How to Reduce Patient No-Shows: What the Trials Support for Multi-Location Groups

Trials support two reminders for every patient, staff calls to high-risk patients and a way to refill canceled slots. Overbooking, fees and ride offers have little or no support.

Written by the Commure Agents Team

Published: November 10, 2026

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11 min read

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What You Need to Know

  • Reduce patient no-shows by reminding every patient twice, having staff call the patients most likely to miss and refilling the slots patients cancel.
  • Reminders raised attendance in a Cochrane review¹, while staff calls to high-risk patients lowered no-show rates in two US trials²,³.
  • Overbooking, no-show fees and ride offers have little or no support in trials⁴,⁵,⁶.

How do you reduce patient no-shows?

Reduce patient no-shows with reminders, calls to the patients most likely to miss and a fast way to refill canceled slots. Reminders are the foundation¹. Staff calls add to them for high-risk patients²,³, while refilled slots recover visits that would otherwise go empty.

  1. Remind every patient by text or phone¹.
  2. Send a second reminder, such as 3 days and 1 day ahead⁷.
  3. Have staff call the patients most likely to miss.²,³
  4. Refill the slots patients cancel.⁸

Overbooking, no-show fees and ride offers have little or no support in trials⁴,⁵,⁶.

What works to reduce patient no-shows, and how strong is the evidence?

Reminders and phone calls aimed at high-risk patients have moderate-certainty evidence¹,⁴.

TacticBest evidenceEffectCertaintyCaveat
Reminder vs noneCochrane reviewAttendance 68% none, 79% text, 80% phone¹ModerateSearch to 2012
Text vs phone callReview; pooled trialsAttendance about equal¹; phone slightly better in one review⁹; no-shows not significantly different⁹ModerateNo-show data, 4 trials
Two reminders vs oneOne RCTMissed 4.4% vs 5.8% and 5.3%⁷Not gradedGain in highest-risk quarter (one system)
Stating the cost of a missed visitTwo RCTsMissed 8.4% vs 11.1%¹⁰Not gradedNHS, no patient charges
Staff calls, high-risk patientsTwo US RCTs22.8% vs 29.2%²; 32.8% vs 36.2%³Not gradedOne site each
Model-targeted phone remindersReview, 3 RCTsMedian RR 0.61, not pooled⁴ModerateStaff callers in 2 of 3 trials, third not stated; targeting vs calling all untested
Automated waitlist offersOne matched studyAcceptors seen about 15 days sooner (primary care)⁸Not gradedPortal opt-in; 8% of offers accepted
OverbookingReviewUnclear; one endoscopy trial's days ran longer⁴Very lowModel-based
No-show feesOne fine study; MGMA pollNo reduction⁵; 25% vs 16% report improvement¹¹Not gradedPoll self-reported
Ride offersOne trialMissed 36.5% vs 36.7%⁶Not gradedMedicaid, one city; low uptake
Advanced accessReviewSignificant drops in 5 of 11, all from baselines of 16% to 43%¹²Not gradedAll at risk of bias; mostly before-after

The evidence supports combining tactics instead of adding more of one. Every patient gets two standard reminders, the cadence one trial tested⁷, while patients above a risk threshold also get a live staff call.

Who places the call is less settled. Three studies in one review compared staff and automated reminder calls, with mixed results⁵. Yet in another study in the same review, human reminder calls in patients' preferred language improved attendance⁵.

Federal rules also limit automated reminders. The FCC treats AI-generated voices as "artificial or prerecorded voice" under the Telephone Consumer Protection Act (TCPA)¹³. Even so, an FCC healthcare exemption may allow a healthcare provider's automated or AI-voice reminder calls and texts to cell phones without prior express consent¹⁴. Every condition must be met¹⁴:

  • Messages must go only to the wireless number the patient provided and must cost the patient nothing¹⁴.
  • Every call and text must state the provider's name and contact information, which a voice call must give at the start¹⁴.
  • Messages must comply with HIPAA privacy rules and carry no telemarketing, solicitation, advertising, accounting, billing, debt-collection or other financial content¹⁴.
  • Purposes are limited to ones such as "appointment and exam confirmations and reminders" and wellness checkups¹⁴.
  • Voice calls must be "generally one minute or less"¹⁴. Texts must be 160 characters or less¹⁴.
  • The provider may send one message a day and three a week to each patient, calls and texts combined¹⁴.
  • Each message needs an easy opt-out, honored immediately¹⁴. Calls a person may answer need a voice or key-press opt-out, while calls that may reach voicemail need a toll-free opt-out number¹⁴. Texts must tell recipients they can reply "STOP"¹⁴.

Because landline messages and consent-based programs follow separate rules, confirm with counsel which rules your program relies on¹⁴.

What does a lower no-show rate look like when it holds?

Staff calls from schedulers and patient service coordinators lowered high-risk patients' no-show rates by about 3 to 6 percentage points in two US trials²,³. Both trials added the calls to automated reminders and used a risk model to pick who got a call²,³.

With staff calls a week ahead, one academic primary care practice cut high-risk patients' no-show rate to 22.8%, against 29.2% with automated calls alone². Even so, completed visits didn't rise significantly².

One safety-net clinic saw a smaller drop³. Scheduler calls on top of automated reminders brought its no-show rate to 32.8%, against 36.2%, in a trial that counted same-day cancellations as no-shows³.

Staff calls also moved cancellations earlier. Patients assigned to the academic practice's staff calls canceled or rescheduled about 8 hours earlier on average². The trial didn't measure whether those slots were refilled².

Refilled slots can get patients seen sooner, but rebooking was low where studies measured it. When one health system's automated waitlist offered open slots to patients who opted in, those who accepted were seen about 15 days sooner in primary care⁸. Patients accepted about 8% of the waitlist's offers, which went by text or email to portal users⁸. In MGMA data, about 27% of canceled visits, including visits practices canceled, were rescheduled within 30 days¹⁵.

Why doesn't adding more reminders keep lowering no-shows?

Extra reminders reach a limit for two reasons. Their gain may concentrate in the patients most likely to miss, while some misses have causes a reminder can't reach.

Gains from extra reminders are uneven. A second reminder mostly helped the highest-risk quarter of patients in one large trial, with risk scored by a model built from the trial's own data⁷. It prevented about one missed visit for every 25 of those patients, against about one for every 1,328 others⁷. In one meta-analysis, trials with more than one text reminder raised attendance more than trials with one, yet their no-shows didn't fall more⁹.

Practice leaders in MGMA's reporting describe misses that reminders don't reach¹⁶,¹⁵. One practice leader told MGMA that patients who don't plan to come still miss, however many reminders they get¹⁶. Comments in MGMA's 2026 poll cite patient costs, transportation, work conflicts and weather¹⁵.

Groups with rising rates in MGMA's 2026 poll listed many of the same tactics as groups whose rates held or improved¹⁵. The shared tactics included text and phone reminders, confirmation calls and no-show fees¹⁵.

Other fixes showed no effect when tested. In an NHS trial, adding the cancellation phone number to a text reminder didn't reduce missed appointments¹⁰. Free rideshare offers to Medicaid patients in one city left missed visits unchanged, although only about 1 in 5 patients reached used a ride⁶.

Two no-show predictors are partly within each group's control. At one academic system, appointments the provider had rescheduled were the top no-show predictor in 12 of 14 specialties¹⁷. A systematic review's abstract lists long lead time among the most commonly reported predictors¹⁸. Lead time also fed the risk model in one safety-net trial³. Neither is a proven cause.

How common are patient no-shows, and what do they cost?

There's no single average no-show rate, because rates vary by specialty and by whether same-day cancellations count¹⁷,¹⁹. A no-show is a booked visit the patient neither attends nor cancels in time to reuse its slot¹⁷,¹⁹. Any plan to reduce patient no-shows starts with what your rate includes.

Published rates vary widely. MGMA's summary put its single-specialty practice aggregate at 6.81% in 2023 but didn't say whether that rate counts same-day cancellations¹¹. One academic health system did count them, with rates from 13% in urogynecology to 32% in pulmonary and allergy clinics in 2014 to 2016¹⁷.

Two studies using 2008 and 2009 data put a dollar figure on no-shows¹⁹,²⁰. One VA medical center put each no-show at $196 on average, based on average cost per visit¹⁹. A simulation of one academic endoscopy suite put an 18% no-show rate at about $725 a day²⁰. Your own revenue per visit is a closer guide than either figure. It reflects your payer mix.

Where do Commure AI Call Center Agents fit in a no-show reduction plan?

Commure AI Call Center Agents work on both sides of a no-show plan. They take patients' calls to confirm, cancel or reschedule at any hour. They can also place outbound reminder calls, run recall campaigns and re-engage patients who missed visits, although deployments typically start with inbound workflows.

Patients can call 24/7/365 in English or Spanish. Commure Agents verify each patient's identity, then confirm, cancel or reschedule the visit, offering open times within scheduling rules set during scoping. Take a patient who decides at 10 p.m. that tomorrow won't work. The patient can cancel and rebook on that call.

Commure reports that 20%+ of inbound calls go unanswered, resulting in missed appointments and missed revenue. Because the platform takes many calls at once, cancellation calls get through during surges without queueing or busy signals. When a patient cancels, the agents state any cancellation policy that applies. They document the reason where your workflow requires it, then update availability instantly, which opens the slot for other patients to book.

Requests outside your rules go to staff, an answering service or a follow-up workflow, with messages logged as structured tasks.

Commure reports typical results of a 30%+ reduction in inbound calls handled by your team. Fewer inbound calls can free schedulers for the staff calls to high-risk patients the trials tested. To see how many confirm, cancel and reschedule calls reach your team today, request a complimentary call center health analysis.

How should a group practice start reducing no-shows?

Reduce patient no-shows across sites by first defining and segmenting your rate. Then aim live staff calls at high-risk patients and track how many freed slots get rebooked.

  1. Write down your definition. Decide whether same-day cancellations count, then use the same rule at every site.
  2. Report the rate by specialty, site, visit type and lead time.
  3. Track rebooking. Measure the share of canceled and missed visits rebooked within 30 days. MGMA's 27% figure uses that window but covers canceled visits only¹⁵.
  4. Audit provider-initiated reschedules. Count how often providers move visits, by site and specialty.
  5. Set a two-reminder cadence, such as 3 days and 1 day ahead⁷. Automated calls and texts to cell phones without prior express consent may rely on the FCC's healthcare exemption only if they meet every condition¹⁴. The conditions include one message a day and three a week per patient, calls and texts combined¹⁴. Ask counsel how the rule treats the rest of your program.
  6. Pick a risk threshold and budget staff calls for patients above it. Staff in both US trials called patients at 15% or higher predicted risk, on top of automated reminders²,³. The 15% cutoff covered about 11% of screened patients at the academic practice but over 70% at the safety-net clinic²,³. Set yours by how many calls your staff can make. At one safety-net clinic, schedulers estimated calling 15 to 20 patients took under an hour a day³. Use your own risk model or your EHR vendor's, which the safety-net trial used³.
  7. Open a cancel-and-rebook path at all hours.
  8. Review the outbound program with counsel before automated or AI-voice reminder calls start. State rules may add limits beyond the federal ones. Cover recording disclosures in the same review, because some states require every party's consent to record certain calls. HHS advises leaving only limited information on answering machines, such as a name, a call-back number and what's needed to confirm the visit²¹.
  9. Get a baseline of the confirm, cancel and reschedule calls your team handles today. A complimentary call center health analysis breaks down call volume by type, length, category and outcome. Read it alongside the healthcare call center metrics you already track.

Sources

  1. Gurol-Urganci, I., de Jongh, T., Vodopivec-Jamsek, V., Atun, R., & Car, J. (2013). Mobile phone messaging reminders for attendance at healthcare appointments. Cochrane Database of Systematic Reviews, (12), CD007458. https://doi.org/10.1002/14651858.CD007458.pub3
  2. Shah, S. J., Cronin, P., Hong, C. S., et al. (2016). Targeted reminder phone calls to patients at high risk of no-show for primary care appointment: A randomized trial. Journal of General Internal Medicine, 31(12), 1460–1466. https://pmc.ncbi.nlm.nih.gov/articles/PMC5130951
  3. Tarabichi, Y., Higginbotham, J., Riley, N., Kaelber, D. C., & Watts, B. (2023). Reducing disparities in no show rates using predictive model-driven live appointment reminders for at-risk patients: A randomized controlled quality improvement initiative. Journal of General Internal Medicine, 38(13), 2921–2927. https://pmc.ncbi.nlm.nih.gov/articles/PMC10150669/
  4. Oikonomidi, T., Norman, G., McGarrigle, L., Stokes, J., van der Veer, S. N., & Dowding, D. (2023). Predictive model-based interventions to reduce outpatient no-shows: A rapid systematic review. Journal of the American Medical Informatics Association, 30(3), 559–569. https://pmc.ncbi.nlm.nih.gov/articles/PMC9933067/
  5. Werner, K., Alsuhaibani, S. A., Alsukait, R. F., Alshehri, R., Herbst, C. H., Alhajji, M., & Lin, T. K. (2023). Behavioural economic interventions to reduce health care appointment non-attendance: A systematic review and meta-analysis. BMC Health Services Research, 23, 1136. https://pmc.ncbi.nlm.nih.gov/articles/PMC10594857/
  6. Chaiyachati, K. H., Hubbard, R. A., Yeager, A., et al. (2018). Association of rideshare-based transportation services and missed primary care appointments: A clinical trial. JAMA Internal Medicine, 178(3), 383–389. https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2671405
  7. Steiner, J. F., Shainline, M. R., Dahlgren, J. Z., Kroll, A., & Xu, S. (2018). Optimizing number and timing of appointment reminders: A randomized trial. American Journal of Managed Care, 24(8), 377–384. https://www.ajmc.com/view/optimizing-number-and-timing-of-appointment-reminders-a-randomized-trial
  8. Chung, S., Martinez, M. C., Frosch, D. L., Jones, V. G., & Chan, A. S. (2020). Patient-centric scheduling with the implementation of health information technology to improve the patient experience and access to care: Retrospective case-control analysis. Journal of Medical Internet Research, 22(6), e16451. https://www.jmir.org/2020/6/e16451/
  9. Robotham, D., Satkunanathan, S., Reynolds, J., Stahl, D., & Wykes, T. (2016). Using digital notifications to improve attendance in clinic: Systematic review and meta-analysis. BMJ Open, 6(10), e012116. https://bmjopen.bmj.com/content/6/10/e012116
  10. Hallsworth, M., Berry, D., Sanders, M., Sallis, A., King, D., Vlaev, I., & Darzi, A. (2015). Stating appointment costs in SMS reminders reduces missed hospital appointments: Findings from two randomised controlled trials. PLOS ONE, 10(9), e0137306. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0137306
  11. MGMA Staff. (2025, January). No-show fees in medical practices on the rise to balance bumpy attendance rates. MGMA Stat. Medical Group Management Association. https://www.mgma.com/mgma-stat/no-show-fees-in-medical-practices-on-the-rise-to-balance-bumpy-attendance-rates
  12. Rose, K. D., Ross, J. S., & Horwitz, L. I. (2011). Advanced access scheduling outcomes: A systematic review. Archives of Internal Medicine, 171(13), 1150–1159. https://pmc.ncbi.nlm.nih.gov/articles/PMC3154021/
  13. Federal Communications Commission. (2024, February 8). Declaratory ruling: Implications of artificial intelligence technologies on protecting consumers from unwanted robocalls and robotexts (FCC 24-17, CG Docket No. 23-362). https://docs.fcc.gov/public/attachments/FCC-24-17A1.pdf
  14. Office of the Federal Register. (n.d.). 47 CFR 64.1200: Delivery restrictions. Electronic Code of Federal Regulations. https://www.ecfr.gov/current/title-47/chapter-I/subchapter-B/part-64/subpart-L/section-64.1200
  15. Harrop, C. (2026, August). About 1 in 3 medical groups see higher no-shows in 2026 as patients face higher costs. MGMA Stat. Medical Group Management Association. https://www.mgma.com/mgma-stat/about-1-in-3-medical-groups-see-higher-no-shows-in-2026-as-patients-face-higher-costs
  16. MGMA Patient Experience Insights. (2026, August 13). Beyond reminders: Segment for no-shows you can't fix. Medical Group Management Association. https://www.mgma.com/articles/beyond-reminders-segment-for-no-shows-you-cant-fix
  17. Ding, X., Gellad, Z. F., Mather, C., 3rd, et al. (2018). Designing risk prediction models for ambulatory no-shows across different specialties and clinics. Journal of the American Medical Informatics Association, 25(8), 924–930. https://pmc.ncbi.nlm.nih.gov/articles/PMC6077778
  18. Dantas, L. F., Fleck, J. L., Cyrino Oliveira, F. L., & Hamacher, S. (2018). No-shows in appointment scheduling: A systematic literature review. Health Policy, 122(4), 412–421. https://doi.org/10.1016/j.healthpol.2018.02.002
  19. Kheirkhah, P., Feng, Q., Travis, L. M., Tavakoli-Tabasi, S., & Sharafkhaneh, A. (2016). Prevalence, predictors and economic consequences of no-shows. BMC Health Services Research, 16, 13. https://pmc.ncbi.nlm.nih.gov/articles/PMC4714455/
  20. Berg, B. P., Murr, M., Chermak, D., et al. (2013). Estimating the cost of no-shows and evaluating the effects of mitigation strategies. Medical Decision Making, 33(8), 976–985. https://pmc.ncbi.nlm.nih.gov/articles/PMC4153419
  21. U.S. Department of Health and Human Services, Office for Civil Rights. (last reviewed 2022, December 28). May physician's offices or pharmacists leave messages for patients at their homes, either on an answering machine or with a family member, to remind them of appointments or to inform them that a prescription is ready? (HIPAA FAQ 198). https://www.hhs.gov/hipaa/for-professionals/faq/198/may-health-care-providers-leave-messages/index.html
  22. Centers for Medicare & Medicaid Services. (2007, June 29; updated 2014, November 12). Charges for missed appointments (MLN Matters MM5613). https://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNMattersArticles/Downloads/MM5613.pdf
  23. Centers for Medicare & Medicaid Services. (1995, June 1). Billing and missed appointments [Guidance document]. U.S. Department of Health and Human Services. https://www.hhs.gov/guidance/document/1995-billing-missed-appointments

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