Digital Pharmacist Digest - 👇 preparing for generative AI in healthtech, identifying low-value safety practices, and more

Hiya 👋

We’re back with another edition of the digital pharmacist digest!

Here are this week's links that are worth your time.

Thanks for reading,
Kevin

📖What I'm reading

🩺💻 Health informatics and 🤖 Artificial Intelligence - Preparing for the World of Generative AI

'Imagine a bad actor or unscrupulous app developer who wants to market software as a medical device (SAMD) but doesn’t want to do the expensive due diligence to analyze tens of thousands of medical images to train its algorithm, and instead develops its “solution” using ChatGPT-enabled fake images. If products like this gain traction in the medical community, it would put lives at risk.'

🏥🩺Patient Safety - Identifying Safety Practices Perceived as Low Value: An Exploratory Survey of Healthcare Staff in the United Kingdom and Australia

Staff identified safety practices that they perceived to be low value. In healthcare systems under strain, removing existing low-value practices should be a priority. Frequently identified categories of practices identified included “paperwork,” “duplication,” and “intentional rounding.” Careful evaluation of these identified safety practices is required to determine whether they are appropriate for deimplementation and, if not, to explore how to better support healthcare workers to perform them.

👨‍💻 Product management - How Meta uses analytics to assess Product Market Fit

Meta measures the following three key metrics to assess the value delivered by products within their markets and gauge product market fit.

1. Stable Retention — Are people coming back predictably?

2. Sustainable Growth — Is the product able to consistently acquire, retain and resurrect users?

3. Deep Engagement — Are people using the product? If so, how long are they using it for?

📈 Data - 2023 State of Databases for Serverless & Edge

"...emerging trends for database companies:

• Databases are increasingly becoming data platforms, including other adjacent solutions like full-text search and analytics.

• The decoupling of storage and compute, popularized by Snowflake (and more), is enabling new players (e.g. Neon et al.) to massively reduce the cost of a “database at rest”. This pairs well with frontend git branch-based workflows, where you want to scale to zero when not being used.

• Increasingly developers don't want to “dial the knobs”. Solutions like DynamoDB (and in some ways S3) provided infinite scale without needing to tweak memory, storage, CPU, clusters, and instances.

• The dream of global data is here, but not how it was predicted. Trying to replicate all data to every network edge is probably not the correct solution most times. Instead, we're seeing specialized data APIs and the emergence of user-specific data stores (e.g. for shopping cart data).

• More databases are embracing serverless, but what “serverless” means to them varies. There are different vectors of autoscaling: connections, storage, compute, and more."