Sat.Jan 25, 2025

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A Comprehensive Guide to HL7 Messages

Arkenea

The healthcare industry is incredibly complex and amasses an ever-increasing amount of fragile patient and specialist data. According to insightful studies, each patient has 80 megabytes of EMR and imaging data each year. In most cases, this data belongs to different medical institutions that process and store it in isolated pools that are poorly structured.

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🧠 Community Wisdom: Building a good foundation in product, workshop exercises for roadmap planning, structuring team syncs, career ladders for designers, and more

Lenny Rachitsky

👋 Hello and welcome to this week’s edition of ✨ Community Wisdom ✨ a subscriber-only email, delivered every Saturday, highlighting the most helpful conversations in our members-only Slack community.

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Application Support Model: Everything You Must Know Today

Arkenea

The strategy of creating a great application isn’t just coding or integrating solution based features, it’s also about providing application support to the customers. As per a report , customer expectation surged in 2020 and the need for speed, ease, and personalized customer support only increased. Hence, the need for implementing a robust application support model for resolving issues of customers is crucial.

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ePrescription Software Development – Detailed Outlook

Arkenea

Key Takeaways A need to control substance usage and reduce medication errors is driving the e-prescribing market growth during the 2023-2030 timeframe. Key audiences to note during the e-prescription software development are the elderly, chronic disease, and bedridden patients. Online medication orders, refills, and reminders help such patients to keep a track of their health care.

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How to Achieve High-Accuracy Results When Using LLMs

Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage

When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m