Crowdsourcing: an overview and applications to ophthalmology

Cardiac screening in school children: Combining auscultation and electrocardiography with a crowdsourcing model

BACKGROUND/PURPOSE

School-based cardiac screening is useful for identifying children and adolescents with a high risk of sudden cardiac death. However, because of challenges associated with cost, distance, and human resources, cardiac screening is not widely implemented, especially in rural areas with limited medical resources. This study aims to establish a cloud-based system suitable for mass cardiac screening of schoolchildren in rural areas with limited medical resources.

METHODS

Students from three schools were included. They or their guardians completed a simple questionnaire, administered in paper or electronic form. Heart sounds were recorded using an electronic stethoscope. Twelve-lead electrocardiograms (ECGs) were recorded and digitalized. The signals were transmitted through Bluetooth to a tablet computer and then uploaded to a cloud server over Wi-Fi. Crowdsourced pediatric cardiologists reviewed those data from a web-based platform and provided remote consultation. In cases in which abnormal heart sounds or ECGs were noted, the students were referred to the hospital for further evaluation.

RESULTS

A total of 1004 students were enrolled in this study. Of the 138 students referred, 62 were diagnosed as having an abnormal heart condition and most had previously been undiagnosed. The interrater agreeability was high.

CONCLUSION

An innovative strategy combining a cloud-based cardiac screening system with remote consultation by crowdsourced experts was established. This system allows pediatric cardiologists to provide consultation and make reliable diagnoses. Combined with crowdsourcing, the system constitutes a viable approach for mass cardiac screening in children and adolescents living in rural areas with insufficient medical resources.

Research on the doctors' win in crowdsourcing competitions: perspectives on service content and competitive environment

Medical crowdsourcing competitions can help patients get more efficient and comprehensive treatment advice than "one-to-one" service, and doctors should be encouraged to actively participate. In the crowdsourcing competitions, winning the crowdsourcing competition is the driving force for doctors to continue to participate in the service. Therefore, how to improve the winning probability needs to be revealed. From the service content and competitive environment perspectives, this study introduces doctor competence indicators to investigate the key influence factors of doctors' wins on the online platform. The results show that the emotional interaction in doctors' service content positively influences doctors' wins. However, the influence of information interaction presents heterogeneity. Conclusive information helps doctors win, while suggestive information negatively affects them. For the competitive environment, the competitive environment negatively moderates the relationship between doctors' service content and doctors' wins. The results of this study provide important contributions to the research on crowdsourcing competitions and online healthcare services and guide the participants of the competition, including patients, doctors, and platforms.

Assessing Interventions on Crowdsourcing Platforms to Nudge Patients for Engagement Behaviors in Primary Care Settings: Randomized Controlled Trial

BACKGROUND

Engaging patients in health behaviors is critical for better outcomes, yet many patient partnership behaviors are not widely adopted. Behavioral economics-based interventions offer potential solutions, but it is challenging to assess the time and cost needed for different options. Crowdsourcing platforms can efficiently and rapidly assess the efficacy of such interventions, but it is unclear if web-based participants respond to simulated incentives in the same way as they would to actual incentives.

OBJECTIVE

The goals of this study were (1) to assess the feasibility of using crowdsourced surveys to evaluate behavioral economics interventions for patient partnerships by examining whether web-based participants responded to simulated incentives in the same way they would have responded to actual incentives, and (2) to assess the impact of 2 behavioral economics-based intervention designs, psychological rewards and loss of framing, on simulated medication reconciliation behaviors in a simulated primary care setting.

METHODS

We conducted a randomized controlled trial using a between-subject design on a crowdsourcing platform (Amazon Mechanical Turk) to evaluate the effectiveness of behavioral interventions designed to improve medication adherence in primary care visits. The study included a control group that represented the participants' baseline behavior and 3 simulated interventions, namely monetary compensation, a status effect as a psychological reward, and a loss frame as a modification of the status effect. Participants' willingness to bring medicines to a primary care visit was measured on a 5-point Likert scale. A reverse-coding question was included to ensure response intentionality.

RESULTS

A total of 569 study participants were recruited. There were 132 in the baseline group, 187 in the monetary compensation group, 149 in the psychological reward group, and 101 in the loss frame group. All 3 nudge interventions increased participants' willingness to bring medicines significantly when compared to the baseline scenario. The monetary compensation intervention caused an increase of 17.51% (P<.001), psychological rewards on status increased willingness by 11.85% (P<.001), and a loss frame on psychological rewards increased willingness by 24.35% (P<.001). Responses to the reverse-coding question were consistent with the willingness questions.

CONCLUSIONS

In primary care, bringing medications to office visits is a frequently advocated patient partnership behavior that is nonetheless not widely adopted. Crowdsourcing platforms such as Amazon Mechanical Turk support efforts to efficiently and rapidly reach large groups of individuals to assess the efficacy of behavioral interventions. We found that crowdsourced survey-based experiments with simulated incentives can produce valid simulated behavioral responses. The use of psychological status design, particularly with a loss framing approach, can effectively enhance patient engagement in primary care. These results support the use of crowdsourcing platforms to augment and complement traditional approaches to learning about behavioral economics for patient engagement.

Leveraging weak supervision to perform named entity recognition in electronic health records progress notes to identify the ophthalmology exam

OBJECTIVE

To develop deep learning models to recognize ophthalmic examination components from clinical notes in electronic health records (EHR) using a weak supervision approach.

METHODS

A corpus of 39,099 ophthalmology notes weakly labeled for 24 examination entities was assembled from the EHR of one academic center. Four pre-trained transformer-based language models (DistilBert, BioBert, BlueBert, and ClinicalBert) were fine-tuned to this named entity recognition task and compared to a baseline regular expression model. Models were evaluated on the weakly labeled test dataset, a human-labeled sample of that set, and a human-labeled independent dataset.

RESULTS

On the weakly labeled test set, all transformer-based models had recall > 0.93, with precision varying from 0.815 to 0.843. The baseline model had lower recall (0.769) and precision (0.682). On the human-annotated sample, the baseline model had high recall (0.962, 95 % CI 0.955-0.067) with variable precision across entities (0.081-0.999). Bert models had recall ranging from 0.771 to 0.831, and precision >=0.973. On the independent dataset, precision was 0.926 and recall 0.458 for BlueBert. The baseline model had better recall (0.708, 95 % CI 0.674-0.738) but worse precision (0.399, 95 % CI -0.352-0.451).

CONCLUSION

We developed the first deep learning system to recognize eye examination components from clinical notes, leveraging a novel opportunity for weak supervision. Transformer-based models had high precision on human-annotated labels, whereas the baseline model had poor precision but higher recall. This system may be used to improve cohort and feature identification using free-text notes.Our weakly supervised approach may help amass large datasets of domain-specific entities from EHRs in many fields.

Twitter-based crowdsourcing: What kind of measures can help to end the COVID-19 pandemic faster?

Background

Crowdsourcing is a low-cost, adaptable, and innovative method to collect ideas from numerous contributors with diverse backgrounds. Crowdsourcing from social media like Twitter can be used for generating ideas in a noticeably brief time based on contributions from globally distributed users. The world has been challenged by the COVID-19 pandemic in the last several years. Measures to combat the pandemic continue to evolve worldwide, and ideas and opinions on optimal counteraction strategies are of high interest.

Objective

This study aimed to validate the use of Twitter as a crowdsourcing platform in order to gain an understanding of public opinion on what measures can help to end the COVID-19 pandemic faster.

Methods

This cross-sectional study was conducted during the period from December 22, 2021, to February 4, 2022. Tweets were posted by accounts operated by the authors, asking “How to faster end the COVID-19 pandemic?” and encouraging the viewers to comment on measures that they perceive would be effective to achieve this goal. The ideas from the users' comments were collected and categorized into two major themes – personal and institutional measures. In the final stage of the campaign, a Twitter poll was conducted to get additional comments and to estimate which of the two groups of measures were perceived to be important amongst Twitter users.

Results

The crowdsourcing campaign generated seventeen suggested measures categorized into two major themes (personal and institutional) that received a total of 1,727 endorsements (supporting comments, retweets, and likes). The poll received a total of 325 votes with 58% of votes underscoring the importance of both personal and institutional measures, 20% favoring personal measures, 11% favoring institutional measures, and 11% of the votes given just out of curiosity to see the vote results.

Conclusions

Twitter was utilized successfully for crowdsourcing ideas on strategies how to end the COVID-19 pandemic faster. The results indicate that the Twitter community highly values the significance of both personal responsibility and institutional measures to counteract the pandemic. This study validates the use of Twitter as a primary tool that could be used for crowdsourcing ideas with healthcare significance.

Video-Based Coaching: Current Status and Role in Surgical Practice (Part 1) From the Society for Surgery of the Alimentary Tract, Health Care Quality and Outcomes Committee

Patient safety and outcomes are directly related to surgical performance. Surgical training emphasizes the importance of the surgeon in determining these outcomes. After training is complete, there is a lack of structured programs for surgeons to audit their skills and continue their individual development. There is a significant linear relationship between surgeon technical skill and surgical outcomes; however, measuring technical performance is difficult. Video-based coaching matches an individual surgeon in practice with a surgical colleague who has been trained in the core principles of coaching for individualizing instruction. It can provide objective assessment for teaching higher-level concepts, such as technical skills, cognitive skills, and decision-making. There are many benefits to video-based coaching. While the concept is gaining acceptance as a method of surgical education, it is still novel in clinical practice. As more surgeons look towards video-based coaching for quality improvement, a consistent definition of the program, goals, and metrics for assessment will be critical. This paper is a review on the status of the video-based coaching as it applies to practicing surgeons.

The Use of Crowdsourcing Technology to Evaluate Preoperative Severity in Patients With Unilateral Cleft Lip in a Multiethnic Population

ABSTRACT

Crowd sourcing has been used in multiple disciplines to quickly generate large amounts of diverse data. The objective of this study was to use crowdsourcing to grade preoperative severity of unilateral cleft lip phenotype in a multiethnic cohort with the hypothesis that crowdsourcing could efficiently achieve similar rankings compared to expert surgeons. Deidentified preoperative photos were collected for patients with primary, unilateral cleft lip with or without cleft palate (CL ± P). A platform was developed with C-SATS for pairwise comparisons utilizing Elo rankings by crowdsource workers through Amazon Mechanical Turk. Images were independently ranked by 2 senior surgeons for comparison. Seventy-six patients with varying severity of unilateral (CL ± P) phenotype were chosen from Operation Smile missions in Bolivia, Madagascar, Vietnam, and Morocco. Patients were an average of 1.2 years' old, ranging from 3 months to 3.3 years. Each image was compared with 10 others, for a total of 380 unique pairwise comparisons. A total of 4627 total raters participated with a median of 12 raters per pair. Data collection was completed in <20 hours. The crowdsourcing ranking and expert surgeon rankings were highly correlated with Pearson correlation coefficient of R = 0.77 (P = 0.0001). Crowdsourcing provides a rapid and convenient method of obtaining preoperative severity ratings, comparable to expert surgeon assessment, across multiple ethnicities. The method serves as a potential solution to the current lack of rating systems for preoperative severity and overcomes the difficulty of acquiring large-scale assessment from expert surgeons.

Crowdsourcing to Assess Speech Quality Associated With Velopharyngeal Dysfunction

OBJECTIVE

To assess crowdsourced responses in the evaluation of speech outcomes in children with velopharyngeal dysfunction (VPD).

DESIGN

Fifty deidentified speech samples were compiled. Multiple pairwise comparisons obtained by crowdsourcing were used to produce a rank order of speech quality. Ratings of overall and specific speech characteristics were also collected. Twelve speech-language pathologists (SLPs) who specialize in VPD were asked to complete the same tasks. Crowds and experts completed each task on 2 separate occasions at least 1 week apart.

SETTING

On-line crowdsourcing platform.

PARTICIPANTS

Crowdsource raters were anonymous and at least 18 years of age, North American English speakers with self-reported normal hearing. Speech-language pathologists were recruited from multiple cleft/craniofacial teams.

INTERVENTIONS

None.

MAIN OUTCOME MEASURE(S)

Correlation of repeated assessments and comparison of crowd and SLP assessments.

RESULTS

We obtained 6331 lay person assessments that met inclusion criteria via crowdsourcing within 8 hours. The crowds provided reproducible Elo rankings of speech quality, ρ(48) = .89; P <.0001, and consistent ratings of intelligibility and acceptability (intraclass correlation coefficient [ICC] = .87 and .92) on repeated assessments. There was a significant correlation of those crowd rankings, ρ(10) = .86; P = .0003, and ratings (ICC = .75 and .79) with those of SLPs. The correlation of more specific speech characteristics by the crowds and SLPs was moderate to weak (ICC < 0.65).

CONCLUSIONS

Crowdsourcing shows promise as a rapid way to obtain large numbers of speech assessments. Reliability of repeated assessments was acceptable. Large groups of naive raters yield comparable evaluations of overall speech acceptability, intelligibility, and quality, but are not consistent with expert raters for specific speech characteristics such as resonance and nasal air emission.

Collective intelligence in medical decision-making: a systematic scoping review

BACKGROUND

Collective intelligence, facilitated by information technology or manual techniques, refers to the collective insight of groups working on a task and has the potential to generate more accurate information or decisions than individuals can make alone. This concept is gaining traction in healthcare and has potential in enhancing diagnostic accuracy. We aim to characterize the current state of research with respect to collective intelligence in medical decision-making and describe a framework for diverse studies in this topic.

METHODS

For this systematic scoping review, we conducted a systematic search for published literature using PubMed, Embase, Web of Science, and CINAHL on August 8, 2017. We included studies that combined the insights of two or more medical experts to make decisions related to patient care. Studies that examined medical decisions such as diagnosis, treatment, and management in the context of an actual or theoretical patient case were included. We include studies of complex medical decision-making rather than identification of a visual finding, as in radiology or pathology. We differentiate between medical decisions, in which synthesis of multiple types of information is required over time, and studies of radiological scans or pathological specimens, in which objective identification of a visual finding is performed. Two reviewers performed article screening, data extraction, and final inclusion for analysis.

RESULTS

Of 3303 original articles, 15 were included. Each study examined the medical decisions of two or more individuals; however, studies were heterogeneous in their methods and outcomes. We present a framework to characterize these diverse studies, and future investigations, based on how they operationalize collective intelligence for medical decision-making: 1) how the initial decision task was completed (group vs. individual), 2) how opinions were synthesized (information technology vs. manual vs. in-person), and 3) the availability of collective intelligence to participants.

DISCUSSION

Collective intelligence in medical decision-making is gaining popularity to advance medical decision-making and holds promise to improve patient outcomes. However, heterogeneous methods and outcomes make it difficult to assess the utility of collective intelligence approaches across settings and studies. A better understanding of collective intelligence and its applications to medicine may improve medical decision-making.

Crowdsourcing the Citation Screening Process for Systematic Reviews: Validation Study

BACKGROUND

Systematic reviews (SRs) are often cited as the highest level of evidence available as they involve the identification and synthesis of published studies on a topic. Unfortunately, it is increasingly challenging for small teams to complete SR procedures in a reasonable time period, given the exponential rise in the volume of primary literature. Crowdsourcing has been postulated as a potential solution.

OBJECTIVE

The feasibility objective of this study was to determine whether a crowd would be willing to perform and complete abstract and full text screening. The validation objective was to assess the quality of the crowd's work, including retention of eligible citations (sensitivity) and work performed for the investigative team, defined as the percentage of citations excluded by the crowd.

METHODS

We performed a prospective study evaluating crowdsourcing essential components of an SR, including abstract screening, document retrieval, and full text assessment. Using CrowdScreenSR citation screening software, 2323 articles from 6 SRs were available to an online crowd. Citations excluded by less than or equal to 75% of the crowd were moved forward for full text assessment. For the validation component, performance of the crowd was compared with citation review through the accepted, gold standard, trained expert approach.

RESULTS

Of 312 potential crowd members, 117 (37.5%) commenced abstract screening and 71 (22.8%) completed the minimum requirement of 50 citation assessments. The majority of participants were undergraduate or medical students (192/312, 61.5%). The crowd screened 16,988 abstracts (median: 8 per citation; interquartile range [IQR] 7-8), and all citations achieved the minimum of 4 assessments after a median of 42 days (IQR 26-67). Crowd members retrieved 83.5% (774/927) of the articles that progressed to the full text phase. A total of 7604 full text assessments were completed (median: 7 per citation; IQR 3-11). Citations from all but 1 review achieved the minimum of 4 assessments after a median of 36 days (IQR 24-70), with 1 review remaining incomplete after 3 months. When complete crowd member agreement at both levels was required for exclusion, sensitivity was 100% (95% CI 97.9-100) and work performed was calculated at 68.3% (95% CI 66.4-70.1). Using the predefined alternative 75% exclusion threshold, sensitivity remained 100% and work performed increased to 72.9% (95% CI 71.0-74.6; P<.001). Finally, when a simple majority threshold was considered, sensitivity decreased marginally to 98.9% (95% CI 96.0-99.7; P=.25) and work performed increased substantially to 80.4% (95% CI 78.7-82.0; P<.001).

CONCLUSIONS

Crowdsourcing of citation screening for SRs is feasible and has reasonable sensitivity and specificity. By expediting the screening process, crowdsourcing could permit the investigative team to focus on more complex SR tasks. Future directions should focus on developing a user-friendly online platform that allows research teams to crowdsource their reviews.

Collaborative intelligence and gamification for on-line malaria species differentiation

Background

Current World Health Organization recommendations for the management of malaria include the need for a parasitological confirmation prior to triggering appropriate treatment. The use of rapid diagnostic tests (RDTs) for malaria has contributed to a better infection recognition and a more targeted treatment. Nevertheless, low-density infections and parasites that fail to produce HRP2 can cause false-negative RDT results. Microscopy has traditionally been the methodology most commonly used to quantify malaria and characterize the infecting species, but the wider use of this technique remains challenging, as it requires trained personnel and processing capacity.

Objective

In this study, the feasibility of an on-line system for remote malaria species identification and differentiation has been investigated by crowdsourcing the analysis of digitalized infected thin blood smears by non-expert observers using a mobile app.

Methods

An on-line videogame in which players learned how to differentiate the young trophozoite stage of the five Plasmodium species has been designed. Images were digitalized with a smartphone camera adapted to the ocular of a conventional light microscope. Images from infected red blood cells were cropped and puzzled into an on-line game. During the game, players had to decide the malaria species (Plasmodium falciparum, Plasmodium malariae, Plasmodium vivax, Plasmodium ovale, Plasmodium knowlesi) of the infected cells that were shown in the screen. After 2 months, each player’s decisions were analysed individually and collectively.

Results

On-line volunteers playing the game made more than 500,000 assessments for species differentiation. Statistically, when the choice of several players was combined (n > 25), they were able to significantly discriminate Plasmodium species, reaching a level of accuracy of 99% for all species combinations, except for P. knowlesi (80%). Non-expert decisions on which Plasmodium species was shown in the screen were made in less than 3 s.

Conclusion

These findings show that it is possible to train malaria-naïve non-experts to identify and differentiate malaria species in digitalized thin blood samples. Although the accuracy of a single player is not perfect, the combination of the responses of multiple casual gamers can achieve an accuracy that is within the range of the diagnostic accuracy made by a trained microscopist.

Electronic supplementary material

The online version of this article (10.1186/s12936-019-2662-9) contains supplementary material, which is available to authorized users.

Crowdsourced versus expert evaluations of the vesico-urethral anastomosis in the robotic radical prostatectomy: is one superior at discriminating differences in automated performance metrics?

Crowdsourcing from the general population is an efficient, inexpensive method of surgical performance evaluation. In this study, we compared the discriminatory ability of experts and crowdsourced evaluators (the Crowd) to detect differences in robotic automated performance metrics (APMs). APMs (instrument motion tracking and events data directly from the robot system) of anterior vesico-urethral anastomoses (VUAs) of robotic radical prostatectomies were captured by the dVLogger (Intuitive Surgical). Crowdsourced evaluators and four expert surgeons evaluated video footage using the Global Evaluative Assessment of Robotic Skills (GEARS) (individual domains and total score). Cases were then stratified into performance groups (high versus low quality) for each evaluator based on GEARS. APMs from each group were compared using the Mann-Whitney U test. 25 VUAs performed by 11 surgeons were evaluated. The Crowd displayed moderate correlation with averaged expert scores for all GEARS domains (r > 0.58, p < 0.01). Bland-Altman analysis showed a narrower total GEARS score distribution by the Crowd compared to experts. APMs compared amongst performance groups for each evaluator showed that through GEARS scoring, the most common differentiated metric by evaluators was the velocity of the dominant instrument arm. The Crowd outperformed two out of four expert evaluators by discriminating differences in three APMs using total GEARS scores. The Crowd assigns a narrower range of GEARS scores compared to experts but maintains overall agreement with experts. The discriminatory ability of the Crowd at discerning differences in robotic movements (via APMs) through GEARS scoring is quite refined, rivaling that of expert evaluators.

Plasmodium species differentiation by non-expert on-line volunteers for remote malaria field diagnosis

Background

Routine field diagnosis of malaria is a considerable challenge in rural and low resources endemic areas mainly due to lack of personnel, training and sample processing capacity. In addition, differential diagnosis of Plasmodium species has a high level of misdiagnosis. Real time remote microscopical diagnosis through on-line crowdsourcing platforms could be converted into an agile network to support diagnosis-based treatment and malaria control in low resources areas. This study explores whether accurate Plasmodium species identification—a critical step during the diagnosis protocol in order to choose the appropriate medication—is possible through the information provided by non-trained on-line volunteers.

Methods

88 volunteers have performed a series of questionnaires over 110 images to differentiate species (Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, Plasmodium malariae, Plasmodium knowlesi) and parasite staging from thin blood smear images digitalized with a smartphone camera adapted to the ocular of a conventional light microscope. Visual cues evaluated in the surveys include texture and colour, parasite shape and red blood size.

Results

On-line volunteers are able to discriminate Plasmodium species (P. falciparum, P. malariae, P. vivax, P. ovale, P. knowlesi) and stages in thin-blood smears according to visual cues observed on digitalized images of parasitized red blood cells. Friendly textual descriptions of the visual cues and specialized malaria terminology is key for volunteers learning and efficiency.

Conclusions

On-line volunteers with short-training are able to differentiate malaria parasite species and parasite stages from digitalized thin smears based on simple visual cues (shape, size, texture and colour). While the accuracy of a single on-line expert is far from perfect, a single parasite classification obtained by combining the opinions of multiple on-line volunteers over the same smear, could improve accuracy and reliability of Plasmodium species identification in remote malaria diagnosis.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2194-8) contains supplementary material, which is available to authorized users.

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options.

Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated.

Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome.

Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record.

Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen and generalize this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.

Crowdsourcing: a valid alternative to expert evaluation of robotic surgery skills

BACKGROUND

Robotic-assisted gynecologic surgery is common, but requires unique training. A validated assessment tool for evaluating trainees' robotic surgery skills is Robotic-Objective Structured Assessments of Technical Skills.

OBJECTIVE

We sought to assess whether crowdsourcing can be used as an alternative to expert surgical evaluators in scoring Robotic-Objective Structured Assessments of Technical Skills.

STUDY DESIGN

The Robotic Training Network produced the Robotic-Objective Structured Assessments of Technical Skills, which evaluate trainees across 5 dry lab robotic surgical drills. Robotic-Objective Structured Assessments of Technical Skills were previously validated in a study of 105 participants, where dry lab surgical drills were recorded, de-identified, and scored by 3 expert surgeons using the Robotic-Objective Structured Assessments of Technical Skills checklist. Our methods-comparison study uses these previously obtained recordings and expert surgeon scores. Mean scores per participant from each drill were separated into quartiles. Crowdworkers were trained and calibrated on Robotic-Objective Structured Assessments of Technical Skills scoring using a representative recording of a skilled and novice surgeon. Following this, 3 recordings from each scoring quartile for each drill were randomly selected. Crowdworkers evaluated the randomly selected recordings using Robotic-Objective Structured Assessments of Technical Skills. Linear mixed effects models were used to derive mean crowdsourced ratings for each drill. Pearson correlation coefficients were calculated to assess the correlation between crowdsourced and expert surgeons' ratings.

RESULTS

In all, 448 crowdworkers reviewed videos from 60 dry lab drills, and completed a total of 2517 Robotic-Objective Structured Assessments of Technical Skills assessments within 16 hours. Crowdsourced Robotic-Objective Structured Assessments of Technical Skills ratings were highly correlated with expert surgeon ratings across each of the 5 dry lab drills (r ranging from 0.75-0.91).

CONCLUSION

Crowdsourced assessments of recorded dry lab surgical drills using a validated assessment tool are a rapid and suitable alternative to expert surgeon evaluation.