Practical AI In Health: The Series

Most of what gets written about AI in health is either a vendor pitch or a research paper. Neither helps much if your job is to decide what to build next quarter, what it will cost to run, and how you will explain it to your privacy officer.

This series is for the people in that position: IT managers, CIOs, clinical informatics leads and executives at Australian hospitals, primary care networks, aged care providers, allied health groups and health software vendors. We assume you know your own environment better than we do. What we can add is what these systems look like once they are running in production, past the demo.

What we mean by practical

Each post takes one use case and works through it properly. What the system actually does, what it costs to build and to keep running, where it breaks, and what you need in place before it is safe to turn on. Where a use case does not justify the effort, we will say so. Some of the most useful advice we give clients is that a well-designed form or a fixed integration will beat an AI feature for the same problem.

We will not be quoting productivity figures from overseas vendor case studies. If we cite a number, it will be one we can point to.

Human review is part of the design

In health, a model output that nobody checks is a clinical or administrative decision made by software. Every pattern we write about assumes a person in the loop, and treats that person’s time as a real cost in the business case. A system that saves ten minutes of drafting but adds fifteen minutes of verification is a system that has failed, and you should be able to see that in the numbers before you build it.

That also shapes the engineering. Outputs need to be traceable back to source documents. Reviewers need enough context to reject something quickly. Logs need to survive an audit.

The Australian context is not an afterthought

The Privacy Act and the Australian Privacy Principles apply to health information whether the processing happens in your data centre or in someone’s US-hosted API. Data residency, secondary use, consent, contracted service providers and record retention all shape the architecture, and they shape it early. We will cover how those requirements land in real design decisions rather than as a compliance checklist bolted on at the end.

PicNet has been building software for Australian organisations since 2001, and for the last few years that has increasingly meant AI systems that have to work every day, not just in a pilot. This series is the version of the advice we give clients, written down.

PicNet builds production AI systems for Australian organisations. Talk to us about what a first project could look like.

Posts in this series

Tagged: #ai-in-healthcare #health-it #privacy-act #human-in-the-loop #production-ai

Practical AI in Health: the series

What we mean by practical

Human review is part of the design

The Australian context is not an afterthought

Posts in this series