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Background Although alert fatigue is blamed for high override rates in contemporary clinical decision support systems, the concept of alert fatigue is poorly defined. We tested hypotheses arising from two possible alert fatigue mechanisms: (A) cognitive overload associated with amount of work, complexity of work, and effort distinguishing informative from uninformative alerts, and (B) desensitization from repeated exposure to the same alert over time. Methods Retrospective cohort study using electronic health record data (both drug alerts and clinical practice reminders) from January 2010 through June 2013 from 112 ambulatory primary care clinicians. The cognitive overload hypotheses were that alert acceptance would be lower with higher workload (number of encounters, number of patients), higher work complexity (patient comorbidity, alerts per encounter), and more alerts low in informational value (repeated alerts for the same patient in the same year). The desensitization hypothesis was that, for newly deployed alerts, acceptance rates would decline after an initial peak. Results On average, one-quarter of drug alerts received by a primary care clinician, and one-third of clinical reminders, were repeats for the same patient within the same year. Alert acceptance was associated with work complexity and repeated alerts, but not with the amount of work. Likelihood of reminder acceptance dropped by 30% for each additional reminder received per encounter, and by 10% for each five percentage point increase in proportion of repeated reminders. The newly deployed reminders did not show a pattern of declining response rates over time, which would have been consistent with desensitization. Interestingly, nurse practitioners were 4 times as likely to accept drug alerts as physicians. Conclusions Clinicians became less likely to accept alerts as they received more of them, particularly more repeated alerts. There was no evidence of an effect of workload per se, or of desensitization over time for a newly deployed alert. Reducing within-patient repeats may be a promising target for reducing alert overrides and alert fatigue.

There is limited evidence on how clinical outcomes differ by socioeconomic conditions among patients with coronavirus disease 2019 (COVID-19). Most studies focused on COVID-19 patients from a single hospital. Results based on patients from multiple health systems have not been reported. The objective of this study is to examine variation in patient characteristics, outcomes, and healthcare utilization by neighborhood social conditions among COVID-19 patients. We extracted electronic health record data for 23,300 community dwelling COVID-19 patients in New York City between March 1st and June 11th, 2020 from all care settings, including hospitalized patients, patients who presented to the emergency department without hospitalization, and patients with ambulatory visits only. Zip Code Tabulation Area-level social conditions were measured by the Social Deprivation Index (SDI). Using logistic regressions and Cox proportional-hazards models, we examined the association between SDI quintiles and hospitalization and death, controlling for race, ethnicity, and other patient characteristics. Among 23,300 community dwelling COVID-19 patients, 60.7% were from neighborhoods with disadvantaged social conditions (top SDI quintile), although these neighborhoods only account for 34% of overall population. Compared to socially advantaged patients (bottom SDI quintile), socially disadvantaged patients (top SDI quintile) were older (median age 55 vs. 53, P<0.001), more likely to be black (23.1% vs. 6.4%, P<0.001) or Hispanic (25.4% vs. 8.5%, P<0.001), and more likely to have chronic conditions (e.g., diabetes: 21.9% vs. 10.5%, P<0.001). Logistic and Cox regressions showed that patients with disadvantaged social conditions had higher risk for hospitalization (odds ratio: 1.68; 95% confidence interval [CI]: [1.46, 1.94]; P<0.001) and mortality (hazard ratio: 1.91; 95% CI: [1.35, 2.70]; P<0.001), adjusting for other patient characteristics. Substantial socioeconomic disparities in health outcomes exist among COVID-19 patients in NYC. Disadvantaged neighborhood social conditions were associated with higher risk for hospitalization, severity of disease, and death.

Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively. With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30–180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations. In this study, the authors characterise post-acute sequelae of SARS-CoV-2 (PASC) in two large cohorts based on electronic health records from the USA. They identify a broad range of PASC-related conditions which were only partially replicated across the two cohorts, indicating possible heterogeneity between populations.

BackgroundThe HERO registry was established to support research on the impact of the COVID-19 pandemic on US healthcare workers.ObjectiveDescribe the COVID-19 pandemic experiences of and effects on individuals participating in the HERO registry.DesignCross-sectional, self-administered registry enrollment survey conducted from April 10 to July 31, 2020.SettingParticipants worked in hospitals (74.4%), outpatient clinics (7.4%), and other settings (18.2%) located throughout the nation.ParticipantsA total of 14,600 healthcare workers.Main MeasuresCOVID-19 exposure, viral and antibody testing, diagnosis of COVID-19, job burnout, and physical and emotional distress.Key ResultsMean age was 42.0 years, 76.4% were female, 78.9% were White, 33.2% were nurses, 18.4% were physicians, and 30.3% worked in settings at high risk for COVID-19 exposure (e.g., ICUs, EDs, COVID-19 units). Overall, 43.7% reported a COVID-19 exposure and 91.3% were exposed at work. Just 3.8% in both high- and low-risk settings experienced COVID-19 illness. In regression analyses controlling for demographics, professional role, and work setting, the risk of COVID-19 illness was higher for Black/African-Americans (aOR 2.32, 99% CI 1.45, 3.70, p < 0.01) and Hispanic/Latinos (aOR 2.19, 99% CI 1.55, 3.08, p < 0.01) compared with Whites. Overall, 41% responded that they were experiencing job burnout. Responding about the day before they completed the survey, 53% of participants reported feeling tired a lot of the day, 51% stress, 41% trouble sleeping, 38% worry, 21% sadness, 19% physical pain, and 15% anger. On average, healthcare workers reported experiencing 2.4 of these 7 distress feelings a lot of the day.ConclusionsHealthcare workers are at high risk for COVID-19 exposure, but rates of COVID-19 illness were low. The greater risk of COVID-19 infection among race/ethnicity minorities reported in the general population is also seen in healthcare workers. The HERO registry will continue to monitor changes in healthcare worker well-being during the pandemic.Trial RegistrationClinicalTrials.gov identifier NCT04342806

BackgroundImproving care for high-cost patients is increasingly important for improving the value of healthcare. Most prior research has focused on identifying patients with high costs, but the extent to which these costs are potentially preventable remains unclear.ObjectiveTo identify patients with persistent preventable utilization and compare their characteristics with high-cost patients.DesignDescriptive analysis using Medicare claims data from 2013 to 2014.ParticipantsMedicare fee-for-service and dual-eligible beneficiaries (N = 515,689) from the New York metropolitan area who were continuously enrolled in Medicare Parts A and B in 2013 and 2014.Main MeasuresThe primary analysis focuses on patients with persistent preventable utilization (at least one preventable emergency department visit, hospitalization, or 30-day readmission in both 2013 and 2014) and high-cost patients in 2014 (top 10% of total annual spending). We compared demographic, medical, behavioral, and social characteristics and total and preventable healthcare utilization between these two groups.Key ResultsPatients with persistent preventable utilization accounted for 4.8% of the overall patient population, 13.4% of overall costs, but 46.2% of preventable costs among all Medicare patients. Compared with high-cost patients, patients with persistent preventable utilization had lower median healthcare costs ($33,383 vs. $56,552), but their median potentially preventable costs were seven times higher ($7151 vs. $928). We also found that 1.9% of patients could be categorized in both the persistent preventable utilization group and the high-cost group. This subset of patients had the highest median Medicare costs and preventable costs and represented over 30% of total preventable spending and 9.4% of overall costs among all Medicare patients.ConclusionDesigning and targeting interventions for patients with persistent preventable utilization may offer an important opportunity to reduce unnecessary utilization and promote high-value care.

This national survey finds that only 4% of physicians use an extensive, fully functional system for electronic health records, and 13% use some form of basic electronic records. Those who use electronic records are generally satisfied with the systems and believe that they improve the quality of care that patients receive. Only 4% of physicians use an extensive, fully functional system for electronic health records, and 13% use some form of basic electronic records. Those who use electronic records believe that they improve the quality of care that patients receive. Health-information technology, such as sophisticated electronic health records, has the potential to improve health care. 1 – 3 Nevertheless, electronic-records systems have been slow to become part of the practices of physicians in the United States. 4 , 5 To date, there have been no definitive national studies that provide reliable estimates of the adoption of electronic health records by U.S. physicians. Recent estimates of such adoption by physicians range from 9 to 29%. 4 , 5 These percentages were derived from studies that either had a small number of respondents or incompletely specified definitions of an electronic health record. 5 , 6 To provide clearer estimates of . . .

While it is known that social deprivation index (SDI) plays an important role on risk for acquiring Coronavirus Disease 2019 (COVID-19), the impact of SDI on in-hospital outcomes such as intubation and mortality are less well-characterized. We analyzed electronic health record data of adults hospitalized with confirmed COVID-19 between March 1, 2020 and February 8, 2021 from the INSIGHT Clinical Research Network (CRN). To compute the SDI (exposure variable), we linked clinical data using patient’s residential zip-code with social data at zip-code tabulation area. SDI is a composite of seven socioeconomic characteristics determinants at the zip-code level. For this analysis, we categorized SDI into quintiles. The two outcomes of interest were in-hospital intubation and mortality. For each outcome, we examined logistic regression and random forests to determine incremental value of SDI in predicting outcomes. We studied 30,016 included COVID-19 patients. In a logistic regression model for intubation, a model including demographics, comorbidity, and vitals had an Area under the receiver operating characteristic curve (AUROC) = 0.73 (95% CI 0.70–0.75); the addition of SDI did not improve prediction [AUROC = 0.73 (95% CI 0.71–0.75)]. In a logistic regression model for in-hospital mortality, demographics, comorbidity, and vitals had an AUROC = 0.80 (95% CI 0.79–0.82); the addition of SDI in Model 2 did not improve prediction [AUROC = 0.81 (95% CI 0.79–0.82)]. Random forests revealed similar findings. SDI did not provide incremental improvement in predicting in-hospital intubation or mortality. SDI plays an important role on who acquires COVID-19 and its severity; but once hospitalized, SDI appears less important.

Pregnancy alters immune responses and clinical manifestations of COVID-19, but its impact on Long COVID remains uncertain. This study investigated Long COVID risk in individuals with SARS-CoV-2 infection during pregnancy compared to reproductive-age females infected outside of pregnancy. A retrospective analysis of two U.S. databases, the National Patient-Centered Clinical Research Network (PCORnet) and the National COVID Cohort Collaborative (N3C), identified 29,975 pregnant individuals (aged 18–50) with SARS-CoV-2 infection in pregnancy from PCORnet and 42,176 from N3C between March 2020 and June 2023. At 180 days after infection, estimated Long COVID risks for those infected during pregnancy were 16.47 per 100 persons (95% CI, 16.00–16.95) in PCORnet using the PCORnet computational phenotype (CP) model and 4.37 per 100 persons (95% CI, 4.18–4.57) in N3C using the N3C CP model. Compared to matched non-pregnant individuals, the adjusted hazard ratios for Long COVID were 0.86 (95% CI, 0.83–0.90) in PCORnet and 0.70 (95% CI, 0.66–0.74) in N3C. The observed risk factors for Long COVID included Black race/ethnicity, advanced maternal age, first- and second-trimester infection, obesity, and comorbid conditions. While the findings suggest a high incidence of Long COVID among pregnant individuals, their risk was lower than that of matched non-pregnant females. The influence of pregnancy on Long COVID is not well understood. Here, the authors use electronic health record data from the United States to compare the incidence of Long COVID in females after infection in pregnancy with matched non-pregnant females of reproductive age.

Objectives A small percentage of patients account for the bulk of population health care utilization and costs in many countries including the United States (US). In the US, 5% of the population has high health care utilization accounting for nearly 50% of health care costs. A subset of this utilization is deemed preventable, and thus potentially cost saving to patients as well as to the health care system. This study sought to identify drivers of preventable utilization from the perspectives of three stakeholder groups in the US: health system leaders; high-need, high-cost (HNHC) patients or their primary caregivers; and physicians. Methods We performed a qualitative study using interviews of health system leaders and focus groups of HNHC patients, caregivers and physicians. We used a mixed inductive deductive approach to analyse transcripts and identify themes. Results We identified three key drivers of preventable high health care utilization: (1) unmet behavioural health needs, (2) socio-economic determinants of health and (3) challenges associated with accessing health care delivery systems. Conclusions To be potentially more effective, interventions to reduce preventable high health care utilization should incorporate the perspectives of patients, health system leaders and physicians. Particularly important to stakeholders is increased access to mental-health resources, support for patients with low socio-economic resources and systemic changes that reduce wait times for primary care visits and allow providers more time during patient visits.

The coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, 4 biologically distinct subphenotypes were derived in the development cohort ( N  = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and rederivation and prediction in the internal ( N  = 3519) and external ( N  = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add insights to health policy to reduce social disparity in the pandemic.