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.

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.

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.

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.

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.

Rationale and design of a navigator-driven remote optimization of guideline-directed medical therapy in patients with heart failure with reduced ejection fraction.

Although optimal pharmacological therapy for heart failure with reduced ejection fraction (HFrEF) is carefully scripted by treatment guidelines, many eligible patients are not treated with guideline-directed medical therapy (GDMT) in clinical practice. We designed a strategy for remote optimization of GDMT on a population scale in patients with HFrEF leveraging nonphysician providers. An electronic health record-based algorithm was used to identify a cohort of patients with a diagnosis of heart failure (HF) and ejection fraction (EF) ≤ 40% receiving longitudinal follow-up at our center. Those with end-stage HF requiring inotropic support, mechanical circulatory support, or transplantation and those enrolled in hospice or palliative care were excluded. Treating providers were approached for consent to adjust medical therapy according to a sequential, stepped titration algorithm modeled on the current American College of Cardiology (ACC)/American Heart Association (AHA) HF Guidelines within a collaborative care agreement. The program was approved by the institutional review board at Brigham and Women's Hospital with a waiver of written informed consent. All patients provided verbal consent to participate. A navigator then facilitated medication adjustments by telephone and conducted longitudinal surveillance of laboratories, blood pressure, and symptoms. Each titration step was reviewed by a pharmacist with supervision as needed from a nurse practitioner and HF cardiologist. Patients were discharged from the program to their primary cardiologist after achievement of an optimal or maximally tolerated regimen. A navigator-led remote management strategy for optimization of GDMT may represent a scalable population-level strategy for closing the gap between guidelines and clinical practice in patients with HFrEF.

Extending i2b2 into a framework for semantic abstraction of EHR to facilitate rapid development and portability of Health IT applications.

The wide gap between a care provider's conceptualization of electronic health record (EHR) and the structures for electronic health record (EHR) data storage and transmission, presents a multitude of obstacles for development of innovative Health IT applications. While developers model the EHR view of the clinicians at one end, they work with a different data view to construct health IT applications. Although there has been considerable progress to bridge this gap by evolution of developer friendly standards and tools for terminology mapping and data warehousing, there is a need for a simplified framework to facilitate development of interoperable applications. To this end, we propose a framework for creating a layer of semantic abstraction on the EHR and describe preliminary work on the implementation of this framework for management of hyperlipidemia and hypertension. Our goal is to facilitate the rapid development and portability of Health IT applications.

Evaluation: A Qualitative Pilot Study of Novel Information Technology Infrastructure to Communicate Genetic Variant Updates.

BACKGROUND: Partners HealthCare Personalized Medicine developed GeneInsight Clinic (GIC), a tool designed to communicate updated variant information from laboratory geneticists to treating clinicians through automated alerts, categorized by level of variant interpretation change. OBJECTIVES: The study aimed to evaluate feedback from the initial users of the GIC, including the advantages and challenges to receiving this variant information and using this technology at the point of care. METHODS: Healthcare professionals from two clinics that ordered genetic testing for cardiomyopathy and related disorders were invited to participate in one-hour semi-structured interviews and/ or a one-hour focus group. Using a Grounded Theory approach, transcript concepts were coded and organized into themes. RESULTS: Two genetic counselors and two physicians from two treatment clinics participated in individual interviews. Focus group participants included one genetic counselor and four physicians. Analysis resulted in 8 major themes related to structuring and communicating variant knowledge, GIC's impact on the clinic, and suggestions for improvements. The interview analysis identified longitudinal patient care, family data, and growth in genetic testing content as potential challenges to optimization of the GIC infrastructure. DISCUSSION: Participants agreed that GIC implementation increased efficiency and effectiveness of the clinic through increased access to genetic variant information at the point of care. CONCLUSION: Development of information technology (IT) infrastructure to aid in the organization and management of genetic variant knowledge will be critical as the genetic field moves towards whole exome and whole genome sequencing. Findings from this study could be applied to future development of IT support for genetic variant knowledge management that would serve to improve clinicians' ability to manage and care for patients.

CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record.

OBJECTIVE: Clinicians' ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS). MATERIALS AND METHODS: The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement. RESULTS: There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information. CONCLUSION: Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended.

A novel clinician interface to improve clinician access to up-to-date genetic results.

OBJECTIVES: To understand the impact of GeneInsight Clinic (GIC), a web-based tool designed to manage genetic information and facilitate communication of test results and variant updates from the laboratory to the clinics, we measured the use of GIC and the time it took for new genetic knowledge to be available to clinicians. METHODS: Usage data were collected across four study sites for the GIC launch and post-GIC implementation time periods. The primary outcome measures were the time (average number of days) between variant change approval and notification of clinic staff, and the time between notification and viewing the patient record. RESULTS: Post-GIC, time between a variant change approval and provider notification was shorter than at launch (average days at launch 503.8, compared to 4.1 days post-GIC). After e-mail alerts were sent at launch, providers clicked into the patient record associated with 91% of these alerts. In the post period, clinic providers clicked into the patient record associated with 95% of the alerts, on average 12 days after the e-mail was sent. DISCUSSION: We found that GIC greatly increased the likelihood that a provider would receive updated variant information as well as reduced the time associated with distributing that variant information, thus providing a more efficient process for incorporating new genetic knowledge into clinical care. CONCLUSIONS: Our study results demonstrate that health information technology systems have the potential effectively to assist providers in utilizing genetic information in patient care.