Healthcare
27 columns
200 rows
8 formats

Healthcare Patient Appointment Patterns

This dataset provides detailed synthetic records of healthcare appointments, including patient demographics, provider and clinic information, scheduling, cancellations, no-shows, and outcomes. It is ideal for optimizing patient flow, improving operational efficiency, and developing predictive analytics models for healthcare organizations.

last updated 21 Aug 2025
by GoMask

The brief that made it

Patient flow optimization and resource allocation

Sample rows

preview · 8 of 200 rows · all 27 columns

appointment_idstring	appointment_typestring	appointment_duration_minutesinteger	reminder_sentboolean	clinic_citystring	patient_idstring	patient_genderstring	patient_ageinteger	appointment_datedate	appointment_timestring	booking_datetimedatetime	cancellation_statusboolean	cancellation_reasonstring	cancellation_datetimedatetime	no_show_statusboolean	appointment_outcomestring	provider_idstring	provider_specialtystring	clinic_idstring	clinic_namestring	clinic_street_addressstring	clinic_statestring	clinic_postal_codestring	clinic_countrystring	patient_contact_phonestring	patient_contact_emailstring	reminder_datetimedatetime
A0001	in-person	45	true	Chicago	P001	female	29	2024-07-12	09:30	2024-06-29T10:21:00	false	blank	blank	false	completed	PR001	general practitioner	C001	Downtown Health Center	123 Main St	IL	60601	USA	(312) 555-0183	lisa29@healthmail.com	2024-07-11T09:00:00
A0002	in-person	60	true	Miami	P002	male	65	2024-08-04	13:15	2024-07-10T08:54:00	false	blank	blank	false	completed	PR002	geriatrics	C002	Miami Senior Clinic	456 Ocean Blvd	FL	33101	USA	305-555-0176	james65@healthmail.com	2024-08-03T11:00:00
A0003	initial consultation	30	true	Houston	P003	female	3	2024-07-07	11:00	2024-06-29T14:07:00	false	blank	blank	false	completed	PR003	pediatrics	C003	Houston Pediatric Care	789 Maple Ave	TX	77002	USA	blank	parent.houston@healthmail.com	2024-07-06T15:00:00
A0004	telemedicine	40	true	Seattle	P004	other	22	2024-07-18	16:15	2024-07-11T13:22:00	false	blank	blank	false	completed	PR004	psychology	C004	Seattle Wellness Group	456 Pine St	WA	98101	USA	+1 206-555-3112	alex22@wellnessmail.com	2024-07-17T17:00:00
A0005	follow-up	25	false	Houston	P005	unknown	11	2024-07-15	08:45	2024-07-02T09:33:00	false	blank	blank	true	no-show	PR003	pediatrics	C003	Houston Pediatric Care	789 Maple Ave	TX	77002	USA	blank	blank	blank
A0006	follow-up	50	true	Portland	P006	female	58	2024-07-26	10:45	2024-07-04T12:15:00	false	blank	blank	false	completed	PR005	cardiology	C005	Portland Heart Clinic	222 Oak St	OR	97201	USA	503-555-9255	mary58@cardiocare.com	2024-07-25T15:00:00
A0007	in-person	55	false	Los Angeles	P007	male	41	2024-07-16	14:00	2024-07-05T11:40:00	false	blank	blank	false	completed	PR006	orthopedics	C006	LA Ortho Center	995 Sunset Blvd	CA	90001	USA	(213) 555-8746	john41@orthomail.com	blank
A0008	in-person	60	false	Miami	P008	female	77	2024-08-12	15:30	2024-07-15T09:12:00	true	Patient hospitalized	2024-08-10T17:30:00	false	cancelled	PR002	geriatrics	C002	Miami Senior Clinic	456 Ocean Blvd	FL	33101	USA	305-555-0199	susan77@healthmail.com	blank
A0009	in-person	35	false	Chicago	P009	male	34	2024-07-19	11:45	2024-07-11T09:18:00	false	blank	blank	false	completed	PR007	sports medicine	C001	Downtown Health Center	123 Main St	IL	60601	USA	312-555-8877	mike34@sportsmail.com	blank
A0010	in-person	20	true	Denver	P010	female	8	2024-07-21	10:00	2024-07-13T12:10:00	false	blank	blank	false	completed	PR008	pediatrics	C007	Denver Child Health	321 Elm St	CO	80203	USA	blank	parent.denver@caremail.com	2024-07-20T16:00:00
A0011	follow-up	40	false	Phoenix	P011	male	42	2024-07-22	17:00	2024-07-09T15:30:00	false	blank	blank	false	completed	PR009	endocrinology	C008	Phoenix Diabetes Center	131 Desert Rd	AZ	85001	USA	602-555-9933	tom42@endomail.com	blank
A0012	in-person	30	true	Chicago	P012	female	27	2024-07-29	08:00	2024-07-14T09:02:00	false	blank	blank	false	completed	PR001	general practitioner	C001	Downtown Health Center	123 Main St	IL	60601	USA	312-555-0192	emma27@healthmail.com	2024-07-28T10:00:00
A0013	follow-up	60	true	Boston	P013	female	56	2024-08-03	12:15	2024-07-17T11:15:00	false	blank	blank	false	completed	PR010	rheumatology	C009	Boston Arthritis Institute	210 Beacon St	MA	02108	USA	617-555-0087	julia56@arthritis.com	2024-08-02T16:30:00
A0014	follow-up	35	false	Boston	P014	male	50	2024-07-28	10:30	2024-07-15T13:00:00	false	blank	blank	false	completed	PR010	rheumatology	C009	Boston Arthritis Institute	210 Beacon St	MA	02108	USA	(617) 555-9912	david50@arthritis.com	blank
A0015	telemedicine	40	true	Seattle	P015	female	19	2024-07-20	16:00	2024-07-14T18:30:00	false	blank	blank	false	completed	PR004	psychology	C004	Seattle Wellness Group	456 Pine St	WA	98101	USA	206-555-6112	emily19@wellnessmail.com	2024-07-19T18:00:00
A0016	in-person	45	false	Portland	P016	male	62	2024-08-08	09:45	2024-07-15T13:45:00	false	blank	blank	true	no-show	PR005	cardiology	C005	Portland Heart Clinic	222 Oak St	OR	97201	USA	503-555-9678	larry62@cardiocare.com	blank
A0017	follow-up	20	true	Denver	P017	male	6	2024-07-31	13:00	2024-07-24T09:12:00	false	blank	blank	false	completed	PR008	pediatrics	C007	Denver Child Health	321 Elm St	CO	80203	USA	blank	parent.denver@caremail.com	2024-07-30T15:00:00
A0018	telemedicine	25	true	Los Angeles	P018	female	37	2024-07-25	11:30	2024-07-10T08:08:00	false	blank	blank	false	completed	PR011	dermatology	C010	LA Skin Institute	101 Wilshire Blvd	CA	90001	USA	(213) 555-9087	sarah37@skinmail.com	2024-07-24T09:00:00
A0019	follow-up	45	false	Portland	P019	male	59	2024-08-06	15:00	2024-07-18T12:20:00	false	blank	blank	false	completed	PR005	cardiology	C005	Portland Heart Clinic	222 Oak St	OR	97201	USA	503-555-9781	robert59@cardiocare.com	blank
A0020	telemedicine	25	false	Los Angeles	P020	female	38	2024-07-27	12:45	2024-07-17T15:41:00	true	Family emergency	2024-07-26T17:00:00	false	cancelled	PR011	dermatology	C010	LA Skin Institute	101 Wilshire Blvd	CA	90001	USA	(213) 555-9087	sarah38@skinmail.com	blank

What the 200 rows show

from the 200-row sample

Telemedicine (appointment type) stands out: 36 of its 37 rows have reminder_sent = true, against 94 of 163 for the rest.

65%reminder_sent = true
35median appointment_duration_minutes
3appointment outcomes
4patient genders
13clinic states
14provider specialties

Reminder sent rate by appointment_typereminder_sent = true

appointment_duration_minutes200 rows, in bands of 10

Median 35, from 15 to 70.

clinic_city200 rows · top 10 of 19 values

San Francisco47
Portland22
Chicago21
Denver18
Miami16
Seattle16
Los Angeles13
Houston10
Boston10
Oakland7

27 columns by typefrom the column list below

string 18
integer 2
date 1
datetime 3
boolean 3

Columns

27 columns in four groups

blueprint · 27 columns
column	type	description	example
Text 18 columns
`appointment_id`	string	Unique identifier for each appointment recordunique	A0001
`patient_id`	string	Unique identifier for the patient	P001
`patient_gender`	string	Gender of the patientmale · female · other · unknown	female
`appointment_time`	string	Scheduled time of the appointment (24-hour format: HH:MM)	09:30
`cancellation_reason`	string	Reason for cancellation, if applicable12 reasons · optional	Patient hospitalized
`appointment_outcome`	string	Outcome of the appointment (completed, cancelled, no-show, rescheduled)completed · cancelled · no-show · rescheduled	completed
`provider_id`	string	Unique identifier for the healthcare provider	PR001
`provider_specialty`	string	Medical specialty of the healthcare provider	general practitioner
`clinic_id`	string	Unique identifier for the clinic or facility	C001
`clinic_name`	string	Name of the clinic or facility	Downtown Health Center
`clinic_street_address`	string	Street address of the clinic	123 Main St
`clinic_city`	string	City where the clinic is located	Chicago
`clinic_state`	string	State or region where the clinic is located	IL
`clinic_postal_code`	string	Postal code of the clinic	60601
`clinic_country`	string	Country where the clinic is located	USA
`appointment_type`	string	Type of appointment (in-person, telemedicine, follow-up, initial consultation)4 values	in-person
`patient_contact_phone`	string	Patient's contact phone numberoptional	(312) 555-0183
`patient_contact_email`	string	Patient's contact email addressoptional	lisa29@healthmail.com
Numbers 2 columns
`patient_age`	integer	Age of the patient at the time of appointment0 to 120	29
`appointment_duration_minutes`	integer	Scheduled duration of the appointment in minutes1 to 480	45
Dates and times 4 columns
`appointment_date`	date	Scheduled date of the appointment	2024-07-12
`booking_datetime`	datetime	Date and time when the appointment was booked	2024-06-29T10:21:00
`cancellation_datetime`	datetime	Date and time when the appointment was cancelled, if applicableoptional	2024-08-10T17:30:00
`reminder_datetime`	datetime	Date and time when the reminder was sent, if applicableoptional	2024-07-11T09:00:00
True or false 3 columns
`cancellation_status`	boolean	Indicates if the appointment was cancelled (true/false)	false
`no_show_status`	boolean	Indicates if the patient did not show up for the appointment (true/false)	false
`reminder_sent`	boolean	Indicates if a reminder was sent to the patient prior to the appointment	true

Use it for

A healthcare dashboard
The reminder_sent rate, appointment_duration_minutes by appointment_type and a breakdown of clinic_city. Excel, Power BI or Tableau.
Why do 130 of 200 rows have reminder_sent = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Appointments200
A000145in-person
A000260in-person
A000525follow-up
A software demo
Believable appointments with patient_id, patient_gender and patient_age to fill a screen in front of a buyer.

This dataset200 rows27 columns

Yours1,000 rows27 columnsclinic_street_address: UK only

blueprint · healthcare-patient-appointment-patterns

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 27 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 type: consultation, checkup, emergency
Cancellation flag at 8% rate
Outcome: attended, missed, rescheduled
Time of booking tracked

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: healthcare-patient-appointment-patterns

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A healthcare dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Healthcare

What should your data show?