diff --git a/02_Statistical Analyses/2023.11.04 -Statistics Final - Project Approach.pdf b/02_Statistical Analyses/2023.11.04 -Statistics Final - Project Approach.pdf deleted file mode 100644 index 457e230..0000000 Binary files a/02_Statistical Analyses/2023.11.04 -Statistics Final - Project Approach.pdf and /dev/null differ diff --git a/02_Statistical Analyses/2023.11.13 - Statistics Final - Quality of Care.pdf b/02_Statistical Analyses/2023.11.13 - Statistics Final - Quality of Care.pdf deleted file mode 100644 index 52e3c7d..0000000 Binary files a/02_Statistical Analyses/2023.11.13 - Statistics Final - Quality of Care.pdf and /dev/null differ diff --git a/02_Statistical Analyses/README.md b/02_Statistical Analyses/README.md deleted file mode 100644 index 1e9427b..0000000 --- a/02_Statistical Analyses/README.md +++ /dev/null @@ -1,71 +0,0 @@ -# Statistical Analyses - R Coding - -## Overview - -Welcome to the Statistical Analyses - R Coding section of my portfolio. This folder contains my final project for Statistics in R, focusing on quality measurement in healthcare. Here, I showcase my ability to apply statistical methods to real-world healthcare problems, providing valuable insights and actionable recommendations. - -## Contents - -### Quality Measurement Project - -This project explores key performance metrics in clinician quality of care within the healthcare industry. The analysis compares internal health system results with national benchmarks, examines internal factors impacting clinician performance, and provides recommendations for quality improvement. - -#### Documents: - -- **Coding Approach:** A detailed document outlining the statistical methods and coding techniques used in the project. -- **Final Paper:** A comprehensive paper presenting the findings, analyses, and recommendations based on the project. - -### Project Details - -#### Introduction - -In today's healthcare industry, understanding clinician quality of care key performance metrics is essential for Ambulatory Quality Directors. These metrics help in drawing conclusions about clinics and clinicians, selecting measure-based interventions, and establishing high-level quality strategies to drive better patient outcomes. - -Comparing internal results to national feedback can be challenging due to the large amount of publicly reported data and the complex variables impacting clinician results. This project addresses these challenges by analyzing internal and external performance data to validate or refute common narratives and provide clear recommendations. - -#### Problem Statement - -How can performance data help an Ambulatory Quality Director establish a measurable, attainable, and actionable quality improvement approach for the upcoming year? - -#### Project Goal - -Develop a clear recommendation for a high-level quality approach, armed with data analyses against false narratives, and provide insights into specific clinicians and clinics that could benefit from increased attention and guidance. - -#### Research Questions - -1. How does clinician performance compare to national benchmarks and internal targets? -2. Are there patterns of performance associated with specific locations? -3. Are there patterns of performance associated with patient panel sizes? -4. What is the relationship between clinician roles (MD, NP, PA) and their performance? -5. Are there any outlier clinicians or practices? -6. How can the insights be translated into actionable strategies for quality improvement? - -#### Analysis and Approach - -The analysis involves comparing internal de-identified clinician quality results with CMS benchmarking data, examining the impact of clinic environments and patient panel sizes, and creating a regression model to predict clinician performance. - -#### Key Findings and Recommendations - -- Set 2024 Target Goals at the 75th percentile of current performance. -- Share 2024 Stretch Goals at the 90th percentile with high-performers. -- Perform site visits to both high and low-performing clinics to identify scalable practices and address specific challenges. -- Review physician oversight policies for mid-level clinicians to ensure adequate support. -- Use a communication plan to address incorrect narratives and encourage data-driven decision-making. - -#### Limitations and Future Work - -- The dataset only covers 2023, limiting the scope of the analysis. -- Further iterations and professional consultation could enhance the models and understanding of advanced statistical techniques. - -## How to Use - -To view the contents: - -1. **Coding Approach:** Open the document to explore the statistical methods and R code used in the project. -2. **Final Paper:** Read the comprehensive paper for detailed findings, analyses, and recommendations. - -Feel free to contact me for any questions or further information. - -Thank you for exploring my Statistical Analyses projects! Continue to check out other sections of my portfolio for more advanced work and projects. - -