Healthcare Appointment No-Show Prediction

This dataset provides detailed logs of healthcare appointment bookings, enriched with patient demographics, medical history, and communication records such as reminders. It enables comprehensive analysis of no-show risk factors, supports predictive modeling, and helps optimize scheduling efficiency in clinical settings.

  • last updated 21 Jul 2025
  • by GoMask
The brief that made it

Predicting patient no-show risk using machine learning models

Sample rows

preview · 8 of 200 rows · all 37 columns
appointment_idstringdepartmentstringappointment_duration_minutesintegerhas_heart_diseasebooleanpatient_race_ethnicitystringpatient_idstringappointment_datetimedatetimebooking_datetimedatetimescheduled_bystringappointment_statusstringno_showbooleanpatient_ageintegerpatient_genderstringpatient_primary_languagestringpatient_phone_numberstringpatient_emailstringaddress_streetstringaddress_citystringaddress_statestringaddress_postal_codestringaddress_countrystringinsurance_typestringchronic_conditions_countintegerhas_diabetesbooleanhas_hypertensionbooleanprevious_no_show_countintegerprevious_appointments_countintegerreminder_sentbooleanreminder_typestringreminder_sent_datetimedatetimeclinic_idstringclinic_namestringprovider_idstringprovider_namestringappointment_typestringbooking_channelstringspecial_accommodationsstring
APT-0001general practice30falseWhitePAT-00012024-07-15T09:00:002024-06-30T10:15:00staff.rosarioattendedfalse29femaleEnglish+1 415-555-7213rachel.hill@example.com125 Main StSan FranciscoCA94105USAprivate0falsefalse02truesms2024-07-14T11:00:00CLIN-1001Bay Primary Care CenterPROV-3010Dr. Ann Rosarioannual examonlineblank
APT-0002cardiology45falseSouth AsianPAT-00022024-07-16T15:45:002024-07-10T14:22:00staff.malhotraattendedfalse64maleHindi+91 22 5557 2020arjun.malhotra@example.in45 MG RoadMumbaiMH400001Indiapublic2truetrue16truephone2024-07-16T09:00:00CLIN-2001Mumbai Cardiac InstitutePROV-4001Dr. Preeti Singhfollow-upphonehearing assistance
APT-0003internal medicine20falseBlackPAT-00032024-07-17T11:30:002024-07-12T12:55:00blankno-showtrue38femaleEnglish+1 312-555-6802tina.collins@example.com335 South Clark StChicagoIL60604USApublic1falsetrue25trueemail2024-07-16T13:00:00CLIN-1002Midwest Medical GroupPROV-3020Dr. John Youngfollow-uponlineblank
APT-0004pediatrics25falseLatinoPAT-00042024-07-18T13:00:002024-07-01T09:05:00system.autoattendedfalse7maleSpanish+34 93 555 0100miguel.olvera@example.esCarrer de Mallorca 210BarcelonaCatalonia08036Spainnone0falsefalse01falsenoneblankCLIN-3001Barcelona Children's HospitalPROV-5001Dra. Elena Ruiznew patientin-personparent present
APT-0005endocrinology40falseAsianPAT-00052024-07-19T08:30:002024-07-17T17:00:00staff.jleeattendedfalse52maleMandarin+1 646-555-8877jian.lee@example.com1105 5th Ave Apt 2BNew YorkNY10028USAprivate2truetrue16truesms2024-07-18T15:00:00CLIN-1003Manhattan Internal MedicinePROV-6001Dr. Linda Wufollow-uponlineblank
APT-0006geriatrics60trueWhitePAT-00062024-07-20T14:20:002024-07-15T08:00:00blankattendedfalse73femaleGerman+49 89 555 3366gerda.schmidt@example.deLudwigstrasse 90MunichBavaria80539Germanypublic3truetrue010truephone2024-07-19T12:00:00CLIN-4001Munich Geriatric CenterPROV-7001Dr. Markus Kleinconsultationreferralwheelchair access
APT-0007cardiology30trueVietnamesePAT-00072024-07-21T10:30:002024-07-18T16:45:00staff.tnguyenno-showtrue44femaleVietnamese+84 28 3823 9999thuy.nguyen@example.vn23 Le Loi BlvdHo Chi Minh CityHCM700000Vietnamnone1falsefalse24falsenoneblankCLIN-5001Saigon Heart ClinicPROV-8001Dr. Binh Tranconsultationphoneblank
APT-0008pediatrics20falsePacific IslanderPAT-00082024-07-22T16:00:002024-07-20T11:00:00blankattendedfalse14femaleSamoan+1 808-555-4422alani.tupuola@example.com72 Kapiolani BlvdHonoluluHI96813USApublic0falsefalse01trueemail2024-07-21T15:00:00CLIN-1004Waikiki Family MedicinePROV-9001Dr. Keoni Akanafollow-upin-personinterpreter

What the 200 rows show

from the 200-row sample

Geriatrics department stands out: 17 of its 20 rows have has_heart_disease = true, against 15 of 168 for the rest.

  • 16%has_heart_disease = true
  • 25median appointment_duration_minutes
  • 3insurance types
  • 4appointment statuses
  • 4patient genders
  • 4booking channels
Has heart disease rate by departmenthas_heart_disease = true
0%50%100%11%int…5 of…0%gen…0 of…0%ped…0 of…85%ger…17 o…0%end…0 of…50%car…8 of…0%pri…0 of…22%wom…2 of…
appointment_duration_minutes200 rows, in bands of 10
0408004380442517000050100appointment_duration_minutes →

Median 25, from 10 to 60.

patient_race_ethnicity191 rows with a value · 9 left blank
  1. White65
  2. Black29
  3. Asian25
  4. Latino15
  5. South Asian13
  6. Pacific Islander11
  7. Hispanic8
  8. Native American8
  9. Other6
  10. Arab5
37 columns by typefrom the column list below
  • string 24
  • integer 5
  • datetime 3
  • boolean 5

Columns

37 columns in four groups
blueprint · 37 columns
columntypedescriptionexample
Text 24 columns
appointment_idstringUnique identifier for each appointment booking.uniqueAPT-0001
patient_idstringUnique identifier for each patient.PAT-0001
scheduled_bystringIdentifier or name of staff or system that scheduled the appointment.optionalstaff.rosario
appointment_statusstringFinal status of the appointment (e.g., attended, no-show, cancelled, rescheduled).attended · no-show · cancelled · rescheduledattended
patient_genderstringGender of the patient.male · female · other · unknownfemale
patient_race_ethnicitystringSelf-reported race or ethnicity of the patient.12 ethnicities · optionalWhite
patient_primary_languagestringPrimary language spoken by the patient.optionalEnglish
patient_phone_numberstringPrimary phone number for patient contact.optional+1 415-555-7213
patient_emailstringPrimary email address for patient contact.optionalrachel.hill@example.com
address_streetstringStreet address of the patient.optional125 Main St
address_citystringCity of the patient's address.optionalSan Francisco
address_statestringState or province of the patient's address.optionalCA
address_postal_codestringPostal or ZIP code of the patient's address.optional94105
address_countrystringCountry of the patient's address.optionalUSA
insurance_typestringType of insurance coverage (e.g., private, public, none).3 types · optionalprivate
reminder_typestringType of reminder sent (e.g., SMS, email, phone call, none).sms · email · phone · none · other · optionalsms
clinic_idstringUnique identifier for the clinic or facility where the appointment is scheduled.CLIN-1001
clinic_namestringName of the clinic or facility.optionalBay Primary Care Center
departmentstringDepartment or specialty for the appointment (e.g., cardiology, pediatrics).8 departments · optionalgeneral practice
provider_idstringUnique identifier for the healthcare provider assigned to the appointment.optionalPROV-3010
provider_namestringName of the healthcare provider assigned to the appointment.optionalDr. Ann Rosario
appointment_typestringType of appointment (e.g., new patient, follow-up, annual exam, telehealth).6 types · optionalannual exam
booking_channelstringChannel used to book the appointment (e.g., online, phone, in-person, referral).4 channels · optionalonline
special_accommodationsstringAny special accommodations requested for the appointment (e.g., wheelchair access, interpreter).7 accommodationses · optionalhearing assistance
Numbers 5 columns
patient_ageintegerAge of the patient at the time of appointment.0 to 12029
chronic_conditions_countintegerNumber of chronic conditions diagnosed for the patient.0 or more · optional0
previous_no_show_countintegerNumber of previous appointments where the patient did not show up.0 or more · optional0
previous_appointments_countintegerTotal number of previous appointments for the patient.0 or more · optional2
appointment_duration_minutesintegerScheduled duration of the appointment in minutes.1 or more · optional30
Dates and times 3 columns
appointment_datetimedatetimeScheduled date and time for the appointment.2024-07-15T09:00:00
booking_datetimedatetimeDate and time when the appointment was booked.2024-06-30T10:15:00
reminder_sent_datetimedatetimeDate and time when the reminder was sent.optional2024-07-14T11:00:00
True or false 5 columns
no_showbooleanIndicates if the patient did not attend the appointment (true = no-show, false = attended or cancelled in advance).false
has_diabetesbooleanIndicates if the patient has a diagnosis of diabetes.optionalfalse
has_hypertensionbooleanIndicates if the patient has a diagnosis of hypertension.optionalfalse
has_heart_diseasebooleanIndicates if the patient has a diagnosis of heart disease.optionalfalse
reminder_sentbooleanIndicates if a reminder was sent for this appointment.optionaltrue

Use it for

  • has heart dise…16%32 of 200 rowsmean appointment dura…25.6inte…21.6gene…18.1pedi…46.0geri…

    A healthcare dashboard

    The has_heart_disease rate, appointment_duration_minutes by department and a breakdown of patient_race_ethnicity. Excel, Power BI or Tableau.

  • Why do 32 of 200 rows have has_heart_disease = 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, appointment_datetime and booking_datetime to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 37 columns, your size and your rules. See 20 rows before you pay.

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This dataset200 rows37 columns
Yours1,000 rows37 columnsaddress_street: UK only

blueprint · healthcare-appointment-no-show-prediction

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 37 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
  • No-show rates vary by patient segment and appointment type
  • Incorporate reminders, lead time, and rescheduling attempts
  • Data includes weather, holidays, and transportation status
  • Sensitive details anonymized for privacy compliance
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
healthcare-appointment-no-show-prediction

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