Leah Kaiser

I came across this insightful 💡 post and comments relating to how important creating high quality clinical documentation is to reducing claim denial rates. The post speaks of the Adonis 2026 Revenue Cycle Management Report http://bit.ly/3ZVoBuo. What I found really interesting is that 66% of respondents identified "automated denial follow-up and resolution" as a "very important" capability for AI for their 2026 RCM strategy. While I agree that AI is very important for RCM, I disagree it should be used to follow up denied claims. We should use A.I. to improve clinical documentation and avoid claim denials altogether! Healthful AI's Falcon 🦅 was created specifically for this purpose. It uses A.I. to assist the clinician to create improved documentation in real-time. Please reach out to me if you'd like to be proactive about tackling claim denial rates. P.S. Thanks Maria, for your post #HealthfulAI #Falcon #AIinHealthcare #ClinicalDocumentationImprovement

7

💭 Ever tried <cd /tmp | ls> and wondered why it just... doesn't work? Yeah, same here. I figured < cd > moves me to the directory and < ls > shows the contents—so why not just pipe them together? 🔧 **Tools I used:** Jumped into **ChatGPT** for this one—because honestly, it’s like having a Bash mentor who never gets tired of my “dumb” questions. It explained the concept in a way I could actually *grasp*, not just read. 🧠 **Key things I asked:** * “What really happens when I run `cd /tmp | ls`?” * “Why doesn’t `cd` pass anything to `ls`?” * “How do stdin and stdout actually work in this pipeline?” 💡 **The aha moment?** Turns out `cd` is a shell built-in and doesn’t send anything down the pipeline. So `ls` doesn’t know we ever moved directories! It finally clicked—**not every command plays nice with `|`**, especially the ones that just change shell state. 🔥 Honestly, using AI to break this down saved me hours of frustration and YouTube rabbit holes. I’ll definitely be using this approach again for tricky Bash stuff. #ALXFE alx_africa

1

1 Comment

Most people think you need a perfect technical background to become a Systems Analyst. You don’t. If you: • Ask why something happened • Notice patterns in data or workflows • Translate between people who speak different “languages” (business ↔ tech) You’re already practicing systems analysis. The title comes later. The mindset comes first. #AspiringAnalyst #SystemsAnalyst #CareerGrowth #SystemsThinking #TechCareers

7

3 Comments

📊 RADV isn’t just an audit—it’s a strategy. CMS continues to raise the bar on Risk Adjustment Data Validation: ✔️ Expanded audits ✔️ Stricter documentation expectations ✔️ Real financial impact through extrapolation 👉 The bottom line: If it’s not documented, it’s not defensible. This means: • Documentation = revenue protection • Education must be accurate + compliant (not leading) • Data must drive action—not just reporting ✨ Organizations that succeed won’t just pass audits—they’ll build audit-ready ecosystems. 🔗 https://lnkd.in/gufG5shZ #RevenueIntegrity #RADV #CDI #HIM #MedicareAdvantage #HealthcareLeadership #Alignment2026

1

Thinking about where to grow your EHR career? Data roles are seeing some of the biggest pay increases. Data Warehouse Developers now earn $142K on average — outpacing most non-manager roles in healthcare IT.

5

System Discovery   I’ve found that fixing RCM doesn’t start with technology, it starts with visibility. Before you fix RCM, you need to understand your system. Too often, we inherit workflows with “hidden” gaps that quietly cause denials, delays, and lost revenue. Those gaps rarely show up in the dashboards. You have to go looking for them. Blue Eagle SMEs bring the experience of 20+ hospitals to uncover: •          Broken processes •          Inefficient workflows •          Revenue leaks Discovery is the first step to transformation. In my experience, getting the right experts in early makes all the difference. #RevenueCycleManagement #HealthcareOptimization #HospitalFinance #BlueEagleConsulting

14

1 Comment

Reprocessing data in production is risky. Doing it without breaking reports is even harder. I supported pipelines processing pharmacy claims and eligibility data, where late corrections and business rule changes meant historical data had to be reprocessed. Taking pipelines down wasn’t an option. How I handled reprocessing & backfills in production: 🖊️ Parameter-driven pipelines I designed Airflow DAGs to accept date ranges and run modes (daily vs backfill). This allowed me to reprocess only impacted windows instead of full reloads. 🖊️ Bronze replay, Silver/Gold rebuild Raw data was always preserved in the Bronze layer. For backfills, I replayed Bronze → Silver → Gold transformations deterministically, ensuring consistent results. 🖊️ Isolation from live loads Backfill jobs ran on separate warehouses / clusters, so daily ingestion and reporting workloads were never impacted. 🖊️ Idempotent MERGE logic Using Spark and Snowflake MERGE statements, reprocessing updated existing records safely without creating duplicates. 🖊️ Validation before promotion Every backfill run had reconciliation checks (counts, sums, deltas). Only after validation did data get promoted to reporting tables. The result: Historical data fixed without downtime No broken Power BI dashboards Zero manual data patches Business trusted that “numbers changing” was intentional Backfills shouldn’t feel like emergencies. When pipelines are designed right, they’re just another controlled operation. #DataEngineering #Backfills #Airflow #Snowflake #HealthcareData #ETL #DataPipelines

4

The workforce crisis in health care isn’t going away, but technology is stepping up. At UPMC Enterprises, we’re investing in solutions that make a real impact. ⚙️🚀 One example is our portfolio company ThriveAP — a platform helping advanced practitioners transition into clinical roles faster and more effectively. New research from the Center for Connected Medicine at UPMC and KLAS Research shows: 🏥 80% of health system leaders say tech is essential to solving workforce challenges. 💰 90% are planning new investments in workforce technology. Explore how innovation is reshaping the future of care delivery. Download the full report here: https://lnkd.in/e5NvCTnp

13

1 Comment

- - End-to-end process visibility across EHR systems - - Root-cause insights in days, not months - - Automation triggers for delays and rework - - Improved care delivery and patient flow

5

This comment highlights a hard reality in healthcare leadership. Too often, C-level leaders treat technology like a utility. What I am saying most leaders expecting it to just “work” without investing the effort required to redesign workflows and operations. That’s never how large-scale systems like Epic worked, and it’s certainly not how AI will work. Technology simply exposes and accelerates whatever processes already exist. If workflows are broken, technology will scale the dysfunction faster. We saw this during the EMR wave, and we’re repeating the same mistake with AI today. Many organizations are hoping AI will be the silver bullet that fixes operational problems. It won’t. The real competitive advantage will belong to organizations willing to do the difficult work first: cleaning up workflows, aligning operations, and governing data properly. Process discipline will always beat technology hype.

4

“This is our moment to really change prior authorizations… and just make it a background function.” — Chronis Manolis, Chief Pharmacy Officer, UPMC Health Plan We don’t need to eliminate #PA - we need to fix it. The right combination of #ePA, #RealTimeBenefitCheck, and #AI-powered decision support is already delivering real results. To truly scale the impact, collaboration is key. Providers. Payers. Pharmacies. Tech partners. Everyone has a role to play in transforming the PA experience—for the better. Explore what’s working and what comes next in our latest white paper with UPMC: https://lnkd.in/eY-tZZd3 #ValueBasedCare #HealthcareTransformation #PriorAuthorization

32

Are you prepared for the challenges facing healthcare documentation and revenue streams? • Understand how leading organizations like UPMC, Mount Sinai Health System, and SGMC Health tackle denials in the context of Medicare Advantage. • Gain insights into improving Clinical Documentation Improvement (CDI) strategies. • Transform your approach to healthcare revenue management. Dive into our white paper, which addresses these pressing topics and offers actionable solutions. Explore the future of healthcare success. Follow the link : https://lnkd.in/gbJaJ7Np #MarketingInnovation #StrategicGrowth #B2BMarketing #DigitalMarketing #MarketingStrategies #LeadershipInMarketing

3

This is 100% true if the company states they will follow up. If they behave badly like this and renig on their commitment, alas, it probably is not an organization for which you want to work. But it is no less frustrating and demoralizing. #keepthefaith

1

At the 2025 Top of Mind Summit, co-hosted with UPMC Enterprises, health care leaders gathered to confront a challenge we can no longer ignore: ➡️ AI won’t transform health care unless we build infrastructure that supports governance, transparency, and validation. From real-world evidence to clinician trust, the message was clear: The health systems that win with AI are the ones building for scale, not just pilots. Explore our key takeaways and where we go from here: https://lnkd.in/dT7npxYi

3

  After years in healthcare eligibility and revenue cycle work, I launched a practical AI-powered real-time coverage locator that identifies coverage issues before the patient arrives. This model demonstrates: • Real-time eligibility & coverage validation • Plan mapping and filing-order intelligence • QMB / MSP indicator extraction • Pre-visit coverage issue detection • Front-end denial prevention workflows View the full model here: https://lnkd.in/gvGxm2Nf

2

Many clinical leaders ask: “What does HIMSS actually look for in Closed-Loop Medication adoption?” In my experience, HIMSS isn’t only checking that technology exists — they’re looking for safe, repeatable practice + evidence. If you’re preparing for HIMSS EMRAM validation (or simply want safer medication workflows), here’s a useful checklist: 1) The workflow is truly “closed-loop” • Provider orders → pharmacy verification/dispense → nursing administration • Right patient / right medication / right dose / right route / right time • Documentation captured electronically (not “after the fact”) 2) Adoption is measurable (not assumed) Leaders should be ready to show unit-level adoption such as: • Barcode scan-to-admin compliance • Override rate + top reasons • Exceptions by unit/shift (where the workflow breaks) 3) Governance is real (not theoretical) HIMSS discussions often focus on: • Who owns medication safety rules (clinical + pharmacy + IT) • How overrides are reviewed (cadence + decisions) • How you reduce alert fatigue while protecting critical alerts 4) Evidence beats opinions What makes the story strong: • Policy/SOP + training evidence • Audit logs or reports (sample screenshots) • Examples of “issue → corrective action → improved metric” 5) Resilience matters (especially during downtime) A mature program includes: • Clear downtime process for medication administration • Controlled documentation during downtime • Reconciliation/back-entry after recovery Closed-loop isn’t a “go-live milestone”. It’s a leadership operating model: governance + measurement + continuous improvement. If you’re a hospital leader, which area is usually hardest in your setting: adoption, exceptions/overrides, or downtime resilience? #MedicationSafety #ClinicalInformatics #HealthIT #HealthcareLeadership #DigitalTransformation #HIMSS #EMRAM

18

1 Comment

A lot of analyst work isn’t about knowing more tools. It’s about recognizing patterns. Most of the real progress I’ve seen in healthcare IT didn’t come from someone working harder or clicking faster. It came from someone pausing long enough to notice: • the same issue surfacing in different places • the same workaround being used by different teams • the same question being asked over and over See more… Once you recognize the pattern, the problem usually gets simpler: You stop solving tickets one-by-one and start fixing the system. You stop reacting and start predicting. You spend less time firefighting and more time preventing the next issue. That’s when work compounds. And it’s often invisible at first — because pattern recognition doesn’t look busy. It looks calm. It looks efficient. It looks like someone who’s seen this before. If you’re an analyst reading this: The people around you probably appreciate your work more than you hear. Your value isn’t just in what you fix — it’s in what you quietly prevent. Happy Monday, and happy New Year to everyone doing the thinking work that keeps systems moving. #healthcareIT #analystlife #epic #radiant #bridges #interoperability #clinicalinformatics #healthIT #consulting #systemsThinking #Epic #Radiant #Interoperability #Integrations #EpicBridges #HL7 #FHIR #PACS #DICOM #HealthcareIT #Rhapsody #corepoint #InterfaceEngines #ImagingIT #HealthTech #DataFlow #ClinicalWorkflows #EpicAnalyst #DigitalHealth #HealthSystems #WorkflowDesign

15

1 Comment

If I was building an EHR marketplace in 2026 - information blocking regulations would be haunting me. I know because I built one in 2023 and 2024 and they haunted me then too. Here's what I would do if I was tasked with building it again. 1. Create a complete separation of the idea of partnership and integrations. Trying to blend these two muddies the water when information blocking regulations create a clear image that the government wants third-party access to be strictly about fair and equal integration capabilities. 2. Create a quick path for third-parties to register as an integration, get sandbox access, and self-serve developer resources. If you're an EHR with thousands of customers, countless third parties will want to join your EHR marketplace and explore integration pathways whether they have customers or not. Build self-service workflows that allow third parties to explore this process of integrating on their own. 3. Ensure you're positioned to capture data on integrations being used. For many platforms, customer integrations can be a black box. Who are our customers always connected to? How many integrations do third parties have? How effective are third parties at helping our customers? 4. Decide on the business model. Whether it's proprietary APIs or FHIR, there's strict government regulations as to whether you can charge at all. The most important thing is to create a model that works at 1, 100, and 1000 partners. Not because it's a smart product idea to plan for scale on day 1 but because the government demands fair treatment across partners. Especially with FHIR, just offering it free to third parties is the most common route today. Other options could be per API transaction for proprietary APIs. 5. Once you're satisfied with your program for managing third party integrations at scale, establish an optional (to the partner) partnerships program focused on marketing and co-selling that has zero impact on third-parties ability to integrate. For EHRs, integrations aren't revenue generating departments. If you're able to brag about how much money third parties have to pay to be a partner, you're likely first in line to get information blocking complaints. And, if you're not ONC certified, current cases show that may not even matter. If you're an EHR with a large enough provider base their still may angles for third parties to take. However, where the data comes into play is identifying where integrations increase utilization, stickiness, where co-selling helps win deals, etc.

20

2 Comments