Does the Relationship Between HIV Stigma Subtypes and Viral Suppression Differ by Age?: A Stratified Analysis of Data from the Florida Medical Monitoring Project.

HIV-related stigma is recognized as a top barrier to achieve viral suppression in the United States, but data describing who is most affected by HIV stigma is limited. The study sought to (1) identify the relationships between HIV-related stigma and unsuppressed viral load and (2) examine whether the association between HIV stigma subtypes and unsuppressed viral load differ by age group (i.e., 18-34, 35-49, and 50+ years-old) using surveillance data from the Florida Medical Monitoring Project (n = 1195). Most participants were 50+ years-old (55%), male (71%), and Black (51%). Enacted stigma was significantly associated with unsuppressed viral loads among the 18-34-year-old age group (OR 1.68, CI 1.09-2.60). After adjusting for potential confounders, only enacted stigma was independently associated with unsuppressed viral load in the 18-34-year-old age group. Results highlight the need for targeted interventions to reduce enacted stigma among younger persons with HIV to achieve viral suppression.

Application of a practice-based approach in variable selection for a prediction model development study of hospital-induced delirium.

BACKGROUND: Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach. METHODS: This study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors. RESULTS: In the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium. CONCLUSIONS: Heterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model.

Experts' Perspectives on Use of Fast Healthcare Interoperable Resources for Computerized Clinical Decision Support.

Barriers to improving the US healthcare system include a lack of interoperability across digital health information and delays in seeking preventative and recommended care. Interoperability can be seen as the lynch pin to reducing fragmentation and improving outcomes related to digital health systems. The prevailing standard for information exchange to enable interoperability is the Health Level Seven International Fast Healthcare Interoperable Resources standard. To better understand Fast Healthcare Interoperable Resources within the context of computerized clinical decision support expert interviews of health informaticists were conducted and used to create a modified force field analysis. Current barriers and future recommendations to scale adoption of Fast Healthcare Interoperable Resources were explored through qualitative analysis of expert interviews. Identified barriers included variation in electronic health record implementation, limited electronic health record vendor support, ontology variation, limited workforce knowledge, and testing limitations. Experts recommended research funders require Fast Healthcare Interoperable Resource usage, development of an "app store," incentives for clinical organizations and electronic health record vendors, and Fast Healthcare Interoperable Resource certification development.

Validation of the tablet-based Turkish-PAINReportIt® for lung cancer patients after thoracotomy in Turkey.

BACKGROUND: Digital pain assessment is advantageous and timely for healthcare priorities in Turkey. However, a multi-dimensional, tablet-based pain assessment tool is not available in the Turkish language. PURPOSE: To validate the Turkish-PAINReportIt® as a multi-dimensional measure of post-thoracotomy pain. METHODS: In the first of a two-phased study, 32 Turkish patients (mean age 47.8 ± 15.6 years, 72 % male) participated in individual cognitive interviews as they completed the tablet-based Turkish-PAINReportIt® once during the first four days post-thoracotomy, and 8 clinicians participated in a focus group discussion of implementation barriers. In the second phase, 80 Turkish patients (mean age 59.0 ± 12.7 years, 80 % male) completed the Turkish-PAINReportIt® preoperatively, on postoperative days 1-4, and at the two-week post-operative follow-up visit. RESULTS: Patients generally interpreted accurately the Turkish-PAINReportIt® instructions and items. We eliminated some items unnecessary for daily assessment based on focus-group suggestions. In the second study phase, pain scores (intensity, quality, pattern) were low pre-thoracotomy for lung cancer and high postoperatively high on day 1, decreasing on days 2, 3 and 4, and back down to pre-surgical levels at 2-weeks. Over time, pain intensity decreased from post-operative day 1 to post-operative day 4 (p < .001) and from post-operative day 1 to post-operative week 2 (p < .001). CONCLUSIONS: The formative research supported proof of concept and informed the longitudinal study. Findings showed strong validity of the Turkish-PAINReportIt® to detect reduced pain over time as healing occurs after thoracotomy.

Prevalence of HIV-related stigma in adults living with HIV & disability in Florida, 2015-2016.

Using data collected from the Florida Medical Monitoring Project, we sought to compare the prevalence of overall HIV-related stigma, including its subdimensions among persons with HIV and disability(s) and persons with HIV without disability in Florida. Disability was classified as having difficulty in one or more areas: activity limitations, participation restrictions, and functional or sensory activities. HIV-related stigma was assessed using the HIV Stigma Scale, which measures (1) overall stigma (2) negative self-image, (3) personalized, and (4) anticipated stigma. Multivariate analysis indicates that the crude prevalence ratios of overall stigma, including negative self-image, personalized, and anticipated stigma among persons with HIV and disability(s) were 1.43, 1.24, 1.20, and 1.23 compared to persons with HIV without disability, respectively. After adjusting for confounders, the prevalence ratios of HIV-related stigma ranged from 1.33-1.07 among persons with HIV and disability(s) compared to persons with HIV without disability. The implications of these findings reveal that persons with HIV and disability(s) are more vulnerable to HIV-related stigma. Researchers could consider distinct stigma interventions tailored towards persons with HIV and disability(s) in Florida.

Language equivalence of the modified falls efficacy scale (MFES) among English- and Spanish-speaking older adults: Rasch analysis.

BACKGROUND: To investigate item-level measurement properties of the Modified Falls Efficacy (MFES) Scale among English- and Spanish-speaking urban-dwelling older adults as a means to evaluate language equivalence of the tool. METHODS: Secondary analysis of survey data from 170 English (n = 83) and Spanish (n = 87) speaking older adults who reported to the emergency department of a quaternary medical center in New York City between February 2010 and August 2011. The Rasch rating scale model was used to investigate item statistics and ordering of items, item and person reliability, and model performance of the Modified Falls Efficacy Scale. RESULTS: The Modified Falls Efficacy Scale, for English- and Spanish-speakers, demonstrated acceptable fit to the Rasch model of a unidimensional measure. While the range of the construct is more limited for the Spanish group, the interval between tasks are much closer, reflecting little to no construct under-representation. CONCLUSION: There is rationale for continued testing of a unidemsional English- and Spanish-MFES among urban community-dwelling older adults. Large-scale international studies linking the unidemsional MFES to patient outcomes will support the validity of this tool for research and practice.

The relationship between individual characteristics and HIV-related stigma in adults living with HIV: medical monitoring project, Florida, 2015-2016.

BACKGROUND: Human Immunodeficiency Virus (HIV) disproportionately affects the Southern United States, accounting for approximately 46% of people living with HIV. HIV-related stigma is recognized as a barrier to testing, treatment, and prevention efforts. However, little is known about HIV-related stigma experiences in Florida. Using data collected from the Florida Medical Monitoring Project, we sought to examine individual characteristics associated with HIV-related stigma. METHODS: We analyzed secondary data from the 2015-2016 Medical Monitoring Project in Florida (n = 603). Stigma was measured using the 10-item HIV Stigma Scale. Exploratory factor analysis of the HIV Stigma Scale revealed three subscales: negative self-image, anticipated, and personalized stigma. Bivariate and multivariate regression models were used to determine the individual characteristics associated with the HIV Stigma Scale. RESULTS: Multivariate analysis indicated that people with severe depression scores (OR: 3.13; CI: 1.38-7.13) and persons with disability (OR: 1.64; CI: 1.03-2.61) had significantly increased odds of higher overall stigma. In the subscale analyses, negative self-image was significantly associated with alcohol misuse (OR: 2.02; CI: 1.15-3.56) depression (OR: 2.81; CI: 1.38-5.72) and/or those who identify as homosexual (OR: 0.54; CI: 0.31-0.93). Anticipated stigma was significantly associated with people who had mild-moderate depression (OR: 3.03; CI: 1.20-7.65), severe depression (OR: 2.87; CI: 1.38-5.98), identified as Black (OR: 0.60; CI: 0.37-0.98), non-injection drug use (OR: 0.55; CI: 0.33-0.91), and/or people aged 50 years and older (OR: 0.28; CI: 0.09-0.82). Personalized stigma was not associated with any of the variables examined. CONCLUSIONS: The implications of these findings reveal that certain individuals are more vulnerable to stigma. Researchers could consider distinct stigma interventions strategies based on the characteristics of specific individuals (i.e., targeting depression, disability, sexual orientation, avoidant coping, racial/ethnic groups, and youth) in Florida.

Fall Prevention Decision Making of Acute Care Registered Nurses.

OBJECTIVE: The aim of this study was to examine acute care registered nurses' (RNs') fall prevention decision-making. BACKGROUND: The RN decision-making process related to fall prevention needs to be investigated to ensure that hospital policies align with nursing workflow and support nursing judgment. METHODS: Qualitative semistructured interviews based on the Critical Decision Method were conducted with RNs about their planning and decision making during their last 12-hour shift worked. RESULTS: Data saturation was achieved with 12 RNs. Nine themes emerged related to the RN decision-making process and included hospital-level (eg, fear of discipline), unit-level (eg, value of bed alarm technology), and nurse-level (eg, professional judgment) factors that could influence fall prevention. CONCLUSIONS: Nursing administrators should consider a multilevel approach to fall prevention policies that includes promoting a practice environment that embraces self-reporting adverse events without fear of shame or being reprimanded, evaluating unit-level practice and technology acceptance and usability, and supporting autonomous nursing practice.

A semi-automated approach for analyzing collages to inform the design of a family health information management system for Hispanic dementia caregivers.

Culturally- and linguistically-tailored health communication is needed for vulnerable populations to manage their health and the health of their families. This presents a significant design challenge. The use of collages is an increasingly popular technique with the flexibility to capture the needs and experiences of individuals with various cultural and language preferences. Collage analysis has typically remained qualitative in nature. We introduce a novel, objective, semi-automated approach that enhances collage analysis to elucidate pattern differences that may not be detectable by natural perception. We present a case scenario of collage analysis based on the expressed experience and self-management needs of Hispanic dementia caregivers (n = 24). We demonstrate how our innovative approach may reveal cultural differences between language groups that could have otherwise been missed using traditional techniques.

Interest in using mobile technology to help self-manage alcohol use among persons living with the human immunodeficiency virus: A Florida Cohort cross-sectional study.

BACKGROUND: Alcohol consumption at hazardous levels is more prevalent and associated with poor health outcomes among persons living with the human immunodeficiency virus (HIV; PLWH). Although PLWH are receptive to using technology to manage health issues, it is unknown whether a cell phone app to self-manage alcohol use would be acceptable among PLWH who drink. The objectives of this study were to determine factors associated with interest in an app to self-manage drinking and to identify differences in baseline mobile technology use among PLWH by drinking level. METHODS: The study population included 757 PLWH recruited from 2014 to 2016 into the Florida Cohort, an ongoing cohort study investigating the utilization of health services and HIV care outcomes among PLWH. Participants completed a questionnaire examining demographics, substance use, mobile technology use, and other health behaviors. Multivariable logistic regression was used to identify factors significantly associated with interest in an app to self-manage drinking. We also determined whether mobile technology use varied by drinking level. RESULTS: Of the sample, 40% of persons who drink at hazardous levels, 34% of persons who drink at nonhazardous levels, and 19% of persons who do not drink were interested in a self-management app for alcohol use. Multivariable logistic regression analysis indicated that nonhazardous drinking (adjusted odds ratio [AOR] = 1.78; confidence interval [CI 95%]: 1.10-2.88) and hazardous drinking (AOR = 2.58; CI: 1.60-4.16) were associated with interest, controlling for age, gender, education, and drug use. Regarding mobile technology use, most of the sample reported smartphone ownership (56%), text messaging (89%), and at least one cell phone app (69%). CONCLUSIONS: Regardless of drinking level, overall mobile technology use among PLWH was moderate, whereas PLWH who consumed alcohol expressed greater interest in a cell phone app to self-manage alcohol use. This indicates that many PLWH who drink would be interested in and prepared for a mobile technology-based intervention to reduce alcohol consumption.

Urban-Dwelling Community Members' Views on Biomedical Research Engagement.

In this study, we explore community members' overall understanding and experience with biomedical research engagement. We conducted a qualitative analysis to explore a concept that emerged but was not specifically addressed in a pre-existing dataset obtained using four focus group sessions with 30 urban-dwelling community members. Transcripts were read in an iterative process, and an emergent content analysis was performed. Five main themes were identified: (a) engaging in research to contribute to personal or greater good, (b) hierarchy of trust, (c) the importance of disclosure and transparency, (d) practical barriers to research engagement, and (e) fear of research procedures. Community members view research engagement as a collaborative process whereby community members and researchers are involved in all stages of the investigation. Focusing on research engagement, and not merely participation, may enhance community knowledge of the research process and advance scientific knowledge.

Quantifying Health Outcome Disparity in Invasive Methicillin-Resistant Staphylococcus aureus Infection using Fairness Algorithms on Real-World Data.

This study quantifies health outcome disparities in invasive Methicillin-Resistant Staphylococcus aureus (MRSA) infections by leveraging a novel artificial intelligence (AI) fairness algorithm, the Fairness-Aware Causal paThs (FACTS) decomposition, and applying it to real-world electronic health record (EHR) data. We spatiotemporally linked 9 years of EHRs from a large healthcare provider in Florida, USA, with contextual social determinants of health (SDoH). We first created a causal structure graph connecting SDoH with individual clinical measurements before/upon diagnosis of invasive MRSA infection, treatments, side effects, and outcomes; then, we applied FACTS to quantify outcome potential disparities of different causal pathways including SDoH, clinical and demographic variables. We found moderate disparity with respect to demographics and SDoH, and all the top ranked pathways that led to outcome disparities in age, gender, race, and income, included comorbidity. Prior kidney impairment, vancomycin use, and timing were associated with racial disparity, while income, rurality, and available healthcare facilities contributed to gender disparity. From an intervention standpoint, our results highlight the necessity of devising policies that consider both clinical factors and SDoH. In conclusion, this work demonstrates a practical utility of fairness AI methods in public health settings.

Practice-Based Knowledge Discovery for Comparative Effectiveness Research: An Organizing Framework.

Electronic health information systems can increase the ability of health-care organizations to investigate the effects of clinical interventions. The authors present an organizing framework that integrates outcomes and informatics research paradigms to guide knowledge discovery in electronic clinical databases. They illustrate its application using the example of hospital acquired pressure ulcers (HAPU). The Knowledge Discovery through Informatics for Comparative Effectiveness Research (KDI-CER) framework was conceived as a heuristic to conceptualize study designs and address potential methodological limitations imposed by using a single research perspective. Advances in informatics research can play a complementary role in advancing the field of outcomes research including CER. The KDI-CER framework can be used to facilitate knowledge discovery from routinely collected electronic clinical data.

Clinical Decision Support Systems for Palliative Care Management: A Scoping Review.

CONTEXT: With the expansion of palliative care services in clinical settings, clinical decision support systems (CDSSs) have become increasingly crucial for assisting bedside nurses and other clinicians in improving the quality of care to patients with life-limiting health conditions. OBJECTIVES: To characterize palliative care CDSSs and explore end-users' actions taken, adherence recommendations, and clinical decision time. METHODS: The CINAHL, Embase, and PubMed databases were searched from inception to September 2022. The review was developed following the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews guidelines. Qualified studies were described in tables and assessed the level of evidence. RESULTS: A total of 284 abstracts were screened, and 12 studies comprised the final sample. The CDSSs selected focused on identifying patients who could benefit from palliative care based on their health status, making referrals to palliative care services, and managing medications and symptom control. Despite the variability of palliative CDSSs, all studies reported that CDSSs assisted clinicians in becoming more informed about palliative care options leading to better decisions and improved patient outcomes. Seven studies explored the impact of CDSSs on end-user adherence. Three studies revealed high adherence to recommendations while four had low adherence. Lack of feature customization and trust in guideline-based in the initial stages of feasibility and usability testing were evident, limiting the usefulness for nurses and other clinicians. CONCLUSION: This study demonstrated that implementing palliative care CDSSs can assist nurses and other clinicians in improving the quality of care for palliative patients. The studies' different methodological approaches and variations in palliative CDSSs made it challenging to compare and validate the applicability under which CDSSs are effective. Further research utilizing rigorous methods to evaluate the impact of clinical decision support features and guideline-based actions on clinicians' adherence and efficiency is recommended.

Risk of bias in prognostic models of hospital-induced delirium for medical-surgical units: A systematic review.

PURPOSE: The purpose of this systematic review was to assess risk of bias in existing prognostic models of hospital-induced delirium for medical-surgical units. METHODS: APA PsycInfo, CINAHL, MEDLINE, and Web of Science Core Collection were searched on July 8, 2022, to identify original studies which developed and validated prognostic models of hospital-induced delirium for adult patients who were hospitalized in medical-surgical units. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was used for data extraction. The Prediction Model Risk of Bias Assessment Tool was used to assess risk of bias. Risk of bias was assessed across four domains: participants, predictors, outcome, and analysis. RESULTS: Thirteen studies were included in the qualitative synthesis, including ten model development and validation studies and three model validation only studies. The methods in all of the studies were rated to be at high overall risk of bias. The methods of statistical analysis were the greatest source of bias. External validity of models in the included studies was tested at low levels of transportability. CONCLUSIONS: Our findings highlight the ongoing scientific challenge of developing a valid prognostic model of hospital-induced delirium for medical-surgical units to tailor preventive interventions to patients who are at high risk of this iatrogenic condition. With limited knowledge about generalizable prognosis of hospital-induced delirium in medical-surgical units, existing prognostic models should be used with caution when creating clinical practice policies. Future research protocols must include robust study designs which take into account the perspectives of clinicians to identify and validate risk factors of hospital-induced delirium for accurate and generalizable prognosis in medical-surgical units.

Usability Testing of an Interoperable Computerized Clinical Decision Support Tool for Fall Risk Management in Primary Care.

BACKGROUND: Falls are a widespread and persistent problem for community-dwelling older adults. Use of fall prevention guidelines in the primary care setting has been suboptimal. Interoperable computerized clinical decision support systems have the potential to increase engagement with fall risk management at scale. To support fall risk management across organizations, our team developed the ASPIRE tool for use in differing primary care clinics using interoperable standards. OBJECTIVES: Usability testing of ASPIRE was conducted to measure ease of access, overall usability, learnability, and acceptability prior to pilot . METHODS: Participants were recruited using purposive sampling from two sites with different electronic health records and different clinical organizations. Formative testing rooted in user-centered design was followed by summative testing using a simulation approach. During summative testing participants used ASPIRE across two clinical scenarios and were randomized to determine which scenario they saw first. Single Ease Question and System Usability Scale were used in addition to analysis of recorded sessions in NVivo. RESULTS: All 14 participants rated the usability of ASPIRE as above average based on usability benchmarks for the System Usability Scale metric. Time on task decreased significantly between the first and second scenarios indicating good learnability. However, acceptability data were more mixed with some recommendations being consistently accepted while others were adopted less frequently. CONCLUSION: This study described the usability testing of the ASPIRE system within two different organizations using different electronic health records. Overall, the system was rated well, and further pilot testing should be done to validate that these positive results translate into clinical practice. Due to its interoperable design, ASPIRE could be integrated into diverse organizations allowing a tailored implementation without the need to build a new system for each organization. This distinction makes ASPIRE well positioned to impact the challenge of falls at scale.

Clinical Prediction Models for Hospital-Induced Delirium Using Structured and Unstructured Electronic Health Record Data: Protocol for a Development and Validation Study.

BACKGROUND: Hospital-induced delirium is one of the most common and costly iatrogenic conditions, and its incidence is predicted to increase as the population of the United States ages. An academic and clinical interdisciplinary systems approach is needed to reduce the frequency and impact of hospital-induced delirium. OBJECTIVE: The long-term goal of our research is to enhance the safety of hospitalized older adults by reducing iatrogenic conditions through an effective learning health system. In this study, we will develop models for predicting hospital-induced delirium. In order to accomplish this objective, we will create a computable phenotype for our outcome (hospital-induced delirium), design an expert-based traditional logistic regression model, leverage machine learning techniques to generate a model using structured data, and use machine learning and natural language processing to produce an integrated model with components from both structured data and text data. METHODS: This study will explore text-based data, such as nursing notes, to improve the predictive capability of prognostic models for hospital-induced delirium. By using supervised and unsupervised text mining in addition to structured data, we will examine multiple types of information in electronic health record data to predict medical-surgical patient risk of developing delirium. Development and validation will be compliant to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement. RESULTS: Work on this project will take place through March 2024. For this study, we will use data from approximately 332,230 encounters that occurred between January 2012 to May 2021. Findings from this project will be disseminated at scientific conferences and in peer-reviewed journals. CONCLUSIONS: Success in this study will yield a durable, high-performing research-data infrastructure that will process, extract, and analyze clinical text data in near real time. This model has the potential to be integrated into the electronic health record and provide point-of-care decision support to prevent harm and improve quality of care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48521.

Optimizing Dynamic Antibiotic Treatment Strategies against Invasive Methicillin-Resistant Staphylococcus Aureus Infections using Causal Survival Forests and G-Formula on Statewide Electronic Health Record Data.

Developing models for individualized, time-varying treatment optimization from observational data with large variable spaces, e.g., electronic health records (EHR), is problematic because of inherent, complex bias that can change over time. Traditional methods such as the g-formula are robust, but must identify critical subsets of variables due to combinatorial issues. Machine learning approaches such as causal survival forests have fewer constraints and can provide fine-tuned, individualized counterfactual predictions. In this study, we aimed to optimize time-varying antibiotic treatment -identifying treatment heterogeneity and conditional treatment effects- against invasive methicillin-resistant Staphylococcus Aureus (MRSA) infections, using statewide EHR data collected in Florida, USA. While many previous studies focused on measuring the effects of the first empiric treatment (i.e., usually vancomycin), our study focuses on dynamic sequential treatment changes, comparing possible vancomycin switches with other antibiotics at clinically relevant time points, e.g., after obtaining a bacterial culture and susceptibility testing. Our study population included adult individuals admitted to the hospital with invasive MRSA. We collected demographic, clinical, medication, and laboratory information from the EHR for these patients. Then, we followed three sequential antibiotic choices (i.e., their empiric treatment, subsequent directed treatment, and final sustaining treatment), evaluating 30-day mortality as the outcome. We applied both causal survival forests and g-formula using different clinical intervention policies. We found that switching from vancomycin to another antibiotic improved survival probability, yet there was a benefit from initiating vancomycin compared to not using it at any time point. These findings show consistency with the empiric choice of vancomycin before confirmation of MRSA and shed light on how to manage switches on course. In conclusion, this application of causal machine learning on EHR demonstrates utility in modeling dynamic, heterogeneous treatment effects that cannot be evaluated precisely using randomized clinical trials.

Management of Fall Risk Among Older Adults in Diverse Primary Care Settings.

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.

Use of a Data Repository to Identify Delirium as a Presenting Symptom of COVID-19 Infection in Hospitalized Adults: Cross-Sectional Cohort Pilot Study.

BACKGROUND: Delirium, an acute confusional state highlighted by inattention, has been reported to occur in 10% to 50% of patients with COVID-19. People hospitalized with COVID-19 have been noted to present with or develop delirium and neurocognitive disorders. Caring for patients with delirium is associated with more burden for nurses, clinicians, and caregivers. Using information in electronic health record data to recognize delirium and possibly COVID-19 could lead to earlier treatment of the underlying viral infection and improve outcomes in clinical and health care systems cost per patient. Clinical data repositories can further support rapid discovery through cohort identification tools, such as the Informatics for Integrating Biology and the Bedside tool. OBJECTIVE: The specific aim of this research was to investigate delirium in hospitalized older adults as a possible presenting symptom in COVID-19 using a data repository to identify neurocognitive disorders with a novel group of International Classification of Diseases, Tenth Revision (ICD-10) codes. METHODS: We analyzed data from 2 catchment areas with different demographics. The first catchment area (7 counties in the North-Central Florida) is predominantly rural while the second (1 county in North Florida) is predominantly urban. The Integrating Biology and the Bedside data repository was queried for patients with COVID-19 admitted to inpatient units via the emergency department (ED) within the health center from April 1, 2020, and April 1, 2022. Patients with COVID-19 were identified by having a positive COVID-19 laboratory test or a diagnosis code of U07.1. We identified neurocognitive disorders as delirium or encephalopathy, using ICD-10 codes. RESULTS: Less than one-third (1437/4828, 29.8%) of patients with COVID-19 were diagnosed with a co-occurring neurocognitive disorder. A neurocognitive disorder was present on admission for 15.8% (762/4828) of all patients with COVID-19 admitted through the ED. Among patients with both COVID-19 and a neurocognitive disorder, 56.9% (817/1437) were aged ≥65 years, a significantly higher proportion than those with no neurocognitive disorder (P<.001). The proportion of patients aged <65 years was significantly higher among patients diagnosed with encephalopathy only than patients diagnosed with delirium only and both delirium and encephalopathy (P<.001). Most (1272/4828, 26.3%) patients with COVID-19 admitted through the ED during our study period were admitted during the Delta variant peak. CONCLUSIONS: The data collected demonstrated that an increased number of older patients with neurocognitive disorder present on admission were infected with COVID-19. Knowing that delirium increases the staffing, nursing care needs, hospital resources used, and the length of stay as previously noted, identifying delirium early may benefit hospital administration when planning for newly anticipated COVID-19 surges. A robust and accessible data repository, such as the one used in this study, can provide invaluable support to clinicians and clinical administrators in such resource reallocation and clinical decision-making.