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CAN YOU PROVIDE MORE INFORMATION ON THE SPECIFIC COMPONENTS OF THE TRANSITIONAL CARE PROGRAM

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Transitional care programs aim to ensure continuity of care and prevent adverse outcomes when patients move from one care setting to another, such as from a hospital to home. Comprehensive transitional care programs typically include several core components to effectively facilitate this transition and reduce the risk of errors, rehospitalizations, or other issues.

The core components of an effective transitional care program include: comprehensive discharge planning, post-discharge follow up, medication reconciliation and management, patient and caregiver education and engagement, and care coordination. Let’s take a closer look at each of these elements:

Comprehensive discharge planning starts during the hospital stay and involves a thorough evaluation of the patient’s needs and living situation upon discharge. Social workers, nurses, and discharge planners work closely with the patient and family to develop an individualized discharge plan. This plan outlines the patient’s diagnosis, treatment course in the hospital, any pending tests or future appointments, instructions for care at home including medication management and follow up care, equipment needs, and availability of family/social support. Good discharge planning results in a clear communication of this plan to both the patient and their outpatient providers.

Post-discharge follow up is a crucial component to catching any issues early and preventing adverse events. This typically involves a nurse practitioner or physician assistant led visit or phone call within 3-7 days of discharge to assess how the patient is coping and managing at home. During this follow up, the care provider comprehensively reviews medications, checks vital signs and wound healing, answers any patient questions, and screens for signs of potential complications or deterioration in condition that may warrant physician follow up. Additional follow ups may be scheduled further out depending on the individual’s needs.

Medication reconciliation involves compiling an accurate list of all prescription medications, over-the-counters, and supplements a patient is taking and comparing this to what is documented in medical records at each transition point. During care transitions, medications are clarified, reconciled, and reported to ensure no errors in dosages or discontinuations occur, and that the discharge instructions are synchronized across all providers. Pharmacists typically take the lead on medication reconciliation during transitions, but nurses and other clinicians also conduct reconciliations.

Patient and caregiver education and engagement is a critical process whereby key information is effectively communicated to promote self-management at home. During the hospitalization and in follow up sessions, clinicians spend dedicated time training patients and families on diagnoses, medication purposes and side effects, activity recommendations, diet, wound/incision care, when to seek help based on symptoms, and health maintenance. Teaching methods are tailored to individual health literacy needs. This facilitates carrying out the discharge plan successfully.

Care coordination ensures all members of the care team are aligned and that patients experience a seamless transition between settings without duplication or gaps in care/information. Formal care coordinators, often nurses or social workers, are designated to communicate with inpatient/outpatient providers, track test results and appointments, troubleshoot barriers, and serve as the single point of contact for patients as issues arise post-discharge. EHR systems further bolster care coordination by giving all providers updated, consolidated views of treatment plans and status.

Additional supportive elements in many transitional care programs include home health monitoring technologies that allow clinicians to maintain visibility into patients’ conditions from afar, telephone/telehealth capabilities for virtual follow up visits to limit travel demands, extensive support for obtaining any needed durable medical equipment or home services, and 24/7 access to clinicians for urgent questions/problems beyond regular business hours. Social determinants that could disrupt care transitions like transportation, housing instability andaffordability of medications/care are also addressed proactively.

The outcomes of comprehensive transitional care programs demonstrate reduced rates of preventable rehospitalizations, Emergency Department visits and healthcare costs through early detection and management of post-discharge issues. Patients also report high satisfaction with clarity of communication and organizational support received during care transitions. As healthcare delivery continues prioritizing value over volume, transitional care models play an important role in maintaining quality while keeping patients healthy in their home environments.

The key components of an effective transitional care program including thorough discharge planning, timely post-discharge follow up visits, medication reconciliation, patient education, care coordination across providers, use of remote monitoring technologies, addressing social factors, and availability of 24/7 clinician support. Together, these elements work to ensure patients experience safe, efficient transitions between care levels with their medical needs met.

WHAT ARE SOME OF THE SPECIFIC SKILLS THAT STUDENTS GAIN THROUGH PARTICIPATING IN NIKE’S CAPSTONE PROGRAM

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Nike’s Capstone program provides high school students with an opportunity to develop important hard and soft skills that will serve them well both in future educational pursuits and career paths. Through this program, students work in teams on a real-world project proposed by Nike to help solve a business challenge. This hands-on experience allows students to gain valuable project management, collaboration, communication, and problem-solving abilities.

Some of the key skills students are able to hone through the Capstone program experience include:

Project Management Skills – Students learn what it takes to successfully plan and execute a complex project from start to finish. They have to define project goals and scope, develop a work plan with timelines and assign responsibilities, track progress, and ensure the project is delivered on schedule and meets requirements. This teaches skills like priority setting, resource allocation, and adapting to changes that are critical for any career.

Collaboration Skills – As members of multidisciplinary teams, students learn effective collaboration techniques for working together toward a common goal. They practice clear communication, active listening, consensus building, handling conflicts constructively, and tapping the diverse strengths each person brings. Participating in team-based problem solving readies students for the many collaborative work environments they will likely face.

Communication Skills – Both oral and written communication skills are sharpened through delivering project presentations and documentation. Students practice organizing information logically, adapting messages for different audiences like clients or stakeholders, and using various media like slides, reports and demonstrations. Delivering persuasive recommendations enhances presentation and public speaking confidence.

Problem Solving Skills – The real-world business challenges provided by Nike require innovative thinking. Students have to analyze complex problems from multiple angles, brainstorm creative solutions, conduct research, test ideas, and iterate based on outcomes. This strengthens critical thinking, research proficiencies, and the ability to tackle open-ended problems—skills integral to any career path.

Design Thinking Skills – Many Capstone projects involve designing new product concepts, prototypes or user experiences. This immerses students in the full iterative design process of empathizing with user needs, defining the problem, ideating solutions, prototyping, testing, and refining based on feedback. Students not only strengthen creative design skills but also learn human-centered approaches through practicing design thinking methodologies.

Research Skills – To thoroughly understand business challenges and solution spaces, students extensively research topics through literature reviews and primary data gathering like surveys, interviews and contextual inquiries. This improves their abilities to efficiently gather, assess validity of, synthesize and apply information from diverse sources—all key attributes of any research-driven career.

Time Management Skills – With tight deadlines and competing priorities across classes, activities and personal lives, students experience the importance of self-discipline, prioritization, planning and organizational abilities needed to effectively manage workload and schedules. The program cultivates time management proficiencies central to work-life balance.

Leadership Skills – While working as a team, students alternate facilitating meetings, motivating others, resolving conflicts, delegating responsibilities, setting examples and driving projects forward under constraints and ambiguity. Even those who may not be formal group leaders gain exposure to developing leadership presence and guiding successful team efforts.

Perseverance – Taking on open-ended challenges that may encounter setbacks along the way builds students’ perseverance, willingness to learn from mistakes/failures, and determination to find solutions—all qualities needed to progress in uncharted problem spaces. The hands-on work gives students confidence to push through obstacles and iterative approaches to continuous improvement.

The diverse hard and soft skills strengthened through participating in Nike’s high-impact Capstone program provide a strong foundation for whatever future studies or careers students may pursue. The real-world, collaborative project experience equips students to become flexible, resourceful problem solvers ready to excel in dynamic, fast-paced work environments of the future.

CAN YOU PROVIDE MORE DETAILS ON THE SPECIFIC ALTERYX TOOLS USED IN THE PROJECT

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The core Alteryx tools utilized in this data analytics project included:

Input Data (Tool): This tool was used for importing various data sources into the Alteryx workflow. It allows bringing in data from a variety of sources like CSV files, SQL databases, excel files etc. For this project, we mainly used it to import customer transaction data, product master files, location details from different SQL databases.

Filter Tool: The Filter tool was extensively used for filtering the data based on certain conditions. For example, filtering customer records belonging to only certain regions, or filtering product records belonging only to certain categories. It helped to reduce the volume of records being analyzed by focusing only on relevant subsets.

Formula Tool: The Formula tool allowed creating new fields and performing calculations on existing fields within the data. For example, we used it to compute aggregations like total sales amount, number of orders etc. per customer/product/location. It was also used to derive new attributes by concatenating or modifying existing fields.

Select Tool: The Select tool helped select only the required fields from the data instead of carrying all fields through the workflow. This optimized the performance and resource usage. We used it to discard unused fields at multiple stages of the workflow.

Join Tool: The Join tool enabled joining multiple data sources based on common key fields. It was useful for linking transaction level detail to master files like linking orders to customer details or products files. Different join types like left, right and inner joins were leveraged based on business requirements.

Aggregate Tool: As the name suggests, this tool allows aggregating data along grouping fields. We extensively used this tool for creating summaries and aggregations. For example, aggregating total sales by customer/product/location combinations using various aggregation functions like sum, count, min, max etc.

Sample Tool: This tool helped in sampling the data for testing purposes. Since the real production data was huge, we took samples of 10,000-50,000 records using this tool before building models for testing model performance on smaller data sets.

Union Tool: The Union tool provided the ability to combine/concatenate multiple similar data streams. It was utilized to merge results from different filtering or aggregation steps in the workflow.

Distinct Tool: This tool removed duplicate records from the data and retained only unique records. It helped in cleaning the data by removing repeated values at intermediate steps.

Split Tool: The Split tool enabled breaking up the data into multiple output ports based on a splitting conditions. This allowed processing different record subsets through separate subsequent logic paths based on field values.

Rank Tool: The Rank tool facilitated ranking records along dimensions. We used it to find top/bottom performing products, customers, locations etc. based on defined ranking criteria like sales amount, profits etc.

Graphic Tools: Alteryx workflow contains various graphic tools like Plot, Map and Gallery for visualizing results. Map tool helped view geographic locations on maps while Plot tool generated different chart types for analysis.

Apart from above, other tools leveraged included Condition, Order, Lookup, Modeler tools for additional data preparation, joins, validations and building predictive models. The Alteryx engine executed the workflow in an optimized manner with automatic parallelization. Intermediate results were cached for better performance during successive runs. The self-service interface with powerful data tools helped tremendously in fast modeling and drawing meaningful insights from the project business objectives.

The above covers the key Alteryx tools implemented for this data analytics project with details on their features, purpose and usage in different stages of the workflow. The self-service, intuitive interface and wide range of data preparation and analytics functionality provided by Alteryx tools helped to efficiently analyze large, complex datasets and fulfill business objectives. The flexible processing environment additionally enabled reusability of workflow modules and iterative model development.

WHAT WERE THE SPECIFIC METRICS USED TO EVALUATE THE PERFORMANCE OF THE PREDICTIVE MODELS

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The predictive models were evaluated using different classification and regression performance metrics depending on the type of dataset – whether it contained categorical/discrete class labels or continuous target variables. For classification problems with discrete class labels, the most commonly used metrics included accuracy, precision, recall, F1 score and AUC-ROC.

Accuracy is the proportion of true predictions (both true positives and true negatives) out of the total number of cases evaluated. It provides an overall view of how well the model predicts the class. It does not provide insights into errors and can be misleading if the classes are imbalanced.

Precision calculates the number of correct positive predictions made by the model out of all the positive predictions. It tells us what proportion of positive predictions were actually correct. A high precision relates to a low false positive rate, which is important for some applications.

Recall calculates the number of correct positive predictions made by the model out of all the actual positive cases in the dataset. It indicates what proportion of actual positive cases were predicted correctly as positive by the model. A model with high recall has a low false negative rate.

The F1 score is the harmonic mean of precision and recall, and provides an overall view of accuracy by considering both precision and recall. It reaches its best value at 1 and worst at 0.

AUC-ROC calculates the entire area under the Receiver Operating Characteristic curve, which plots the true positive rate against the false positive rate at various threshold settings. The higher the AUC, the better the model is at distinguishing between classes. An AUC of 0.5 represents a random classifier.

For regression problems with continuous target variables, the main metrics used were Mean Absolute Error (MAE), Mean Squared Error (MSE) and R-squared.

MAE is the mean of the absolute values of the errors – the differences between the actual and predicted values. It measures the average magnitude of the errors in a set of predictions, without considering their direction. Lower values mean better predictions.

MSE is the mean of the squared errors, and is most frequently used due to its intuitive interpretation as an average error energy. It amplifies larger errors compared to MAE. Lower values indicate better predictions.

R-squared calculates how close the data are to the fitted regression line and is a measure of how well future outcomes are likely to be predicted by the model. Its best value is 1, indicating a perfect fit of the regression to the actual data.

These metrics were calculated for the different predictive models on designated test datasets that were held out and not used during model building or hyperparameter tuning. This approach helped evaluate how well the models would generalize to new, previously unseen data samples.

For classification models, precision, recall, F1 and AUC-ROC were the primary metrics whereas for regression tasks MAE, MSE and R-squared formed the core evaluation criteria. Accuracy was also calculated for classification but other metrics provided a more robust assessment of model performance especially when dealing with imbalanced class distributions.

The metric values were tracked and compared across different predictive algorithms, model architectures, hyperparameters and preprocessing/feature engineering techniques to help identify the best performing combinations. Benchmark metric thresholds were also established based on domain expertise and prior literature to determine whether a given model’s predictive capabilities could be considered satisfactory or required further refinement.

Ensembling and stacking approaches that combined the outputs of different base models were also experimented with to achieve further boosts in predictive performance. The same evaluation metrics on holdout test sets helped compare the performance of ensembles versus single best models.

This rigorous and standardized process of model building, validation and evaluation on independent datasets helped ensure the predictive models achieved good real-world generalization capability and avoided issues like overfitting to the training data. The experimentally identified best models could then be deployed with confidence on new incoming real-world data samples.

CAN YOU PROVIDE EXAMPLES OF SPECIFIC CAPSTONE PROJECTS COMPLETED BY CAPELLA UNIVERSITY STUDENTS

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One student in the Bachelor of Science in Business Management program completed a capstone project examining strategies for improving employee retention at a small manufacturing company. For their project, the student conducted interviews with 20 current employees to understand their reasons for staying or considering leaving the organization. They also did benchmarking research on employee retention best practices at similar companies. In their capstone paper and presentation, they proposed a combination of improved management training, competitive compensation and benefits packages, enhanced opportunities for advancement, and expanded work-life balance programs. Some of their key recommendations that were later implemented included the introduction of flexible work schedules, an annual employee satisfaction survey to gather ongoing feedback, and the creation of internal mentorship and development programs.

In the Master of Science in Information Assurance and Cybersecurity program, a student focused their capstone project on enhancing the security of a mid-sized financial services firm’s cloud infrastructure and applications. Through vulnerability assessments and penetration testing, they identified several gaps in access controls, authentication protocols, and network security that could expose sensitive customer data. In their project report and presentation to IT leadership, they recommended an integrated solution involving Multi-Factor Authentication, increased encryption of data in transit and at rest, regular security awareness training for all employees, and deploying cloud security tools to monitor for malicious activity and abnormal behavior. The company was so impressed with the findings and proposed roadmap that they hired the student as their new Cloud Security Engineer after graduation to help implement the changes.

A student in the Doctor of Education in Organizational Leadership program completed a program evaluation capstone to assess the effectiveness of an after-school tutoring program at a local Title 1 elementary school. For their project, they developed surveys to collect feedback from students, parents, and teachers on perceived strengths and weaknesses of the existing tutoring model. They also analyzed standardized test score data from past years to see if program participation correlated with improved academic performance. Their final paper presented both qualitative and quantitative findings. Some of the major recommendations included tailoring tutoring sessions to individual student needs based on formative assessments, involving parents more directly in the program through volunteer opportunities, and securing additional grant funding to expand the scope and resources available. The school district was pleased with the comprehensive evaluation and subsequently implemented several of the proposed improvements.

In the Master of Science in Information Technology program, one capstone involved developing a proof-of-concept prototype for an innovative mobile application aimed at helping parents easily locate and connect with local babysitters, nannies, and childcare providers. Through user interviews and competitor research, the student identified pain points in existing solutions and opportunities to address unmet needs. Their prototype application included customizable family profiles, real-time availability calendars for care providers, secure payment processing capabilities, parental controls, and integrated background check verification. Their project report contained a full business plan outlining user acquisition strategies, pricing models, partnerships, staffing requirements, and financial projections. Investors were impressed with the clarity of vision and early validation findings, resulting in seed funding being secured to further develop the concept into a product.

These are just a few examples of the diverse, impactful capstone projects completed through Capella’s competency-based programs. A hallmark of Capella’s model is developing applied research and evaluation skills to address real-world organizational and community issues. Students successfully collaborate with industry partners and stakeholders to design solutions informed by evidence and tailored to specific needs. By completing rigorous projects with measurable outcomes, Capella graduates gain proven ability to effectively problem solve, communicate recommendations, and drive meaningful change in their respective fields and workplaces.