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.

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.

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.

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.

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.

Unveiling Fall Risk Factors: Nurse-Driven Corpus Development for Natural Language Processing.

Hospital-acquired falls are a continuing clinical concern. The emergence of advanced analytical methods, including NLP, has created opportunities to leverage nurse-generated data, such as clinical notes, to better address the problem of falls. In this nurse-driven study, we employed an iterative process for expert manual annotation of RNs clinical notes to enable the training and testing of an NLP pipeline to extract factors related to falls. The resulting annotated data corpus had moderately high interrater reliability (F-score=0.74) and captured a breadth of clinical concepts for extraction with potential utility beyond patient falls. Further research is needed to determine which annotation tasks most benefit from nursing expert annotators, to optimize efficiency when tapping into the invaluable resource represented by the nursing workforce.

The journey to building a diverse, equitable, and inclusive American Medical Informatics Association.

OBJECTIVE: The American Medical Informatics Association (AMIA) Task Force on Diversity, Equity, and Inclusion (DEI) was established to address systemic racism and health disparities in biomedical and health informatics, aligning with AMIA's mission to transform healthcare. AMIA's DEI initiatives were spurred by member voices responding to police brutality and COVID-19's impact on Black/African American communities. MATERIALS AND METHODS: The Task Force, consisting of 20 members across 3 groups aligned with AMIA's 2020-2025 Strategic Plan, met biweekly to develop DEI recommendations with the help of 16 additional volunteers. These recommendations were reviewed, prioritized, and presented to the AMIA Board of Directors for approval. RESULTS: In 9 months, the Task Force (1) created a logic model to support workforce diversity and raise AMIA's DEI awareness, (2) conducted an environmental scan of other associations' DEI activities, (3) developed a DEI framework for AMIA meetings, (4) gathered member feedback, (5) cultivated DEI educational resources, (6) created a Board nominations and diversity session, (7) reviewed the Board's Strategic Planning for DEI alignment, (8) led a program to increase diversity at the 2020 AMIA Virtual Annual Symposium, and (9) standardized socially-assigned race and ethnicity data collection. DISCUSSION: The Task Force proposed actionable recommendations that focused on AMIA's role in addressing systemic racism and health equity, helping the organization understand its member diversity. CONCLUSION: This work supported marginalized groups, broadened the research agenda, and positioned AMIA as a DEI leader while reinforcing the need for ongoing transformation within informatics.

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.

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.

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.

Fall risk management using clinical decision support in a rural nursing-led primary care practice.

BACKGROUND: The leading cause of injuries among older adults in the United States is unintentional falls. The American Geriatrics Society/British Geriatrics Society promote fall risk management in primary care; however, this is challenging in low-resource settings. LOCAL PROBLEM: Archer Family Health Care (AFHC), an Advanced Practice Registered Nurse (APRN)-managed and federally designated rural health clinic, identified a care gap with falls adherence to guidelines for patients at higher risk for falls. METHODS: The aim of this quality improvement effort was to integrate an evidence-based fall risk management tool in a rural nurse-managed primary care practice. A standardized fall risk management process with a new brief paper-based clinical decision support (CDS) tool was developed and tested in two phases. INTERVENTION: Phase 1 focused on developing a fall risk management CDS tool, identifying the primary care visit workflow, communicating the workflow patterns to the AFHC staff, and collaborating with the staff to identify when and who should implement the tool. Phase 2 focused on implementation of the fall risk management CDS tool into standard practice among older adults aged 65 years and older. RESULTS: We found that integrating the tool did not disrupt the workflow of primary care visits at AFHC. The most common recommended intervention for patients at risk of falling was daily vitamin D supplementation. CONCLUSION: This project revealed that it is feasible to introduce a brief fall risk management decision support tool in an APRN-managed rural primary care practice.

Clinical Decision Support for Fall Prevention: Defining End-User Needs.

BACKGROUND AND SIGNIFICANCE: Falls in community-dwelling older adults are common, and there is a lack of clinical decision support (CDS) to provide health care providers with effective, individualized fall prevention recommendations. OBJECTIVES: The goal of this research is to identify end-user (primary care staff and patients) needs through a human-centered design process for a tool that will generate CDS to protect older adults from falls and injuries. METHODS: Primary care staff (primary care providers, care coordinator nurses, licensed practical nurses, and medical assistants) and community-dwelling patients aged 60 years or older associated with Brigham & Women's Hospital-affiliated primary care clinics and the University of Florida Health Archer Family Health Care primary care clinic were eligible to participate in this study. Through semi-structured and exploratory interviews with participants, our team identified end-user needs through content analysis. RESULTS: User needs for primary care staff (n = 24) and patients (n = 18) were categorized under the following themes: workload burden; systematic communication; in-person assessment of patient condition; personal support networks; motivational tools; patient understanding of fall risk; individualized resources; and evidence-based safe exercises and expert guidance. While some of these themes are specific to either primary care staff or patients, several address needs expressed by both groups of end-users. CONCLUSION: Our findings suggest that there are many care gaps in fall prevention management in primary care and that personalized, actionable, and evidence-based CDS has the potential to address some of these gaps.

Application of persuasive systems design principles to design a self-management application user interface for Hispanic informal dementia caregivers: user preferences and perceptions.

OBJECTIVE: We designed an mHealth application (app) user interface (UI) prototype informed by participatory design sessions, persuasive systems design (PSD) principles, and Lorig and Holman's self-management behavior framework to support self-management activities of Hispanic informal dementia caregivers and assessed their perceptions and preferences regarding features and functions of the app. MATERIALS AND METHODS: Our observational usability study design employed qualitative methods and forced choice preference assessments to identify: (1) the relationship between user preferences for UI features and functions and PSD principles and (2) user preferences for UI design features and functions and app functionality. We evaluated 16 pairs of mHealth app UI prototype designs. Eight paper-based paired designs were used to assess the relationship between PSD principles and caregiver preferences for UI features and functions to support self-management. An Apple iPad WIFI 32GB was used to display another 8 paired designs and assess caregiver preferences for UI functions to support the self-management process. RESULTS: Caregivers preferred an app UI with features and functions that incorporated a greater number of PSD principles and included an infographic to facilitate self-management. Moreover, caregivers preferred a design that did not depend on manual data entry, opting instead for functions such as drop-down list, drag-and-drop, and voice query to prioritize, choose, decide, and search when performing self-management activities. CONCLUSION: Our assessment approaches allowed us to discern which UI features, functions, and designs caregivers preferred. The targeted application of PSD principles in UI designs holds promise for supporting personalized problem identification, goal setting, decision-making, and action planning as strategies for improving caregiver self-management confidence.

A Stress and Pain Self-management mHealth App for Adult Outpatients With Sickle Cell Disease: Protocol for a Randomized Controlled Study.

BACKGROUND: This paper describes the research protocol for a randomized controlled trial of a self-management intervention for adults diagnosed with sickle cell disease (SCD). People living with SCD experience lifelong recurrent episodes of acute and chronic pain, which are exacerbated by stress. OBJECTIVE: This study aims to decrease stress and improve SCD pain control with reduced opioid use through an intervention with self-management relaxation exercises, named You Cope, We Support (YCWS). Building on our previous findings from formative studies, this study is designed to test the efficacy of YCWS on stress intensity, pain intensity, and opioid use in adults with SCD. METHODS: A randomized controlled trial of the short-term (8 weeks) and long-term (6 months) effects of YCWS on stress, pain, and opioid use will be conducted with 170 adults with SCD. Patients will be randomized based on 1:1 ratio (stratified on pain intensity [≤5 or >5]) to be either in the experimental (self-monitoring of outcomes, alerts or reminders, and use of YCWS [relaxation and distraction exercises and support]) or control (self-monitoring of outcomes and alerts or reminders) group. Patients will be asked to report outcomes daily. During weeks 1 to 8, patients in both groups will receive system-generated alerts or reminders via phone call, text, or email to facilitate data entry (both groups) and intervention use support (experimental). If the participant does not enter data after 24 hours, the study support staff will contact them for data entry troubleshooting (both groups) and YCWS use (experimental). We will time stamp and track patients' web-based activities to understand the study context and conduct exit interviews on the acceptability of system-generated and staff support. This study was approved by our institutional review board. RESULTS: This study was funded by the National Institute of Nursing Research of the National Institutes of Health in 2020. The study began in March 2021 and will be completed in June 2025. As of April 2022, we have enrolled 45.9% (78/170) of patients. We will analyze the data using mixed effects regression models (short term and long term) to account for the repeated measurements over time and use machine learning to construct and evaluate prediction models. Owing to the COVID-19 pandemic, the study was modified to allow for mail-in consent process, internet-based consent process via email or Zoom videoconference, devices delivered by FedEx, and training via Zoom videoconference. CONCLUSIONS: We expect the intervention group to report reductions in pain intensity (primary outcome; 0-10 scale) and in stress intensity (0-10 scale) and opioid use (Wisepill event medication monitoring system), which are secondary outcomes. Our study will contribute to advancing the use of nonopioid therapy such as guided relaxation and distraction techniques for managing SCD pain. TRIAL REGISTRATION: ClinicalTrials.gov NCT04484272; https://clinicaltrials.gov/ct2/show/NCT04484272. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/33818.

Information technology, nurse staffing, and patient needs.

As health care organizations increasingly adopt health information technology, time-sensitive data that track patients' requirements for nursing care and nurses' responsiveness to these needs might be available to support evidence-based nurse staffing decisions. care information technologies available in hospitals and on nursing units may provide valuable sources of information that can be translated into usable data. In this study, the usefulness of electronic data obtained from a nurse tracking call light system as a source of information for quality measurement was explored. The findings point to what might be under-utilization of existing health information technology to track patients' needs and nurses' responsiveness, patient census, and patient movements. The authors recommend health information technology be used less as support for other organizational systems and more as an administrative resource that can allow nurse executives to be more actively engaged within and across nursing environments.

Nutrition Support Therapies on the Medication Administration Record: Impacts on Staff Perception of Nutrition Care.

BACKGROUND: It has been reported that many hospitals in the United States have fragmented and ineffective ordering, administration, documentation, and evaluation/monitoring of nutrition therapies. This paper reports on a project to investigate if perceived hospital staff awareness and documentation of nutrition support therapies (NSTs) improves by including them as part of the medication administration record (MAR). METHODS: Surveys were conducted with nursing staff, physicians, and dietitians before and after adding NSTs to the MAR to evaluate the perceived impact on the outcome of interest. The outcomes of interest include nurses' perception of ease of finding information, awareness of an order, and ability to assess administration and documentation and dietitian, nurse, and physician staff perceptions of impact of intervention on aspects of the nutrition care process. RESULTS: After adding NST to the MAR, nursing staff perceived improvement in knowing that their patient had an oral nutritional supplement (ONS) order (P = .01), when and how much product was last administered (P = .01), and documentation of the type of product consumed (P = .01) and volume of product consumed (P = .01). The majority of dietitian and nurses surveyed reported perceived improvement in placing and finding ONS orders, in administration of ONS, in ability to evaluate patient nutrition status, and in ONS intake and a positive impact on clinical practice. CONCLUSION: Inclusion of NST in the MAR presents an innovative solution to enhance staff awareness of ordered therapies and perception of improved documentation of nutrition interventions for hospitalized patients.