Healthcare Leaders Need Trusted Data Foundation for Decision-Making

Grateful to Healthcare Business Outlook for the opportunity to contribute.   As a physician and informaticist, I’ve seen how often healthcare teams look at the same data and reach different conclusions. Not because anyone is wrong, but because there isn’t a shared understanding of what the data actually means. That’s where friction shows up. Decisions slow down, rework increases, and over time it becomes a real constraint on performance.   More data hasn’t fixed this. In many cases, it’s made it harder. What I’m seeing more of now is organizations pairing their internal expertise with external partners to help bring context and clarity to that data so decisions can happen earlier and with more confidence. AI can help, but only if the foundation is right. Otherwise, it just accelerates the noise.   I’d value your perspective on this, especially from those working inside health systems.   https://ow.ly/HLyM50Z15T9   #Claritev #Healthcare #HealthTech #DataAnalytics #AI

Why does Healthcare in the US seem stuck despite nonstop technology advances and ever-increasing budgets? Can AI help us get out of this rut? Today's article on Digital Perspectives explores these questions... https://lnkd.in/gXcjDdmk

For more than 30 years, I have worked inside healthcare systems - with payers, providers, government programs, care coordination models, and healthcare technology platforms. One lesson keeps repeating itself: Healthcare does not fail because people lack intelligence. It fails because systems do not coordinate. That is exactly what is happening with AI now. Over the last 18 months, healthcare organizations moved aggressively toward AI across prior authorization, documentation, triage, utilization review, and clinical administration. The promise was simple: 1. Faster decisions. 2. Lower administrative burden. 3. Better workflows. But healthcare is not a simple workflow environment. Every decision touches multiple parties: The physician. The patient. The payer. The administrator. The compliance team. The technology vendor. This is where AI deployment becomes difficult. Not because the model cannot generate an answer. Because the healthcare system cannot always govern what happens after the answer is produced. A recommendation moves across fragmented systems, different stakeholders, different rules, and different accountability structures. Then the real questions begin: Who owns the decision? Who has the authority to act? Who is accountable if the outcome is wrong? Can the decision be audited later? Where did the data move? If those questions are not answered at the infrastructure level, AI will not scale in high-risk healthcare workflows. The American Medical Association reported that 94% of physicians say prior authorization delays patient care. That is not a model problem. It is a coordination problem. Adding AI on top of a fragmented infrastructure does not automatically create trust. It can make systems faster, but not necessarily safer. That is why healthcare is moving toward governed AI environments with human oversight, clear accountability, auditability, and controlled execution. This is not resistance to AI. This is how real healthcare adoption works. The next phase of healthcare AI will not be won by the companies with the best demo. It will be won by the companies that understand how healthcare systems actually operate - and build infrastructure that can support trust at scale. #HealthcareAI #AIInfrastructure #DigitalHealth #EnterpriseAI

AI is about to expose more problems in healthcare than it solves. Everyone is chasing AI right now. But here’s the uncomfortable truth: AI doesn’t fix broken operations. It exposes them. When you introduce AI into a physician group or an ASC, three things happen fast: Variability in provider performance becomes visible Inefficiencies in scheduling and staffing get harder to hide Revenue leakage shows up in real numbers That’s where most organizations stall. Not because the technology fails— but because the operating model can’t support it. The groups actually getting value from AI aren’t the ones buying the most tools. They’re the ones that: Standardize workflows first Align physicians around performance Then layer in AI to scale what already works AI isn’t a shortcut. It’s a multiplier. And if the foundation isn’t right, it just accelerates the problem. That’s the gap I’m seeing across healthcare right now. If you’re evaluating AI for your organization, start with your operating model—not the vendor list. And if you’re serious about making it actually work inside your business, that’s exactly where I focus. Booth Healthcare Advisory https://lnkd.in/ee9cT7CB #HealthcareOperations #PhysicianPractices #HealthcareLeadership #ValueBasedCare #HealthcareStrategy #ScalingHealthcare #OperationalExcellence #PrivateEquityHealthcare #MSO #HealthcareConsulting

🏥 This #NationalHospitalWeek, let’s talk about the future of healthcare technology. 🚀 Why does MCP matter to health systems now more than ever? Dive into our latest blog to explore the role of MCP in healthcare, and review 5 key considerations for its success: https://hubs.la/Q04dJYbJ0     #Micromedex #ClinicalDecisionSupport  #MCPHealthcare #AIagents #ModelContextProtocolNews  #ModelContextProtocolHealthcareNews #HealthcareTechnology #HospitalWeek

The Governance Institute (an NRC Health company) put out a great report today on how health system aren't investing enough in the structure and ongoing development of their boards. They note "In many organizations, [#AI] capabilities are advancing faster than the boards responsible for governing them." Question -- Is your board governing use of AI? If so, are they using AI to help them govern AI? If not, I'd wager they're either not doing their job or they're holding back critical innovation. https://lnkd.in/gc3KvbXc

In healthcare, decisions don’t wait for perfect data. They happen in real time, often under pressure, and with incomplete visibility. What’s evolving isn’t the decision itself, but the intelligence behind it. With connected systems, real-time data, and AI-driven insights now embedded into daily workflows, healthcare teams are moving beyond fragmented reports and delayed information. Instead, they’re operating with a clearer, more unified view. The result? ✔️ Proactive care instead of reactive responses ✔️ Smarter, more accurate operational planning ✔️ Reduced financial inefficiencies and unexpected costs This shift isn’t just about improving processes. It’s about enabling faster, more confident decisions across the entire healthcare ecosystem. Want to see how data, analytics, and AI are redefining decision-making in healthcare? Explore the full blog here: https://lnkd.in/gCVfpWd7 #HealthcareAnalytics #HealthcareAI #DataDrivenHealthcare #HealthTech #AIInHealthcare #AIForBusiness #DataAnalytics #Dotsquares

Honey Health is increasingly being brought into MSOs, ACOs, MSPs, roll-ups, and other types of platforms in healthcare to solve a unique problem to "platforms." Platform organizations sit on top of dozens or hundreds of practices. In an ideal world, they're pushing and pulling information and actions between their centralized databases and all of the practices and EMRs underneath them. Bi-directional. Constant. Automated. In reality, that's very far from the truth. Getting data out of each practice is a manual grind. Staff pulling records from the EMR, compiling spreadsheets, uploading to a portal. Different EHRs at every site. Different workflows. Different people. And even when the data makes it up to the platform, pushing action back down is just as painful. Getting care gap alerts into visit notes. Coordinating follow-ups with scheduling teams. Making sure providers actually see the right information at the right moment. All of it manual. All of it fragmented. This is the bi-directional problem that platforms have been trying to solve for years. There hasn't really been a great solution to this until Honey Health's data fetching and entry AI staff came online. Today, Honey is orchestrating hundreds of thousands of these pushes and pulls every single day, eliminating the slow manual work to orchestrate insights and actions across these platforms. As one healthcare leader put it: "You are giving our platform the arms we always wanted ..."

Bridging the data gap between platforms and the practices underneath them — that's exactly what our AI staff are built to handle.