Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study.

BACKGROUND: For an emergent pandemic, such as COVID-19, the statistics of symptoms based on hospital data may be biased or delayed due to the high proportion of asymptomatic or mild-symptom infections that are not recorded in hospitals. Meanwhile, the difficulty in accessing large-scale clinical data also limits many researchers from conducting timely research. OBJECTIVE: Given the wide coverage and promptness of social media, this study aimed to present an efficient workflow to track and visualize the dynamic characteristics and co-occurrence of symptoms for the COVID-19 pandemic from large-scale and long-term social media data. METHODS: This retrospective study included 471,553,966 COVID-19-related tweets from February 1, 2020, to April 30, 2022. We curated a hierarchical symptom lexicon for social media containing 10 affected organs/systems, 257 symptoms, and 1808 synonyms. The dynamic characteristics of COVID-19 symptoms over time were analyzed from the perspectives of weekly new cases, overall distribution, and temporal prevalence of reported symptoms. The symptom evolutions between virus strains (Delta and Omicron) were investigated by comparing the symptom prevalence during their dominant periods. A co-occurrence symptom network was developed and visualized to investigate inner relationships among symptoms and affected body systems. RESULTS: This study identified 201 COVID-19 symptoms and grouped them into 10 affected body systems. There was a significant correlation between the weekly quantity of self-reported symptoms and new COVID-19 infections (Pearson correlation coefficient=0.8528; P<.001). We also observed a 1-week leading trend (Pearson correlation coefficient=0.8802; P<.001) between them. The frequency of symptoms showed dynamic changes as the pandemic progressed, from typical respiratory symptoms in the early stage to more musculoskeletal and nervous symptoms in the later stages. We identified the difference in symptoms between the Delta and Omicron periods. There were fewer severe symptoms (coma and dyspnea), more flu-like symptoms (throat pain and nasal congestion), and fewer typical COVID symptoms (anosmia and taste altered) in the Omicron period than in the Delta period (all P<.001). Network analysis revealed co-occurrences among symptoms and systems corresponding to specific disease progressions, including palpitations (cardiovascular) and dyspnea (respiratory), and alopecia (musculoskeletal) and impotence (reproductive). CONCLUSIONS: This study identified more and milder COVID-19 symptoms than clinical research and characterized the dynamic symptom evolution based on 400 million tweets over 27 months. The symptom network revealed potential comorbidity risk and prognostic disease progression. These findings demonstrate that the cooperation of social media and a well-designed workflow can depict a holistic picture of pandemic symptoms to complement clinical studies.

Post-Acute COVID-19 Respiratory Symptoms in Patients With Asthma: An Electronic Health Records-Based Study.

BACKGROUND: Post-viral respiratory symptoms are common among patients with asthma. Respiratory symptoms after acute COVID-19 are widely reported in the general population, but large-scale studies identifying symptom risk for patients with asthma are lacking. OBJECTIVE: To identify and compare risk for post-acute COVID-19 respiratory symptoms in patients with and without asthma. METHODS: This retrospective, observational cohort study included COVID-19-positive patients between March 4, 2020, and January 20, 2021, with up to 180 days of health care follow-up in a health care system in the Northeastern United States. Respiratory symptoms recorded in clinical notes from days 28 to 180 after COVID-19 diagnosis were extracted using natural language processing. Cohorts were stratified by hospitalization status during the acute COVID-19 period. Univariable and multivariable analyses were used to compare symptoms among patients with and without asthma adjusting for demographic and clinical confounders. RESULTS: Among 31,084 eligible patients with COVID-19, 2863 (9.2%) had hospitalization during the acute COVID-19 period; 4049 (13.0%) had a history of asthma, accounting for 13.8% of hospitalized and 12.9% of nonhospitalized patients. In the post-acute COVID-19 period, patients with asthma had significantly higher risk of shortness of breath, cough, bronchospasm, and wheezing than patients without an asthma history. Incident respiratory symptoms of bronchospasm and wheezing were also higher in patients with asthma. Patients with asthma who had not been hospitalized during acute COVID-19 had additionally higher risk of cough, abnormal breathing, sputum changes, and a wider range of incident respiratory symptoms. CONCLUSION: Patients with asthma may have an under-recognized burden of respiratory symptoms after COVID-19 warranting increased awareness and monitoring in this population.

Use of Hospital Capacity Command Centers to Improve Patient Flow and Safety: A Scoping Review.

OBJECTIVES: Delayed emergency department (ED) and hospital patient throughput is recognized as a critical threat to patient safety. Increasingly, hospitals are investing significantly in deploying command centers, long used in airlines and the military, to proactively manage hospital-wide patient flow. This scoping review characterizes the evidence related to hospital capacity command centers (CCCs) and synthesizes current data regarding their implementation. METHODS: As no consensus definition exists for CCCs, we characterized them as units (i) involving interdisciplinary, permanently colocated teams, (ii) using real-time data, and (iii) managing 2 or more patient flow functions (e.g., bed management, transfers, discharge planning, etc.), to distinguish CCCs from transfer centers. We undertook a scoping review of the medical and gray literature published through April 2019 related to CCCs meeting these criteria. RESULTS: We identified 8 eligible articles (including 4 peer-reviewed studies) describing 7 CCCs of varying designs. The most common CCC outcome measures related to transfer volume (n = 5) and ED boarding (n = 4). Several CCCs also monitored patient-level clinical parameters. Although all articles reported performance improvements, heterogeneity in CCC design and evidence quality currently restricts generalizability of findings. CONCLUSIONS: Numerous anecdotal accounts suggest that CCCs are being widely deployed in an effort to improve hospital patient flow and safety, yet peer-reviewed evidence regarding their design and effectiveness is in its earliest stages. The costs, objectives, and growing deployment of CCCs merit an investment in rigorous research to better measure their processes and outcomes. We propose a standard definition, conceptual framework, research priorities, and reporting standards to guide future investigation of CCCs.

The Association of the First Surge of the COVID-19 Pandemic with the High- and Low-Value Outpatient Care Delivered to Adults in the USA.

BACKGROUND: The first surge of the COVID-19 pandemic entirely altered healthcare delivery. Whether this also altered the receipt of high- and low-value care is unknown. OBJECTIVE: To test the association between the April through June 2020 surge of COVID-19 and various high- and low-value care measures to determine how the delivery of care changed. DESIGN: Difference in differences analysis, examining the difference in quality measures between the April through June 2020 surge quarter and the January through March 2020 quarter with the same 2 quarters' difference the year prior. PARTICIPANTS: Adults in the MarketScan® Commercial Database and Medicare Supplemental Database. MAIN MEASURES: Fifteen low-value and 16 high-value quality measures aggregated into 8 clinical quality composites (4 of these low-value). KEY RESULTS: We analyzed 9,352,569 adults. Mean age was 44 years (SD, 15.03), 52% were female, and 75% were employed. Receipt of nearly every type of low-value care decreased during the surge. For example, low-value cancer screening decreased 0.86% (95% CI, -1.03 to -0.69). Use of opioid medications for back and neck pain (DiD +0.94 [95% CI, +0.82 to +1.07]) and use of opioid medications for headache (DiD +0.38 [95% CI, 0.07 to 0.69]) were the only two measures to increase. Nearly all high-value care measures also decreased. For example, high-value diabetes care decreased 9.75% (95% CI, -10.79 to -8.71). CONCLUSIONS: The first COVID-19 surge was associated with receipt of less low-value care and substantially less high-value care for most measures, with the notable exception of increases in low-value opioid use.

Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications.

OBJECTIVE: Understanding public discourse on emergency use of unproven therapeutics is essential to monitor safe use and combat misinformation. We developed a natural language processing-based pipeline to understand public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter across time. METHODS: This retrospective study included 609 189 US-based tweets between January 29, 2020 and November 30, 2021 on 4 drugs that gained wide public attention during the COVID-19 pandemic: (1) Hydroxychloroquine and Ivermectin, drug therapies with anecdotal evidence; and (2) Molnupiravir and Remdesivir, FDA-approved treatment options for eligible patients. Time-trend analysis was used to understand the popularity and related events. Content and demographic analyses were conducted to explore potential rationales of people's stances on each drug. RESULTS: Time-trend analysis revealed that Hydroxychloroquine and Ivermectin received much more discussion than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin were highly politicized, related to conspiracy theories, hearsay, celebrity effects, etc. The distribution of stance between the 2 major US political parties was significantly different (P < .001); Republicans were much more likely to support Hydroxychloroquine (+55%) and Ivermectin (+30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (+7%) more than the general population; in contrast, the general population was more likely to support Ivermectin (+14%). CONCLUSION: Our study found that social media users with have different perceptions and stances on off-label versus FDA-authorized drug use across different stages of COVID-19, indicating that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation for promoting safe drug use. Our analysis pipeline and stance detection models are made public at https://github.com/ningkko/COVID-drug.

Demographic Disparity in Use of Telemedicine for Ambulatory General Surgical Consultation During the COVID-19 Pandemic: Analysis of the Initial Public Health Emergency and Second Phase Periods.

BACKGROUND: Surgical patients with limited digital literacy may experience reduced telemedicine access. We investigated racial/ethnic and socioeconomic disparities in telemedicine compared with in-person surgical consultation during the coronavirus disease 2019 (COVID-19) pandemic. STUDY DESIGN: Retrospective analysis of new visits within the Division of General & Gastrointestinal Surgery at an academic medical center occurring between March 24 through June 23, 2020 (Phase I, Massachusetts Public Health Emergency) and June 24 through December 31, 2020 (Phase II, relaxation of restrictions on healthcare operations) was performed. Visit modality (telemedicine/phone vs in-person) and demographic data were extracted. Bivariate analysis and multivariable logistic regression were performed to evaluate associations between patient characteristics and visit modality. RESULTS: During Phase I, 347 in-person and 638 virtual visits were completed. Multivariable modeling demonstrated no significant differences in virtual compared with in-person visit use across racial/ethnic or insurance groups. Among patients using virtual visits, Latinx patients were less likely to have video compared with audio-only visits than White patients (OR, 0.46; 95% CI 0.22-0.96). Black race and insurance type were not significant predictors of video use. During Phase II, 2,922 in-person and 1,001 virtual visits were completed. Multivariable modeling demonstrated that Black patients (OR, 1.52; 95% CI 1.12-2.06) were more likely to have virtual visits than White patients. No significant differences were observed across insurance types. Among patients using virtual visits, race/ethnicity and insurance type were not significant predictors of video use. CONCLUSION: Black patients used telemedicine platforms more often than White patients during the second phase of the COVID-19 pandemic. Virtual consultation may help increase access to surgical care among traditionally under-resourced populations.

Successfully Implementing Digital Health to Ensure Future Global Health Security During Pandemics: A Consensus Statement.

Importance: COVID-19 has highlighted widespread chronic underinvestment in digital health that hampered public health responses to the pandemic. Recognizing this, the Riyadh Declaration on Digital Health, formulated by an international interdisciplinary team of medical, academic, and industry experts at the Riyadh Global Digital Health Summit in August 2020, provided a set of digital health recommendations for the global health community to address the challenges of current and future pandemics. However, guidance is needed on how to implement these recommendations in practice. Objective: To develop guidance for stakeholders on how best to deploy digital health and data and support public health in an integrated manner to overcome the COVID-19 pandemic and future pandemics. Evidence Review: Themes were determined by first reviewing the literature and Riyadh Global Digital Health Summit conference proceedings, with experts independently contributing ideas. Then, 2 rounds of review were conducted until all experts agreed on the themes and main issues arising using a nominal group technique to reach consensus. Prioritization was based on how useful the consensus recommendation might be to a policy maker. Findings: A diverse stakeholder group of 13 leaders in the fields of public health, digital health, and health care were engaged to reach a consensus on how to implement digital health recommendations to address the challenges of current and future pandemics. Participants reached a consensus on high-priority issues identified within 5 themes: team, transparency and trust, technology, techquity (the strategic development and deployment of technology in health care and health to achieve health equity), and transformation. Each theme contains concrete points of consensus to guide the local, national, and international adoption of digital health to address challenges of current and future pandemics. Conclusions and Relevance: The consensus points described for these themes provide a roadmap for the implementation of digital health policy by all stakeholders, including governments. Implementation of these recommendations could have a significant impact by reducing fatalities and uniting countries on current and future battles against pandemics.

Sequential coupling of dry and wet COVID-19 screening to reduce the number of quarantined individuals.

INTRODUCTION: Currently, several countries are facing severe public health and policy challenges when designing their COVID-19 screening strategy. A quantitative analysis of the potential impact that combing the Rapid Antigen Test (RAT; Wet screening) and digital checker (Dry screening) can have on the healthcare system is lacking. METHOD: We created a hypothetical COVID-19 cohort for the analysis. The population size was set as 10 million with three levels of disease prevalence (10%, 1%, or 0.1%) under the assumption that a positive test result will lead to quarantine. A digital checker and two RATs are used for analysis. We further hypothesized two scenarios: RAT only and RAT plus digital checker. We then calculated the number of quarantined in both scenarios and compared the two to understand the benefits of sequential coupling of a digital checker with a RAT. RESULT: Sequential coupling of the digital checker and RAT can significantly reduce the number of individuals quarantined to 0.95-1.33M, 0.86-1.29M, and 0.86-1.29M, respectively, under the three different prevalence levels. CONCLUSION: Sequential coupling of digital checker and RAT at a population level for COVID-19 positive test to reduce the number of people who require quarantine and alleviating stress on the overburdened healthcare systems during the COVID-19 pandemic.

Opioid prescribing among new users for non-cancer pain in the USA, Canada, UK, and Taiwan: A population-based cohort study.

BACKGROUND: The opioid epidemic in North America has been driven by an increase in the use and potency of prescription opioids, with ensuing excessive opioid-related deaths. Internationally, there are lower rates of opioid-related mortality, possibly because of differences in prescribing and health system policies. Our aim was to compare opioid prescribing rates in patients without cancer, across 5 centers in 4 countries. In addition, we evaluated differences in the type, strength, and starting dose of medication and whether these characteristics changed over time. METHODS AND FINDINGS: We conducted a retrospective multicenter cohort study of adults who are new users of opioids without prior cancer. Electronic health records and administrative health records from Boston (United States), Quebec and Alberta (Canada), United Kingdom, and Taiwan were used to identify patients between 2006 and 2015. Standard dosages in morphine milligram equivalents (MMEs) were calculated according to The Centers for Disease Control and Prevention. Age- and sex-standardized opioid prescribing rates were calculated for each jurisdiction. Of the 2,542,890 patients included, 44,690 were from Boston (US), 1,420,136 Alberta, 26,871 Quebec (Canada), 1,012,939 UK, and 38,254 Taiwan. The highest standardized opioid prescribing rates in 2014 were observed in Alberta at 66/1,000 persons compared to 52, 51, and 18/1,000 in the UK, US, and Quebec, respectively. The median MME/day (IQR) at initiation was highest in Boston at 38 (20 to 45); followed by Quebec, 27 (18 to 43); Alberta, 23 (9 to 38); UK, 12 (7 to 20); and Taiwan, 8 (4 to 11). Oxycodone was the first prescribed opioid in 65% of patients in the US cohort compared to 14% in Quebec, 4% in Alberta, 0.1% in the UK, and none in Taiwan. One of the limitations was that data were not available from all centers for the entirety of the 10-year period. CONCLUSIONS: In this study, we observed substantial differences in opioid prescribing practices for non-cancer pain between jurisdictions. The preference to start patients on higher MME/day and more potent opioids in North America may be a contributing cause to the opioid epidemic.

PASCLex: A comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon derived from electronic health record clinical notes.

OBJECTIVE: To develop a comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon (PASCLex) from clinical notes to support PASC symptom identification and research. METHODS: We identified 26,117 COVID-19 positive patients from the Mass General Brigham's electronic health records (EHR) and extracted 328,879 clinical notes from their post-acute infection period (day 51-110 from first positive COVID-19 test). PASCLex incorporated Unified Medical Language System® (UMLS) Metathesaurus concepts and synonyms based on selected semantic types. The MTERMS natural language processing (NLP) tool was used to automatically extract symptoms from a development dataset. The lexicon was iteratively revised with manual chart review, keyword search, concept consolidation, and evaluation of NLP output. We assessed the comprehensiveness of PASCLex and the NLP performance using a validation dataset and reported the symptom prevalence across the entire corpus. RESULTS: PASCLex included 355 symptoms consolidated from 1520 UMLS concepts of 16,466 synonyms. NLP achieved an averaged precision of 0.94 and an estimated recall of 0.84. Symptoms with the highest frequency included pain (43.1%), anxiety (25.8%), depression (24.0%), fatigue (23.4%), joint pain (21.0%), shortness of breath (20.8%), headache (20.0%), nausea and/or vomiting (19.9%), myalgia (19.0%), and gastroesophageal reflux (18.6%). DISCUSSION AND CONCLUSION: PASC symptoms are diverse. A comprehensive lexicon of PASC symptoms can be derived using an ontology-driven, EHR-guided and NLP-assisted approach. By using unstructured data, this approach may improve identification and analysis of patient symptoms in the EHR, and inform prospective study design, preventative care strategies, and therapeutic interventions for patient care.

The intersection of big data and epidemiology for epidemiologic research: The impact of the COVID-19 pandemic.

Big data epidemiology facilitates pandemic response by providing data-driven insights by utilizing big data tools that differ from traditional methods. Aspects regarding 'garbage in, garbage out', such as insufficient data, inaccessibility of data, missing data, uncertainty in handling data and bias in analysis or common findings are addressable by combining techniques across disciplines.

TechQuity is an imperative for health and technology business: Let's work together to achieve it.

Open discussions of social justice and health inequities may be an uncommon focus within information technology science, business, and health care delivery partnerships. However, the COVID-19 pandemic-which disproportionately affected Black, indigenous, and people of color-has reinforced the need to examine and define roles that technology partners should play to lead anti-racism efforts through our work. In our perspective piece, we describe the imperative to prioritize TechQuity-equity and social justice as a technology business strategy-through collaborating in partnerships that focus on eliminating racial and social inequities.

Telehealth in US hospitals: State-level reimbursement policies no longer influence adoption rates.

OBJECTIVES: Prior to COVID-19, levels of adoption of telehealth were low in the U.S., though they exploded during the pandemic. Following the pandemic, it will be critical to identify the characteristics that were associated with adoption of telehealth prior to the pandemic as key drivers of adoption and outside of a public health emergency. MATERIALS AND METHODS: We examined three data sources: The American Telemedicine Association's 2019 state telehealth analysis, the American Hospital Association's 2018 annual survey of acute care hospitals and its Information Technology Supplement. Telehealth adoption was measured through five telehealth categories. Independent variables included seven hospital characteristics and five reimbursement policies. After bivariate comparisons, we developed a multivariable model using logistic regression to assess characteristics associated with telehealth adoption. RESULTS: Among 2923 US hospitals, 73% had at least one telehealth capability. More than half of these hospitals invested in telehealth consultation services and stroke care. Non-profit hospitals, affiliated hospitals, major teaching hospitals, and hospitals located in micropolitan areas (those with 10-50,000 people) were more likely to adopt telehealth. In contrast, hospitals that lacked electronic clinical documentation, were unaffiliated with a hospital system, or were investor-owned had lower odds of adopting telehealth. None of the statewide policies were associated with adoption of telehealth. CONCLUSIONS: Telehealth policy requires major revisions soon, and we suggest that these policies should be national rather than at the state level. Further steps as incentivizing rural hospitals for adopting interoperable systems and expanding RPM billing opportunities will help drive adoption, and promote equity.

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases.

Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

Utilizing health information technology in the treatment and management of patients during the COVID-19 pandemic: Lessons from international case study sites.

OBJECTIVE: The study sought to develop an in-depth understanding of how hospitals with a long history of health information technology (HIT) use have responded to the COVID-19 (coronavirus disease 2019) pandemic from an HIT perspective. MATERIALS AND METHODS: We undertook interviews with 44 healthcare professionals with a background in informatics from 6 hospitals internationally. Interviews were informed by a topic guide and were conducted via videoconferencing software. Thematic analysis was employed to develop a coding framework and identify emerging themes. RESULTS: Three themes and 6 subthemes were identified. HITs were employed to manage time and resources during a surge in patient numbers through fast-tracked governance procedures, and the creation of real-time bed capacity tracking within electronic health records. Improving the integration of different hospital systems was identified as important across sites. The use of hard-stop alerts and order sets were perceived as being effective at helping to respond to potential medication shortages and selecting available drug treatments. Utilizing information from multiple data sources to develop alerts facilitated treatment. Finally, the upscaling/optimization of telehealth and remote working capabilities was used to reduce the risk of nosocomial infection within hospitals. DISCUSSION: A number of the HIT-related changes implemented at these sites were perceived to have facilitated more effective patient treatment and management of resources. Informaticians generally felt more valued by hospital management as a result. CONCLUSIONS: Improving integration between data systems, utilizing specialized alerts, and expanding telehealth represent strategies that hospitals should consider when using HIT for delivering hospital care in the context of the COVID-19 pandemic.

Disparities In Telehealth Use Among California Patients With Limited English Proficiency.

Telehealth services that allow remote communication between the patient and the clinical team are an emerging part of care delivery. Given language barriers, patients with limited English proficiency present a unique set of challenges in integrating telehealth and ensuring equity. Using data from 84,419 respondents in the 2015-18 California Health Interview Survey, we assessed the association between limited English proficiency and telehealth use (telephone and video visits) and evaluated the impact of telehealth use on health care access and use. We found that patients with limited English proficiency had lower rates of telehealth use (4.8 percent versus 12.3 percent) compared with proficient English speakers. In weighted multivariable logistic regression, patients with limited English proficiency still had about half the odds of using telehealth. Telehealth use was associated with increased emergency department use for all patients. This study suggests that policy makers and clinicians must focus on limited English proficiency as an important dimension to promote telehealth equity and decrease digital divides.