The Penn Medicine BioBank (PMBB) is an electronic health record (EHR)-linked biobank at the University of Pennsylvania (Penn Medicine). A large variety of health-related information, ranging from diagnosis codes to laboratory measurements, imaging data and lifestyle information, is integrated with genomic and biomarker data in the PMBB to facilitate discoveries and translational science. To date, 174,712 participants have been enrolled into the PMBB, including approximately 30% of participants of non-European ancestry, making it one of the most diverse medical biobanks. There is a median of seven years of longitudinal data in the EHR available on participants, who also consent to permission to recontact. Herein, we describe the operations and infrastructure of the PMBB, summarize the phenotypic architecture of the enrolled participants, and use body mass index (BMI) as a proof-of-concept quantitative phenotype for PheWAS, LabWAS, and GWAS. The major representation of African-American participants in the PMBB addresses the essential need to expand the diversity in genetic and translational research. There is a critical need for a “medical biobank consortium” to facilitate replication, increase power for rare phenotypes and variants, and promote harmonized collaboration to optimize the potential for biological discovery and precision medicine.

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The Penn Medicine BioBank: Towards a Genomics-Enabled Learning Healthcare System to Accelerate Precision Medicine in a Diverse Population

To protect personal patient information and comply with regulations, data security is a necessary aspect of the healthcare business. Because patient data is written on paper and stored in filing cabinets, it is simple to protect and secure it. However, all data is now saved digitally, such as patient records, which are now stored on computers, servers, and storage devices. Poor wireless network security, data breaches, legacy system use, malware, viruses, and other hostile attacks are all risk considerations in health care. When compared to other businesses, one of the key reasons to safeguard health care data is that healthcare companies gather and provide comprehensive patient records, including the date of birth, address, and social security number. This paper aims to discuss the issues and solutions to achieving digital healthcare using blockchain technology. 

Digital Healthcare using Blockchain

Abstract Background Germline genetic testing is recommended by the National Comprehensive Cancer Network (NCCN) for individuals including, but not limited to, those with a personal history of ovarian cancer, young-onset (&lt; 50 years) breast cancer, and a family history of ovarian cancer or male breast cancer. Genetic testing is underused overall, and rates are consistently lower among Black and Hispanic populations. Behavioral economics-informed implementation strategies, or nudges, directed towards patients and clinicians may increase the use of this evidence-based clinical practice. Methods Patients meeting eligibility for germline genetic testing for breast and ovarian cancer will be identified using electronic phenotyping algorithms. A pragmatic cohort study will test three sequential strategies to promote genetic testing, two directed at patients and one directed at clinicians, deployed in the electronic health record (EHR) for patients in OB-GYN clinics across a diverse academic medical center. We will use rapid cycle approaches informed by relevant clinician and patient experiences, health equity, and behavioral economics to optimize and de-risk our strategies and methods before trial initiation. Step 1 will send patients messages through the health system patient portal. For non-responders, step 2 will reach out to patients via text message. For non-responders, Step 3 will contact patients’ clinicians using a novel “pend and send” tool in the EHR. The primary implementation outcome is engagement with germline genetic testing for breast and ovarian cancer predisposition, defined as a scheduled genetic counseling appointment. Patient data collected through the EHR (e.g., race/ethnicity, geocoded address) will be examined as moderators of the impact of the strategies. Discussion This study will be one of the first to sequentially examine the effects of patient- and clinician-directed strategies informed by behavioral economics on engagement with breast and ovarian cancer genetic testing. The pragmatic and sequential design will facilitate a large and diverse patient sample, allow for the assessment of incremental gains from different implementation strategies, and permit the assessment of moderators of strategy effectiveness. The findings may help determine the impact of low-cost, highly transportable implementation strategies that can be integrated into healthcare systems to improve the use of genomic medicine. Trial registration ClinicalTrials.gov. NCT05721326. Registered February 10, 2023. https://www.clinicaltrials.gov/study/NCT05721326 

Protocol to evaluate sequential electronic health record-based strategies to increase genetic testing for breast and ovarian cancer risk across diverse patient populations in gynecology practices

Customizing the electronic health record for delivery of pharmacogenetics

Evolving From Discrete Molecular Data Integrations to Actionable Molecular Insights Within the Electronic Health Record.

PURPOSE To develop an electronic health record (EHR)–based clinical decision support (CDS) tool to promote guideline-recommended cancer risk management among patients with Lynch syndrome (LS), an inherited cancer syndrome that confers an increased risk of colorectal and other cancer types. MATERIALS AND METHODS We conducted a cross-sectional study to determine the baseline prevalence and predictors of guideline-recommended colonic surveillance and annual genetics program visits among patients with LS. Multivariable log-binomial regressions estimated prevalence ratios (PRs) of cancer risk management adherence by baseline sociodemographic and clinical characteristics. These analyses provided rationale for the development of an EHR-based CDS tool to support patients and clinicians with LS-related endoscopic surveillance and annual genetics program visits. The CDS leverages an EHR platform linking discrete genetic data to LS Genomic Indicators, in turn driving downstream clinician- and patient-facing CDS. RESULTS Among 323 patients with LS, cross-sectional adherence to colonic surveillance and annual genetics program visits was 69.3% and 55.4%, respectively. Patients with recent electronic patient portal use were more likely to be adherent to colonic surveillance (PR, 1.67; 95% CI, 1.11 to 2.52). Patients more recently diagnosed with LS were more likely to be adherent to annual genetics program visits (PR, 0.58; 95% CI, 0.44 to 0.76 for 2-4 years; PR, 0.62; 95% CI, 0.51 to 0.75 for ≥4 compared with &lt;2 years). Our EHR-based CDS tool is now active for 421 patients with LS throughout our health system. CONCLUSION We have successfully developed an EHR-based CDS tool to promote guideline-recommended cancer risk management among patients with LS. 

Development of an Electronic Health Record-Based Clinical Decision Support Tool for Patients With Lynch Syndrome.

Impact of integrating genomic data into the electronic health record on genetics care delivery.

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Customizing the electronic health record for delivery of pharmacogenetics

Development of an Electronic Health Record-Based Clinical Decision Support Tool for Patients With Lynch Syndrome.

Impact of integrating genomic data into the electronic health record on genetics care delivery.