Primary Care Appointment Scheduling

This dataset provides detailed records of appointment requests and scheduling outcomes for primary care clinics, including patient demographics, preferences, wait times, and location details. It enables healthcare organizations to analyze scheduling efficiency, identify bottlenecks, and optimize appointment allocation to reduce patient wait times and improve resource utilization.

  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Optimizing clinic schedules to reduce patient wait times

Sample rows

preview · 8 of 200 rows · all 21 columns
appointment_request_idstringappointment_typestringpatient_ageintegerappointment_location_statestringpatient_idstringclinic_idstringprovider_idstringrequest_datetimedatetimepreferred_datedatepreferred_timestringscheduled_datetimedatetimescheduled_provider_idstringscheduling_statusstringwait_time_minutesintegerappointment_location_streetstringappointment_location_citystringappointment_location_postal_codestringappointment_location_countrystringpatient_genderstringcancellation_reasonstringnotesstring
AR0001routine34ILP1001C01PR3202024-03-24T09:05:002024-03-28morning2024-03-28T10:00:00PR320scheduled57602612 N Clark StChicago60614USAfemaleblankAnnual wellness check, first appointment at this clinic.
AR0002urgent12CAP1002C02PR2152024-03-22T13:30:002024-03-24afternoon2024-03-22T15:00:00PR215scheduled901457 Mission StSan Francisco94103USAmaleblankParent requested urgent appointment after minor injury.
AR0003other47NYP1003C03blank2024-03-25T10:45:00blankblankblankblankpendingblank888 8th AveNew York10019USAfemaleblankRequest for travel immunizations, pending insurance approval.
AR0004annual62MAP1004C04PR3082024-03-21T08:15:002024-03-27morning2024-03-27T09:00:00PR308scheduled8645262 Boylston StBoston02116USAmaleblankAnnual physical exam, senior patient.
AR0005follow-up54ILP1005C01PR3202024-03-30T15:20:002024-04-02afternoon2024-04-02T13:00:00PR320scheduled43002612 N Clark StChicago60614USAfemaleblankPost-operative follow-up after outpatient surgery.
AR0006urgent7FLP1006C05blank2024-02-20T14:00:002024-02-25eveningblankblankpendingblank2295 Biscayne BlvdMiami33137USAfemaleblankHigh fever reported, awaiting triage assignment.
AR0007routine29WAP1007C06PR4512024-03-19T11:00:002024-03-20morning2024-03-20T09:30:00PR453scheduled1350401 Pine StSeattle98101USAmaleblankRoutine checkup; provider reassigned due to availability.
AR0008other41CAP1008C07blank2024-03-18T14:30:00blankeveningblankblankdeniedblank700 S Flower StLos Angeles90017USAprefer_not_to_sayblankRequest denied due to coverage restrictions.

What the 200 rows show

from the 200-row sample

Urgent (appointment type) stands out: mean patient_age is 16.8, against 45.6 for the rest.

  • 41median patient_age
  • 3preferred times
  • 4patient genders
  • 6scheduling statuses
  • 50clinics
  • 53appointment location cities
Mean patient_age by appointment_type200 rows
0408036.9routine60 rows16.8urgent28 rows55.1follow…48 rows52.6annual32 rows40.9other32 rows
patient_age200 rows, in bands of 10
0255015122442363326921050100patient_age →

Median 41, from 2 to 99.

appointment_location_state200 rows · top 10 of 28 values
  1. TX29
  2. CA24
  3. IL21
  4. FL17
  5. AZ15
  6. NY11
  7. WA11
  8. PA10
  9. MA9
  10. MO8
21 columns by typefrom the column list below
  • string 16
  • integer 2
  • date 1
  • datetime 2

Columns

21 columns in three groups
blueprint · 21 columns
columntypedescriptionexample
Text 16 columns
appointment_request_idstringUnique identifier for each appointment requestuniqueAR0001
patient_idstringUnique identifier for the patient requesting the appointmentP1001
clinic_idstringUnique identifier for the primary care clinicC01
provider_idstringUnique identifier for the healthcare provider assigned to the appointmentoptionalPR320
preferred_timestringPatient's preferred time for the appointment (e.g., 'morning', 'afternoon', 'evening', or specific time)morning · afternoon · evening · optionalmorning
appointment_typestringType of appointment requested (e.g., 'routine', 'urgent', 'follow-up', 'annual')routine · urgent · follow-up · annual · otherroutine
scheduled_provider_idstringUnique identifier for the provider with whom the appointment was scheduledoptionalPR320
scheduling_statusstringOutcome of the appointment request (e.g., 'scheduled', 'cancelled', 'pending', 'no-show', 'rescheduled', 'denied')6 valuesscheduled
cancellation_reasonstringReason for cancellation if the appointment was cancelledoptionalPatient cancelled due to …
appointment_location_streetstringStreet address of the appointment locationoptional2612 N Clark St
appointment_location_citystringCity of the appointment locationoptionalChicago
appointment_location_statestringState of the appointment locationoptionalIL
appointment_location_postal_codestringPostal code of the appointment locationoptional60614
appointment_location_countrystringCountry of the appointment locationoptionalUSA
patient_genderstringGender of the patientmale · female · other · prefer_not_to_say · optionalfemale
notesstringAdditional notes or comments related to the appointment requestoptionalAnnual wellness check, fi…
Numbers 2 columns
wait_time_minutesintegerNumber of minutes between request and scheduled appointment (calculated)0 or more · optional5760
patient_ageintegerAge of the patient at the time of request0 or more · optional34
Dates and times 3 columns
request_datetimedatetimeTimestamp when the appointment request was made2024-03-24T09:05:00
preferred_datedatePatient's preferred date for the appointmentoptional2024-03-28
scheduled_datetimedatetimeDate and time when the appointment was scheduledoptional2024-03-28T10:00:00

Use it for

  • median patient…41200 rowsmean patient age by a…36.9rout…16.8urge…55.1foll…52.6annu…

    A healthcare dashboard

    Patient_age by appointment_type and a breakdown of appointment_location_state. Excel, Power BI or Tableau.

  • Why do the 28 urgent rows have a mean patient_age of 16.8?

    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 appointment requests with patient_id, clinic_id and provider_id to fill a screen in front of a buyer.

Not quite right?

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This dataset200 rows21 columns
Yours1,000 rows21 columnsappointment_location_street: UK only

blueprint · primary-care-appointment-scheduling

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 21 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
  • Request type classified (routine/emergency)
  • Appointment outcome logged
  • Lead time before appointment recorded
  • Patient no-show flagged
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
primary-care-appointment-scheduling

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