IUPUI Faculty Mentoring Exchange: A Platform to Seek and Volunteer Mentoring

poster abstractMentoring combines the impact of learning with the human need for social connection; it helps individuals achieve their best potential in a competitive and challenging world. Within educational organizations, people are willing to seek advice on various topics, including teaching, research, service, civic engagement, and mutual concerns. The IUPUI Faculty Mentoring Exchange is an application, which allows, faculty to seek mentoring, or offer to mentor to those who could benefit from their insights, expertise, and experience. The Mentoring Exchange delivers three core functionalities: 1.) Voluntary mentoring, 2.) A way to seek to mentor, and 3.) Establishing a social connection chain among the faculty from diverse departments and locations. Participating in the Mentoring Exchange is entirely voluntary. Users fill out a checklist to describe the types of insights and experience they wish to discuss with a colleague. The Mentoring Exchange will then offer a list of potential mentors who have described themselves as having such insights to offer. Then, it is up to the user to contact any of these colleagues and take it from there. Human-centered design processes were followed to deliver the system: user research, prototyping, implementation and iterative analysis. After observations and contextual inquiry, data was collected for sketching and prototyping. We learned about the types of mentoring faculty wish to seek out. Senior faculties were asked about their willingness to provide mentoring. Currently, we are evaluating what users are searching for with an ongoing collection of data. Next, we plan build intelligent social matchmaking ecosystem using machinelearning algorithm. Our work contributes to the enhancing of mentoring culture and creating social connections within the organization

Comptracker: Research Administration Tool

poster abstractComptracker is a project to research and develop a platform that can be used by hospitals and educational institutions to track effort and compensation of research personnel working on multiple grant accounts and income sources. The platform is expected to streamline the process of tracking researchers' effort and periodic changes in salary and salary caps. The project is focused on IU School of Medicine (IUSOM) faculty who practice at IU Health to ensure compliance with NIH requirements for institutions recovering clinical salaries on NIH grant accounts. Comptracker is designed to replace a system of spreadsheets which is nearing its capacity to handle the data involved. A web application with a database will be used. The following functionalities have been identified to make this a viable platform: Method for calculating variance in effort. Actual effort from payroll and labor ledger data is compared with projected effort. Variances are highlighted, and records requiring intervention are flagged. Method for tracking Purchase Orders (PO's). All accounts must have PO's set up, and accounts without PO's are flagged. PO's that have expected effort, but no actual effort are flagged. Method for generating monthly invoice data. Where salaries are cost-shared between IUSOM and IU Health, monthly invoices must allocate the cost between institutions. Method for reconciling effort and invoices. Where discrepancies occur, a method to certify actual effort and salary costs must allow corrections to be made. Method for calculating cost-sharing effort report. Where personnel are shared between institutions, a method to resolve discrepancies in effort allocation is required. Method for accommodating changes. Salary changes and NIH salary cap requirements must be accommodated by the system

CAREERS: A Career Guidance Tool for Student Recruiting

poster abstractThe Careers Application for iOS devices helps the School of Informatics and Computing (SOIC) Student Outreach Department provide direction to current high school students for Bachelor's Degree programs and courses offered through SOIC. With the help of this application, the students can get suggestions about the different program majors available at SOIC that might be suitable for their profile. The app asks the students a series of multiple choice questions. The students select answers as per their interests. Depending on the students' responses, the application calculates suitable degree and program matches, and provides these as suggestions to the student. In addition, potential salary expectations for graduates from these programs is provided. The app is a combination of the mobile application and a server-based web application. Students create an account using an e-mail address, and log in. A set of eight questions are selected at random from a database, and displayed in multiple choice format. The student's answers are recorded on the device and sent to the server application for storing and processing. An algorithm evaluates the student's interests on dimensions that correspond to the various programs offered through SOIC, and responds with a ranked list, which includes the program name, degree offered, and expected salary levels for graduates in that program. Since the questions are randomly selected, students can try the app repeatedly. Student Outreach personnel will begin using the app during the coming months, and expect to use it to generate interest among high school students, as well as learn more about the interests of incoming students from their responses

Beyond Data Capture: Using REDCap™ to Facilitate Web-Based Therapeutic Intervention Research

Background Limited guidelines to assist nurse researchers who use web-based interventions are available. Nurses must develop the supporting technology enabling participants to complete study activities and collected data while maintaining data security and participant confidentiality. Objectives To describe how the authors used advanced Research Electronic Data Capture (REDCapTM) functionality to support the data management infrastructure of an interactive, web-based therapeutic intervention. Methods The data management infrastructure for the WISER intervention pilot study consisted of two components: a website for presentation of the intervention and participant account management and a REDCap project for data capture and storage. REDCap application programming interface (API) connected these two components using HTML links and data exchanges. Results We completed an initial pilot study of WISER with 14 participants using the REDCap-based infrastructure. Minimal technical difficulties were encountered. Discussion REDCap is cost-effective, readily available, and through its advanced functionality is able to facilitate confidential, secure interactions with participants, robust data management, and seamless participant progression in web-based intervention research

Development and Usability Testing of a Computer-Tailored Decision Support Tool for Lung Cancer Screening: Study Protocol

Background: Awareness of lung cancer screening remains low in the screening-eligible population, and when patients visit their clinician never having heard of lung cancer screening, engaging in shared decision making to arrive at an informed decision can be a challenge. Therefore, methods to effectively support both patients and clinicians to engage in these important discussions are essential. To facilitate shared decision making about lung cancer screening, effective methods to prepare patients to have these important discussions with their clinician are needed. Objective: Our objective is to develop a computer-tailored decision support tool that meets the certification criteria of the International Patient Decision Aid Standards instrument version 4.0 that will support shared decision making in lung cancer screening decisions. Methods: Using a 3-phase process, we will develop and test a prototype of a computer-tailored decision support tool in a sample of lung cancer screening-eligible individuals. In phase I, we assembled a community advisory board comprising 10 screening-eligible individuals to develop the prototype. In phase II, we recruited a sample of 13 screening-eligible individuals to test the prototype for usability, acceptability, and satisfaction. In phase III, we are conducting a pilot randomized controlled trial (RCT) with 60 screening-eligible participants who have never been screened for lung cancer. Outcomes tested include lung cancer and screening knowledge, lung cancer screening health beliefs (perceived risk, perceived benefits, perceived barriers, and self-efficacy), perception of being prepared to engage in a patient-clinician discussion about lung cancer screening, occurrence of a patient-clinician discussion about lung cancer screening, and stage of adoption for lung cancer screening. Results: Phases I and II are complete. Phase III is underway. As of July 15, 2017, 60 participants have been enrolled into the study, and have completed the baseline survey, intervention, and first follow-up survey. We expect to have results by December 31, 2017 and to have data analysis completed by March 1, 2018. Conclusions: Results from usability testing indicate that the computer-tailored decision support tool is easy to use, is helpful, and provides a satisfactory experience for the user. At the conclusion of phase III (pilot RCT), we will have preliminary effect sizes to inform a future fully powered RCT on changes in (1) knowledge about lung cancer and screening, (2) perceived risk of lung cancer, (3) perceived benefits of lung cancer screening, (4) perceived barriers to lung cancer screening, (5) self-efficacy for lung cancer screening, and (6) perceptions of being adequately prepared to engage in a discussion with their clinician about lung cancer screening