Fred Lee

Recently in a client interaction I called POC an outdated concept, this article has gone one step further and says it's dead, can't agree more. You have to deploy and test simultaneously and evaluate results and not do a POC on some random sample or fudged data!! A full blown implementation is the way forward in AI projects else start ups will waste a lot of time and resources!!

8

Enterprise #SaaS is giving way to #AI-native SaaS not just in technological #innovation but also in the #valuation it commands. Terrific data and perspective from Ryan Allis and three of the key messages ring true: 1. Recurring revenue still matters but not all #recurringrevenue is created equal 2. #Distribution and #GTM models are increasingly internal to the platform vs. discrete actions I.e. adoption and expansion are the norm, not the exception 3. #Scale and #growth are not defined by unit economic acceleration but rather by growth in #systems #intelligence over the same cost base At a macro level, it will be interesting to see how enterprise SaaS platforms in #healthcare manage this rapid evolution. How do you see this taking shape?

39

7 Comments

Ignition K.K. 𝗛𝗼𝘄 𝘄𝗶𝗹𝗹 𝗔𝗜 𝗿𝗲𝗮𝗹𝗹𝘆 𝗰𝗵𝗮𝗻𝗴𝗲 𝗿𝗲𝗰𝗿𝘂𝗶𝘁𝗺𝗲𝗻𝘁 𝗮𝗴𝗲𝗻𝗰𝗶𝗲𝘀? There’s a lot of noise about AI replacing recruiters. The reality is probably simpler — and more practical. I see two big shifts coming for large agencies. 𝟭. 𝗠𝗶𝗱𝗱𝗹𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗴𝗲𝘁𝘀 𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲𝗱 Many middle layers today focus on reporting, KPIs, and internal oversight. But once AI has access to company data, it can instantly generate reports, forecasts, and performance insights. Much of that operational work becomes automated. So the value of managers has to shift. Less internal control. More external impact. Future leaders will need to win clients, open doors, and create opportunities — not manage spreadsheets. Rainmakers, not administrators. 𝟮. 𝗧𝗵𝗲 𝗹𝗼𝘄𝗲𝗿 𝗲𝗻𝗱 𝗼𝗳 𝘁𝗵𝗲 𝘃𝗮𝗹𝘂𝗲 𝗰𝗵𝗮𝗶𝗻 𝘀𝗵𝗿𝗶𝗻𝗸𝘀 Sourcing, screening, and admin are increasingly automated. That likely means fewer junior and mid-tier “process” roles, and more focus on higher-value work: senior hiring, niche searches, and trusted advisory relationships. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲 Recruitment won’t disappear — it moves up the value chain. The transactional parts get automated. The human, trust-based parts become more valuable. And if AI ever truly replaced all recruitment? It probably means it replaced most jobs too — and then there’d be nothing left to recruit for anyway. AI disruption will have essentially eaten itself and caused its own extinction...;) More likely, the industry just evolves. Agencies that adapt will thrive. Those that don’t will struggle. If you’d like to better understand the recruitment market in Japan and the options available to you, please feel free to get in touch Michael Craven #rec2rec #japanjobs #ignitionkk

23

2 Comments

Vendor sprawl is a hidden tax. I’ve seen $2.3M saved just by renegotiating contracts and cutting overlap. In many sub‑250 bed hospitals and FQHCs, the tech stack looks normal on paper. But under the surface, legacy systems and duplicate tools drive IT Opex up by 25–40% over years. Silent drains. Multiple EMRs and bolt‑ons. Three vendors for the same service. Old apps kept alive “for one report.” The data is clear. Healthcare studies show phased modernization and cloud moves cut IT run costs by 25–40%. EMR consolidation case work shows about 30% lower maintenance after legacy systems merge into one modern platform. Application rationalization is the fastest lever. Analysts estimate 20–30% of IT resources in mature health systems are redundant. One KLAS‑profiled health system retired nearly 800 applications and unlocked over $65M in net annual savings, about $80K per app per year. Cloud and contracts add more upside. A HIMSS‑referenced survey shows over half of healthcare IT leaders who moved to cloud saw more than 25% infrastructure savings with higher reliability. One network saw $2.3M per year back from telecom and vendor optimization alone. Here is how I guide community systems. Inventory and score every app and contract. Target high‑cost, low‑value systems for retirement or consolidation. Move heavy workloads to cloud with clear TCO goals. Layer automation and AI only after the stack is clean. Modernization then funds itself. Many organizations see 15–30% IT spend back in year one and 20–40% long‑term Opex reduction without new capital. If your health system wants a 60‑minute tech performance and cost review, I am open to walk your team through where vendor sprawl is taxing your margin. Info@transformativehlth.io

3

When we took over a SaaS product upgrade earlier this year, the founder thought they had a dev problem. They didn’t. They had a 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 problem. No one was documenting changes. Feature requests came through DMs. Deadlines were “whenever possible.” We introduced a reporting system, Asana integration, and transparent sprint updates. Productivity went up 40% without adding a single engineer. Most founders think they need more talent. What they actually need is more 𝘷𝘪𝘴𝘪𝘣𝘪𝘭𝘪𝘵𝘺.

2

🧬 We talk about “health data” as if it’s one thing, but it’s really hundreds of incompatible languages trying (and failing) to talk to each other. Every layer speaks a different dialect: • EHRs: HL7 v2, CDA, FHIR • Claims: X12 837, UB-04, CMS-1500 • Labs: LOINC, SNOMED CT • Devices: DICOM, IEEE 11073 • Genomics: VCF, FASTQ, BAM Each was built for a single purpose, not interoperability. The result? 🚑 A patient’s data is scattered across 40+ systems, each with its own schema, timestamps, and access controls. But things are shifting. Newer models are moving beyond formats to: • Graph-based data structures • Semantic layers • Federated architectures These approaches preserve context, not just content, across systems. FHIR paved the road. But the next frontier is semantic interoperability. That’s not just data exchange; it’s data understanding. 🧠 The future of healthcare intelligence isn’t in collecting more data, it’s in connecting meaning. #HealthTech #DataInteroperability #FHIR #HealthcareAI #KnowledgeGraphs #SemanticWeb

146

15 Comments