Introduction to health information exchange

To support health care and public health in managing the array of information available about patients and populations, health systems have adopted a variety of information and communications technologies (ICT). Examples include electronic health record systems that document patient symptoms, diseases, and medications as well as health care processes. Yet, many ICT systems operate as islands unto themselves, unable to connect or share information with other ICT systems. Such fragmentation of data and information is an impediment to achieving the goal of efficient, coordinated health care delivery. It was further a major challenge during the COVID-19 pandemic when information was rapidly needed yet challenging to access. Health information exchange (HIE) seeks to address the challenges of connecting disparate ICT systems, enabling information to be available when and where it is needed by clinicians, administrators, and public health authorities. This chapter robustly defines HIE, including its core components and various forms. This chapter further discusses the role of HIE in supporting care delivery and public health. © 2023 Elsevier Inc. All rights reserved.

Shared longitudinal health records for clinical and population health

The ability of a health information exchange (HIE) to consolidate information, collected from multiple, disparate information systems, into a single, person-centric health record can provide a comprehensive and longitudinal representation of an individual's medical history. Shared, longitudinal health records can be leveraged to enhance the delivery of individual clinical care and provide opportunities to improve health outcomes at the population level. This chapter describes the clinical benefits imparted by the shared health record (SHR) component an HIE infrastructure. It also characterizes the potential public health benefits of the aggregate level, population health indicators calculated, stored, and distributed by a health management information system (HMIS) component. Tools for visualizing health indicators from the HMIS, including disease surveillance systems developed during the COVID-19 pandemic, are also described. Postpandemic components such as the SHR and HMIS will likely play critical roles in strengthening health information infrastructures in states and nations. © 2023 Elsevier Inc. All rights reserved.

Quantitative Study of a Regional Patient Portal Usage in the Pandemic Period.

In 2013 using a Public Procurement of Innovation procedure the Region of Galicia developed a patient portal called "E-Saúde", that went live in 2015. COVID situation in 2019 produced a high demand of e-health services, scaling by 10x the number of users in 2021. OBJECTIVE: In this study a quantitative description of patient portal usage from 2018 to 2022 is made to show the behaviour of usage trends of a patient portal before, during and after COVID pandemic. METHODS: Two main data sets were obtained from patient portal logs to obtain: 1) Enrolment of new users and number of sessions opened in the portal. 2) Detailed usage of relevant functionalities. Descriptive statistical methods were applied to show the usage of the portal in a biannual time series description. RESULTS: Prior to the pandemic, the portal was gradually being introduced to citizens. During pandemics, more than 1 million users were registered and a peak of 15x usage could be observed. After COVID, the level of usage of portal services decreased, but kept a sustained trend five times higher than in Pre-COVID situation. CONCLUSION: There is limited information available on metrics, functionalities and acceptability for general purpose patient portals, but the analysis performed on usage levels shows that after a high peak reached during COVID period, explained by the need of direct access to clinical information, the level of usage of the patient portal remains five times higher than in pre-pandemic situation for all functionalities of the patient portal.

Blockchain-based EHR System for Indian Healthcare Industry using Aadhar

In today's world, digital technologies are advancing at a rapid pace. Almost every industry has benefited from this ongoing change. In the health sector, the digitization of medical records was proposed decades ago. Whereas some developed countries have successfully adopted and implemented Electronic Health Record (EHR) systems. Developing countries like India still heavily rely on paper-based medical records. Although there are a number of systems for electronic medical record management, they have issues related to interoperability, timely access, and storage. Due to poor infrastructure and design, the current systems are not robust for communicating and tracking medical records. The need for a better EHR system was highly emphasized during the COVID-19 pandemic. The two major shortcomings of the existing system are a lack of interoperability, which causes delays in sharing the information, and a lack of standardization, due to which the data quality of the data that is shared suffers. To mitigate these issues, we need a nationwide EHR system. Another issue is the lack of a ubiquitous UPI (Unique Patient Identifier). In a country like India, the second most populated country in the world, Aadhar is the best option for UPI, which can be used for creating a national EHR system. In this paper, we have presented a framework for a standardized, interoperable, and unified EHR system based on blockchain technology with Aadhar as the UPI. Using blockchain as the base of this model provides numerous advantages over a cloud-based system, like decentralization, better security, immutability, and traceability. © 2023 IEEE.

Electronic Medical Record System Use and Determinants in Ethiopia: Systematic Review and Meta-Analysis.

BACKGROUND: The strategic plan of the Ethiopian Ministry of Health recommends an electronic medical record (EMR) system to enhance health care delivery and streamline data systems. However, only a few exhaustive systematic reviews and meta-analyses have been conducted on the degree of EMR use in Ethiopia and the factors influencing success. This will emphasize the factors that make EMR effective and increase awareness of its widespread use among future implementers in Ethiopia. OBJECTIVE: This study aims to determine the pooled estimate of EMR use and success determinants among health professionals in Ethiopia. METHODS: We developed a protocol and searched PubMed, Web of Sciences, African Journals OnLine, Embase, MEDLINE, and Scopus to identify relevant studies. To assess the quality of each included study, we used the Joanna Briggs Institute quality assessment tool using 9 criteria. The applicable data were extracted using Microsoft Excel 2019, and the data were then analyzed using Stata software (version 11; StataCorp). The presence of total heterogeneity across included studies was calculated using the index of heterogeneity I2 statistics. The pooled size of EMR use was estimated using a random effect model with a 95% CI. RESULTS: After reviewing 11,026 research papers, 5 papers with a combined total of 2439 health workers were included in the evaluation and meta-analysis. The pooled estimate of EMR usage in Ethiopia was 51.85% (95% CI 37.14%-66.55%). The subgroup study found that the northern Ethiopian region had the greatest EMR utilization rate (58.75%) and that higher (54.99%) utilization was also seen in publications published after 2016. Age groups <30 years, access to an EMR manual, EMR-related training, and managerial support were identified factors associated with EMR use among health workers. CONCLUSIONS: The use of EMR systems in Ethiopia is relatively low. Belonging to a young age group, accessing an EMR manual, receiving EMR-related training, and managerial support were identified as factors associated with EMR use among health workers. As a result, to increase the use of EMRs by health care providers, it is essential to provide management support and an EMR training program and make the EMR manual accessible to health professionals.

COVID-19 phenomics

Phenomic studies of coronavirus disease 2019 (COVID-19) attempt to comprehensively describe the range of phenotypes associated with disease-related outcomes, by either breadth or depth of characterization. The primary aims of such studies are the unbiased generation of hypotheses concerning COVID-19 pathophysiology and the empirical determination of effective prognostic indicators. Of particular relevance to COVID-19 are phenome-wide association studies—large-scale, data-driven studies evaluating associations between a multitude of phenotypic traits and COVID-19 severity or other outcomes of interest, often employing bioinformatic and statistical approaches for the analysis of databases of electronic health records. This type of extensive phenotyping, in combination with intensive interrogation of particular aspects of the pathophysiological response, also allows investigators to reconstruct the network of phenomena that underpin disease, of particular significance because of the systemic nature of COVID-19. Because of their ability to detect novel associations, another great utility of extensive phenomic analyses applied to COVID-19 is in the development of prognostic tools and biomarkers that improve the efficacy of patient care. Finally, when applied to those in the convalescent phase, phenomics has helped to elucidate both the nature of postacute sequalae of COVID-19 and the characteristics that predispose an individual toward them. Hence, phenomics provides an additional and unique perspective which is crucial to our understanding of COVID-19 to better equip us against unforeseen adverse outcomes of this pandemic and potential infectious outbreaks in the future. © 2023 Elsevier Inc. All rights reserved.

Blockchain Meets Federated Learning in Healthcare: A Systematic Review with Challenges and Opportunities

Recently, innovations in the Internet-of-Medical- Things (IoMT), information and communication technologies, and Machine Learning (ML) have enabled smart healthcare. Pooling medical data into a centralised storage system to train a robust ML model, on the other hand, poses privacy, ownership, and regulatory challenges. Federated Learning (FL) overcomes the prior problems with a centralised aggregator server and a shared global model. However, there are two technical challenges: FL members need to be motivated to contribute their time and effort, and the centralised FL server may not accurately aggregate the global model. Therefore, combining the blockchain and FL can overcome these issues and provide high-level security and privacy for smart healthcare in a decentralised fashion. This study integrates two emerging technologies, blockchain and FL, for healthcare. We describe how blockchain-based FL plays a fundamental role in improving competent healthcare, where edge nodes manage the blockchain to avoid a single point of failure, while IoMT devices employ FL to use dispersed clinical data fully. We discuss the benefits and limitations of combining both technologies based on a content analysis approach. We emphasise three main research streams based on a systematic analysis of blockchain-empowered (i) IoMT, (ii) Electronic Health Records (EHR) and Electronic Medical Records (EMR) management, and (iii) digital healthcare systems (internal consortium/secure alerting). In addition, we present a novel conceptual framework of blockchain-enabled FL for the digital healthcare environment. Finally, we highlight the challenges and future directions of combining blockchain and FL for healthcare applications. IEEE

Prediction of Sudden Death Due to COVID-19 Using Machine Learning Models

The early classification of COVID-19 patients severity can help save lives by giving to doctors valuable instructions and guidelines for the cases that may need more attention to survive. This paper aims to classify cases depending on their severity into three classes: "survivor”, "sudden death” and "death” using electronic health records (HER). The first class represents positive cases discharged from the hospital after being treated for COVID-19. While the second and the third classes are describing the level of cases severity based on the interval of death. We called the highest severity class "sudden death” to identify critical cases with a high risk of death in the first two days of admission, while the "death” class includes severe cases with an interval of death beyond two days. The sudden death class represents the biggest challenge for this classification as the number of samples representing this case is very small. This paper presents a triage system for COVID-19 cases using four machine learning algorithms (KNN, Logistic Regression, SVM, and Decision tree). The best classification results were obtained using Logistic Regression and SVM models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients.

BACKGROUND: Two years into the COVID-19 pandemic and with more than five million deaths worldwide, the healthcare establishment continues to struggle with every new wave of the pandemic resulting from a new coronavirus variant. Research has demonstrated that there are variations in the symptoms, and even in the order of symptom presentations, in COVID-19 patients infected by different SARS-CoV-2 variants (e.g., Alpha and Omicron). Textual data in the form of admission notes and physician notes in the Electronic Health Records (EHRs) is rich in information regarding the symptoms and their orders of presentation. Unstructured EHR data is often underutilized in research due to the lack of annotations that enable automatic extraction of useful information from the available extensive volumes of textual data. METHODS: We present the design of a COVID Interface Terminology (CIT), not just a generic COVID-19 terminology, but one serving a specific purpose of enabling automatic annotation of EHRs of COVID-19 patients. CIT was constructed by integrating existing COVID-related ontologies and mining additional fine granularity concepts from clinical notes. The iterative mining approach utilized the techniques of 'anchoring' and 'concatenation' to identify potential fine granularity concepts to be added to the CIT. We also tested the generalizability of our approach on a hold-out dataset and compared the annotation coverage to the coverage obtained for the dataset used to build the CIT. RESULTS: Our experiments demonstrate that this approach results in higher annotation coverage compared to existing ontologies such as SNOMED CT and Coronavirus Infectious Disease Ontology (CIDO). The final version of CIT achieved about 20% more coverage than SNOMED CT and 50% more coverage than CIDO. In the future, the concepts mined and added into CIT could be used as training data for machine learning models for mining even more concepts into CIT and further increasing the annotation coverage. CONCLUSION: In this paper, we demonstrated the construction of a COVID interface terminology that can be utilized for automatically annotating EHRs of COVID-19 patients. The techniques presented can identify frequently documented fine granularity concepts that are missing in other ontologies thereby increasing the annotation coverage.

Characterization of COVID-19 Hospitalized Patients in Three United States Electronic Health Record Databases.

COVID-19 infections have contributed to substantial increases in hospitalizations. This study describes demographics, baseline clinical characteristics and treatments, and clinical outcomes among U.S. patients admitted to hospitals with COVID-19 during the prevaccine phase of the pandemic. A total of 20,446 hospitalized patients with a positive COVID-19 nucleic acid amplification test were identified from three large electronic health record databases during 5 February-30 November 2020 (Academic Health System: n = 4504; Explorys; n = 7492; OneFlorida: n = 8450). Over 90% of patients were ≥30 years of age, with an even distribution between sexes. At least one comorbidity was recorded in 84.6-96.1% of patients; cardiovascular and respiratory conditions (28.8-50.3%) and diabetes (25.6-44.4%) were most common. Anticoagulants were the most frequently reported medications on or up to 28 days after admission (44.5-81.7%). Remdesivir was administered to 14.1-24.6% of patients and increased over time. Patients exhibited higher COVID-19 severity 14 days following admission than the 14 days prior to and on admission. The length of in-patient hospital stay ranged from a median of 4 to 6 days, and over 85% of patients were discharged alive. These results promote understanding of the clinical characteristics and hospital-resource utilization associated with hospitalized COVID-19 over time.

Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C).

BACKGROUND: Multi-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical interventions. They provide access to clinical data from large pooled patient populations in addition to laboratory measurements unavailable in insurance claims-based data. However, secondary use of these data for research requires specialized knowledge and careful evaluation of data quality and completeness. We discuss data quality assessments undertaken during the conduct of prep-to-research, focusing on the investigation of treatment safety and effectiveness. METHODS: Using the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria typical in non-interventional inpatient drug effectiveness studies. We present the challenges encountered when constructing this dataset, beginning with an examination of data quality across data partners. We then discuss the methods and best practices used to operationalize several important study elements: exposure to treatment, baseline health comorbidities, and key outcomes of interest. RESULTS: We share our experiences and lessons learned when working with heterogeneous EHR data from over 65 healthcare institutions and 4 common data models. We discuss six key areas of data variability and quality. (1) The specific EHR data elements captured from a site can vary depending on source data model and practice. (2) Data missingness remains a significant issue. (3) Drug exposures can be recorded at different levels and may not contain route of administration or dosage information. (4) Reconstruction of continuous drug exposure intervals may not always be possible. (5) EHR discontinuity is a major concern for capturing history of prior treatment and comorbidities. Lastly, (6) access to EHR data alone limits the potential outcomes which can be used in studies. CONCLUSIONS: The creation of large scale centralized multi-site EHR databases such as N3C enables a wide range of research aimed at better understanding treatments and health impacts of many conditions including COVID-19. As with all observational research, it is important that research teams engage with appropriate domain experts to understand the data in order to define research questions that are both clinically important and feasible to address using these real world data.

Experiences and Expectations of Information and Communication Technologies in Flexible Assertive Community Treatment Teams: Qualitative Study.

BACKGROUND: Flexible Assertive Community Treatment (FACT) is a model of integrated care for patients with long-term serious mental illness. FACT teams deliver services using assertive outreach to treat patients who can be hard to reach by the health care service, and focus on both the patient's health and their social situation. However, in Norway, FACT team members have challenges with their information and communication (ICT) solutions. OBJECTIVE: The aim of this study was to explore Norwegian FACT teams' experiences and expectations of their ICT solutions, including electronic health records, electronic whiteboards, and calendars. METHODS: We gathered data in two phases. In the first phase, we conducted semistructured interviews with team leaders and team coordinators, and made observations in FACT teams targeting adults. In the second phase, we conducted semistructured group interviews in FACT teams targeting youth. We performed a thematic analysis of the data in a theoretical manner to address the specific objectives of the study. RESULTS: A total of 8 teams were included, with 5 targeting adults and 3 targeting youth. Due to the COVID-19 pandemic, we were not able to perform observations in 2 of the teams targeting adults. Team leaders and coordinators in all 5 teams targeting adults were interviewed, with a total of 7 team members participating in the teams targeting youth. We found various challenges with communication, documentation, and organization for FACT teams. The COVID-19 pandemic was challenging for the teams and changed the way they used ICT solutions. There were issues with some technical solutions used in the teams, including electronic health records, electronic whiteboards, and calendars. Lack of integration and access to data were some of the main issues identified. CONCLUSIONS: Despite the FACT model being successfully implemented in Norway, there are several issues regarding the ICT solutions they use, mainly related to access to data and integration. Further research is required to detail how improved ICT solutions should be designed. While FACT teams targeting adults and youth differ in some ways, their needs for ICT solutions are largely similar.

Impact of COVID-19 Severity on Long-term Events in US Veterans using the Veterans Affairs Severity Index for COVID-19 (VASIC).

In this retrospective cohort study of 94,595 SARS-CoV-2 positive cases, we developed and validated an algorithm to assess the association between COVID-19 severity and long-term complications (stroke, myocardial infarction, pulmonary embolism/deep vein thrombosis, heart failure, and mortality). COVID-19 severity was associated with a greater risk of experiencing a long-term complication days 31-120 post-infection. Most incident events occurred days 31-60 post-infection and diminished after day 91, except heart failure for severe patients and death for moderate patients, which peaked days 91-120. Understanding the differential impact of COVID-19 severity on long-term events provide insight into possible intervention modalities and critical prevention strategies.

Utilizing Policy and Electronic Health Record (EHR) System Modifications to Implement and Sustain Routine Opt-Out HIV Screening and Linkage to Care During the COVID-19 Pandemic.

The burden of HIV infection disproportionately impacts Black people across the United States. New York City (NYC) has taken substantial steps to End the HIV Epidemic, boasting reductions in new HIV infections by 40% since 2015; however, racial inequities persist. In 2019, Black people living in NYC accounted for 24% of the population, yet represented 46.1% of new HIV diagnoses and 48.7% of HIV deaths. To address the high incidence of HIV in a predominately Black community in Central Brooklyn, Brookdale Hospital Medical Center (BHMC) developed a multi-faceted approach to increase routine opt-out HIV screening and linkage. In order to integrate HIV testing into routine clinical care, BHMC leadership updated screening policies; developed an Electronic Health Record (EHR) algorithm to trigger HIV screening in five BHMC ambulatory clinics; and modified the EHR to transmit positive HIV screening results to patient navigators dedicated to linking patients to HIV care. During the height of the COVID-19 pandemic, between March and April 2020, HIV screening across all five ambulatory sites decreased by 87.3%. After activation of the EHR algorithm in three ambulatory sites in June 2020, HIV screening increased 216.3% from the prior month. By the time the final EHR algorithm launched in August 2020, HIV testing had fully rebounded to pre-pandemic levels. Policies supporting routine opt-out HIV screening coupled with EHR-prompted screening can improve and sustain HIV testing in a Black community with a high incidence and prevalence of HIV.

COVID-19 vaccination and venous thromboembolism risk in older veterans.

Introduction: It is important for SARS-CoV-2 vaccine providers, vaccine recipients, and those not yet vaccinated to be well informed about vaccine side effects. We sought to estimate the risk of post-vaccination venous thromboembolism (VTE) to meet this need. Methods: We conducted a retrospective cohort study to quantify excess VTE risk associated with SARS-CoV-2 vaccination in US veterans age 45 and older using data from the Department of Veterans Affairs (VA) National Surveillance Tool. The vaccinated cohort received at least one dose of a SARS-CoV-2 vaccine at least 60 days prior to 3/06/22 (N = 855,686). The control group was those not vaccinated (N = 321,676). All patients were COVID-19 tested at least once before vaccination with a negative test. The main outcome was VTE documented by ICD10-CM codes. Results: Vaccinated persons had a VTE rate of 1.3755 (CI: 1.3752-1.3758) per thousand, which was 0.1 percent over the baseline rate of 1.3741 (CI: 1.3738-1.3744) per thousand in the unvaccinated patients, or 1.4 excess cases per 1,000,000. All vaccine types showed a minimal increased rate of VTE (rate of VTE per 1000 was 1.3761 (CI: 1.3754-1.3768) for Janssen; 1.3757 (CI: 1.3754-1.3761) for Pfizer, and for Moderna, the rate was 1.3757 (CI: 1.3748-1.3877)). The tiny differences in rates comparing either Janssen or Pfizer vaccine to Moderna were statistically significant (p < 0.001). Adjusting for age, sex, BMI, 2-year Elixhauser score, and race, the vaccinated group had a minimally higher relative risk of VTE as compared to controls (1.0009927 CI: 1.007673-1.0012181; p < 0.001). Conclusion: The results provide reassurance that there is only a trivial increased risk of VTE with the current US SARS-CoV-2 vaccines used in veterans older than age 45. This risk is significantly less than VTE risk among hospitalized COVID-19 patients. The risk-benefit ratio favors vaccination, given the VTE rate, mortality, and morbidity associated with COVID-19 infection.

Vaccine Effectiveness, School Reopening, and Risk of Omicron Infection Among Adolescents Aged 12-17 Years.

PURPOSE: The BNT162b2 (Pfizer-BioNTech) is approved for adolescents aged 12-17 years. We estimated BNT162b2 vaccine effectiveness (VE) and a booster dose effectiveness in adolescents aged 12-17 years and the impact of opening schools and the Omicron variant on risk of SARS-CoV-2 infection in adolescents. METHODS: We used logistic regression with a test-negative design controlling for gender and race to estimate BNT162b2 VE and the effectiveness of a booster dose in adolescents aged 12-17 years. To evaluate the effect of school opening on Omicron transmission, we used Cox proportional hazards regression to compare adolescents to a reference group of adults aged 22-33 or aged 65+ years, investigating whether risk for adolescents increased relative to the reference group after school opened. RESULTS: We found that adolescents who received two BNT162b2 doses had significant protection against Omicron infection in the first three months following their second dose (VE = 54.5%, confidence interval [CI]: [17.8%-76.9%], p = .014) but no protection afterwards. Receiving a booster dose was associated with lower risk of infection (odds ratio = 0.48, CI: [0.33-0.69], p < .0001) and restored efficacy to a similar level (VE = 56.3%, CI: [36.5%-70.6%], p < .0001). We observed a statistically significant increase (p = .04) in adolescent infection risk relative to adults in the period of Omicron predominance. DISCUSSION: The BNT162b2 vaccine is effective at preventing SARS-CoV-2 infection in adolescents but immunity against Omicron wanes rapidly and booster doses are needed to retain protection. More research is needed to determine the effect of school reopening on spread in the Omicron-dominant period.

Measuring Training Disruptions Using an Informatics Based Tool.

OBJECTIVE: Training disruptions, such as planned curricular adjustments or unplanned global pandemics, impact residency training in ways that are difficult to quantify. Informatics-based medical education tools can help measure these impacts. We tested the ability of a software platform driven by electronic health record data to quantify anticipated changes in trainee clinical experiences during the COVID-19 pandemic. METHODS: We previously developed and validated the Trainee Individualized Learning System (TRAILS) to identify pediatric resident clinical experiences (i.e. shifts, resident provider-patient interactions (rPPIs), and diagnoses). We used TRAILS to perform a year-over-year analysis comparing pediatrics residents at a large academic children's hospital during March 15-June 15 in 2018 (Control #1), 2019 (Control #2), and 2020 (Exposure). RESULTS: Residents in the exposure cohort had fewer shifts than those in both control cohorts (P < .05). rPPIs decreased an average of 43% across all PGY levels, with interns experiencing a 78% decrease in Continuity Clinic. Patient continuity decreased from 23% to 11%. rPPIs with common clinic and emergency department diagnoses decreased substantially during the exposure period. CONCLUSIONS: Informatics tools like TRAILS may help program directors understand the impact of training disruptions on resident clinical experiences and target interventions to learners' needs and development.

[Digitalization in healthcare: today and in the future]. / Digitalisierung im Gesundheitswesen: heute und in Zukunft.

Although Germany continues to struggle with the digital transformation of healthcare, there is reason for optimism. The political will to improve healthcare with digital technologies has been underpinned by numerous legal initiatives since 2018. In addition, there is growing acceptance among healthcare providers and the population. The latter has clearly been driven by the corona pandemic, which underscored the need for more digitized care.Digitalization in healthcare has three key drivers: the rapid technological development in data processing, the ever-improving understanding of the biological basis of human life, and growing patient sovereignty coupled with a growing desire for transparency. Prerequisites for digital medicine are data interoperability and the establishment of a networking (telematics) infrastructure (TI). The status of the most important digital TI applications affecting German healthcare are described: the electronic patient record (ePA) as its core as well as electronic prescriptions, medication plans, and communication tools such as Communication in Medicine (KIM) and TI Messenger (TIM). In addition, various telemedical offerings are discussed as well as the introduction of digital health applications (DiGA) into the statutory healthcare system, which Germany has pioneered. Furthermore, the use of medical data as the basis for artificial intelligence (AI) algorithms is discussed. While helpful and capable of improving diagnostics as well as medical therapy, such AI tools will not replace doctors and nurses.

Analyzing historical diagnosis code data from NIH N3C and RECOVER Programs using deep learning to determine risk factors for Long Covid

Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an emerging medical condition that has been observed in several patients with a positive diagnosis for COVID-19. Historical Electronic Health Records (EHR) like diagnosis codes, lab results and clinical notes have been analyzed using deep learning and have been used to predict future clinical events. In this paper, we propose an interpretable deep learning approach to analyze historical diagnosis code data from the National COVID Cohort Collective (N3C)1 to find the risk factors contributing to developing Long COVID. Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70.48%. We are then able to examine the trained model using Gradient-weighted Class Activation Mapping (GradCAM) to give each input diagnoses a score. The highest scored diagnosis were deemed to be the most important for making the correct prediction for a patient. We also propose a way to summarize these top diagnoses for each patient in our cohort and look at their temporal trends to determine which codes contribute towards a positive Long COVID diagnosis. © 2022 IEEE.

Third dose vaccination with mRNA-1273 or BNT162b2 vaccines improves protection against SARS-CoV-2 infection.

As of 2021 November 29, booster vaccination against SARS-CoV-2 infection has been recommended for all individuals aged 18 years and older in the United States. A key reason for this recommendation is the expectation that a booster vaccine dose can alleviate observed waning of vaccine effectiveness (VE). Although initial reports of booster effectiveness have been positive, the level of protection from booster vaccination is unclear. We conducted two studies to assess the impact of booster vaccination, with BNT162b2 or mRNA-1273, on the incidence of SARS-CoV-2 infection between August and December 2021. We first compared SARS-CoV-2 infection incidence in cohorts of 3-dose vaccine recipients to incidence in matched cohorts of 2-dose vaccine recipients (cohort size = 24,539 for BNT162b2 and 14,004 for mRNA-1273). Additionally, we applied a test-negative study design to compare the level of protection against symptomatic infection in 3-dose recipients to that observed in recent 2-dose primary vaccine series recipients. The 3-dose recipients experienced a significantly lower incidence rate of SARS-CoV-2 infection than the matched 2-dose cohorts (BNT162b2 Incidence Rate Ratio: 0.11, 95% CI: 0.09 to 0.13 and mRNA-1273 IRR: 0.11, 95% CI: 0.08 to 0.15). Results from the test-negative study showed the third vaccine dose mitigated waning of VE, with the risk of symptomatic infection in 3-dose recipients being comparable to that observed 7 to 73 days after the primary vaccine series. These results show that 3-dose vaccine regimens with BNT162b2 or mRNA-1273 are effective at reducing SARS-CoV-2 infection and support the widespread administration of booster vaccine doses.