Stakeholder Analysis: Medicare Diabetes Prevention Program Awareness and Implementation

Study Design: We conducted a qualitative stakeholder analysis project with suppliers of the MDPP and health care providers. Am J Manag Care. 2023;29(6):In Press _____ Takeaway Points More than 5 years after CMS enacted coverage of the CDC-approved Medicare Diabetes Prevention Program (MDPP) in 2018, little is known about why MDPP uptake is so limited. * Findings of our stakeholder analysis with program suppliers and health care providers reinforced existing evidence on insufficient reimbursement and low awareness of the program. * Newer insights include recommendations about lagged payments, ongoing virtual delivery, and formally diagnosing prediabetes among MDPP participants. * Our findings on barriers and facilitators can inform policy to refine the MDPP and research on the MDPP, particularly within the field of implementation science. _____ Population-level strategies to prevent type 2 diabetes are urgently needed for the more than 24 million older adults with prediabetes in the United States.1 Evidence-based lifestyle interventions can prevent diabetes onset, per evidence from the landmark Diabetes Prevention Program trial.2 Thus, the CDC launched the National Diabetes Prevention Program (NDPP) in 2010.3 Significant reductions in weight and medical spending were observed among Medicare beneficiaries who participated in the NDPP,4 prompting CMS to fully cover the Medicare Diabetes Prevention Program (MDPP) starting in 2018.5 Despite unprecedented Medicare coverage for a disease prevention program, MDPP uptake is limited. Regarding awareness, national guidelines recommend referral to lifestyle intervention for adults aged 40 to 70 years with prediabetes.9 Yet less than 5% of adults eligible for a NDPP reported receiving a referral,10 which may stem from limited awareness among health care providers.11 Thus, we conducted a qualitative stakeholder analysis to learn about regional awareness of, referral to, facilitators of, and barriers to the MDPP. The 8 interviewees included 5 program directors (3 from YMCAs, 1 from a private organization, and 1 from a hospital system) and 3 health care providers (2 family physicians and 1 dietitian).

Q&A

In this interview, Sujay Jadhav, global vice president, study start-up, Oracle Health Sciences, touches on how COVID has affected study start-up and what new perspectives it has forced the industry to have on its own challenges. [...]assessing site ability to leverage telehealth will be a factor in site selection. Andy Studna is an Assistant Editor for Applied Clinical Trials Sujay Jadhav Global Vice President, Study Start-Up, Oracle Health Sciences Problems with startup, more than any other phase of a clinical trial, have the greatest potential to increase timelines and budgets.

Maternal and Neonatal Outcomes After Monoclonal Antibody Use

INTRODUCTION: The study aimed to evaluate whether the administration of monoclonal antibodies (MABs) in mildly symptomatic unvaccinated COVID-19-positive pregnant patients reduced the need for maternal hospitalization and to evaluate whether this medication affected the rate of adverse neonatal outcomes and severe maternal disease. We hypothesized that MAB use would reduce the need for hospitalization. METHODS: This retrospective cohort study was completed by obtaining electronic medical record data of all pregnant patients between August 2020 and January 2022 who met criteria for MAB therapy. The two comparison groups were patients who received outpatient MAB therapy during pregnancy and those who were eligible for therapy but declined. Demographic and hospitalization data were obtained. Exclusion criteria included severe illness upon diagnosis requiring hospitalization, or patients for whom delivery and neonatal data were not available. RESULTS: During the study period, 49 patients qualified for MAB therapy, of which delivery data were available for 39 patients. Twenty patients (51%) elected to receive MAB therapy and 19 (49%) declined. Among those who received MAB therapy, 10 (26% of the population) were vaccinated, and among those who declined, 6 (15% of the population) were vaccinated. The two groups were similar in gestational age at delivery (38 weeks 4 days versus 37 weeks 5 days) and gestational age at diagnosis (19 weeks 0 days versus 22 weeks 6 days). Among patients who did not receive MABs, both absolute and relative maternal hospitalization rate was higher (26.3% versus 5%, 12.8% versus 3%, P >.05). When stratified by vaccination status, those who were vaccinated had a 5% probability of hospitalization regardless of MAB therapy. The probability of hospitalization was highest among unvaccinated women who did not receive MAB therapy (67%) and lowest among unvaccinated women who received MAB therapy (0%). CONCLUSION: Unvaccinated patients who declined MAB therapy had a higher rate of hospitalization, although not statistically significant. These preliminary findings warrant further study with a larger cohort. [ FROM AUTHOR] Copyright of Obstetrics & Gynecology is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Healthcare costs vis-à-vis economic growth in pandemic crisis with technology adoption

COVID-19 pandemic brought up issues with healthcare costs, national economic development and welfare of the society in forefront. Nations across the globe followed different approaches to deal with COVID-19, such as zero tolerance, herd immunity, containment to build treatment capability. National healthcare became a contentious sociopolitical issue involving healthcare costs, technologies and societal health. In the United States even during the COVID-19 pandemic, the government approach was pursuing a sustainable improvement in patient care through adoption of medical and information technologies. The national healthcare policies are framed around technological interventions with the assumption that deployment of technologies could keep healthcare costs under control and at the same time improve health outcomes. However, evidences show that the healthcare costs are in the rise even with impressive progress in technological deployment. This article highlights some of the recent trends in healthcare costs, technological preparedness, medical technology developments in managing COVID-19 pandemic. The US government mandated electronic health record (EHR) systems implementation and assess its impact on healthcare costs and health outcomes. This article emphasizes the need for understanding the interconnectedness of costs, technology and societal health.

Building Research Capacity in Primary Care Practices That Serve Predominantly Racial and Ethnic Minority Populations

In this commentary, we report on lessons learned over 2 years (2020-2022) from conducting primary care research through a novel alliance of an ACO consisting of independent practices, a health plan, and several academic researchers, with the support of a private foundation. Am J Manag Care. 2023;29(6):In Press _____ Takeaway Points The process of collaborating on research was mutually beneficial for a network of independent practices and a group of academic researchers. * The process benefited the practices by facilitating more precise thinking about quality improvement, motivating the staff, and enabling readiness for health system change. * The process benefited the researchers by illuminating nuances of clinical and organizational workflow and revealing the practices' in-depth understanding of the communities they serve. * If practices have more federally funded opportunities to consistently participate in research, it could help speed greater adoption of payment reform models to promote health equity at the state and national levels. _____ A 2021 National Academies of Sciences, Engineering, and Medicine report, Implementing High-Quality Primary Care, has called out the persistent "neglect of basic primary care research" in the United States.1 A 2020 study by the RAND Corporation found that primary care research represents only 1% of all federally funded projects (including projects funded by the National Institutes of Health, the Agency for Healthcare Research and Quality [AHRQ], and the Veterans Health Administration).2 However, innovation in primary care is central to advancing health care delivery. Leaders in health care innovation recently called for CMS to test a proposal for primary care payment reform in accountable care organizations (ACOs) composed of independent practices (ie, practices not owned by hospitals).3 By innovating in independent practices, these leaders argued that CMS would provide incentives for those practices to stay independent, thereby potentially decreasing the vertical market consolidation that contributes to rising health care costs.3 Yet these same practices may have less experience with the kind of systematic innovation that leads to generalizable insights, because what little funding is available for primary care research is mostly awarded to large academic medical centers.1 AHRQ's practice-based research networks have not fully addressed this gap, as they have struggled to find infrastructure and maintain funding.1 In this commentary, we report on the lessons we learned over 2 years (2020-2022) from conducting primary care research through a novel alliance of an ACO consisting of independent practices, a health plan, and several academic researchers, with the support of a private foundation. [...]ACPNY found that experience with research facilitates innovation and readiness for health system change (lesson 1C).

Overcoming the Challenges of COVID-19

Agile and inventive pandemic response augurs in future-ready clinical trial management The onset of a global health emergency set innovation engines rolling, with active participation across the clinical research spectrum to find effective solutions. Here are 5 landmark solutions that were woven into the existing clinical trial fabric, with agility and innovation, to strengthen processes and make it relevant to current needs and beyond. 1.Accelerated trials to market lifesaving drugs faster When scientists began the process of developing a vaccine against COVID-19 in early 2020, the fastest vaccine that had been developed till then was the mumps vaccine in 4 years during the early 1960s. Navitas Life Sciences' OneClinical® Analytics platform is an artificial intelligence tool that has been shown to reduce clinical trial monitoring costs by 50% while bringing down cycle time by 30%.3 The near-real-time data insights gained from such tools aid in taking proactive corrective action helps resolve critical clinical trial issues at the onset, allowing intelligent deployment of resources.

Research in brief

The deadly complication Scientists failed to find evidence that COVID-19 causes a "cytokine storm” leading to death in patients with COVID-19 but they did find that secondary bacterial pneumonia that does not resolve was a key driver of death in patients with COVID-19 and may have exceeded death rates from the viral infection itself. The approach grouped similar ICU patient-days into clinical states based on electronic health record data and allowed the scientists to discover how complications such as bacterial pneumonia impacted the course of illness. For more on complications in COVID19 see J Clin Investig 2023;published online April 27. https://doi.org/10.1172/JCI170682 For more on efficacious monoclonal antibodies see Ann Intern Med 2023;published online April 18. https://doi.org/10.7326/M22-3428 For more on targets for herpes virus see Sci Adv 2023;9: eadf3977 For more on an RSV vaccine in pregnancy see N Engl J Med 2023;388: 1451–64 For more on Pillar[5]arene see Nat Commun 2023;14: 2141 For more on doxycycline for STIs see N Engl J Med 2023;388: 1296–306 For more on immunity in tuberculosis see Nat Immunol 2023;24: 753–54

Economics of a Health System's Direct-to-Consumer Telemedicine for Its Employees

Am J Manag Care. 2023;29(6):In Press _____ Takeaway Points The value of direct-to-consumer (DTC) telemedicine services offered by academic health systems is understudied. * DTC telemedicine services for low-acuity or minor illnesses are increasingly offered as an employee benefit, but any per-episode unit cost advantage may be offset by overuse of care. * DTC telemedicine staffed by an academic health system and offered to its employees resulted in lower per-episode unit costs for care within 7 days and only marginally increased the use of services. * DTC telemedicine staffed by an academic health system and offered directly to employees was cost-saving. _____ Employers in the United States have increasingly been offering a direct-to-consumer (DTC) telemedicine benefit for low-acuity or minor illnesses to their employees.1-3 By 2021, more than 95% of employers with 50 or more employees provided some coverage for DTC telemedicine in their largest health plan;more than 75% felt that offering telemedicine was important and nearly 20% either limited or eliminated cost sharing for telemedicine.4 Despite these trends among general employers, few health systems have directly provided DTC telemedicine to their own employees. [...]because these services are easy to access (often available immediately, around the clock, and without travel), they may induce overuse of care, especially for self-limited conditions such as viral upper respiratory infections for which the alternative to in-person care is no care at all, thus increasing the overall cost of care.5-11 Telemedicine will save money relative to in-person care if any unit price advantages are not overwhelmed by the increased use of care overall, induced by its convenience. Employers provide health insurance coverage for 158 million Americans or nearly 50% of the population. Since the COVID-19 pandemic began, telemedicine has represented a significantly larger portion of all medical claims—consistently more than 5% of all medical claims by mid-202112-15—and the estimated value of the global telemedicine industry is projected to reach a quarter of a trillion dollars by 2024.13 Yet, the future of telemedicine remains undetermined with reimbursement rates in debate,16-18 driven in large part because its economic value is understudied and uncertain. Penn Medicine is self-insured and more than 95% of employees use its only employer-sponsored plan—a preferred provider organization (PPO) plan—rather than insurance obtained individually or through a family member. Since 2017, these PPO-insured employees have been offered Penn Medicine OnDemand,19 a 24/7 DTC telemedicine benefit to employees and their adult (≥ 18 years) dependents.

Q&A

In this interview, Jody Casey, vice president, healthcare partnerships at EUigo Health Research, highlights how the pandemic has put the spotlight on diversity in trials, how EUigo is working with physicians to make studies more accessibie, as well as what is in store for the future of appropriate representation in trials. There needs to be a greater breadth of population and diversity in trials. Because a COVID vaccine is so critically important for all Americans, it's been brought to light the fact that trials are generally lacking in diversity. Casey: A unique aspect of Elligo is that we securely access electronic health record (EHR) data from our physician partners.

Clinical Trial Technology: New Kids on the Block are Changing Industry Dynamics

Machine learning depends on training algorithms on large sets of data, Limaye notes, but many pharmaceutical companies securely protect their own clinical trial data in a way that's impenetrable to machine learning algorithms. Limaye adds that federated learning technologies, in which algorithms access data that never leaves its secure location, are emerging solutions to this problem that many drug manufacturers are embracing. According to Ngang, Amgen takes great pains to ensure that everyone, regardless of language or literacy level, understands what they are agreeing to as they begin a clinical trial, and says the same care can be taken with ensuring that all clinical trial participants know how to use a wearable sensor or other digital device. [...]ObvioHealth has developed a platform that continuously collects data gathered from wearables such as blood pressure levels, oxygenation levels, or heart rates of patients in clinical trials for different treatments-with the data fed to the research site in real time (or as soon as possible if the patient loses connectivity).

Will the COVID-19 cloud change pediatric medicine forever?

With the United States still in the throes of a pandemic, nearly 400 pediatric health care providers share their struggles in getting patients back to the office, advocating for a COVID-19 vaccine, and working their way toward optimism in the face of the biggest health care challenge of their lives. [...]although the reasons around the pessimism remained the same in both 2013 and 2019 (insufficient time with patients, inadequate reimbursement, and health care reform), this year-no surprise-the top reason was concerns about adequately treating patients with COVID-19 and multisystem inflammatory syndrome in children (MIS-C). In 2019, when asked what the top 2 challenges to their practice were, 45% of health care providers said transitioning to electronic health records (EHRs) and dealing with insurance (42%) were the greatest obstacles.

Facilitating COVID recognition from X-rays with computer vision models and transfer learning.

Multimedia data plays an important role in medicine and healthcare since EHR (Electronic Health Records) entail complex images and videos for analyzing patient data. In this article, we hypothesize that transfer learning with computer vision can be adequately harnessed on such data, more specifically chest X-rays, to learn from a few images for assisting accurate, efficient recognition of COVID. While researchers have analyzed medical data (including COVID data) using computer vision models, the main contributions of our study entail the following. Firstly, we conduct transfer learning using a few images from publicly available big data on chest X-rays, suitably adapting computer vision models with data augmentation. Secondly, we aim to find the best fit models to solve this problem, adjusting the number of samples for training and validation to obtain the minimum number of samples with maximum accuracy. Thirdly, our results indicate that combining chest radiography with transfer learning has the potential to improve the accuracy and timeliness of radiological interpretations of COVID in a cost-effective manner. Finally, we outline applications of this work during COVID and its recovery phases with future issues for research and development. This research exemplifies the use of multimedia technology and machine learning in healthcare.

Associations Between Insurance, Race and Ethnicity, and COVID-19 Hospitalization, Beyond Underlying Health Conditions: A Retrospective Cohort Study.

Introduction: : People of lower socioeconomic position (SEP) and people of color (POC) experience higher risks of severe COVID-19, but understanding of these associations beyond the effect of underlying health conditions (UHCs) is limited. Moreover, few studies have focused on young adults, who have had the highest incidence of COVID-19 during much of the pandemic. Methods: : We conducted a retrospective cohort study using electronic health record data from the University of Washington Medicine healthcare system. Our study population included individuals aged 18-39 years who tested positive for SARS-CoV-2 from February 2020 to March 2021. Using regression modeling, we estimated adjusted risk ratios (aRRs) and differences (aRDs) of COVID-19 hospitalization by SEP (using health insurance as a proxy) and race and ethnicity. We adjusted for any UHC to examine these associations beyond the effect of UHCs. Results: Among 3,101 individuals, the uninsured/publicly insured had a 1.9-fold higher risk of hospitalization (aRR [95% CI]=1.9 [1.0, 3.6]) and 9 additional hospitalizations per 1,000 SARS-CoV-2 positive persons (aRD [95% CI]=9 [-1, 20]) compared to the privately insured. Hispanic or Latine, non-Hispanic (NH) Asian, NH Black, and NH Native Hawaiian or Pacific Islander patients had a 1.5-, 2.7-, 1.4-, and 2.1-fold-higher risk of hospitalization (aRR [95% CI]=1.5 [0.7, 3.1]; 2.7 [1.1, 6.5]; 1.4 [0.6, 3.3]; 2.1 [0.5, 9.1]), respectively, compared to NH White patients. Conclusions: Though they should be interpreted with caution given low precision, our findings suggest the increased risk of COVID-19 hospitalization among young adults of lower SEP and young adults of color may be driven by forces other than UHCs, including social determinants of health.

A global federated real-world data and analytics platform for research.

Objective: This article describes a scalable, performant, sustainable global network of electronic health record data for biomedical and clinical research. Materials and Methods: TriNetX has created a technology platform characterized by a conservative security and governance model that facilitates collaboration and cooperation between industry participants, such as pharmaceutical companies and contract research organizations, and academic and community-based healthcare organizations (HCOs). HCOs participate on the network in return for access to a suite of analytics capabilities, large networks of de-identified data, and more sponsored trial opportunities. Industry participants provide the financial resources to support, expand, and improve the technology platform in return for access to network data, which provides increased efficiencies in clinical trial design and deployment. Results: TriNetX is a growing global network, expanding from 55 HCOs and 7 countries in 2017 to over 220 HCOs and 30 countries in 2022. Over 19 000 sponsored clinical trial opportunities have been initiated through the TriNetX network. There have been over 350 peer-reviewed scientific publications based on the network's data. Conclusions: The continued growth of the TriNetX network and its yield of clinical trial collaborations and published studies indicates that this academic-industry structure is a safe, proven, sustainable path for building and maintaining research-centric data networks.

Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data.

OBJECTIVES: As the real-world electronic health record (EHR) data continue to grow exponentially, novel methodologies involving artificial intelligence (AI) are becoming increasingly applied to enable efficient data-driven learning and, ultimately, to advance healthcare. Our objective is to provide readers with an understanding of evolving computational methods and help in deciding on methods to pursue. TARGET AUDIENCE: The sheer diversity of existing methods presents a challenge for health scientists who are beginning to apply computational methods to their research. Therefore, this tutorial is aimed at scientists working with EHR data who are early entrants into the field of applying AI methodologies. SCOPE: This manuscript describes the diverse and growing AI research approaches in healthcare data science and categorizes them into 2 distinct paradigms, the bottom-up and top-down paradigms to provide health scientists venturing into artificial intelligent research with an understanding of the evolving computational methods and help in deciding on methods to pursue through the lens of real-world healthcare data.

Risks of neurological and psychiatric sequelae 2 years after hospitalisation or intensive care admission with COVID-19 compared to admissions for other causes.

The association between COVID-19 and subsequent neurological and psychiatric disorders is well established. However, two important questions remain unanswered. First, what are the risks in those admitted to intensive care unit (ICU) with COVID-19? Admission to ICU is itself associated with neurological and psychiatric sequelae and it is not clear whether COVID-19 further increases those risks or changes their profile. Second, what are the trajectories of neurological and psychiatric risks in patients admitted to hospital or ICU with COVID-19, and when do the risks subside? We sought to answer these two questions using a retrospective cohort study based on electronic health records (EHR) data from the TriNetX Analytics Network (covering 89 million patients, mostly in the USA). Cohorts of patients admitted to hospital or ICU with COVID-19 were propensity score-matched (for 82 covariates capturing risk factors for COVID-19 and more severe COVID-19 illness) to patients admitted to hospital or ICU (respectively) for any other reason. Matched cohorts were followed for up to two years and the risk of 14 neurological and psychiatric outcomes were compared. A total of 280,173 patients admitted to hospital and 46,573 patients admitted to ICU with COVID-19 were successfully matched to an equal number of patients admitted to hospital or ICU for any other reason. Those hospitalised with COVID-19 were found to be at a greater risk of a range of neurological and psychiatric outcomes including seizure/epilepsy, encephalitis, myoneural junction/muscle disease, Guillain-Barré syndrome (GBS), dementia, cognitive deficits, psychotic disorder, mood and anxiety disorders, but not ischaemic stroke or intracranial haemorrhage. When risks were elevated after COVID-19, most remained so for the whole two years of follow-up (except for mood and anxiety disorders). Risk profiles and trajectories were substantially different among those admitted to ICU: compared to those admitted for any other reasons, those admitted with COVID-19 were at a greater risk of myoneural junction/muscle disease, GBS, cognitive deficits and anxiety disorder, but at a significantly lower risk of ischaemic stroke, intracranial haemorrhage, encephalitis, and mood disorder. When elevated, the risks in those admitted to ICU with COVID-19 were mostly short-lived. In summary, risks of neurological and psychiatric sequelae in patients hospitalised with COVID-19 are wide ranging and long standing whereas those in patients admitted to ICU with COVID-19 are similar to, or lower than, the risks observed post-ICU admission for any other cause. These contrasting risk trajectories are relevant for researchers, clinicians, patients, and policymakers.

EVOLVING CHARACTERISTICS OF COVID-19 INPATIENTS AT AN AUSTRALIAN CENTRE

Since the declaration of the global pandemic in March 2020, the novel coronavirus disease (COVID-19) has caused dynamic pressures on healthcare systems worldwide. This study aims to compare the demographic and clinical characteristics, management, and outcomes of patients with COVID-19 at a single centre in Sydney, Australia. Using the clinical coding data, we identified all patients aged > 16 years admitted to our centre between February 1st, 2020, and March 31st, 2022. We categorised the time periods 'pre-delta' (February 1st, 2020 – June 15th, 2021), 'delta' (June 16th, 2021 – November 25th, 2021), and 'omicron' (November 26th, 2021 – March 31st, 2022). We retrospectively extracted the demographic, administrative, and clinical data from the electronic medical records and performed a descriptive analysis of the data. A total of 1842 patients were identified (pre-delta N=18;delta N=788;omicron N=1036). The percentage of male patients was 83%, 54%, and 56% and the median ages at admission were 55, 49, and 70 years during the pre-delta, delta, and omicron periods, respectively. The median length of hospital stay was the longest during the pre-delta period (8.5 days vs. 7 vs. 6). ICU admission rate was 39%, 21%, and 10% for each period and of the ICU-admitted patients 43%, 40%, and 36% respectively required mechanical ventilation. Inhospital mortality was the highest during the omicron period (pre-delta inhospital mortality 5.6%;delta 4.1%;omicron 7.3%). A total of 219 (28%) patients of delta and 185 (18%) of omicron periods received at least one dose of dexamethasone and 175 (22%) and 44 (4.2%) respectively received at least one dose of remdesivir. The demographic and clinical characteristics of admitted COVID-19 patients varied across the 'pre-delta', 'delta', and 'omicron' time periods. The median age at admission and in-hospital mortality was the highest during the omicron period. [ FROM AUTHOR] Copyright of International Journal of Infectious Diseases is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Exploring the topic structure and evolutionary trends of health informatics research in library and information science

PurposeThis study aims to reveal the topic structure and evolutionary trends of health informatics research in library and information science.Design/methodology/approachUsing publications in Web of Science core collection, this study combines informetrics and content analysis to reveal the topic structure and evolutionary trends of health informatics research in library and information science. The analyses are conducted by Pajek, VOSviewer and Gephi.FindingsThe health informatics research in library and information science can be divided into five subcommunities: health information needs and seeking behavior, application of bibliometrics in medicine, health information literacy, health information in social media and electronic health records. Research on health information literacy and health information in social media is the core of research. Most topics had a clear and continuous evolutionary venation. In the future, health information literacy and health information in social media will tend to be the mainstream. There is room for systematic development of research on health information needs and seeking behavior.Originality/valueTo the best of the authors' knowledge, this is the first study to analyze the topic structure and evolutionary trends of health informatics research based on the perspective of library and information science. This study helps identify the concerns and contributions of library and information science to health informatics research and provides compelling evidence for researchers to understand the current state of research.

De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository.

Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH's All of Us study partnered to reproduce the output of N3C's trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics.

Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond.

OBJECTIVE: The COVID-19 pandemic has demonstrated the value of real-world data for public health research. International federated analyses are crucial for informing policy makers. Common data models (CDM) are critical for enabling these studies to be performed efficiently. Our objective was to convert the UK Biobank, a study of 500,000 participants with rich genetic and phenotypic data to the Observational Medical Outcomes Partnership (OMOP) CDM. MATERIALS AND METHODS: We converted UK Biobank data to OMOP CDM v. 5.3. We transformedparticipant research data on diseases collected at recruitment and electronic health records (EHR) from primary care, hospitalizations, cancer registrations, and mortality from providers in England, Scotland, and Wales. We performed syntactic and semantic validations and compared comorbidities and risk factors between source and transformed data. RESULTS: We identified 502,505 participants (3,086 with COVID-19) and transformed 690 fields (1,373,239,555 rows) to the OMOP CDM using eight different controlled clinical terminologies and bespoke mappings. Specifically, we transformed self-reported non-cancer illnesses 946,053 (83.91% of all source entries), cancers 37,802 (70.81%), medications 1,218,935 (88.25%), and prescriptions 864,788 (86.96%). In EHR, we transformed 1,3028,182 (99.95%) hospital diagnoses, 6,465,399 (89.2%) procedures, 337,896,333 primary care diagnoses (CTV3, SNOMED-CT), 139,966,587 (98.74%) prescriptions (dm+d) and 77,127 (99.95%) deaths (ICD-10). We observed good concordance across demographic, risk factor, and comorbidity factors between source and transformed data. DISCUSSION AND CONCLUSION: Our study demonstrated that the OMOP CDM can be successfully leveraged to harmonize complex large-scale biobanked studies combining rich multimodal phenotypic data. Our study uncovered several challenges when transforming data from questionnaires to the OMOP CDM which require further research. The transformed UK Biobank resource is a valuable tool that can enable federated research, like COVID-19 studies.