Most health plans measure chart retrieval volume.

Very few measure retrieval value.

In this episode of The Execution Gap, Dr. Peter Saah introduces the concept of the Retrieval Yield Gap and explains why retrieving more charts does not automatically create more value.

A health plan can retrieve thousands of records, hit every retrieval target, and still see little improvement in measure performance, audit readiness, or Stars outcomes.

The real question isn't:

"How many charts did we retrieve?"

The real question is:

"What value did those charts create?"

In this episode, you'll learn:

• Why retrieval volume and retrieval value are not the same thing
• The Retrieval Yield framework
• The Retrieval Yield Pyramid
• How to identify high-value retrieval opportunities
• Why some gap closures never survive audit review
• The hidden cost of False Yield
• How CCD data and medical records play different roles in evidence strategy
• Why the same chart is worth more in March than it is in October
• The three questions every health plan should ask before retrieving a chart

If you're responsible for HEDIS®, Stars, ECDS, quality improvement, risk adjustment, chart retrieval, abstraction operations, or audit readiness, this episode will challenge how you think about evidence collection and performance improvement.

Because retrieval volume measures effort.

Retrieval yield measures impact.

#HealthcareQuality #HEDIS #MedicareAdvantage #StarsRatings #PopulationHealth #RiskAdjustment #ECDS #MedicalRecords #ChartRetrieval #QualityImprovement #HealthcareAnalytics #AuditReadiness #HealthcareOperations

Episode 11: The Evidence Utilization Gap — Why the Chart You Retrieved May Be More Valuable Than You Think

Most Medicare Advantage plans have become very good at retrieving clinical evidence.

Very few have become equally good at utilizing it.

In this episode of The Execution Gap Podcast, Peter Saah introduces the concept of the Evidence Utilization Gap — the difference between the evidence an organization acquires and the value it actually extracts from that evidence.

The central argument is simple:

Many plans have already paid for the evidence they need. They simply haven't extracted all the value from it.

Peter explores why the chart itself isn't the asset — the evidence inside the chart is — and why treating evidence as a departmental resource instead of an enterprise asset may be creating one of the largest hidden inefficiencies in Medicare Advantage operations today.

From HEDIS and risk adjustment to RADV readiness and provider abrasion, this episode examines what happens when organizations focus on evidence acquisition but underinvest in evidence utilization.

• Why the chart isn't the asset — the evidence is

• The Evidence Utilization Gap and why it matters

• The "dual-use chart" and the hidden value inside retrieved records

• Why the most expensive chart in healthcare may be the one you've already retrieved but never fully utilized

• CMS-HCC V28 and the growing importance of documentation specificity

• RADV expansion and the increasing importance of evidence traceability

• Why chart retrieval and abstraction should be viewed as enterprise capabilities

• The provider abrasion cost of duplicate retrieval workflows

• The CFO Test: explaining your evidence workflow to finance leadership

• The difference between evidence acquisition and evidence utilization

• Why the organizations that win may not retrieve the most charts — they may simply extract the most value from every chart they retrieve

Key Takeaways:

Retrieval is an activity. Evidence utilization is an outcome.

The plans that perform best over the next decade may not be the ones retrieving the most charts. They may be the ones extracting the most value from every chart they retrieve.

Pilot inquiries: poderohealth.com/demo

Connect with Peter Saah on LinkedIn: https://www.linkedin.com/in/dr-peter-saah-dba-mba-pmp-cphq-0b50a572/

Website: poderohealth.com

Episode 9: The Audit Readiness Gap

Most healthcare quality teams focus on collecting evidence.

Far fewer focus on whether that evidence can be defended.

In this episode, Peter Saah explores a growing challenge facing health plans as ECDS reporting expands: the difference between a closed gap and an audit-ready gap.

Using a practical framework built around evidence acquisition, validation, and interpretation, Peter explains why audit readiness is not an audit-season activity—it is a production discipline that starts with the first chart retrieved, the first CCD processed, and the first abstraction decision recorded.

Topics include:

• What audit readiness actually means

• Why evidence can be clinically correct but still create audit exposure

• The three most common audit failure modes

• How ECDS increases the importance of source validation and traceability

• The Audit Defensibility Framework

• Why high-performing plans build audit readiness into daily operations

This episode is intended for healthcare quality leaders, HEDIS professionals, Medicare Stars teams, population health leaders, and health plan executives navigating the transition toward increasingly digital quality measurement.

Learn more about Podero Health at:
https://www.poderohealth.com

Request a pilot:
https://www.poderohealth.com/demo

Episode 8: Your ECDS Data Is Only as Good as Your Provider's EHR WorkflowMost discussions about ECDS focus on interoperability, CCD ingestion, FHIR, data exchanges, and measure engines.But what if the biggest ECDS performance problem starts before any of that?What if the determining factor is the way providers document care at the point of care?In this episode of *The Execution Gap*, Peter Saah explores one of the least discussed drivers of ECDS performance: **documentation maturity**.The reality is that two providers can deliver the exact same care to the exact same patient and produce two completely different reporting outcomes. One workflow generates measure-ready data. The other generates clinically meaningful documentation that never becomes compliant evidence.The result?Care happened.Documentation exists.The measure stays open.This episode breaks down why provider workflow design has become one of the strongest predictors of ECDS success, how health plans can identify documentation maturity gaps across their network, and why many organizations are investing in downstream technology to solve an upstream workflow problem.---### In This Episode✅ What NCQA's ECDS guidance reveals about provider readiness✅ Why documentation maturity matters as much as data exchange maturity✅ The difference between clinically meaningful documentation and measure-ready data✅ How the same clinical event can produce completely different ECDS outcomes✅ Why your provider network is not a uniform data environment✅ The provider segmentation analysis most plans have never run✅ How to identify providers generating structurally incomplete ECDS data✅ When targeted chart retrieval becomes the right operational response✅ Why ECDS performance often reflects provider workflow design more than technology investments---### Key Takeaway**Most plans segment providers by performance. Very few segment providers by documentation maturity.**As ECDS adoption expands, documentation maturity may become one of the strongest predictors of quality performance.---### About Podero HealthPodero Health helps health plans improve quality performance through:• CCD processing and evidence normalization• Documentation maturity analysis• Provider-network segmentation• Targeted chart retrieval• AI-assisted clinical abstraction with human validation• Audit-ready evidence workflows---🌐 Website: www.poderohealth.com📩 Pilot Inquiries: www.poderohealth.com/demo🔗 Connect with Peter Saah on LinkedIn: https://www.linkedin.com/in/dr-peter-saah-dba-cphq-0b50a572/---### Timestamps00:00 — Where ECDS Performance Really Begins00:30 — Why Provider Workflow Matters01:20 — What NCQA Is Really Telling Us03:00 — The Documentation Gap Explained05:20 — Your Provider Network Is Not Uniform07:30 — Three Operational Implications09:30 — The Analysis Most Plans Should Run10:50 — Closing Thoughts#ECDS #HEDIS #MedicareStars #QualityImprovement #HealthcareQuality #HealthPlan #PopulationHealth #ClinicalData #Interoperability #FHIR #CCD #DigitalQuality #HealthcareOperations #QualityManagement #ProviderData #NCQA #MedicareAdvantage #HealthcareAnalytics #PoderoHealth #TheExecutionGapPodcast

The promise of ECDS was real: real-time digital data replacing expensive, retrospective chart retrieval. The operational reality of MY2026 is more complicated. In this episode, Peter Saah breaks down exactly where the data quality problem went when hybrid reporting gave way to ECDS — why COL-E rates have been declining under ECDS since MY2024, why BPC-E is about to produce the same challenge at larger scale, why the administrative-to-ECDS shift for measures like SPC-E requires clinical data infrastructure most plans haven't built, and what the integrated operational model actually looks like when digital data collection, targeted chart retrieval, and abstraction quality work together correctly.

This episode is for health plan quality directors, VPs of operations, and HEDIS program leads who have invested in ECDS transition and are now asking why the gaps are not closing the way the technology promised.

Topics covered:

→ What ECDS was designed to fix — and the published data showing COL-E rates declining under ECDS versus prior hybrid rates

→ Why NCQA's "no large population impact" statement and member-level closure failures are measuring two different things

→ BPC-E: why blood pressure evidence under ECDS is the hardest clinical data to normalize and why it will expose data infrastructure gaps at scale in MY2026

→ Why SPC-E represents a category shift — pharmacy and administrative measures now requiring integrated clinical data infrastructure most plans weren't built for

→ The three simultaneous data programs most MA plans are running in MY2026 — and what nobody budgeted for in the governance layer

→ Why chart retrieval is not over — it is being repositioned as a prospective completion mechanism for the members ECDS data can't close alone

→ The audit exposure that increases when you add ECDS data sources without adding measure-level validation infrastructure

→ What the integrated operational model looks like when CCD processing, targeted retrieval, and abstraction quality work as one pipeline

Pilot inquiries — CCD data processing, chart retrieval, abstraction:https://poderohealth.com/Contact

Connect with Peter Saah: https://www.linkedin.com/in/dr-peter-saah-dba-cphq-0b50a572/

Website: poderohealth.com

___________________________________________________

© Podero Health. All rights reserved.

The care happened.

The physician documented it.

The chart was retrieved.

The evidence existed.

And the measure still stayed open.

That is not a retrieval failure. That is an abstraction failure. And it is one of the least measured — but most financially consequential — problems in payer quality operations today.

In Episode 6 of The Execution Podcast, Peter Saah breaks down exactly where abstraction fails, why your vendor's sub-one-percent error rate doesn't tell you what you think it does, why AI-assisted abstraction shifts the judgment layer without eliminating it, and what the plans managing abstraction well actually do differently.

This episode is for health plan quality directors, VPs of operations, and HEDIS program leads who want to understand why Stars performance keeps coming in below forecast — and why the answer may not be in their retrieval rate at all.

---

WHAT YOU'LL LEARN IN THIS EPISODE:

→ Why most Stars misses are not missing care — they are wrong interpretation of care that was already documented and retrieved

→ Why vendor-reported sub-1% error rates measure internal consistency — not whether the shared interpretation logic is correct against the measure specification

→ Why AI-assisted abstraction (NLP, evidence extraction) shifts the judgment layer without eliminating it — and the three failure modes that remain in the human decision layer on top of AI output

→ The seven ways abstraction fails in real HEDIS production environments — from clinical misinterpretation and specification misapplication to the silent failure that leaves no trace in the system

→ Why COL-E has five different lookback windows by procedure type — and why misclassifying CT colonography and optical colonoscopy is a material error, not a cosmetic one

→ The three root causes behind all seven failure modes: interpretation variance, specification complexity, and unmeasured human adjudication accuracy

→ The financial calculation that connects abstraction error rates to Stars measure weighting — and why the same error rate on a high-weight measure costs materially more than on a lower-weight one

→ Why Q4 abstraction accuracy is systematically lower than Q2 — and why most plans don't staff for it

→ Four specific operational practices that separate plans managing abstraction from plans assuming it works

ABOUT THE EXECUTION PODCAST:

The Execution Podcast is hosted by Peter Saah, DBA, MBA, CPHQ — CEO and Co-Founder of Podero Health. Each episode covers the operational realities of HEDIS performance, Stars strategy, and quality data execution for health plan leaders. No fluff. No vendor pitch. Just what's actually happening in quality operations — and what to do about it.

New episodes drop regularly on Spotify and YouTube.

---

PODERO HEALTH:

Podero Health helps health plans validate and close care gaps at the data layer — ensuring that chart retrieval, EMR feeds, lab feeds, and CCD data actually satisfy HEDIS measure specifications, not just land in a system.

Request a demo or pilot: https://poderohealth.com/Contact

Connect with Peter Saah on LinkedIn: https://www.linkedin.com/in/dr-peter-saah-dba-cphq-0b50a572/

Website: poderohealth.com

---

© Podero Health. All rights reserved.

Most health plans are running two data programs simultaneously right now — chart retrieval for hybrid measures still in traditional reporting, and ECDS digital feeds for measures like COL-E, CCS-E, CIS-E, and IMA-E that have already transitioned. Two vendor relationships. Two sets of timelines. One member population sitting in the middle.

Nobody's budgeted for what happens when they disagree.

In Episode 5, Peter Saah breaks down the reconciliation problem — the governance gap that lives between your vendor programs, shows up in nobody's SOW, and quietly leaks Stars performance in a way that no vendor report will ever surface for you. 

In this episode:

→ Why reconciliation is a governance problem, not a technology fix — and what that distinction means for how you solve it

→ The unresolved member: a specific population every plan has and almost none have a protocol for

→ Why your Stars forecast for MY2026 may be built on historical data that was never accurate — and why that error runs in two directions simultaneously

→ The retrospective attribution analysis that tells you how reliable your closure rates actually are

→ What the ECDS transition to MY2029 means for reconciliation complexity over the next three measurement years

→ Three things that actually fix the problem — structurally, not just operationally

If you're running both chart retrieval and digital feeds and you've never formally answered the question "what happens when they conflict on the same member" — this episode is for you.

Built for health plan quality directors, VPs of operations, and HEDIS program leads.

Note: Now — some of you are thinking: isn’t this what my HEDIS analytics engineer handles? Or isn’t this what my HEDIS engine is supposed to catch? Fair question. And the honest answer is: partly. Your HEDIS engine — is exceptionally good at one thing: applying NCQA measure logic to data that has already been ingested and normalized. It will tell you whether the data in the system satisfies the measure. What it cannot tell you is which data to trust when your chart retrieval program and your digital feed disagree about whether an encounter happened — before that data enters the engine. That decision happens in abstraction, governed by whatever source hierarchy protocol you have documented. The engine executes the logic. It doesn’t make the call. And your analytics engineer validates the output of the engine — they don’t sit upstream resolving source conflicts in real time for individual members during production. That’s the gap. That’s what nobody has formally built a workflow for.

---

Learn more about Podero Health: https://poderohealth.com/Contact

Request a demo or pilot: poderohealth.com/demo

Connect with Peter Saah: https://www.linkedin.com/in/dr-peter-saah-dba-cphq-0b50a572/

---

EPISODE TAGS

HEDIS, Stars Ratings, Medicare Advantage, Health Plan, Care Gaps, Quality Improvement, ECDS, Data Collection, Abstraction, Managed Care, Health Tech

--- 

©2026 Podero Health. All rights reserved.

Following on Episode 3's look at late-year chart retrieval, this episode challenges the assumption that digital data collection solves the underlying problem. [Name] breaks down the three failure modes that make digital feeds dangerous precisely because they're invisible: the completeness illusion, sequencing latency, and source conflicts that documented protocols often can't actually resolve under audit scrutiny. Includes a direct look at where NCQA's Data Aggregator Validation program ends — and where your abstraction logic has to begin.

Topics covered:

• Why digital data collection fails silently where chart retrieval fails loudly
• Where NCQA's Data Aggregator Validation (DAV) program ends and measure-level completeness begins
• The completeness illusion: how a DAV-validated CCD can still be measure-incomplete
• Feed sequencing latency and its real impact on outreach accuracy
• Source conflicts: why documented protocols often don't survive auditor scrutiny
• What measure-level validation actually means versus format-level validation
• Why members who need both digital and manual retrieval are the ones most plans miss systematically

Request a demo or pilot: https://poderohealth.com/Contact?intent=pilot

Connect: https://www.linkedin.com/in/dr-peter-saah-dba-cphq-0b50a572/

________________________________________________________

© 2026 Podero Health. All rights reserved.

A retrieved chart isn't a closed gap. It's raw material. And raw material that arrives too late doesn't become finished product — it becomes write-off. Peter walks through exactly how late-year retrieval silently degrades abstraction quality, burns outreach budget on already-closed gaps, and creates submission risk that doesn't show up until it's too late to fix. Plus the Biased Rate designation — what it is, what it costs, and why it's hiding in plain sight in the Star Ratings Technical Notes.

Bring your Q4 retrieval data to Podero Health and we'll show you your yield number — probably for the first time. Demo, pilot, or yield rate analysis: https://poderohealth.com/Contact?intent=pilot

Provider outreach isn’t failing—it’s being used at the wrong point in the process.

In this episode, we break down why outreach volume stays high while gap closure remains low. The issue isn’t effort. It’s premature outreach execution—requests triggered before gaps are validated and without control over what happens next.

We cover:

• Why outreach activity ≠ gap closure
• How unvalidated gaps create duplicate and unnecessary requests
• What happens when outreach operates without visibility into retrieval and abstraction
• Why providers aren’t the primary constraint

You’ll also learn what controlled execution looks like—where outreach declines as closure increases—versus uncontrolled environments where outreach stays high and outcomes stall.

Visit Podero Health to see the graphical breakdown of controlled vs uncontrolled upstream execution - https://poderohealth.com/insights/why-provider-outreach-does-not-close-gaps

Outreach doesn’t drive closure. Execution control does.