Student Name Western Governors University D159 Evidence-Based Measures for Evaluating Healthcare Improvements Prof. Name Date D159 Improving CMS Star Ratings: Specialized Education & OASIS Training Project Overview and Purpose The purpose of this quality improvement project is to evaluate the impact of a targeted documentation enhancement initiative designed to improve CMS Quality of Care Star Ratings at Pathways Healthcare (PHC). The initiative centers on a structured, two-part educational intervention combined with cross-training in OASIS documentation standards for clinicians. By strengthening clinician knowledge and application of CMS-aligned documentation practices, the project seeks to improve both care delivery and reporting accuracy. To determine the effectiveness of the intervention, three primary outcome measures have been identified: clinician attendance and engagement in training sessions, corridor intervention rates related to documentation corrections, and changes in CMS Star Rating scores. Together, these indicators provide a comprehensive evaluation of knowledge acquisition, documentation performance, and overall organizational quality outcomes. Evaluation Metrics and Rationale Clinician Attendance and Engagement Clinician participation in the educational sessions is a foundational requirement for achieving project success. Attendance alone is insufficient; active engagement through discussion, case review, and practical application exercises is essential to ensure clinicians internalize updated CMS and OASIS requirements. High attendance rates increase the likelihood of consistent documentation practices across disciplines, thereby reducing variability and errors. Low participation poses a significant risk to the initiative, as inadequate exposure to updated standards perpetuates misinformation, incomplete documentation, and misaligned clinical assessments. These deficiencies directly contribute to persistently low CMS Star Ratings. Therefore, monitoring attendance and engagement serves as an early indicator of potential project success or failure. Corridor Intervention Rates Corridor intervention rates measure the frequency with which submitted clinical documentation requires correction following initial review. PHC partners with Corridor Group, a nationally recognized organization specializing in technology-enabled clinical documentation and coding audits. Corridor interventions often highlight discrepancies in functional scoring, diagnosis selection, and congruence across disciplines. A post-training reduction in corridor interventions would indicate improved clinician competency, more accurate OASIS scoring, and enhanced interdisciplinary consistency. These improvements are particularly critical during admission and recertification assessments, as functional outcomes captured at these points significantly influence CMS quality measures and patient discharge outcomes. CMS Star Rating Scores CMS Star Ratings represent the most direct and outcome-focused indicator of project effectiveness. These ratings aggregate multiple process and outcome measures into a single, publicly reported score that reflects agency quality performance. The project specifically evaluates whether a specialized OASIS education program can measurably improve PHC’s Star Rating. CMS updates Star Ratings quarterly in January, April, July, and October. Clinician education is scheduled from December 2, 2024, through January 28, 2025, followed by a structured implementation phase from January 29 to March 28, 2025. Performance will be evaluated by comparing the January 2025 baseline scores with those published in April 2025. Although CMS provides a 15-day preview period prior to publication, limited OASIS submissions during this window may reduce its predictive reliability. Data Management Strategy What Data Will Be Collected and How? Clinician Attendance RatesAttendance data will be collected using structured sign-in sheets for each training session. The target benchmark is 95% attendance and participation among clinicians from all four teams within the M01 branch. Team managers are responsible for verifying attendance and submitting finalized rosters to the Project Manager for consolidation and analysis. Corridor Intervention Rates and Star RatingsData related to corridor interventions and CMS Star Ratings will be obtained through PHC’s Quality Assurance and Performance Improvement (QAPI) Committee. These data are reviewed weekly during standing QAPI meetings, where the Quality Assurance nurse presents trend analyses. The Project Manager will attend these meetings to systematically collect and track data relevant to the project’s evaluation. Key Performance Indicators The following table outlines the primary Key Performance Indicators (KPIs) used to assess project outcomes: KPI Number Focus Area Description Measurement Period Success Indicator KPI #1 Training Completion Measures the proportion of clinicians completing the full two-part educational program By January 28, 2025 95% clinician attendance with active engagement KPI #2 Employee Performance Progress Evaluates post-training documentation quality and practical application of OASIS standards January 29 – March 28, 2025 Improved accuracy, consistency, and interdisciplinary congruence in OASIS documentation Project Goal and Timeline The overarching goal of this initiative is to increase PHC’s CMS Quality of Care Star Rating from 2.5 to 4.0 by enhancing clinician proficiency in OASIS assessments, functional scoring, and documentation accuracy. Progress will be evaluated across the seven CMS quality measures that directly contribute to the Star Rating calculation. CMS Quality Measures PHC Baseline Score (Out of 5) Timely Initiation of Care (TIOC) 2.0 Improvement in Ambulation 2.5 Improvement in Bed Transferring 1.5 Improvement in Bathing 1.5 Improvement in Dyspnea 1.5 Management of Oral Medications 2.0 Potentially Preventable Hospitalizations 4.0 Overall Star Rating 2.5 The education phase will occur from December 2, 2024, through January 28, 2025. Clinicians will then apply newly acquired knowledge during routine practice from January 29 to March 28, 2025. Data collected during this implementation period will be compared with baseline performance to determine improvement trends. Data Collection, Analysis, and Security Attendance data will be captured in real time, while corridor intervention and Star Rating data will be extracted from Strategic Healthcare Programs (SHP). SHP provides near–real-time analytics and automated alerts, enabling PHC to identify and correct documentation inconsistencies prior to CMS submission. To ensure data confidentiality, all patient identifiers will be replaced with numerical codes, and clinician identities will be anonymized in analytic datasets. Both physical and electronic records will be securely stored with access restricted to authorized project team members. Key data parameters include: Data analysis will rely on descriptive statistics, including percentages and averages, to evaluate attendance rates, corridor intervention frequency, and changes in Star Ratings. Weekly summary reports will be used to monitor trends and identify barriers requiring timely intervention. Success Criteria and Anticipated Challenges The project will be considered successful if clinician engagement reaches or exceeds 95%, corridor intervention rates demonstrate a sustained decline, and CMS Star Ratings improve beyond the baseline score of 2.5.

Student Name Western Governors University D159 Evidence-Based Measures for Evaluating Healthcare Improvements Prof. Name Date D159: Shared Governance Implementation in Home Care Programs The health improvement project centers on the implementation of a shared governance framework within the Home & Community-Based Care (HCBC) department at the Iron Mountain Veterans Affairs (VA) facility. Shared governance is designed to enhance staff engagement, professional accountability, and collaborative decision-making by involving frontline staff directly in organizational processes. The primary goal of this initiative is to empower staff through education and active participation in performance improvement activities. To evaluate the effectiveness of the shared governance initiative, the project team identified three essential data elements. These include assessing frontline staff knowledge of shared governance before and after educational interventions and monitoring the number of performance improvement projects initiated by staff. Together, these metrics provide both quantitative and qualitative insight into knowledge acquisition and practical application. The first data element involves a pre-education survey administered to frontline staff to establish baseline knowledge. Because staff backgrounds, prior leadership exposure, and professional experiences vary, baseline data are necessary to identify gaps in understanding shared governance principles. The second data element is a post-education survey, which evaluates the effectiveness of the educational intervention and determines whether staff can articulate and apply shared governance concepts in their daily practice. The third data element tracks the initiation and execution of staff-led performance improvement projects, serving as a tangible indicator of shared governance integration into practice. Employee feedback mechanisms, such as surveys, are critical tools in organizational development. Huebner and Zacher (2021) emphasize that the true value of employee surveys lies not only in data collection but also in the follow-up actions and planning that translate feedback into meaningful improvement. Data Management Plan What is the source of data for the project? The primary data source for this project consists of digitally administered surveys distributed to frontline HCBC staff before and after the shared governance educational sessions. The pre-education survey captures baseline knowledge and perceptions, while the post-education survey measures knowledge gains and perceived applicability of shared governance concepts. The Agency for Healthcare Research and Quality (AHRQ, 2020) underscores the importance of structured data in healthcare settings, noting that data support workflow development, quality reporting, and improvement incentives. Aggregated, practice-level data facilitate continuous quality improvement, process standardization, and enhanced patient outcomes. However, data must be systematically analyzed and incorporated into a quality improvement framework to produce actionable results. To ensure confidentiality and data integrity, all surveys will be conducted through an encrypted digital platform accessible only via VA-issued computers. Survey data will be stored and reviewed on secure VA systems and accessed solely by the project team and designated stakeholders. Key Performance Indicators (KPIs) and Deliverables The project’s success will be evaluated using clearly defined KPIs aligned with shared governance objectives. These indicators focus on both educational outcomes and behavioral change among staff. KPI Number Description Intended Outcome KPI #1 Development and delivery of shared governance educational materials Increased staff knowledge, confidence, and engagement in shared governance activities such as councils, committees, and improvement initiatives KPI #2 Strengthening collaborative relationships between leadership and frontline staff Enhanced shared decision-making, critical thinking, and communication demonstrated through staff participation in leadership forums The primary deliverables associated with these KPIs include measurable improvement in staff knowledge scores and an increase in staff-initiated problem-solving and performance improvement activities. These outcomes reflect key principles of shared governance, including empowerment, accountability, and professional ownership. To measure success, the project team established an internal benchmark of a 30% increase in shared governance knowledge by July 31, 2025. This benchmark ensures that educational interventions remain effective, relevant, and aligned with departmental needs. Data Collection and Analysis Methods How will the data be collected and analyzed? Data will be collected through digitally administered surveys containing multiple-choice questions focused on foundational shared governance concepts. The pre-education survey will also solicit staff input regarding potential applications of shared governance in their roles and preferred educational formats, allowing the project team to tailor educational delivery methods. Survey participation rates will be closely monitored to ensure adequate representation. Results from the pre-education survey will guide refinements to educational content, ensuring that identified knowledge gaps are addressed effectively. Data analysis will rely on descriptive statistics, including calculation of correct response percentages, overall completion rates, and comparison of pre- and post-education results. Visual data representations will include bar graphs comparing knowledge scores before and after education and pie charts summarizing staff-identified improvement project themes. Houser (2023) notes that effective data analysis supports informed decision-making and may include descriptive, diagnostic, predictive, or prescriptive analytics depending on project goals. In this project, descriptive analytics are most appropriate for evaluating knowledge acquisition and participation trends. Potential Challenges and Mitigation Strategies What barriers might impact the project’s success? One anticipated challenge is low survey participation, which could limit data validity and introduce response bias. Additionally, securing sustained buy-in from both leadership and frontline staff may be difficult during periods of organizational change and federal-level uncertainty within the VA system. To mitigate these risks, leadership support will be emphasized, and the value of shared governance will be clearly communicated to staff. Educational sessions will highlight how shared governance enhances professional autonomy, improves communication, and supports staff-driven change. By demonstrating relevance to daily practice, the project aims to increase engagement and participation. Despite potential barriers, the initiative offers substantial organizational value by promoting staff ownership of professional development and departmental improvement efforts. Dissemination of Results Upon completion of data collection and analysis, findings will be shared with the Pathway to Excellence team to evaluate the feasibility of expanding early shared governance education across departments. The High Reliability Organization (HRO) lead will also be informed to encourage staff participation in advanced training opportunities, including Yellow Belt and Green Belt process improvement programs. If the initiative demonstrates positive outcomes, the shared governance implementation strategy will be presented to additional department leaders to support replication throughout the facility. Project Closure and Sustainability How will the project conclude

Student Name Western Governors University D159 Evidence-Based Measures for Evaluating Healthcare Improvements Prof. Name Date Leadership and Management E-Portfolio Template Phase 1 Task Overview Phase 1 of the Leadership and Management E-Portfolio focuses on foundational planning, documentation, and initial project conceptualization. The tasks completed during this phase establish the framework for effective project implementation and evaluation. Table 1Phase 1 Task Completion Overview Task Estimated Time to Complete Date Completed CPE Table 1 hour 2/11/2025 Project Report Template 4 hours 2/12/2025 GoReact Videos 2 hours 2/14/2025 GoReact Video Summary 1 hour 2/14/2025 Project Report Plan What Is the Project Aim? The primary aim of this project is to design and implement a structured telehealth follow-up process in which nurse case managers contact psychiatric patients within seven days following discharge from an acute behavioral health hospitalization. This initiative seeks to strengthen continuity of care, enhance patient engagement in outpatient psychiatric services, and ultimately reduce 30-day psychiatric rehospitalization rates. Early post-discharge contact has been shown to support treatment adherence and mitigate relapse risk during a highly vulnerable transition period. Who Is the Project Manager? Amanda Howell serves as the Project Manager for this initiative. In this leadership role, she is responsible for coordinating project activities, ensuring adherence to timelines, overseeing data collection and analysis, and facilitating communication among stakeholders. Her responsibilities also include monitoring performance metrics and guiding continuous quality improvement efforts. Essential Data for Analysis What Data Are Essential for Evaluating Project Outcomes? To accurately evaluate the effectiveness of the telehealth follow-up intervention, several critical data elements will be collected and analyzed. These data points support both outcome measurement and process evaluation. Key data elements include demographic characteristics of psychiatric patients discharged from the behavioral health unit, 30-day rehospitalization rates, and the percentage of patients who complete an outpatient psychiatric follow-up appointment within seven days of discharge. Collectively, these indicators allow for assessment of both patient outcomes and system-level performance. Data Security and Confidentiality How Will Patient Data Be Protected? Patient data will be accessed exclusively through a secure organizational database that complies with healthcare privacy regulations. Access permissions will be tightly controlled by the project manager, ensuring that only authorized project team members can view or handle sensitive information. Data extracted for analysis will be stored in an encrypted Excel file housed on a secure network. This approach minimizes the risk of unauthorized access while maintaining compliance with institutional policies and regulatory standards such as HIPAA. Data Presentation and Reporting How Will Project Results Be Presented? Project findings will be communicated using both graphical and tabular formats to support clarity and stakeholder understanding. Comparative charts will illustrate changes in 30-day rehospitalization rates and 7-day follow-up compliance before and after implementation of the telehealth intervention. Tables will provide precise numerical comparisons, enabling leadership to quickly assess trends, progress, and areas requiring improvement. Project Team Roles and Responsibilities Who Are the Project Team Members and What Are Their Roles? The project team is led by the project manager, who assumes primary responsibility for oversight and evaluation. Weekly data reviews will be conducted to monitor 30-day psychiatric rehospitalization rates and 7-day follow-up appointment compliance. These reviews enable timely identification of barriers, support accountability, and inform any necessary adjustments to the intervention strategy. Targeted Performance Metrics What Metrics Will Be Used to Measure Success? Specific performance metrics have been identified to evaluate both implementation fidelity and project outcomes. These metrics align with quality improvement principles and organizational goals. Table 2Targeted Project Metrics Metric Target Outcome Staff Training 100% of relevant staff trained prior to project launch Audit Frequency Weekly audits conducted by the project manager Psychiatric Rehospitalization Measurable reduction in 30-day rehospitalization rates Follow-Up Appointment Compliance Increase in 7-day outpatient appointment attendance GoReact Video Summary (Phase 1) The Phase 1 GoReact video emphasized the development of a telehealth-based follow-up process for psychiatric patients transitioning from inpatient to outpatient care. The project manager’s role was highlighted as central to data oversight, staff engagement, and outcome evaluation. Key data elements include patient demographics, rehospitalization rates, and follow-up compliance metrics. Secure data management practices and regular progress monitoring through weekly audits were identified as essential components of project success. Phase 2 Task Overview Phase 2 builds upon the planning foundation established in Phase 1 and emphasizes structured data management, performance tracking, and outcome evaluation. Table 3Phase 2 Task Completion Overview Task Estimated Time to Complete Date Completed CPE Table 1 hour 2/11/2025 Data Management Plan Template 4 hours 2/13/2025 GoReact Videos 2 hours 2/14/2025 GoReact Video Summary 1 hour 2/14/2025 Data Management Plan What Are the Three Primary Data Elements to Track? The project focuses on three essential data elements: the number of project team members educated on the telehealth follow-up process, 30-day psychiatric rehospitalization rates, and patient compliance with outpatient follow-up appointments completed within seven days of discharge. These data elements collectively assess readiness, implementation effectiveness, and patient outcomes. What Data Sources Will Be Used to Evaluate Project Success? The electronic medical record (EMR) system will serve as the primary data source. Relevant data will be extracted from the EMR and transferred into a secure Excel spreadsheet. Access to this file will be restricted to authorized project team members to preserve data integrity and confidentiality. Key Performance Indicators and Benchmarks What Are the Key Performance Indicators for This Process? Table 4Key Performance Indicators KPI Description Staff Training Completion All designated staff complete telehealth process education prior to launch Weekly Audits Ongoing monitoring of rehospitalization and follow-up compliance rates What Benchmarks Align With the SMART Goal? The project benchmarks are aligned with SMART criteria by targeting a 5% increase in 7-day outpatient follow-up compliance and a 2% reduction in psychiatric rehospitalization rates. These benchmarks are specific, measurable, achievable, relevant, and time-bound, supporting meaningful performance evaluation. Quantitative Data Collection and Analysis How Will Quantitative Data Be Collected? Quantitative data will be collected through systematic extraction from the EMR by the project manager. Data collection will focus on patients discharged from the psychiatric unit and will include readmission status