Chapter 27 — Deployment and validation
Lesson map
Lesson 27 published. Map, example, EX079.
flowchart TD
N0["Learning outcomes"]
N1["27.1 Deployment"]
N2["27.2 Validation"]
N3["Lab — Reported share in new and existing clinics"]
N4["27.3 Local adaptation"]
N5["Synthesis"]
N0 --> N1 --> N2 --> N3 --> N4 --> N5
N5 --> EX["Worked example"]
EX --> Q["Exercise EX079"]
Logical drawing
flowchart LR
N0["Learning outcomes"]
N1["27.1 Deployment"]
N2["27.2 Validation"]
N3["Lab — Reported share in new and existing clinics"]
N4["27.3 Local adaptation"]
N5["Synthesis"]
N0 --> N1 --> N2 --> N3 --> N4 --> N5 --> Q["EX079"]
[Learning outcomes]
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v
[27.1 Deployment]
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v
[27.2 Validation]
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v
[Lab — Reported share in new and existing clinics]
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v
[27.3 Local adaptation]
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v
[Synthesis]
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v
[Worked example] --> [Exercise EX079]
Learning outcomes
You will (1) deploy the programme to two new clinics; (2) validate that it works in the new setting; (3) adapt for local differences.
27.1 Deployment
Brightwell acquires two more clinics. Lucas and the IT contractor deploy the programme there using the runbooks: report buttons, same-day replies, callback cards, payment holds and security keys for staff with patient data access. They run a short session for staff in each clinic, explaining what reporting is for and that unsure reports are welcome. Deployment takes two weeks per clinic. Using runbooks tests whether they work for someone other than their author, which reveals two missing steps. Both steps are added to the runbooks.
27.2 Validation
Lucas validates the deployment by checking outcomes, not just installation. After two months, he compares reported shares and verdict times in the new clinics with those in the existing ones. The lab shows that reporting in the new clinics is lower but rising. He interviews two staff, who say the previous owner had a policy of disciplining people who clicked suspicious links, so they were wary of admitting anything. Validation reveals that culture, not tools, is the remaining gap. He shares this finding carefully.
Lab — Reported share in new and existing clinics
# Reported share in new and existing clinics (synthetic).
print("existing clinics: 80%")
for m, (r, t) in enumerate([(5, 11), (8, 13)], 1): print(f"new clinics month {m}: {r}/{t} = {r / t:.0%}")
Run with python3 lab_newclinics.py. Verified output:
existing clinics: 80%
new clinics month 1: 5/11 = 45%
new clinics month 2: 8/13 = 62%
27.3 Local adaptation
To address the cultural gap, the practice manager visits the new clinics and states clearly that nobody will be disciplined for reporting or for honest mistakes. The clinic leads share their own stories of reporting suspicious messages. Lucas suggests a short follow-up survey after three months. Adapting for local history shows that the same controls can have different effects depending on what staff have experienced before, and that leaders' words and actions matter for trust. The survey is scheduled with the leads.
Worked example
Lucas deploys the programme to two acquired clinics, validates outcomes, finds a blame culture legacy and supports leaders in addressing it.
Synthetic case. Data are synthetic.
Errors and limits
Two months is short. Culture changes slowly.
Exercises
- EX079 — Missing steps. What did using runbooks in new clinics reveal? Rubric: 3 = two missing steps;; 2 = gaps;; 1 = vague;; 0 = none.
- EX080 — Lab comparison. How did new clinics compare in the lab? Rubric: 3 = 45% then 62% versus 80% in existing clinics;; 2 = lower but rising;; 1 = vague;; 0 = none.
- EX081 — Legacy cause. Why were new clinic staff wary? Rubric: 3 = the previous owner disciplined people who clicked;; 2 = blame;; 1 = vague;; 0 = none.
Synthesis
Validation checks outcomes in new settings and can reveal cultural gaps that tools cannot fix.
Continue studying — chapter 27 of 30
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