Healthcare
24 columns
100 rows
8 formats

Joint Replacement Registry - Orthopedic Implants

This dataset provides a comprehensive record of orthopedic joint replacement surgeries, including implant details, manufacturer information, surgical approach, complications, revisions, patient-reported outcomes, and implant survival. It enables robust analysis of implant performance, surgical quality, and patient outcomes, supporting continuous improvement in orthopedic care and device safety.

last updated 2 Nov 2025
by GoMask

The brief that made it

Tracking implant performance and survival rates

Sample rows

preview · 8 of 100 rows · all 24 columns

registry_idinteger	implant_typestring	patient_age_at_surgeryinteger	complication_occurredboolean	implant_sizestring	patient_idinteger	implant_idinteger	manufacturer_namestring	implant_modelstring	implant_materialstring	surgery_datedate	surgical_approachstring	surgeon_idinteger	hospital_idinteger	complication_typestring	revision_requiredboolean	revision_datedate	revision_reasonstring	implant_survival_yearsfloat	patient_reported_outcome_scorefloat	outcome_score_typestring	follow_up_datedate	patient_genderstring	patient_bmifloat
10001	hip	65	false	medium	5001	3001	Zimmer Biomet	ZB-HipPro X1	titanium	2019-02-11	posterior	201	1001	blank	false	blank	blank	4.5	78.2	WOMAC	2023-04-03	female	28.1
10002	knee	73	true	large	5002	3002	DePuy Synthes	DPS-KneeFlex 2	polyethylene	2022-09-19	anterior	202	1002	infection	true	2023-03-27	infection	0.5	40.7	Oxford Knee Score	2023-09-10	male	31.4
10003	shoulder	57	false	small	5003	3003	Smith & Nephew	SN-ShoulderElite	ceramic	2020-06-04	lateral	203	1003	blank	false	blank	blank	3.8	82.9	ASES	2023-06-12	female	24.7
10004	ankle	62	false	medium	5004	3004	Stryker	STR-AnkleMax	titanium	2021-01-17	anterior	204	1004	blank	false	blank	blank	2.7	65.3	FAOS	2022-12-28	male	29
10005	knee	74	false	large	5005	3005	Zimmer Biomet	ZB-KneeSmart	polyethylene	2018-08-24	posterior	205	1001	blank	false	blank	blank	5.4	73.5	WOMAC	2023-04-11	female	27.9
10006	hip	70	true	medium	5006	3006	DePuy Synthes	DPS-HipUltra	ceramic	2020-03-03	lateral	206	1002	dislocation	false	blank	blank	3.1	59.8	WOMAC	2022-10-19	male	32.2
10007	elbow	60	false	small	5007	3007	Smith & Nephew	SN-ElbowFlex	titanium	2019-05-12	posterior	207	1003	blank	false	blank	blank	4	70.1	MEPI	2023-02-20	female	26.3
10008	hip	67	true	large	5008	3008	Stryker	STR-HipAdvance	ceramic	2017-11-02	anterior	208	1004	fracture	true	2018-02-11	fracture	0.3	45.6	WOMAC	2019-03-18	male	35.7
10009	shoulder	54	false	medium	5009	3009	Zimmer Biomet	ZB-ShoulderPrime	titanium	2021-06-29	lateral	209	1001	blank	false	blank	blank	2.2	77.3	ASES	2023-03-09	female	25.5
10010	knee	75	false	medium	5010	3010	DePuy Synthes	DPS-KneeActive	polyethylene	2018-05-20	posterior	210	1002	blank	false	blank	blank	5.8	80.4	Oxford Knee Score	2023-04-15	male	29.8
10011	ankle	58	false	medium	5011	3011	Smith & Nephew	SN-AnkleElite	titanium	2021-11-22	lateral	211	1003	blank	false	blank	blank	2.1	63.2	FAOS	2023-01-19	female	27.2
10012	elbow	63	false	small	5012	3012	Stryker	STR-ElbowPrime	polyethylene	2019-08-15	anterior	212	1004	blank	false	blank	blank	4.1	68.7	MEPI	2022-11-10	male	28.5
10013	hip	68	true	large	5013	3013	Zimmer Biomet	ZB-HipFlex	ceramic	2017-03-14	posterior	213	1001	infection	true	2017-11-20	infection	0.7	39.2	WOMAC	2018-01-09	female	33.1
10014	shoulder	56	false	medium	5014	3014	DePuy Synthes	DPS-ShoulderMax	titanium	2021-02-16	lateral	214	1002	blank	false	blank	blank	2.7	79.5	ASES	2023-02-23	male	25.8
10015	knee	72	false	large	5015	3015	Smith & Nephew	SN-KneeActive	polyethylene	2018-12-05	anterior	215	1003	blank	false	blank	blank	5.1	74.7	Oxford Knee Score	2023-03-14	female	29.5
10016	ankle	61	false	medium	5016	3016	Stryker	STR-AnklePrime	titanium	2020-10-18	posterior	216	1004	blank	false	blank	blank	3.3	66.8	FAOS	2023-03-01	male	30.6
10017	elbow	64	false	small	5017	3017	Zimmer Biomet	ZB-ElbowUltra	ceramic	2019-01-08	lateral	217	1001	blank	false	blank	blank	4.9	72.1	MEPI	2023-02-17	female	27.7
10018	hip	66	false	large	5018	3018	DePuy Synthes	DPS-HipPrime	titanium	2017-07-26	anterior	218	1002	blank	false	blank	blank	6	83.3	WOMAC	2023-03-22	male	28.9
10019	shoulder	59	true	medium	5019	3019	Smith & Nephew	SN-ShoulderPrime	polyethylene	2021-07-07	posterior	219	1003	dislocation	false	blank	blank	1.9	51.5	ASES	2022-10-04	female	30.1
10020	knee	71	false	large	5020	3020	Stryker	STR-KneeElite	ceramic	2018-11-30	anterior	220	1004	blank	false	blank	blank	5.3	78	Oxford Knee Score	2023-03-29	male	28.6

What the 100 rows show

from the 100-row sample

Knee (implant type) stands out: mean patient_age_at_surgery is 73.2, against 61.2 for the rest.

22%complication_occurred = true
64.5median patient_age_at_surgery
2patient genders
3complication types
3revision reasons
4implant materials

Mean patient_age_at_surgery by implant_type100 rows

patient_age_at_surgery100 rows, in bands of 5

Median 64.5, from 52 to 78.

implant_size100 rows · 7 values

medium21
large19
M17
L12
XL12
small10
S9

24 columns by typefrom the column list below

string 10
integer 6
float 3
date 3
boolean 2

Columns

24 columns in four groups

blueprint · 24 columns
column	type	description	example
Text 10 columns
`manufacturer_name`	string	Name of the implant manufacturer5 names	Zimmer Biomet
`implant_type`	string	Type of orthopedic implant (e.g., hip, knee, shoulder)6 values	hip
`implant_model`	string	Model name or number of the implant	ZB-HipPro X1
`implant_material`	string	Primary material of the implant (e.g., titanium, ceramic, polyethylene)4 materials · optional	titanium
`implant_size`	string	Size specification of the implant7 sizes · optional	medium
`surgical_approach`	string	Surgical approach used (e.g., anterior, posterior, lateral)4 approaches	posterior
`complication_type`	string	Type of complication if any occurred (e.g., infection, dislocation, fracture)3 types · optional	infection
`revision_reason`	string	Reason for revision surgery (e.g., implant loosening, infection)3 reasons · optional	infection
`outcome_score_type`	string	Type of patient-reported outcome score used6 types · optional	WOMAC
`patient_gender`	string	Patient's gendermale · female · other · unknown	female
Numbers 9 columns
`registry_id`	integer	Unique identifier for each joint replacement registry recordunique	10001
`patient_id`	integer	Unique identifier for the patient receiving the implant	5001
`implant_id`	integer	Unique identifier for the orthopedic implant used	3001
`surgeon_id`	integer	Unique identifier for the surgeon performing the procedure	201
`hospital_id`	integer	Unique identifier for the hospital or surgical center	1001
`implant_survival_years`	float	Number of years the implant survived before revision or last follow-up0 or more · optional	4.5
`patient_reported_outcome_score`	float	Patient-reported outcome score (e.g., WOMAC, Oxford Hip Score)0 or more · optional	78.2
`patient_age_at_surgery`	integer	Patient's age at the time of surgery0 or more	65
`patient_bmi`	float	Patient's Body Mass Index at time of surgery0 or more · optional	28.1
Dates and times 3 columns
`surgery_date`	date	Date of the joint replacement surgery	2019-02-11
`revision_date`	date	Date of revision surgery if performedoptional	2023-03-27
`follow_up_date`	date	Date of last follow-up for outcome assessmentoptional	2023-04-03
True or false 2 columns
`complication_occurred`	boolean	Indicates if any complication occurred during or after surgery	false
`revision_required`	boolean	Indicates if a revision surgery was required	false

Use it for

A healthcare dashboard
The complication_occurred rate, patient_age_at_surgery by implant_type and a breakdown of implant_size. Excel, Power BI or Tableau.
Why do the 26 knee rows have a mean patient_age_at_surgery of 73.2?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Rows100
1000165hip
1000273knee
1000670hip
A software demo
Believable rows with patient_id, implant_id and manufacturer_name to fill a screen in front of a buyer.

This dataset100 rows24 columns

Yours1,000 rows24 columns

blueprint · joint-replacement-registry-orthopedic-implants

Behind this dataset

Same schema. As many rows as you need.

These 100 rows came out of a blueprint — 24 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 joint replacement
Joint replaced: hip, knee, shoulder, ankle
Laterality: left, right, bilateral
Primary vs revision surgery
Indication: osteoarthritis, fracture, AVN, inflammatory
Implant manufacturer and model number
Bearing surface: metal-on-polyethylene, ceramic-on-ceramic, etc.
Fixation: cemented, uncemented, hybrid

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: joint-replacement-registry-orthopedic-implants

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?