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Background For some SARS-CoV-2 survivors, recovery from the acute phase of the infection has been grueling with lingering effects. Many of the symptoms characterized as the post-acute sequelae of COVID-19 (PASC) could have multiple causes or are similarly seen in non-COVID patients. Accurate identification of PASC phenotypes will be important to guide future research and help the healthcare system focus its efforts and resources on adequately controlled age- and gender-specific sequelae of a COVID-19 infection. Methods In this retrospective electronic health record (EHR) cohort study, we applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19. We evaluated the post-test phenotypes in two temporal windows at 3–6 and 6–9 months after the test and by age and gender. Data from longitudinal diagnosis records stored in EHRs from Mass General Brigham in the Boston Metropolitan Area was used for the analyses. Statistical analyses were performed on data from March 2020 to June 2021. Study participants included over 96 thousand patients who had tested positive or negative for COVID-19 and were not hospitalized. Results We identified 33 phenotypes among different age/gender cohorts or time windows that were positively associated with past SARS-CoV-2 infection. All identified phenotypes were newly recorded in patients’ medical records 2 months or longer after a COVID-19 RT-PCR test in non-hospitalized patients regardless of the test result. Among these phenotypes, a new diagnosis record for anosmia and dysgeusia (OR 2.60, 95% CI [1.94–3.46]), alopecia (OR 3.09, 95% CI [2.53–3.76]), chest pain (OR 1.27, 95% CI [1.09–1.48]), chronic fatigue syndrome (OR 2.60, 95% CI [1.22–2.10]), shortness of breath (OR 1.41, 95% CI [1.22–1.64]), pneumonia (OR 1.66, 95% CI [1.28–2.16]), and type 2 diabetes mellitus (OR 1.41, 95% CI [1.22–1.64]) is one of the most significant indicators of a past COVID-19 infection. Additionally, more new phenotypes were found with increased confidence among the cohorts who were younger than 65. Conclusions The findings of this study confirm many of the post-COVID-19 symptoms and suggest that a variety of new diagnoses, including new diabetes mellitus and neurological disorder diagnoses, are more common among those with a history of COVID-19 than those without the infection. Additionally, more than 63% of PASC phenotypes were observed in patients under 65 years of age, pointing out the importance of vaccination to minimize the risk of debilitating post-acute sequelae of COVID-19 among younger adults.

Made available in DSpace on 2022-04-29T08:35:59Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-12-01 Division of Intramural Research, National Institute of Allergy and Infectious Diseases Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases National Institute of Allergy and Infectious Diseases National Center for Advancing Translational Sciences National Heart, Lung, and Blood Institute National Institute of Environmental Health Sciences U.S. National Library of Medicine National Institute of Neurological Disorders and Stroke Division of Cancer Prevention, National Cancer Institute Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January–September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7–7.8%, pFDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7–10.5%, pFDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19–25%), cerebrovascular diseases (24%, 13–35%), nontraumatic intracranial hemorrhage (34%, 20–50%), encephalitis and/or myelitis (37%, 17–60%) and myopathy (72%, 67–77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Department of Biomedical Informatics Harvard Medical School Department of Neurology University of Pittsburgh, Biomedical Science Tower 3, Suite 7014, 3501 5th Avenue Department of Pediatrics Wake Forest School of Medicine Department of Critical Care National University Health Systems Department of Preventive Medicine Northwestern University Department of Biomedical Informatics University of Pittsburgh Department of Surgery National University Health Systems Department of Internal Medicine University of Kansas Medical Center Data Analytics Center University of Pennsylvania Health System Department of Computational Medicine and Bioinformatics University of Michigan Department of Biomedical Informatics University of Kentucky Department of Legal Economic and Social Sciences University Magna Graecia of Catanzaro Health Catalyst INC. Department of Surgery ASST Pavia Lombardia Region Health System Clinical Research Unit of Botucatu Medical School São Paulo State University Pediatric Emergency Department Hôpital Necker-Enfants Malades Assistance Publique-Hôpitaux de Paris National Center for Infectious Diseases Tan Tock Seng Hospital Departments of Biomedical Informatics Pediatrics Cincinnati Children’s Hospital Medical Center University of Cincinnati Department of Pediatrics Medical University of South Carolina Department of Surgery St. Luke’s University Health Network Department of Medicine David Geffen School of Medicine at UCLA UOC Ricerca Innovazione e Brand Reputation ASST Papa Giovanni XXIII Department of Electrical Computer and Biomedical Engineering University of Pavia IT Department Innovation & Data APHP Greater Paris University Hospital I.T. Department ASST Pavia Health Informatics Hospital Universitario, 12 de Octubre Strategy and Transformation Department APHP Greater Paris University Hospital Faculty of Medicine and Medical Center University of Freiburg Digital Research Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children Scientific Direction IRCCS Ca’ Granda Ospedale Maggiore Policlinico di Milano North Carolina Translational and Clinical Sciences (NC TraCS) Institute University of North Carolina BIOMERIS (BIOMedical Research Informatics Solutions) Department of Biomedical Informatics HEGP APHP Greater Paris University Hospital Department of Medical and Surgical Sciences Data Analytics Research Center University Magna Graecia of Catanzaro Department of Anesthesia St. Luke’s University Health Network Université Paris-Saclay Inria CEA INRIA Sophia-Antipolis–ZENITH Team LIRMM Computational Health Informatics Program Boston Children’s Hospital Department of Internal Medicine University of Kentucky Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Department of Internal Medicine and Therapeutics University of Pavia Informatics Institute University of Alabama at Birmingham IAM Unit Bordeaux University Hospital/ERIAS-Inserm U1219 BPH Biomedical Informatics Center Medical University of South Carolina Clinical Research Informatics Boston Children’s Hospital Department of Biomedical and Health Informatics Children’s Hospital of Philadelphia SED/SIERRA Inria Centre de Paris Health Information Technology & Services University of Michigan Internal Medicine Department Botucatu Medical School São Paulo State University Heinrich-Lanz-Center for Digital Health University Medicine Mannheim Heidelberg University Department of Anesthesiology Critical Care and Pain Medicine Boston Children’s Hospital Department of Learning Health Sciences University of Michigan Medical School MSHI Medical University of South Carolina Department of Medicine Massachusetts General Hospital Division of Human Genetics Department of Pediatrics The Children’s Hospital of Philadelphia Center for Medical Information and Communication Technology University Hospital Renaissance Computing Institute/Department of Computer Science University of North Carolina Clinical Research Unit Saint Antoine Hospital APHP Greater Paris University Hospital Clevy.io Department of Biostatistics Harvard T.H. Chan School of Public Health VA Informatics and Computing Infrastructure VA Salt Lake City Health Care System MICHR Informatics University of Michigan Laboratory of Informatics and Systems Engineering for Clinical Research Istituti Clinici Scientifici Maugeri SpA SB IRCCS Harvard Catalyst Harvard Medical School Clinical Research Unit Paris Saclay APHP Greater Paris University Hospital Department of Anesthesiology and Critical Care Children’s Hospital of Philadelphia VA Informatics and Computing Infrastructure Tennessee Valley Healthcare System Veterans Affairs Medical Center École Normale Supérieure PSL Université Paris BIG-ARC The University of Texas Health Science Center at Houston School of Biomedical Informatics Pediatric Infectious Disease Department Hospital Universitario, 12 de Octubre Department of Infectious Diseases Great Ormond Street Hospital for Children Department of Neurology Massachusetts General Hospital Internal Medicine Department of Botucatu Medical School São Paulo State University Department of Pediatrics Boston Children’s Hospital Center for Biomedical Informatics Wake Forest School of Medicine Department of Medical Informatics University of Erlangen-Nürnberg Department of Veterans Affairs Office of Research and Development Biomedical Data Science Lab ITACA Institute Universitat Politècnica de València Nurse Department of FMB-Medicine School of Botucatu Management Engineering ASST Pavia Lombardia Region Health System Department of Anesthesiology University Hospital Erlangen FAU Erlangen-Nürnberg Critical Care Medicine Department of Medicine St. Luke’s University Health Network Department of Medicine National University Hospital Department of Information Management Chang Gung University Clinical & Translational Science Institute Medical College of Wisconsin Montréal Neurological Institute McGill University SequeL Inria Lille Respiratory Department ICS S Maugeri IRCCS Department of Health Management and Informatics University of Missouri Department of Oncology ASST Papa Giovanni XXIII Clinical Research Unit of Botucatu Medical School São Paulo State University Internal Medicine Department Botucatu Medical School São Paulo State University Internal Medicine Department of Botucatu Medical School São Paulo State University Division of Intramural Research, National Institute of Allergy and Infectious Diseases: AI11679 Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases: AI11679 National Institute of Allergy and Infectious Diseases: AI11679 National Center for Advancing Translational Sciences: CTSA Award #UL1TR001878 National Center for Advancing Translational Sciences: CTSA Award #UL1TR002366 National Heart, Lung, and Blood Institute: K23HL148394 National Institute of Environmental Health Sciences: P30ES017885 U.S. National Library of Medicine: R01LM012095 National Institute of Neurological Disorders and Stroke: R01NS098023 Division of Cancer Prevention, National Cancer Institute: U24CA210967 National Center for Advancing Translational Sciences: UL1TR001420 National Center for Advancing Translational Sciences: UL1TR001857

Quality reporting that relies on coded administrative data alone may not completely and accurately depict providers' performance. To assess this concern with a test case, we developed and evaluated a natural language processing (NLP) approach to identify falls risk screenings documented in clinical notes of patients without coded falls risk screening data. Extracting information from 1,558 clinical notes (mainly progress notes) from 144 eligible patients, we generated a lexicon of 38 keywords relevant to falls risk screening, 26 terms for pre-negation, and 35 terms for post-negation. The NLP algorithm identified 62 (out of the 144) patients who falls risk screening documented only in clinical notes and not coded. Manual review confirmed 59 patients as true positives and 77 patients as true negatives. Our NLP approach scored 0.92 for precision, 0.95 for recall, and 0.93 for F-measure. These results support the concept of utilizing NLP to enhance healthcare quality reporting.

The purpose of writing medical notes in a computer system goes beyond documentation for medical-legal purposes or billing. The structure of documentation is a checklist that serves as a cognitive aid and a potential index to retrieve information for learning from the record. For the past 50 years, one of the primary organizing structures for physicians' clinical documentation have been the SOAP note (Subjective, Objective, Assessment, Plan). The cognitive check list is well-suited to differential diagnosis but may not support detection of changes in systems and/or learning from cases. We describe an alternative cognitive checklist called the OODA Loop (Observe, Orient, Decide, Act. Through incorporation of projections of anticipated course events with and without treatment and by making "Decisions" an explicit category of documentation in the medical record in the context of a variable temporal cycle for observations, OODA may enhance opportunities to learn from clinical care.

Discharge summaries (DCS) frequently fail to improve the continuity of care. A chart review of 188 DCS was performed to identify specific components that could be improved through health information technology. Medication reconciliations were analyzed for completeness and for medical reasoning. Documentation of pending results and follow-up details were analyzed. Patient preferences, patient goals, and the handover tone were noted. Patients were discharged on an average of 9.8 medications, only 3% of medication reconciliations were complete and medical reasoning was frequently absent. There were 358 pending results in 188 hospital discharges though only 14% were mentioned in the DCS. Documentation of clear, timely follow-up was present for less than 50% of patients. Patient preferences, patient goals, and lessons learned were rarely included. A handover tone was in only 17% of the DCS. Evaluating the DCS as a clinical handover is novel but information for safe handovers is frequently missing.

Incident Command systems often achieve situational awareness through manual paper-tracking systems. Such systems often produce high latencies and in-complete data, resulting in inefficient and ineffective resource deployment. WIISARD (Wireless Internet Information System for Medical Response in Disasters) collects much more data than a paper-based system, dramatically reducing latency while increasing the kinds and quality of information available to incident commanders. Yet, the introduction of IT into a disaster setting is not problem-free. Notably, system component failures can delay the delivery of data. The type and extent of a failure can have varying effects on the usefulness of information displays. We describe a small, coherent set of customizble information overlays to address this problem, and we discuss reactions to these displays by medical commanders.

The WIISARD disaster response patient tracking program that allows for improved process flow, communication, and patient care using sophisticated wireless technology to coordinate and enhance the care of mass casualties in terrorist attacks or natural disasters. The MICN device has been developed as the link between the Base Station Mobile Intensive Care Nurse and Incident Command in the field. This tool allows the MICN coordinating the incident from the hospital side to more effectively and efficiently communicate with the Incident Command for the accurate and rapid distribution of patients from the scene to the hospitals.

In existing Incident Command systems1, situational awareness is achieved manually through paper tracking systems. Such systems often produce high latencies and incomplete data, resulting in inefficient and ineffective resource deployment. The WIISARD2 system collects much more data than a paper-based system, dramatically reducing latency while increasing the kinds and quality of information available to Incident Commanders. The WIISARD Command Center solves the problem of data overload and uncertainty through the careful use of limited screen area and novel visualization techniques.

Most medical facilities practice managing the large numbers of seriously injured patients expected during catastrophic events. During mass casualty events, as the demands on the healthcare team increase, and the challenges faced by managers escalate, workflow bottlenecks begin to develop and system capacity decreases. This paper describes MASCAL, an integrated software-hardware system designed to enhance management of resources at a hospital during a mass casualty situation. MASCAL uses active 802.11b asset tags to track patients, equipment and staff during the response to a disaster. The system integrates tag position information with data from personnel databases, medical information systems, registration applications and the US Navy's TACMEDCS triage application in a custom visual disaster management environment. MASCAL includes interfaces for a hospital command center, local area managers (emergency room, operating suites, radiology, etc.) and registration personnel. MASCAL is an operational system undergoing functional evaluation at the Naval Medical Center, San Diego, CA.

We study how Metropolitan Medical Response System units conceptualize the physical space of a disaster and their organized response. Using a variety of ethnographic methods before, during, and after a disaster drill, we have developed an initial ontology for geospatial and context-aware technology. The conceptual map of first responders is far more complex than a geographical map. Zones and Areas are used to describe documented concepts critical to MMRS operations. Ad hoc locations also play a critical role, helping first responders communicate tactics in spatial terms. Such distinctions play an important role in the way our experts think about their activity. Successful geoaware alerting systems must incorporate these notions if they are to seamlessly fit into the work flow of first responders.

When medical care is initiated at a mass casualty event, the first activity is the triage of victims, which is the grouping by victims severity of injury. Paper triage tags are often used to mark victims' triage status and to record information on injuries and treatments administered in the field. In this paper we describe the design and development of an"Intelligent Triage Tag" (ITT), an electronic device to coordinate patient field care. ITTs combine the basic functionality of a paper triage tag with sensors, nonvolatile memory, a microprocessor and 802.11 wireless transmission capabilities. ITTs not only display victims' triage status but also signal alerts, and mark patients for transport or immediate medical attention. ITTs record medical data for later access offsite and help organize care by relaying information on the location of the victims during field treatment. ITTs are a part of the Wireless Information System for Medical Response in Disasters (WIISARD) architecture.

Wireless Internet Information System for medicAl Response to Disasters (WIISARD) is a developing wireless technology to coordinate and enhance the care of mass casualties at disaster sites. Mid-tier personnel (area supervisors) play a critical role in disaster response, supervising care processes in the Triage, Treatment and Transport areas of the attack site. The design of a software tool to support mid-tier activities focuses on providing supervisors aggregate information on patient conditions and needs, real-time data on ambulance availability and location, and hospital status and on coordinating care delivery among Triage, Treatment and Transport areas.

One of the most difficult tasks in cost-effectiveness analysis is the measurement of quality weights (utilities) for health states. The task is difficult because subjects often lack familiarity with health states they are asked to rate and because utilities measures such as the standard gamble, ask subjects to perform tasks that are complex and far from everyday experience. A large body of research suggests that computer methods can play an important role in explaining health states and measuring utilities. However, administering computer surveys to a "general public" sample, the most relevant sample for cost-effectiveness analysis, is logistically difficult. In this paper, we describe a software system designed to allow the study of general population preferences in a volunteer Internet survey panel. The approach, which relied on over sampling of ethnic groups and older members of the panel, produced a data set with an ethnically, chronologically and geographically diverse group of respondents, but was not successful in replicating the joint distribution of demographic patterns in the population.