Healthcare
20 columns
200 rows
8 formats

Healthcare Telemedicine Usage Patterns

This dataset provides detailed records of telemedicine appointment usage, including patient demographics, provider information, technology platforms, and satisfaction outcomes. It enables healthcare organizations to analyze remote care trends, optimize digital health programs, and identify barriers to telemedicine adoption across populations.

last updated 20 Aug 2025
by GoMask

The brief that made it

Analyzing telemedicine adoption and usage patterns

Sample rows

preview · 8 of 200 rows · all 20 columns

appointment_idstring	connection_qualitystring	patient_satisfaction_scorefloat	appointment_completedboolean	patient_location_statestring	patient_idstring	provider_idstring	appointment_datetimedatetime	appointment_duration_minutesinteger	appointment_typestring	specialtystring	patient_ageinteger	patient_genderstring	patient_location_citystring	patient_location_postal_codestring	patient_location_countrystring	platform_usedstring	device_typestring	cancellation_reasonstring	follow_up_requiredboolean
APPT00001	excellent	4.7	true	IL	PAT1001	PROV01	2024-05-01T09:13:00	52	initial_consultation	primary care	32	female	Chicago	60614	USA	Zoom	laptop	blank	true
APPT00002	good	4.2	true	TX	PAT1002	PROV02	2024-05-02T14:40:00	37	follow_up	cardiology	77	male	Houston	77005	USA	Epic	tablet	blank	true
APPT00003	fair	blank	false	WA	PAT1003	PROV03	2024-05-03T18:15:00	12	urgent_care	pediatrics	6	female	Seattle	98109	USA	Doxy.me	smartphone	Patient unable to connect	blank
APPT00004	good	4.6	true	NY	PAT1004	PROV04	2024-05-04T10:00:00	50	routine_check	dermatology	43	male	New York	10022	USA	Epic	desktop	blank	false
APPT00005	poor	blank	false	MA	PAT1005	PROV05	2024-05-05T21:10:00	16	urgent_care	psychiatry	14	male	Boston	02118	USA	Amwell	tablet	Connection dropped repeatedly	blank
APPT00006	excellent	4.8	true	CA	PAT1006	PROV06	2024-05-06T08:30:00	39	follow_up	gastroenterology	68	female	San Francisco	94105	USA	Teladoc	desktop	blank	true
APPT00007	good	4.9	true	PA	PAT1007	PROV07	2024-05-07T15:25:00	63	routine_check	primary care	56	female	Philadelphia	19103	USA	Epic	laptop	blank	false
APPT00008	good	4.3	true	AZ	PAT1008	PROV08	2024-05-08T19:47:00	22	other	physical therapy	71	female	Phoenix	85016	USA	MDLive	tablet	blank	true
APPT00009	excellent	5	true	CO	PAT1009	PROV09	2024-05-09T13:33:00	75	initial_consultation	psychiatry	23	other	Denver	80218	USA	Zoom	desktop	blank	true
APPT00010	fair	3.9	true	FL	PAT1010	PROV10	2024-05-10T11:17:00	44	routine_check	rheumatology	80	male	Miami	33134	USA	MDLive	tablet	blank	false
APPT00011	poor	blank	false	TX	PAT1011	PROV11	2024-05-11T15:59:00	11	urgent_care	dermatology	2	female	Dallas	75201	USA	Amwell	smartphone	Appointment cancelled at provider request	blank
APPT00012	good	4.1	true	CA	PAT1012	PROV12	2024-05-12T17:45:00	26	other	endocrinology	41	female	San Diego	92103	USA	Teladoc	laptop	blank	false
APPT00013	excellent	4.9	true	GA	PAT1013	PROV13	2024-05-13T08:55:00	59	initial_consultation	psychiatry	27	prefer_not_to_say	Atlanta	30309	USA	Zoom	desktop	blank	true
APPT00014	good	4.5	true	blank	PAT1014	PROV14	2024-05-14T12:23:00	21	follow_up	gastroenterology	75	female	Toronto	M5V 2T6	CAN	Epic	tablet	blank	true
APPT00015	good	4.6	true	blank	PAT1015	PROV15	2024-05-15T16:40:00	33	routine_check	primary care	50	male	London	SW1A 1AA	GBR	Epic	laptop	blank	false
APPT00016	poor	blank	false	blank	PAT1016	PROV16	2024-05-16T19:20:00	17	urgent_care	pediatrics	8	male	Berlin	10115	DEU	Doxy.me	tablet	Patient lost connection	blank
APPT00017	fair	3.7	true	blank	PAT1017	PROV17	2024-05-17T08:10:00	24	other	dermatology	39	female	Madrid	28001	ESP	Amwell	tablet	blank	false
APPT00018	excellent	4.8	true	blank	PAT1018	PROV18	2024-05-18T13:30:00	54	initial_consultation	primary care	26	female	Paris	75008	FRA	Zoom	laptop	blank	true
APPT00019	poor	blank	false	blank	PAT1019	PROV19	2024-05-19T20:41:00	15	urgent_care	dermatology	5	male	Sydney	2000	AUS	Doxy.me	smartphone	Technical issues with provider	blank
APPT00020	fair	3.2	true	blank	PAT1020	PROV20	2024-05-20T07:52:00	42	follow_up	cardiology	84	female	Vancouver	V6Z 2H7	CAN	MDLive	tablet	blank	true

What the 200 rows show

from the 200-row sample

Fair (connection quality) stands out: mean patient_satisfaction_score is 3.3, against 4.5 for the rest.

79%appointment_completed = true
4.4median patient_satisfaction_score
4patient genders
4device types
5appointment types
6platform useds

Mean patient_satisfaction_score by connection_quality157 rows

patient_satisfaction_score157 rows, in bands of 0.5

Median 4.4, from 2.2 to 5.0.

patient_location_state126 rows with a value · 74 left blank

TX23
CA19
IL9
ON8
NY7
AZ7
FL7
WA6
MA6
QC5

20 columns by typefrom the column list below

string 14
integer 2
float 1
datetime 1
boolean 2

Columns

20 columns in four groups

blueprint · 20 columns
column	type	description	example
Text 14 columns
`appointment_id`	string	Unique identifier for each telemedicine appointmentunique	APPT00001
`patient_id`	string	Unique identifier for the patient	PAT1001
`provider_id`	string	Unique identifier for the healthcare provider conducting the appointment	PROV01
`appointment_type`	string	Type of telemedicine appointment (e.g., initial consultation, follow-up, urgent care)5 values	initial_consultation
`specialty`	string	Medical specialty for the appointment (e.g., cardiology, dermatology, primary care)9 specialties	primary care
`patient_gender`	string	Gender of the patientmale · female · other · prefer_not_to_say	female
`patient_location_city`	string	City of the patient's location during the appointmentoptional	Chicago
`patient_location_state`	string	State or province of the patient's location during the appointmentoptional	IL
`patient_location_postal_code`	string	Postal code of the patient's location during the appointmentoptional	60614
`patient_location_country`	string	Country of the patient's location during the appointment10 countries	USA
`platform_used`	string	Telemedicine platform or software used for the appointment (e.g., Zoom, Doxy.me, Epic)6 useds	Zoom
`device_type`	string	Type of device used by the patient (e.g., smartphone, tablet, desktop, laptop)smartphone · tablet · desktop · laptop · other	laptop
`connection_quality`	string	Quality of the internet connection during the appointment (e.g., excellent, good, fair, poor)excellent · good · fair · poor · unknown · optional	excellent
`cancellation_reason`	string	Reason for appointment cancellation, if applicableoptional	Patient lost connection
Numbers 3 columns
`appointment_duration_minutes`	integer	Duration of the telemedicine appointment in minutes1 or more	52
`patient_age`	integer	Age of the patient at the time of appointment0 to 120	32
`patient_satisfaction_score`	float	Patient's satisfaction score for the appointment (0.0 to 5.0)0 to 5 · optional	4.7
Dates and times 1 column
`appointment_datetime`	datetime	Date and time when the telemedicine appointment occurred	2024-05-01T09:13:00
True or false 2 columns
`appointment_completed`	boolean	Indicates whether the appointment was successfully completed	true
`follow_up_required`	boolean	Indicates if a follow-up appointment is recommendedoptional	true

Use it for

A healthcare dashboard
The appointment_completed rate, patient_satisfaction_score by connection_quality and a breakdown of patient_location_state. Excel, Power BI or Tableau.
Why do the 29 fair rows have a mean patient_satisfaction_score of 3.3?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Appointments200
APPT000014.7excellent
APPT000024.2good
APPT00003fair
A software demo
Believable appointments with patient_id, provider_id and appointment_datetime to fill a screen in front of a buyer.

This dataset200 rows20 columns

Yours1,000 rows20 columnspatient_location_city: UK only

blueprint · healthcare-telemedicine-usage-patterns

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 20 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

Include appointment platform type
Segment by specialty
Track usage frequency per patient
Flag technical issues reported

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: healthcare-telemedicine-usage-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?