Medical Appointment Booking Patterns

This dataset provides detailed records of medical appointment bookings, capturing patient demographics, booking channels, scheduling lead times, provider and clinic details, and appointment outcomes. It enables analysis of patient booking behaviors, identification of optimal scheduling windows, and detection of overbooking trends to improve healthcare operational efficiency and patient experience.

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

Analyzing patient appointment booking patterns to optimize scheduling windows

Sample rows

preview · 8 of 200 rows · all 22 columns
appointment_idstringappointment_typestringpatient_ageintegeroverbookedbooleanclinic_idstringpatient_idstringpatient_genderstringappointment_datedateappointment_timestringbooking_datetimedatetimebooking_channelstringlead_time_daysintegerprovider_idstringprovider_specialtystringclinic_namestringclinic_citystringclinic_statestringclinic_postal_codestringclinic_countrystringappointment_statusstringcancellation_reasonstringrescheduled_from_appointment_idstring
APPT001checkup31falseCLINIC01PAT1001female2024-06-2509:152024-06-19T11:47:00online6PRV101general practitionerDowntown Health CenterSan FranciscoCA94103USAcompletedblankblank
APPT002urgent14trueCLINIC02PAT1002male2024-06-2110:002024-06-20T08:04:00in-person1PRV104pediatricianKids Medical GroupAustinTX73301USAbookedblankblank
APPT003specialist53falseCLINIC03PAT1003female2024-07-0114:452024-06-18T16:31:00phone13PRV106cardiologistHeartwell CardiologyMiamiFL33101USAcompletedblankblank
APPT004follow-up31falseCLINIC01PAT1001female2024-06-2713:052024-06-23T14:22:00app4PRV101general practitionerDowntown Health CenterSan FranciscoCA94103USAbookedblankblank
APPT005checkup46falseCLINIC04PAT1004other2024-06-2611:202024-06-23T09:32:00online3PRV102dermatologistSkin Wellness ClinicChicagoIL60601USAcompletedblankblank
APPT006urgent7trueCLINIC02PAT1005female2024-06-2208:302024-06-21T12:08:00app1PRV104pediatricianKids Medical GroupAustinTX73301USAcompletedblankblank
APPT007checkup36falseCLINIC01PAT1006male2024-06-2115:302024-06-20T17:27:00online1PRV101general practitionerDowntown Health CenterSan FranciscoCA94103USAcompletedblankblank
APPT008specialist60falseCLINIC05PAT1007male2024-07-0210:552024-06-20T15:41:00phone12PRV107neurologistNeuro Health CenterTorontoONM4B1B3Canadabookedblankblank

What the 200 rows show

from the 200-row sample

Urgent (appointment type) stands out: 36 of its 39 rows have overbooked = true, against 5 of 161 for the rest.

  • 21%overbooked = true
  • 37.5median patient_age
  • 4patient genders
  • 4clinic countries
  • 5booking channels
  • 5appointment statuses
Overbooked rate by appointment_typeoverbooked = true
0%50%100%0%checkup0 of 680%follow…0 of 3692%urgent36 of 398%specia…4 of 5325%other1 of 4
patient_age200 rows, in bands of 20
0459030815720111060120patient_age →

Median 37.5, from 0 to 120.

clinic_id200 rows · top 10 of 33 values
  1. CLINIC2322
  2. CLINIC0118
  3. CLINIC2016
  4. CLINIC2814
  5. CLINIC2613
  6. CLINIC2512
  7. CLINIC2410
  8. CLINIC218
  9. CLINIC047
  10. CLINIC307
22 columns by typefrom the column list below
  • string 17
  • integer 2
  • date 1
  • datetime 1
  • boolean 1

Columns

22 columns in four groups
blueprint · 22 columns
columntypedescriptionexample
Text 17 columns
appointment_idstringUnique identifier for each medical appointment booking.uniqueAPPT001
patient_idstringUnique identifier for the patient booking the appointment.PAT1001
patient_genderstringGender of the patient.male · female · other · unknown · optionalfemale
appointment_timestringScheduled time of the appointment (24-hour format, e.g., '14:30').09:15
booking_channelstringChannel used to book the appointment (e.g., online, phone, in-person, app).online · phone · in-person · app · otheronline
appointment_typestringType of appointment (e.g., checkup, follow-up, urgent, specialist).checkup · follow-up · urgent · specialist · othercheckup
provider_idstringUnique identifier for the healthcare provider or doctor.PRV101
provider_specialtystringMedical specialty of the provider (e.g., general practitioner, cardiologist).11 specialties · optionalgeneral practitioner
clinic_idstringUnique identifier for the clinic or facility where the appointment is scheduled.CLINIC01
clinic_namestringName of the clinic or facility.optionalDowntown Health Center
clinic_citystringCity where the clinic is located.optionalSan Francisco
clinic_statestringState or province where the clinic is located.optionalCA
clinic_postal_codestringPostal code of the clinic location.optional94103
clinic_countrystringCountry where the clinic is located.4 countries · optionalUSA
appointment_statusstringCurrent status of the appointment (e.g., booked, cancelled, completed, no-show, rescheduled).booked · cancelled · completed · no-show · rescheduledcompleted
cancellation_reasonstringReason for appointment cancellation, if applicable.11 reasons · optionalPatient hospitalized
rescheduled_from_appointment_idstringIf rescheduled, the appointment_id of the original appointment.optionalAPPT008
Numbers 2 columns
patient_ageintegerAge of the patient at the time of booking.0 to 12031
lead_time_daysintegerNumber of days between booking and appointment date.0 or more6
Dates and times 2 columns
appointment_datedateScheduled date of the appointment.2024-06-25
booking_datetimedatetimeDate and time when the appointment was booked.2024-06-19T11:47:00
True or false 1 column
overbookedbooleanIndicates if the appointment was booked in an overbooked time slot.false

Use it for

  • overbooked21%41 of 200 rowsmean patient age by a…31.1chec…38.7foll…21.0urge…64.2spec…

    A healthcare dashboard

    The overbooked rate, patient_age by appointment_type and a breakdown of clinic_id. Excel, Power BI or Tableau.

  • Why do 41 of 200 rows have overbooked = 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_age and patient_gender to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 22 columns, your size and your rules. See 20 rows before you pay.

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This dataset200 rows22 columns
Yours1,000 rows22 columnsclinic_city: UK only

blueprint · medical-appointment-booking-patterns

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 22 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
  • One record per booking activity.
  • Includes patient segment and booking channel.
  • Time to appointment and cancellation reason included.
  • Doctor specialty and slot type listed.
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
medical-appointment-booking-patterns

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