Emma Nodine

Daily Summaries 

Tuesday – 04/07/2026

On this day, I continued  my appointment timing analysis by adding columns to the appointment spreadsheet I made for total appointment time, the difference between scheduled and utilized appointment time, and overall appointment duration. I then organized all appointments since 12/31/2025 by appointment category, then by provider, and then by length of time. This organization allowed me to begin identifying any potential patterns and trends in how appointment time is used across different procedure types and providers, with the goal of generating data-driven scheduling improvement suggestions. I continued this analysis throughout the afternoon, further examining the data for potential trends.

Thursday – 04/09/2026

On this day, I sorted the appointment data within each category and provider by time to determine the most common procedure duration for each appointment type. After establishing these baseline timing patterns, I used formulas in the excel document to identify statistical outliers within each category and cross-referenced each outlier with the corresponding patient’s chart to investigate the cause of the irregular timing for each appointment. In the afternoon, I began going through each patient with an outlier scheduling gap in depth, working to identify the specific reasons and any recurring patterns behind why those appointments ran beyond their scheduled time.

Summary of Week 12 with Competencies 

• 4.3 Manage the collection and analysis of evaluation and/or research data using appropriate technology
  • 4.3.5 Prepare data for analysis
  • 4.3.6 Analyze data
    • This week, I focused heavily on preparing and analyzing appointment timing data to support scheduling improvement efforts. I added calculated columns to the appointment dataset, including total appointment time, scheduled vs. utilized time difference, and appointment duration, and then organized and grouped the data by category, provider, and time. I then analyzed the data to identify the most common procedure durations and locate statistical outliers, forming the core of the data analysis phase of this project.
• 4.4 Interpret data
  • 4.4.1 Explain how findings address the questions and/or hypotheses
  • 4.4.4 Draw conclusions based on findings
    • After identifying outlier appointments in the timing data, I investigated the root cause of each by reviewing individual patient charts. This process moved beyond data collection into active interpretation, connecting scheduling irregularities to specific clinical or patient-related factors. This work is directly informing early conclusions about why appointment overruns occur and what patterns may be contributing to scheduling inefficiency.
• 1.3 Analyze the data to determine the health of the priority population(s) and the factors that influence health
  • 1.3.6 List the needs of the priority population(s)
    • By examining provider-level scheduling data and identifying where appointment time is consistently over- or underutilized, I began identifying operational needs within the clinic’s scheduling system. Understanding which procedure categories and providers are most affected by timing discrepancies is a key step toward defining what improvements are needed and for whom they are most relevant.
• 2.4 Develop plans and materials for implementation and evaluation
  • 2.4.3 Address factors that influence implementation
    • The outlier analysis this week involved identifying specific factors, such as patient complexity, procedure type, or provider variation, that cause appointments to deviate from their scheduled time. Recognizing these factors is essential to developing realistic and effective scheduling recommendations that account for the realities of clinical practice.

Week 12 Summaries

 

During my twelfth week with the UGA UHC Dental Clinic, I advanced significantly in the data analysis phase of the scheduling improvement project. At the start of the week, I expanded the appointment timing dataset by adding calculated columns for total appointment time, the difference between scheduled and utilized time, and appointment duration. I then reorganized the full dataset of  all appointments since 12/31/2025 by appointment category, provider, and length of time. This structured approach enabled me to begin identifying meaningful patterns and trends in how appointment time is allocated and used across different procedure types and providers.

I then deepened the analysis by sorting appointment data within each category per provider to determine the most common duration for each procedure type. I then identified outliers in the timing data and investigated each one individually by reviewing the corresponding patient charts. This chart review process allowed me to begin connecting scheduling irregularities to specific causes, whether related to patient complexity, procedure variation, or other clinical factors.

By the end of the week, I had begun compiling a picture of why certain appointments consistently run over their scheduled time and what patterns underlie those overruns. This outlier analysis is a critical component of building evidence-based scheduling recommendations, as it moves the project from broad data organization into targeted, root-cause investigation. Overall, Week 12 was defined by rigorous data preparation, pattern identification, and the beginning of meaningful interpretation that will directly inform scheduling improvement suggestions for the clinic.