Analyzing Factors and User Demographics Influencing No-Show Rates in VISN Clinics

Patient no-shows were quietly eroding clinic efficiency and patient care. Leadership lacked clarity on why attendance was dropping and what levers could improve it. By analyzing years of historical data, Lunexa Insights uncovered the patterns and demographics driving no-shows, giving clinics the insight to act fast, reduce wasted appointments, and strengthen care delivery.

Context

The U.S. Department of Veterans Affairs manages a large network of healthcare facilities under its Veterans Integrated Services Networks (VISNs). Missed appointments strain clinical resources, impact patient outcomes, and contribute to inefficiencies. The VEText initiative was implemented to reduce no-shows via appointment reminders, but further analysis was needed to understand patterns across clinical groups, facilities, and demographics.

Objective

To analyze historical appointment data across VISN clinics from FY17 to FY21 to identify trends and factors contributing to patient no-show rates, with the goal of improving attendance and operational efficiency. The analysis encompasses all 50 states, targeting the 2018 influenza season and making use of historical data to predict staffing needs.

Role

Lead Data Analyst (designed and implemented all reporting and analysis)

Duration

6 weeks (data exploration, metric development, analysis and visual dashboard delivery)

Tools & Methodologies

SQL, Excel, Power BI
Time-Series Analysis: Measured month-over-month no-show rates from FY17 to FY21 across all VISN clinics to observe long-term trends.
Stratified Segmentation: Grouped data by clinical category (Mental Health, Primary Care, Specialty Care), demographics, and geography to isolate high-risk populations.
Comparative Analysis: Benchmarked clinic and VISN-level no-show rates against national averages.
Categorical Cross-Tabulation: Evaluated no-show behavior based on appointment attributes such as time of day and appointment duration.
Outlier Detection: Flagged facilities and stop codes with consistently elevated no-show rates (>13%) for focused investigation.

The Approach and Process

Data Analysis

Data Collection & Preprocessing

Extracted appointment-level data from the VEText system covering FY17–FY21.
Filtered records to include completed and missed appointments across all VISNs.
Ensured completeness of fields: patient demographics, clinical group, VISN/facility, appointment time, stop codes, and length.

National No-Show Trend Calculation

Grouped data by month and fiscal year.
Calculated national average no-show rate for each time point.
Created visual trend lines to assess seasonality and long-term shifts.

Segmentation by Clinical Group

Labeled appointments into three main categories: Mental Health, Primary Care, and Specialty Care (All Other) using primary stop code groupings.
Calculated and plotted average no-show rate trends for each clinical group over time.

VISN-Level and Facility-Level Analysis

Aggregated data by VISN and facility to identify regions consistently above the national average (>10–13%).
Created heatmaps and tables to highlight high-risk VISNs (e.g., VISN 04, 12, and 22).
Focused further on individual facilities within these VISNs for detailed review.

Demographic Risk Profiling

Grouped data by:

Age brackets
Race and ethnicity
Gender identity
Rurality status

2. Computed no-show rates by group and ranked by risk.

3. Identified significantly at-risk groups (e.g., patients under 35, Black and Indigenous patients, transgender/non-binary individuals, highly rural areas).

Appointment Attributes Analysis

Time of Day: Grouped by appointment hour blocks (e.g., 0700–0800, 1500–1600), calculated no-show rates, and visualized trends.
Length of Appointment: Grouped durations into 10-minute buckets and compared rates. Longer durations correlated with higher no-show rates.
Stop Codes: Analyzed both primary and secondary stop codes to identify services with the highest missed appointment rates (e.g., substance use, mental health, HUD/VASH).

Outlier Detection

Flagged stop codes, facilities, and demographic segments with consistently high no-show rates (>15%).
Validated findings by cross-checking across VISNs, service lines, and demographics to ensure patterns weren’t isolated incidents.

Synthesis and Visualization

Built summary tables and comparative bar charts for each major category.
Compiled key trends into visuals for executive-level consumption.

Key Findings

Overall National Trend

Average no-show rate: 8.24%
Steady patterns with minor fluctuations over time

By Clinic Type (FY2023 as a reference):

Mental Health: 11.91%
Primary Care: 7.74%
Specialty Clinics: 7.14%
Mental health consistently had the highest no-show rates over time.

Demographic Trends:

Younger patients (<35) had significantly higher no-show rates (~13–14%) than seniors (5–7%).
Black, American Indian, and Native Hawaiian veterans had higher no-show rates (>10%) than White and Asian veterans (<8%).
Transgender and non-binary individuals reported no-show rates >11%.

Rurality & Geography:

Highly rural and urban patients showed elevated no-show patterns (~9–10%) compared to rural (~7%).
VISNs 04, 12, and 22 had facilities consistently above 13%, with some over 20% (e.g., Greater Los Angeles, CA).

Appointment Timing:

Afternoon appointments (3–5 PM) had higher no-show rates (~9%) than morning slots (~6–7%).
Extremely early or late slots (e.g., 6–7 AM or 8–9 PM) had the lowest no-show rates (<2%).

Appointment Length:

Shorter visits (10–30 minutes) had lower no-show rates (~5–9%) vs. longer appointments (60–210 mins) with up to 29.85% no-shows.

End Results & Recommendations

Recommendations

1. Targeted Interventions for Mental Health:

Double down on VEText reminders and check-in prompts for mental health appointments.
Consider follow-up calls or personalized outreach for high-risk individuals.

2. Reschedule Optimization:

Shift more appointments to morning hours for high-risk patients.
Avoid scheduling long appointments late in the day.

3. Facility-Level Strategy:

Deep-dive audits in high-no-show VISNs (e.g., VISN 22).
Consider reallocating resources or enhancing transportation and telehealth access.

4. Demographic Outreach:

Develop culturally competent outreach for minority groups with elevated no-show rates.
Include gender-inclusive messaging and community-based partnerships.

5. Data Integration:

Integrate EHR and appointment system data to automate flagging high-risk appointments.

Future Considerations

Expand analysis to post-pandemic FY22–FY25 data to account for telehealth adoption and behavioral shifts.
Test predictive models using historical no-show patterns to flag at-risk appointments in real time.
Pilot transportation or incentive programs in VISNs with persistently high no-show rates.
Link no-show data to clinical outcomes to assess downstream impact.

Conclusion

Consistent no-show challenges across mental health services, certain VISNs, and among specific demographics. By leveraging appointment timing, service type, and targeted patient characteristics, healthcare systems can design smarter interventions to lower no-show rates and improve care delivery.

Final Dashboard.

Company