Healthcare
23 columns
200 rows
8 formats

Healthcare Patient Satisfaction Scores

This dataset contains detailed patient satisfaction survey responses linked to healthcare visit records, including ratings on various service aspects, patient demographics, provider and department information, and identified service gaps. It enables comprehensive analysis of patient experience, service quality, and areas for improvement in healthcare delivery.

last updated 20 Aug 2025
by GoMask

The brief that made it

Modeling patient satisfaction and predicting service quality trends

Sample rows

preview · 8 of 200 rows · all 23 columns

patient_idstring	address_countrystring	overall_satisfaction_scorefloat	survey_completedboolean	patient_ethnicitystring	visit_idstring	visit_datedate	provider_idstring	departmentstring	patient_ageinteger	patient_genderstring	address_streetstring	address_citystring	address_statestring	address_postal_codestring	wait_time_scorefloat	provider_communication_scorefloat	facility_cleanliness_scorefloat	staff_courtesy_scorefloat	recommend_facilityboolean	additional_commentsstring	service_gap_identifiedboolean	service_gap_descriptionstring
P001	USA	4.7	true	White	V1001	2022-03-14	DR001	GeneralPractice	28	female	45 Elm St	New York	NY	10001	4.2	4.8	4.6	4.9	true	Great care, staff very attentive and helpful.	false	blank
P002	USA	4.9	true	Black	V1002	2023-05-21	DR002	Cardiology	70	male	102 Main Ave	San Jose	CA	95112	4.7	4.9	4.9	5	true	Exceptional doctor and team!	false	blank
P003	USA	3.9	true	Asian	V1003	2021-11-30	DR003	Pediatrics	7	female	271 Oak Lane	Chicago	IL	60614	3.5	4.2	4	4.1	true	My daughter's doctor was very caring.	false	blank
P004	USA	4.2	true	Hispanic	V1004	2022-08-02	DR004	Oncology	84	male	395 Willow Dr	Baltimore	MD	21201	2.7	4.4	4.1	4.3	true	Long wait time but very good care.	true	Wait times in the oncology department need improvement.
P005	USA	4.5	true	South Asian	V1005	2023-01-16	DR005	Dermatology	34	female	88 1st Ave	Los Angeles	CA	90012	4.3	4.6	4.8	4.6	true	Clean office and helpful staff.	false	blank
P006	USA	blank	false	White	V1006	2022-04-11	DR006	PhysicalTherapy	41	male	2349 Pine St	Dallas	TX	75201	blank	blank	blank	blank	blank	blank	blank	blank
P007	USA	3.2	true	Black	V1007	2021-07-22	DR007	Radiology	11	male	17 Maple St	Phoenix	AZ	85004	2.3	3.4	3.5	3.1	false	Radiology wait was too long for a child.	true	Children should have priority in radiology.
P008	USA	4.8	true	Hispanic	V1008	2023-03-17	DR008	OBGYN	30	female	610 Cedar St	Miami	FL	33101	4.9	5	5	4.8	true	Very comfortable visit, highly recommend.	false	blank
P009	USA	1.7	true	Native American	V1009	2022-09-27	DR009	Emergency	52	male	Null	blank	blank	blank	1.2	2.1	2.4	1.9	false	Unacceptable wait and poor communication.	true	Emergency department needs faster triage.
P010	USA	blank	false	White	V1010	2023-07-05	DR010	Gastroenterology	69	female	1753 Spruce Rd	Boston	MA	02118	blank	blank	blank	blank	blank	blank	blank	blank
P011	USA	2.2	true	East Asian	V1011	2022-05-18	DR011	Neurology	44	other	5012 Birch St	Seattle	WA	98109	1.9	2.6	2.5	2.4	false	Provider did not explain diagnosis well.	true	Neurology team should improve patient education.
P012	USA	4.5	true	White	V1012	2021-12-13	DR012	Ophthalmology	78	female	318 Spruce St	San Diego	CA	92101	4.2	4.7	4.8	4.6	true	Vision test was thorough and staff friendly.	false	blank
P013	USA	blank	false	Middle Eastern	V1013	2023-04-03	DR013	Urology	53	male	1225 Walnut St	Houston	TX	77002	blank	blank	blank	blank	blank	blank	blank	blank
P014	USA	4.1	true	blank	V1014	2022-06-25	DR014	Allergy	12	prefer_not_to_say	876 Cedar Ave	Philadelphia	PA	19103	4	4.3	4.2	4	true	Allergy shots were easy and quick.	false	blank
P015	blank	1.5	true	Black	V1015	2021-09-10	DR015	Psychiatry	33	female	blank	blank	blank	blank	1	1.7	2	1.4	false	Not satisfied, long delay in care.	true	More mental health staff needed for timely care.
P016	USA	3.4	true	Asian	V1016	2023-08-09	DR016	Orthopedics	46	male	300 Magnolia Blvd	San Francisco	CA	94102	3	3.5	3.6	3.7	false	Orthopedics team seemed rushed.	false	blank
P017	USA	blank	false	White	V1017	2022-10-14	DR017	PhysicalMedicine	37	female	952 Chestnut Rd	Austin	TX	78701	blank	blank	blank	blank	blank	blank	blank	blank
P018	USA	4	true	Latine	V1018	2023-02-22	DR018	Endocrinology	66	male	211 Lakeview Dr	Orlando	FL	32801	3.9	4.2	4.1	4	true	Endocrinology team is knowledgeable.	false	blank
P019	blank	4.3	true	Black	V1019	2022-12-05	DR019	Hematology	89	female	Null	blank	blank	blank	4.4	4.5	4.2	4.6	true	Hematology staff are very kind.	false	blank
P020	USA	blank	false	Pacific Islander	V1020	2021-10-19	DR020	Pulmonology	57	male	1637 Holly St	Denver	CO	80203	blank	blank	blank	blank	blank	blank	blank	blank

What the 200 rows show

from the 200-row sample

66%survey_completed = true
4.2median overall_satisfaction_score
4patient genders
28departments
46address states
41median patient_age

address_country182 rows by value

overall_satisfaction_score131 rows, in bands of 0.5

Median 4.2, from 1.5 to 5.0.

patient_ethnicity188 rows with a value · 12 left blank

White52
Black34
Asian25
Latine13
Other12
Native American11
Hispanic9
South Asian8
East Asian8
Middle Eastern8

23 columns by typefrom the column list below

string 13
integer 1
float 5
date 1
boolean 3

Columns

23 columns in four groups

blueprint · 23 columns
column	type	description	example
Text 13 columns
`patient_id`	string	Unique identifier for each patientunique	P001
`visit_id`	string	Unique identifier for each healthcare visitunique	V1001
`provider_id`	string	Unique identifier for the healthcare provider seen during the visit	DR001
`department`	string	Department or specialty where the patient was treated (e.g., Cardiology, Pediatrics)	GeneralPractice
`patient_gender`	string	Gender of the patientmale · female · other · prefer_not_to_say	female
`patient_ethnicity`	string	Ethnicity of the patient11 ethnicities · optional	White
`address_street`	string	Street address of the patientoptional	45 Elm St
`address_city`	string	City of the patient's addressoptional	New York
`address_state`	string	State of the patient's addressoptional	NY
`address_postal_code`	string	Postal code of the patient's addressoptional	10001
`address_country`	string	Country of the patient's address3 countries · optional	USA
`additional_comments`	string	Free-text comments or feedback provided by the patientoptional	Friendly and efficient.
`service_gap_description`	string	Description of any service gaps identified by the patientoptional	Wait times in the oncolog…
Numbers 6 columns
`patient_age`	integer	Age of the patient at the time of visit0 to 120	28
`overall_satisfaction_score`	float	Overall satisfaction score given by the patient (scale: 1.0 to 5.0)1 to 5 · optional	4.7
`wait_time_score`	float	Patient rating of wait time (scale: 1.0 to 5.0)1 to 5 · optional	4.2
`provider_communication_score`	float	Patient rating of provider communication (scale: 1.0 to 5.0)1 to 5 · optional	4.8
`facility_cleanliness_score`	float	Patient rating of facility cleanliness (scale: 1.0 to 5.0)1 to 5 · optional	4.6
`staff_courtesy_score`	float	Patient rating of staff courtesy (scale: 1.0 to 5.0)1 to 5 · optional	4.9
Dates and times 1 column
`visit_date`	date	Date of the healthcare visit	2022-03-14
True or false 3 columns
`survey_completed`	boolean	Indicates if the patient completed the satisfaction survey for the visit	true
`recommend_facility`	boolean	Indicates if the patient would recommend the facility to othersoptional	true
`service_gap_identified`	boolean	Indicates if the patient identified any service gaps during the visitoptional	false

Use it for

A healthcare dashboard
The survey_completed rate, overall_satisfaction_score by address_country and a breakdown of patient_ethnicity. Excel, Power BI or Tableau.
Why do 131 of 200 rows have survey_completed = true?
A class exercise
Hand out the rows and one question. Everyone works from the same 200 rows.
Patients200
P0014.7USA
P0024.9USA
P006USA
A software demo
Believable patients with visit_id, visit_date and provider_id to fill a screen in front of a buyer.

This dataset200 rows23 columns

Yours1,000 rows23 columnsaddress_street: UK only

blueprint · healthcare-patient-satisfaction-scores

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 23 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 visit and provider info
Log satisfaction survey scores
Flag follow-up visits
Integrate wait time data
Aggregate by clinic and specialty

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-patient-satisfaction-scores

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A healthcare dashboard

A class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Healthcare

What should your data show?