Healthcare
32 columns
200 rows
8 formats

Chronic Disease Patient Registry

This dataset provides a comprehensive registry of chronic disease patients, including demographics, diagnoses, clinical parameters, medications, and risk stratification metrics. It is designed to support advanced analytics, care management workflows, and risk prediction models for population health management and clinical decision support.

last updated 22 Jul 2025
by GoMask

The brief that made it

Risk stratification and identification of high-risk patients for targeted interventions

Sample rows

preview · 8 of 200 rows · all 32 columns

patient_idstring	risk_categorystring	egfr_ml_min_1_73m2float	secondary_diagnosesstring	first_namestring	last_namestring	date_of_birthdate	genderstring	phone_numberstring	emailstring	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring	primary_diagnosisstring	diagnosis_datedate	last_clinic_visit_datedate	smoker_statusstring	alcohol_usestring	height_cmfloat	weight_kgfloat	bmifloat	systolic_bpinteger	diastolic_bpinteger	hba1c_percentfloat	ldl_cholesterol_mg_dlfloat	medicationsstring	allergiesstring	care_managerstring	risk_scorefloat	last_updateddatetime
P000001	moderate	95.2	hyperlipidemia	Isabella	Reed	1982-05-13	female	+1-212-555-0184	isabella.reed82@gmail.com	425 West 62nd St	New York	NY	10023	USA	hypertension	2016-09-29	2024-03-17	never	current	167.4	81.2	29.1	144	86	blank	137.8	amlodipine, atorvastatin	none	CM001	0.48	2024-04-04T11:25:55
P000002	moderate	51.6	hypertension, CKD	Samuel	Kang	1975-07-02	male	+82 2-1234-5678	samkang75@naver.com	21 Gangnam-daero	Seoul	Seoul	06030	South Korea	diabetes	2012-12-18	2024-01-08	former	current	172	93.5	31.6	152	94	8.6	167.2	metformin, lisinopril, furosemide	blank	CM004	0.69	2024-03-21T13:42:20
P000003	low	98	blank	Ayaan	Patel	2016-11-20	male	blank	blank	D-48, Ring Road	Ahmedabad	GJ	380015	India	asthma	2022-01-23	2024-04-09	never	never	130.2	31.8	18.7	100	68	blank	82.3	salbutamol inhaler	dust	blank	0.19	2024-04-13T15:02:10
P000004	moderate	34.7	hypertension, anemia	Maya	Gonzalez	1955-04-12	female	+34 91 555 4351	maya.gonzalez55@correo.es	Calle Mayor 12	Madrid	Madrid	28013	Spain	CKD	2017-08-04	2024-02-22	never	former	160.5	68.1	26.5	138	84	blank	126.5	losartan, darbepoetin	blank	CM002	0.61	2024-03-11T10:57:32
P000005	low	88.3	blank	Oluwafemi	Adebayo	2009-09-15	male	blank	blank	blank	Lagos	blank	100001	Nigeria	sickle cell disease	2014-04-25	2023-12-11	never	never	145.1	38.4	18.2	106	70	blank	74.1	hydroxyurea	blank	CM010	0.23	2024-04-06T16:14:23
P000006	low	104.8	blank	Emily	Thompson	1994-12-06	female	+1-415-212-9876	emilyt94@outlook.com	77 Lombard St	San Francisco	CA	94133	USA	PCOS	2019-02-17	2024-03-23	never	never	162.3	71.9	27.3	116	72	blank	109.2	metformin	penicillin	blank	0.15	2024-04-09T09:31:17
P000007	high	56.9	hypertension, hyperlipidemia	Sean	McAllister	1968-03-30	male	+44 20 7946 2172	sean.mcallister68@nhs.uk	15 Oxford Rd	London	England	NW1 5BG	UK	heart failure	2015-07-20	2024-04-01	current	current	178.2	99.4	31.3	161	98	blank	154.7	carvedilol, ramipril, atorvastatin	blank	CM004	0.81	2024-04-08T17:46:09
P000008	low	102.1	blank	Lina	Zhou	2001-01-27	female	+86 10 6552 1346	lina.zhou01@sina.cn	88 Dongzhimen St	Beijing	Beijing	100007	China	rheumatoid arthritis	2020-11-15	2024-02-28	never	never	160.7	57.5	22.3	109	68	blank	91.9	methotrexate	blank	blank	0.21	2024-03-16T12:19:04
P000009	high	40.8	hypertension, retinopathy	Jean	Dubois	1942-11-11	male	+33 1 45 25 13 45	blank	3 Rue de la Paix	Paris	Île-de-France	75002	France	diabetes	2009-02-03	2023-11-30	former	former	168.9	91.6	32.1	143	86	9.5	169.4	insulin glargine, lisinopril	blank	CM001	0.73	2024-03-19T15:42:14
P000010	moderate	94.5	hypertension	Tatiana	Ivanova	1979-08-24	female	+7 495 123 4456	tatiana.ivanova79@yandex.ru	Leninsky Prospekt 47	Moscow	Moscow	119334	Russia	obesity	2015-10-11	2024-03-18	never	current	159.8	102.3	40	137	87	7.4	141.2	orlistat, amlodipine	none	CM002	0.59	2024-04-14T13:08:42
P000011	low	112	blank	Diego	Silva	1989-02-10	male	+55 11 99999-1234	dsilva89@gmail.com	R. Augusta, 1122	São Paulo	SP	01304-001	Brazil	asthma	2014-05-13	2024-03-27	never	never	174.5	85.3	28	121	76	blank	104.8	montelukast	pollen	blank	0.17	2024-04-04T13:37:50
P000012	high	65.4	hyperlipidemia, obesity	Chloe	Dubois	1960-05-28	female	+33 1 42 78 34 21	chloe.dubois60@orange.fr	12 Rue de l'Université	Paris	Île-de-France	75007	France	hypertension	2013-06-12	2024-03-08	former	former	164.9	96.6	35.6	154	93	blank	159.7	amlodipine, simvastatin	blank	CM003	0.77	2024-04-12T11:03:29
P000013	low	92.7	blank	Mpho	Ndlovu	2006-06-16	female	blank	blank	blank	Harare	blank	blank	Zimbabwe	sickle cell disease	2011-10-22	2024-03-04	never	never	141.3	37	18.5	105	64	blank	75	hydroxyurea	blank	blank	0.18	2024-04-07T14:20:45
P000014	moderate	84.6	hypertension	David	Smith	1985-10-04	male	+1-617-320-9995	dsmith85@gmail.com	62 Beacon St	Boston	MA	02108	USA	diabetes	2017-08-18	2024-04-12	never	current	175.6	91.8	29.8	148	88	7.8	133.7	metformin, lisinopril	blank	CM004	0.52	2024-04-13T10:10:38
P000015	low	89.7	blank	Yara	Fahim	1973-04-19	female	+971 4 556 7812	yara.fahim73@gmail.com	Al Wasl Rd 14	Dubai	blank	00000	UAE	lupus	2020-02-02	2024-03-30	never	never	163.7	62.4	23.3	116	74	blank	107.1	hydroxychloroquine	blank	blank	0.37	2024-03-31T11:04:46
P000016	moderate	37.5	hypertension	Olga	Serban	1963-02-12	female	+40 21 213 4567	olga.serban63@gmail.com	Strada Polona 18	Bucharest	blank	010494	Romania	CKD	2011-01-09	2024-01-19	former	former	158.7	70.2	27.9	136	83	blank	122.6	enalapril, darbepoetin	blank	CM010	0.65	2024-03-21T09:48:14
P000017	moderate	74.3	blank	Mateusz	Nowak	1982-06-13	male	+48 22 498 7321	mateusz.nowak82@onet.pl	ul. Marszałkowska 34	Warszawa	blank	00-626	Poland	COPD	2018-03-22	2024-04-02	current	former	180.4	77.2	23.7	119	72	blank	97.9	tiotropium	none	CM009	0.47	2024-04-08T07:33:12
P000018	low	94	blank	Sara	Morris	2011-03-17	female	blank	blank	blank	Chicago	IL	60614	USA	type 1 diabetes	2018-12-05	2024-03-11	never	never	144.7	39.6	18.9	98	61	8.3	77.8	insulin aspart	blank	blank	0.22	2024-03-29T15:55:01
P000019	moderate	72.1	hyperlipidemia	Sophie	Müller	1970-11-05	female	+49 30 12345678	sophie.mueller70@gmx.de	Friedrichstr. 55	Berlin	BE	10117	Germany	hypertension	2010-07-13	2024-03-25	former	current	165.2	76.1	27.9	139	88	blank	146	amlodipine, simvastatin	blank	CM008	0.66	2024-04-07T12:36:48
P000020	critical	22.6	hypertension, CKD	Ahmed	Hassan	1951-09-26	male	blank	blank	blank	Cairo	blank	blank	Egypt	heart failure	2010-06-11	blank	current	former	169	78.7	27.6	143	91	blank	142.9	furosemide, bisoprolol, enalapril	blank	CM004	0.92	2024-03-20T11:23:55

What the 200 rows show

from the 200-row sample

Critical (risk category) stands out: mean egfr_ml_min_1_73m2 is 21.0, against 84.6 for the rest.

88.0median egfr_ml_min_1_73m2
3genders
4smoker statuses
4alcohol uses
10allergieses
15care managers

Mean egfr_ml_min_1_73m2 by risk_category200 rows

egfr_ml_min_1_73m2200 rows, in bands of 20

Median 88.0, from 6.7 to 113.2.

secondary_diagnoses94 rows with a value · 106 left blank

hypertension33
hyperlipidemia16
hypertension, hyperlipidemia7
CKD5
diabetes, hypertension4
hypertension,hyperlipidemia4
hypertension, CKD3
hypertension, anemia2
hyperlipidemia, obesity2
obesity2

32 columns by typefrom the column list below

string 19
integer 2
float 7
date 3
datetime 1

Columns

32 columns in three groups

blueprint · 32 columns
column	type	description	example
Text 19 columns
`patient_id`	string	Unique identifier for each patient in the registryunique	P000001
`first_name`	string	Patient's first name	Isabella
`last_name`	string	Patient's last name	Reed
`gender`	string	Patient's gendermale · female · other · unknown	female
`phone_number`	string	Patient's primary contact phone numberoptional	+1-212-555-0184
`email`	string	Patient's email addressoptional	samkang75@naver.com
`address_street`	string	Patient's street addressoptional	425 West 62nd St
`address_city`	string	Patient's city of residenceoptional	New York
`address_state`	string	Patient's state or province of residenceoptional	NY
`address_postal_code`	string	Patient's postal or ZIP codeoptional	10023
`address_country`	string	Patient's country of residenceoptional	USA
`primary_diagnosis`	string	Primary chronic disease diagnosis (e.g., diabetes, hypertension)	hypertension
`secondary_diagnoses`	string	Comma-separated list of secondary chronic disease diagnosesoptional	hyperlipidemia
`smoker_status`	string	Patient's smoking statusnever · former · current · unknown · optional	never
`alcohol_use`	string	Patient's alcohol use statusnever · former · current · unknown · optional	current
`medications`	string	Comma-separated list of current medicationsoptional	amlodipine, atorvastatin
`allergies`	string	Comma-separated list of known allergies10 allergieses · optional	none
`care_manager`	string	Name or identifier of the assigned care manageroptional	CM001
`risk_category`	string	Assigned risk category based on risk scorelow · moderate · high · critical · optional	moderate
Numbers 9 columns
`height_cm`	float	Patient's height in centimeters30 to 300 · optional	167.4
`weight_kg`	float	Patient's weight in kilograms2 to 500 · optional	81.2
`bmi`	float	Patient's Body Mass Index (BMI)10 to 80 · optional	29.1
`systolic_bp`	integer	Most recent systolic blood pressure measurement (mmHg)50 to 300 · optional	144
`diastolic_bp`	integer	Most recent diastolic blood pressure measurement (mmHg)30 to 200 · optional	86
`hba1c_percent`	float	Most recent HbA1c measurement as a percentage3 to 20 · optional	8.6
`ldl_cholesterol_mg_dl`	float	Most recent LDL cholesterol measurement (mg/dL)10 to 500 · optional	137.8
`egfr_ml_min_1_73m2`	float	Most recent estimated glomerular filtration rate (eGFR) in mL/min/1.73m21 to 200 · optional	95.2
`risk_score`	float	Calculated risk score for patient stratification (0-1 scale)0 to 1 · optional	0.48
Dates and times 4 columns
`date_of_birth`	date	Patient's date of birth	1982-05-13
`diagnosis_date`	date	Date of initial chronic disease diagnosis	2016-09-29
`last_clinic_visit_date`	date	Date of the patient's most recent clinic visitoptional	2024-03-17
`last_updated`	datetime	Timestamp of the last update to this patient record	2024-04-04T11:25:55

Use it for

A healthcare dashboard
Egfr_ml_min_1_73m2 by risk_category and a breakdown of secondary_diagnoses. Excel, Power BI or Tableau.
Why do the 21 critical rows have a mean egfr_ml_min_1_73m2 of 21.0?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Patients200
P00000195.2moderate
P00000251.6moderate
P00000398low
A software demo
Believable patients with first_name, last_name and date_of_birth to fill a screen in front of a buyer.

This dataset200 rows32 columns

Yours1,000 rows32 columnsaddress_street: UK only

blueprint · chronic-disease-patient-registry

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 32 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

Each patient record includes demographics, diagnosis, and key metrics.
Multiple chronic conditions allowed per patient.
Lab results, medication, and outcome fields provided.
Anonymized provider and visit details included.

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: chronic-disease-patient-registry

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?