Healthcare
25 columns
200 rows
8 formats

Healthcare Preventive Screening Uptake

This dataset provides a comprehensive record of patient participation in preventive screening programs, including demographic details, screening types and outcomes, provider information, outreach activities, and follow-up actions. It enables healthcare organizations to analyze screening uptake, target outreach efforts, and monitor population health trends for improved preventive care.

last updated 20 Aug 2025
by GoMask

The brief that made it

Analyze preventive screening rates and identify gaps in population health

Sample rows

preview · 8 of 200 rows · all 25 columns

screening_record_idinteger	screening_resultstring	follow_up_flagboolean	address_statestring	patient_idinteger	first_namestring	last_namestring	date_of_birthdate	genderstring	address_streetstring	address_citystring	address_postal_codestring	address_countrystring	contact_phonestring	contact_emailstring	screening_typestring	screening_datedate	provider_idinteger	provider_namestring	provider_typestring	outreach_flagboolean	outreach_datedate	outreach_methodstring	follow_up_datedate	notesstring
1	normal	false	WA	1001	Jessica	Morris	1977-05-19	female	401 2nd Ave	Seattle	98101	USA	+1-206-555-1937	jessica.morris1977@wellhealth.org	mammogram	2022-09-15	501	Seattle Health Hospital	hospital	false	blank	blank	blank	blank
2	abnormal	true	blank	1002	Leon	Petrov	1952-09-29	male	48 Nevsky Prospekt	Saint Petersburg	191025	Russia	+7-812-555-4829	leon.petrov52@mail.ru	colonoscopy	2023-03-10	502	Nevsky Clinic	clinic	true	2023-02-20	phone	2023-03-24	Patient scheduled for repeat colonoscopy; findings discussed.
3	normal	false	blank	1003	Ahmed	Suleiman	1985-08-04	male	102 Sheik Zayed Rd	Abu Dhabi	11001	UAE	+971-2-555-8102	ahmed.suleiman1985@emirateshealth.ae	cholesterol	2022-10-05	503	Emirates Health Center	hospital	false	blank	blank	blank	blank
4	pending	true	blank	1004	Mia	Fujimoto	1992-11-17	female	3-2-1 Shibuya	Tokyo	150-0002	Japan	+81-3-5555-3217	mia.fujimoto1992@tokyomed.jp	cervical_cancer	2023-07-08	504	Tokyo Medical Clinic	clinic	true	2023-06-21	email	2023-07-22	Awaiting pathology results; patient notified by email.
5	normal	false	ID	1005	Lucas	Johnson	2014-03-23	male	88 Pine St	Boise	83702	USA	+1-208-555-8323	lucas.johnson2014@kidshealth.net	diabetes	2023-05-12	505	Boise Pediatric Health	clinic	false	blank	blank	blank	blank
6	normal	false	blank	1006	Olivia	Fernandez	1982-01-10	female	Calle Mayor 21	Madrid	28013	Spain	+34-91-555-1021	olivia.fernandez82@spainhealth.es	cholesterol	2022-11-11	506	Madrid Central Hospital	hospital	false	blank	blank	blank	blank
7	normal	false	NY	1007	Ryan	Chen	1979-04-07	male	18 Market St	New York	10002	USA	+1-212-555-1807	ryan.chen1979@nycmed.org	blood_pressure	2023-01-24	507	NYC Medical Group	physician	false	blank	blank	blank	blank
8	normal	false	blank	1008	Sophie	Leblanc	1960-06-29	female	12 Rue de la Paix	Paris	75002	France	+33-1-555-1229	sophie.leblanc1960@parisheath.fr	mammogram	2023-06-03	508	Paris Health Clinic	hospital	false	blank	blank	blank	blank
9	follow_up_required	true	TX	1009	Ethan	Williams	1988-12-12	male	303 Main St	El Paso	79901	USA	+1-915-555-3030	ethan.williams88@borderhealth.us	cholesterol	2022-08-18	509	El Paso Regional Clinic	clinic	true	2022-08-21	sms	2022-09-01	Patient advised to schedule follow-up for high cholesterol.
10	pending	true	blank	1010	Anna	Garcia	2009-07-15	female	77 Calle Flores	Madrid	28029	Spain	+34-91-555-7715	anna.garcia2009@kidsclinic.es	blood_pressure	2023-04-20	510	Madrid Kids Clinic	clinic	true	2023-04-18	mail	2023-05-01	Pending further tests for inconsistent readings.
11	normal	false	blank	1011	Maria	Silva	1971-03-01	female	Rua Augusta 94	Lisbon	1100-048	Portugal	+351-21-555-9401	maria.silva1971@lishealth.pt	diabetes	2023-02-25	511	Lisbon Health Center	community_center	false	blank	blank	blank	blank
12	normal	false	WA	1001	Jessica	Morris	1977-05-19	female	401 2nd Ave	Seattle	98101	USA	+1-206-555-1937	jessica.morris1977@wellhealth.org	cholesterol	2021-11-14	501	Seattle Health Hospital	hospital	false	blank	blank	blank	blank
13	normal	false	blank	1012	Noah	Kim	1996-02-27	male	22 Park Ave	London	SW1A 1AA	UK	+44-20-555-2270	noah.kim96@londonmed.uk	blood_pressure	2022-06-13	512	London Medical Practice	physician	false	blank	blank	blank	blank
14	normal	false	TN	1013	Emily	Turner	1981-10-30	female	56 Oak Lane	Nashville	37209	USA	+1-615-555-5630	emily.turner81@tnhealth.com	mammogram	2022-05-19	513	Nashville Women's Clinic	clinic	false	blank	blank	blank	blank
15	abnormal	true	blank	1014	Ava	Nguyen	1942-01-11	female	blank	Ho Chi Minh City	700000	Vietnam	+84-28-555-0114	ava.nguyen1942@vnhospital.vn	colonoscopy	2023-03-03	514	Vietnam National Hospital	hospital	true	2023-02-23	in_person	2023-03-17	Patient recommended for further GI evaluation.
16	normal	false	blank	1015	Mikhail	Ivanov	1970-12-05	male	Prospekt Mira 8	Moscow	129090	Russia	+7-495-555-1290	mikhail.ivanov70@mosmed.ru	diabetes	2022-12-18	515	Moscow Medical Center	clinic	false	blank	blank	blank	blank
17	normal	false	blank	1016	Chloe	Martins	1989-08-23	female	Rua da Liberdade 32	Lisbon	1250-096	Portugal	+351-21-555-8321	chloe.martins89@lisclinic.pt	cervical_cancer	2022-09-29	511	Lisbon Health Center	community_center	false	blank	blank	blank	blank
18	normal	false	WA	1017	Jayden	Smith	2002-10-14	male	400 9th Ave	Seattle	98104	USA	+1-206-555-9140	jayden.smith2002@seattlehealth.com	diabetes	2022-01-09	501	Seattle Health Hospital	hospital	false	blank	blank	blank	blank
19	normal	false	blank	1018	Dmitri	Volkov	1950-05-20	male	Ulitsa Tverskaya 15	Moscow	125009	Russia	+7-495-555-1520	dmitri.volkov50@mosclinic.ru	colonoscopy	2021-10-28	515	Moscow Medical Center	clinic	false	blank	blank	blank	blank
20	normal	false	blank	1002	Leon	Petrov	1952-09-29	male	48 Nevsky Prospekt	Saint Petersburg	191025	Russia	+7-812-555-4829	leon.petrov52@mail.ru	cholesterol	2022-02-01	502	Nevsky Clinic	clinic	false	blank	blank	blank	blank

What the 200 rows show

from the 200-row sample

Abnormal (screening result) stands out: 18 of its 18 rows have follow_up_flag = true, against 18 of 182 for the rest.

18%follow_up_flag = true
4genders
4provider types
5outreach methods
6screening types
25address countries

Follow up flag rate by screening_resultfollow_up_flag = true

address_state79 rows with a value · 121 left blank

WA15
NY11
ID10
TX10
CA10
IL4
ON4
CO3
TN2
AZ2

25 columns by typefrom the column list below

string 16
integer 3
date 4
boolean 2

Columns

25 columns in four groups

blueprint · 25 columns
column	type	description	example
Text 16 columns
`first_name`	string	Patient's first name	Jessica
`last_name`	string	Patient's last name	Morris
`gender`	string	Patient's gendermale · female · other · unknown	female
`address_street`	string	Patient's street addressoptional	401 2nd Ave
`address_city`	string	Patient's cityoptional	Seattle
`address_state`	string	Patient's state or provinceoptional	WA
`address_postal_code`	string	Patient's postal or ZIP codeoptional	98101
`address_country`	string	Patient's country of residenceoptional	USA
`contact_phone`	string	Patient's primary contact phone numberoptional	+1-206-555-1937
`contact_email`	string	Patient's email addressoptional	leon.petrov52@mail.ru
`screening_type`	string	Type of preventive screening (e.g., mammogram, colonoscopy, blood pressure)7 values	mammogram
`screening_result`	string	Result of the screening (e.g., normal, abnormal, follow-up required)5 values	normal
`provider_name`	string	Name of the healthcare provider or facility	Seattle Health Hospital
`provider_type`	string	Type of provider (e.g., hospital, clinic, physician)hospital · clinic · physician · community_center · other · optional	hospital
`outreach_method`	string	Method used for outreach (e.g., phone, email, mail, in-person)6 values · optional	phone
`notes`	string	Additional notes or comments related to the screening or outreachoptional	Awaiting lab results.
Numbers 3 columns
`screening_record_id`	integer	Unique identifier for each preventive screening recordunique	1
`patient_id`	integer	Unique identifier for each patient	1001
`provider_id`	integer	Unique identifier for the healthcare provider who performed the screening	501
Dates and times 4 columns
`date_of_birth`	date	Patient's date of birth	1977-05-19
`screening_date`	date	Date when the preventive screening was performed	2022-09-15
`outreach_date`	date	Date when outreach was conducted (if applicable)optional	2023-02-20
`follow_up_date`	date	Scheduled date for follow-up (if required)optional	2023-03-24
True or false 2 columns
`outreach_flag`	boolean	Indicates if the patient was contacted for screening outreach	false
`follow_up_flag`	boolean	Indicates if a follow-up is required based on screening result	false

Use it for

A healthcare dashboard
The follow_up_flag rate, follow_up_flag by screening_result and a breakdown of address_state. Excel, Power BI or Tableau.
Why do 36 of 200 rows have follow_up_flag = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Rows200
1normal
2abnormal
4pending
A software demo
Believable rows with patient_id, first_name and last_name to fill a screen in front of a buyer.

This dataset200 rows25 columns

Yours1,000 rows25 columnsaddress_street: UK only

blueprint · healthcare-preventive-screening-uptake

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 25 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

Monitor screening attendance by age group
Segment data by screening type
Flag missed screening intervals
Include follow-up outcomes

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-preventive-screening-uptake

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?