Your Patient Journey Data Template

This attribute defines the steps taken during the patient journey, ranging from administrative tasks like registration to clinical interventions like medication administration or surgery. It provides the necessary granularity to visualize the process map.

Analysts use this field to identify bottlenecks, loops, and deviations from the ideal clinical pathway. Standardizing these names across different departments is often necessary to ensure a clean and readable process discovery view.

It defines the nodes in the process map and is required to understand what happened.

Derived from transaction logs, audit trails, or order status history tables.

This timestamp marks the chronological point of the activity, enabling the sequencing of events within the patient episode. High precision (down to the second or minute) is preferred to accurately capture rapid workflows in emergency or critical care settings.

It forms the basis for all time-based calculations, including throughput times between stages, total cycle time, and waiting times. Accurate timestamping is critical for identifying delays in medication administration or diagnostic results.

Required to order events chronologically and calculate duration metrics.

Found in transaction logs, often labeled as creation date, execution time, or event time.

This metadata field tracks the freshness of the data used in the analysis. It helps ensure that dashboards reflect the most current state of patient care and aids in troubleshooting data pipeline latency.

While not used directly in the process flow visualization, it is critical for data governance and for validating that the process mining model is running on up-to-date information.

Ensures data freshness and assists in incremental data loading strategies.

Generated by the ETL process or available in the source system's record modification timestamp.

This attribute serves as the central case key for the process mining analysis, linking all distinct activities within a single care journey together. It distinguishes one hospital admission, emergency visit, or outpatient consultation from another, even for the same patient.

In analysis, this identifier allows for the reconstruction of the end-to-end flow from registration to discharge. It is essential for calculating case-level metrics such as Total Length of Stay and for identifying unique process variants.

It is the fundamental Case ID required to group events into a process instance.

Typically found in the encounter, visit, or admission header tables of the EHR.

In complex healthcare environments, data often flows from multiple systems such as the core EHR, Laboratory Information Systems (LIS), and Radiology Information Systems (RIS). This attribute identifies the origin of each data point.

It is useful for technical validation and for filtering the process map to specific system interactions. It also helps in identifying data quality issues that may be isolated to a specific integration or software module.

Provides lineage and context, especially in multi-system hospital environments.

Hardcoded during extraction or mapped from system metadata fields.

This attribute localizes the activity within the hospital structure, such as the Emergency Room, ICU, or Surgery Department. It is fundamental for analyzing inter-departmental flows and handovers.

Using this field, analysts can visualize the physical movement of patients and identify bottlenecks at specific transfer points. It also supports resource planning by showing load distribution across different units.

Essential for analyzing patient flow between units and transfer efficiency.

Activity log location fields or unit master tables.

This attribute categorizes the nature of the visit, which heavily influences the expected process flow. For example, an emergency visit has a radically different ideal pathway than a scheduled outpatient consultation.

Segmenting analysis by this field allows for fair comparisons and more accurate benchmarking. It ensures that KPIs like Length of Stay are evaluated within the correct context.

Critical for segmenting the data, as different types follow different process logic.

Encounter or admission header tables.

While the start timestamp captures when an action began, the end timestamp allows for the calculation of the active processing time (service time) for a specific step. This is distinct from the waiting time between steps.

This attribute is particularly useful for analyzing the efficiency of procedures, surgeries, or consultations. It helps differentiate between the time a patient waits for a doctor and the time the doctor actually spends with the patient.

Enables calculation of active service time versus waiting time.

Transaction logs for activities that have duration (e.g., surgery, scans).

Unlike the Episode ID, this attribute remains constant for the same individual across different visits. It enables cross-case analysis, such as identifying frequent flyers or tracking readmission rates within 30 days.

By grouping episodes by this identifier, analysts can view the long-term patient history and analyze care patterns that extend beyond a single hospitalization. It is essential for population health metrics and longitudinal care analysis.

Crucial for identifying readmissions and linking multiple visits to one individual.

Patient master index or demographic tables (e.g., MRN).

This attribute captures the clinical reason for the encounter using standard coding systems like ICD-10 or SNOMED. It enables the analysis of clinical pathways specific to certain conditions, such as Sepsis, Stroke, or Heart Failure.

By filtering on this attribute, analysts can check for conformance to condition-specific protocols and identifying variations in care for patients with the same diagnosis.

Enables clinical pathway analysis and conformance checking against medical protocols.

Diagnosis tables linked to the encounter, often termed 'Admitting Diagnosis' or 'Final Diagnosis'.

This attribute tracks the specific resource—such as a doctor, nurse, or technician—responsible for the activity. It allows for the analysis of resource utilization, workload balancing, and variation in practice patterns among staff.

It is often used to identify training opportunities or to highlight best practices by comparing outcomes across different providers. Privacy considerations usually require this to be anonymized or hashed in general reporting.

Allows for resource utilization analysis and variation by staff member.

Transaction logs, user ID fields, or staff master tables.

This attribute records the outcome of the episode, such as 'Discharged to Home', 'Transferred to SNF', or 'Left Against Medical Advice'. It is a key outcome metric for the patient journey.

It is used to analyze the efficiency of discharge planning and to correlate process variations with specific outcomes. For example, it helps identify if certain pathways lead to higher rates of transfer to other facilities.

Defines the outcome of the process and is critical for length of stay analysis.

Discharge summary records or admission/discharge/transfer (ADT) messages.

This attribute provides the specific detail for medication activities. While 'Administer Medication' is the generic activity, this attribute identifies what was given, such as 'Acetaminophen' or 'Vancomycin'.

It is crucial for analyzing specific treatment protocols, such as antibiotic timing for sepsis patients or pain management effectiveness. It allows for detailed drill-downs into medication administration delays.

Required for analyzing specific treatment protocols and medication timeliness KPIs.

Medication Administration Record (MAR) or pharmacy order tables.

This attribute groups the thousands of possible specific orders into manageable buckets. It allows for high-level analysis of diagnostic versus therapeutic activities within the patient journey.

Analysts use this to visualize the density of testing versus treatment and to identify heavy diagnostic phases that may contribute to length of stay extensions.

Helps group granular activities into meaningful phases (e.g., Diagnostic vs. Treatment).

Order entry tables, often a lookup or type field associated with the order ID.

This numerical or categorical value indicates how urgent the patient's condition was upon arrival, often using scales like ESI (Emergency Severity Index). It is a vital context for analyzing waiting times and prioritization.

Analyzing process flows based on acuity helps determining if high-risk patients are being fast-tracked correctly and if lower-acuity patients are experiencing disproportionate delays.

Contextualizes waiting times; high acuity should correlate with faster initial throughput.

Triage assessment records or ED tracking boards.