Key Takeaways
- 14.0% of adults reported not getting needed care due to costs in 2023, meaning a portion of demand never enters the care pipeline (effectively affecting waiting lists).
- 35% of patients in a 2021 Canadian survey reported waiting longer than expected for specialist appointments, indicating expectation gaps can worsen perceived delays.
- 3.0 million people were waiting for elective surgery in England in October 2023, meaning the backlog scale exceeded 2023 single-digit millions.
- 1.8x higher odds of appointment delay were found for rural residents vs urban residents in a 2020 peer-reviewed U.S. study, contributing to uneven waiting list experiences.
- In England, the number of consultant-led referrals was 6.8 million in 2023/24, contributing to inflows into elective waiting lists.
- 6.2x growth in ransomware-related cyber incidents targeting healthcare organizations from 2019 to 2023 was reported by IBM, meaning operational disruptions can worsen patient flow and waiting.
- 27% of patients reported they had to repeat information when scheduling care in 2020 (survey), meaning administrative friction increases appointment lead times.
- Wait-time reductions of 20–40% were reported for lean/flow redesign in outpatient clinics across multiple trials (systematic review, 2021), meaning process changes can shorten queues.
- 4.4% of clinic-level patient encounters in a 2022 EHR-based study experienced delayed scheduling beyond 14 days, meaning queueing was measurable in routine workflows.
- 9.4% reduction in no-show rates after implementing automated reminders was reported in a 2020 systematic review/meta-analysis, improving throughput and reducing waits.
- Automatic SMS reminders increased appointment adherence by 4.9 percentage points in a 2019 systematic review, reducing queue bottlenecks.
- $8.2 billion estimated annual economic burden in the U.S. attributable to delayed care (system-level estimate, 2021), meaning waiting has measurable cost consequences.
- U.S. healthcare IT spending was $198.0 billion in 2023, indicating budgets for systems that manage scheduling and queueing.
- The global healthcare analytics market was valued at $40.3 billion in 2023, indicating spending capacity for analytics that can optimize waitlists.
- The global patient scheduling and appointment management software market was forecast to grow from $4.3 billion in 2023 to $9.6 billion by 2030, indicating demand for queue management systems.
Costs and operational shocks are shrinking access, making elective and specialist waiting persist despite better scheduling tools.
Related reading
01 · Category
User Adoption2 stats
User Adoption Interpretation
02 · Category
Industry Trends4 stats
Industry Trends Interpretation
03 · Category
Operational Impact5 stats
Operational Impact Interpretation
04 · Category
Performance Metrics4 stats
Performance Metrics Interpretation
05 · Category
Cost Analysis2 stats
Cost Analysis Interpretation
06 · Category
Market Size2 stats
Market Size Interpretation
More related reading
07 · Category
Clinical Bottlenecks1 stats
Clinical Bottlenecks Interpretation
08 · Category
Capacity Constraints2 stats
Capacity Constraints Interpretation
09 · Category
Market Adoption2 stats
Market Adoption Interpretation
10 · Category
Waiting Time Measurement1 stats
Waiting Time Measurement Interpretation
11 · Category
Economic And Operational Impacts3 stats
Economic And Operational Impacts Interpretation
How big is the waitlist problem?
Backlogs and demand gaps show both scale (patients waiting) and pressure points (people who never reach care, plus longer-than-expected waits).
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
David Sutherland. (2026, February 13). Waitlist Statistics. Gitnux. https://gitnux.org/waitlist-statistics
David Sutherland. "Waitlist Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/waitlist-statistics.
David Sutherland. 2026. "Waitlist Statistics." Gitnux. https://gitnux.org/waitlist-statistics.
Sources & references
28 datasets cited across this report · attribution is report-level
+8 additional datasets cited (not shown individually)

