Patient Appointment No-show Logs

This dataset provides detailed logs of scheduled healthcare appointments, including patient demographics, appointment details, reminders, and attendance outcomes (attended, no-show, or cancelled). It enables comprehensive analysis of patient attendance patterns, supports predictive modeling for no-shows, and helps optimize healthcare resource allocation.

  • last updated 12 Jul 2025
  • by GoMask
The brief that made it

Predicting patient no-shows to improve scheduling efficiency

Sample rows

preview · 8 of 200 rows · all 26 columns
appointment_idstringbooking_channelstringno_showbooleanaddress_statestringpatient_idstringpatient_namestringpatient_dobdatepatient_genderstringpatient_phonestringappointment_datedateappointment_timestringappointment_typestringdepartmentstringprovider_idstringprovider_namestringscheduled_datetimedatetimereminder_sentbooleanreminder_methodstringreminder_datetimedatetimecancellationbooleancancellation_datetimedatetimeaddress_streetstringaddress_citystringaddress_postal_codestringaddress_countrystringnotesstring
APT000001onlinefalseblankPAT10001Ava Müller2012-03-21female+49 151 234567892024-04-1514:30immunizationpediatricsDOC201Dr. Marie Schmidt2024-03-20T10:15:00trueSMS2024-04-13T09:00:00falseblankLuisenstrasse 7Berlin10117GermanyAccompanied by parent
APT000002phonefalseCAPAT10002Noah Lee1952-10-13male(415) 222-33442024-05-2209:15annual checkupgeriatricsDOC129Dr. Raymond Green2024-05-01T13:40:00truephone call2024-05-20T15:45:00falseblank1212 Market StSan Francisco94102USANeeds wheelchair
APT000003appfalseblankPAT10003Lucas Dupont1989-07-30male+33 1 44 55 66 772024-06-0411:00consultationdermatologyDOC112Dr. Sophie Laurent2024-05-10T16:00:00trueemail2024-06-02T08:00:00falseblank14 Rue de RivoliParis75004FrancePrefers online communication
APT000004in-personfalseblankPAT10004Emma O’Connor2008-12-07female+44 20 7946 09582024-01-2716:45screeningophthalmologyDOC145Dr. Karen Patel2023-12-18T11:25:00trueSMS2024-01-24T10:00:00falseblank22 Queen's RdLondonSW1A 1AAUKCheck for allergies
APT000005onlinefalseblankPAT10005Fatima Al Fahad1985-04-10female+971 4 123 45672024-07-1010:15consultationobstetricsDOC222Dr. Munira Khalifa2024-06-15T09:30:00trueSMS2024-07-08T18:45:00falseblankSheikh Zayed Rd 88Dubai00000UAEPrefers female provider
APT000006appfalseblankPAT10006Oscar Rivera1996-01-27male+34 91 234 56782024-03-1313:00screeningcardiologyDOC153Dr. Marta Reyes2024-02-15T14:10:00trueSMS2024-03-11T17:00:00falseblankCalle Mayor 11Madrid28013SpainFirst-time patient
APT000007referralfalseblankPAT10007Jin Park1977-09-10male+82 2-312-12342024-09-2408:25consultationneurologyDOC188Dr. Hyun Kim2024-08-30T12:00:00falseblankblankfalseblankGangnam-daero 320Seoul06040South KoreaProvider not yet assigned
APT000008in-personfalseblankPAT10008Charlotte White2010-05-09femaleblank2024-02-1917:50vaccinationpediatricsDOC215Dr. Anne-Pauline Chevalier2024-01-29T15:20:00falseblankblankfalseblank123 Abbey RdLondonNW8 9AYUKMinor, accompanied by parent

What the 200 rows show

from the 200-row sample

In-person (booking channel) stands out: 12 of its 32 rows have no_show = true, against 8 of 168 for the rest.

  • 10%no_show = true
  • 3reminder methods
  • 4patient genders
  • 9appointment types
  • 14departments
  • 45address countries
No show rate by booking_channelno_show = true
0%50%100%5%onli…3 of …0%phone0 of …38%in-p…12 of…13%app4 of …0%refe…0 of …5%walk…1 of …0%mobi…0 of 3
address_state50 rows with a value · 150 left blank
  1. NY7
  2. BE5
  3. CA4
  4. RM3
  5. ON3
  6. SP3
  7. MH3
  8. MD3
  9. BY3
  10. FL1
26 columns by typefrom the column list below
  • string 18
  • date 2
  • datetime 3
  • boolean 3

Columns

26 columns in three groups
blueprint · 26 columns
columntypedescriptionexample
Text 18 columns
appointment_idstringUnique identifier for each scheduled appointment.uniqueAPT000001
patient_idstringUnique identifier for the patient.PAT10001
patient_namestringFull name of the patient.optionalAva Müller
patient_genderstringGender of the patient.male · female · other · unknown · optionalfemale
patient_phonestringContact phone number for the patient.optional+49 151 23456789
appointment_timestringScheduled time of the appointment (HH:MM, 24-hour format).14:30
appointment_typestringType of appointment (e.g., consultation, follow-up, screening).9 types · optionalimmunization
departmentstringDepartment or specialty for the appointment (e.g., cardiology, pediatrics).optionalpediatrics
provider_idstringUnique identifier for the healthcare provider assigned to the appointment.optionalDOC201
provider_namestringName of the healthcare provider assigned to the appointment.optionalDr. Marie Schmidt
booking_channelstringChannel used to book the appointment (e.g., phone, online, in-person).7 channels · optionalonline
reminder_methodstringMethod used to send the reminder (e.g., SMS, email, phone call).3 methods · optionalSMS
address_streetstringStreet address of the healthcare facility where the appointment is scheduled.optionalLuisenstrasse 7
address_citystringCity of the healthcare facility.optionalBerlin
address_statestringState or region of the healthcare facility.optionalCA
address_postal_codestringPostal code of the healthcare facility.optional10117
address_countrystringCountry of the healthcare facility.optionalGermany
notesstringAdditional notes or comments related to the appointment.optionalAccompanied by parent
Dates and times 5 columns
patient_dobdateDate of birth of the patient.optional2012-03-21
appointment_datedateScheduled date of the appointment.2024-04-15
scheduled_datetimedatetimeDate and time when the appointment was scheduled.2024-03-20T10:15:00
reminder_datetimedatetimeDate and time when the reminder was sent.optional2024-04-13T09:00:00
cancellation_datetimedatetimeDate and time when the appointment was cancelled, if applicable.optional2024-08-18T10:30:00
True or false 3 columns
reminder_sentbooleanIndicates if a reminder was sent to the patient prior to the appointment.optionaltrue
no_showbooleanIndicates whether the patient failed to attend the appointment (true = no-show, false = attended).false
cancellationbooleanIndicates if the appointment was cancelled prior to the scheduled time.optionalfalse

Use it for

  • no show10%20 of 200 rowsno show by booking ch…5%onli…0%phone38%in-p…13%app

    A healthcare dashboard

    The no_show rate, no_show by booking_channel and a breakdown of address_state. Excel, Power BI or Tableau.

  • Why do 20 of 200 rows have no_show = true?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable appointments with patient_id, patient_name and patient_dob to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 26 columns, your size and your rules. See 20 rows before you pay.

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This dataset200 rows26 columns
Yours1,000 rows26 columnsaddress_street: UK only

blueprint · patient-appointment-no-show-logs

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 26 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Appointment date, time, and department logged
  • Label as attended or no-show
  • Add patient age and appointment type
  • No PHI included
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
patient-appointment-no-show-logs

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