Healthcare
28 columns
200 rows
8 formats

Medical Appointment No-Show Records

This dataset provides detailed records of medical appointments, including patient demographics, health conditions, appointment details, reminders, and attendance outcomes. It enables analysis of no-show patterns, the impact of reminders, and patient history to optimize healthcare resource utilization and improve patient engagement.

last updated 12 Jul 2025
by GoMask

The brief that made it

Predicting and reducing patient no-shows using machine learning models.

Sample rows

preview · 8 of 200 rows · all 28 columns

appointment_idstring	appointment_typestring	ageinteger	has_hypertensionboolean	address_statestring	patient_idstring	appointment_datedate	appointment_timestring	booking_datedate	genderstring	address_streetstring	address_citystring	address_postal_codestring	address_countrystring	has_diabetesboolean	has_heart_diseaseboolean	has_asthmaboolean	other_chronic_conditionsstring	sms_reminder_sentboolean	email_reminder_sentboolean	reminder_days_beforeinteger	previous_appointmentsinteger	previous_no_showsinteger	departmentstring	attendedboolean	cancellation_reasonstring	insurance_typestring	notesstring
A10001	checkup	34	false	IL	P5001	2024-07-09	09:15	2024-06-25	female	120 Oakridge Blvd	Chicago	60616	USA	false	false	false	blank	true	true	3	5	1	general practice	true	blank	private	Requested early morning slot.
A10002	consultation	48	true	MA	P5002	2024-07-10	16:45	2024-06-19	male	87 Elm St	Boston	02118	USA	false	false	false	blank	true	false	2	7	2	cardiology	true	blank	public	Discuss blood pressure management.
A10003	checkup	0	false	CA	P5003	2024-07-11	08:00	2024-07-03	female	302 Spruce Ave	San Francisco	94107	USA	false	false	false	blank	true	true	7	0	0	pediatrics	true	blank	private	Newborn checkup.
A10004	follow-up	70	true	OH	P5004	2024-07-12	15:30	2024-07-01	male	61 Willow Rd	Cleveland	44109	USA	true	false	false	arthritis	false	false	blank	14	3	geriatrics	true	blank	public	Requires wheelchair access.
A10005	consultation	27	false	WA	P5005	2024-07-13	23:00	2024-07-11	other	1967 Main St	Seattle	98105	USA	false	false	true	anxiety	false	false	blank	2	1	mental health	false	blank	uninsured	No-show, late night.
A10006	procedure	56	false	TX	P5006	2024-07-14	10:00	2024-06-29	female	44 Westview Dr	Austin	73301	USA	true	true	false	blank	true	false	4	18	2	orthopedics	true	blank	public	Arrived with assistance.
A10007	consultation	41	false	NY	P5007	2024-07-15	18:30	2024-06-26	male	5 Broadway Ave	New York	10003	USA	false	false	false	blank	true	true	2	5	0	dermatology	true	blank	private	Requested evening slot.
A10008	follow-up	75	true	AZ	P5008	2024-07-09	07:30	2024-06-20	female	888 Meadow Ln	Phoenix	85001	USA	true	true	false	osteoporosis	false	false	blank	21	1	cardiology	true	blank	public	Needs extra time for mobility.
A10009	other	24	false	FL	P5009	2024-07-17	12:00	2024-07-02	male	55 River Rd	Miami	33101	USA	false	false	false	blank	true	false	1	1	1	sports medicine	false	blank	uninsured	Walk-in, did not attend.
A10010	checkup	62	true	CA	P5010	2024-07-18	14:45	2024-06-28	female	2129 Sunset Ave	Sacramento	95814	USA	true	false	false	blank	false	false	blank	12	2	endocrinology	true	blank	public	Diabetes follow-up.
A10011	consultation	38	false	OR	P5011	2024-07-19	11:20	2024-06-27	other	134 Pine St	Portland	97201	USA	false	false	true	eczema	true	false	2	4	0	dermatology	true	blank	private	Discussed allergy options.
A10012	other	0	blank	blank	P5012	2024-07-20	06:00	2024-07-12	unknown	blank	blank	blank	blank	blank	blank	blank	blank	false	false	blank	0	0	urgent care	false	No-show on holiday.	other	Minimal data provided.
A10013	checkup	29	false	CO	P5013	2024-07-21	13:30	2024-07-06	female	218 Park Dr	Denver	80202	USA	false	false	false	blank	true	false	4	3	1	general practice	true	blank	private	Annual checkup.
A10014	follow-up	48	true	MA	P5002	2024-07-22	17:00	2024-07-18	male	87 Elm St	Boston	02118	USA	false	false	false	blank	false	false	blank	8	2	internal medicine	false	Canceled by patient due to travel.	public	Canceled in advance.
A10015	consultation	19	false	TX	P5014	2024-07-23	20:30	2024-07-22	other	blank	Houston	blank	USA	false	false	true	anxiety	false	false	blank	1	1	mental health	false	blank	uninsured	Walk-in, minimal data.
A10016	procedure	54	true	CA	P5015	2024-07-24	22:15	2024-07-20	male	663 Valley Rd	Los Angeles	90001	USA	false	false	false	blank	false	false	blank	11	2	urology	false	blank	public	No-show, late evening.
A10017	checkup	39	false	CA	P5016	2024-07-25	12:30	2024-07-05	female	301 Maple St	San Diego	92103	USA	false	false	false	blank	true	false	3	7	0	general practice	true	blank	private	Annual physical.
A10018	follow-up	82	true	AZ	P5017	2024-07-26	19:00	2024-07-18	male	18 Cedar Dr	Tucson	85701	USA	true	true	false	COPD	false	false	blank	19	2	geriatrics	true	blank	public	Needs transportation assistance.
A10019	checkup	2	false	OH	P5018	2024-07-27	08:15	2024-07-10	female	89 Lincoln Ln	Columbus	43215	USA	false	false	false	blank	true	true	6	1	0	pediatrics	true	blank	public	Well child visit.
A10020	consultation	23	false	DC	P5019	2024-07-28	15:00	2024-07-20	other	1501 14th St	Washington	20009	USA	false	false	true	allergy	false	false	blank	2	1	allergy	false	blank	uninsured	No-show, minimal contact info.

What the 200 rows show

from the 200-row sample

Follow-up (appointment type) stands out: 28 of its 30 rows have has_hypertension = true, against 36 of 158 for the rest.

34%has_hypertension = true
41median age
2address countries
4genders
4insurance types
28departments

Has hypertension rate by appointment_typehas_hypertension = true

age200 rows, in bands of 20

Median 41, from 0 to 120.

address_state166 rows with a value · 34 left blank

CA18
TX12
OH11
MO10
FL8
AZ7
NC7
NE7
WA6
OR6

28 columns by typefrom the column list below

string 15
integer 4
date 2
boolean 7

Columns

28 columns in four groups

blueprint · 28 columns
column	type	description	example
Text 15 columns
`appointment_id`	string	Unique identifier for each medical appointment record.unique	A10001
`patient_id`	string	Unique identifier for each patient.	P5001
`appointment_time`	string	Scheduled time of the appointment in 24-hour format (HH:MM).	09:15
`gender`	string	Gender of the patient.male · female · other · unknown	female
`address_street`	string	Street address of the patient.optional	120 Oakridge Blvd
`address_city`	string	City of the patient's address.optional	Chicago
`address_state`	string	State or region of the patient's address.optional	IL
`address_postal_code`	string	Postal or ZIP code of the patient's address.optional	60616
`address_country`	string	Country of the patient's address.2 countries · optional	USA
`other_chronic_conditions`	string	Comma-separated list of other chronic conditions reported by the patient.optional	arthritis
`appointment_type`	string	Type of appointment (e.g., consultation, follow-up, checkup, procedure).consultation · follow-up · checkup · procedure · other	checkup
`department`	string	Medical department or specialty for the appointment.	general practice
`cancellation_reason`	string	Reason provided for cancellation, if appointment was canceled.optional	No-show on holiday.
`insurance_type`	string	Type of insurance coverage (e.g., public, private, uninsured).public · private · uninsured · other · optional	private
`notes`	string	Additional notes or comments related to the appointment.optional	Newborn checkup.
Numbers 4 columns
`age`	integer	Age of the patient at the time of appointment.0 to 120	34
`reminder_days_before`	integer	Number of days before the appointment that the reminder was sent.0 to 30 · optional	3
`previous_appointments`	integer	Total number of previous appointments the patient has had at this facility.0 or more · optional	5
`previous_no_shows`	integer	Number of previous appointments the patient did not attend (no-shows).0 or more · optional	1
Dates and times 2 columns
`appointment_date`	date	Scheduled date of the medical appointment.	2024-07-09
`booking_date`	date	Date when the appointment was booked.	2024-06-25
True or false 7 columns
`has_diabetes`	boolean	Indicates if the patient has a diagnosis of diabetes.optional	false
`has_hypertension`	boolean	Indicates if the patient has a diagnosis of hypertension.optional	false
`has_heart_disease`	boolean	Indicates if the patient has a diagnosis of heart disease.optional	false
`has_asthma`	boolean	Indicates if the patient has a diagnosis of asthma.optional	false
`sms_reminder_sent`	boolean	Indicates if an SMS reminder was sent prior to the appointment.optional	true
`email_reminder_sent`	boolean	Indicates if an email reminder was sent prior to the appointment.optional	true
`attended`	boolean	Indicates whether the patient attended the appointment (true) or was a no-show (false).	true

Use it for

A healthcare dashboard
The has_hypertension rate, age by appointment_type and a breakdown of address_state. Excel, Power BI or Tableau.
Why do 64 of 188 rows have has_hypertension = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Appointments200
A1000134checkup
A1000248consulta…
A1000470follow-up
A software demo
Believable appointments with patient_id, appointment_date and appointment_time to fill a screen in front of a buyer.

This dataset200 rows28 columns

Yours1,000 rows28 columnsaddress_street: UK only

blueprint · medical-appointment-no-show-records

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 28 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

No-shows labeled with reason if available
Appointments for minors require guardian info
Repeat no-shows identified
All dates in ISO 8601 format

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: medical-appointment-no-show-records

What should your data show?

No signup. No card.

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?