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HOW CAN STUDENTS BENEFIT FROM THE MENTORSHIP AND FEEDBACK THEY RECEIVE DURING THE CAPSTONE PROCESS

The capstone project is intended to be the culminating experience for students nearing the end of their academic program. It gives students an opportunity to integrate and apply what they have learned over the course of their studies to a substantial project of their own design. While conducting independent work on the capstone is valuable for developing self-guided research, writing, and project management skills, receiving mentorship and feedback during the process provides students with immense additional benefits. Thoughtful guidance from advisors can help students improve their work, gain valuable career skills and experience, and obtain a greater sense of fulfillment from completing their capstone.

Receiving mentorship allows students to access the expertise, experience, and perspectives of faculty members, practitioners in their field of study, or other experts that are involved in reviewing and advising on capstone work. Advisors can point students toward important resources they may have otherwise overlooked, suggest innovative approaches to tackle challenges, and expose them to new ways of thinking about their topic or industry that expands their knowledge beyond what is in textbooks or classrooms. They also role model real-world problem-solving techniques and strategies for juggling responsibilities that students will encounter in future careers or graduate studies. The back-and-forth dialogue between student and mentor simulates collaboration styles common in professional environments.

Thorough feedback on draft capstone proposals, outlines, initial research findings, and works-in-progress is extremely useful for strengthening student work prior to the final submission. Advisors can catch gaps, flaws, or areas needing further development early in the writing process when it is still easy to implement improvements. They may point out inaccurate assumptions, unclear or weak arguments, unnecessary sections, improper citations, formatting issues, grammatical errors, and more. With feedback, capstone quality rises as students refine and polish their work based on expert outside perspectives. Students also gain experience responding professionally to critiques, which is a core career-readiness competency.

Feedback pushes students’ critical thinking further by prompting them to thoroughly evaluate their own arguments and approach from an objective lens. When advisors pose challenging questions, it trains students to become more rigorous in assessing strengths and limitations. Defending methodologies and interpretations to an advisor boosts analytical skills. Strategic suggestions for more sophisticated analyses offer a glimpse of what higher levels of academic or professional work require. This enhances students’ capacity for independent and self-guided learning far beyond graduation.

The mentorship relationship has additional interpersonal benefits. Students receive encouragement, advice, and reality checks on timelines, scope, and requirements from someone invested in their success. This provides reassurance and accountability when ambitious projects become daunting. Knowing an expert is available for consultation promotes confidence. Regular check-ins keep isolated work on track. Advisors may also write letters of recommendation, facilitating career or postgraduate opportunities if students earn strong recommendations through excellent capstone work.

The mentorship and feedback received during the capstone experience immeasurably strengthens final learning outcomes and prepares students for future challenges. It accelerates learning through access to high-level insights. Feedback drives capstone quality upwards. The process boosts real-world, self-guided, analytical, and collaborative skills critical for any field. And relationships with advisors have intangible confidence-building and career-related benefits. While undertaking an independent capstone provides learning, guidance from mentors expands the impact, helping ensure students achieve their fullest potential and are well-equipped for life after college. The enhanced capstone from mentorship readies graduates to hit the ground running in their professional lives.

HOW WILL THE FEEDBACK FROM CLINICAL EXPERTS AND PATIENTS BE COLLECTED AND ANALYZED

Collecting meaningful and useful feedback from clinical experts and patients is crucial for the development of new medical treatments and technologies. A robust feedback process allows researchers and developers to gain valuable insights that can help improve outcomes for patients. Some key aspects of how feedback could be collected and analyzed at various stages of the development process include:

During early research and development stages, focus groups and design thinking workshops with clinicians and patients can help inform what needs exist and how new solutions may help address unmet needs. Audio recordings of these sessions would be transcribed to capture all feedback and ideas. Transcripts would then be analyzed for themes, pain points, and common insights using qualitative data analysis software. This early feedback is formative and helps shape the direction of the project.

Once prototypes are developed, usability testing sessions with clinicians and patients would provide feedback on early user experiences. These sessions would be video recorded with participants’ consent to capture interactions with the prototypes. Recordings would then be reviewed and analyzed to identify any usability issues, things participants struggled with, aspects they found intuitive, and overall impressions. Researchers may use qualitative coding techniques to systematically analyze the recordings for reoccurring themes. Feedback from these sessions helps make refinements and improvements to prototypes before larger pilot studies.

When pilot studies involve real-world use of new technologies or treatments, multiple methods are useful for collecting comprehensive feedback. Clinicians and patients in pilot studies could be asked to complete online questionnaires about their experiences at various time points such as initial use, one week follow up, one month follow up, and study completion. Questions would address impact on clinical workflows, ease of use, patient experience and outcomes, and overall impressions. Questionnaires would be designed using best practices for question wording and response scales to produce high quality quantitative data.

In addition to questionnaires, pilot study participants could optionally participate in 30-60 minute interviews or focus groups. A semi-structured interview guide would be used consistently across all interviews and groups to allow for systematic comparative analysis while still permitting open discussion of experiences. Interviews and groups would be audio recorded with consent for transcription and analysis. Recordings may be transcribed using speech recognition software and transcriptions would then be coded and analyzed thematically. Quantitative questionnaire data and qualitative interview/group data combined provide a comprehensive picture of real-world experiences.

To analyze feedback at scale from large pilot studies or post-market surveillance, Natural Language Processing (NLP) techniques may be applied to unstructured text data like questionnaires comments, transcripts, clinical notes, and patient/clinician written reviews. NLP involves using machine learning algorithms to extract semantic meaning from vast amounts of free-form text. It allows for sentiment analysis to understand if feedback is positive or negative, and also topic modeling to surface common themes or concerns that emerge from the data. Combined with techniques like statistical analysis of Likert scale responses, this approach analyzes both qualitative and quantitative feedback at a large scale with a level of rigor not possible through manual coding alone.

All analyzed feedback would be systematically tracked in a searchable database along with key details about when and from whom the feedback was received. Clinicians, researchers and product developers would have access to review feedback themes, Sentiments, and identified issues/enhancements. Regular reports on gathered feedback would also help inform strategic product roadmaps and planning for future research studies. The database allows feedback to have a visible impact and influence on the continuous improvement of solutions over time based on real-world input from intended end users.

Collecting feedback from multiple qualitative and quantitative sources at various stages of development, coupled with robust analytic techniques helps uncover valuable insights that can strengthen new medical solutions to better serve clinicians and improve patient outcomes. A systematic, multifaceted approach to feedback collection and analysis ensures a continuous learning process throughout the lifecycle of developing technologies and treatments.

WHAT WERE THE KEY FINDINGS FROM THE FAILURE MODES AND EFFECTS ANALYSIS

A failure modes and effects analysis (FMEA) is a systematic process for evaluating potential failure modes within a system or design and assessing the relative impact of those failures. By conducting a thorough FMEA, engineers can gain valuable insights into ways the system may fail and assess how to minimize risk and the effects of any potential failures that do occur. Some key findings that could emerge from a comprehensive FMEA may include:

The FMEA would carefully examine each component, subsystem and interface within the overall system or design. Engineers would evaluate potential ways that each part could fail to perform its intended function, considering factors such as material defects, wear and tear, excessive stresses, improper assembly, incorrect operational parameters, etc. Through this process, certain components may be identified as having higher failure potential due to their complexity, number of failure modes, operating stresses or other risk factors. For example, some parts that interface with users or are exposed to harsh environmental conditions could emerge as particular risk areas based on potential failure modes.

Upon determining all potential failure modes, the team would then assess the impact or severity of each failure on system performance, safety and other critical attributes. Some failure modes, even if relatively unlikely, may carry catastrophic or critical consequences like injury, system damage or inability to complete a primary function. Other failures may only cause minor quality issues or inconveniences. This severity analysis helps identify where design or process changes could help minimize overall risk. Certain component failures or failure combinations ranked with high severity may warrant immediate design focus or additional controls.

An important consideration would be the likelihood or probability of each specific failure mode occurring. Factors like history of similar parts, design maturity, manufacturing processes and component stresses are evaluated. Failures seen as very likely due to high risks require special attention versus others seen as only remotely possible. Combining severity and occurrence evaluations into an overall risk priority number, the FMEA can objectively pinpoint the highest priority issues to address proactively through design or process improvements.

Patterns may emerge implicating certain suppliers, manufacturing steps, environmental conditions or other root causes as contributing factors in multiple failure modes. For example, if many failures can be traced to variations in a critical material property, material certification and testing processes may need review. Such systematic insights help prioritize the most valuable corrective and preventive actions to take.

Recommended actions are formulated to reduce occurrence and/or minimize impact of the highest risk failures. These may include design changes like adding features to reinforce weaknesses, improve inspection points, or adding redundancies. Process recommendations could involve tightening controls, adding process validation checks, supplying staff training and so on. An effective FMEA drives continuous improvement by prioritizing actions supported by objective analysis.

Once improvements are made, the FMEA should be recalculated or revisited periodically over the system’s life cycle to verify effectiveness and consider additional learning from field data. New potential failure modes may emerge as designs or usage profiles evolve too. Periodic review ensures the analysis stays aligned with current conditions.

A robust FMEA process involves cross-functional perspectives in the analysis and uses its findings to help develop comprehensive reliability test plans as well as maintenance and inspection protocols. The end goal is achieving an optimal balance of high reliability, safety and cost-effectiveness throughout the system’s lifecycle. When consistently applied and maintained, FMEA can significantly reduce development and operational risks.

A thorough failure modes and effects analysis provides a rigorous, evidence-based process for identifying and prioritizing reliability and safety concerns within a system or design. Its key findings light the path for targeted improvements to minimize overall risks and their impacts on performance, schedule and budgets. Used effectively, FMEA drives powerful gains that resonate throughout the development, production and field support phases of any product or operation.