Rebecca R. Carter

Crowdsourcing Awareness: Exploration of the Ovarian Cancer Knowledge Gap through Amazon Mechanical Turk

Background Ovarian cancer is the most lethal gynecologic disease in the United States, with more women dying from this cancer than all gynecological cancers combined. Ovarian cancer has been termed the “silent killer” because some patients do not show clear symptoms at an early stage. Currently, there is a lack of approved and effective early diagnostic tools for ovarian cancer. There is also an apparent severe knowledge gap of ovarian cancer in general and of its indicative symptoms among both public and many health professionals. These factors have significantly contributed to the late stage diagnosis of most ovarian cancer patients (63% are diagnosed at Stage III or above), where the 5-year survival rate is less than 30%. The paucity of knowledge concerning ovarian cancer in the United States is unknown. Methods The present investigation examined current public awareness and knowledge about ovarian cancer. The study implemented design strategies to develop an unbiased survey with quality control measures, including the modern application of multiple statistical analyses. The survey assessed a reasonable proxy of the US population by crowdsourcing participants through the online task marketplace Amazon Mechanical Turk, at a highly condensed rate of cost and time compared to traditional recruitment methods. Conclusion Knowledge of ovarian cancer was compared to that of breast cancer using repeated measures, bias control and other quality control measures in the survey design. Analyses included multinomial logistic regression and categorical data analysis procedures such as correspondence analysis, among other statistics. We confirmed the relatively poor public knowledge of ovarian cancer among the US population. The simple, yet novel design should set an example for designing surveys to obtain quality data via Amazon Mechanical Turk with the associated analyses.

Addiction and "Generation Me": Narcissistic and Prosocial Behaviors of Adolescents with Substance Dependency Disorder in Comparison to Normative Adolescents

The purpose of this study is to explore narcissistic and prosocial behaviors as reported by adolescents with and without substance dependency disorder (SDD). This study employs a quasi-experimental design using adolescents with SDD compared with two normative samples of adolescents. In comparison to normative adolescents, adolescents with SDD were strongly distinguished by overt narcissistic behaviors and less monetary giving. Levels of narcissistic and prosocial behaviors among adolescents with SDD suggest a connection between self-centeredness and addiction. Results also suggest volunteerism as a potential option to counter narcissism in adolescents who are substance dependent.

Evaluation of a novel Conjunctive Exploratory Navigation Interface for consumer health information: a crowdsourced comparative study.

Background Numerous consumer health information websites have been developed to provide consumers access to health information. However, lookup search is insufficient for consumers to take full advantage of these rich public information resources. Exploratory search is considered a promising complementary mechanism, but its efficacy has never before been rigorously evaluated for consumer health information retrieval interfaces. Objective This study aims to (1) introduce a novel Conjunctive Exploratory Navigation Interface (CENI) for supporting effective consumer health information retrieval and navigation, and (2) evaluate the effectiveness of CENI through a search-interface comparative evaluation using crowdsourcing with Amazon Mechanical Turk (AMT). Methods We collected over 60,000 consumer health questions from NetWellness, one of the first consumer health websites to provide high-quality health information. We designed and developed a novel conjunctive exploratory navigation interface to explore NetWellness health questions with health topics as dynamic and searchable menus. To investigate the effectiveness of CENI, we developed a second interface with keyword-based search only. A crowdsourcing comparative study was carefully designed to compare three search modes of interest: (A) the topic-navigation-based CENI, (B) the keyword-based lookup interface, and (C) either the most commonly available lookup search interface with Google, or the resident advanced search offered by NetWellness. To compare the effectiveness of the three search modes, 9 search tasks were designed with relevant health questions from NetWellness. Each task included a rating of difficulty level and questions for validating the quality of answers. Ninety anonymous and unique AMT workers were recruited as participants. Results Repeated-measures ANOVA analysis of the data showed the search modes A, B, and C had statistically significant differences among their levels of difficulty (P<.001). Wilcoxon signed-rank test (one-tailed) between A and B showed that A was significantly easier than B (P<.001). Paired t tests (one-tailed) between A and C showed A was significantly easier than C (P<.001). Participant responses on the preferred search modes showed that 47.8% (43/90) participants preferred A, 25.6% (23/90) preferred B, 24.4% (22/90) preferred C. Participant comments on the preferred search modes indicated that CENI was easy to use, provided better organization of health questions by topics, allowed users to narrow down to the most relevant contents quickly, and supported the exploratory navigation by non-experts or those unsure how to initiate their search. Conclusions We presented a novel conjunctive exploratory navigation interface for consumer health information retrieval and navigation. Crowdsourcing permitted a carefully designed comparative search-interface evaluation to be completed in a timely and cost-effective manner with a relatively large number of participants recruited anonymously. Accounting for possible biases, our study has shown for the first time with crowdsourcing that the combination of exploratory navigation and lookup search is more effective than lookup search alone.

A Survey and Analysis of the American Public's Perceptions and Knowledge About Antibiotic Resistance

A crowdsourced survey found that most Americans are aware that antibiotic misuse contributes to antibiotic resistance. The survey also found that Americans are less aware that antibiotic resistance is a significant problem and of the biologic mechanisms of bacterial resistance.

Personalized prediction of chronic wound healing: An exponential mixed effects model using stereophotogrammetric measurement

STUDY AIM Stereophotogrammetric digital imaging enables rapid and accurate detailed 3D wound monitoring. This rich data source was used to develop a statistically validated model to provide personalized predictive healing information for chronic wounds. MATERIALS 147 valid wound images were obtained from a sample of 13 category III/IV pressure ulcers from 10 individuals with spinal cord injury. METHODS Statistical comparison of several models indicated the best fit for the clinical data was a personalized mixed-effects exponential model (pMEE), with initial wound size and time as predictors and observed wound size as the response variable. Random effects capture personalized differences. RESULTS Other models are only valid when wound size constantly decreases. This is often not achieved for clinical wounds. Our model accommodates this reality. Two criteria to determine effective healing time outcomes are proposed: r-fold wound size reduction time, t(r-fold), is defined as the time when wound size reduces to 1/r of initial size. t(δ) is defined as the time when the rate of the wound healing/size change reduces to a predetermined threshold δ < 0. Healing rate differs from patient to patient. Model development and validation indicates that accurate monitoring of wound geometry can adaptively predict healing progression and that larger wounds heal more rapidly. Accuracy of the prediction curve in the current model improves with each additional evaluation. CONCLUSION Routine assessment of wounds using detailed stereophotogrammetric imaging can provide personalized predictions of wound healing time. Application of a valid model will help the clinical team to determine wound management care pathways.

Mixed-Methods Pilot Study to Assess Perceptions of Antimicrobial Stewardship in Nursing Homes

To identify features of community nursing home (NH) environments associated with lower rates of overall antibiotic use.

A virtual clinic improves pneumococcal vaccination for asplenic veterans at high risk for pneumococcal disease

We developed a “virtual clinic” to improve pneumococcal vaccination among asplenic adults. Using an electronic medical record, we identified patients, assessed their vaccination status, entered orders, and notified patients and providers. Within 180 days, 38 of 76 patients (50%) received a pneumococcal vaccination. A virtual clinic may optimize vaccinations among high-risk patients.

A virtual clinic improves pneumococcal vaccination coverage among patients living with HIV at a Veterans Affairs Medical Center

ABSTRACT People living with the human immunodeficiency virus (HIV) should receive pneumococcal vaccinations as part of their routine health maintenance. Our goal was to create a “virtual clinic” to help increase rates of pneumococcal vaccination among people living with HIV without adding substantially to the workload of primary providers. We used administrative data from our Veterans Affairs (VA) medical center to identify a cohort of veterans living with HIV who were not current with either the 13-valent pneumococcal conjugate vaccine (PCV13), the 23-valent pneumococcal polysaccharide vaccine (PPSV23) or both. We enrolled these individuals (n = 99) into a virtual clinic, notified providers via the electronic medical record and mailed letters to the veterans recommending they receive a pneumococcal vaccine. We also wrote orders for the appropriate pneumococcal vaccine that expired after 90 days. Among the virtual clinic cohort, 38% (38/99) of patients received the recommended vaccine within 180 days. Concurrent with our intervention, the Veterans Health Administration deployed a system-wide pneumococcal vaccine clinical reminder that incorporated recent PCV13 recommendations. To discern any effect of the virtual clinic beyond that of the clinical reminder, we compared the rate of PCV13 vaccinations among all HIV-positive veterans at our institution to the equivalent population from 2 other VA medical centers in Ohio. With consideration of the VHA’s system-wide clinical reminder, the proportion of HIV-positive patients who received PCV13 in the first 90 days following the virtual clinic intervention was greater at our facility compared to another Ohio VA medical center (P < 0.05). The virtual clinic improved the pneumococcal vaccine coverage among HIV-positive veterans. These outcomes suggest that even in conjunction with a system-wide clinical reminder, the virtual clinic strategy improves vaccination rates among a high-risk population.

Design and Implementation of a Comprehensive Web-based Survey for Ovarian Cancer Survivorship with an Analysis of Prediagnosis Symptoms via Text Mining

Ovarian cancer (OvCa) is the most lethal gynecologic disease in the United States, with an overall 5-year survival rate of 44.5%, about half of the 89.2% for all breast cancer patients. To identify factors that possibly contribute to the long-term survivorship of women with OvCa, we conducted a comprehensive online Ovarian Cancer Survivorship Survey from 2009 to 2013. This paper presents the design and implementation of our survey, introduces its resulting data source, the OVA-CRADLE™ (Clinical Research Analytics and Data Lifecycle Environment), and illustrates a sample application of the survey and data by an analysis of prediagnosis symptoms, using text mining and statistics. The OVA-CRADLE™ is an application of our patented Physio-MIMI technology, facilitating Web-based access, online query and exploration of data. The prediagnostic symptoms and association of early-stage OvCa diagnosis with endometriosis provide potentially important indicators for future studies in this field.