What Should You Ask Simana AI?

In our introductory article (Supercharging Healthcare Improvement with Simana AI), we explored how Simana AI can help healthcare teams bring improvement expertise directly into the flow of their work. Rather than acting like a generic AI assistant, Simana AI has been designed specifically to support quality improvement, measurement, SPC charting, project reporting, and portfolio oversight.

As more teams begin using Simana AI, one of the most common questions is simple: what should I ask it?

That question matters. Simana AI can support a wide range of improvement tasks, but users will often get more useful answers when they provide clear context about their project, measures, data, audience, or decision. A short prompt can be helpful, but a more specific prompt can help Simana AI give guidance that is more relevant, practical, and easier to act on.

To help users get started, we have published a new Simana AI Prompt Guide. It includes example prompts for frontline project teams, improvement advisors, operational leaders, and those responsible for governing improvement portfolios.

Prompting Is An Improvement Skill

Asking useful questions has always been central to improvement work. Teams ask what problem they are trying to solve, what changes they can test, what measures will show whether improvement is happening, and what the data is telling them. Simana AI brings that same questioning mindset into the improvement workspace.

The aim is not to learn technical AI language. The aim is to ask clear improvement questions.

For example, a user could ask Simana AI to:

• Improve an aim statement
• Suggest measures
• Create an SPC chart
• Interpret variation
• Summarise project progress
• Identify projects that may need support

Each of these prompts can be simple, but they become more useful when the user includes enough context for Simana AI to understand the improvement challenge.

Instead of asking:

• “Suggest some measures”

A team might ask:

• “Suggest outcome, process, and balancing measures for a project aiming to reduce discharge delays in the Emergency Department. Include practical operational definitions and suggest how often each measure should be collected.”

That second version gives Simana AI a clearer picture of the project, the setting, and the type of output needed. The result is likely to be more specific, more useful, and easier for the team to discuss.

Helping Frontline Teams Get Started

For frontline teams, one of the most valuable uses of Simana AI is support at the beginning of an improvement project. Many teams have a good sense of the problem they want to solve, but need help turning that idea into a clear project structure.

A team might ask:

• “Help me turn this improvement idea into a clear quality improvement project.”
• “Based on this project description, suggest a clear aim statement.”
• “What questions should we answer before starting this improvement project?”

These prompts help teams move from a broad concern to a more focused improvement effort. They can also help teams think through assumptions, risks, stakeholders, and the practical steps needed to begin work with more confidence.

Choosing Better Measures

Choosing what to measure is one of the hardest parts of improvement work. Teams need measures that are meaningful, practical to collect, and capable of showing whether changes are leading to improvement. They also need to balance outcome measures with process and balancing measures, so they do not miss important parts of the story.

Simana AI can help teams think through their measurement approach. For example, users can ask:

• “Suggest outcome, process, and balancing measures for this project.”
• “Review these proposed measures and suggest how they could be strengthened.”
• “Help us reduce the burden of collecting this data.”

These prompts are especially useful because they do not simply produce a list of possible measures. They help users think more critically about whether the measures are practical, whether they reflect the aim of the project, and whether they will support better decision-making over time.

Creating Charts From Data

Once measures have been agreed and data collection begins, teams often face the next challenge: turning the data into a meaningful chart. Many healthcare teams only create SPC charts occasionally, so choosing the right chart type and structuring the data correctly can feel difficult.

Simana AI can act as an embedded analytical advisor. A user might ask:

• “Turn this spreadsheet into an SPC chart and recommend the most appropriate chart type.”
• “Review this spreadsheet and tell me whether the data is suitable for a run chart or SPC chart.”
• “Create a P chart from this spreadsheet.”

The prompt can also include more detail about presentation and interpretation. For example, a team could ask Simana AI to:

• "Add a process break after a particular intervention"
• "Suggest useful annotations"
  

Understanding What The Data Is Telling You

Creating a chart is important, but it is rarely the end of the challenge. Teams then need to understand whether the chart shows normal variation, special cause variation, improvement, deterioration, or uncertainty.

This is where Simana AI can help teams build confidence in interpretation. Users can ask:

• “Analyse this chart and tell me what it suggests about variation and performance.”
• “Are there any shifts, trends, or special cause signals in this chart?”
• “Should we recalculate the centre line on this chart? Explain why or why not.”

These kinds of prompts help teams avoid overreacting to normal variation, missing meaningful signals, or drawing conclusions that the data does not support. Simana AI can also help explain the reasoning, so users understand not just what the chart may be showing, but why.

Deciding What To Do Next

Improvement work depends on decisions. Teams need to decide what to test, adapt, continue, stop, implement, or scale. These decisions are often made under pressure, and it is not always obvious what the next step should be.

Simana AI can support that thinking. A team might ask:

• “Based on this chart and project activity, what should our next step be?”
• “Generate PDSA cycle ideas for testing this change idea.”
• “Are we ready to move from testing to implementation? Explain your reasoning.”

The value of these prompts is not that Simana AI makes the decision for the team. The value is that it helps structure the decision, surface risks, connect data to activity, and suggest practical options the team can review together.

Summarising And Reporting Progress

Improvement teams also spend a significant amount of time summarising their work for others. Project updates, governance papers, leadership briefings, handovers, and benefits summaries all take time to prepare, especially when teams need to explain technical improvement work in plain language.

Simana AI can help reduce that administrative burden. Users can ask:

• “Create a concise update covering progress, evidence of impact, risks, and next steps.”
• “Summarise this project for a senior leadership audience.”
• “Help us describe the impact of this project without overstating the evidence.”

These prompts can help teams communicate more clearly and consistently. They can also help ensure that project reporting remains connected to evidence, learning, and next steps, rather than becoming a simple activity update.

Supporting Improvement Leaders And Portfolio Governance

Simana AI is not only useful for individual project teams. It can also support improvement leaders, operational leaders, and governance groups who need to understand what is happening across a wider portfolio of improvement work.

Leaders might ask:

• “Which projects have not been active in the last 30 days?”
• “Which projects are missing outcome measures?”
• “Which active projects are showing measurable improvement?”

At portfolio level, Simana AI can help identify risks, themes, stalled work, support needs, and opportunities for spread. For example, a governance lead could ask:

• “Summarise the current improvement portfolio by theme, service, and status.”
• “Which projects may be suitable for spread or scale?”
• “What common themes are emerging across projects focused on this priority?”

This makes it easier for organisations to learn from improvement work, not just manage it. It can help leaders spot patterns, identify where teams may need support, and make better use of the improvement activity already taking place across the organisation.

Simana AI Supports Human Judgement

Simana AI is designed to support improvement thinking, not replace it.

It can help teams generate ideas, improve project content, interpret charts, summarise progress, and review portfolios. However, final decisions should still be made by the people responsible for the work, including project teams, improvement advisors, analysts, clinical leaders, operational managers, and governance groups.

This is especially important in healthcare, where decisions may affect patient care, staff workload, resources, and service performance. Simana AI should be used as an embedded improvement advisor that helps people think more clearly, not as a black-box decision maker.

Explore The Simana AI Prompt Guide

The new Simana AI Prompt Guide brings together example prompts across the different stages of improvement work, from starting a project and choosing measures to interpreting SPC charts, planning next steps, reporting progress, and governing a portfolio.

The guide is intended to be practical and adaptable. Users can copy prompts directly, change them to fit their own context, or use them as inspiration for better questions.

The more clearly users can describe what they are trying to improve, what information they are working with, who the output is for, and what decision they need to make, the more useful Simana AI can become.

Explore the full guide here: Simana AI Prompt Guide.