Healthcare18 columns

Clinical Pathway Adherence Data

This dataset provides comprehensive records of clinical pathway adherence, detailing prescribed versus followed interventions for individual patients across healthcare encounters. It enables health systems to monitor compliance, identify deviations, and analyze reasons for non-adherence, supporting targeted quality improvement and evidence-based care optimization.

opened 2 timeslast updated 30 Oct 2025by GoMask

The brief that made it

Monitoring and improving clinical pathway compliance

Blueprint

blueprint · 18 columns

column	type	description
adherence_id	string	Unique identifier for each clinical pathway adherence record
patient_id	string	Unique identifier for the patient
encounter_id	string	Unique identifier for the clinical encounter or visit
pathway_id	string	Identifier for the clinical pathway being monitored
pathway_name	string	Name of the clinical pathway (e.g., Diabetes Management, Heart Failure)
prescribed_intervention_code	string	Code for the intervention prescribed according to the pathway (e.g., medication, procedure code)
prescribed_intervention_description	string	Description of the prescribed intervention
followed_intervention_code	string	Code for the intervention actually followed or administered
followed_intervention_description	string	Description of the intervention actually followed
adherence_status	string	Indicates if the intervention followed matches the prescribed pathway (e.g., adherent, non-adherent, partially adherent)
adherence_reason	string	Reason for non-adherence or deviation from the pathway, if applicable
clinician_id	string	Unique identifier for the clinician responsible for the intervention
clinician_role	string	Role of the clinician (e.g., physician, nurse, pharmacist)
intervention_date	date	Date when the intervention was administered
documentation_date	datetime	Date and time when adherence was documented
facility_id	string	Unique identifier for the healthcare facility
facility_name	string	Name of the healthcare facility
department	string	Department or unit within the facility (e.g., Cardiology, Endocrinology)

Sample rows

preview · 4 of 200 rows

adherence_id	patient_id	encounter_id	pathway_id	pathway_name
AR0001	P1001	E50001	PATH01	Diabetes Management
AR0002	P1002	E50002	PATH01	Diabetes Management
AR0003	P1003	E50003	PATH02	Heart Failure
AR0004	P1004	E50004	PATH03	Hypertension Adult

Behind this dataset

blueprint · clinical-pathway-adherence-data

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.

1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data FactoryFree tier covers your first 1,000 rows.

More in Healthcare

Browse all →

Pharmacy Inventory Sales ForecastThis dataset provides detailed historical sales and inventory records for top pharmacy products, including batch-level expiration and restock information, store locations, and transaction details. It enables precise inventory optimization, demand forecasting, and proactive management of product expirations across multiple pharmacy locations.Pharmacy Inventory OptimizationThis dataset provides detailed, location-specific pharmacy inventory records, including product stock levels, demand forecasts, reorder patterns, and risk flags for stockouts and excess inventory. It enables supply chain optimization, predictive analytics, and operational efficiency for pharmacies by integrating product, demand, and location data. Ideal for inventory management, demand planning, and reducing waste in pharmaceutical retail.Pharmacy Inventory Movement DatasetThis dataset provides detailed records of pharmacy inventory movements, including receipts, dispenses, adjustments, returns, and transfers. Each transaction is linked to pharmacy, product, supplier, and patient entities, with granular tracking of stock levels, costs, and transaction types. Ideal for inventory optimization, demand forecasting, and stockout prevention in healthcare supply chains.