Healthcare
19 columns
80 rows
8 formats

Patient Appointment No-Show Data

This dataset provides detailed records of patient appointments, including attendance status, patient demographics, health conditions, and reminder interventions. It enables healthcare providers and analysts to identify patterns and factors contributing to missed appointments, supporting the development of targeted interventions to improve patient engagement and clinic efficiency.

last updated 12 Jul 2025
by GoMask

The brief that made it

Predictive modeling of appointment no-shows

Sample rows

preview · 8 of 80 rows · all 19 columns

appointment_idstring	appointment_typestring	ageinteger	no_showboolean	neighbourhoodstring	patient_idstring	appointment_datedate	appointment_timestring	scheduled_datedate	scheduled_timestring	genderstring	scholarshipboolean	hypertensionboolean	diabetesboolean	alcoholismboolean	handicapinteger	sms_receivedboolean	clinic_idstring	clinic_namestring
APT10001	general	28	false	Brooklyn	PAT0001	2024-06-10	08:30	2024-06-05	15:21	female	false	false	false	false	0	true	CLINIC01	Greenwood Medical
APT10002	specialist	53	false	Harlem	PAT0002	2024-06-12	09:45	2024-06-07	16:10	male	false	true	false	false	1	true	CLINIC02	Central Health
APT10003	follow-up	34	true	Queens Village	PAT0003	2024-06-13	11:00	2024-06-09	13:35	female	true	false	false	false	0	false	CLINIC03	Riverbend Clinic
APT10004	general	28	false	Brooklyn	PAT0001	2024-06-16	14:15	2024-06-14	12:10	female	false	false	false	false	0	false	CLINIC01	Greenwood Medical
APT10005	general	42	true	Upper East Side	PAT0004	2024-06-14	10:00	2024-06-10	09:40	male	false	false	false	true	1	true	CLINIC02	Central Health
APT10006	specialist	66	false	Staten Island	PAT0005	2024-06-15	15:30	2024-06-11	10:50	male	false	true	false	false	2	false	CLINIC04	Southside Family Health
APT10007	follow-up	58	false	Williamsburg	PAT0006	2024-06-20	08:00	2024-06-17	17:15	female	true	true	true	false	1	true	CLINIC03	Riverbend Clinic
APT10008	general	19	true	Chelsea	PAT0007	2024-06-21	13:20	2024-06-13	14:44	female	false	false	false	false	0	false	CLINIC05	Broadway Health Center
APT10009	specialist	36	false	Astoria	PAT0008	2024-06-18	12:40	2024-06-14	11:15	other	true	false	true	false	0	true	CLINIC06	Astoria Care
APT10010	follow-up	73	false	Harlem	PAT0009	2024-06-22	16:45	2024-06-20	09:55	male	false	true	true	false	3	false	CLINIC02	Central Health
APT10011	general	22	true	Brooklyn	PAT0010	2024-06-23	09:10	2024-06-19	08:42	female	true	false	false	false	0	true	CLINIC01	Greenwood Medical
APT10012	specialist	45	false	Midtown	PAT0011	2024-06-24	10:30	2024-06-15	15:03	unknown	false	true	true	false	2	false	CLINIC07	Hudson Medical Group
APT10013	follow-up	61	false	Flushing	PAT0012	2024-06-25	12:00	2024-06-20	11:25	male	false	true	false	true	2	true	CLINIC08	Flushing Family Practice
APT10014	general	37	false	Soho	PAT0013	2024-06-26	13:45	2024-06-21	14:55	female	false	false	false	false	1	false	CLINIC09	Soho Wellness
APT10015	specialist	49	true	Harlem	PAT0014	2024-06-27	14:10	2024-06-23	10:32	male	true	false	true	true	0	true	CLINIC02	Central Health
APT10016	follow-up	64	false	Bronx	PAT0015	2024-06-28	15:30	2024-06-25	14:17	female	false	true	false	false	3	false	CLINIC10	Bronx Medical
APT10017	general	18	false	Chelsea	PAT0016	2024-07-01	08:45	2024-06-28	16:10	male	true	false	false	false	0	true	CLINIC05	Broadway Health Center
APT10018	specialist	56	true	Midtown	PAT0017	2024-07-01	11:30	2024-06-29	10:55	female	false	true	false	false	2	false	CLINIC07	Hudson Medical Group
APT10019	follow-up	41	false	Astoria	PAT0018	2024-07-02	13:00	2024-06-30	11:30	other	true	false	true	false	1	true	CLINIC06	Astoria Care
APT10020	general	53	false	Harlem	PAT0002	2024-07-02	16:50	2024-06-28	12:05	male	false	true	false	false	1	false	CLINIC02	Central Health

What the 80 rows show

from the 80-row sample

General (appointment type) stands out: mean age is 32.3, against 53.7 for the rest.

30%no_show = true
42median age
4genders
15clinics
15clinic names
1median handicap

Mean age by appointment_type80 rows

age80 rows, in bands of 10

Median 42, from 18 to 77.

neighbourhood80 rows · top 10 of 21 values

Greenfield6
Riverside6
Brooklyn5
Harlem5
Old Town5
Maple Heights5
Brooklyn West5
Elmwood5
Williamsburg4
Astoria4

19 columns by typefrom the column list below

string 9
integer 2
date 2
boolean 6

Columns

19 columns in four groups

blueprint · 19 columns
column	type	description	example
Text 9 columns
`appointment_id`	string	Unique identifier for each appointment recordunique	APT10001
`patient_id`	string	Unique identifier for each patient	PAT0001
`appointment_time`	string	Scheduled time of the appointment (24-hour format, e.g., '14:30')	08:30
`scheduled_time`	string	Time when the appointment was scheduled/booked (24-hour format, e.g., '09:15')	15:21
`gender`	string	Patient's gendermale · female · other · unknown · optional	female
`neighbourhood`	string	Patient's residential neighborhood or districtoptional	Brooklyn
`appointment_type`	string	Type or category of the appointment (e.g., general, specialist, follow-up)3 types · optional	general
`clinic_id`	string	Unique identifier for the clinic or facility where the appointment is scheduledoptional	CLINIC01
`clinic_name`	string	Name of the clinic or facilityoptional	Greenwood Medical
Numbers 2 columns
`age`	integer	Patient's age at the time of the appointment0 to 120 · optional	28
`handicap`	integer	Degree of disability/handicap (0 = none, 1 = mild, 2 = moderate, 3 = severe, 4 = very severe)0 to 4 · optional	0
Dates and times 2 columns
`appointment_date`	date	Scheduled date of the appointment	2024-06-10
`scheduled_date`	date	Date when the appointment was scheduled/booked	2024-06-05
True or false 6 columns
`no_show`	boolean	Indicates whether the patient missed the appointment (true = no-show, false = attended)	false
`scholarship`	boolean	Indicates if the patient is enrolled in a welfare program or scholarshipoptional	false
`hypertension`	boolean	Indicates if the patient has been diagnosed with hypertensionoptional	false
`diabetes`	boolean	Indicates if the patient has been diagnosed with diabetesoptional	false
`alcoholism`	boolean	Indicates if the patient has a history of alcoholismoptional	false
`sms_received`	boolean	Indicates if the patient received an SMS reminder for the appointmentoptional	true

Use it for

A healthcare dashboard
The no_show rate, age by appointment_type and a breakdown of neighbourhood. Excel, Power BI or Tableau.
Why do the 38 general rows have a mean age of 32.3?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Appointments80
APT1000128general
APT1000334follow-up
APT1000542general
A software demo
Believable appointments with patient_id, appointment_date and appointment_time to fill a screen in front of a buyer.

This dataset80 rows19 columns

Yours1,000 rows19 columns

blueprint · patient-appointment-no-show-data

Behind this dataset

Same schema. As many rows as you need.

These 80 rows came out of a blueprint — 19 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

Patients must have at least one appointment
Missed appointments are recorded with reasons when available
Reminders sent 24 hours before confirmed appointments
Patients with 3+ no-shows flagged for follow-up

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

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: patient-appointment-no-show-data

What should your data show?

No signup. No card.

Sample rows

What the 80 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?