HAGER process stressor data management

2. Collect stress data types quantitative metrics qualitative inputs

Reduce stressor by feeding the AI Model with data quality determine the result quality of your enquiry

Combining Structured + Unstructured Data from different data silos / data resources ( email, sharpoint, CRM customer relationship management )

the knowledge hub data base has curated data from raw material from different data silos / data resources

Example: user 1 asks Healthcare Chatbot to get confidential customized chatbot – voice answer based on knowledge hub data base, user 2 asks Healthcare Chatbot gets confidential customized chatbot – email answer based on knowledge hub data base

Quantitative Metrics via IT

Cycle time / lead time (especially spikes or delays)
Error rates / defect rates
Task retries / rework levels
Utilization rates (e.g., human or machine capacity)
Downtime / system lags

Qualitative Inputs via SWISS HAGER

Employee surveys (e.g., using Likert scales to assess stress)
Interviews or focus groups
Observation notes
Customer feedback (indirect indicator of stress impact)

Metadata allows to search via filters like author
Structured data: patient vitals, diagnosis codes
Unstructured data: doctor’s notes, patient conversations
Generation: personalized explanations, summaries, discharge instruction

Process Mining tool to collect data

Process mining can be used to collect and analyze different types of stress-related data by uncovering patterns in event logs that reflect user behavior, system usage, and workflow dynamics. While process mining itself doesn’t directly measure stress (like cortisol levels), it can infer or correlate stress indicators from behavioral patterns and system interactions.

Example 🏥 Healthcare Data Transformation Example

📥 Step 1: Collect Raw Data

Sources: Vital monitors, lab machines, hospital ERP systems
Raw data:
- Heart rate: HR=135, HR=90
- Blood sugar: GLU=260mg/dL
- Lab result: POSITIVE, NEGATIVE

🧹 Step 2: Clean the Data

Remove incorrect or corrupt entries
Handle missing values
Standardize formats (e.g., all timestamps in ISO 8601)

🧱 Step 3: Structure the Data

Organize into tables or records like:

Patient ID	Timestamp	Heart Rate	Blood Sugar	Lab Result
P001	2025-06-24T08:00:00Z	135	260	POSITIVE

P002

2025-06-24T08:01:00Z

105

NEGATIVE

🏷️ Step 4: Enrich & Label

Add context:
- Flag abnormal vitals (HR > 120 → High HR)
- Link to diagnosis codes (ICD-10, etc.)
- Tag lab results with metadata (e.g., lab type, confidence score)

📊 Step 5: Curate & Present

Store in a secure database
Use in dashboards, patient reports, ML models, or alert systems
Ensure compliance with privacy regulations (e.g., HIPAA)

Here’s how process mining can help collect or infer different stress-related data types, along with relevant examples:

1. Behavioral Event Logs (Digital Behavior)

Type: System logs, activity logs, keystroke/mouse dynamics
Inferred Stress Indicator: Unusual interaction patterns, erratic or rapid clicks, frequent backtracking
Example Tools: Event logs from ERP, CRM, or web-based systems
Use in Process Mining:

Detect deviations from usual task execution paths
Identify bottlenecks and frequent rework cycles
Cluster users with inefficient or error-prone behavior
Higher click frequency or task repetition can indicate stress or confusion

2. Time-Based Metrics

Type: Duration logs, timestamped events, idle time
Inferred Stress Indicator: Long task durations, abrupt time gaps, excessive overtime
Example Tools: Workflow engines, ticketing systems, time-tracking software
Use in Process Mining:

Visualize time spent on each process step
Compare stressed vs. non-stressed user groups
Identify steps causing delays or rushed completions

3. Error and Exception Logs

Type: System or process error reports
Inferred Stress Indicator: Increased error frequency may reflect cognitive overload or task complexity
Example Tools: Application logs, bug trackers, system diagnostics
Use in Process Mining:

Identify process variants with high error rates
Flag critical process paths with repeated exceptions
Correlate error frequency with user activity patterns

4. Communication Patterns

Type: Email metadata, chat transcripts, meeting logs
Inferred Stress Indicator: Increased volume, response delays, terse language
Example Tools: Microsoft Exchange logs, Slack/Teams exports
Use in Process Mining:

Visualize communication flows (e.g., stress during project deadlines)
Analyze increased collaboration or communication breakdowns

5. Physiological Data (when integrated)

Type: Wearable data (heart rate, EDA, sleep)
Inferred Stress Indicator: Biometric stress signals
Example Tools: Smartwatches, biometric sensors (must be integrated with task logs)
Use in Process Mining:

Correlate process steps with physiological stress markers
Detect which tasks or users show physical stress responses

6. Self-reported or Survey Data (Supplementary)

Type: Questionnaires, stress scales, feedback forms
Inferred Stress Indicator: Subjective experience of stress
Example Tools: Google Forms, Qualtrics, internal surveys
Use in Process Mining:

Enrich process variants with subjective stress indicators
Identify correlations between stress levels and process steps

Practical Example

Imagine applying process mining to a customer support system:

Agents‘ workflows are analyzed.
Longer ticket resolution times + frequent escalations = process stress points.
Combined with increased HR-reported burnout → supports changes in workflows or task distribution.

Summary Table

Data Type	Source	Stress Indicator
Event Logs	ERP/CRM logs	Task rework, high click density
Time Logs	Workflow systems	Long duration, delay, multitasking
Error Logs	Application/system logs	High error/retry rates
Communication Logs	Email/chat metadata	Delay, frequency, sentiment
Physiological Signals	Wearables (e.g., Fitbit)	Heart rate variability, EDA spikes
Survey Data	Feedback forms	Self-reported stress

3. data analyzing: Identify stress patterns in stress data types

Goal:

Use AI to detect stress patterns from:

Quantitative data (e.g., sensor readings, app usage, biometric data)
Qualitative data (e.g., surveys, text, voice notes)

Data Types

Quantitative Metrics via IT Systems

Collected passively or through sensors:

Heart Rate Variability (HRV), Skin Conductance (EDA/GSR), Cortisol levels
Sleep duration, steps, screen time, app usage logs
Keyboard/mouse activity
Timestamped physiological data (CSV, APIs, IoT)

Qualitative Inputs

Collected actively via:

Stress diaries
Self-reports (Likert scales, Perceived Stress Scale)
Voice logs
Open-ended survey text

Stress Pattern Analysis Framework

Step	Action	Tools / AI Workloads
1. Ingest	Collect and unify structured and unstructured data	Pandas, APIs, IoT pipelines
2. Preprocess	Clean data, normalize metrics, parse text/audio	SciPy, spaCy, Librosa
3. Feature Extraction	Time-series features (quant), sentiment/emotion (qual)	`tsfresh`, `transformers`, `librosa`
4. Pattern Detection	Cluster/label patterns of high/low stress	KMeans, DBSCAN, LSTM
5. Prediction/Insight	Train AI to predict stress levels or flags	XGBoost, RandomForest, BERT, Multimodal DL
6. Visualization	Dashboard for time-trend stress monitoring	Plotly, Dash, PowerBI

AI Workloads for Each Data Type

Quantitative (Time-Series / Biometric)

LSTM / GRU: Time-based stress prediction
Autoencoders: Anomaly detection in stress episodes
XGBoost/RandomForest: Feature importance (e.g., HRV drop, EDA spike)

Qualitative (Text / Voice)

BERT or DistilBERT: Emotion/Sentiment detection in text
Wav2Vec2 + RNN: Stress emotion detection in voice
Clustering: Themes from open-ended text (KMeans, LDA)

Sample: AI Workflow Summary

Example:

You collect:

HRV (Quant)
Weekly diary entries (Qual)

Workflow:

Normalize HRV → extract mean, RMSSD, SDNN
Analyze diary with emotion classifier (BERT)
Label entries: High HRV drop + negative tone → „high stress“
Train a classifier: Predict stress risk next week
Output insights: HRV drop + sad diary = 80% stress likelihood

Klick here for Stress insight

https://markushager.com/wp-content/uploads/2025/06/2025-06-Stress-Insight-Qualitative-and-Quantitive-.png

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Newsletter English

Innovative Tipp 42 – HAGER Linkedin Newsletter series part 2 – HAGER process stress management