Pharmacogenomic testing and prescribing patterns for patients with cancer in a large national precision medicine cohort.

Population databases could help patients with cancer and providers better understand current pharmacogenomic prescribing and testing practices. This retrospective observational study analysed patients with cancer, drugs with pharmacogenomic evidence and related genetic testing in the National Institutes of Health All of Us database. Most patients with cancer (19 633 (88.3%) vs 2590 (11.7%)) received ≥1 drug and 36 (0.2%) received genetic testing, with a significant association between receiving ≥1 drug and age group (p<0.001), but not sex (p=0.612), race (p=0.232) or ethnicity (p=0.971). Drugs with pharmacogenomic evidence-but not genetic testing-were common for patients with cancer, reflecting key gaps preventing precision medicine from becoming standard of care.

Using informatics to guide public health policy during the COVID-19 pandemic in the USA.

BACKGROUND: Current and future pandemics will require informatics solutions to assess the risks, resources and policies to guide better public health decision-making. METHODS: Cross-sectional study of all COVID-19 cases and deaths in the USA on a population- and resource-adjusted basis (as of 24 April 2020) by applying biomedical informatics and data visualization tools to several public and federal government datasets, including analysis of the impact of statewide stay-at-home orders. RESULTS: There were 2753.2 cases and 158.0 deaths per million residents, respectively, in the USA with variable distributions throughout divisions, regions and states. Forty-two states and Washington, DC, (84.3%) had statewide stay-at-home orders, with the remaining states having population-adjusted characteristics in the highest risk quartile. CONCLUSIONS: Effective national preparedness requires clearly understanding states' ability to predict, manage and balance public health needs through all stages of a pandemic. This will require leveraging data quickly, correctly and responsibly into sound public health policies.

Software-Related Recalls of Health Information Technology and Other Medical Devices: Implications for FDA Regulation of Digital Health.

Policy Points: Medical software has become an increasingly critical component of health care, yet the regulation of these devices is inconsistent and controversial. No studies of medical devices and software assess the impact on patient safety of the FDA's current regulatory safeguards and new legislative changes to those standards. Our analysis quantifies the impact of software problems in regulated medical devices and indicates that current regulations are necessary but not sufficient for ensuring patient safety by identifying and eliminating dangerous defects in software currently on the market. New legislative changes will further deregulate health IT, reducing safeguards that facilitate the reporting and timely recall of flawed medical software that could harm patients. CONTEXT: Medical software has become an increasingly critical component of health care, yet the regulatory landscape for digital health is inconsistent and controversial. To understand which policies might best protect patients, we examined the impact of the US Food and Drug Administration's (FDA's) regulatory safeguards on software-related technologies in recent years and the implications for newly passed legislative changes in regulatory policy. METHODS: Using FDA databases, we identified all medical devices that were recalled from 2011 through 2015 primarily because of software defects. We counted all software-related recalls for each FDA risk category and evaluated each high-risk and moderate-risk recall of electronic medical records to determine the manufacturer, device classification, submission type, number of units, and product details. FINDINGS: A total of 627 software devices (1.4 million units) were subject to recalls, with 12 of these devices (190,596 units) subject to the highest-risk recalls. Eleven of the devices recalled as high risk had entered the market through the FDA review process that does not require evidence of safety or effectiveness, and one device was completely exempt from regulatory review. The largest high-risk recall categories were anesthesiology and general hospital, with one each in cardiovascular and neurology. Five electronic medical record systems (9,347 units) were recalled for software defects classified as posing a moderate risk to patient safety. CONCLUSIONS: Software problems in medical devices are not rare and have the potential to negatively influence medical care. Premarket regulation has not captured all the software issues that could harm patients, evidenced by the potentially large number of patients exposed to software products later subject to high-risk and moderate-risk recalls. Provisions of the 21st Century Cures Act that became law in late 2016 will reduce safeguards further. Absent stronger regulations and implementation to create robust risk assessment and adverse event reporting, physicians and their patients are likely to be at risk from medical errors caused by software-related problems in medical devices.

Practical Aspects of Using Large Language Models to Screen Abstracts for Cardiovascular Drug Development: Cross-Sectional Study.

Unlabelled: Cardiovascular drug development requires synthesizing relevant literature about indications, mechanisms, biomarkers, and outcomes. This short study investigates the performance, cost, and prompt engineering trade-offs of 3 large language models accelerating the literature screening process for cardiovascular drug development applications.

Informatics and Artificial Intelligence-Guided Assessment of the Regulatory and Translational Research Landscape of First-in-Class Oncology Drugs in the United States, 2018-2022.

PURPOSE: Cancer drug development remains a critical but challenging process that affects millions of patients and their families. Using biomedical informatics and artificial intelligence (AI) approaches, we assessed the regulatory and translational research landscape defining successful first-in-class drugs for patients with cancer. METHODS: This is a retrospective observational study of all novel first-in-class drugs approved by the US Food and Drug Administration (FDA) from 2018 to 2022, stratified by cancer versus noncancer drugs. A biomedical informatics pipeline leveraging interoperability standards and ChatGPT performed integration and analysis of public databases provided by the FDA, National Institutes of Health, and WHO. RESULTS: Between 2018 and 2022, the FDA approved a total of 247 novel drugs, of which 107 (43.3%) were first-in-class drugs involving a new biologic target. Of these first-in-class drugs, 30 (28%) treatments were indicated for patients with cancer, including 19 (63.3%) for solid tumors and the remaining 11 (36.7%) for hematologic cancers. A median of 68 publications of basic, clinical, and other relevant translational science preceded successful FDA approval of first-in-class cancer drugs, with oncology-related treatments involving fewer median years of target-based research than therapies not related to cancer (33 v 43 years; P < .05). Overall, 94.4% of first-in-class drugs had at least 25 years of target-related research papers, while 85.5% of first-in-class drugs had at least 10 years of translational research publications. CONCLUSION: Novel first-in-class cancer treatments are defined by diverse clinical indications, personalized molecular targets, dependence on expedited regulatory pathways, and translational research metrics reflecting this complex landscape. Biomedical informatics and AI provide scalable, data-driven ways to assess and even address important challenges in the drug development pipeline.

Precision Medicine Landscape of Genomic Testing for Patients With Cancer in the National Institutes of Health All of Us Database Using Informatics Approaches.

PURPOSE: The rapid growth of biomedical data ecosystems has catalyzed research for oncology and precision medicine. We leverage federal cloud-based precision medicine databases and tools to better understand the current landscape of precision medicine and genomic testing for patients with cancer. METHODS: Retrospective observational study of genomic testing for patients with cancer in the National Institutes of Health All of Us Research Program, with the cancer cohort defined as having at least two documented or reported cancer diagnoses. RESULTS: There were 5,678 (1.8%) All of Us participants in the cancer cohort, with a significant difference between cancer status by age category, sex, race, and ethnicity (P < .001 for all). There were 295 (5.2%) patients with cancer who received genomic testing compared with 6,734 (2.2%) of noncancer patients, with 752 genomic tests commonly focused on gene mutations (primarily pharmacogenomics), molecular pathology, or clinical cytogenetic reports. CONCLUSION: Although not yet ubiquitous, diverse clinical genomic analyses in oncology can set the stage to grow the practice of precision medicine by integrating research patient data repositories, cancer data ecosystems, and biomedical informatics.

Practical Aspects of Implementing and Applying Health Care Cloud Computing Services and Informatics to Cancer Clinical Trial Data.

PURPOSE: Cloud computing has led to dramatic growth in the volume, variety, and velocity of cancer data. However, cloud platforms and services present new challenges for cancer research, particularly in understanding the practical tradeoffs between cloud performance, cost, and complexity. The goal of this study was to describe the practical challenges when using a cloud-based service to improve the cancer clinical trial matching process. METHODS: We collected information for all interventional cancer clinical trials from ClinicalTrials.gov and used the Google Cloud Healthcare Natural Language Application Programming Interface (API) to analyze clinical trial Title and Eligibility Criteria text. An informatics pipeline leveraging interoperability standards summarized the distribution of cancer clinical trials, genes, laboratory tests, and medications extracted from cloud-based entity analysis. RESULTS: There were a total of 38,851 cancer-related clinical trials found in this study, with the distribution of cancer categories extracted from Title text significantly different than in ClinicalTrials.gov (P < .001). Cloud-based entity analysis of clinical trial criteria identified a total of 949 genes, 1,782 laboratory tests, 2,086 medications, and 4,902 National Cancer Institute Thesaurus terms, with estimated detection accuracies ranging from 12.8% to 89.9%. A total of 77,702 API calls processed an estimated 167,179 text records, which took a total of 1,979 processing-minutes (33.0 processing-hours), or approximately 1.5 seconds per API call. CONCLUSION: Current general-purpose cloud health care tools-like the Google service in this study-should not be used for automated clinical trial matching unless they can perform effective extraction and classification of the clinical, genetic, and medication concepts central to precision oncology research. A strong understanding of the practical aspects of cloud computing will help researchers effectively navigate the vast data ecosystems in cancer research.

Health IT, hacking, and cybersecurity: national trends in data breaches of protected health information.

OBJECTIVE: The rapid adoption of health information technology (IT) coupled with growing reports of ransomware, and hacking has made cybersecurity a priority in health care. This study leverages federal data in order to better understand current cybersecurity threats in the context of health IT. MATERIALS AND METHODS: Retrospective observational study of all available reported data breaches in the United States from 2013 to 2017, downloaded from a publicly available federal regulatory database. RESULTS: There were 1512 data breaches affecting 154 415 257 patient records from a heterogeneous distribution of covered entities (P < .001). There were 128 electronic medical record-related breaches of 4 867 920 patient records, while 363 hacking incidents affected 130 702 378 records. DISCUSSION AND CONCLUSION: Despite making up less than 25% of all breaches, hacking was responsible for nearly 85% of all affected patient records. As medicine becomes increasingly interconnected and informatics-driven, significant improvements to cybersecurity must be made so our health IT infrastructure is simultaneously effective, safe, and secure.

Assessing the readiness of precision medicine interoperabilty: An exploratory study of the National Institutes of Health genetic testing registry.

BACKGROUND:   Precision medicine involves three major innovations currently taking place in healthcare:  electronic health records, genomics, and big data.  A major challenge for healthcare providers, however, is understanding the readiness for practical application of initiatives like precision medicine. OBJECTIVE:   To better understand the current state and challenges of precision medicine interoperability using a national genetic testing registry as a starting point, placed in the context of established interoperability formats. METHODS:   We performed an exploratory analysis of the National Institutes of Health Genetic Testing Registry.  Relevant standards included Health Level Seven International Version 3 Implementation Guide for Family History, the Human Genome Organization Gene Nomenclature Committee (HGNC) database, and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT).  We analyzed the distribution of genetic testing laboratories, genetic test characteristics, and standardized genome/clinical code mappings, stratified by laboratory setting. RESULTS: There were a total of 25472 genetic tests from 240 laboratories testing for approximately 3632 distinct genes.  Most tests focused on diagnosis, mutation confirmation, and/or risk assessment of germline mutations that could be passed to offspring.  Genes were successfully mapped to all HGNC identifiers, but less than half of tests mapped to SNOMED CT codes, highlighting significant gaps when linking genetic tests to standardized clinical codes that explain the medical motivations behind test ordering.  Conclusion:  While precision medicine could potentially transform healthcare, successful practical and clinical application will first require the comprehensive and responsible adoption of interoperable standards, terminologies, and formats across all aspects of the precision medicine pipeline.