Classifying early infant feeding status from clinical notes using natural language processing and machine learning.

The objective of this study is to develop and evaluate natural language processing (NLP) and machine learning models to predict infant feeding status from clinical notes in the Epic electronic health records system. The primary outcome was the classification of infant feeding status from clinical notes using Medical Subject Headings (MeSH) terms. Annotation of notes was completed using TeamTat to uniquely classify clinical notes according to infant feeding status. We trained 6 machine learning models to classify infant feeding status: logistic regression, random forest, XGBoost gradient descent, k-nearest neighbors, and support-vector classifier. Model comparison was evaluated based on overall accuracy, precision, recall, and F1 score. Our modeling corpus included an even number of clinical notes that was a balanced sample across each class. We manually reviewed 999 notes that represented 746 mother-infant dyads with a mean gestational age of 38.9 weeks and a mean maternal age of 26.6 years. The most frequent feeding status classification present for this study was exclusive breastfeeding [n = 183 (18.3%)], followed by exclusive formula bottle feeding [n = 146 (14.6%)], and exclusive feeding of expressed mother's milk [n = 102 (10.2%)], with mixed feeding being the least frequent [n = 23 (2.3%)]. Our final analysis evaluated the classification of clinical notes as breast, formula/bottle, and missing. The machine learning models were trained on these three classes after performing balancing and down sampling. The XGBoost model outperformed all others by achieving an accuracy of 90.1%, a macro-averaged precision of 90.3%, a macro-averaged recall of 90.1%, and a macro-averaged F1 score of 90.1%. Our results demonstrate that natural language processing can be applied to clinical notes stored in the electronic health records to classify infant feeding status. Early identification of breastfeeding status using NLP on unstructured electronic health records data can be used to inform precision public health interventions focused on improving lactation support for postpartum patients.

Development of a Probabilistic Boolean network (PBN) to model intraoperative blood pressure management.

BACKGROUND: Blood pressure is a vital sign for organ perfusion that anesthesiologists measure and modulate during surgery. However, current decision-making processes rely heavily on clinicians' experience, which can lead to variability in treatment across surgeries. With the advent of machine learning, we can now create models to predict the outcomes of interventions and guide perioperative decision-making. The first step in this process involves translating the clinical decision-making process into a framework understood by an algorithm. Probabilistic Boolean networks (PBNs) provide an information-rich approach to this problem. A PBN trends toward a steady state, and its decisions are easily understood via its Boolean predictor functions. We hypothesize that a PBN can be developed that corrects hemodynamic instability in patients by selecting clinical interventions to maintain blood pressure within a given range. METHODS: Data on patients over the age of 65 undergoing surgery with general anesthesia from 2018 to 2020 were drawn from the UF Health PRECEDE data set with IRB approval (IRB201700747). Parameters examined included heart rate, blood pressure, and frequency of medications given 15 min after anesthetic induction and 15 min before awakening. The medication frequency data were truncated into a 66/33 split for the training and validation set used in the PBN. The model was coded using Python 3 and evaluated by comparing the frequency of medications chosen by the program to the values in the testing set via linear regression analysis. RESULTS: The network developed successfully models a hemodynamically unstable patient and corrects the imbalance by administering medications. This is evidenced by the model achieving a stable, steady state matrix in all iterations. However, the model's ability to emulate clinical drug selection was variable. It was successful with its use of vasodilator selection but struggled with the appropriate selection of vasopressors. CONCLUSIONS: The PBN has demonstrated the ability to choose appropriate interventions based on a patient's current vitals. Additional work must be done to have the network emulate the frequency at which drugs are selected from in clinical practice. In its current state, the model provides an understanding of how a PBN behaves in the context of correcting hemodynamic instability and can aid in developing more robust models in the future.

Racial disparities in septic shock mortality: a retrospective cohort study.

Background: Patients with septic shock have the highest risk of death from sepsis, however, racial disparities in mortality outcomes in this cohort have not been rigorously investigated. Our objective was to describe the association between race/ethnicity and mortality in patients with septic shock. Methods: Our study is a retrospective cohort study of adult patients in the OneFlorida Data Trust (Florida, United States of America) admitted with septic shock between January 2012 and July 2018. We identified patients as having septic shock if they received vasopressors during their hospital encounter and had either an explicit International Classification of Disease (ICD) code for sepsis, or had an infection ICD code and received intravenous antibiotics. Our primary outcome was 90-day mortality. Our secondary outcome was in-hospital mortality. Multiple logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) for variable selection was used to assess associations. Findings: There were 13,932 patients with septic shock in our cohort. The mean age was 61 years (SD 16), 68% of the cohort identified as White (n = 9419), 28% identified as Black (n = 3936), 2% (n = 294) identified as Hispanic ethnicity, and 2% as other races not specified in the previous groups (n = 283). In our logistic regression model for 90-day mortality, patients identified as Black had 1.57 times the odds of mortality (95% CI 1.07-2.29, p = 0.02) compared to White patients. Other significant predictors included mechanical ventilation (OR 3.66, 95% CI 3.35-4.00, p < 0.01), liver disease (OR 1.75, 95% CI 1.59-1.93, p < 0.01), laboratory components of the Sequential Organ Failure Assessment score (OR 1.18, 95% CI 1.16-1.21, p < 0.01), lactate (OR 1.10, 95% CI 1.08-1.12, p < 0.01), congestive heart failure (OR 1.19, 95% CI 1.10-1.30, p < 0.01), human immunodeficiency virus (OR 1.35, 95% CI 1.04-1.75, p = 0.03), age (OR 1.04, 95% CI 1.04-1.04, p < 0.01), and the interaction between age and race (OR 0.99, 95% CI 0.99-1.00, p < 0.01). Among younger patients (<45 years), patients identified as Black accounted for a higher proportion of the deaths. Results were similar in the in-hospital mortality model. Interpretation: In this retrospective study of septic shock patients, we found that patients identified as Black had higher odds of mortality compared to patients identified as non-Hispanic White. Our findings suggest that the greatest disparities in mortality are among younger Black patients with septic shock. Funding: National Institutes of Health National Center for Advancing Translational Sciences (1KL2TR001429); National Institute of Health National Institute of General Medical Sciences (1K23GM144802).

Telehealth Implementation Response to COVID-19 in the OneFlorida+ Clinical Research Network: Perspectives of Clinicians and Health Systems Leaders.

Introduction: The COVID-19 pandemic forced health systems worldwide to make rapid adjustments to patient care. Nationwide stay-at-home mandates and public health concerns increased demand for telehealth to maintain patients' continuity of care. These circumstances permitted observation of telehealth implementation in real-world settings at a large scale. This study aimed to understand clinician and health system leader (HSL) experiences in expanding, implementing, and sustaining telehealth during COVID-19 in the OneFlorida+ clinical research network. Methods: We conducted semistructured videoconference interviews with 5 primary care providers, 7 specialist providers, and 12 HSLs across 7 OneFlorida+ health systems and settings. Interviews were audiorecorded, transcribed, and summarized using deductive team-based template coding. We then used matrix analysis to organize the qualitative data and identify inductive themes. Results: Rapid telehealth implementation occurred even among sites with low readiness, facilitated by responsive planning, shifts in resource allocation, and training. Common hurdles in routine telehealth use, including technical and reimbursement issues, were also barriers to telehealth implementation. Acceptability of telehealth was influenced by benefits such as the providers' ability to view a patient's home environment and the availability of tools to enhance patient education. Lower acceptability stemmed from the inability to conduct physical examinations during the shutdown. Conclusions: This study identified a broad range of barriers, facilitators, and strategies for implementing telehealth within large clinical research networks. The findings can contribute to optimizing the effectiveness of telehealth implementation in similar settings, and point toward promising directions for telehealth provider training to improve acceptability and promote sustainability.

Machine learning-based prediction models for home discharge in patients with COVID-19: Development and evaluation using electronic health records.

OBJECTIVE: This study aimed to develop and validate predictive models using electronic health records (EHR) data to determine whether hospitalized COVID-19-positive patients would be admitted to alternative medical care or discharged home. METHODS: We conducted a retrospective cohort study using deidentified data from the University of Florida Health Integrated Data Repository. The study included 1,578 adult patients (≥18 years) who tested positive for COVID-19 while hospitalized, comprising 960 (60.8%) female patients with a mean (SD) age of 51.86 (18.49) years and 618 (39.2%) male patients with a mean (SD) age of 54.35 (18.48) years. Machine learning (ML) model training involved cross-validation to assess their performance in predicting patient disposition. RESULTS: We developed and validated six supervised ML-based prediction models (logistic regression, Gaussian Naïve Bayes, k-nearest neighbors, decision trees, random forest, and support vector machine classifier) to predict patient discharge status. The models were evaluated based on the area under the receiver operating characteristic curve (ROC-AUC), precision, accuracy, F1 score, and Brier score. The random forest classifier exhibited the highest performance, achieving an accuracy of 0.84 and an AUC of 0.72. Logistic regression (accuracy: 0.85, AUC: 0.71), k-nearest neighbor (accuracy: 0.84, AUC: 0.63), decision tree (accuracy: 0.84, AUC: 0.61), Gaussian Naïve Bayes (accuracy: 0.84, AUC: 0.66), and support vector machine classifier (accuracy: 0.84, AUC: 0.67) also demonstrated valuable predictive capabilities. SIGNIFICANCE: This study's findings are crucial for efficiently allocating healthcare resources during pandemics like COVID-19. By harnessing ML techniques and EHR data, we can create predictive tools to identify patients at greater risk of severe symptoms based on their medical histories. The models developed here serve as a foundation for expanding the toolkit available to healthcare professionals and organizations. Additionally, explainable ML methods, such as Shapley Additive Explanations, aid in uncovering underlying data features that inform healthcare decision-making processes.

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.

Study protocol for a type III hybrid effectiveness-implementation trial to evaluate scaling interoperable clinical decision support for patient-centered chronic pain management in primary care.

BACKGROUND: The US continues to face public health crises related to both chronic pain and opioid overdoses. Thirty percent of Americans suffer from chronic noncancer pain at an estimated yearly cost of over $600 billion. Most patients with chronic pain turn to primary care clinicians who must choose from myriad treatment options based on relative risks and benefits, patient history, available resources, symptoms, and goals. Recently, with attention to opioid-related risks, prescribing has declined. However, clinical experts have countered with concerns that some patients for whom opioid-related benefits outweigh risks may be inappropriately discontinued from opioids. Unfortunately, primary care clinicians lack usable tools to help them partner with their patients in choosing pain treatment options that best balance risks and benefits in the context of patient history, resources, symptoms, and goals. Thus, primary care clinicians and patients would benefit from patient-centered clinical decision support (CDS) for this shared decision-making process. METHODS: The objective of this 3-year project is to study the adaptation and implementation of an existing interoperable CDS tool for pain treatment shared decision making, with tailored implementation support, in new clinical settings in the OneFlorida Clinical Research Consortium. Our central hypothesis is that tailored implementation support will increase CDS adoption and shared decision making. We further hypothesize that increases in shared decision making will lead to improved patient outcomes, specifically pain and physical function. The CDS implementation will be guided by the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. The evaluation will be organized by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. We will adapt and tailor PainManager, an open source interoperable CDS tool, for implementation in primary care clinics affiliated with the OneFlorida Clinical Research Consortium. We will evaluate the effect of tailored implementation support on PainManager's adoption for pain treatment shared decision making. This evaluation will establish the feasibility and obtain preliminary data in preparation for a multi-site pragmatic trial targeting the effectiveness of PainManager and tailored implementation support on shared decision making and patient-reported pain and physical function. DISCUSSION: This research will generate evidence on strategies for implementing interoperable CDS in new clinical settings across different types of electronic health records (EHRs). The study will also inform tailored implementation strategies to be further tested in a subsequent hybrid effectiveness-implementation trial. Together, these efforts will lead to important new technology and evidence that patients, clinicians, and health systems can use to improve care for millions of Americans who suffer from pain and other chronic conditions. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05256394 , Registered 25 February 2022.

Telehealth and racial disparities in colorectal cancer screening: A pilot study of how virtual clinician characteristics influence screening intentions.

Introduction: Racial disparities in colorectal cancer (CRC) can be addressed through increased adherence to screening guidelines. In real-life encounters, patients may be more willing to follow screening recommendations delivered by a race concordant clinician. The growth of telehealth to deliver care provides an opportunity to explore whether these effects translate to a virtual setting. The primary purpose of this pilot study is to explore the relationships between virtual clinician (VC) characteristics and CRC screening intentions after engagement with a telehealth intervention leveraging technology to deliver tailored CRC prevention messaging. Methods: Using a posttest-only design with three factors (VC race-matching, VC gender, intervention type), participants (N = 2267) were randomised to one of eight intervention treatments. Participants self-reported perceptions and behavioral intentions. Results: The benefits of matching participants with a racially similar VC trended positive but did not reach statistical significance. Specifically, race-matching positively influenced screening intentions for Black participants but not for Whites (b = 0.29, p = 0.10). Importantly, perceptions of credibility, attractiveness, and message relevance significantly influenced screening intentions and the relationship with race-matching. Conclusions: To reduce racial CRC screening disparities, investments are needed to identify patient-focused interventions to address structural barriers to screening. This study suggests that telehealth interventions that match Black patients with a Black VC can enhance perceptions of credibility and message relevance, which may then improve screening intentions. Future research is needed to examine how to increase VC credibility and attractiveness, as well as message relevance without race-matching.

The effects of experimentally manipulated social status and subjective social status on physical activity among Hispanic adolescents: An RCT.

BACKGROUND: Low objective socioeconomic status (SES) and subjective social status (SSS), one's perceived social rank, are associated with obesity. This association may be due, in part, to social status-related differences in energy expenditure. Experimental studies are needed to assess the extent to which SES and SSS relate to energy expenditure. OBJECTIVE: Assess the effects of experimentally manipulated social status and SSS on moderate-to-vigorous physical activity (MVPA) and sedentary behaviour. METHODS: One hundred thirty-three Hispanic adolescents aged 15-21 were randomized to a high or low social status position, facilitated through a rigged game of Monopoly™. SSS was assessed with MacArthur Scales. Post-manipulation 24-h MVPA and sedentary behaviour were assessed via accelerometry. Analyses were conducted with general linear regression models. RESULTS: Experimentally manipulated social status did not significantly affect the total time spent in MVPA or sedentary behaviour; however, identifying as low SSS was significantly associated with less MVPA (p = 0.0060; 18.76 min less). CONCLUSIONS: Tewnty-four-hour MVPA and sedentary behaviour are not affected by an acute experimental manipulation of social status. However, low SSS, independent of SES, was associated with clinically significant differences in MVPA. SSS may be a better predictor of MVPA than SES among Hispanic adolescents, potentially influencing obesity, and other health-related outcomes.

A Subjective Culture Approach to Cancer Prevention: Rural Black and White Adults' Perceptions of Using Virtual Health Assistants to Promote Colorectal Cancer Screening.

In the US, Black adults are less likely than White adults to be screened for colorectal cancer (CRC). This study uses a subjective culture approach to describe and compare perceptions of a CRC screening intervention delivered via virtual health assistants (VHAs) among rural Black and White study participants. We analyzed 28 focus groups with Black (n = 85) and White (n = 69) adults aged 50-73. Participants, largely recruited through community engagement efforts, tested the VHA intervention on mobile phones provided by the research team. Moderated discussions were recorded, transcribed, and analyzed using thematic analysis. All groups preferred the VHA to be friendly. Other important cues included trustworthiness, authority, and expertise. Black participants expressed a preference for receiving information about their CRC risk from the VHA compared with White adults. Black participants also expressed the importance of sharing the intervention and the CRC screening messages with younger members of their networks, including family members who could benefit from screening messages before reaching the recommended age for screening. The key similarities and differences between Black and White adults' perceptions of the intervention that were identified in this study can help inform future efforts to develop effective communication strategies and reduce cancer screening inequities.

A Mobile App to Support Self-management of Chronic Kidney Disease: Development Study.

BACKGROUND: Chronic kidney disease (CKD) is a common and costly condition that is usually accompanied by multiple comorbidities including type 2 diabetes, hypertension, and obesity. Proper management of CKD can delay or prevent kidney failure and help mitigate cardiovascular disease risk, which increases as kidney function declines. Smart device apps hold potential to enhance patient self-management of chronic conditions including CKD. OBJECTIVE: The objective of this study was to develop a mobile app to facilitate self-management of nondialysis-dependent CKD. METHODS: Our stakeholder team included 4 patients with stage 3-4 nondialysis-dependent CKD; a kidney transplant recipient; a caretaker; CKD care providers (pharmacists, a nurse, primary care physicians, a nephrologist, and a cardiologist); 2 health services and CKD researchers; a researcher in biomedical informatics, nutrition, and obesity; a system developer; and 2 programmers. Focus groups and in-person interviews with the patients and providers were conducted using a focus group and interview guide based on existing literature on CKD self-management and the mobile app quality criteria from the Mobile App Rating Scale. Qualitative analytic methods including the constant comparative method were used to analyze the focus group and interview data. RESULTS: Patients and providers identified and discussed a list of requirements and preferences regarding the content, features, and technical aspects of the mobile app, which are unique for CKD self-management. Requirements and preferences centered along themes of communication between patients and caregivers, partnership in care, self-care activities, adherence to treatment regimens, and self-care self-efficacy. These identified themes informed the features and content of our mobile app. The mobile app user can enter health data including blood pressure, weight, and blood glucose levels. Symptoms and their severity can also be entered, and users are prompted to contact a physician as indicated by the symptom and its severity. Next, mobile app users can select biweekly goals from a set of predetermined goals with the option to enter customized goals. The user can also keep a list of medications and track medication use. Our app includes feedback mechanisms where in-range values for health data are depicted in green and out-of-range values are depicted in red. We ensured that data entered by patients could be downloaded into a user-friendly report, which could be emailed or uploaded to an electronic health record. The mobile app also includes a mechanism that allows either group or individualized video chat meetings with a provider to facilitate either group support, education, or even virtual clinic visits. The CKD app also includes educational material on CKD and its symptoms. CONCLUSIONS: Patients with CKD and CKD care providers believe that a mobile app can enhance CKD self-management by facilitating patient-provider communication and enabling self-care activities including treatment adherence.

Development of a Credible Virtual Clinician Promoting Colorectal Cancer Screening via Telehealth Apps for and by Black Men: Qualitative Study.

BACKGROUND: Traditionally, promotion of colorectal cancer (CRC) screening among Black men was delivered by community health workers, patient navigators, and decision aids (printed text or video media) at clinics and in the community setting. A novel approach to increase CRC screening of Black men includes developing and utilizing a patient-centered, tailored message delivered via virtual human technology in the privacy of one's home. OBJECTIVE: The objective of this study was to incorporate the perceptions of Black men in the development of a virtual clinician (VC) designed to deliver precision messages promoting the fecal immunochemical test (FIT) kit for CRC screening among Black men in a future clinical trial. METHODS: Focus groups of Black men were recruited to understand their perceptions of a Black male VC. Specifically, these men identified source characteristics that would enhance the credibility of the VC. The modality, agency, interactivity, and navigability (MAIN) model, which examines how interface features affect the user's psychology through four affordances (modality, agency, interactivity, and navigability), was used to assess the presumed credibility of the VC and likability of the app from the focus group transcripts. Each affordance triggers heuristic cues that stimulate a positive or a negative perception of trustworthiness, believability, and understandability, thereby increasing source credibility. RESULTS: In total, 25 Black men were recruited from the community and contributed to the development of 3 iterations of a Black male VC over an 18-month time span. Feedback from the men enhanced the visual appearance of the VC, including its movement, clothing, facial expressions, and environmental surroundings. Heuristics, including social presence, novelty, and authority, were all recognized by the final version of the VC, and creditably was established. The VC was named Agent Leveraging Empathy for eXams (ALEX) and referred to as "brother-doctor," and participants stated "wanting to interact with ALEX over their regular doctor." CONCLUSIONS: Involving Black men in the development of a digital health care intervention is critical. This population is burdened by cancer health disparities, and incorporating their perceptions in telehealth interventions will create awareness of the need to develop targeted messages for Black men.

Accessing Artificial Intelligence for Clinical Decision-Making.

Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US.

Key changes to improve social presence of a virtual health assistant promoting colorectal cancer screening informed by a technology acceptance model.

BACKGROUND: Understanding how older, minoritized patients attend to cues when interacting with web-based health messages may provide opportunities to improve engagement with novel health technologies. We assess acceptance-promoting and acceptance-inhibiting cues of a web-based, intervention promoting colorectal cancer (CRC) screening with a home stool test among Black women. MATERIALS AND METHODS: Focus group and individual interview data informed iterative changes to a race- and gender-concordant virtual health assistant (VHA). A user-centered design approach was used across 3 iterations to identify changes needed to activate cues described as important; such as portraying authority and expertise. Questionnaire data were analyzed using non-parametric tests for perceptions of cues. Analysis was guided by the Technology Acceptance Model. RESULTS: Perceptions of interactivity, social presence, expertise, and trust were important cues in a VHA-delivered intervention promoting CRC screening. Features of the web-based platform related to ease of navigation and use were also discussed. Participant comments varied across the 3 iterations and indicated acceptance of or a desire to improve source cues for subsequent iterations. We highlight the specific key changes made at each of three iterative versions of the interactive intervention in conjunction with user perception of changes. DISCUSSION: Virtual agents can be adapted to better meet patient expectations such as being a trustworthy and expert source. Across three evolving versions of a Black, VHA, cues for social presence were particularly important. Social presence cues helped patients engage with CRC screening messages delivered in this novel digital context. CONCLUSIONS: When using a VHA to disseminate health information, cues associated with acceptability can be leveraged and adapted as needed for diverse audiences. Patient characteristics (age, identity, health status) are important to note as they may affect perceptions of a novel health technologies ease of use and relevancy according to the leading models.

A Pilot Study Examining the Efficacy of Delivering Colorectal Cancer Screening Messages via Virtual Health Assistants.

INTRODUCTION: Patients are more likely to complete colorectal cancer screening when recommended by a race-concordant healthcare provider. Leveraging virtual healthcare assistants to deliver tailored screening interventions may promote adherence to colorectal cancer screening guidelines among diverse patient populations. The purpose of this pilot study is to determine the efficacy of the Agent Leveraging Empathy for eXams virtual healthcare assistant intervention to increase patient intentions to talk to their doctor about colorectal cancer screening. It also examines the influence of animation and race concordance on intentions to complete colorectal cancer screening. METHODS: White and Black adults (N=1,363) aged 50-73 years and not adherent to colorectal cancer screening guidelines were recruited from Qualtrics Panels in 2018 to participate in a 3-arm (animated virtual healthcare assistant, static virtual healthcare assistant, attention control) message design experiment. In 2020, a probit regression model was used to identify the intervention effects. RESULTS: Participants assigned to the animated virtual healthcare assistant (p<0.01) reported higher intentions to talk to their doctor about colorectal cancer screening than participants assigned to the other conditions. There was a significant effect of race concordance on colorectal cancer screening intentions but only in the static virtual healthcare assistant condition (p=0.04). Participant race, age, trust in healthcare providers, health literacy, and cancer information overload were also significant predictors of colorectal cancer screening intentions. CONCLUSIONS: Animated virtual healthcare assistants were efficacious compared with the static virtual healthcare assistant and attention control conditions. The influence of race concordance between source and participant was inconsistent across conditions. This warrants additional investigation in future studies given the potential for virtual healthcare assistant‒assisted interventions to promote colorectal cancer screening within guidelines.

Impact of the Early Phase of the COVID-19 Pandemic on US Healthcare Workers: Results from the HERO Registry.

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

Application of machine learning to the prediction of postoperative sepsis after appendectomy.

BACKGROUND: We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with postoperative sepsis in these patients. METHODS: The National Surgery Quality Improvement Program database was used to identify patients undergoing appendectomy between 2005 and 2017. Logistic regression, support vector machines, random forest decision trees, and extreme gradient boosting machines were used to model the occurrence of postoperative sepsis. RESULTS: In the study, 223,214 appendectomies were identified; 2,143 (0.96%) were indicated as having postoperative sepsis. Logistic regression (area under the curve 0.70; 95% confidence interval, 0.68-0.73), random forest decision trees (area under the curve 0.70; 95% confidence interval, 0.68-0.73), and extreme gradient boosting (area under the curve 0.70; 95% confidence interval, 0.68-0.73) afforded similar performance, while support vector machines (area under the curve 0.51; 95% confidence interval, 0.50-0.52) had worse performance. Variable importance analyses identified preoperative congestive heart failure, transfusion, and acute renal failure as predictors of postoperative sepsis. CONCLUSION: Machine learning methods can be used to predict the development of sepsis after appendectomy with moderate accuracy. Such predictive modeling has potential to ultimately allow for preoperative recognition of patients at risk for developing postoperative sepsis after appendectomy thus facilitating early intervention and reducing morbidity.

mMotiv8: A smartphone-based contingency management intervention to promote smoking cessation.

Cigarette smoking is the leading preventable cause of death and illness in the United States. We tested the usability, acceptability, and efficacy of a smartphone-based contingency management treatment to promote cessation. We used a nonconcurrent multiple-baseline design. Participants (N = 14) provided breath carbon monoxide (CO) samples by using a CO meter that was connected to the user's smartphone. An app (mMotiv8) housed on participants' smartphones automatically captured pictures of the CO sampling procedure to validate the end user's identity, and it prompted submissions via a push message delivered to participants' smartphones. Participants earned a $10 incentive for daily abstinence, which was added to a reloadable debit card. Overall, 4% of the CO samples were negative during baseline, and 89% were negative during treatment. Self-reported usability and acceptability were high, and 85% of the prompted samples were submitted. A smartphone intervention could be scalable and reduce the health consequences and costs associated with cigarette smoking, particularly in rural and low-income populations.

Tailoring virtual human-delivered interventions: A digital intervention promoting colorectal cancer screening for Black women.

OBJECTIVE: Despite efforts to reduce cancer disparities, Black women remain underrepresented in cancer research. Virtual health assistants (VHAs) are one promising digital technology for communicating health messages and promoting health behaviors to diverse populations. This study describes participant responses to a VHA-delivered intervention promoting colorectal cancer (CRC) screening with a home-stool test. METHODS: We recruited 53 non-Hispanic Black women 50 to 73 years old to participate in focus groups and think-aloud interviews and test a web-based intervention delivered by a race- and gender-concordant VHA. A user-centered design approach prioritized modifications to three successive versions of the intervention based on participants' comments. RESULTS: Participants identified 26 cues relating to components of the VHA's credibility, including trustworthiness, expertise, and authority. Comments on early versions revealed preferences for communicating with a human doctor and negative critiques of the VHA's appearance and movements. Modifications to specific cues improved the user experience, and participants expressed increased willingness to engage with later versions of the VHA and the screening messages it delivered. Informed by the Modality, Agency, Interactivity, Navigability Model, we present a framework for developing credible VHA-delivered cancer screening messages. CONCLUSIONS: VHAs provide a systematic way to deliver health information. A culturally sensitive intervention designed for credibility promoted user interest in engaging with guideline-concordant CRC screening messages. We present strategies for effectively using cues to engage audiences with health messages, which can be applied to future research in varying contexts.

Simple tests of cardiorespiratory fitness in a pediatric population.

A progressive, treadmill-based VO2max is the gold standard of cardiorespiratory fitness determination but is rarely used in pediatric clinics due to time requirements and cost. Simpler and shorter fitness tests such as the Squat Test or Step Test may be feasible and clinically useful alternatives. However, performance comparisons of these tests to treadmill VO2max tests are lacking. The primary aim of this cross-sectional study was to assess the correlation between Squat and Step Test scores and VO2max in a pediatric population. As secondary outcomes, we calculated correlations between Rated Perceived Exertion Scale (RPE) scores, NIH PROMIS Physical Activity scores, and BMI z-score with VO2max, and we also evaluated the ability of each fitness test to discriminate low and high-risk patients based on the FITNESSGram. Forty children aged 10-17 completed these simple cardiorespiratory fitness tests. Statistically significant correlations were observed between VO2max and the Step Test (r = -0.549) and Squat Test (r = -0.429) scores, as well as participant BMI z-score (r = -0.458). RPE and PROMIS scores were not observed to be correlated with VO2max. Area Under the Receiver Operator Curve was relatively high for BMI z-scores and the Step Test (AUC = 0.813, 0.713 respectively), and lower for the Squat Test (AUC = 0.610) in discriminating risk according to FITNESSGram Scores. In this sample, the Step Test performed best overall. These tests were safe, feasible, and may add great value in assessing cardiorespiratory fitness in a clinical setting.