The Wallet Biopsy: Medical Crowdfunding for Heart Transplantation.

Financial considerations continue to impact access to heart transplantation. Transplant recipients face various costs, including, but not limited to, the index hospitalization, immunosuppressive medications, and lodging and travel to appointments. In this study, we sought to describe the state of crowdfunding for individuals being evaluated for heart transplantation. Using the search term heart transplant, 1000 GoFundMe campaigns were reviewed. After exclusions, 634 (63.4%) campaigns were included. Most campaigns were in support of white individuals (57.8%), males (63.1%) and adults (76.7%). Approximately 15% of campaigns had not raised any funds. The remaining campaigns fundraised a median of $53.24 dollars per day. Of the patients, 44% were admitted at the time of the fundraising. Within the campaigns in the United States, the greatest proportions were in the Southeast United States in non-Medicaid expansion states. These findings highlight the significant financial toxicities associated with heart transplantation and the need for advocacy at the governmental and payer levels to improve equitable access and coverage for all.

Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis.

BACKGROUND: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality. OBJECTIVE: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data. METHODS: In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips. RESULTS: Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance. CONCLUSIONS: Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems.

The effectiveness of warning statements in reducing careless responding in crowdsourced online surveys.

Carelessness or insufficient effort responding is a widespread problem in online research, with estimates ranging from 3% to almost 50% of participants in online surveys being inattentive. While detecting carelessness has been subject to multiple studies, the factors that reduce or prevent carelessness are not as well understood. Initial evidence suggests that warning statements prior to study participation may reduce carelessness, but there is a lack of conclusive high-powered studies. This preregistered randomized controlled experiment aimed to test the effectiveness of a warning statement and an improved implementation of a warning statement in reducing participant inattention. A study with 812 participants recruited on Amazon Mechanical Turk was conducted. Results suggest that presenting a warning statement is not effective in reducing carelessness. However, requiring participants to actively type the warning statement statistically significantly reduced carelessness as measured with self-reported diligence, even-odd consistency, psychometric synonyms and antonyms, and individual response variability. The active warning statements also led to statistically significantly more attrition and potentially deterred those who were likely to be careless from even participating in this study. We show that the current standard practice of implementing warning statements is ineffective and novel methods to prevent and deter carelessness are needed.

Enabling Health Information Recommendation Using Crowdsourced Refinement in Web-Based Health Information Applications: User-Centered Design Approach and EndoZone Informatics Case Study.

BACKGROUND: In the digital age, search engines and social media platforms are primary sources for health information, yet their commercial interests-focused algorithms often prioritize irrelevant content. Web-based health applications by reputable sources offer a solution to circumvent these biased algorithms. Despite this advantage, there remains a significant gap in research on the effective integration of content-ranking algorithms within these specialized health applications to ensure the delivery of personalized and relevant health information. OBJECTIVE: This study introduces a generic methodology designed to facilitate the development and implementation of health information recommendation features within web-based health applications. METHODS: We detail our proposed methodology, covering conceptual foundation and practical considerations through the stages of design, development, operation, review, and optimization in the software development life cycle. Using a case study, we demonstrate the practical application of the proposed methodology through the implementation of recommendation functionalities in the EndoZone platform, a platform dedicated to providing targeted health information on endometriosis. RESULTS: Application of the proposed methodology in the EndoZone platform led to the creation of a tailored health information recommendation system known as EndoZone Informatics. Feedback from EndoZone stakeholders as well as insights from the implementation process validate the methodology's utility in enabling advanced recommendation features in health information applications. Preliminary assessments indicate that the system successfully delivers personalized content, adeptly incorporates user feedback, and exhibits considerable flexibility in adjusting its recommendation logic. While certain project-specific design flaws were not caught in the initial stages, these issues were subsequently identified and rectified in the review and optimization stages. CONCLUSIONS: We propose a generic methodology to guide the design and implementation of health information recommendation functionality within web-based health information applications. By harnessing user characteristics and feedback for content ranking, this methodology enables the creation of personalized recommendations that align with individual user needs within trusted health applications. The successful application of our methodology in the development of EndoZone Informatics marks a significant progress toward personalized health information delivery at scale, tailored to the specific needs of users.

Design and evaluation of crowdsourcing platforms based on users' confidence judgments.

Crowdsourcing deals with solving problems by assigning them to a large number of non-experts called crowd using their spare time. In these systems, the final answer to the question is determined by summing up the votes obtained from the community. The popularity of these systems has increased by facilitating access for community members through mobile phones and the Internet. One of the issues raised in crowdsourcing is how to choose people and how to collect answers. Usually, users are separated based on their performance in a pre-test. Designing the pre-test for performance calculation is challenging; The pre-test questions should be selected to assess characteristics in individuals that are relevant to the main questions. One of the ways to increase the accuracy of crowdsourcing systems is by considering individuals' cognitive characteristics and decision-making models to form a crowd and improve the estimation of their answer accuracy to questions. People can estimate the correctness of their responses while making a decision. The accuracy of this estimate is determined by a quantity called metacognition ability. Metacoginition is referred to the case where the confidence level is considered along with the answer to increase the accuracy of the solution. In this paper, by both mathematical and experimental analysis, we would answer the following question: Is it possible to improve the performance of a crowdsourcing system by understanding individuals' metacognition and recording and utilizing users' confidence in their answers?

Crowdsourced human-based computational approach for tagging peripheral blood smear sample images from Sickle Cell Disease patients using non-expert users.

In this paper, we present a human-based computation approach for the analysis of peripheral blood smear (PBS) images images in patients with Sickle Cell Disease (SCD). We used the Mechanical Turk microtask market to crowdsource the labeling of PBS images. We then use the expert-tagged erythrocytesIDB dataset to assess the accuracy and reliability of our proposal. Our results showed that when a robust consensus is achieved among the Mechanical Turk workers, probability of error is very low, based on comparison with expert analysis. This suggests that our proposed approach can be used to annotate datasets of PBS images, which can then be used to train automated methods for the diagnosis of SCD. In future work, we plan to explore the potential integration of our findings with outcomes obtained through automated methodologies. This could lead to the development of more accurate and reliable methods for the diagnosis of SCD.

Protocol for the crowdsourced image-based morbidity hotspot surveillance for neglected tropical diseases (CIMS-NTDs).

INTRODUCTION: Efficient NTDs elimination strategies require effective surveillance and targeted interventions. Traditional methods are costly and time-consuming, often failing to cover entire populations in case of movement restrictions. To address these challenges, a morbidity image-based surveillance system is being developed. This innovative approach which leverages the smartphone technology aims at simultaneous surveillance of multiple NTDs, enhancing cost-efficiency, reliability, and community involvement, particularly in areas with movement constraints. Moreover, it holds promise for post-elimination surveillance. METHODOLOGY: The pilot of this method will be conducted across three states in southern Nigeria. It will target people affected by Neglected Tropical Diseases and members of their communities. The new surveillance method will be introduced to target communities in the selected states through community stakeholder's advocacy meetings and awareness campaigns. The pilot which is set to span eighteen months, entails sensitizing NTDs-affected individuals and community members using signposts, posters, and handbills, to capture photos of NTDs manifestations upon notice using smartphones. These images, along with pertinent demographic information, will be transmitted to a dedicated server through WhatsApp or Telegram accounts. The received images will be reviewed and organized at backend and then forwarded to a panel of experts for identification and annotation to specific NTDs. Data generated, along with geocoordinate information, will be used to create NTDs morbidity hotspot maps using ArcGIS. Accompanying metadata will be used to generate geographic and demographic distributions of various NTDs identified. To protect privacy, people will be encouraged to send manifestation photos of the affected body part only without any identifiable features. EVALUATION PROTOCOL: NTDs prevalence data obtained using conventional surveillance methods from both the pilot and selected control states during the pilot period will be compared with data from the CIMS-NTDs method to determine its effectiveness. EXPECTED RESULTS AND CONCLUSION: It is expected that an effective, privacy-conscious, population inclusive new method for NTDs surveillance, with the potential to yield real-time data for the identification of morbidity hotspots and distribution patterns of NTDs will be established. The results will provide insights into the effectiveness of the new surveillance method in comparison to traditional approaches, potentially advancing NTDs elimination strategies.

Developing Crowdsourced Clinical Registry Studies.

ABSTRACT: Crowdsourced registries have been used to quickly gather information, especially during emerging public health concerns. Registries that began during the COVID-19 pandemic were used to rapidly answer key questions on coinfections, experimental treatments, and morbidity and mortality outcomes. Registries are also used more frequently to support clinical trials and track long-term outcomes in patient populations. This article reviews registry methodology, including the collection of data from crowdsourcing and real-world sources, that can be applied to nurse researcher and clinical research nurse skill sets. The authors illustrate a recently reported crowdsourced COVID-19 and cryptococcal disease registry that followed project management strategies and the Agency for Healthcare Research and Quality registry guidelines for planning, execution, and analysis of registries and other research methods.

LGBTQ+ inequity in crowdfunding cancer costs: The influence of online reach and LGBTQ+ state policy.

BACKGROUND: Emerging literature suggests that LGBTQ+ cancer survivors are more likely to experience financial burden than non-LGBTQ+ survivors. However, LGBTQ+ cancer survivors experience with cost-coping behaviors such as crowdfunding is understudied. METHODS: We aimed to assess LGBTQ+ inequity in cancer crowdfunding by combining community-engaged and technology-based methods. Crowdfunding campaigns were web-scraped from GoFundMe and classified as cancer-related and LGBTQ+ or non-LGBTQ+ using term dictionaries. Bivariate analyses and generalized linear models were used to assess differential effects in total goal amount raised by LGBTQ+ status. Stratified models were run by online reach and LGBTQ+ inclusivity of state policy. RESULTS: A total of N = 188,342 active cancer-related crowdfunding campaigns were web-scraped from GoFundMe in November 2022, of which N = 535 were LGBTQ+ and ranged from 2014 to 2022. In multivariable models of recent campaigns (2019-2022), LGBTQ+ campaigns raised $1608 (95% CI: -2139, -1077) less than non-LGBTQ+ campaigns. LGBTQ+ campaigns with low (26-45 donors), moderate (46-87 donors), and high (88-240 donors) online reach raised on average $1152 (95% CI: -$1589, -$716), $1050 (95% CI: -$1737, -$364), and $2655 (95% CI: -$4312, -$998) less than non-LGBTQ+ campaigns respectively. When stratified by LGBTQ+ inclusivity of state level policy states with anti-LGBTQ+ policy/lacking equitable policy raised on average $1910 (95% CI: -2640, -1182) less than non-LGBTQ+ campaigns from the same states. CONCLUSIONS AND RELEVANCE: Our findings revealed LGBTQ+ inequity in cancer-related crowdfunding, suggesting that LGBTQ+ cancer survivors may be less able to address financial burden via crowdfunding in comparison to non-LGBTQ+ cancer survivors-potentially widening existing economic inequities.

Crowdsourced Feedback to Improve Resident Physician Error Disclosure Skills: A Randomized Clinical Trial.

Importance: Residents must prepare for effective communication with patients after medical errors. The video-based communication assessment (VCA) is software that plays video of a patient scenario, asks the physician to record what they would say, engages crowdsourced laypeople to rate audio recordings of physician responses, and presents feedback to physicians. Objective: To evaluate the effectiveness of VCA feedback in resident error disclosure skill training. Design, Setting, and Participants: This single-blinded, randomized clinical trial was conducted from July 2022 to May 2023 at 7 US internal medicine and family medicine residencies (10 total sites). Participants were second-year residents attending required teaching conferences. Data analysis was performed from July to December 2023. Intervention: Residents completed 2 VCA cases at time 1 and were randomized to the intervention, an individual feedback report provided in the VCA application after 2 weeks, or to control, in which feedback was not provided until after time 2. Residents completed 2 additional VCA cases after 4 weeks (time 2). Main Outcomes and Measures: Panels of crowdsourced laypeople rated recordings of residents disclosing simulated medical errors to create scores on a 5-point scale. Reports included learning points derived from layperson comments. Mean time 2 ratings were compared to test the hypothesis that residents who had access to feedback on their time 1 performance would score higher at time 2 than those without feedback access. Residents were surveyed about demographic characteristics, disclosure experience, and feedback use. The intervention's effect was examined using analysis of covariance. Results: A total of 146 residents (87 [60.0%] aged 25-29 years; 60 female [41.0%]) completed the time 1 VCA, and 103 (70.5%) completed the time 2 VCA (53 randomized to intervention and 50 randomized to control); of those, 28 (54.9%) reported reviewing their feedback. Analysis of covariance found a significant main effect of feedback between intervention and control groups at time 2 (mean [SD] score, 3.26 [0.45] vs 3.14 [0.39]; difference, 0.12; 95% CI, 0.08-0.48; P = .01). In post hoc comparisons restricted to residents without prior disclosure experience, intervention residents scored higher than those in the control group at time 2 (mean [SD] score, 3.33 [0.43] vs 3.09 [0.44]; difference, 0.24; 95% CI, 0.01-0.48; P = .007). Worse performance at time 1 was associated with increased likelihood of dropping out before time 2 (odds ratio, 2.89; 95% CI, 1.06-7.84; P = .04). Conclusions and Relevance: In this randomized clinical trial, self-directed review of crowdsourced feedback was associated with higher ratings of internal medicine and family medicine residents' error disclosure skill, particularly for those without real-life error disclosure experience, suggesting that such feedback may be an effective way for residency programs to address their requirement to prepare trainees for communicating with patients after medical harm. Trial Registration: ClinicalTrials.gov Identifier: NCT06234085.