What the AI found
“Your client's reported 'brain fog' correlated 80% of the time with sleep duration under 6.5 hours in the past two weeks, rather than their assumed dietary triggers.”
Before
Disorganised Client Notes
After
Focused Cognitive Insights
The same system, three states — real screens, not a screenshot
| Client A (ID 001) - Last review | 23/10 |
| Cognition notes | scattered, recent mention of 'brain fog' |
| Sleep logs | partial, from wearable data |
| Dietary intake | client journal via photo uploads |
Prompt
Analyse the provided anonymised client data from the past two weeks. The client reports 'brain fog'. Correlate the reported 'brain fog' instances with sleep duration (under 6.5 hours vs. over), and dietary patterns (high carb, low carb, balanced). Quantify any significant correlations with a percentage.
Analyse the provided anonymised client data from the past two weeks. The client reports 'brain fog'. Correlate the reported 'brain fog' instances with sleep duration (under 6.5 hours vs. over), and dietary patterns (high carb, low carb, balanced). Quantify any significant correlations with a percentage.
AI
Analysis of Client ID 001 data over the past 14 days shows a notable correlation: 80% of reported 'brain fog' instances occurred on days following sleep duration of less than 6.5 hours. No significant correlation was observed with specific dietary patterns within this period.80%
Sleep < 6.5 hrs & Fog
No significant finding
Dietary Trigger
7 min
Review Time / Client
Weekly Cognitive Health Review in 7 Minutes
A practitioner moves from fragmented client notes to a streamlined, AI-assisted cognitive health review.
A nutritionist running a small EU practice
Tools used
The real tools used here — swap any for your own equivalent. Each links to how we’d set it up.
Starting state
Before anything was set up
Before implementing the structured review, client notes for cognitive health were spread across various handwritten scraps and digital documents. Key observations, dietary changes, and reported symptoms were often siloed, making it challenging to identify patterns or track progress systematically. A holistic view required considerable manual effort, often leading to overlooked connections.
| Client A (ID 001) - Last review | 23/10 |
| Cognition notes | scattered, recent mention of 'brain fog' |
| Sleep logs | partial, from wearable data |
| Dietary intake | client journal via photo uploads |
Working state
Resources, doing its job
The practitioner then used the Wellness & AI Cognitive Health Review playbook to standardise client data entry into Google Sheets. With structured daily logs, a weekly check-in note was quickly drafted. This note, alongside anonymised sleep and dietary inputs, was fed into Gemini, prompting an analysis for correlations the human eye might miss.
Prompt
Analyse the provided anonymised client data from the past two weeks. The client reports 'brain fog'. Correlate the reported 'brain fog' instances with sleep duration (under 6.5 hours vs. over), and dietary patterns (high carb, low carb, balanced). Quantify any significant correlations with a percentage.
Analyse the provided anonymised client data from the past two weeks. The client reports 'brain fog'. Correlate the reported 'brain fog' instances with sleep duration (under 6.5 hours vs. over), and dietary patterns (high carb, low carb, balanced). Quantify any significant correlations with a percentage.
AI
Analysis of Client ID 001 data over the past 14 days shows a notable correlation: 80% of reported 'brain fog' instances occurred on days following sleep duration of less than 6.5 hours. No significant correlation was observed with specific dietary patterns within this period.Use case implemented
The finished system, running on its own
The system now runs weekly. Each client's data is entered into the standardised Google Sheet. The practitioner spends seven minutes each Friday morning extracting the key data points, feeding them into Gemini with a pre-prepared prompt, and receiving a targeted summary of potential correlations, enabling more precise, evidence-informed client conversations.
80%
Sleep < 6.5 hrs & Fog
No significant finding
Dietary Trigger
7 min
Review Time / Client
What an outside observer would notice
Reduced by 60%
Manual Data Collation Time
Increased by detecting non-obvious patterns
Correlation Accuracy
More specific, data-backed advice
Client Insight Delivery
The stack — build it yourself
Familiar, flexible for custom tracking, and easily shareable for collaboration.
Excellent for natural language prompting to analyse diverse data inputs and identify correlations.
These are the tools used in this story. Any can be swapped for an equivalent you already trust.
Go deeper
Do this yourself
See the full workflow
This story runs on Resources. The tools and prompts above are the real build — swap any tool for your own equivalent and follow the same steps.