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Diversity in Healthcare.

At Mizzeto, we are proud to be a minority and women-owned business. We believe that varied perspectives and inclusive thinking drive innovation and creativity, enabling us to deliver cutting-edge healthcare solutions. We are dedicated to building a future where everyone has the opportunity to thrive.

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Article

AI Data Governance - Mizzeto Collaborates with Fortune 25 Payer

AI Data Governance

The rapid acceleration of AI in healthcare has created an unprecedented challenge for payers. Many healthcare organizations are uncertain about how to deploy AI technologies effectively, often fearing unintended ripple effects across their ecosystems. Recognizing this, Mizzeto recently collaborated with a Fortune 25 payer to design comprehensive AI data governance frameworks—helping streamline internal systems and guide third-party vendor selection.

This urgency is backed by industry trends. According to a survey by Define Ventures, over 50% of health plan and health system executives identify AI as an immediate priority, and 73% have already established governance committees. 

Define Ventures, Payer and Provider Vision for AI Survey

However, many healthcare organizations struggle to establish clear ownership and accountability for their AI initiatives. Think about it, with different departments implementing AI solutions independently and without coordination, organizations are fragmented and leave themselves open to data breaches, compliance risks, and massive regulatory fines.  

Principles of AI Data Governance  

AI Data Governance in healthcare, at its core, is a structured approach to managing how AI systems interact with sensitive data, ensuring these powerful tools operate within regulatory boundaries while delivering value.  

For payers wrestling with multiple AI implementations across claims processing, member services, and provider data management, proper governance provides the guardrails needed to safely deploy AI. Without it, organizations risk not only regulatory exposure but also the potential for PHI data leakage—leading to hefty fines, reputational damage, and a loss of trust that can take years to rebuild. 

Healthcare AI Governance can be boiled down into 3 key principles:  

  1. Protect People Ensuring member data privacy, security, and regulatory compliance (HIPAA, GDPR, etc.). 
  1. Prioritize Equity – Mitigating algorithmic bias and ensuring AI models serve diverse populations fairly. 
  1. Promote Health Value - Aligning AI-driven decisions with better member outcomes and cost efficiencies. 

Protect People – Safeguarding Member Data 

For payers, protecting member data isn’t just about ticking compliance boxes—it’s about earning trust, keeping it, and staying ahead of costly breaches. When AI systems handle Protected Health Information (PHI), security needs to be baked into every layer, leaving no room for gaps.

To start, payers can double down on essentials like end-to-end encryption and role-based access controls (RBAC) to keep unauthorized users at bay. But that’s just the foundation. Real-time anomaly detection and automated audit logs are game-changers, flagging suspicious access patterns before they spiral into full-blown breaches. Meanwhile, differential privacy techniques ensure AI models generate valuable insights without ever exposing individual member identities.

Enter risk tiering—a strategy that categorizes data based on its sensitivity and potential fallout if compromised. This laser-focused approach allows payers to channel their security efforts where they’ll have the biggest impact, tightening defenses where it matters most.

On top of that, data minimization strategies work to reduce unnecessary PHI usage, and automated consent management tools put members in the driver’s seat, letting them control how their data is used in AI-powered processes. Without these layers of protection, payers risk not only regulatory crackdowns but also a devastating hit to their reputation—and worse, a loss of member trust they may never recover.

Prioritize Equity – Building Fair and Unbiased AI Models 

AI should break down barriers to care, not build new ones. Yet, biased datasets can quietly drive inequities in claims processing, prior authorizations, and risk stratification, leaving certain member groups at a disadvantage. To address this, payers must start with diverse, representative datasets and implement bias detection algorithms that monitor outcomes across all demographics. Synthetic data augmentation can fill demographic gaps, while explainable AI (XAI) tools ensure transparency by showing how decisions are made.

But technology alone isn’t enough. AI Ethics Committees should oversee model development to ensure fairness is embedded from day one. Adversarial testing—where diverse teams push AI systems to their limits—can uncover hidden biases before they become systemic issues. By prioritizing equity, payers can transform AI from a potential liability into a force for inclusion, ensuring decisions support all members fairly. This approach doesn’t just reduce compliance risks—it strengthens trust, improves engagement, and reaffirms the commitment to accessible care for everyone.

Promote Health Value – Aligning AI with Better Member Outcomes 

AI should go beyond automating workflows—it should reshape healthcare by improving outcomes and optimizing costs. To achieve this, payers must integrate real-time clinical data feeds into AI models, ensuring decisions account for current member needs rather than outdated claims data. Furthermore, predictive analytics can identify at-risk members earlier, paving the way for proactive interventions that enhance health and reduce expenses.

Equally important are closed-loop feedback systems, which validate AI recommendations against real-world results, continuously refining accuracy and effectiveness. At the same time, FHIR-based interoperability enables AI to seamlessly access EHR and provider data, offering a more comprehensive view of member health.

To measure the full impact, payers need robust dashboards tracking key metrics such as cost savings, operational efficiency, and member outcomes. When implemented thoughtfully, AI becomes much more than a tool for automation—it transforms into a driver of personalized, smarter, and more transparent care.

Integrated artificial intelligence compliance
FTI Technology

Importance of an AI Governance Committee

An AI Governance Committee is a necessity for payers focused on deploying AI technologies in their organization. As artificial intelligence becomes embedded in critical functions like claims adjudication, prior authorizations, and member engagement, its influence touches nearly every corner of the organization. Without a central body to oversee these efforts, payers risk a patchwork of disconnected AI initiatives, where decisions made in one department can have unintended ripple effects across others. The stakes are high: fragmented implementation doesn’t just open the door to compliance violations—it undermines member trust, operational efficiency, and the very purpose of deploying AI in healthcare.

To be effective, the committee must bring together expertise from across the organization. Compliance officers ensure alignment with HIPAA and other regulations, while IT and data leaders manage technical integration and security. Clinical and operational stakeholders ensure AI supports better member outcomes, and legal advisors address regulatory risks and vendor agreements. This collective expertise serves as a compass, helping payers harness AI’s transformative potential while protecting their broader healthcare ecosystem.

Mizzeto’s Collaboration with a Fortune 25 Payer

At Mizzeto, we’ve partnered with a Fortune 25 payer to design and implement advanced AI Data Governance frameworks, addressing both internal systems and third-party vendor selection. Throughout this journey, we’ve found that the key to unlocking the full potential of AI lies in three core principles: Protect People, Prioritize Equity, and Promote Health Value. These principles aren’t just aspirational—they’re the bedrock for creating impactful AI solutions while maintaining the trust of your members.

If your organization is looking to harness the power of AI while ensuring safety, compliance, and meaningful results, let’s connect. At Mizzeto, we’re committed to helping payers navigate the complexities of AI with smarter, safer, and more transformative strategies. Reach out today to see how we can support your journey.

February 14, 2025

5

min read

Feb 21, 20242 min read

Article

The Marketplace Members You Are About to Lose Are Already Calling You

The fallout from this year's premium shock did not wait for open enrollment. When the enhanced premium tax credits expired at the end of 2025, the price of 2026 coverage moved for almost everyone who buys it on the individual market, and it moved sharply. Average premium payments for subsidized Marketplace enrollees more than doubled heading into 20261, and effectuated enrollment is on track to fall from 22.3 million to roughly 17.5 million2.

For a Marketplace plan, that is not an abstract policy shift. It is a wave of confused, price sensitive members calling right now, mid plan year, to ask why their bill changed, whether they still qualify for help, and whether a cheaper plan exists. Those calls are happening months before the next open enrollment window even opens, and the decision to stay or leave is being made on them well ahead of any renewal file. Marketplace member retention is being decided on the member service line today, and most plans are barely listening to it.

The 2026 reset changed who is on the phone

The expiration of the enhanced tax credits did more than raise prices. It changed the mix of people calling. A disproportionate share of the enrollment drop, about 27 percent, came from households just above the old subsidy cliff, even though that group made up only 3 percent of plan selections the year before2. These are members who lost eligibility for help entirely and are now weighing coverage on price alone.

The result is higher call volume made up of harder calls. Billing questions, subsidy confusion, and plan comparison requests are exactly the interactions that resolve least often on the first attempt. And first call resolution is where this problem turns expensive: SQM Group benchmarks put first call resolution for complaint calls at 47 percent, the lowest of any call type and far below the healthcare insurance average3. The same research shows that in a given year, roughly 40 percent of customers who do not get their issue resolved on the first call defect to another company4. A price shocked Marketplace member whose billing question is transferred twice and never resolved is not a service statistic. That member is a renewal the plan is about to lose.

Churn shows up in the call before it shows up in the data

Retention data is a lagging indicator. By the time a member appears in a disenrollment report, the decision was made and the window to intervene has closed. The signal that predicts that outcome is audible much earlier, in the tone and content of the call itself.

What this looks like in practice: a member calls in October asking why the subsidy that covered most of the premium shrank. The agent explains the tax credit change, cannot fully resolve the affordability concern, and the call ends. Nothing flags the member as at risk. No follow up is triggered. In January the member is gone, and the plan learns about it from a report rather than from the call that predicted it.

Why sampling misses the members who matter most

Most plans still evaluate member calls the way they did a decade ago, by pulling a small manual sample and scoring it after the fact. When a plan reviews less than 5 percent of its member calls, the leaving decision almost always forms inside the 95 percent no one listens to. The signals that predict churn, the second unresolved call, the audible frustration, the mention of a competitor's premium, sit in the calls that were never selected.

The gap is widest exactly where the 2026 population is most exposed. Non-English calls are rarely part of a manual sample at all, yet language access is where affordability confusion compounds fastest. A sampling model does not just miss volume. It systematically misses the members whose experience is deteriorating and who are most likely to leave.

How a sampling model and a full call model compare

Sampling based call review Full call member intelligence
Reviews less than 5% of member calls Reviews 100% of member calls
At risk members found by chance, if at all At risk members identified by sentiment and tone
Distress surfaces later in surveys and disenrollment data Distress surfaced while there is still time to intervene
Non-English calls rarely sampled Every language translated and scored
Quality data owned and reported by a vendor Intelligence owned and controlled by the plan

What to look for in a member call intelligence approach

The problem is not that plans lack member data. It is that the most predictive data, what members actually say when they call, is captured and then discarded. When evaluating how to close that gap, plans should look for:

  • Coverage of every member call, not a sample, so at-risk members are identified rather than missed by chance
  • Sentiment and risk detection that surfaces the members most likely to disenroll while there is still time to act
  • Translation and scoring of calls in every language your membership actually calls in, so non-English members are not the least understood
  • Transparency the plan controls, with the logic and the intelligence owned by the plan rather than a vendor
  • Signal that reaches retention and enrollment teams quickly enough to intervene before renewal, not after

Claro by Mizzeto was built for this. Member Experience Insights is one of four capability areas within Claro, and it is the one focused specifically on this problem: identifying at-risk members before they disenroll, using the actual content and tone of their calls rather than a survey that arrives months later. It reviews 100 percent of member calls, scores sentiment and risk across every language a plan's membership calls in, and surfaces the members most likely to leave in time for retention teams to act, so outreach can happen before open enrollment closes rather than after the member is already gone.

The bottom line

The 2026 subsidy reset handed Marketplace plans a harder, more price sensitive population and a narrower margin for error. Retention this year will not be won by surveys that arrive after the decision or reports that confirm a loss already booked. It will be won on the call, in the moment a member is deciding whether the plan is worth the new price. Plans that can hear every one of those calls will keep members that sampling based plans never knew were leaving.

To see how full call member intelligence identifies at-risk Marketplace members before they disenroll, send us a sample of your calls and we will return scored transcripts before you commit to anything.

References

1. KFF. Analysis of premium payment increases for subsidized Marketplace enrollees following the expiration of enhanced premium tax credits, 2026. www.kff.org

2. Congressional Budget Office. Projected effects of the expiration of enhanced premium tax credits on Marketplace enrollment, 2026, including subsidy cliff impact by income band. www.cbo.gov

3. SQM Group. First call resolution benchmarks by call type, healthcare and insurance industry comparison. www.sqmgroup.com

4. SQM Group. Customer defection rates following unresolved first-call issues. www.sqmgroup.com

Jan 30, 20246 min read

August 10, 2026

2

min read

Article

This Year, Retention Is a Fight on Two Fronts

In most years, Medicare Advantage disenrollment is a defensive problem: a plan keeps the members it has, and the losses are gradual. 2026 broke that pattern. A wave of plan exits pushed roughly 2.9 million Medicare Advantage members, about 10 percent of enrollees, out of plans that stopped serving their counties, a nearly tenfold jump from the 1 percent average that held from 2018 through 2024.1 The market filled with switchers, and every plan is now fighting on two fronts, defending the members it has and competing for a large pool of switchers. The same thing decides both: whether the plan is easy to be a member of.

The same failure now costs a plan twice

Forced disenrollment, the county exits that drove the 2026 spike, is not a service problem, and no plan can prevent it. But everything the disruption set in motion afterward is within a plan's control. A market this unsettled taxes a weak member experience twice, once as the plan loses its own members and again as it fails to keep the switchers it wins. A new member arrives with no goodwill in reserve. A tenured member might forgive a bad call after ten good years; a member of ten days simply shops again.

Members leave for reasons a plan can see coming

On both fronts, the reasons are documented, and they are not mainly about price. In the Medicare Current Beneficiary Survey, the strongest predictors of leaving were difficulty accessing care, which raised the odds by about a third, and low plan generosity, by roughly half; cost alone was not significant once the other factors were accounted for.2 CMS confirms it in its own monthly Disenrollment Reasons Survey,3 where close to a fifth of disenrollees cite problems getting services covered and roughly one in eight cite customer service.4 Those are the substance of member service calls.

The decision is usually audible before it is final

An honest caveat first: access and benefit design, the largest drivers, are not call center problems in origin, and listening does not widen a narrow network. What it does is surface the trouble first. Members rarely leave over a benefit design in the abstract; they phone to ask why a service was denied or a drug dropped, and hang up without an answer long before they fill out a form. A member who has called three times about one issue is not satisfied, yet first call resolution in health insurance is only about 72 percent,5 and for complaint calls just 47 percent.6 Traditional quality assurance reviews only 2 to 5 percent of calls,7 so the slow walk to disenrollment, and nearly every call in a language other than English, goes unheard.

Why the loss counts twice on the books

The cost shows up plainly. CMS caps broker pay at $694 for a new Medicare Advantage enrollment in 2026 and $347 for a renewal, so acquiring a member costs at least twice keeping one.8 In a churning market a plan pays that premium at volume, and winning a switcher only to lose them a year later to an unreviewed call means it bought nothing. On top sits the Star Ratings bonus: eligibility turns on the four star threshold, and a half star slip from 4.0 to 3.5 erases the entire 5 percent bonus,9 several million dollars a year for a midsize plan.

CMS just removed the disenrollment scorecard

The timing is unkind. On April 2, 2026, the Contract Year 2027 Final Rule removed 11 Star Ratings measures, including Members Choosing to Leave the Plan, the measure that graded plans on voluntary disenrollment, effective with the 2029 Star Ratings.10 That looks like relief, but nothing underneath it changed. CMS still fields the Disenrollment Reasons Survey and reports disenrollment publicly, CAHPS remains in the formula at double weight,11 and a lost member still has to be replaced. CMS stopped keeping score in the very year a plan can least afford to look away.

What to look for in a way to see it coming

Finding these members while there is still time to act is a different exercise from grading agents on a sample. A plan should look for:

  • Whole population analysis rather than sampling, so a single member's escalating calls and the patterns across the book are both visible.
  • Scoring that reads the member, weighing sentiment, unresolved issues, and repeat contact, not just whether the agent's greeting was correct.
  • The same rigor for every language, including interpreter lines, which Section 1557 of the Affordable Care Act makes a legal duty.
  • Plan ownership of the data and scoring models, so the retention signal survives changes in vendors, staffing, and telephony.
  • A direct link from calls to CAHPS, grievances, and disenrollment, so sentiment does not sit in a report no one connects.

Two ways to manage member retention

Reactive retention (sampling model) Predictive retention (whole population analysis)
Call coverage2 to 5 percent of calls sampledClose to 100 percent of calls analyzed
What gets measuredAgent greeting, script, and etiquetteMember sentiment, unresolved issues, members at risk
Calls not in EnglishRarely reviewed or scoredAnalyzed at the same depth as English calls
New members you just wonTreated like any other call in the sampleFirst calls flagged before a new member sours
When a leaving member becomes visibleAfter the member disenrolls, on a surveyWhile the member is still enrolled and reachable
Systemic patternsInvisible inside a small sampleSurfaced across the full member population
Data and scoring logicHeld in a vendor or reporting layerOwned by the plan and portable across changes

Sources: SQM Group call center benchmarking; CMS Contract Year 2027 Final Rule.

This is the gap Claro by Mizzeto was built to close. Claro analyzes the full volume of member calls, including the languages and interpreter lines sampling never reaches, and its Member Sentiment & At-Risk Identification scoring surfaces the unresolved, escalating conversations that turn into disenrollment months later, for tenured and newly won members alike, while the plan keeps ownership of the data. For the upstream work, see how payers can fix their call centers.

The bottom line

For one year at least, retention is a contest a plan can lose on both sides at once. The reasons people leave Medicare Advantage plans are documented, they turn on usability more than price, and they surface on calls long before an enrollment form, for members of a decade or a week. CMS took away the measure. The cost, paid twice over in a churning market, did not fall.

References

1.  Johns Hopkins Bloomberg School of Public Health and Georgetown University, JAMA (Feb. 18, 2026): approximately 2.9 million Medicare Advantage members, about 10 percent of enrollees, faced forced disenrollment for 2026 as plans exited markets, up from a mean of 1 percent from 2018 to 2024 and 6.9 percent in 2025. jamanetwork.com

2.  Health Affairs, study of Medicare Advantage disenrollment using the Medicare Current Beneficiary Survey (2015 to 2020): difficulty accessing care associated with roughly 1.33 times greater likelihood of disenrollment; low plan generosity roughly 1.47 times; dissatisfaction with care quality significant; dissatisfaction with cost alone not a significant independent predictor (approx. 1.03 times). healthaffairs.org

3.  Centers for Medicare and Medicaid Services, Medicare Advantage and Prescription Drug Plan Disenrollment Reasons Survey. Captures why beneficiaries voluntarily leave; results publicly reported in the annual Star Ratings Data Table and Display Measures. cms.gov

4.  The Commonwealth Fund, analysis of CMS Medicare Advantage Disenrollment Reasons Survey data: approximately 18 percent of disenrollees cited problems getting the plan to cover services and approximately 13 percent cited customer service issues such as trouble obtaining accurate information; voluntary disenrollment across MA contracts rose about 70 percent between 2017 and 2021. commonwealthfund.org

5.  SQM Group, First Call Resolution Benchmarking by Industry: health insurance first call resolution approximately 72 percent. sqmgroup.com

6.  SQM Group, first call resolution by call type: complaint calls resolve on first contact approximately 47 percent of the time, the lowest of all call types. sqmgroup.com

7.  SQM Group, call center quality assurance benchmarking: traditional programs review roughly 2 to 5 percent of interactions. sqmgroup.com

8.  Centers for Medicare and Medicaid Services, Agent and Broker Compensation memorandum for Contract Year 2026 (June 18, 2025): national maximum Fair Market Value of $694 for a new Medicare Advantage enrollment and $347 for a renewal. cms.gov

9.  Centers for Medicare and Medicaid Services, Medicare Advantage Quality Bonus Payment methodology: bonus payments apply to contracts at or above the four star threshold; loss of four star status removes the 5 percent quality bonus applied to the benchmark. cms.gov

10.  Centers for Medicare and Medicaid Services, Contract Year 2027 Medicare Advantage and Part D Final Rule (April 2, 2026): removal of 11 Star Ratings measures, including Members Choosing to Leave the Plan, effective with the 2029 Star Ratings. cms.gov; Federal Register, April 6, 2026.

11.  Centers for Medicare and Medicaid Services, 2026 Medicare Part C and D Star Ratings Technical Notes: CAHPS and patient experience measure weights reduced from quadruple (4x) to double (2x) effective with the 2026 Star Ratings. cms.gov

Jan 30, 20246 min read

July 31, 2026

2

min read

Article

The Grievance That Had a Six Week Warning

By the time a formal grievance reaches a health plan’s Grievance and Appeals team, it arrives looking like an emergency. The member is angry. A response deadline is already running. A regulator may eventually read the file. What the file rarely shows is that the grievance did not begin that week, or even that month. It began on an ordinary call that did not get resolved and was never reviewed. The warning was there. No one was assigned to look for it.

Most health plans treat reducing member grievances as a downstream chore: staff the queue, meet the deadline, close the case. That posture quietly concedes the grievance as inevitable, and it is not. A grievance is the visible end of an escalation that usually runs about six weeks, and nearly all of it is recorded, in the plan’s own phone system, in the member’s own words. The signal is not missing. It is simply never listened to.

A grievance is a six week escalation, not a single event

CMS defines a grievance as a complaint about a plan’s delivery of service, and the rules let a member arrive there slowly. A Medicare Advantage enrollee has up to 60 days after the triggering event to file, and the plan then has 30 days to resolve a standard grievance, with a 14 day extension.5 That window is the outer edge of a story that almost always starts earlier, on the phone.

The arc is familiar to any member services team. It opens with a single call about a denied claim, a benefit change, or a stalled prior authorization, and the member hangs up with an answer that is incomplete or simply wrong. That is common. SQM Group puts first call resolution for health insurance at roughly 72 percent, so close to three in ten member calls are not settled the first time.1 The member calls back, and calls again, and these repeat calls are the hardest to fix, because complaint calls resolve on the first contact only 47 percent of the time, the lowest rate of any call type SQM tracks.2 By the fifth or sixth week the member gives up and files. Only then does the plan open a case.

The shape of it is mundane. A member is told on the phone that a drug is covered, learns at the pharmacy counter that it is not, gets a different answer from a second agent, and files after a third. Three recorded calls, one avoidable grievance, and a member now drifting toward disenrollment. Every call was captured. None was flagged.

Appeals run on the same current. A denial explained badly prompts an appeal, not just frustration. The recording of that call is the clearest account of what the member was told, and the one document the appeals file rarely contains. A single unreviewed call can feed both outcomes, a grievance about the service and an appeal against the decision, both audible weeks before either was filed.

Why the warning stays invisible

The reason is not indifference. It is arithmetic. Traditional call center quality assurance, whether member service is run internally, outsourced, or split between the two, reviews somewhere between 2 and 5 percent of calls.3 The other 95 percent, which includes nearly every repeat call in an escalating grievance, is never heard by anyone whose job is to catch problems. A sample that small will almost never contain the three or four particular calls that make up one member’s slow walk to a filing, and it is even less likely to reveal the shape of the trouble when the same benefit is being miscommunicated to hundreds of members at once.

Sample size is only half of the failure. The deeper flaw is what the sampling was built to measure. A conventional scorecard asks whether the agent greeted the member, verified identity, and read the required disclosures. It does not ask whether the member’s problem was actually solved, or whether the member hung up angrier than they picked up. A call can earn a clean score and still be a grievance in motion. The program was designed to grade etiquette, and etiquette is not the thing that turns into a filing. We have made this case before, that most grievances start as calls that never got reviewed, and nothing in the underlying mechanics has changed since.

The calls in languages other than English are the least visible of all. When a member with limited English proficiency cannot be helped without an interpreter, the quality of the resolution is harder to verify, the member is less likely to push back on an answer they do not fully understand, and the interaction is almost never scored at all. Those are precisely the calls where a small misunderstanding hardens, unseen, into a grievance.

CMS just removed your grievance scorecard

On April 2, 2026, CMS issued the Contract Year 2027 Medicare Advantage and Part D Final Rule and, with it, removed 11 measures from the Star Ratings. Four of them speak directly to this problem, and all four leave the formula beginning with the 2029 Star Ratings: Complaints about the Health and Drug Plan, Members Choosing to Leave the Plan, Plan Makes Timely Decisions about Appeals, and Reviewing Appeals Decisions.4 Read in a hurry, that looks like a reprieve. The measures that once turned complaints, disenrollment, and appeals handling into a Star score are going away, and the natural temptation is to slide grievance and appeals monitoring down the list of things worth watching.

That reading has it exactly backward. The measures are leaving the scorecard. The exposure is staying exactly where it was. CMS still runs the Complaints Tracking Module, and plans are still bound to the resolution timelines set at 42 CFR 422.125 and 422.564.5 The complaint and customer service questions remain on the CAHPS survey, and the survey based measures carry heavy and rising weight in the Medicare Advantage formula.6 A member who leaves still leaves. What actually changed is narrower and more dangerous than relief: the warning light that used to sit on the Star dashboard has gone dark. Plans that had quietly relied on those measures as their grievance scorecard now have no scorecard, and precisely the same risk underneath it.

What it takes to see it coming

Seeing a grievance coming means catching the escalation while it is still a service problem, not a case number. It rests on a reversal: stop sampling calls to grade agents, and start reading all of them to find members. A plan that analyzes every interaction can connect the several calls behind one escalation and watch the same complaint surface across the population, where a single systemic fix replaces hundreds of grievances not yet filed.

The point is not more scores but earlier ones. A member who has called three times about one decision is a retention risk however politely each call was handled, and no rubric that grades greetings will say so. A few practical tests separate a system that sees a grievance coming from one that only counts calls afterward:

  • Whole population analysis, not sampling, so one member’s escalating calls and the patterns forming across the book are both visible before anyone files.
  • Scoring that reads the member, not just the agent, weighing sentiment, unresolved issues, and repeat contact rather than whether the greeting was correct.
  • The same rigor for calls in every language, including interpreter lines, because that is where hidden escalation collects and Section 1557 makes language access a legal duty.
  • Plan ownership of the conversation data, scoring models, and trend lines, so the early warning system survives changes in telephony and staffing and can be retuned in days.
  • Alerts that reach the people who can act, so member services, grievance and appeals, and compliance see a trend while there is still time to intervene.

Two ways to run grievance and appeals operations

Reactive grievance handling (sampling model) Predictive early warning (whole population analysis)
Call coverage 2 to 5 percent of calls sampled Close to 100 percent of calls analyzed
What gets measured Agent greeting, script, and etiquette Member sentiment, unresolved issues, members at risk
Calls not in English Rarely reviewed or scored Analyzed at the same depth as English calls
When a problem surfaces After the grievance is filed While it is still a service issue, weeks earlier
Systemic patterns Invisible inside a small sample Surfaced across the full member population
Data and scoring logic Held in a vendor or reporting layer Owned by the plan and portable across changes

Sources: SQM Group call center benchmarking; CMS Contract Year 2027 Final Rule.

Mizzeto built Claro for exactly this gap. It analyzes the full volume of member calls, including the ones in other languages and on interpreter lines that sampled QA never reaches, and its Member Sentiment & At-Risk Identification scoring is built to surface the unresolved, escalating conversations that become grievances and appeals weeks later, all while the plan keeps ownership of the underlying data and the logic that scores it. If a grievance and appeals team is meeting every deadline and still watching volume climb, the explanation is usually sitting in calls the plan has already recorded and has never had a way to hear.

The bottom line

A formal grievance is the most expensive way a health plan can learn about a problem it could have seen six weeks earlier. The information was never missing. It was sitting, unheard, in the 95 percent of calls no one reviews. CMS has taken away the measures that once forced plans to watch their complaints and their appeals, but the price of a grievance, paid in CAHPS, in disenrollment, and in compliance exposure, has not fallen by a cent. Reducing member grievances begins with a decision to stop waiting for them to arrive.

Any plan can find out what its own calls are already saying. Mizzeto will score a sample of them and show a plan the warnings hidden inside before it commits to anything further. For the upstream work that keeps these calls from going wrong in the first place, see how payers can fix their call centers.

References

1.  SQM Group, “First Call Resolution Benchmarking by Industry” (health insurance first call resolution approx. 72 percent). sqmgroup.com

2.  SQM Group, first call resolution by call type (complaint calls resolve at 47 percent, the lowest of all call types). sqmgroup.com

3.  SQM Group, call center quality assurance benchmarking (traditional programs review roughly 2 to 5 percent of interactions). sqmgroup.com

4.  Centers for Medicare & Medicaid Services, “Contract Year 2027 Medicare Advantage and Part D Final Rule,” April 2, 2026 (removal of 11 Star Ratings measures, including Complaints about the Health and Drug Plan, Members Choosing to Leave the Plan, Plan Makes Timely Decisions about Appeals, and Reviewing Appeals Decisions, effective with the 2029 Star Ratings). cms.gov; Federal Register, April 6, 2026.

5.  42 CFR 422.564 (grievance procedures: 60 day filing window, 30 day standard resolution, 14 day extension) and 42 CFR 422.125 (resolution of complaints in the Complaints Tracking Module). ecfr.gov

6.  Analysis of the CY2027 Final Rule finding relative Star Ratings weight shifting toward survey based measures such as CAHPS for Medicare Advantage plans (e.g., Crowell & Moring; Holland & Knight client alerts, April 2026).

Jan 30, 20246 min read

July 14, 2026

2

min read

Article

Patient Experience Starts in the Call Center, Not the Exam Room

Most health systems measure patient experience after it is over. The survey arrives weeks after the visit, asks about the physician, the nurse, and the discharge instructions, and lands as a number on a dashboard a full quarter after the care happened. By the time anyone reads it, the experience is already history.

For a large share of patients, though, the experience did not begin in the exam room. It began on the phone. It began when they called to book an appointment, asked a question about a bill, tried to reach someone in a language other than English, or waited on hold to learn whether a procedure was covered. That first call sets the tone for everything that follows, and most organizations have almost no visibility into how it went.

The call center is the front door to the health system, and also the least measured room in it. Most organizations review fewer than 5 percent of their calls, which means the interactions that decide whether a patient stays or leaves are usually the ones nobody hears. You cannot improve an experience you only sample. This article looks at why patient experience starts in the call center, what it costs when those calls go unreviewed, and what to look for in a way of measuring them that does not depend on a survey arriving months too late.

The first impression is usually a phone call

The first call is the first test of the relationship, and patients grade it against every other service they use. In a recent Harris Poll, 61 percent of Americans said they want their healthcare experience to feel more like a convenience app such as Amazon Prime or Uber, and 60 percent said they find the process of seeing a new provider frustrating.1 For most of them, that judgment forms on the phone.

That call carries more weight than it appears to. Accenture, which has surveyed more than 21,000 consumers on healthcare experience, found that 30 percent of patients selected a new provider in 2021, and that nearly 80 percent of those who switched cited poor navigation factors as the reason, including difficulty doing business and bad experiences with administrative staff.2 Navigation and administrative staff are not abstractions. They are the scheduling line, the billing line, and the front desk phone.

What this looks like in practice is familiar. A patient calls to schedule, gets transferred twice, sits on hold, never gets a clear answer about cost, and quietly books somewhere else. No survey ever captures that call, because the caller never became a patient. They simply did not come back.

The survey is a rear-view mirror

Patient experience surveys are valuable, but they share a structural limitation. HCAHPS for hospitals and CG-CAHPS for medical groups measure experience after the fact, on a sample of patients, and report it weeks or months later. They tell you what happened. They cannot tell you what is happening right now, while you can still do something about it.

A survey score also cannot explain itself. It can tell you a patient rated you a six. It cannot tell you that the patient was transferred three times trying to reschedule, that your Spanish-speaking callers are routinely routed to English-only agents, or that a billing conversation went sideways and broke trust. The reason behind the score lives in the call, not in the survey.

And the score is not a vanity metric. Through the CMS Hospital Value-Based Purchasing program, 2 percent of a participating hospital’s Medicare payments are withheld and redistributed based on performance, and the patient experience domain measured by HCAHPS accounts for 25 percent of the Total Performance Score.3 For a mid-sized hospital, that is millions of dollars tied to patient experience, a meaningful share of which is shaped before the patient ever arrives for care. That direct exposure is the hospital case. Physician groups do not sit under Hospital Value-Based Purchasing, but they answer to their own version through CG-CAHPS and value-based programs such as MIPS. The mechanism differs by setting. The underlying point does not: the experience that drives the score is built on calls, and it is rarely captured by them.

The calls that matter are the ones you never hear

Here is the part that does not get said often enough. Traditional manual call quality assurance reviews a small fraction of total calls, typically less than 5 percent. This is not a failure of the people doing the work. A human QA team, however skilled, can only listen to so many calls in a day. It is a limitation of the measurement model, and it holds true whether your contact center is in-house, outsourced, or a combination of both.

The problem is what that small sample misses. The calls that drive complaints, switching, and low scores are outliers by definition, and a random sample of a few percent is structurally poor at finding outliers. The frustrated caller, the dropped handoff, the limited English patient who never got a qualified interpreter: these are precisely the calls that fall outside the sample. The 95 percent nobody reviews is exactly where the risk lives.

When you can only see a sliver, you end up managing to averages. Average handle time, abandonment rate, and service level tell you the center is busy. They do not tell you whether patients felt heard, whether a financial conversation damaged trust, or whether a caller is one bad interaction away from leaving. The warning signs of a patient about to switch are audible long before they ever surface in a survey: repeat calls about the same unresolved issue, rising frustration in a caller’s tone, questions that never get a straight answer. Caught in the moment, those are coachable and fixable. Caught in a survey a quarter later, they are already lost revenue and a lower score.

What to look for in a way to measure patient calls

The goal is not to survey harder. It is to actually hear what happens on your calls, all of them, in a way you can act on. When evaluating how to do that, whether your contact center is run in-house, through a partner, or as a hybrid, look for a few things.

  • Full coverage, not a sample. The system should review 100 percent of calls, not the small share a manual team can reach. Outliers only become visible at full coverage.
  • Experience, not just compliance. It should score patient sentiment and the quality of agent communication, not only whether a script was followed. Compliance checklists miss the human signal that actually moves HCAHPS.
  • Every language, automatically. Non-English calls should be reviewed the same way English calls are. Section 1557 of the Affordable Care Act requires meaningful access for patients with limited English proficiency,4 so a tool that cannot read those calls leaves both an experience gap and a compliance gap.
  • Real time, not retrospective. It should surface issues while you can still intervene, not weeks later. A survey is a rear-view mirror. Live call intelligence is a windshield.
  • Owned by your organization. You should own the data and the real-time intelligence so your teams can act on it directly and continuously, rather than at quarterly reporting intervals.
  • Built to fit your stack. It should connect to the systems you already run, without a disruptive replacement project.

What you are measuring Survey-based measurement Call intelligence across every call
Coverage A sample of patients, after the visit Every call, as it happens
What it captures A score The conversation behind the score
Timing Retrospective, reported quarterly Real time
Non-English calls Often underrepresented or excluded Reviewed in every language
Primary use Reporting and benchmarking Coaching and early intervention

The difference between the two models is the difference between knowing your score and knowing why you earned it.

This is the gap Claro by Mizzeto was built to close. Claro is an AI-powered contact center intelligence platform that audits 100 percent of patient and provider calls, including calls in languages other than English, scoring patient sentiment, agent communication, and compliance in real time rather than on a sample weeks after the fact, and it connects to the systems you already use. The result is the ability to see and coach to what is actually happening on every call, in every language, instead of inferring it from a fraction of them. You can read more about how this reshapes day-to-day operations in our overview of modern call center operations.

The bottom line

The visit is not where patient experience begins. For a growing share of patients, it begins on the phone, and it often ends there too, before anyone in a white coat is involved. Health systems have spent years measuring experience after the fact and running the contact center to averages, while the moments that decide loyalty, reputation, and a meaningful slice of value-based reimbursement play out on calls nobody hears.

The organizations that move first to hear every call, in every language, as it happens will hold a real advantage: stronger HCAHPS performance, better patient retention, and operational decisions grounded in what patients actually experienced rather than what a delayed survey implied. Patient experience starts on the phone. The only question is whether you can hear it.

References

  1. Medical Economics, “69% of patients would switch providers for better services” (The Harris Poll), 2026. medicaleconomics.com
  1. American Hospital Association, “Why Patients Leave: 4 Nonnegotiable Consumer Expectations” (Accenture consumer research), 2023. aha.org
  1. Centers for Medicare and Medicaid Services, “Hospital Value-Based Purchasing.” cms.gov
  1. HCAHPS and Hospital VBP, Patient Experience of Care domain weighting. hcahpsonline.org
  1. U.S. Department of Health and Human Services, Office for Civil Rights, Section 1557 non discrimination final rule, 2024. hhs.gov

Jan 30, 20246 min read

June 25, 2026

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