Mansi Soni

Mansi SoniOct 7, 2026

🚨 Prior Authorization is becoming a data problem — not just a workflow problem. For years, prior authorization has often meant: Request → Wait → Follow up → Approval/Denial But healthcare is moving toward more electronic prior authorization. CMS is requiring certain impacted payers to implement Prior Authorization APIs beginning January 1, 2027. The goal is to make the process more electronic and give providers better information about authorization requests and decisions. But there is another important change happening here. More digital workflows can also mean more structured data. And that creates an opportunity for RCM data analysts. Instead of only asking: “How many authorizations were denied?” we can start asking: Why are they being denied? Which payer has the most delays? Which services need authorization most often? How long does approval take? How many authorization problems later become claim denials? For example: Prior Authorization Data → Find Patterns → Identify Bottlenecks → Reduce Rework → Improve RCM This is where I think the role of an RCM data analyst becomes more valuable. The goal isn't just to build a dashboard showing numbers. The goal is to understand what those numbers are telling us about the revenue cycle. Healthcare is becoming more digital. So RCM analytics should move from simply reporting what happened to helping teams understand why it happened and where they can improve. Prior authorization may be a workflow today. But the data generated by that workflow can become an important source of RCM intelligence. Source:- https://lnkd.in/dKzNstxV Follow Mansi Soni for more such content. #HealthcareRCM #RevenueCycleManagement #HealthcareAnalytics #HealthcareData #PriorAuthorization #USHealthcare #RCMAnalytics #DataAnalytics #HealthcareTechnology #CMS

Mansi SoniOct 8, 2026

🤖 AI + Healthcare Data: AI Can Find the Pattern, but Can Your Data Explain It? AI is becoming a bigger part of healthcare, and I believe this will also change how RCM teams work with data. But there is one important question for RCM and healthcare data analysts: What happens when AI finds a pattern, but the underlying data is incomplete, inconsistent, or not properly validated? CMS is moving healthcare toward more connected and standardized digital data exchange through its Health Technology Ecosystem and interoperability initiatives. This creates opportunities for healthcare organizations to work with more structured data across different systems. For an RCM data analyst, this can become very valuable. Instead of only reporting: “Our denial rate increased.” We can use data and AI to ask better questions: Which payer is driving the increase? Which providers or specialties are affected? Which services are involved? What are the main denial reasons? When did the pattern start? Did something change in the workflow or payer requirements? AI can help us find these patterns faster, but the analyst still needs to validate the data and understand what is actually happening. A simple approach could be: Reliable Data → AI Finds Patterns → Analyst Validates → RCM Team Takes Action → Measure the Result For example, if AI identifies an unusual increase in claim denials, we should not immediately assume that the AI has found the root cause. We should first check whether the data is complete, whether the comparison is valid, what changed during that period, and whether the pattern continues after further analysis. The goal is not simply to build another dashboard. The goal is to understand what the data is telling us and turn that information into better RCM decisions. My takeaway: AI can find patterns faster. Good data and human analysis help us understand whether those patterns actually mean something. I don't see the future of RCM analytics as AI vs. Data Analysts. I see it as: AI + Reliable Healthcare Data + Human Validation = Better RCM Analytics Follow Mansi Soni for more insights on US Healthcare RCM, Data Analytics, and AI. official CMS sources:- https://lnkd.in/dFqgaC2z https://lnkd.in/d59Vys6T https://lnkd.in/dYSx2Gza #HealthcareRCM #RevenueCycleManagement #HealthcareAnalytics #HealthcareData #HealthcareAI #DataAnalytics #USHealthcare #RCMAnalytics #ArtificialIntelligence #CMS

Mansi SoniOct 9, 2026

🚨 October 1, 2026: A small CMS update that can create a big RCM impact. CMS has updated the Q4 2026 NCCI Procedure-to-Procedure (PTP) Edits, effective October 1, 2026. In simple terms, NCCI PTP edits help identify certain CPT/HCPCS code combinations that generally should not be reported or paid separately under Medicare. Why should an RCM data analyst care? Because a coding or claims-processing rule can eventually show up as a denial, payment change, or unusual trend in the dashboard. So when October denials increase, the first question should not always be, “What did the billing team do wrong?” A better question is: “Did a CMS coding or claims-processing rule change?” An RCM analyst should compare September vs. October and check which CPT/HCPCS combinations, providers, payers, and denial reasons were affected. CMS update → Coding/Edit → Claim → Payment/Denial → RCM Data → Dashboard That is where healthcare data analytics becomes more than reporting numbers. It helps explain why the numbers changed. 📌 I’ll explain the NCCI PTP update and its impact on RCM data in the carousel. Follow Mansi Soni for such content. Source link : https://lnkd.in/dJ92Geuk #HealthcareRCM #MedicalBilling #HealthcareAnalytics #NCCI #RevenueCycleManagement #cms #UShealthcare