Task-Boundary Mode
How task-instance boundaries are drawn from the event stream. Applies to every Task SoP, Step SoP, and Variants view.
Assessment
📝 Task SoP
Mined from 856 instances
Open Adverse Event Review
How 12 analysts execute this task across 459 cases — auto-generated Standard Operating Procedure with adherence scoring.
80.6
Avg Adherence Score (0-100)
Median 82.5 · Range 48.3–97.5
14.6 min
Avg Task Duration
0.5 min median
2.6
Avg Sub-Steps per Instance
55.0% have multiple sub-steps
45.0%
Reference Path Coverage
Instances following mined SoP exactly
Reference Baseline
45.0% of instances follow this exactly
Open Adverse Event Review — Mined SoP
The most common sub-step sequence observed across 903 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
Open Adverse Events Section
0.3s
0.1 edits
Veeva Safety
100%
Variant Explorer
Reference
Open Adverse Event Review Reference Path
406 instances · 11 analysts
This is the mined reference path — 406 instances across 11 analysts follow this exact sequence. Treat as the proposed Standard Operating Procedure for Open Adverse Event Review.
Genuine Complexity
Complex-Case Execution Path
24 instances · 4 analysts
· Δ adherence +1.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Open Case Assessment Results
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.6 (delta +1.9). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
17 instances · 3 analysts
· Δ adherence +1.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Open Reference Numbers
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.6 (delta +1.9). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
11 instances · 4 analysts
· Δ adherence +8.8
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Select List Item
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 89.3 vs reference 80.6 (delta +8.8). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
9 instances · 2 analysts
· Δ adherence +1.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
View Case Assessment Results
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.6 (delta +1.9). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
8 instances · 6 analysts
· Δ adherence +1.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Delete Item
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.6 (delta +1.9). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
8 instances · 3 analysts
· Δ adherence +2.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
View Event Details Table
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 83.4 vs reference 80.6 (delta +2.9). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Cross-App Swivel
Cross-App Lookup to Microsoft Teams
8 instances · 4 analysts
· Δ adherence -13.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Microsoft Teams
extra step (reference ends at step 1)
Cross-App Swivel — 4 analysts pivot into Microsoft Teams during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Cross-App Swivel
Cross-App Lookup to Microsoft Word
8 instances · 2 analysts
· Δ adherence -13.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Microsoft Word
extra step (reference ends at step 1)
Cross-App Swivel — 2 analysts pivot into Microsoft Word during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Cross-App Swivel
Cross-App Lookup to Microsoft Excel
6 instances · 4 analysts
· Δ adherence -13.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Microsoft Excel
extra step (reference ends at step 1)
Cross-App Swivel — 4 analysts pivot into Microsoft Excel during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Genuine Complexity
Complex-Case Execution Path
6 instances · 3 analysts
· Δ adherence +3.2
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Open Test Results Section
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 83.8 vs reference 80.6 (delta +3.2). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
6 instances · 1 analysts
· Δ adherence +1.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Select Adverse Events
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.6 (delta +1.9). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
5 instances · 4 analysts
· Δ adherence +1.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Select Patient
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.6 (delta +1.9). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Veeva Safety | 2,014 | 84.6% | 97.6% |
| Microsoft Word | 62 | 2.6% | 0.0% |
| Acrobat | 58 | 2.4% | 0.0% |
| Microsoft Excel | 56 | 2.4% | 0.0% |
| Microsoft Teams | 55 | 2.3% | 0.0% |
| Microsoft Outlook | 30 | 1.3% | 0.0% |
| milkeyway.merck.com | 16 | 0.7% | 0.0% |
| collaboration.merck.com | 14 | 0.6% | 0.0% |
| YoudaoDict | 9 | 0.4% | 0.0% |
| ONENOTE | 8 | 0.3% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Veeva Safety | Acrobat | 45 |
| Veeva Safety | Microsoft Teams | 38 |
| Veeva Safety | Microsoft Word | 37 |
| Veeva Safety | Microsoft Excel | 36 |
| Veeva Safety | Microsoft Outlook | 27 |
| Acrobat | Veeva Safety | 25 |
| Microsoft Word | Veeva Safety | 23 |
| Microsoft Excel | Veeva Safety | 18 |
| Microsoft Teams | Veeva Safety | 16 |
| Microsoft Outlook | Veeva Safety | 9 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Veeva Safety DOM 8,858 events 459 cases 106.1 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Adverse Events"
964×
pyzeClick
<span>
"Complete"
549×
pyzeClick
<span>
"Narrative"
400×
pyzeClick
<a>
"Show more"
361×
pyzeClick
<span>
"Products"
294×
pyzeClick
<span>
"Patient"
224×
pyzeClick
<span>
"Submissions & Distributions"
224×
pyzeClick
<span>
"Documents"
206×
Phobos DOM 1,214 events 173 cases 34.3 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
152×
pyzeClick
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
129×
pyzeClick
<button>
"Save Comments"
113×
pyzeClick
<button>
"Advance"
92×
inputld
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
74×
pyzeClick
<button>
"Send to process"
70×
pyzeClick
<select>
"Select Compound · - sitagliptin phosphate Tablet (G"
62×
pyzeClick
<div>
"Edit info and/or add comments · × · Compound · Select C"
38×
Microsoft Word Document 329 events 90 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
V-Narrative 552979 (English) (26689105_0_2_348832)
1076×
file
V-Narrative 543823 (English) (26544362_0_2_343646)
895×
file
V-Narrative 549707 (English) (26645831_0_1_347236)
556×
file
V-Narrative 549550 (English) (26643764_0_1_347593)
526×
file
V-Narrative 534252 (English) (26399499_0_2_343143)
301×
file
V-Narrative 533936 (English) (26397592_0_1_338360)
264×
file
V-Narrative 542512 (English) (26522671_0_2_344761)
255×
file
V-Narrative 540358 (English) (26494608_0_2_342069)
228×
Acrobat App 267 events 66 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
4,151 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Teams App 231 events 100 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
2,848 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Excel Document 212 events 85 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
Case tracking list
973×
file
2329445 (v2.1)
370×
file
Priorización
234×
file
I-0000346875
167×
file
I-0000354334
153×
file
I-0000353124
142×
file
I-0000350382
142×
file
I-0000340178
109×
Microsoft Outlook Message 121 events 71 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
collaboration.merck.com App 90 events 46 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
ONENOTE Document 40 events 24 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
RIM Vault DOM 38 events 5 cases 0.3 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.08
Swivel Rate
events in non-primary app / total
0.94
Zero-Edit Visit Rate
inspection-only clicks
1.13
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.94
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.94
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.13
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.08
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2352006 | Analyst 03 | 48.3 | 3.8m | 9 | 0.89 | 0.00 | 4 |
| 2346158 | Analyst 08 | 52.5 | 0.3m | 6 | 0.83 | 0.00 | 3 |
| 2240449 | Analyst 09 | 52.5 | 6.2m | 6 | 0.83 | 0.00 | 3 |
| 2332282 | Analyst 13 | 56.0 | 3.4m | 31 | 0.58 | 0.00 | 5 |
| 2350433 | Analyst 08 | 56.0 | 0.6m | 5 | 0.80 | 0.00 | 2 |
Users on This Task
12 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
11
04
17
14
01
03
12
08
09
05
02
13
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 11 | 3 | 3.0 | 85.2 | Zero-Edit Visits | 0.00 | 0.00 | 9 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 11's Dominant Sequence
— 1 of 3 instances
1
Open Adverse Events Section
2
View Blinded SAE Case
3
View Case Lock Status
4
Select Case Lock Status
5
Select User
This analyst exhibits 3 distinct sequences across their 3 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 04 | 66 | 6.0 | 83.8 | Zero-Edit Visits | 0.00 | 0.00 | 503 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 04's Dominant Sequence
— 34 of 66 instances
1
Open Adverse Events Section
This analyst exhibits 30 distinct sequences across their 66 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 17 | 1 | 1.0 | 82.5 | Zero-Edit Visits | 0.00 | 0.00 | 0 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 17's Dominant Sequence
— 1 of 1 instances
1
Open Adverse Events Section
|
||||||||
| ▸ | Analyst 14 | 5 | 2.5 | 82.4 | Zero-Edit Visits | 0.00 | 0.00 | 23 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 14's Dominant Sequence
— 3 of 5 instances
1
Open Adverse Events Section
This analyst exhibits 3 distinct sequences across their 5 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 16 | 1.8 | 82.1 | Zero-Edit Visits | 0.01 | 0.00 | 30 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 01's Dominant Sequence
— 4 of 16 instances
1
Open Adverse Events Section
This analyst exhibits 13 distinct sequences across their 16 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 247 | 19.0 | 81.2 | Zero-Edit Visits | 0.07 | 0.00 | 3,287 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 03's Dominant Sequence
— 98 of 247 instances
1
Open Adverse Events Section
This analyst exhibits 111 distinct sequences across their 247 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 39 | 4.9 | 80.6 | Zero-Edit Visits | 0.09 | 0.00 | 1,967 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 12's Dominant Sequence
— 15 of 39 instances
1
Open Adverse Events Section
This analyst exhibits 25 distinct sequences across their 39 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 08 | 152 | 10.9 | 80.0 | Zero-Edit Visits | 0.08 | 0.00 | 5,212 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 08's Dominant Sequence
— 102 of 152 instances
1
Open Adverse Events Section
This analyst exhibits 29 distinct sequences across their 152 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 277 | 17.3 | 79.8 | Zero-Edit Visits | 0.11 | 0.00 | 1,594 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 09's Dominant Sequence
— 114 of 277 instances
1
Open Adverse Events Section
This analyst exhibits 122 distinct sequences across their 277 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 05 | 28 | 3.1 | 79.4 | Zero-Edit Visits | 0.12 | 0.00 | 407 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 05's Dominant Sequence
— 11 of 28 instances
1
Open Adverse Events Section
This analyst exhibits 17 distinct sequences across their 28 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 44 | 3.7 | 79.3 | Zero-Edit Visits | 0.12 | 0.00 | 74 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 02's Dominant Sequence
— 14 of 44 instances
1
Open Adverse Events Section
This analyst exhibits 29 distinct sequences across their 44 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 13 | 25 | 2.8 | 78.9 | Zero-Edit Visits | 0.14 | 0.00 | 56 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 13's Dominant Sequence
— 10 of 25 instances
1
Open Adverse Events Section
This analyst exhibits 16 distinct sequences across their 25 instances; the dominant one is shown.
|
||||||||
80.4
Avg Adherence Score (0-100)
Median 82.5 · Range 37.0–97.5
16.7 min
Avg Task Duration
0.6 min median
2.9
Avg Sub-Steps per Instance
58.4% have multiple sub-steps
41.6%
Reference Path Coverage
Instances following mined SoP exactly
Reference Baseline
41.6% of instances follow this exactly
Open Adverse Event Review — Mined SoP
The most common sub-step sequence observed across 903 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
Open Adverse Events Section
0.3s
0.1 edits
Veeva Safety
100%
Variant Explorer
Reference
Open Adverse Event Review Reference Path
376 instances · 11 analysts
This is the mined reference path — 376 instances across 11 analysts follow this exact sequence. Treat as the proposed Standard Operating Procedure for Open Adverse Event Review.
Genuine Complexity
Complex-Case Execution Path
23 instances · 4 analysts
· Δ adherence +2.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Open Case Assessment Results
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.4 (delta +2.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
17 instances · 3 analysts
· Δ adherence +2.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Open Reference Numbers
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.4 (delta +2.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
11 instances · 4 analysts
· Δ adherence +8.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Select List Item
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 89.3 vs reference 80.4 (delta +8.9). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
9 instances · 2 analysts
· Δ adherence +2.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
View Case Assessment Results
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.4 (delta +2.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
8 instances · 6 analysts
· Δ adherence +2.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Delete Item
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.4 (delta +2.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
8 instances · 3 analysts
· Δ adherence +2.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
View Event Details Table
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.4 (delta +2.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Cross-App Swivel
Cross-App Lookup to Microsoft Word
8 instances · 2 analysts
· Δ adherence -12.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Microsoft Word
extra step (reference ends at step 1)
Cross-App Swivel — 2 analysts pivot into Microsoft Word during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Cross-App Swivel
Cross-App Lookup to Microsoft Teams
7 instances · 4 analysts
· Δ adherence -12.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Microsoft Teams
extra step (reference ends at step 1)
Cross-App Swivel — 4 analysts pivot into Microsoft Teams during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Cross-App Swivel
Cross-App Lookup to Microsoft Excel
6 instances · 4 analysts
· Δ adherence -12.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Microsoft Excel
extra step (reference ends at step 1)
Cross-App Swivel — 4 analysts pivot into Microsoft Excel during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Genuine Complexity
Complex-Case Execution Path
5 instances · 4 analysts
· Δ adherence +2.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Select Patient
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.4 (delta +2.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
5 instances · 2 analysts
· Δ adherence +3.6
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Open Test Results Section
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 84.0 vs reference 80.4 (delta +3.6). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
5 instances · 3 analysts
· Δ adherence +3.6
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Open Medical History Section
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 84.0 vs reference 80.4 (delta +3.6). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Veeva Safety | 2,186 | 83.8% | 97.9% |
| Microsoft Word | 78 | 3.0% | 0.0% |
| Acrobat | 66 | 2.5% | 0.0% |
| Microsoft Teams | 64 | 2.5% | 0.0% |
| Microsoft Excel | 58 | 2.2% | 0.0% |
| Microsoft Outlook | 37 | 1.4% | 0.0% |
| collaboration.merck.com | 18 | 0.7% | 0.0% |
| milkeyway.merck.com | 16 | 0.6% | 0.0% |
| YoudaoDict | 9 | 0.3% | 0.0% |
| ONENOTE | 8 | 0.3% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Veeva Safety | Acrobat | 47 |
| Veeva Safety | Microsoft Teams | 43 |
| Veeva Safety | Microsoft Word | 42 |
| Veeva Safety | Microsoft Excel | 36 |
| Acrobat | Veeva Safety | 31 |
| Veeva Safety | Microsoft Outlook | 31 |
| Microsoft Word | Veeva Safety | 28 |
| Microsoft Excel | Veeva Safety | 19 |
| Microsoft Teams | Veeva Safety | 18 |
| Microsoft Teams | Microsoft Word | 12 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Veeva Safety DOM 8,858 events 459 cases 106.1 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Adverse Events"
964×
pyzeClick
<span>
"Complete"
549×
pyzeClick
<span>
"Narrative"
400×
pyzeClick
<a>
"Show more"
361×
pyzeClick
<span>
"Products"
294×
pyzeClick
<span>
"Patient"
224×
pyzeClick
<span>
"Submissions & Distributions"
224×
pyzeClick
<span>
"Documents"
206×
Phobos DOM 1,214 events 173 cases 34.3 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
152×
pyzeClick
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
129×
pyzeClick
<button>
"Save Comments"
113×
pyzeClick
<button>
"Advance"
92×
inputld
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
74×
pyzeClick
<button>
"Send to process"
70×
pyzeClick
<select>
"Select Compound · - sitagliptin phosphate Tablet (G"
62×
pyzeClick
<div>
"Edit info and/or add comments · × · Compound · Select C"
38×
Microsoft Word Document 329 events 90 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
V-Narrative 552979 (English) (26689105_0_2_348832)
1076×
file
V-Narrative 543823 (English) (26544362_0_2_343646)
895×
file
V-Narrative 549707 (English) (26645831_0_1_347236)
556×
file
V-Narrative 549550 (English) (26643764_0_1_347593)
526×
file
V-Narrative 534252 (English) (26399499_0_2_343143)
301×
file
V-Narrative 533936 (English) (26397592_0_1_338360)
264×
file
V-Narrative 542512 (English) (26522671_0_2_344761)
255×
file
V-Narrative 540358 (English) (26494608_0_2_342069)
228×
Acrobat App 267 events 66 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
4,151 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Teams App 231 events 100 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
2,848 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Excel Document 212 events 85 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
Case tracking list
973×
file
2329445 (v2.1)
370×
file
Priorización
234×
file
I-0000346875
167×
file
I-0000354334
153×
file
I-0000353124
142×
file
I-0000350382
142×
file
I-0000340178
109×
Microsoft Outlook Message 121 events 71 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
collaboration.merck.com App 90 events 46 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
ONENOTE Document 40 events 24 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
RIM Vault DOM 38 events 5 cases 0.3 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.09
Swivel Rate
events in non-primary app / total
0.94
Zero-Edit Visit Rate
inspection-only clicks
1.16
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.94
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.94
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.16
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.09
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2350507 | Analyst 09 | 37.0 | 16.3m | 20 | 0.85 | 0.00 | 9 |
| 2352006 | Analyst 03 | 48.3 | 3.8m | 9 | 0.89 | 0.00 | 4 |
| 2346158 | Analyst 08 | 52.5 | 0.3m | 6 | 0.83 | 0.00 | 3 |
| 2240449 | Analyst 09 | 52.5 | 6.2m | 6 | 0.83 | 0.00 | 3 |
| 2351074 | Analyst 08 | 56.0 | 0.5m | 5 | 0.80 | 0.00 | 2 |
Users on This Task
12 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
11
04
14
17
01
03
12
08
09
05
02
13
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 11 | 3 | 3.0 | 85.2 | Zero-Edit Visits | 0.00 | 0.00 | 9 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 11's Dominant Sequence
— 1 of 3 instances
1
Open Adverse Events Section
2
View Blinded SAE Case
3
View Case Lock Status
4
Select Case Lock Status
5
Select User
This analyst exhibits 3 distinct sequences across their 3 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 04 | 66 | 6.0 | 83.9 | Zero-Edit Visits | 0.00 | 0.00 | 540 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 04's Dominant Sequence
— 32 of 66 instances
1
Open Adverse Events Section
This analyst exhibits 32 distinct sequences across their 66 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 14 | 5 | 2.5 | 83.0 | Zero-Edit Visits | 0.00 | 0.00 | 38 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 14's Dominant Sequence
— 2 of 5 instances
1
Open Adverse Events Section
This analyst exhibits 4 distinct sequences across their 5 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 17 | 1 | 1.0 | 82.5 | Zero-Edit Visits | 0.00 | 0.00 | 0 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 17's Dominant Sequence
— 1 of 1 instances
1
Open Adverse Events Section
|
||||||||
| ▸ | Analyst 01 | 16 | 1.8 | 82.1 | Zero-Edit Visits | 0.01 | 0.00 | 30 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 01's Dominant Sequence
— 4 of 16 instances
1
Open Adverse Events Section
This analyst exhibits 13 distinct sequences across their 16 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 247 | 19.0 | 81.0 | Zero-Edit Visits | 0.07 | 0.00 | 4,836 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 03's Dominant Sequence
— 88 of 247 instances
1
Open Adverse Events Section
This analyst exhibits 120 distinct sequences across their 247 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 39 | 4.9 | 80.8 | Zero-Edit Visits | 0.09 | 0.00 | 2,004 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 12's Dominant Sequence
— 13 of 39 instances
1
Open Adverse Events Section
This analyst exhibits 27 distinct sequences across their 39 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 08 | 152 | 10.9 | 79.9 | Zero-Edit Visits | 0.09 | 0.00 | 5,226 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 08's Dominant Sequence
— 100 of 152 instances
1
Open Adverse Events Section
This analyst exhibits 31 distinct sequences across their 152 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 277 | 17.3 | 79.8 | Zero-Edit Visits | 0.11 | 0.00 | 1,730 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 09's Dominant Sequence
— 106 of 277 instances
1
Open Adverse Events Section
This analyst exhibits 130 distinct sequences across their 277 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 05 | 28 | 3.1 | 79.5 | Zero-Edit Visits | 0.12 | 0.00 | 432 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 05's Dominant Sequence
— 9 of 28 instances
1
Open Adverse Events Section
This analyst exhibits 18 distinct sequences across their 28 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 44 | 3.7 | 78.7 | Zero-Edit Visits | 0.13 | 0.00 | 132 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 02's Dominant Sequence
— 12 of 44 instances
1
Open Adverse Events Section
This analyst exhibits 32 distinct sequences across their 44 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 13 | 25 | 2.8 | 78.2 | Zero-Edit Visits | 0.16 | 0.00 | 91 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 13's Dominant Sequence
— 9 of 25 instances
1
Open Adverse Events Section
This analyst exhibits 17 distinct sequences across their 25 instances; the dominant one is shown.
|
||||||||
80.4
Avg Adherence Score (0-100)
Median 82.5 · Range 48.3–97.5
15.4 min
Avg Task Duration
0.5 min median
2.8
Avg Sub-Steps per Instance
54.3% have multiple sub-steps
45.7%
Reference Path Coverage
Instances following mined SoP exactly
Reference Baseline
45.7% of instances follow this exactly
Open Adverse Event Review — Mined SoP
The most common sub-step sequence observed across 858 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
Open Adverse Events Section
0.3s
0.1 edits
Veeva Safety
100%
Variant Explorer
Reference
Open Adverse Event Review Reference Path
392 instances · 11 analysts
This is the mined reference path — 392 instances across 11 analysts follow this exact sequence. Treat as the proposed Standard Operating Procedure for Open Adverse Event Review.
Genuine Complexity
Complex-Case Execution Path
22 instances · 4 analysts
· Δ adherence +2.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Open Case Assessment Results
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.4 (delta +2.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
17 instances · 3 analysts
· Δ adherence +2.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Open Reference Numbers
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.4 (delta +2.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
10 instances · 4 analysts
· Δ adherence +8.8
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Select List Item
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 89.2 vs reference 80.4 (delta +8.8). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
8 instances · 6 analysts
· Δ adherence +2.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Delete Item
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.4 (delta +2.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
8 instances · 3 analysts
· Δ adherence +2.7
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
View Event Details Table
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 83.1 vs reference 80.4 (delta +2.7). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
8 instances · 1 analysts
· Δ adherence +2.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
View Case Assessment Results
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.4 (delta +2.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Cross-App Swivel
Cross-App Lookup to Microsoft Teams
7 instances · 4 analysts
· Δ adherence -12.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Microsoft Teams
extra step (reference ends at step 1)
Cross-App Swivel — 4 analysts pivot into Microsoft Teams during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Cross-App Swivel
Cross-App Lookup to Microsoft Excel
6 instances · 4 analysts
· Δ adherence -12.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Microsoft Excel
extra step (reference ends at step 1)
Cross-App Swivel — 4 analysts pivot into Microsoft Excel during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Cross-App Swivel
Cross-App Lookup to Microsoft Word
6 instances · 2 analysts
· Δ adherence -12.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Microsoft Word
extra step (reference ends at step 1)
Cross-App Swivel — 2 analysts pivot into Microsoft Word during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Genuine Complexity
Complex-Case Execution Path
6 instances · 1 analysts
· Δ adherence +2.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Select Adverse Events
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.4 (delta +2.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
4 instances · 4 analysts
· Δ adherence +2.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Select Documents
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.4 (delta +2.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Cross-App Swivel
Cross-App Lookup to Microsoft Outlook
4 instances · 2 analysts
· Δ adherence -12.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Microsoft Outlook
extra step (reference ends at step 1)
Cross-App Swivel — 2 analysts pivot into Microsoft Outlook during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Veeva Safety | 2,014 | 84.6% | 97.6% |
| Microsoft Word | 62 | 2.6% | 0.0% |
| Acrobat | 58 | 2.4% | 0.0% |
| Microsoft Excel | 56 | 2.4% | 0.0% |
| Microsoft Teams | 55 | 2.3% | 0.0% |
| Microsoft Outlook | 30 | 1.3% | 0.0% |
| milkeyway.merck.com | 16 | 0.7% | 0.0% |
| collaboration.merck.com | 14 | 0.6% | 0.0% |
| YoudaoDict | 9 | 0.4% | 0.0% |
| ONENOTE | 8 | 0.3% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Veeva Safety | Acrobat | 45 |
| Veeva Safety | Microsoft Teams | 38 |
| Veeva Safety | Microsoft Word | 37 |
| Veeva Safety | Microsoft Excel | 35 |
| Veeva Safety | Microsoft Outlook | 27 |
| Acrobat | Veeva Safety | 25 |
| Microsoft Word | Veeva Safety | 24 |
| Microsoft Excel | Veeva Safety | 18 |
| Microsoft Teams | Veeva Safety | 16 |
| Microsoft Excel | milkeyway.merck.com | 9 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Veeva Safety DOM 8,858 events 459 cases 106.1 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Adverse Events"
964×
pyzeClick
<span>
"Complete"
549×
pyzeClick
<span>
"Narrative"
400×
pyzeClick
<a>
"Show more"
361×
pyzeClick
<span>
"Products"
294×
pyzeClick
<span>
"Submissions & Distributions"
224×
pyzeClick
<span>
"Patient"
224×
pyzeClick
<span>
"Documents"
206×
Phobos DOM 1,214 events 173 cases 34.3 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
152×
pyzeClick
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
129×
pyzeClick
<button>
"Save Comments"
113×
pyzeClick
<button>
"Advance"
92×
inputld
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
74×
pyzeClick
<button>
"Send to process"
70×
pyzeClick
<select>
"Select Compound · - sitagliptin phosphate Tablet (G"
62×
pyzeClick
<div>
"Edit info and/or add comments · × · Compound · Select C"
38×
Microsoft Word Document 329 events 90 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
V-Narrative 552979 (English) (26689105_0_2_348832)
1076×
file
V-Narrative 543823 (English) (26544362_0_2_343646)
895×
file
V-Narrative 549707 (English) (26645831_0_1_347236)
556×
file
V-Narrative 549550 (English) (26643764_0_1_347593)
526×
file
V-Narrative 534252 (English) (26399499_0_2_343143)
301×
file
V-Narrative 533936 (English) (26397592_0_1_338360)
264×
file
V-Narrative 542512 (English) (26522671_0_2_344761)
255×
file
V-Narrative 540358 (English) (26494608_0_2_342069)
228×
Acrobat App 267 events 66 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
4,151 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Teams App 231 events 100 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
2,848 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Excel Document 212 events 85 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
Case tracking list
973×
file
2329445 (v2.1)
370×
file
Priorización
234×
file
I-0000346875
167×
file
I-0000354334
153×
file
I-0000353124
142×
file
I-0000350382
142×
file
I-0000340178
109×
Microsoft Outlook Message 121 events 71 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
collaboration.merck.com App 90 events 46 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
ONENOTE Document 40 events 24 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
RIM Vault DOM 38 events 5 cases 0.3 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.09
Swivel Rate
events in non-primary app / total
0.94
Zero-Edit Visit Rate
inspection-only clicks
1.17
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.94
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.94
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.17
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.08
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2352006 | Analyst 03 | 48.3 | 3.8m | 9 | 0.89 | 0.00 | 4 |
| 2346158 | Analyst 08 | 52.5 | 0.3m | 6 | 0.83 | 0.00 | 3 |
| 2240449 | Analyst 09 | 52.5 | 6.2m | 6 | 0.83 | 0.00 | 3 |
| 2332282 | Analyst 13 | 56.0 | 3.4m | 31 | 0.58 | 0.00 | 5 |
| 2351074 | Analyst 08 | 56.0 | 0.5m | 5 | 0.80 | 0.00 | 2 |
Users on This Task
12 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
11
04
17
14
01
03
12
08
09
02
05
13
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 11 | 3 | 3.0 | 85.2 | Zero-Edit Visits | 0.00 | 0.00 | 9 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 11's Dominant Sequence
— 1 of 3 instances
1
Open Adverse Events Section
2
View Blinded SAE Case
3
View Case Lock Status
4
Select Case Lock Status
5
Select User
This analyst exhibits 3 distinct sequences across their 3 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 04 | 64 | 5.8 | 83.7 | Zero-Edit Visits | 0.00 | 0.00 | 506 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 04's Dominant Sequence
— 34 of 64 instances
1
Open Adverse Events Section
This analyst exhibits 28 distinct sequences across their 64 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 17 | 1 | 1.0 | 82.5 | Zero-Edit Visits | 0.00 | 0.00 | 0 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 17's Dominant Sequence
— 1 of 1 instances
1
Open Adverse Events Section
|
||||||||
| ▸ | Analyst 14 | 5 | 2.5 | 82.4 | Zero-Edit Visits | 0.00 | 0.00 | 23 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 14's Dominant Sequence
— 3 of 5 instances
1
Open Adverse Events Section
This analyst exhibits 3 distinct sequences across their 5 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 16 | 1.8 | 82.1 | Zero-Edit Visits | 0.01 | 0.00 | 30 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 01's Dominant Sequence
— 4 of 16 instances
1
Open Adverse Events Section
This analyst exhibits 13 distinct sequences across their 16 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 228 | 17.5 | 80.9 | Zero-Edit Visits | 0.07 | 0.00 | 3,306 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 03's Dominant Sequence
— 95 of 228 instances
1
Open Adverse Events Section
This analyst exhibits 102 distinct sequences across their 228 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 37 | 4.6 | 80.5 | Zero-Edit Visits | 0.09 | 0.00 | 1,968 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 12's Dominant Sequence
— 14 of 37 instances
1
Open Adverse Events Section
This analyst exhibits 24 distinct sequences across their 37 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 08 | 148 | 10.6 | 79.9 | Zero-Edit Visits | 0.08 | 0.00 | 5,213 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 08's Dominant Sequence
— 99 of 148 instances
1
Open Adverse Events Section
This analyst exhibits 31 distinct sequences across their 148 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 261 | 16.3 | 79.7 | Zero-Edit Visits | 0.11 | 0.00 | 1,600 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 09's Dominant Sequence
— 108 of 261 instances
1
Open Adverse Events Section
This analyst exhibits 120 distinct sequences across their 261 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 44 | 3.7 | 79.3 | Zero-Edit Visits | 0.12 | 0.00 | 74 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 02's Dominant Sequence
— 14 of 44 instances
1
Open Adverse Events Section
This analyst exhibits 29 distinct sequences across their 44 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 05 | 26 | 2.9 | 79.1 | Zero-Edit Visits | 0.13 | 0.00 | 407 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 05's Dominant Sequence
— 10 of 26 instances
1
Open Adverse Events Section
This analyst exhibits 16 distinct sequences across their 26 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 13 | 25 | 2.8 | 78.9 | Zero-Edit Visits | 0.14 | 0.00 | 56 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 13's Dominant Sequence
— 10 of 25 instances
1
Open Adverse Events Section
This analyst exhibits 16 distinct sequences across their 25 instances; the dominant one is shown.
|
||||||||
80.3
Avg Adherence Score (0-100)
Median 82.5 · Range 37.0–97.5
17.6 min
Avg Task Duration
0.6 min median
3.0
Avg Sub-Steps per Instance
57.4% have multiple sub-steps
42.6%
Reference Path Coverage
Instances following mined SoP exactly
Reference Baseline
42.6% of instances follow this exactly
Open Adverse Event Review — Mined SoP
The most common sub-step sequence observed across 856 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
Open Adverse Events Section
0.3s
0.1 edits
Veeva Safety
100%
Variant Explorer
Reference
Open Adverse Event Review Reference Path
365 instances · 11 analysts
This is the mined reference path — 365 instances across 11 analysts follow this exact sequence. Treat as the proposed Standard Operating Procedure for Open Adverse Event Review.
Genuine Complexity
Complex-Case Execution Path
21 instances · 4 analysts
· Δ adherence +2.2
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Open Case Assessment Results
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.3 (delta +2.2). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
17 instances · 3 analysts
· Δ adherence +2.2
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Open Reference Numbers
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.3 (delta +2.2). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
10 instances · 4 analysts
· Δ adherence +9.0
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Select List Item
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 89.2 vs reference 80.3 (delta +9.0). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
8 instances · 3 analysts
· Δ adherence +1.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
View Event Details Table
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.2 vs reference 80.3 (delta +1.9). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
8 instances · 1 analysts
· Δ adherence +2.2
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
View Case Assessment Results
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.3 (delta +2.2). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
7 instances · 5 analysts
· Δ adherence +2.2
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Delete Item
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.3 (delta +2.2). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Cross-App Swivel
Cross-App Lookup to Microsoft Teams
6 instances · 4 analysts
· Δ adherence -12.8
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Microsoft Teams
extra step (reference ends at step 1)
Cross-App Swivel — 4 analysts pivot into Microsoft Teams during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Linked Remediation Opportunities
Cross-App Swivel
Cross-App Lookup to Microsoft Excel
6 instances · 4 analysts
· Δ adherence -12.8
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Microsoft Excel
extra step (reference ends at step 1)
Cross-App Swivel — 4 analysts pivot into Microsoft Excel during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Linked Remediation Opportunities
Cross-App Swivel
Cross-App Lookup to Microsoft Word
6 instances · 2 analysts
· Δ adherence -12.8
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Microsoft Word
extra step (reference ends at step 1)
Cross-App Swivel — 2 analysts pivot into Microsoft Word during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Genuine Complexity
Complex-Case Execution Path
5 instances · 1 analysts
· Δ adherence +2.2
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Select Adverse Events
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.3 (delta +2.2). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
4 instances · 4 analysts
· Δ adherence +2.2
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Select Documents
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.3 (delta +2.2). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
4 instances · 4 analysts
· Δ adherence +2.2
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Open Adverse Events Section
2
Select Patient
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 80.3 (delta +2.2). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Veeva Safety | 2,186 | 83.8% | 97.9% |
| Microsoft Word | 78 | 3.0% | 0.0% |
| Acrobat | 66 | 2.5% | 0.0% |
| Microsoft Teams | 64 | 2.5% | 0.0% |
| Microsoft Excel | 58 | 2.2% | 0.0% |
| Microsoft Outlook | 37 | 1.4% | 0.0% |
| collaboration.merck.com | 18 | 0.7% | 0.0% |
| milkeyway.merck.com | 16 | 0.6% | 0.0% |
| YoudaoDict | 9 | 0.3% | 0.0% |
| ONENOTE | 8 | 0.3% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Veeva Safety | Acrobat | 49 |
| Veeva Safety | Microsoft Teams | 42 |
| Veeva Safety | Microsoft Word | 41 |
| Veeva Safety | Microsoft Excel | 37 |
| Veeva Safety | Microsoft Outlook | 31 |
| Acrobat | Veeva Safety | 30 |
| Microsoft Word | Veeva Safety | 29 |
| Microsoft Excel | Veeva Safety | 19 |
| Microsoft Teams | Veeva Safety | 18 |
| Microsoft Teams | Microsoft Word | 12 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Veeva Safety DOM 8,858 events 459 cases 106.1 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Adverse Events"
964×
pyzeClick
<span>
"Complete"
549×
pyzeClick
<span>
"Narrative"
400×
pyzeClick
<a>
"Show more"
361×
pyzeClick
<span>
"Products"
294×
pyzeClick
<span>
"Submissions & Distributions"
224×
pyzeClick
<span>
"Patient"
224×
pyzeClick
<span>
"Documents"
206×
Phobos DOM 1,214 events 173 cases 34.3 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
152×
pyzeClick
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
129×
pyzeClick
<button>
"Save Comments"
113×
pyzeClick
<button>
"Advance"
92×
inputld
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
74×
pyzeClick
<button>
"Send to process"
70×
pyzeClick
<select>
"Select Compound · - sitagliptin phosphate Tablet (G"
62×
pyzeClick
<div>
"Edit info and/or add comments · × · Compound · Select C"
38×
Microsoft Word Document 329 events 90 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
V-Narrative 552979 (English) (26689105_0_2_348832)
1076×
file
V-Narrative 543823 (English) (26544362_0_2_343646)
895×
file
V-Narrative 549707 (English) (26645831_0_1_347236)
556×
file
V-Narrative 549550 (English) (26643764_0_1_347593)
526×
file
V-Narrative 534252 (English) (26399499_0_2_343143)
301×
file
V-Narrative 533936 (English) (26397592_0_1_338360)
264×
file
V-Narrative 542512 (English) (26522671_0_2_344761)
255×
file
V-Narrative 540358 (English) (26494608_0_2_342069)
228×
Acrobat App 267 events 66 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
4,151 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Teams App 231 events 100 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
2,848 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Excel Document 212 events 85 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
Case tracking list
973×
file
2329445 (v2.1)
370×
file
Priorización
234×
file
I-0000346875
167×
file
I-0000354334
153×
file
I-0000350382
142×
file
I-0000353124
142×
file
I-0000340178
109×
Microsoft Outlook Message 121 events 71 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
collaboration.merck.com App 90 events 46 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
ONENOTE Document 40 events 24 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
RIM Vault DOM 38 events 5 cases 0.3 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.09
Swivel Rate
events in non-primary app / total
0.94
Zero-Edit Visit Rate
inspection-only clicks
1.21
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.94
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.94
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.21
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.09
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2350507 | Analyst 09 | 37.0 | 16.3m | 20 | 0.85 | 0.00 | 9 |
| 2352006 | Analyst 03 | 48.3 | 3.8m | 9 | 0.89 | 0.00 | 4 |
| 2346158 | Analyst 08 | 52.5 | 0.3m | 6 | 0.83 | 0.00 | 3 |
| 2240449 | Analyst 09 | 52.5 | 6.2m | 6 | 0.83 | 0.00 | 3 |
| 2350433 | Analyst 08 | 56.0 | 0.6m | 5 | 0.80 | 0.00 | 2 |
Users on This Task
12 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
11
04
14
17
01
03
12
08
09
05
02
13
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 11 | 3 | 3.0 | 85.2 | Zero-Edit Visits | 0.00 | 0.00 | 9 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 11's Dominant Sequence
— 1 of 3 instances
1
Open Adverse Events Section
2
View Blinded SAE Case
3
View Case Lock Status
4
Select Case Lock Status
5
Select User
This analyst exhibits 3 distinct sequences across their 3 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 04 | 64 | 5.8 | 83.9 | Zero-Edit Visits | 0.00 | 0.00 | 543 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 04's Dominant Sequence
— 32 of 64 instances
1
Open Adverse Events Section
This analyst exhibits 30 distinct sequences across their 64 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 14 | 5 | 2.5 | 83.0 | Zero-Edit Visits | 0.00 | 0.00 | 38 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 14's Dominant Sequence
— 2 of 5 instances
1
Open Adverse Events Section
This analyst exhibits 4 distinct sequences across their 5 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 17 | 1 | 1.0 | 82.5 | Zero-Edit Visits | 0.00 | 0.00 | 0 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 17's Dominant Sequence
— 1 of 1 instances
1
Open Adverse Events Section
|
||||||||
| ▸ | Analyst 01 | 16 | 1.8 | 82.1 | Zero-Edit Visits | 0.01 | 0.00 | 30 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 01's Dominant Sequence
— 4 of 16 instances
1
Open Adverse Events Section
This analyst exhibits 13 distinct sequences across their 16 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 228 | 17.5 | 80.7 | Zero-Edit Visits | 0.07 | 0.00 | 4,855 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 03's Dominant Sequence
— 86 of 228 instances
1
Open Adverse Events Section
This analyst exhibits 110 distinct sequences across their 228 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 36 | 4.5 | 80.6 | Zero-Edit Visits | 0.09 | 0.00 | 2,010 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 12's Dominant Sequence
— 13 of 36 instances
1
Open Adverse Events Section
This analyst exhibits 24 distinct sequences across their 36 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 08 | 148 | 10.6 | 79.8 | Zero-Edit Visits | 0.09 | 0.00 | 5,226 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 08's Dominant Sequence
— 98 of 148 instances
1
Open Adverse Events Section
This analyst exhibits 33 distinct sequences across their 148 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 260 | 16.2 | 79.6 | Zero-Edit Visits | 0.11 | 0.00 | 1,736 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 09's Dominant Sequence
— 100 of 260 instances
1
Open Adverse Events Section
This analyst exhibits 126 distinct sequences across their 260 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 05 | 26 | 2.9 | 79.3 | Zero-Edit Visits | 0.13 | 0.00 | 431 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 05's Dominant Sequence
— 8 of 26 instances
1
Open Adverse Events Section
This analyst exhibits 17 distinct sequences across their 26 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 44 | 3.7 | 78.7 | Zero-Edit Visits | 0.13 | 0.00 | 132 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 02's Dominant Sequence
— 12 of 44 instances
1
Open Adverse Events Section
This analyst exhibits 32 distinct sequences across their 44 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 13 | 25 | 2.8 | 78.2 | Zero-Edit Visits | 0.16 | 0.00 | 91 |
|
Reference SoP
1
Open Adverse Events Section
Analyst 13's Dominant Sequence
— 9 of 25 instances
1
Open Adverse Events Section
This analyst exhibits 17 distinct sequences across their 25 instances; the dominant one is shown.
|
||||||||