Healthcare
30 columns
100 rows
8 formats

Diabetes Registry and Hemoglobin A1C Tracking

This dataset provides a comprehensive registry of diabetes patients, including key clinical metrics such as Hemoglobin A1C, blood glucose logs, complications, medication adherence, and preventive exam results. It is ideal for chronic disease management, population health analytics, and quality improvement initiatives in healthcare organizations.

last updated 2 Nov 2025
by GoMask

The brief that made it

Population health management and risk stratification

Sample rows

preview · 8 of 100 rows · all 30 columns

patient_idstring	foot_exam_resultstring	medication_adherence_scorefloat	complications_nephropathyboolean	address_statestring	first_namestring	last_namestring	date_of_birthdate	genderstring	address_streetstring	address_citystring	address_postal_codestring	address_countrystring	diabetes_typestring	diagnosis_datedate	latest_a1c_valuefloat	latest_a1c_datedate	average_blood_glucosefloat	blood_glucose_log_countinteger	complications_retinopathyboolean	complications_neuropathyboolean	complications_cvdboolean	eye_exam_datedate	eye_exam_resultstring	foot_exam_datedate	quality_measure_bp_controlledboolean	quality_measure_lipid_controlledboolean	quality_measure_smoking_statusstring	primary_care_providerstring	last_visit_datedate
P001	normal	89.5	false	MA	Maria	Santos	1980-07-15	female	12 Lakeview Blvd	Boston	02118	USA	type 2	2015-03-22	8.2	2023-12-15	168.1	23	false	true	false	2023-11-29	normal	2024-02-01	true	true	former	Dr. A. Patel	2024-03-10
P002	normal	81.2	false	WA	James	Nguyen	1965-02-08	male	55 Summer St	Seattle	98109	USA	type 2	2012-10-05	9.7	2024-04-02	198.3	15	true	false	true	2023-10-15	abnormal	2024-03-05	false	true	current	Dr. K. Chen	2024-03-28
P003	normal	95.3	false	GA	Olivia	Johnson	1992-09-21	female	101 Maple Ave	Atlanta	30303	USA	type 1	2002-04-18	7.1	2024-03-09	145.2	27	false	false	false	2024-01-17	normal	2023-12-12	true	true	never	Dr. S. Parker	2024-04-03
P004	normal	97.8	false	CA	Rajesh	Desai	1975-05-12	male	220 Pine St	San Francisco	94111	USA	type 2	2017-08-30	6.8	2024-03-14	121.7	37	false	false	false	2023-11-05	normal	2024-03-07	true	true	never	Dr. R. Lee	2024-03-24
P005	abnormal	67.4	true	CO	Linda	Martin	1956-04-22	female	18 Oak Dr	Denver	80216	USA	type 2	2010-07-13	11.6	2024-01-30	228.9	12	true	true	true	2024-02-08	abnormal	2024-02-08	false	false	former	Dr. K. Lee	2024-03-02
P006	normal	73.5	false	FL	George	Williams	1948-01-18	male	77 Cedar Lane	Tampa	33605	USA	type 2	2005-09-25	7.9	2024-02-19	174.4	29	false	true	true	2023-12-23	normal	2024-01-05	false	false	current	Dr. J. Patel	2024-02-18
P007	normal	98	false	IL	Emily	Taylor	1988-11-03	female	1105 Windy Way	Chicago	60613	USA	type 1	2007-05-27	6.5	2024-03-01	112.6	43	false	false	false	2024-02-16	normal	2024-03-08	true	true	never	Dr. E. Oliver	2024-03-12
P008	abnormal	62.7	true	TX	Robert	Garcia	1970-06-30	male	9 Willow St	Houston	77007	USA	type 2	2016-04-18	10.2	2023-12-04	215.8	11	true	true	false	2024-01-21	abnormal	2024-03-02	false	false	former	Dr. S. Larkin	2024-03-15
P009	normal	92.4	false	AZ	Angela	Barnes	1990-12-25	female	223 Forest Rd	Phoenix	85018	USA	type 1	2008-09-03	7.7	2024-01-28	140.6	34	false	false	false	2023-10-27	normal	2024-02-11	true	true	never	Dr. T. Nelson	2024-03-05
P010	abnormal	79.8	false	OH	Kenneth	Brown	1962-03-14	male	890 Main St	Cleveland	44102	USA	type 2	2014-06-11	8.8	2024-02-28	185.7	18	true	true	true	2024-03-10	abnormal	2024-03-10	false	false	current	Dr. A. Carter	2024-03-21
P011	normal	90.4	false	OR	Samantha	Lee	1979-08-17	female	44 Spruce St	Portland	97205	USA	type 2	2019-01-24	7.4	2024-03-14	158.9	26	false	false	false	2024-02-18	normal	2024-02-18	true	true	never	Dr. J. Wu	2024-03-18
P012	abnormal	61.2	true	TX	David	Kim	1955-10-02	male	1022 Elm St	Dallas	75201	USA	type 2	2011-06-30	12.4	2024-03-07	246.4	9	true	true	true	2024-01-14	abnormal	2024-03-01	false	false	current	Dr. D. Young	2024-03-09
P013	normal	96.1	false	MN	Jessica	Clark	1982-06-15	female	780 Birch St	Minneapolis	55403	USA	type 1	1994-12-03	7	2024-04-04	132.1	32	false	false	false	2023-12-22	normal	2024-01-15	true	true	never	Dr. H. Morales	2024-03-26
P014	normal	83.6	false	NV	Michael	Rodriguez	1969-05-28	male	39 Aspen Ave	Las Vegas	89101	USA	type 2	2013-08-09	9.1	2024-03-12	176.9	21	true	true	false	2024-02-11	abnormal	2023-12-29	false	true	former	Dr. S. Wu	2024-03-19
P015	normal	91.7	false	MD	Elizabeth	Davis	1977-12-01	female	65 Poplar Way	Baltimore	21202	USA	type 2	2018-02-28	8	2023-11-14	156.2	25	false	false	false	2024-03-03	normal	2024-01-11	true	true	never	Dr. A. Foster	2024-03-22
P016	normal	97.5	false	MO	Joshua	White	1983-07-25	male	33 Highland Blvd	Kansas City	64108	USA	type 1	1996-03-16	6.2	2024-03-17	128.5	41	false	false	false	2024-01-23	normal	2024-02-20	true	true	never	Dr. B. Evans	2024-04-02
P017	normal	93.6	false	NC	Grace	Thomas	2000-04-14	female	410 River Rd	Raleigh	27609	USA	type 1	2013-07-04	6.9	2024-03-27	137.2	35	false	false	false	2024-02-05	normal	2024-02-05	true	true	never	Dr. G. King	2024-03-30
P018	normal	78.3	false	OH	Ethan	Moore	1972-10-09	male	95 Valley Rd	Columbus	43215	USA	type 2	2015-11-13	8.7	2024-03-13	184.9	22	true	false	false	2023-12-30	abnormal	2024-01-27	false	true	former	Dr. M. Patel	2024-03-21
P019	normal	99.1	false	FL	Ashley	Martinez	1986-08-22	female	200 Vista Dr	Orlando	32801	USA	gestational	2024-01-09	5.8	2024-03-04	101.5	17	false	false	false	2024-02-22	normal	2024-03-06	true	true	never	Dr. L. Young	2024-03-16
P020	abnormal	77.9	true	PA	Christopher	Lopez	1967-09-05	male	4119 Spruce St	Philadelphia	19104	USA	type 2	2012-02-11	8.9	2023-11-22	180.7	16	true	false	true	2024-02-24	abnormal	2024-02-24	false	false	current	Dr. D. Carter	2024-03-17

What the 100 rows show

from the 100-row sample

Abnormal (foot exam result) stands out: 21 of its 23 rows have complications_nephropathy = true, against 2 of 76 for the rest.

23%complications_nephropathy = true
91.8median medication_adherence_score
2eye exam results
3genders
3diabetes types
3quality measure smoking statuses

Complications nephropathy rate by foot_exam_resultcomplications_nephropathy = true

Abnormal (foot exam result)'s mean medication_adherence_score is 72.0, against 92.6 for normal.

medication_adherence_score100 rows, in bands of 10

Median 91.8, from 54.7 to 99.7.

address_state100 rows · top 10 of 36 values

30 columns by typefrom the column list below

string 14
integer 1
float 3
date 6
boolean 6

Columns

30 columns in four groups

blueprint · 30 columns
column	type	description	example
Text 14 columns
`patient_id`	string	Unique identifier for each diabetes patientunique	P001
`first_name`	string	Patient's first name	Maria
`last_name`	string	Patient's last name	Santos
`gender`	string	Patient's gendermale · female · other · unknown	female
`address_street`	string	Patient's street addressoptional	12 Lakeview Blvd
`address_city`	string	Patient's cityoptional	Boston
`address_state`	string	Patient's state or provinceoptional	MA
`address_postal_code`	string	Patient's postal codeoptional	02118
`address_country`	string	Patient's countryoptional	USA
`diabetes_type`	string	Type of diabetes diagnosedtype 1 · type 2 · gestational · other	type 2
`eye_exam_result`	string	Result of most recent diabetic eye examnormal · abnormal · not performed · unknown · optional	normal
`foot_exam_result`	string	Result of most recent diabetic foot examnormal · abnormal · not performed · unknown · optional	normal
`quality_measure_smoking_status`	string	Patient's smoking statusnever · former · current · unknown · optional	former
`primary_care_provider`	string	Name or identifier of patient's primary care provideroptional	Dr. A. Patel
Numbers 4 columns
`latest_a1c_value`	float	Most recent Hemoglobin A1C measurement (percentage)3 to 20 · optional	8.2
`average_blood_glucose`	float	Average blood glucose level over the last 30 days (mg/dL)40 to 600 · optional	168.1
`blood_glucose_log_count`	integer	Number of blood glucose readings logged in the last 30 days0 or more · optional	23
`medication_adherence_score`	float	Medication adherence score (percentage, 0-100)0 to 100 · optional	89.5
Dates and times 6 columns
`date_of_birth`	date	Patient's date of birth	1980-07-15
`diagnosis_date`	date	Date of diabetes diagnosis	2015-03-22
`latest_a1c_date`	date	Date of most recent A1C measurementoptional	2023-12-15
`eye_exam_date`	date	Date of most recent diabetic eye examoptional	2023-11-29
`foot_exam_date`	date	Date of most recent diabetic foot examoptional	2024-02-01
`last_visit_date`	date	Date of patient's most recent clinic visitoptional	2024-03-10
True or false 6 columns
`complications_retinopathy`	boolean	Indicates if patient has diabetic retinopathyoptional	false
`complications_neuropathy`	boolean	Indicates if patient has diabetic neuropathyoptional	true
`complications_nephropathy`	boolean	Indicates if patient has diabetic nephropathyoptional	false
`complications_cvd`	boolean	Indicates if patient has cardiovascular complicationsoptional	false
`quality_measure_bp_controlled`	boolean	Indicates if patient's blood pressure is controlled per guidelinesoptional	true
`quality_measure_lipid_controlled`	boolean	Indicates if patient's lipid levels are controlled per guidelinesoptional	true

Use it for

A healthcare dashboard
The complications_nephropathy rate, medication_adherence_score by foot_exam_result and a breakdown of address_state. Excel, Power BI or Tableau.
Why do 23 of 100 rows have complications_nephropathy = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Patients100
P00189.5normal
P00567.4abnormal
P00862.7abnormal
A software demo
Believable patients with first_name, last_name and date_of_birth to fill a screen in front of a buyer.

This dataset100 rows30 columns

Yours1,000 rows30 columnsaddress_street: UK only

blueprint · diabetes-registry-and-hemoglobin-a1c-tracking

Behind this dataset

Same schema. As many rows as you need.

These 100 rows came out of a blueprint — 30 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

Registry ID unique per diabetic patient
Diabetes type: Type 1, Type 2, gestational, other
Diagnosis date and duration of diabetes
Hemoglobin A1C value and date (target <7% for most)
A1C testing frequency (minimum every 6 months)
Blood glucose logs: fasting, pre-meal, post-meal
Diabetes medications: metformin, sulfonylureas, insulin, GLP-1, SGLT-2
Complications: retinopathy, nephropathy, neuropathy, foot ulcers

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: diabetes-registry-and-hemoglobin-a1c-tracking

What should your data show?

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Sample rows

What the 100 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?