Doctor Appointment Scheduling Patterns

This dataset provides detailed records of doctor appointment bookings, including patient demographics, appointment timing, booking channels, and status outcomes. It enables analysis of scheduling patterns, resource utilization, and patient engagement, supporting data-driven optimization of healthcare operations and reduction of idle doctor time.

  • opened 9 times
  • last updated 12 Jul 2025
  • by GoMask
The brief that made it

Analyzing appointment booking trends to optimize doctor schedules and reduce idle time

Sample rows

preview · 8 of 120 rows · all 24 columns
appointment_idstringappointment_typestringpatient_ageintegeris_first_time_patientbooleanpatient_citystringpatient_idstringdoctor_idstringdepartmentstringappointment_datedateappointment_start_timedatetimeappointment_end_timedatetimebooking_datetimedatetimebooking_channelstringappointment_statusstringcancellation_reasonstringrescheduled_from_appointment_idstringpatient_genderstringpatient_statestringpatient_postal_codestringpatient_countrystringdoctor_namestringdoctor_specialtystringwait_time_minutesintegerdoctor_idle_time_minutesinteger
APT001routine checkup72falseSan DiegoP001D001Cardiology2024-07-052024-07-05T09:00:002024-07-05T09:30:002024-06-23T15:12:00phonecompletedblankblankfemaleCA92101USADr. Michael CarterCardiologist24
APT002routine checkup4falseDenverP002D002Pediatrics2024-07-052024-07-05T10:15:002024-07-05T10:35:002024-07-03T09:08:00phonecompletedblankblankmaleCO80203USADr. Susan LeePediatrician12
APT003new consultation33trueDallasP003D003General Practice2024-07-062024-07-06T13:30:002024-07-06T13:55:002024-06-28T11:24:00onlinescheduledblankblankfemaleTX75201USADr. Emily SmithGeneral Practitionerblankblank
APT004new consultation28trueTorontoP004D004Dermatology2024-07-072024-07-07T14:10:002024-07-07T14:40:002024-07-06T13:25:00onlinescheduledblankblankmaleONM5V1K4CanadaDr. Hannah PatelDermatologistblankblank
APT005new consultation25trueChicagoP005D005Psychiatry2024-07-082024-07-08T09:45:002024-07-08T10:15:002024-07-01T16:12:00appcompletedblankblankotherIL60611USADr. Daniel MurphyPsychiatrist07
APT006other9falseHoustonP006D006ENT2024-07-082024-07-08T11:00:002024-07-08T11:20:002024-07-05T14:50:00phonecompletedblankblankfemaleTX77002USADr. Olivia ChenENT Specialist24
APT007follow-up46falseVancouverP007D007Ophthalmology2024-07-092024-07-09T08:40:002024-07-09T09:10:002024-07-07T17:16:00onlinecompletedblankblankmaleBCV6B1A1CanadaDr. James WilsonOphthalmologist16
APT008emergency59falseNew YorkP008D008Emergency2024-07-092024-07-09T23:45:002024-07-10T00:05:002024-07-09T23:30:00in-personcompletedblankblankfemaleNY10019USADr. Mark EvansEmergency Medicine03

What the 120 rows show

from the 120-row sample

New consultation (appointment type) stands out: 27 of its 27 rows have is_first_time_patient = true, against 3 of 93 for the rest.

  • 25%is_first_time_patient = true
  • 38median patient_age
  • 4booking channels
  • 4patient genders
  • 5appointment statuses
  • 9patient countries
Is first time patient rate by appointment_typeis_first_time_patient = true
0%50%100%100%new co…27 of 270%follow…0 of 380%routin…0 of 4222%emerge…2 of 925%other1 of 4
patient_age120 rows, in bands of 10
01530180192515101413605090patient_age →

Median 38, from 2 to 83.

patient_city120 rows · top 10 of 35 values
  1. San Diego7
  2. Toronto6
  3. Sydney6
  4. Houston5
  5. Vancouver5
  6. Mumbai5
  7. London5
  8. Melbourne5
  9. Berlin5
  10. Manchester5
24 columns by typefrom the column list below
  • string 16
  • integer 3
  • date 1
  • datetime 3
  • boolean 1

Columns

24 columns in four groups
blueprint · 24 columns
columntypedescriptionexample
Text 16 columns
appointment_idstringUnique identifier for each appointment bookinguniqueAPT001
patient_idstringUnique identifier for the patient booking the appointmentP001
doctor_idstringUnique identifier for the doctor with whom the appointment is scheduledD001
departmentstringMedical department or specialty associated with the appointment (e.g., Cardiology, Pediatrics)12 departmentsCardiology
booking_channelstringChannel used to book the appointment (e.g., online, phone, in-person, app)online · phone · in-person · appphone
appointment_statusstringCurrent status of the appointment (e.g., scheduled, completed, cancelled, no-show, rescheduled)scheduled · completed · cancelled · no-show · rescheduledcompleted
cancellation_reasonstringReason for appointment cancellation, if applicable6 reasons · optionalPatient feeling better
rescheduled_from_appointment_idstringReference to the original appointment_id if this appointment was rescheduled from anotheroptionalAPT032
patient_genderstringGender of the patientmale · female · other · prefer_not_to_say · optionalfemale
patient_citystringCity of residence of the patientoptionalSan Diego
patient_statestringState or province of residence of the patientoptionalCA
patient_postal_codestringPostal code of the patient's residenceoptional92101
patient_countrystringCountry of residence of the patient9 countries · optionalUSA
doctor_namestringFull name of the doctoroptionalDr. Michael Carter
doctor_specialtystringSpecialty of the doctor (e.g., General Practitioner, Cardiologist)optionalCardiologist
appointment_typestringType of appointment (e.g., new consultation, follow-up, routine checkup, emergency)5 values · optionalroutine checkup
Numbers 3 columns
patient_ageintegerAge of the patient at the time of appointment booking0 or more · optional72
wait_time_minutesintegerNumber of minutes between scheduled appointment start and actual start (if available)0 or more · optional2
doctor_idle_time_minutesintegerNumber of minutes the doctor was idle between appointments (if calculable)0 or more · optional4
Dates and times 4 columns
appointment_datedateDate of the scheduled appointment2024-07-05
appointment_start_timedatetimeScheduled start datetime of the appointment2024-07-05T09:00:00
appointment_end_timedatetimeScheduled end datetime of the appointment2024-07-05T09:30:00
booking_datetimedatetimeDatetime when the appointment was booked by the patient2024-06-23T15:12:00
True or false 1 column
is_first_time_patientbooleanIndicates if this is the patient's first appointment with the doctoroptionalfalse

Use it for

  • is first time …25%30 of 120 rowsmean patient age by a…29.8new …42.7foll…50.5rout…50.3emer…

    A healthcare dashboard

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

  • Why do 30 of 120 rows have is_first_time_patient = 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, doctor_id and department to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 24 columns, your size and your rules. See 20 rows before you pay.

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This dataset120 rows24 columns
Yours1,000 rows24 columnspatient_city: UK only

blueprint · doctor-appointment-scheduling-patterns

Behind this dataset

Same schema. As many rows as you need.

These 120 rows came out of a blueprint — 24 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
  • Record patient type (new/returning)
  • Track lead time from booking to appointment
  • Flag last-minute cancellations
  • Document preferred appointment slots
  • Log appointment status (attended, canceled, rescheduled)
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
doctor-appointment-scheduling-patterns

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