Health Care Decisions Research Articles

A thematic analysis of shared decision-making in consultations with patients with a presumed brain tumor and neurosurgeons

Abstract Background Patients with a brain tumor, family members and healthcare professionals face complex healthcare decisions that can significantly impact everyday life. Engaging in shared decision-making (SDM) can help ensure treatment decisions align with patients’ preferences and goals. Little is known about the decision-making process in neurosurgical consultations. This study aims to qualitatively explore and describe the SDM process during neurosurgical consultations for patients with a presumed brain tumor. Methods The first consultation between patients with a presumed glioma or meningioma and their neurosurgeons was audio-recorded and transcribed. Decision-making models were used as sensitization concepts, with coding carried out inductively. We employed a reflexive thematic analysis to develop themes and sub-themes until saturation was beginning to emerge. Results Analyzing eleven consultations, we developed three main themes and twelve sub-themes. The results depict decision-making guided by neurosurgeons (theme 1), with varying degrees of engagement from patients and family members (theme 2) and personalization of the decision-making process based on the medical situation and everyday life priorities (theme 3). The consultations lacked specific attention to everyday life priorities of the patients. Conclusions The decision-making processes seemed to focus primarily on medically informing patients with a presumed brain tumor, in line with IDM. However, less emphasis was placed on learning more about the patient’s everyday life and preferences to integrate into the decision-making process, which is key to SDM. Incorporating patient goals may lead to greater personalization and decisions that align with both the patient’s medical situation and their goals.

Mental healthcare and pragmatic shared decision-making in general practice: An interview study.

Healthcare providers in general practice are expected to deliver mental healthcare to patients through shared decision-making (SDM). It is unclear if they perceive their SDM to be affected by challenging circumstances in mental healthcare, eg, how waiting time for therapy plays a role. To explore how healthcare providers and patients in general practice engage in SDM, given the challenging circumstances in mental healthcare. A qualitative interview study in seven Dutch general practices. Semi-structured interviews were conducted with general practitioners (GPs; N=9), practice nurses mental health (PNMHs; N=8) and patients that sought mental healthcare (N=18). The interviews were deductively and inductively thematically analyzed. The participants mainly reported on SDM regarding treatment in secondary mental healthcare. The PNMHs explained they lack an overview of available treatments and waiting times in facilities. The PNMHs therefore instruct patient to also search for options themselves. Most patients found this approach burdensome, especially those new to mental healthcare. These patients were said to often express no strong treatment preferences and rely on advice by their healthcare providers. The GPs and PNMHs explained that in such cases, they often adopt a pragmatic approach and for example refer indecisive patients to facilities with little waiting time. The healthcare providers and patients in general practice experience they adapt their approach to SDM in reaction to the circumstances in mental healthcare. Further exploration of how SDM is implemented and shaped by challenging circumstances across different healthcare settings is needed.

Norwegian and Swedish value sets for the EORTC QLU-C10D utility instrument.

This study aimed to develop utility weights for the European Organization for Research and Treatment of Cancer (EORTC) QLU-C10D, a cancer-specific utility instrument, tailored to the Norwegian and Swedish populations. The utility weights are intended for use in the specific welfare contexts of Norway and Sweden to support more precise healthcare decision-making in cancer treatment and care. This cross-sectional study included 1019 Norwegian and 1048 Swedish participants representative in age and gender of the two general populations. Participants completed a discrete choice experiment involving 960 choice sets, each consisting of two EORTC QLU-C10D health states described by the instrument's domains and the duration of each state. Utility weights were calculated using generalized estimation equation models, and non-monotonic levels were merged to ensure consistent valuation. In the Norwegian participants, the largest utility decrements were seen for the domain of physical functioning (decrement of -0.263 for highest level "very much"), followed by pain (decrement -0.205 for level "very much") and role functioning (-0.139). Among the cancer-specific domains, nausea had the largest utility decrement (-0.124). In the Swedish participants, the largest utility decrements were also observed for physical functioning (-0.207 for the response "very much"), followed by pain (-0.139), role functioning (-0.133), and nausea (-0.119). Emotional functioning also exhibited a sizable utility decrement (-0.115). This study provides the first set of utility weights for the EORTC QLU-C10D specific to Norway and Sweden, reflecting the unique health preferences of these populations. The generated utility decrements can inform cost-utility analyses and optimize resource allocation in cancer care within the Norwegian and Swedish healthcare systems.

Developing and Evaluating SEE-Diabetes: A Patient-Centered Educational Decision Support System for Diabetes Car.

This feasibility study evaluated the effectiveness of Support-Engage-Empower-Diabetes (SEE-Diabetes), a patient-centered educational tool designed to promote shared decision-making of diabetes management in older adults. We aimed to assess SEE-Diabetes's ability to facilitate patient engagement and collaborative goal setting, as measured by the Observational Patient Involvement (OPTION) scale and Shared Decision-Making Questionnaire (SDM-Q-Doc). We hypothesized that these instruments would effectively differentiate between healthcare providers who actively leveraged SEE-Diabetes to guide patient-centric conversations and set goals compared to those who did not. SEE-Diabetes, developed through a 4-year user-centered design process, was employed in simulated clinical encounters at the University of Missouri Health Care. We conducted an analysis of 12 clinical encounters using video recordings. This analysis involved three simulated patients and four providers, two internals and two externals, utilizing a mixed-methods approach. We assessed the decision-making process using SEE-Diabetes by SDM-Q-Doc, OPTION scale, and conversation analysis. The average scores for the SDM-Q-Doc and the OPTION scale, out of a possible 100, were 52.6 and 75.9, respectively. Our findings revealed that active provider engagement with SEE-Diabetes during patient interactions served as an effective medium to facilitate shared decision-making and to set patient-centered goals. Providers who actively utilized SEE-Diabetes to guide conversations, ask open-ended questions, and incorporate patient input into goal setting demonstrated significantly higher OPTION and SDM-Q-Doc scores compared to those who used the tool less frequently or primarily for documentation purposes. Providers expressed positive feedback, highlighting its conciseness, patient-centricity, and optimism about integrating SEE-Diabetes into their future practices. SEE-Diabetes showed considerable promise in improving interactions between patients and providers, presenting an innovative approach to diabetes management for older adults. This tool has the potential to not only close communication gaps but also enable patients to take a more active role in their healthcare decisions.

Accuracy of Prospective Assessments of 4 Large Language Model Chatbot Responses to Patient Questions About Emergency Care: Experimental Comparative Study.

Recent surveys indicate that 48% of consumers actively use generative artificial intelligence (AI) for health-related inquiries. Despite widespread adoption and the potential to improve health care access, scant research examines the performance of AI chatbot responses regarding emergency care advice. We assessed the quality of AI chatbot responses to common emergency care questions. We sought to determine qualitative differences in responses from 4 free-access AI chatbots, for 10 different serious and benign emergency conditions. We created 10 emergency care questions that we fed into the free-access versions of ChatGPT 3.5 (OpenAI), Google Bard, Bing AI Chat (Microsoft), and Claude AI (Anthropic) on November 26, 2023. Each response was graded by 5 board-certified emergency medicine (EM) faculty for 8 domains of percentage accuracy, presence of dangerous information, factual accuracy, clarity, completeness, understandability, source reliability, and source relevancy. We determined the correct, complete response to the 10 questions from reputable and scholarly emergency medical references. These were compiled by an EM resident physician. For the readability of the chatbot responses, we used the Flesch-Kincaid Grade Level of each response from readability statistics embedded in Microsoft Word. Differences between chatbots were determined by the chi-square test. Each of the 4 chatbots' responses to the 10 clinical questions were scored across 8 domains by 5 EM faculty, for 400 assessments for each chatbot. Together, the 4 chatbots had the best performance in clarity and understandability (both 85%), intermediate performance in accuracy and completeness (both 50%), and poor performance (10%) for source relevance and reliability (mostly unreported). Chatbots contained dangerous information in 5% to 35% of responses, with no statistical difference between chatbots on this metric (P=.24). ChatGPT, Google Bard, and Claud AI had similar performances across 6 out of 8 domains. Only Bing AI performed better with more identified or relevant sources (40%; the others had 0%-10%). Flesch-Kincaid Reading level was 7.7-8.9 grade for all chatbots, except ChatGPT at 10.8, which were all too advanced for average emergency patients. Responses included both dangerous (eg, starting cardiopulmonary resuscitation with no pulse check) and generally inappropriate advice (eg, loosening the collar to improve breathing without evidence of airway compromise). AI chatbots, though ubiquitous, have significant deficiencies in EM patient advice, despite relatively consistent performance. Information for when to seek urgent or emergent care is frequently incomplete and inaccurate, and patients may be unaware of misinformation. Sources are not generally provided. Patients who use AI to guide health care decisions assume potential risks. AI chatbots for health should be subject to further research, refinement, and regulation. We strongly recommend proper medical consultation to prevent potential adverse outcomes.

Investigation of reporting bias in interrupted time series (ITS) studies: a study protocol

Background Interrupted time-series (ITS) studies are commonly used to examine the effects of interventions targeted at populations. Suppression of ITS studies or results within these studies, known as reporting bias, has the potential to bias the evidence-base on a particular topic, with potential consequences for healthcare decision-making. Therefore, we aim to determine whether there is evidence of reporting bias among ITS studies. Methods We will conduct a search for published protocols of ITS studies and reports of their results in PubMed, MEDLINE, and Embase up to December 31, 2022. We contact the authors of the ITS studies to seek information about their study, including submission status, data for unpublished results, and reasons for non-publication or non-reporting of certain outcomes. We will examine if there is evidence of publication bias by examining whether time-to-publication is influenced by the statistical significance of the study’s results for the primary research question using Cox proportional hazards regression. We will examine whether there is evidence of discrepancies in outcomes by comparing those specified in the protocols with those in the reports of results, and we will examine whether the statistical significance of an outcome’s result is associated with how completely that result is reported using multivariable logistic regression. Finally, we will examine discrepancies between protocols and reports of results in the methods by examining the data collection processes, model characteristics, and statistical analysis methods. Discrepancies will be summarized using descriptive statistics. Discussion These findings will inform systematic reviewers and policymakers about the extent of reporting biases and may inform the development of mechanisms to reduce such biases.

The human rights act and the community nurse.

Iwan Dowie talks about the Human Rights Act, a controversial piece of statutory legislation. The article emphasises the critical role of community nurses in upholding human rights within their practice and outlines the historical context of human rights protections in the UK. The article also addresses the legal implications of resource constraints in healthcare decisions.

Engagement in Healthcare Decision-Making and Associated Factors among Patients with Chronic Non-Communicable Diseases in Southwest Ethiopia, 2023

BackgroundChronic Non-communicable diseases require lifelong care involving patients, families, a team of their care providers, and significant others. Additionally, engaging patients in their healthcare decision-making is crucial for achieving better clinical outcomes. This study aimed to assess patients' engagement in healthcare decision-making and associated factors among adults with chronic non-communicable diseases in Southwest Ethiopia MethodsA cross-sectional study design was used among 422 participants at the public hospital of Jimma town. Study participants were consecutively recruited from May 01 to June 01, 2023. A structured Patient Activation Measure was used to measure patient engagement in healthcare decision-making. Descriptive statistics and binary logistic regression analysis describe the association between dependent and independent variables. ResultA total of 422 patients with chronic diseases participated in the study yielding a response rate of 99.3%. Slightly greater than one-third (35.8% (95% CI: 31.19, 40.41)) of participants in The study area had a high engagement in healthcare decision-making. Residence (Odd ratio 0.27; 95% confidence interval 0.137-0.534), educational level(Odd ratio 0.168; 95% confidence interval 0.064-0.443), access to information materials(Odd ratio 0.226; 95% confidence interval 0.112-0.458), duration(Odd ratio 3.864; 95% confidence interval 2.083-7.169), health literacy(Odd ratio 1.192; 95% confidence interval 1.111-1.28), and preference for autonomy(Odd ratio 1.097; 95% confidence interval 1.058-1.136) were factors statistically associated with patient engagement in healthcare decision-making. ConclusionOne-third of study participants reported high engagement in healthcare decision-making. Thus, healthcare providers should pay attention to patients with low educational levels, who do not have access to information materials, low health literacy levels, and their preference for autonomy to engage them in their healthcare decision-making.

Effectiveness of EHR systems on decision support in primary healthcare: a technology acceptance model 3 perspective.

This study aims to evaluate healthcare practitioners' perceptions of electronic health record (EHR) systems and their effectiveness in supporting clinical decision-making in Tanzanian Primary Healthcare (PHC) facilities. A mixed-methods approach was employed, combining quantitative data from structured questionnaires and qualitative insights from open-ended responses. The study was conducted in the Dodoma region of Tanzania, focusing on a diverse representation of PHC facilities, including district hospitals, health centres and dispensaries. Data were analysed using multiple linear regression for quantitative data, and thematic analysis was applied to qualitative responses. The results revealed that while EHR systems are widely used in Tanzanian PHC facilities, their impact on clinical decision-making remains limited. Only a moderate portion of practitioners perceived EHR systems as effective in decision support, and frequent system use was negatively correlated with user satisfaction. Challenges such as inadequate training and support, system crashes, slow performance and poor usability and integration into clinical workflows were significant barriers to effectively utilising EHR systems. This study contributes to the limited literature on EHR system implementation in low-resource settings, specifically Tanzania, by focusing on decision-support features within EHR systems. The findings offer valuable insights for healthcare policymakers, system designers and practitioners to optimise EHR implementation and improve healthcare outcomes in resource-constrained environments.

Addressing Social and Structural Determinants of Health in the Delivery of Reproductive Health Care: ACOG Committee Statement No. 11.

Social and structural determinants of health include historical, social, political, and economic forces, many of which are rooted in racism and inequality, that shape the relationship between environmental conditions and individual health. Unmet social needs can increase the risk of many conditions treated by obstetrician-gynecologists (ob-gyns), including, but not limited to, preterm birth, unintended pregnancy, infertility, cervical cancer, breast cancer, and maternal mortality. An individual health care professional's biases (whether overt or unconscious) affect delivery of care and may exacerbate and reinforce health disparities through inequitable treatment. Obstetrician-gynecologists and other health care professionals should seek to understand patients' health care decision making not simply as patients' individual-level behavior, but rather as the result of intersecting sociopolitical conditions, structural inequities, and social needs that create and maintain inequalities in health and health care. Recognizing the importance of social and structural determinants of health can help ob-gyns and other health care professionals to better understand patients, effectively communicate about health-related conditions and behavior, and contribute to improved health outcomes, including patients' experience of care and their trust in the health care system.

Mitigating Racial Bias in Health Care Algorithms: Improving Fairness in Access to Supportive Housing.

Algorithms for guiding health care decisions have come under increasing scrutiny for being unfair to certain racial and ethnic groups. The authors describe their multistep process, using data from 3,465 individuals, to reduce racial and ethnic bias in an algorithm developed to identify state Medicaid beneficiaries experiencing homelessness and chronic health needs who were eligible for coordinated health care and housing supports. Through an iterative process of adjusting inputs, reviewing outputs with diverse stakeholders, and performing quality assurance, the authors developed an algorithm that achieved racial and ethnic parity in the selection of eligible Medicaid beneficiaries.

Tempering Medicine with Mercy: How Moral Luck Promotes More Equitable Healthcare Decisions

The concept of moral luck challenges our traditional understanding of morality, responsibility, and accountability in healthcare. It forces us to confront the reality that many factors that influence health outcomes and medical decisions are beyond our control. By recognizing the role of luck in health outcomes, we can promote a more compassionate, equitable approach to healthcare — one that acknowledges the complex interplay of factors that shape our lives and our decisions.

Concordance of ChatGPT With American Burn Association Guidelines on Acute Burns.

Burn injuries often require immediate assistance and specialized care for optimal management and outcomes. The emergence of accessible artificial intelligence technology has just recently started being applied to healthcare decision making and patient education. However, its role in clinical recommendations is still under scrutiny. This study aims to evaluate ChatGPT's outputs and the appropriateness of its responses to commonly asked questions regarding acute burn care when compared to the American Burn Association Guidelines. Twelve commonly asked questions were formulated by a fellowship-trained burn surgeon to address the American Burn Association's recommendations on burn injuries, management, and patient referral. These questions were prompted into ChatGPT, and each response was compared with the aforementioned guidelines, the gold standard for accurate and evidence-based burn care recommendations. Three burn surgeons independently evaluated the appropriateness and comprehensiveness of each ChatGPT response based on the guidelines according to the modified Global Quality Score scale. The average score for ChatGPT-generated responses was 4.56 ± 0.65, indicating the responses were exceptional quality with the most important topics covered and in high concordance with the guidelines. This initial comparison of ChatGPT-generated responses and the American Burn Association guidelines demonstrates that ChatGPT can accurately and comprehensibly describe appropriate treatment and management plans for acute burn injuries. We foresee that ChatGPT may play a role as a complementary tool in medical decision making and patient education, having a profound impact on clinical practice, research, and education.

Assessing the Readability of Online English and Spanish Resources for Polydactyly and Syndactyly.

Online patient education materials (PEMs) that are difficult to read disproportionately affect patients with low health literacy and educational attainment. Patients may not be fully informed or empowered to engage meaningfully with providers and advocate for their goals. We aim to assess the readability of online PEMs regarding polydactyly and syndactyly. Google was used to query "polydactyly" and "syndactyly" in English and Spanish. The first 50 results were categorized into institutional (government, medical school, teaching hospital), noninstitutional (private practice, blog), and academic (journal articles, book chapters). Readability scores were generated using the Simple Measure of Gobbledygook and Spanish Simple Measure of Gobbledygook scales. All polydactyly PEMs and >95% of syndactyly PEMs exceeded the National Institutes of Health recommended 6th-grade reading level. Altogether, English PEMs had an average reading level of a university freshman and Spanish PEMs had an average reading level of nearly a high school sophomore. For both diagnoses, English PEMs were harder to read than Spanish PEMs overall and when compared across the 3 categories between the 2 languages. Generally, noninstitutional PEMs were more difficult to read than their institutional counterparts. To improve patient education, health literacy, and language equity, online resources for polydactyly and syndactyly should be written at the 6th-grade level. Currently, these PEMs are too advanced, which can make accessing, understanding, and pursuing healthcare decisions more challenging. Understanding health conditions and information is crucial to empower patients, regardless of literacy.

Recommendations on the Use of Structured Expert Elicitation Protocols for Healthcare Decision Making: A Good Practices Report of an ISPOR Task Force

Recommendations on the Use of Structured Expert Elicitation Protocols for Healthcare Decision Making: A Good Practices Report of an ISPOR Task Force

The administration of immune checkpoint inhibitors via an elastomeric pump versus conventional intravenous infusion: an economic perspective

BackgroundRecent studies have underscored the potential of innovative administration methods to mitigate the capacity burden on healthcare systems, without compromising the quality of care. This study assessed and compared the resource utilization and associated costs of two distinct administration modes of immune checkpoint inhibitors: the innovative elastomeric pump and conventional intravenous infusion. This comparison can inform sustainable healthcare practices and healthcare decision-making to optimize treatment efficiency in an era of escalating healthcare demands.MethodsIn this micro-costing study, data on resource use and time allocation for drug preparation and administration were collected using an observational, non-interventional study design. Data were registered at the oncology daycare unit and hospital pharmacy. Cost categories included drug acquisition, disposable materials, healthcare professional time for drug administration, drug preparation, and patient time spent at the oncology day care unit.ResultsDrug administration through the elastomeric pump resulted in substantially lower healthcare costs when compared to conventional infusion, particularly due to reduced labor and chair time. The elastomeric pump reduced the total chair time by 78% and nurse time by 55%. Total average costs (excluding drug costs) were €103,47 and €77.99 for conventional infusion and the elastomeric pump, respectively, showcasing potential savings of €25.48 (P < 0.001) per administration.ConclusionsThis study demonstrated that the elastomeric pump not only offers substantial cost savings but also enhances the treatment capacity of the oncology day care unit. These findings support the adoption of the elastomeric pump in clinical settings as a cost-saving and efficient alternative to conventional infusion.Trial registrationThis study has been registered in the National Trial Register (NTR), with the reference number NTR NL9473. Registration date: 05-05-2021.

Artificial Intelligence and Artificial Stupidity: The Inseparables

Artificial intelligence (AI) has long been heralded for its ability to simulate human intelligence, enabling machines to perform complex tasks such as decision-making, problem-solving, and data analysis. However, alongside the advancements in AI, the concept of artificial stupidity (AS) has gained attention. AS refers to the limitations and errors made by AI systems, often resulting from incomplete data, biased algorithms, or the inherent restrictions placed on AI to simulate more human-like decision-making. These instances of "stupidity" can lead to nonsensical or harmful outcomes, especially when AI is applied to critical areas such as healthcare, autonomous systems, and legal decision-making. This narrative review explores the duality between AI's potential and its flaws, emphasizing the importance of understanding both AI and AS in developing robust, safe, and ethical AI applications. By addressing the causes of artificial stupidity, such as algorithmic limitations and poor data quality, researchers and developers can improve the reliability and decision-making capabilities of AI systems. There is also the need for human oversight and ethical considerations to mitigate the negative impacts of artificial stupidity, especially in high-stakes environments.

Expert consensus on pharmacoeconomic evaluations alongside clinical trials (2024 edition)

As a technical tool to guide the optimal allocation of healthcare resources, pharmacoeconomic evaluation has been gradually applied to national healthcare decisions, including the adjustment of the National Reimbursement Drug List in China. With the acceleration of drug market approval and access to insurance coverage, the demand for pharmacoeconomic evaluation in healthcare decision-making is also increasing. Designing a pharmacoeconomic evaluation alongside clinical trials has become an important research direction and the source of evidence. In order to facilitate the standardization of this research field, the current consensus focuses on the topic of how to conduct a pharmacoeconomic evaluation alongside clinical trials from the Chinese healthcare system and decision-making context. The consensus group reconciled expert opinions and developed recommendations covering the entire process of study design, data collection and management, data analysis, and methods and results reporting. A total of 24 recommendations regarding to methodology and practice were developed from eighteen aspects including target population, selection of comparators, calculation of sample size, data collection of healthcare resource utilization, data collection of health-related quality of life, cost analysis, health outcome analysis, addressing the missing and censored data, etc., with the aim to provide reference for scholars and decision makers in this research filed.

Adaptation and Validation of Decision-Making Questionnaire Among Physicians of Obstetrics and Gynecology

Abstract: Introduction: Healthcare decision-making is a critical yet understudied aspect of medical education. It holds significant importance among healthcare professionals and influences their overall behavior. We adapted and validated the Decision-Making Questionnaire, which measures factors affecting decision-making among obstetricians and gynecologists. Methods: We used a cross-sectional survey design to establish the construct validity of the DMQ. We established content validity using the Lawshe content validity ratio and conducted a survey to establish the construct validity. Results: Exploratory factor analysis ( n = 150) revealed three factors, task-related, context-related, and individual-related, having eigenvalues greater than 1 and adequate alpha reliability. Confirmatory factor analysis ( n = 197) found adequate model fit indices. We assessed the reliability coefficient of the Decision-Making Questionnaire using Cronbach’s alpha and McDonald’s Omega coefficient, which showed an acceptable range (0.86–0.90). Hence, the scale appears to be psychometrically sound and a practical measure for obstetricians and gynecologists. Discussion: The Decision-Making Questionnaire demonstrated convergent validity by positively correlating with the Team Decision-Making Questionnaire and divergent validity by showing correlations with the measure of professional quality of life. The scale is useful for practitioners and researchers to measure factors affecting decision-making in naturalistic settings.

Right Information, Right Care, Right Patient, Right Time: Community Preferences to Inform a Self-Management Support Tool for Upper Respiratory Symptoms.

During and after the COVID-19 pandemic, communities must cope with several conditions that cause similar upper-respiratory symptoms but are managed differently. We describe community reactions to a self-management toolkit for patients with upper respiratory symptoms to inform mobile e-health app development. The toolkit is based on the '4R' (Right Information, Right Care, Right Patient, Right Time) care planning and management model. The 4R Cold, Flu and COVID-19 Information Tool (4R-Toolkit) along with a brief evaluation survey were distributed in three ways: through a Bronx NY Allergy/Asthma clinic, through the Bronx Borough President's Office listserv, and through peer recruitment. The survey assessed respondents' perceptions of the 4R-Toolkit's accessibility, preferences for sharing symptoms with clinicians, social media use, and e-health literacy. We obtained a diverse sample of 106 Bronx residents, with 83% reporting personal or a social contact with symptoms suggestive of COVID-19. Respondents varied in the information sources they preferred: computer (39%); smart phone (28%); paper (11%) and no preference (22%). Most (67%) reported that social media had at least some impact on their healthcare decisions. Regardless of media preferences, respondents were positive about the 4R-Toolkit. Out of 106 respondents, 91% believed the 4R-Toolkit would help people self-manage upper respiratory symptoms and 85% found it easy to understand. Respondents strongly endorsed retention of all 4R-Toolkit content domains with 81% indicating that they would be willing to share symptoms with providers using a 4R-Toolkit smartphone app. The 4R-Toolkit can offer patients and community members accurate and up-to-date information on COVID-19, the common cold, and the flu. The user-friendly tool is accessible to diverse individuals, including those with limited e-health literacy. It has potential to support self-management of upper respiratory symptoms and promote patient engagement with providers.