ABSTRACT
COVID-19 has impacted billions of people and health care systems globally. However, there is currently no publicly available chatbot for patients and care providers to determine the potential severity of a COVID-19 infection or the possible biological system responses and comorbidities that can contribute to the development of severe cases of COVID-19. This preliminary investigation assesses this lack of a COVID-19 case-by-case chatbot into consideration when building a decision tree with binary classification that was stratified by age and body system, viral infection, comorbidities, and any manifestations. After reviewing the relevant literature, a decision tree was constructed using a suite of tools to build a stratified framework for a chatbot application and interaction with users. A total of 212 nodes were established that were stratified from lung to heart conditions along body systems, medical conditions, comorbidities, and relevant manifestations described in the literature. This resulted in a possible 63,360 scenarios, offering a method toward understanding the data needed to validate the decision tree and highlighting the complicated nature of severe cases of COVID-19. The decision tree confirms that stratification of the viral infection with the body system while incorporating comorbidities and manifestations strengthens the framework. Despite limitations of a viable clinical decision tree for COVID-19 cases, this prototype application provides insight into the type of data required for effective decision support.
ABSTRACT
BACKGROUND: There are no psychometrically validated measures of the willingness to engage in public health screening and prevention efforts, particularly mobile health (mHealth)-based tracking, that can be adapted to future crises post-COVID-19. OBJECTIVE: The psychometric properties of a novel measure of the willingness to participate in pandemic-related screening and tracking, including the willingness to use pandemic-related mHealth tools, were tested. METHODS: Data were from a cross-sectional, national probability survey deployed in 3 cross-sectional stages several weeks apart to adult residents of the United States (N=6475; stage 1 n=2190, 33.82%; stage 2 n=2238, 34.56%; and stage 3 n=2047, 31.62%) from the AmeriSpeak probability-based research panel covering approximately 97% of the US household population. Five items asked about the willingness to use mHealth tools for COVID-19-related screening and tracking and provide biological specimens for COVID-19 testing. RESULTS: In the first, exploratory sample, 3 of 5 items loaded onto 1 underlying factor, the willingness to use pandemic-related mHealth tools, based on exploratory factor analysis (EFA). A 2-factor solution, including the 3-item factor, fit the data (root mean square error of approximation [RMSEA]=0.038, comparative fit index [CFI]=1.000, standardized root mean square residual [SRMR]=0.005), and the factor loadings for the 3 items ranged from 0.849 to 0.893. In the second, validation sample, the reliability of the 3-item measure was high (Cronbach α=.90), and 1 underlying factor for the 3 items was confirmed using confirmatory factor analysis (CFA): RMSEA=0, CFI=1.000, SRMR=0 (a saturated model); factor loadings ranged from 1.000 to 0.962. The factor was independently associated with COVID-19-preventive behaviors (eg, "worn a face mask": r=0.313, SE=0.041, P<.001; "kept a 6-foot distance from those outside my household": r=0.282, SE=0.050, P<.001) and the willingness to provide biological specimens for COVID-19 testing (ie, swab to cheek or nose: r=0.709, SE=0.017, P<.001; small blood draw: r=0.684, SE=0.019, P<.001). In the third, multiple-group sample, the measure was invariant, or measured the same thing in the same way (ie, difference in CFI [ΔCFI]<0.010 across all grouping categories), across age groups, gender, racial/ethnic groups, education levels, US geographic region, and population density (ie, rural, suburban, urban). When repeated across different samples, factor-analytic findings were essentially the same. Additionally, there were mean differences (ΔM) in the willingness to use mHealth tools across samples, mainly based on race or ethnicity and population density. For example, in SD units, suburban (ΔM=-0.30, SE=0.13, P=.001) and urban (ΔM=-0.42, SE=0.12, P<.001) adults showed less willingness to use mHealth tools than rural adults in the third sample collected on May 30-June 8, 2020, but no differences were detected in the first sample collected on April 20-26, 2020. CONCLUSIONS: Findings showed that the screener is psychometrically valid. It can also be adapted to future public health crises. Racial and ethnic minority adults showed a greater willingness to use mHealth tools than White adults. Rural adults showed more mHealth willingness than suburban and urban adults. Findings have implications for public health screening and tracking and understanding digital health inequities, including lack of uptake.
ABSTRACT
BACKGROUND: At the start of the COVID-19 pandemic, unprecedented pressure was placed on health care services globally. An opportunity to alleviate this pressure was to introduce a digital health platform that provided COVID-19-related advice and helped individuals understand and manage their COVID-19 symptoms. Therefore, in July 2020, the Your COVID Recovery website was launched by the National Health Service of England with the aim of creating a practical tool that provides advice and support to individuals recovering from COVID-19. The website includes information on many of the key COVID-19 symptoms. To date, public use of the Your COVID Recovery website and user behavior remain unknown. However, this information is likely to afford insight into the impact of the website and most commonly experienced COVID-19 symptoms. OBJECTIVE: This study aimed to evaluate public use of the Your COVID Recovery website, a digital health platform that provides support to individuals recovering from COVID-19, and determine user behavior during its first year of operation. METHODS: Google Analytics software that was integrated into the Your COVID Recovery website was used to assess website use and user behavior between July 31, 2020, and July 31, 2021. Variables that were tracked included the number of users, user country of residence, traffic source, number of page views, number of session views, and mean session duration. User data were compared to COVID-19 case data downloaded from the UK government's website. RESULTS: During the study period, 2,062,394 users accessed the Your COVID Recovery website. The majority of users were located in the United Kingdom (1,265,061/2,062,394, 61.30%) and accessed the website via a search engine (1,443,057/2,062,394, 69.97%). The number of daily website users (n=15,298) peaked on January 18, 2021, during the second wave of COVID-19 in the United Kingdom. The most frequently visited pages after the home page were for the following COVID-19 symptoms: Cough (n=550,190, 12.17%), Fatigue (n=432,421, 9.56%), Musculoskeletal pain (n=406,859, 9.00%), Taste and smell (n=270,599, 5.98%), and Breathlessness (n=203,136, 4.49%). The average session duration was 1 minute 13 seconds. CONCLUSIONS: A large cohort of individuals actively sought help with their COVID-19 recovery from the website, championing the potential of this tool to target an unmet health care need. User behavior demonstrated that individuals were primarily seeking advice on how to relieve and manage COVID-19 symptoms, especially symptoms of cough, fatigue, and musculoskeletal pain. COVID-19 rehabilitation programs should use the results of this study to ensure that the program content meets the needs of the post-COVID-19 population.
ABSTRACT
BACKGROUND: Access to accurate information in health care is a key point for caregivers to avoid medication errors, especially with the reorganization of staff and drug circuits during health crises such as the COVID19 pandemic. It is, therefore, the role of the hospital pharmacy to answer caregivers' questions. Some may require the expertise of a pharmacist, some should be answered by pharmacy technicians, but others are simple and redundant, and automated responses may be provided. OBJECTIVE: We aimed at developing and implementing a chatbot to answer questions from hospital caregivers about drugs and pharmacy organization 24 hours a day and to evaluate this tool. METHODS: The ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model was used by a multiprofessional team composed of 3 hospital pharmacists, 2 members of the Innovation and Transformation Department, and the IT service provider. Based on an analysis of the caregivers' needs about drugs and pharmacy organization, we designed and developed a chatbot. The tool was then evaluated before its implementation into the hospital intranet. Its relevance and conversations with testers were monitored via the IT provider's back office. RESULTS: Needs analysis with 5 hospital pharmacists and 33 caregivers from 5 health services allowed us to identify 7 themes about drugs and pharmacy organization (such as opening hours and specific prescriptions). After a year of chatbot design and development, the test version obtained good evaluation scores: its speed was rated 8.2 out of 10, usability 8.1 out of 10, and appearance 7.5 out of 10. Testers were generally satisfied (70%) and were hoping for the content to be enhanced. CONCLUSIONS: The chatbot seems to be a relevant tool for hospital caregivers, helping them obtain reliable and verified information they need on drugs and pharmacy organization. In the context of significant mobility of nursing staff during the health crisis due to the COVID-19 pandemic, the chatbot could be a suitable tool for transmitting relevant information related to drug circuits or specific procedures. To our knowledge, this is the first time that such a tool has been designed for caregivers. Its development further continued by means of tests conducted with other users such as pharmacy technicians and via the integration of additional data before the implementation on the 2 hospital sites.
ABSTRACT
BACKGROUND: In November 2020, WA Notify, Washington State's COVID-19 digital exposure notification tool, was launched statewide to mitigate ongoing COVID-19 transmission. WA Notify uses the Bluetooth proximity-triggered, Google/Apple Exposure Notification Express framework to distribute notifications to users who have added or activated this tool on their smartphones. This smartphone-based tool relies on sufficient population-level activation to be effective; however, little is known about its adoption among communities disproportionately impacted by the COVID-19 pandemic or what barriers might limit its adoption and use among diverse populations. OBJECTIVE: We sought to (1) conduct a formative exploration of equity-related issues that may influence the access, adoption, and use of WA Notify, as perceived by community leaders of populations disproportionately impacted by the COVID-19 pandemic; and (2) generate recommendations for promoting the equitable access to and impact of this novel intervention for these communities. METHODS: We used a 2-step data collection process to gather the perspectives of community leaders across Washington regarding the launch and implementation of WA Notify in their communities. A web-based, brief, and informational survey measured the perceptions of the community-level familiarity and effectiveness of WA Notify at slowing the spread of COVID-19 and identified potential barriers and concerns to accessing and adopting WA Notify (n=17). Semistructured listening sessions were conducted to expand upon survey findings and explore the community-level awareness, barriers, facilitators, and concerns related to activating WA Notify in greater depth (n=13). RESULTS: Our findings overlap considerably with those from previous mobile health equity studies. Digital literacy, trust, information accessibility, and misinformation were highlighted as key determinants of the adoption and use of WA Notify. Although WA Notify does not track users or share data, community leaders expressed concerns about security, data sharing, and personal privacy, which were cited as outweighing the potential benefits to adoption. Both the survey and informational sessions indicated low community-level awareness of WA Notify. Community leaders recommended the following approaches to improve engagement: tailoring informational materials for low-literacy levels, providing technology navigation, describing more clearly that WA Notify can help the community, and using trusted messengers who are already engaged with the communities to communicate about WA Notify. CONCLUSIONS: As digital public health tools, such as WA Notify, emerge to address public health problems, understanding the key determinants of adoption and incorporating equity-focused recommendations into the development, implementation, and communication efforts around these tools will be instrumental to their adoption, use, and retention.
ABSTRACT
BACKGROUND: Digital mental health (DMH) tools use technology (eg, websites and mobile apps) to conveniently deliver mental health resources to users in real time, reducing access barriers. Underserved communities facing health care provider shortages and limited mental health resources may benefit from DMH tools, as these tools can help improve access to resources. OBJECTIVE: This study described the development and feasibility evaluation of the Emotional Needs Evaluation and Resource Guide for You (ENERGY) System, a DMH tool to meet the mental health and resource needs of youth and their families developed in the context of the COVID-19 pandemic. The ENERGY System offers a brief assessment of resource needs; problem-solving capabilities; and symptoms of depression, anxiety, trauma, and alcohol and substance use followed by automated, personalized feedback based on the participant's responses. METHODS: Individuals aged ≥15 years were recruited through community partners, community events, targeted electronic health record messages, and social media. Participants completed screening questions to establish eligibility, entered demographic information, and completed the ENERGY System assessment. Based on the participant's responses, the ENERGY System immediately delivered digital resources tailored to their identified areas of need (eg, relaxation). A subset of participants also voluntarily completed the following: COVID-19 Exposure and Family Impact Survey (CEFIS) or COVID-19 Exposure and Family Impact Survey Adolescent and Young Adult Version (CEFIS-AYA); resource needs assessment; and feedback on their experience using the ENERGY System. If resource needs (eg, housing and food insecurity) were endorsed, lists of local resources were provided. RESULTS: A total of 212 individuals accessed the ENERGY System link, of which 96 (45.3%) completed the screening tool and 86 (40.6%) received resources. Participant responses on the mental health screening questions triggered on average 2.04 (SD 1.94) intervention domains. Behavioral Activation/Increasing Activities was the most frequently launched intervention domain (56%, 54/96), and domains related to alcohol or substance use were the least frequent (4%, 4/96). The most frequently requested support areas were finances (33%, 32/96), transportation (26%, 25/96), and food (24%, 23/96). The CEFIS and CEFIS-AYA indicated higher than average impacts from the pandemic (ie, average scores >2.5). Participants were satisfied with the ENERGY System overall (65%, 39/60) as well as the length of time it took to answer the questions (90%, 54/60), which they found easy to answer (87%, 52/60). CONCLUSIONS: This study provided initial support for the feasibility of the ENERGY System, a DMH tool capable of screening for resource and mental health needs and providing automated, personalized, and free resources and techniques to meet the identified needs. Future studies should seek direct feedback from community members to further improve the ENERGY System and its dissemination to encourage use.