Choosing AI Vendor Without Framework is Like Choosing Coffee Without Roast

How do you choose the right AI vendor for your medical practice? The answer is that it depends on your specific needs. That might not feel like a complete answer, so let me share a framework to help you make this decision. After talking with many companies, I’ve noticed two main ways to measure value. Most look to show a calculated ROI based on either cash or capacity. To figure out capacity, multiply the hours saved or created by the average cost of that team member’s rate. With that in mind, it’s important to go into this search with a clear vision of what you hope to get out of the system. These goals will land in GROWTH, MARGIN EXPANSION, and CAPABILITIES. If your goal is growth, saving or creating those hours can help your team be more productive and reach their potential. If you care most about margins, focus on the cash return. If you want new capabilities, look for a solution that balances both. When you start with a clear vision, you’ll know what to focus on in your discussions. You can also visit our website to find a full framework for building your AI department. #MedicalPractice #PracticeManagement #HealthcareProfessionals #AIinHealthcare

🤖 AI is quickly moving from curiosity to necessity in healthcare. While much of the conversation focuses on documentation, some of the most interesting applications are emerging in patient engagement, scheduling, referral management, outcomes tracking, and business intelligence. For private practice owners, the question is no longer whether AI will impact your business, but where it can create the greatest value without compromising patient care. If you're exploring AI solutions for your practice, PTPN members may want to take a look at several of our Preferred Vendors offering AI-powered tools designed to help improve efficiency, reduce administrative burden, and support practice growth. What's one area of your practice where technology has made the biggest difference? #PhysicalTherapy #HealthcareInnovation #PrivatePractice #AIinHealthcare #Rehabilitation #PracticeManagement

Patient support is a clear use case for AI, yet teams are still turning to traditional solutions to manage rapidly growing volume. We’re hosting Steve Hind, Co-Founder of Lorikeet, for a practical conversation on what it takes to leverage AI for patient support safely and effectively. We’ll dig into: - How teams are solving patient support today and where AI can help - How guardrails work to enforce the line between admin support and clinical judgement - Getting clinical and compliance teams on board - Navigating implementation with fragmented data and outdated SOPs - Measuring direct and downstream impact - Evaluating AI support vendors If you’re responsible for patient support and weighing AI against more hiring or BPO capacity, this will be a useful discussion. Register here: https://lnkd.in/gfKZvr3k Sponsored by Lorikeet

AI is everywhere in revenue cycle management right now, but is it actually paying off? The numbers tell an interesting story. Healthcare organizations using AI somewhere in the revenue cycle sit at 63%, with 27% running it at scale. Yet only 15% report positive ROI. That is a significant gap between adoption and actual return. Hidden costs are part of the problem. Post-launch expenses like model retraining, staff training, and ongoing compliance audits rarely appear in the initial vendor pitch. These can add up to 25-45% of your total project investment. Then there is the "Touch Tax." This is the cumulative cost of work that fails to produce a financial outcome. Completing more tasks is not the same as generating more revenue. If a claim still needs payer follow-up, appeals, or corrections downstream, the administrative burden simply shifts elsewhere. For some functions, AI in RCM can pay for itself. Clinical documentation AI can break even in 3-8 months. Nearly 1 in 3 providers still cite cost as their top barrier to adoption, and 41% say accuracy remains a trust issue. Are we measuring AI success by outcomes or just by activity? This article is a great read on the topic: https://lnkd.in/dRMvgcGA #RevenueRycleManagement #RCM #HealthcareBilling #MedicalBilling #AIinHealthcare #HealthcareAI #ArtificialIntelligence #AIAdoption #HealthcareLeadership #HealthcareInnovation #FutureOfHealthcare #HealthcareFinance #HealthcareOperations

Artificial intelligence is rapidly reshaping healthcare revenue cycle operations, but it has also created a dangerous misconception. AI cannot independently resolve the complex reimbursement disputes that determine whether your hospital actually recovers its earned revenue. Predictive models and automated work queues are excellent for surfacing hidden reimbursement trends and identifying underpayments. However, when an AI tool flags a massive variance, it cannot read a managed care contract to explain why the payments differ from your negotiated agreement. Technology accelerates insight, but people deliver results. The MAS Advantage At Medical Accounts Systems (MAS), we use advanced analytics to prioritize complex claims—but we never let the software work alone. We combine intelligent automation with experienced revenue cycle professionals and attorney-driven oversight to interpret contracts, evaluate payer obligations, and successfully resolve high-value disputes. Executive Action Plan: 🤖 Use AI for Trends: Deploy automation to flag patterns, not to replace experienced problem-solving. 🔍 Validate with Expertise: Always validate technology findings through strict contract analysis. ⚖️ Combine Forces: The strongest revenue cycle strategies combine intelligent automation with collaborative payer engagement and legal insight. Executive Question: Does your technology simply identify reimbursement opportunities, or does your organization have the legal expertise to actually convert them into recovered revenue? 👇 Check the comments below for the link to read Issue No. 4 of Revenue Recovery Insights (AI Won't Recover Your Revenue Alone) and see how MAS transforms data into meaningful financial outcomes! #HealthcareFinance #RevenueCycleManagement #HealthcareAI #MedicalAccountsSystems #RCM #HealthcareCFO #RevenueIntegrity #HospitalAdministration

🤖 The future of vendor governance isn't more reports. It's better recommendations. Every governance team reviews data. The real challenge is deciding what deserves attention first. 🔎 Clinical Maestro Vision uses 🤖 AI to analyze operational data and generate actionable recommendations, helping sponsors prioritize: ✔ Emerging risks 🚨 ✔ Vendors requiring immediate attention 🎯 ✔ Performance trends that matter 📈 ✔ Actions that support proactive sponsor oversight ✅ Instead of spending time searching for signals across multiple reports, governance teams can focus on making faster, more informed decisions. As clinical outsourcing grows in complexity, intelligent prioritization becomes just as important as visibility. ❓If AI could recommend the three most important vendor issues to discuss every week, how would that change your governance meetings? #ClinicalMaestroVision #AIVendorGovernance #VendorGovernance #ClinicalTrials #ClinicalOperations #SponsorOversight #ICHE6R3 #ExecutiveInsights

AI can give clinicians more information. More recommendations. More support at the point of decision. But someone still has to decide what fits the patient in front of them. That takes judgment. And judgment needs something healthcare doesn't always protect well: the capacity to think. A clinician can have excellent technology and still be making decisions between interruptions, unfinished documentation, inbox messages, and competing priorities. That's why human capacity has to be part of the AI conversation. As the technology becomes more capable, we should be creating more room for clinicians to think clearly. Not less. Because the goal isn't to replace human judgment. It's to support it. AI can support the decision. A human still has to make it. better workdays, better care.

Finding a meeting time should not require ten emails, three phone calls, and a spreadsheet. That is the problem the AI Scheduler solves. It coordinates availability across multiple calendars, communicates with clients or patients, and books the right time automatically: without the endless back-and-forth. For example, a medical practice can use it to coordinate appointments between patients, doctors, and treatment rooms. A consulting firm can use it to schedule client meetings across several consultants’ calendars. The result is simple: Faster booking. Less administrative work. Fewer missed appointments. Fewer double-bookings. The AI Scheduler is not just another calendar tool. It is an AI employee that handles a repetitive operational process so your team can focus on higher-value work. The best AI solutions do not add complexity. They remove it. See how the AI Scheduler can improve your workflow: ai.pclinkup.com Start building a smarter, more efficient business today. #AIForBusiness #BusinessAutomation #ArtificialIntelligence #OperationalEfficiency #Pclinkup

Healthcare AI procurement often starts with a model performance question. But in real-world care, the decisive question is operational accountability. Ask vendors: “What is your process when clinicians follow an incorrect AI recommendation?” Then listen for specifics on: ✅ Notification + escalation pathway ✅ Documentation requirements (what gets recorded, by whom, where) ✅ Human review loop (who signs off and how) ✅ Feedback mechanism (how errors improve the system) ✅ Liability ownership and governance controls If those answers are vague, you’re not evaluating AI you’re evaluating marketing. We ask these questions before we build so the audit trail, escalation pathways, and human review layer are designed from day one. Deployed in 8–12 weeks. What’s the most important question you ask in demos? #ClinicalAI #HealthcareGovernance #PatientSafety

My feed this morning carried four separate posts about AI in healthcare operations. Not one of them mentioned utilization management. That is a strange omission, because UM is where the preconditions for useful automation are unusually good. The inputs are structured. The decision rules are already written down. The volume is high and repetitive. The feedback loop is fast, because you learn within days whether a status determination held. What I would want from a tool in that space is narrow and fairly unglamorous. Surface the cases where the documented clinical picture and the assigned status have quietly drifted apart, while the patient is still in the building rather than after the denial arrives. Rank them, so a physician advisor spends the first hour of the day on the twenty charts that will move and not the two hundred that will not. Most of what is being marketed to hospitals right now is aimed at the note. The larger unrecovered value sits one step later, in whether the right decision was made and then defensibly recorded. That is a quieter problem. It is also a more expensive one to keep ignoring. #UtilizationManagement #HospitalOperations