinical notes for the past two years and developed a workflow of clinical note-based question answering system powered by RAG architecture and GPT-4 that we named RECTIFIER (RAG-Enabled Clinical Trial Infrastructure for Inclusion Exclusion Review). We used notes from 100 patients as a development dataset, 282 patients as a validation dataset, and 1894 patients as a test set. An expert clinician completed a blinded review of patients' charts to answer the eligibility questions and determine the "gold standard" answers. We calculated the sensitivity, specificity, accuracy, and Matthews correlation coefficient (MCC) for each question and screening method. We also performed bootstrapping to calculate the confidence intervals for each statistic. Results: Both RECTIFIER and study staff answers closely aligned with the expert clinician answers across criteria with accuracy ranging between 97.9% and 100% (MCC 0.837 and 1) for RECTIFIER and 91.7% and 100% (MCC 0.644 and 1) for study staff. RECTIFIER performed better than study staff to determine the inclusion criteria of "symptomatic heart failure" with an accuracy of 97.9% vs 91.7% and an MCC of 0.924 vs 0.721, respectively. Overall, the sensitivity and specificity of determining eligibility for the RECTIFIER was 92.3% (CI) and 93.9% (CI), and study staff was 90.1% (CI) and 83.6% (CI), respectively. Conclusion: GPT-4 based solutions have the potential to improve efficiency and reduce costs in clinical trial screening. When incorporating new tools such as RECTIFIER, it is important to consider the potential hazards of automating the screening process and set up appropriate mitigation strategies such as final clinician review before patient engagement.

Methods, rationale, and design for a remote pharmacist and navigator-driven disease management program to improve guideline-directed medical therapy in patients with type 2 diabetes at elevated cardiovascular and/or kidney risk.

AIM: Describe the rationale for and design of Diabetes Remote Intervention to improVe use of Evidence-based medications (DRIVE), a remote medication management program designed to initiate and titrate guideline-directed medical therapy (GDMT) in patients with type 2 diabetes (T2D) at elevated cardiovascular (CV) and/or kidney risk by leveraging non-physician providers. METHODS: An electronic health record based algorithm is used to identify patients with T2D and either established atherosclerotic CV disease (ASCVD), high risk for ASCVD, chronic kidney disease, and/or heart failure within our health system. Patients are invited to participate and randomly assigned to either simultaneous education and medication management, or a period of education prior to medication management. Patient navigators (trained, non-licensed staff) are the primary points of contact while a pharmacist or nurse practitioner reviews and authorizes each medication initiation and titration under an institution-approved collaborative drug therapy management protocol with supervision from a cardiologist and/or endocrinologist. Patient engagement is managed through software to support communication, automation, workflow, and standardization. CONCLUSION: We are testing a remote, navigator-driven, pharmacist-led, and physician-overseen management strategy to optimize GDMT for T2D as a population-level strategy to close the gap between guidelines and clinical practice for patients with T2D at elevated CV and/or kidney risk.

A survey of informatics approaches to whole-exome and whole-genome clinical reporting in the electronic health record.

PURPOSE: Genome-scale clinical sequencing is being adopted more broadly in medical practice. The National Institutes of Health developed the Clinical Sequencing Exploratory Research (CSER) program to guide implementation and dissemination of best practices for the integration of sequencing into clinical care. This study describes and compares the state of the art of incorporating whole-exome and whole-genome sequencing results into the electronic health record, including approaches to decision support across the six current CSER sites. METHODS: The CSER Medical Record Working Group collaboratively developed and completed an in-depth survey to assess the communication of genome-scale data into the electronic health record. We summarized commonalities and divergent approaches. RESULTS: Despite common sequencing platform (Illumina) adoptions, there is a great diversity of approaches to annotation tools and workflow, as well as to report generation. At all sites, reports are human-readable structured documents available as passive decision support in the electronic health record. Active decision support is in early implementation at two sites. CONCLUSION: The parallel efforts across CSER sites in the creation of systems for report generation and integration of reports into the electronic health record, as well as the lack of standardized approaches to interfacing with variant databases to create active clinical decision support, create opportunities for cross-site and vendor collaborations.

Results of a Remotely Delivered Hypertension and Lipid Program in More Than 10 000 Patients Across a Diverse Health Care Network.

Importance: Blood pressure (BP) and cholesterol control remain challenging. Remote care can deliver more effective care outside of traditional clinician-patient settings but scaling and ensuring access to care among diverse populations remains elusive. Objective: To implement and evaluate a remote hypertension and cholesterol management program across a diverse health care network. Design, Setting, and Participants: Between January 2018 and July 2021, 20 454 patients in a large integrated health network were screened; 18 444 were approached, and 10 803 were enrolled in a comprehensive remote hypertension and cholesterol program (3658 patients with hypertension, 8103 patients with cholesterol, and 958 patients with both). A total of 1266 patients requested education only without medication titration. Enrolled patients received education, home BP device integration, and medication titration. Nonlicensed navigators and pharmacists, supported by cardiovascular clinicians, coordinated care using standardized algorithms, task management and automation software, and omnichannel communication. BP and laboratory test results were actively monitored. Main Outcomes and Measures: Changes in BP and low-density lipoprotein cholesterol (LDL-C). Results: The mean (SD) age among 10 803 patients was 65 (11.4) years; 6009 participants (56%) were female; 1321 (12%) identified as Black, 1190 (11%) as Hispanic, 7758 (72%) as White, and 1727 (16%) as another or multiple races (including American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, unknown, other, and declined to respond; consolidated owing to small numbers); and 142 (11%) reported a preferred language other than English. A total of 424 482 BP readings and 139 263 laboratory reports were collected. In the hypertension program, the mean (SD) office BP prior to enrollment was 150/83 (18/10) mm Hg, and the mean (SD) home BP was 145/83 (20/12) mm Hg. For those engaged in remote medication management, the mean (SD) clinic BP 6 and 12 months after enrollment decreased by 8.7/3.8 (21.4/12.4) and 9.7/5.2 (22.2/12.6) mm Hg, respectively. In the education-only cohort, BP changed by a mean (SD) -1.5/-0.7 (23.0/11.1) and by +0.2/-1.9 (30.3/11.2) mm Hg, respectively (P < .001 for between cohort difference). In the lipids program, patients in remote medication management experienced a reduction in LDL-C by a mean (SD) 35.4 (43.1) and 37.5 (43.9) mg/dL at 6 and 12 months, respectively, while the education-only cohort experienced a mean (SD) reduction in LDL-C of 9.3 (34.3) and 10.2 (35.5) mg/dL at 6 and 12 months, respectively (P < .001). Similar rates of enrollment and reductions in BP and lipids were observed across different racial, ethnic, and primary language groups. Conclusions and Relevance: The results of this study indicate that a standardized remote BP and cholesterol management program may help optimize guideline-directed therapy at scale, reduce cardiovascular risk, and minimize the need for in-person visits among diverse populations.

Communicating new knowledge on previously reported genetic variants.

Genetic tests often identify variants whose significance cannot be determined at the time they are reported. In many situations, it is critical that clinicians be informed when new information emerges on these variants. It is already extremely challenging for laboratories to provide these updates. These challenges will grow rapidly as an increasing number of clinical genetic tests are ordered and as the amount of patient DNA assayed per test expands; the challenges will need to be addressed before whole-genome sequencing is used on a widespread basis.Information technology infrastructure can be useful in this context. We have deployed an infrastructure enabling clinicians to receive knowledge updates when a laboratory changes the classification of a variant. We have gathered statistics from this deployment regarding the frequency of both variant classification changes and the effects of these classification changes on patients. We report on the system's functionality as well as the statistics derived from its use.Genet Med advance online publication 5 April 2012.

Usability of a novel clinician interface for genetic results.

The complexity and rapid growth of genetic data demand investment in information technology to support effective use of this information. Creating infrastructure to communicate genetic information to healthcare providers and enable them to manage that data can positively affect a patient's care in many ways. However, genetic data are complex and present many challenges. We report on the usability of a novel application designed to assist providers in receiving and managing a patient's genetic profile, including ongoing updated interpretations of the genetic variants in those patients. Because these interpretations are constantly evolving, managing them represents a challenge. We conducted usability tests with potential users of this application and reported findings to the application development team, many of which were addressed in subsequent versions. Clinicians were excited about the value this tool provides in pushing out variant updates to providers and overall gave the application high usability ratings, but had some difficulty interpreting elements of the interface. Many issues identified required relatively little development effort to fix suggesting that consistently incorporating this type of analysis in the development process can be highly beneficial. For genetic decision support applications, our findings suggest the importance of designing a system that can deliver the most current knowledge and highlight the significance of new genetic information for clinical care. Our results demonstrate that using a development and design process that is user focused helped optimize the value of this application for personalized medicine.

Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis.

Analysis of health data typically requires development of queries using structured query language (SQL) by a data-analyst. As the SQL queries are manually created, they are prone to errors. In addition, accurate implementation of the queries depends on effective communication with clinical experts, that further makes the analysis error prone. As a potential resolution, we explore an alternative approach wherein a graphical interface that automatically generates the SQL queries is used to perform the analysis. The latter allows clinical experts to directly perform complex queries on the data, despite their unfamiliarity with SQL syntax. The interface provides an intuitive understanding of the query logic which makes the analysis transparent and comprehensible to the clinical study-staff, thereby enhancing the transparency and validity of the analysis. This study demonstrates the feasibility of using a user-friendly interface that automatically generate SQL for analysis of health data. It outlines challenges that will be useful for designing user-friendly tools to improve transparency and reproducibility of data analysis.

A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources.

OBJECTIVE: The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. MATERIALS AND METHODS: Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. RESULTS: Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. DISCUSSION: Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.

The GeneInsight Suite: a platform to support laboratory and provider use of DNA-based genetic testing.

The future of personalized medicine will hinge on effective management of patient genetic profiles. Molecular diagnostic testing laboratories need to track knowledge surrounding an increasingly large number of genetic variants, incorporate this knowledge into interpretative reports, and keep ordering clinicians up to date as this knowledge evolves. Treating clinicians need to track which variants have been identified in each of their patients along with the significance of these variants. The GeneInsight(SM) Suite assists in these areas. The suite also provides a basis for interconnecting laboratories and clinicians in a manner that increases the scalability of personalized medicine processes.

The elusive challenge of leadership development at faith-based institutions.

This article describes the rationale given by many faith-based institutions of higher education for creating and promoting leadership programs. A brief overview of the history of faith based educational institutions is followed by an explanation of the motivations of several different schools for creating and funding leadership programs, as well as the limitations of those programs. The article concludes with suggestions for how these programs can benefit more students and the wider community.