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Identifying Signs of Employee Burnout

Overview: Work-life balance is quickly morphing into that of work-life integration. Employees are hyper-connected across multiple platforms and devices to their organizations, work, their teams and there is less compartmentalization. Instead of offering traditional wellness programs and initiatives as one-off offerings, there is a need to create a continuous, integrated focus on preventing burnout, and proactively identifying triggers that can cause burnout.  

Challenge: Monitor the behavioural and personality traits of the employee to identify and predict the signs of burnout. Starting with the causes of burnout, it should be possible to predict burnout in the early stages.

Useful for employees - Early self-identifying burnout occurs too late in case of employees, effective support of task/workload management, help build an effective support system around stress-related issues.

Useful for employers - A stressed-out employee cannot produce their 100% productive capability which in turn impacts the employer as well, build affective mental fatigue score – This, in turn, can be used to draw insights into various performance aspects, better project/team allocation/management, and a better control on improving employee happiness index.

Task:  Identify burnout symptoms like  Job dissatisfaction, Work-related Stress Level, Social support, Workplace dynamics, Communication, Sleep, Sick Days/Leaves.

How?

1. Workload – Draw insights based on this data : 
   a. The factors to derive from this data would be,
   i. Multiple projects or deliverables
   ii. Timelines or Deadlines for each deliverable
   iii. Peer(s) workload in the same team
2. SAP Talk/Feedback Data
   a. Manager’s feedback can be used to proactively identify,
   i. Performance
   ii. Missed deadlines
   iii. Partly – Job satisfaction (Score)
3. MS Teams – User activity report
   a. This data will be used to capture,
   i. Number of unplanned meetings
   ii. Number of meetings organized
   iii. Number of meetings participated
   iv. Average meeting time per day/week
   b. This data helps to determine how much control on the day’s activities the employee has
4. MS Teams – Device usage report
   a. Lack of sleep and obsession over issues at work are two symptoms of burnout and this data would be used to capture,
   i. Time series view of the usage of teams for the purpose of communication or documents
5. Leave Data
   a. Leave data provides insights into,
   i. General leave patterns – By marrying this data along with the workload and meetings(planned), derive the factor of escapist mentality
   ii. Sick leave(s) – Derive the aspects of degrading health due to long working hours 
6. Multi-modalities of data
   a. Audio conversation: voice fatigue, emotional analysis, Speed of talking, Tone?
   b. Video of the meeting: drowsiness, yawn, distraction, mood recognition
   c. Communication – text:  Fatigue / emotion detection from text writings/mails/communication, Grammar and spelling mistakes
   d. On an office laptop: Screen time, and how he is spending his browsing habits – too many emails, too much code, on the screen on working hours, attending too many calls, what causes a burnout for an IT employee
   e. Periodic employee survey feedbacks
   f. External factors: Environmental stress – work or home…, disturbances, ambience, workplace, office AC etc.
   g. Personality style, having a link to communication… systems together, suddenly the employee speaks more compared to shy personality, different behaviors. Time spent on social interactions reduced drastically, stressed out, speed of typing, etc.

References:

• Interventions to alleviate burnout symptoms and to support return to work among employees with burnout
• Chronic job burnout and daily functioning: A theoretical analysis

Public Datasets: 

• Employee Burnout Approximation in Hackerearth
• (MBTI) Myers-Briggs Personality Type

Overview Session Recording

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