Medical Device Usage Analytics

This dataset provides detailed, synthetic logs of patient and operator interactions with connected medical devices, capturing device, user, compliance, and anomaly information. It enables comprehensive analysis of device usage patterns, operator behavior, procedural compliance, and early detection of anomalies or errors in clinical settings. The dataset is valuable for improving device usability, supporting regulatory audits, and enhancing patient safety.

  • last updated 22 Jul 2025
  • by GoMask
The brief that made it

Analyzing device usage patterns for workflow optimization

Sample rows

preview · 8 of 200 rows · all 18 columns
log_idstringinteraction_resultstringinteraction_duration_secondsfloatanomaly_flagbooleanoperator_rolestringtimestampdatetimedevice_idstringdevice_typestringdevice_firmware_versionstringpatient_idstringoperator_idstringinteraction_typestringsession_idstringlocation_departmentstringlocation_roomstringcompliance_flagbooleanerror_codestringnotesstring
LOG000001success12.4falsenurse2024-05-01T07:32:00ZDEV-ICU-10001InfusionPump-X100v2.5.1PAT-00001OP-1201startSESS-ICU-101A-001ICURoom 101AtrueblankMedication scheduled as per physician order. No issues observed.
LOG000002success4.2falsenurse2024-05-01T07:36:19ZDEV-ICU-10001InfusionPump-X100v2.5.1PAT-00001OP-1201pauseSESS-ICU-101A-001ICURoom 101AtrueblankPump paused briefly at patient request. All parameters normal.
LOG000003success3.9falsenurse2024-05-01T07:38:05ZDEV-ICU-10001InfusionPump-X100v2.5.1PAT-00001OP-1201resumeSESS-ICU-101A-001ICURoom 101AtrueblankInfusion resumed without incident. Patient stable.
LOG000004success10.1falsenurse2024-05-01T07:50:45ZDEV-ICU-10001InfusionPump-X100v2.5.1PAT-00001OP-1201stopSESS-ICU-101A-001ICURoom 101AtrueblankInfusion completed. Pump stopped and line flushed.
LOG000005success2.6falseblank2024-05-01T08:00:00ZDEV-ICU-10001InfusionPump-X100v2.5.1PAT-00001blankdata_uploadSESS-ICU-101A-001ICURoom 101AtrueblankUsage data uploaded to central EMR. No errors.
LOG000006success13.8falsenurse2024-05-01T05:14:00ZDEV-ICU-10002InfusionPump-X200v2.7.4PAT-00002OP-1217startSESS-ICU-102B-002ICURoom 102BtrueblankIV antibiotics started as scheduled. No alarms.
LOG000007success2.2falsenurse2024-05-01T05:15:22ZDEV-ICU-10002InfusionPump-X200v2.7.4PAT-00002OP-1217pauseSESS-ICU-102B-002ICURoom 102BtrueblankPump temporarily paused for line inspection.
LOG000008success3.2falsenurse2024-05-01T05:21:35ZDEV-ICU-10002InfusionPump-X200v2.7.4PAT-00002OP-1217resumeSESS-ICU-102B-002ICURoom 102BtrueblankInfusion resumed following tubing check.

What the 200 rows show

from the 200-row sample

Warning (interaction result) stands out: 15 of its 17 rows have anomaly_flag = true, against 20 of 183 for the rest.

  • 18%anomaly_flag = true
  • 4.9median interaction_duration_seconds
  • 9location departments
  • 10interaction types
  • 19device types
  • 52device firmware versions
Anomaly flag rate by interaction_resultanomaly_flag = true
0%50%100%0%succe…0 of 1…100%failu…10 of …88%warni…15 of …100%anoma…10 of …0%pendi…0 of 70%other0 of 4
interaction_duration_seconds200 rows, in bands of 1,000
09018017222130203,0006,000interaction_duration_seconds →

Median 4.9, from 0.0 to 6,000.

operator_role150 rows with a value · 50 left blank
  1. nurse62
  2. doctor27
  3. paramedic17
  4. robotic_surgeon15
  5. anesthesiologist13
  6. lab_technician10
  7. technician6
18 columns by typefrom the column list below
  • string 14
  • float 1
  • datetime 1
  • boolean 2

Columns

18 columns in four groups
blueprint · 18 columns
columntypedescriptionexample
Text 14 columns
log_idstringUnique identifier for each device usage log entryuniqueLOG000001
device_idstringUnique identifier for the medical deviceDEV-ICU-10001
device_typestringType or model of the medical deviceInfusionPump-X100
device_firmware_versionstringFirmware version installed on the device during the interactionoptionalv2.5.1
patient_idstringUnique identifier for the patient using the devicePAT-00001
operator_idstringUnique identifier for the operator (e.g., clinician, nurse, technician) interacting with the deviceoptionalOP-1201
operator_rolestringRole of the operator (e.g., nurse, doctor, technician)7 roles · optionalnurse
interaction_typestringType of interaction performed (e.g., start, stop, pause, error, maintenance, calibration, data_upload)10 valuesstart
interaction_resultstringOutcome of the interaction (e.g., success, failure, warning, anomaly_detected)6 values · optionalsuccess
session_idstringIdentifier for the session or usage period, grouping related interactionsoptionalSESS-ICU-101A-001
location_departmentstringHospital or facility department where the device was used (e.g., ICU, ER, Cardiology)9 departments · optionalICU
location_roomstringSpecific room or area within the facility where the device was usedoptionalRoom 101A
error_codestringError code generated during the interaction, if applicableoptionalSRX-E998
notesstringFree-text notes or comments about the interactionoptionalMedication scheduled as p…
Numbers 1 column
interaction_duration_secondsfloatDuration of the interaction in seconds0 or more · optional12.4
Dates and times 1 column
timestampdatetimeDate and time when the interaction occurred2024-05-01T07:32:00Z
True or false 2 columns
compliance_flagbooleanIndicates whether the interaction complied with standard operating proceduresoptionaltrue
anomaly_flagbooleanIndicates whether an anomaly was detected during this interactionoptionalfalse

Use it for

  • anomaly flag18%35 of 200 rowsmean interaction dura…303.4succ…0.0fail…726.5warn…600.0anom…

    A healthcare dashboard

    The anomaly_flag rate, interaction_duration_seconds by interaction_result and a breakdown of operator_role. Excel, Power BI or Tableau.

  • Why do 35 of 200 rows have anomaly_flag = true?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable logs with timestamp, device_id and device_type to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 18 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset200 rows18 columns
Yours1,000 rows18 columnslocation_department: UK only

blueprint · medical-device-usage-analytics

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 18 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 unique device and session identifiers
  • Record time-stamped user actions and device states
  • Simulate device errors and warnings
  • Flag compliance versus non-compliance events
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
medical-device-usage-analytics

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