Spike Technologies

Health apps can now explain why metrics changed, not just report data, and IoT devices make it possible. Examples from the market: → Eight Sleep tracks sleep stages + bed temperature, letting apps show users exactly which temperature settings improved their HRV by 23% → Baracoda Group BMind embeds meditation into bathroom routines (no separate app sessions needed), then correlates which practices improve sleep quality → Apps integrating Equa bottles + Nix biosensor patches can tell runners "drink 16oz within 30 minutes post-run to match your 450ml/hour sweat rate" instead of generic hydration advice The retention driver: users see tangible improvements tied to specific interventions. We break down what leading IoT products do, how apps integrate the data, and why it keeps users engaged: https://lnkd.in/drQavt2r

Across U.S. hospitals, AI use for billing jumped from 36% to 61% in a single year, and AI scheduling tools grew from 51% to 67% over the same period. This trajectory makes it clear that AI in healthcare is no longer limited to early adopters and is instead becoming the baseline. We analyzed the rise of agentic AI in healthcare and what's available for PT, OT, and SLP clinics: https://lnkd.in/dXbvUDTg

Health and wellness apps seeking new ways to differentiate and get ahead should consider incorporating lab test analysis. Users have to return to the app to see whether the intervention plan worked, understand what changed, what's driving it, and what to do next. Here's how industry leaders are already leveraging it for user retention: → WHOOP Advanced Labs connects biomarker results to daily coaching, adjusting recovery, sleep, and other targets based on what the tests show. → Midi Health allows users to order blood work inside its menopause platform, then feeds results into treatment plans and suggests follow-up testing, creating an active health process. →  Function Health builds a personalized intervention plan based on 160+ biomarker tests. The results feed into dietary and supplement protocols, giving users a reason to return and see if the plan worked. If you are considering integrating lab reports, we put together a guide on what's happening in the space and how lab reports can increase user retention in your health app: https://lnkd.in/dW3u-AeW

Proud to partner with Stedi in helping physical, occupational, and speech-language pathology clinics spend less time on paperwork and focus on what matters most: patients.

For businesses exploring wellness programs, there are more options than step tracking. These workplace devices are reshaping how companies approach employee health: → Air quality monitors like Awair and Kaiterra Sensedge track CO2, particulate matter, and VOCs → Posture tracking devices like Upright Go 2 and smart desks from Fully Jarvis monitor ergonomic health → Smart hydration bottles like HidrateSpark PRO reveal water consumption patterns throughout the workday We compiled a market overview of what's available and what to consider when building an employee wellness program. Read the full piece: https://lnkd.in/dtCv67rh

67% of patients who access their lab results online report confusion about what they mean. The problem isn't access to lab data. It's the gap between the result and what to do about it. In 2026, this gap is closing fast:  → OpenAI launched ChatGPT Health for AI-assisted lab interpretation  → Anthropic rolled out Claude for Healthcare to help interpret medical data  → Quest released an AI Companion designed to help patients interpret their lab test results  → Perplexity Health now combines lab results, wearables, and medical records in one interface Users expect actionable advice when they upload a lab report, creating both a challenge and an opportunity for developers building health apps. Read the full blog: https://lnkd.in/dx6aN9A5

Adding lab reports to a health app may sound straightforward, but it’s not. Every lab formats results differently: different templates, units, and languages. "Hemoglobin," "Hb," and "HGB" are the same marker, but your database doesn't know that without LOINC mapping. And OCR alone won't save you: extracting structured data from dense, tabular medical documents requires AI-powered extraction on top, plus a validation layer for scans that arrive blurry. Before you build, there are six decisions worth thinking through: → Which file formats to support  → How to handle OCR and structured extraction  → Whether you need LOINC mapping (and when you can skip it)  → What to do with low-quality or uncertain extractions  → What structured lab data should look like in your app  → Whether lab data should connect to your broader health stack → To build it all or buy We broke down each one: https://lnkd.in/dA3B9sjT

LOINC is the universal standard for lab test results. → 100,000+ terms covering everything from basic panels to genetic tests → The most widely adopted lab terminology standard worldwide → 2,000 codes cover 99% of the tests labs actually run day-to-day For developers building health apps, it's the difference between building translation logic for dozens of proprietary formats and getting ahead. Learn more about LOINC: https://lnkd.in/ddTqHCqf

Every clinic owner asks the same question before automating their front office. "How will this benefit my clinic financially?" "Can I trust AI and automation in my clinical workflow?" These are questions that deserve straight answers, not a sales pitch. We put together answers to the 5 questions PT, OT, and SLP clinic owners ask most before automating, from cost and trust to what happens when workflows change. Read here: https://lnkd.in/dnSQYcHT

The front desk staff at PT, OT, and SLP clinics are buried in verification calls and seemingly endless admin tasks. Human errors result in incomplete verification, missed authorization requirements, and outdated coverage data. These compound downstream into claim denials and write-offs. We put together a guide on where manual revenue cycle processes are lacking and how Voice AI Agents are changing the revenue cycle for PT, OT, and SLP practices. What's inside: → Why 26% of denials trace back to intake errors → The downstream cost of verification call mistakes → How to automate payor-specific processes Get your guide: https://lnkd.in/dtsXymVG