Daily Summaries 

Tuesday – 4/14/2026

On this day, I continued the outlier analysis by sorting through the Appliance Delivery, Crown, and Crown Delivery appointment categories for both Dr. Zaleski and Dr. Leffert. For each outlier identified, I reviewed the patient’s check-in time, appointment notes, and the surrounding schedule to assess whether other appointments were running late or if double-booking may have contributed to the extended appointment time. I also examined whether any additional procedures were completed during the visit that could account for the longer duration. In the afternoon, I continued this same process for the Exam appointment category, which represented the largest volume of appointments in the dataset. I worked through all of Dr. Leffert’s Exam appointment outliers from 2026, carefully reviewing each for patient arrival lateness, schedule congestion, and any additional procedures performed that may have contributed to appointments running beyond their scheduled time.

Thursday – 4/16/2026

On this day, I continued the outlier analysis by working through Dr. Zaleski’s Exam appointment outliers from 2026, applying the same review process of examining patient check-in times, appointment notes, the surrounding schedule for overlapping or double-booked appointments, patient arrival lateness, and any additional procedures performed during the visit. After completing the Exam category, I met with Larissa in IT to review the appointment timing report she had pulled for this project. We went through the data together to confirm the reporting structure aligned with my analysis goals and to clarify how the information could best support the scheduling improvement project going forward. In the afternoon, I continued the outlier review process by sorting through the Extraction and Follow-Up appointment categories for both providers, examining each outlier for the same contributing factors to identify patterns behind why those appointments ran beyond their scheduled time.

Summary of Week 13 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 continued preparing and systematically analyzing appointment timing data across multiple appointment categories. I organized the outlier appointments for Appliance Delivery, Crown, Crown Delivery, and Exam categories by provider and reviewed each one individually to investigate the source of the scheduling irregularity. By examining check-in times, appointment notes, surrounding schedule congestion, and any additional procedures performed, I ensured that the data preparation and analysis process was both structured and thorough.
• 4.4 Interpret data
  • 4.4.1 Explain how findings address the questions and/or hypotheses
  • 4.4.4 Draw conclusions based on findings
    • The outlier review process this week moved beyond data organization into active interpretation. By connecting each irregular appointment duration to a specific contributing factor (such as patient lateness, a double-booked schedule, or an additional procedure) I began drawing early conclusions about the root causes of scheduling overruns across the most common appointment types. These findings are directly informing the project’s core question of where and why scheduling inefficiencies occur.
• 1.2 Obtain primary data, secondary data, and other evidence-informed sources
  • 1.2.2 Establish collaborative relationships and agreements that facilitate access to data
    • This week, I continued strengthening my collaborative relationship with Larissa, the Clinical Informatics Analyst, by meeting with her to review the appointment timing report she pulled for the project. This meeting helped confirm that the report structure aligned with the needs of my analysis and ensured that the data being extracted from the clinic’s system would be usable and meaningful for the scheduling improvement project. Maintaining this ongoing working relationship is essential to accessing the data necessary to support the project’s goals.
• 7.1 Coordinate relationships with partners and stakeholders
  • 7.1.3 Involve partners and stakeholders throughout the health education and promotion process
  • 7.1.4 Maintain ongoing engagement with stakeholders (referenced from Week 10 framing)
    • This week, I continued to actively involve Larissa as a key stakeholder in the project by meeting with her to review the pulled report and align our understanding of the data. Rather than working in isolation, I consistently engaged her throughout the analytical process to ensure the final report will reflect both the clinical insights I am developing and the informatics capabilities of her team.
• 2.4 Develop plans and materials for implementation and evaluation
  • 2.4.3 Address factors that influence implementation
    • A central focus of this week’s analysis was identifying the specific factors (including patient arrival time, schedule overlap, double-booking, and additional procedures) that cause appointments to exceed their scheduled duration. Recognizing and documenting these factors across multiple appointment categories is a foundational step toward developing realistic scheduling recommendations that reflect the actual conditions of clinical practice.

Week 13 Reflection 

During my thirteenth week with the UGA UHC Dental Clinic, I continued working through the appointment outlier analysis across several appointment categories, including Appliance Delivery, Crown, Crown Delivery, and Exam appointments for both providers. I also met with the Clinical Informatics Analyst to review the report she pulled and began sorting through the Extraction and Follow-Up categories. Through this work, I learned how many different variables can quietly shape a single appointment’s outcome. Patient arrival time, schedule congestion, double-booking, and additional procedures performed can individually or compound to cause an appointment to run significantly longer than planned. What stood out most to me was how rarely a single cause is responsible for an overrun. In most cases, I found that the outliers were the result of overlapping circumstances, which made the investigation feel less like data sorting and more like piecing together a clinical story for each patient visit.

This matters because scheduling in a healthcare setting directly affects the quality of care patients receive and the working conditions of providers. When appointments consistently run long without a clear systemic understanding of why, the burden falls on providers and staff to absorb the inefficiency without any structural support. By identifying patterns in why these overruns occur, this project has the potential to offer real, evidence-based adjustments that could reduce that burden and improve the overall flow of the clinic. It is also reinforcing to me how much operational health of a clinic depends on the quality of its data management and the willingness of different departments, including clinical, IT, and administrative, to work together.

Moving forward, I plan to continue working through the remaining appointment categories and complete the outlier analysis across all procedure types. Once the full picture is assembled, I will need to begin synthesizing the individual findings into broader patterns and trends that can be communicated as actionable scheduling recommendations. I am also thinking about how to best present these findings to stakeholders in a way that is clear and useful to both clinical and non-clinical audiences.