Coditation

Healthcare AI: The Midcycle Revolution | Success Stories #2 A physician we worked with recently told us something that stuck:  "I became 𝗮 𝗱𝗼𝗰𝘁𝗼𝗿 𝘁𝗼 𝗵𝗲𝗮𝗹 𝗽𝗲𝗼𝗽𝗹𝗲, not to fight with paperwork at midnight." He's not the only one! It has been observed that physicians spend 𝟰-𝟲 𝗵𝗼𝘂𝗿𝘀 𝗱𝗮𝗶𝗹𝘆 on documentation and that's more time than they spend with their actual patients. We set out to change that! Through our AI agent deployments in healthcare, we've watched the transformation unfold in real-time. 𝗔𝗺𝗯𝗶𝗲𝗻𝘁 𝘀𝗰𝗿𝗶𝗯𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 that listens, understands and generates clinical notes while doctors focus on what matters the most - the patient in front of them. The impact isn't marginal and it's transformational. Three hours returned to clinicians every single day! 𝗔 𝘄𝗵𝗼𝗽𝗽𝗶𝗻𝗴 𝟯𝟬% 𝗱𝗿𝗼𝗽 in after-hours documentation and the burnout scores cut by 40%. But here's where it gets interesting: the ripple effects extend far beyond. Our revenue cycle AI agents have tackled the chaos that drains healthcare finances.  1. 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗰𝗼𝗱𝗶𝗻𝗴 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 that lifts first-pass claims by 15-22%.   2. 𝗣𝗿𝗶𝗼𝗿 𝗮𝘂𝘁𝗵𝗼𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 became 40% faster.   3. 𝗘𝗹𝗶𝗴𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝘃𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 which slashes denials by 31%. One organization we partnered with was able to eliminate $32.2 million in operational waste over the period 30 months. From the front desk to the back office, AI agents are rewriting the playbook: With 24/7 scheduling that reduced no-shows by 30-40% along with patient intake check ins compressed from 15 minutes to under 3. Referral coordination that actually works boosting completion rates by 40%. These aren't hypothetical case studies.  They're projects we've engineered, deployed and scaled. Here's what makes us different: We build with your infrastructure in mind.   • FHIR-compliant integration.   • Human oversight baked in.   • Security that meets HIPAA from day one. This is our first deep-dive into healthcare AI transformations. More stories coming!! Ready to turn administrative burden into competitive advantage? #AIEngineering #DataSolutions #AIinHealthcare #DigitalHealth #ClinicalWorkflow #RevenueCycleManagement #HealthTech #AIAutomation #PhysicianBurnout

Today, One thing I keep hearing in customer conversations around 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗶𝗻 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 is that : The interest is real, but people are now looking well beyond the technology itself. The first question is no longer what AI can do. It is what outcome it will actually improve. 1. Will it help clinicians spend less time on documentation and more time with patients? 2. Can it reduce delays in claims processing and improve turnaround times? 3. Will it make patient communication feel more timely and personal? And most importantly,  Will the teams using it every day 𝘁𝗿𝘂𝘀𝘁 𝗶𝘁 𝗲𝗻𝗼𝘂𝗴𝗵 to rely on it consistently? From a customer success standpoint, this is where the real journey begins. A pilot can generate excitement but long-term success depends on adoption, trust and outcomes that show up in the numbers and in daily workflows. At 𝗖𝗼𝗱𝗶𝘁𝗮𝘁𝗶𝗼𝗻, What we have seen across healthcare engagements is that the most successful ones start with a clear,  Shared definition of what success actually looks like. Sometimes that means reducing administrative burden for care teams. Other times it means improving operational efficiency or speeding up decision-making. When customers start seeing those outcomes reflected in how their teams work day to day then confidence builds quickly. This is when AI stops being an initiative and starts becoming a real business capability. For me, Customer success in this space comes down to :  • Staying close to customer goals,   • Listening to feedback continuously   • Improving the solution as real-world needs evolve. Technology may open the conversation. But 𝘁𝗿𝘂𝘀𝘁, 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 and 𝗿𝗲𝗮𝗹𝗶𝘇𝗲𝗱 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 are what define whether it lasts. What do you think matters most after the pilot phase: adoption, trust or measurable outcomes? #GenerativeAI #AIinHealthcare #CustomerSuccess #CustomerOutcomes #AIAdoption #TrustInAI #EnterpriseAI #DigitalTransformation

Last year, a healthcare provider approached us with a challenge that was quietly affecting both staff efficiency and patient care. On paper, the hospital had invested in the right technology. Over time, they had implemented several digital systems to manage different parts of their operations. But those systems were working in isolation rather than together. A doctor trying to review a patient’s full medical history had to navigate multiple platforms. Nurses often had to manually transfer information between systems which was a time-consuming search. During assessment we realised that patient data was spread across 𝟭𝟮 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀, including EHR systems, laboratory software, billing systems and imaging archives. Important clinical details were scattered across multiple systems and duplicate patient records occasionally caused confusion during care handoffs. The American Health Information Management Association estimates that 𝟴 𝘁𝗼 𝟭𝟮 % 𝗼𝗳 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗿𝗲𝗰𝗼𝗿𝗱𝘀 𝗰𝗼𝗻𝘁𝗮𝗶𝗻 𝗱𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀, often caused by fragmented systems and inconsistent patient identifiers. Our engineering team discovered that some systems relied on HL7 messaging standards, while others had already adopted 𝗥𝗘𝗦𝗧𝗳𝘂𝗹 𝗔𝗣𝗜𝘀 built on the 𝗙𝗛𝗜𝗥 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱. Instead of replacing systems our focus was integration. We built 𝗶𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 using 𝗛𝗟𝟳 𝗺𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 and 𝗙𝗛𝗜𝗥 𝗔𝗣𝗜𝘀 to connect the hospital’s systems into a unified data layer. Real-time synchronization ensured updates in one system appeared across others instantly. Within a few months, ·    Time spent locating patient information 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝗯𝘆 𝗮𝗯𝗼𝘂𝘁 𝟯𝟬%, ·    Duplicate record incidents fell by 𝗮𝗿𝗼𝘂𝗻𝗱 𝟰𝟱%, ·   Administrative reconciliation workload 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝗯𝘆 ~𝟮𝟱% If fragmented systems are slowing down your healthcare operations, it may be time to rethink about smoother integration. #HealthcareIT #HealthTech #FHIR #HL7 #DigitalHealth #Coditation #ArtificialIntelligence #AI

Over the last couple of years, Coditation has been part of building 30+ GenAI solutions across industries. Not all of them went smoothly. Some worked well from the start. Others needed a reset before they got to a good place. Looking back, the ones that struggled early ended up teaching us the most. Across these projects, we started noticing patterns. Certain factors kept showing up as strong predictors of outcomes, regardless of the industry or use case. We felt it was worth documenting those learnings honestly, not as a polished framework, but as a reflection of what actually happened in the field. That is what eventually became the GenAI Playbook. (link : https://lnkd.in/dNNu9dKq) It is a practical collection of observations from building, testing, fixing, and improving real systems. A big thanks to Rajesh Darak, adish katkar and Aniket Deshpande for contributing their insights and keeping it grounded in real experience. If this is relevant to what you are working on, the Playbook is available on our website.

I recently came across an interesting GenAI report: State of AI 2026 Global Report. It states 60-70 % of employees now had access to AI tools but fewer than half were using them consistently - 𝘁𝗵𝗲 𝗔𝗰𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻 𝗚𝗮𝗽. We analyzed the adoption data of GenAI solutions we have delivered and looked at the empirical insights shared by our customers. The goal was to see if we can uncover some patterns: 𝟭. 𝗧𝗵𝗲 𝗴𝗮𝗽 𝗶𝘀 𝗿𝗲𝗮𝗹, 𝗯𝘂𝘁 𝘂𝗻𝗲𝘃𝗲𝗻: We've seen it and helped customers work through it. But the severity varies wildly depending on the use-case, the org, and how the AI was introduced. 𝟮. 𝗘𝗺𝗯𝗲𝗱𝗱𝗲𝗱 𝗯𝗲𝗮𝘁𝘀 𝘀𝗶𝗱𝗲-𝗰𝗮𝗿 𝘀𝘁𝘆𝗹𝗲 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗼𝗳 𝗔𝗜 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀: Especially outside software engineering, copilot-style chatbots don't do as well as AI woven into tools people already use. Users adopt better when AI is delivered via what feels familiar. For instance, an insurance provider's IT team saw much higher than average adoption when we delivered the AI automation right inside the ticketing system, rather than as a separate chatbot. 𝟯. 𝗨𝘀𝗲-𝗰𝗮𝘀𝗲 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻, 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗲𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻 𝘀𝗲𝘁𝘁𝗶𝗻𝗴 𝗮𝗹𝗹 𝗺𝗮𝘁𝘁𝗲𝗿. But if the solution doesn't deliver real value, no amount of change management will sustain adoption. 𝟰. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗱𝗿𝗶𝘃𝗲𝘀 𝘁𝗿𝘂𝘀𝘁: LLMs are inherently non-deterministic, but agents with guardrails, validation layers and well-defined scope can deliver the consistency users need. When outputs feel unpredictable, usage drops. We recently built an AI agent to automate the RFI/RFQ process. By implementing guardrails and validation steps, we were able to ensure the outputs were consistent and reliable, which significantly increased user trust and adoption. 𝟱. 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗹𝗼𝗼𝗽 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 - but so does the review experience. Everyone agrees oversight is important. What's underappreciated is that if the review workflow is clunky or adds friction, it becomes the bottleneck that kills adoption. The best implementations treat the review interface as a first-class product. As an example, we worked with an industrial enterprise to implement an AI system for quality control. The human review process was designed with a focus on usability and efficiency, which significantly boosted adoption rates among the quality inspectors. 𝗧𝗵𝗲 𝗮𝗰𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻 𝗴𝗮𝗽 𝗿𝗲𝗮𝗹𝗹𝘆 𝗶𝘀𝗻'𝘁 𝗼𝗻𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. It's the 𝗰𝗼𝗺𝗽𝗼𝘂𝗻𝗱 𝗲𝗳𝗳𝗲𝗰𝘁 of choices across product design, process design and trust calibration. 𝗖𝗮𝗿𝗲𝗳𝘂𝗹𝗹𝘆 𝗰𝗿𝗮𝗳𝘁𝗲𝗱 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 that consider all these factors help organizations drive adoption and achieve the desired RoI.