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Introduction: Healthcare organizations are under increasing pressure from policymakers, payers, and advocates to screen for and address patients’ health-related social needs (HRSN). The emergency department (ED) presents several challenges to HRSN screening, and patients are frequently not screened for HRSNs. Predictive modeling using machine learning and artificial intelligence, approaches may address some pragmatic HRSN screening challenges in the ED. Because predictive modeling represents a substantial change from current approaches, in this study we explored the acceptability of HRSN predictive modeling in the ED.Methods: Emergency clinicians, ED staff, and patient perspectives on the acceptability and usage of predictive modeling for HRSNs in the ED were obtained through in-depth semi-structured interviews (eight per group, total 24). All participants practiced at or had received care from an urban, Midwest, safety-net hospital system. We analyzed interview transcripts using a modified thematic analysis approach with consensus coding.Results: Emergency clinicians, ED staff, and patients agreed that HRSN predictive modeling must lead to actionable responses and positive patient outcomes. Opinions about using predictive modeling results to initiate automatic referrals to HRSN services were mixed. Emergency clinicians and staff wanted transparency on data inputs and usage, demanded high performance, and expressed concern for unforeseen consequences. While accepting, patients were concerned that prediction models can miss individuals who required services and might perpetuate biases.Conclusion: Emergency clinicians, ED staff, and patients expressed mostly positive views about using predictive modeling for HRSNs. Yet, clinicians, staff, and patients listed several contingent factors impacting the acceptance and implementation of HRSN prediction models in the ED.

ObjectiveThis study aimed to examine changes in child emergency department (ED) discharges and hospitalizations for primary general medical (GM) and primary psychiatric disorders; prevalence of psychiatric disorders among acute care encounters; and change in acute mental health (MH) care encounters by disorder type and, within these categories, by child sociodemographic characteristics before and after statewide COVID-19–related school closure orders.MethodsThis retrospective, cross-sectional cohort study used the Pediatric Health Information System database to assess percent changes in ED discharges and hospitalizations (N=2,658,474 total encounters) among children ages 3–17 years in 44 U.S. children’s hospitals in 2020 compared with 2019, by using matched data for 36- and 12-calendar-week intervals.ResultsDecline in MH ED discharges accounted for about half of the decline in ED discharges and hospitalizations for primary GM disorders (−24.8% vs. −49.1%), and MH hospitalizations declined 3.4 times less (−8.0% vs. −26.8%) in 2020. Suicide attempt or self-injury and depressive disorders accounted for >50% of acute MH care encounters before and after the statewide school closures. The increase in both ED discharges and hospitalizations for suicide attempt or self-injury was 5.1 percentage points (p

ObjectiveTo measure univariate and covariate-adjusted trends in children's mental health-related emergency department (MH-ED) use across geographically diverse areas of the U.S. during the first wave of the Coronavirus-2019 (COVID-19) pandemic.MethodThis is a retrospective, cross-sectional cohort study using electronic health records from four academic health systems, comparing percent volume change and adjusted risk of child MH-ED visits among children aged 3-17 years, matched on 36-week (3/18/19-11/25/19 vs. 3/16/20-11/22/20) and 12-week seasonal time intervals. Adjusted incidence rate ratios (IRR) were calculated using multivariate Poisson regression.ResultsVisits declined during spring-fall 2020 (n = 3892 vs. n = 5228, -25.5%) and during spring (n = 1051 vs. n = 1839, -42.8%), summer (n = 1430 vs. n = 1469, -2.6%), and fall (n = 1411 vs. n = 1920, -26.5%), compared with 2019. There were greater declines among males (28.2% vs. females -22.9%), children 6-12-year (-28.6% vs. -25.9% for 3-5 years and -22.9% for 13-17 years), and Black children (-34.8% vs. -17.7% to -24.9%). Visits also declined for developmental disorders (-17.0%) and childhood-onset disorders (e.g., attention deficit and hyperactivity disorders; -18.0%). During summer-fall 2020, suicide-related visits rose (summer +29.8%, fall +20.4%), but were not significantly elevated from 2019 when controlling for demographic shifts. In contrast, MH-ED use during spring-fall 2020 was significantly reduced for intellectual disabilities (IRR 0.62 [95% CI 0.47-0.86]), developmental disorders (IRR 0.71 [0.54-0.92]), and childhood-onset disorders (IRR 0.74 [0.56-0.97]).ConclusionsThe early pandemic brought overall declines in child MH-ED use alongside co-occurring demographic and diagnostic shifts. Children vulnerable to missed detection during instructional disruptions experienced disproportionate declines, suggesting need for future longitudinal research in this population.

PurposeRoutinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Patients and methodsWe conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.ResultsWe aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.ConclusionWe constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.

Background Hybrid coronary revascularization (HCR) involves the integration of coronary artery bypass grafting (CABG) and percutaneous coronary intervention to treat multivessel coronary artery disease. Our objective was to perform a comparative analysis with long-term follow-up between HCR and conventional off-pump CABG. Methods and Results We compared all double off-pump CABG (n=216) and HCR (n=147; robotic-assisted minimally invasive direct CABG of the left internal thoracic artery to the left anterior descending artery and percutaneous coronary intervention to one of the non-left anterior descending vessels) performed at a single institution between March 2004 and November 2015. To adjust for the selection bias of receiving either off-pump CABG or HCR, we performed a propensity score analysis using inverse-probability weighting. Both groups had similar results in terms of re-exploration for bleeding, perioperative myocardial infarction, stroke, blood transfusion, in-hospital mortality, and intensive care unit length of stay. HCR was associated with a higher in-hospital reintervention rate (CABG 0% versus HCR 3.4%; P=0.03), lower prolonged mechanical ventilation (>24 hours) rate (4% versus 0.7%; P=0.02), and shorter hospital length of stay (8.1±5.8  versus 4.5±2.1 days; P

With the advent of interoperability standards such as FHIR, SMART, CDS Hooks, and CQL, interoperable clinical decision support (CDS) holds great promise for improving healthcare. In 2018, the Agency for Healthcare Research and Quality (AHRQ)-sponsored Patient-Centered CDS Learning Network (PCCDS LN) chartered a Technical Framework Working Group (TechFWG) to identify barriers, facilitators, and potential solutions for interoperable CDS, with a specific focus on addressing the opioid epidemic. Through an open, multi-stakeholder process that engaged 54 representatives from healthcare, industry, and academia, the TechFWG identified barriers in 6 categories: regulatory environment, data integration, scalability, business case, effective and useful CDS, and care planning and coordination. Facilitators and key recommendations were also identified for overcoming these barriers. The key insights were also extrapolated to CDS-facilitated care improvement outside of the specific opioid use case. If applied broadly, the recommendations should help advance the availability and impact of interoperable CDS delivered at scale.

Background: Health-related social needs (HRSNs), such as housing instability, food insecurity, and financial strain, are increasingly prevalent among patients. Healthcare organizations must first correctly identify patients with HRSNs to refer them to appropriate services or offer resources to address their HRSNs. Yet, current identification methods are suboptimal, inconsistently applied, and cost prohibitive. Machine learning (ML) predictive modeling applied to existing data sources may be a solution to systematically and effectively identify patients with HRSNs. The performance of ML predictive models using data from electronic health records (EHRs) and other sources has not been compared to other methods of identifying patients needing HRSN services. Methods: A screening questionnaire that included housing instability, food insecurity, transportation barriers, legal issues, and financial strain was administered to adult ED patients at a large safety-net hospital in the mid-Western United States (n = 1,101). We identified those patients likely in need of HRSN-related services within the next 30 days using positive indications from referrals, encounters, scheduling data, orders, or clinical notes. We built an XGBoost classification algorithm using responses from the screening questionnaire to predict HRSN needs (screening questionnaire model). Additionally, we extracted features from the past 12 months of existing EHR, administrative, and health information exchange data for the survey respondents. We built ML predictive models with these EHR data using XGBoost (ML EHR model). Out of concerns of potential bias, we built both the screening question model and the ML EHR model with and without demographic features. Models were assessed on the validation set using sensitivity, specificity, and Area Under the Curve (AUC) values. Models were compared using the Delong test. Results: Almost half (41%) of the patients had a positive indicator for a likely HRSN service need within the next 30 days, as identified through referrals, encounters, scheduling data, orders, or clinical notes. The screening question model had suboptimal performance, with an AUC = 0.580 (95%CI = 0.546, 0.611). Including gender and age resulted in higher performance in the screening question model (AUC = 0.640; 95%CI = 0.609, 0.672). The ML EHR models had higher performance. Without including age and gender, the ML EHR model had an AUC = 0.765 (95%CI = 0.737, 0.792). Adding age and gender did not improve the model (AUC = 0.722; 95%CI = 0.744, 0.800). The screening questionnaire models indicated bias with the highest performance for White non-Hispanic patients. The performance of the ML EHR-based model also differed by race and ethnicity. Conclusion: ML predictive models leveraging several robust EHR data sources outperformed models using screening questions only. Nevertheless, all models indicated biases. Additional work is needed to design predictive models for effectively identifying all patients with HRSNs. [ABSTRACT FROM AUTHOR]

Background:Tele-oncology became a widely used tool during the COVID-19 pandemic, but there was limited understanding of how patient–clinician communication occurred using the technology. Our goal was to identify how communication transpired during tele-oncology consultations compared with in-person appointments. Methods:A convergent parallel mixed-method design was utilized for the web-based survey, and follow-up interviews were conducted with cancer patients from March to December 2020. Participants were recruited from the University of Florida Health Cancer Center and two national cancer organizations. During the survey, participants rated their clinician's patient-centered communication behaviors. Open-ended survey responses and interview data were combined and analyzed thematically using the constant comparative method. Results:A total of 158 participants completed the survey, and 33 completed an interview. Ages ranged from 19 to 88 years (mean = 64.2; standard deviation = 13.0); 53.2% identified as female and 44.9% as male. The majority of respondents (76%) considered communication in tele-oncology equal to in-person visits. Preferences for tele-oncology included the ability to get information from the clinician, with 13.5% rating tele-oncology as better than in-person appointments. Tele-oncology was considered worse than in-person appointments for eye contact (n = 21, 12.4%) and virtual waiting room times (n = 50, 29.4%). The following qualitative themes corresponded with several quantitative variables: (1) commensurate to in-person appointments, (2) uncertainty with the digital platform, (3) lack of a personal connection, and (4) enhanced patient experience. Conclusion:Patient-centered communication behaviors were mostly viewed as equally prevalent during tele-oncology and in-person appointments. Addressing the challenges of tele-oncology is necessary to improve the patient experience. [ABSTRACT FROM AUTHOR]

PURPOSE Patients are frequently asked to share their personal health information, The objective of this study was to compare the effects on patient experiences of 3 electronic consent (e-consent) versions asking patients to share their health records for research, METHODS A multi-arm randomized controlled trial was conducted from November 2017 through November 2018. Adult patients (n = 734) were recruited from 4 family medicine clinics in Florida, Using a tablet computer, participants were randomized to (1) a standard e-consent (standard), (2) an e-consent containing standard information plus hyperlinks to additional interactive details (interactive), or (3) an e-consent containing standard information, interactive hyperlinks, and factual messages about data protections and researcher training (trust-enhanced), Satisfaction (1 to 5), subjective understanding (0 to 100), and other outcomes were measured immediately, at 1 week, and at 6 months, RESULTS A majority of participants (94%) consented to future uses of their health record information for research. No differences in study outcomes between versions were observed at immediate or 1-week follow-up, At 6-month follow-up, compared with the standard e-consent, participants who used the interactive e-consent reported greater satisfaction (B = 0,43; SE = 0,09; P CONCLUSIONS Patients who used e-consents with interactive research details and trust-enhancing messages reported higher satisfaction and understanding at 6-month follow-up, Research institutions should consider developing and further validating e-consents that interactively deliver information beyond that required by federal regulations, including facts that may enhance patient trust in research, Key words: consumer health informatics; electronic health records; informed consent; health communication; trust https://doi.org/10.1370/afm.2610, INTRODUCTION Growing use of electronic health record (EHR) data for research has created practical and ethical challenges for health systems and researchers. (1,2-4) In 2018, the Federal Policy for the [...]

Background/Objectives: Concurrent opioid (OPI) and benzodiazepine (BZD) use may exacerbate injurious fall risk (e.g., falls and fractures) compared to no use or use alone. Yet, patients may need concurrent OPI-BZD use for co-occurring conditions (e.g., pain and anxiety). Therefore, we examined the association between longitudinal OPI-BZD dosing patterns and subsequent injurious fall risk. Methods: We conducted a retrospective cohort study including non-cancer fee-for-service Medicare beneficiaries initiating OPI and/or BZD in 2016–2018. We identified OPI-BZD use patterns during the 3 months following OPI and/or BZD initiation (i.e., trajectory period) using group-based multi-trajectory models. We estimated the time to first injurious falls within the 3-month post-trajectory period using inverse-probability-of-treatment-weighted Cox proportional hazards models. Results: Among 622,588 beneficiaries (age ≥ 65 = 84.6%, female = 58.1%, White = 82.7%; having injurious falls = 0.45%), we identified 13 distinct OPI-BZD trajectories: Group (A): Very-low OPI-only (early discontinuation) (44.9% of the cohort); (B): Low OPI-only (rapid decline) (15.1%); (C): Very-low OPI-only (late discontinuation) (7.7%); (D): Low OPI-only (gradual decline) (4.0%); (E): Moderate OPI-only (rapid decline) (2.3%); (F): Very-low BZD-only (late discontinuation) (11.5%); (G): Low BZD-only (rapid decline) (4.5%); (H): Low BZD-only (stable) (3.1%); (I): Moderate BZD-only (gradual decline) (2.1%); (J): Very-low OPI (rapid decline)/Very-low BZD (late discontinuation) (2.9%); (K): Very-low OPI (rapid decline)/Very-low BZD (increasing) (0.9%); (L): Very-low OPI (stable)/Low BZD (stable) (0.6%); and (M): Low OPI (gradual decline)/Low BZD (gradual decline) (0.6%). Compared with Group (A), six trajectories had an increased 3-month injurious falls risk: (C): HR = 1.78, 95% CI = 1.58–2.01; (D): HR = 2.24, 95% CI = 1.93–2.59; (E): HR = 2.60, 95% CI = 2.18–3.09; (H): HR = 2.02, 95% CI = 1.70–2.40; (L): HR = 2.73, 95% CI = 1.98–3.76; and (M): HR = 1.96, 95% CI = 1.32–2.91. Conclusions: Our findings suggest that 3-month injurious fall risk varied across OPI-BZD trajectories, highlighting the importance of considering both dose and duration when assessing injurious fall risk of OPI-BZD use among older adults. [ABSTRACT FROM AUTHOR]

BackgroundElectronic health records (EHRs) hold great potential for longitudinal mother-baby studies, ranging from assessing study feasibility to facilitating patient recruitment to streamlining study visits and data collection. Existing studies on the perspectives of pregnant and breastfeeding women on EHR use have been limited to the use of EHRs to engage in health care rather than to participate in research. ObjectiveThe aim of this study is to explore the perspectives of pregnant and breastfeeding women on releasing their own and their infants’ EHR data for longitudinal research to identify factors affecting their willingness to participate in research. MethodsWe conducted semistructured interviews with pregnant or breastfeeding women from Alachua County, Florida. Participants were asked about their familiarity with EHRs and EHR patient portals, their comfort with releasing maternal and infant EHR data to researchers, the length of time of the data release, and whether individual research test results should be included in the EHR. The interviews were transcribed verbatim. Transcripts were organized and coded using the NVivo 12 software (QSR International), and coded data were thematically analyzed using constant comparison. ResultsParticipants included 29 pregnant or breastfeeding women aged between 22 and 39 years. More than half of the sample had at least an associate degree or higher. Nearly all participants (27/29, 93%) were familiar with EHRs and had experience accessing an EHR patient portal. Less than half of the participants (12/29, 41%) were willing to make EHR data available to researchers for the duration of a study or longer. Participants’ concerns about sharing EHRs for research purposes emerged in 3 thematic domains: privacy and confidentiality, transparency by the research team, and surrogate decision-making on behalf of infants. The potential release of sensitive or stigmatizing information, such as mental or sexual health history, was considered in the decisions to release EHRs. Some participants viewed the simultaneous use of their EHRs for both health care and research as potentially beneficial, whereas others expressed concerns about mixing their health care with research. ConclusionsThis exploratory study indicates that pregnant and breastfeeding women may be willing to release EHR data to researchers if researchers adequately address their concerns regarding the study design, communication, and data management. Pregnant and breastfeeding women should be included in EHR-based research as long as researchers are prepared to address their concerns.

BackgroundMobile health apps related to maternal and infant health (MIH) are prevalent and frequently used. Some of these apps are extremely popular and have been downloaded over 5 million times. However, the understanding of user behavior and user adoption of these apps based on consumer preferences for different app features and categories is limited. ObjectiveThis study aimed to examine the relationship between MIH app characteristics and users’ perceived satisfaction and intent to use. MethodsThe associations between app characteristics, ratings, and downloads were assessed in a sample of MIH apps designed to provide health education or decision-making support to pregnant women or parents and caregivers of infants. Multivariable linear regression was used to assess the relationship between app characteristics and user ratings, and ordinal logistic regression was used to assess the relationship between app characteristics and user downloads. ResultsThe analyses of user ratings and downloads included 421 and 213 apps, respectively. The average user rating was 3.79 out of 5. Compared with the Apple App Store, the Google Play Store was associated with high user ratings (beta=.33; P=.005). Apps with higher standardized user ratings (beta=.80; P

Objectives: Falls are persistent among community-dwelling older adults despite existing prevention guidelines. We described how urban and rural primary care staff and older adults manage fall risk and factors important to integration of computerized clinical decision support (CCDS). Methods: Interviews, contextual inquiries, and workflow observations were analyzed using content analysis and synthesized into a journey map. Sociotechnical and PRISM domains were applied to identify workflow factors important to sustainable CCDS integration. Results: Participants valued fall prevention and described similar approaches. Available resources differed between rural and urban locations. Participants wanted evidence-based guidance integrated into workflows to bridge skills gaps. Discussion: Sites described similar clinical approaches with differences in resource availability. This implies that a single intervention would need to be flexible to environments with differing resources. Electronic Health Record's inherent ability to provide tailored CCDS is limited. However, CCDS middleware could integrate into different settings and increase evidence use. [ABSTRACT FROM AUTHOR]