The High Cost of the Hidden Three Percent

Sagility Healthcare Power Ups

Healthcare Power Ups Episode 1: The Three Percent Problem There is a number worth remembering. Three percent. Three percent sounds small. If a phone battery drops three percent, most people barely notice. If a restaurant raises its prices three percent, dinner probably stays on the calendar. And if someone says a business process is 96 percent accurate, the first reaction might be that 96 percent sounds pretty good. But in Medicare Advantage, three percent can be worth millions of dollars. And the strange thing is that the money does not disappear in one obvious place. It leaks out slowly. A record needs to be corrected. A payment gets delayed. A claim gets paid incorrectly. An encounter gets rejected. A member leaves. Someone downstream has to recover money that never should have been lost in the first place. Suddenly, something that looked like a three percent operational problem becomes a multimillion dollar financial problem. But this story is not really about enrollment. It is about something much bigger. It is about what happens when an industry built around making work cheaper realizes that cheaper is no longer enough. Welcome to Healthcare Power Ups, where the complexity of healthcare is taken apart to better understand the ideas, economics, and decisions changing how the industry works. This is The Three Percent Problem.

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Years ago, a Medicare enrollment operation in California suddenly began generating hundreds of exceptions. Something was wrong. Member records were failing validation. The team started investigating. Was the eligibility information incorrect? Had something changed at CMS? Was there a problem with the data feed? No. The culprit was punctuation. An apostrophe. In some cases, two of them. A validation rule did not like how certain California addresses were formatted. The addresses were legitimate. The members were legitimate. The enrollments were legitimate. The computer disagreed. Hundreds of records that should have moved seamlessly through the enrollment process had suddenly become work. Someone had to find them. Someone had to investigate them. Someone had to correct them. And someone had to make sure those corrections reached the next system. And the next one. And the next one. It is an almost comically small problem. But it captures the Medicare enrollment challenge surprisingly well. Enrollment data comes from everywhere.

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Forms. Brokers. CMS. Commercial platforms. Homegrown applications. Internal systems. Unlike processes that are highly algorithmic, enrollment can generate exceptions that nobody anticipated. And all of it has to work because the member needs access to care. That is the tension. Healthcare organizations operate enormously sophisticated systems, yet sometimes the difference between success and failure comes down to an apostrophe. Before there can be a claim, a call, a prior authorization, or a care management interaction, something much more basic has to happen. A person has to successfully become a member. Health plan members are often discussed as though they simply appear. There is a consumer. And then somehow there is a member. But between those two states sits an extraordinary amount of machinery. A health plan first designs its Medicare benefits. Those benefits must be configured and submitted through the appropriate CMS processes. They eventually make their way into sales materials. Brokers need to understand them. Consumers need to understand them. Then applications arrive. Eligibility must be verified. Enrollment information moves back and forth with CMS. Billing must be established. A primary care physician may need to be assigned. ID cards must be produced. Welcome materials have to arrive. Eventually, if everything works, a consumer becomes a member. The journey can be summarized simply: Consumer. Pre member. Member.

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The technology underneath that journey is often anything but simple. A health plan might have one commercial platform handling one part of the process. Another system handles enrollment intake. Homegrown code manages eligibility. Another platform communicates with CMS. A separate system manages billing. There might also be Excel. Maybe SharePoint. And somewhere inside the operation are a handful of people who understand how the entire thing works because they have spent 15 years making all of those systems cooperate. That raises an important question. What exactly is the system? Is it the technology? Or is it the three people who know how to make five pieces of technology work together? For decades, much of the healthcare services industry concentrated its energy somewhere else. On the people doing the work. The traditional outsourcing proposition was beautifully simple. If a transaction cost four dollars to perform onshore, perhaps it could be performed offshore for one dollar. Four becomes one. That is compelling economics. And the healthcare industry became very good at it. Move the work. Standardize it. Improve it. Automate pieces of it. Lower the unit cost. But eventually, that model runs into a mathematical problem.

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What happens when the labor is already relatively efficient? Consider a health plan spending $2.5 million on an offshore operation. That is meaningful spend. But underneath the operation might sit $50 million in technology costs. Platforms. Licenses. Infrastructure. Maintenance. Integrations. Teams whose job is simply keeping everything connected. A proposal to reduce the $2.5 million operations cost by another 10 percent creates roughly $250,000 in savings. That is useful. But a proposal to redesign technology and operations together and move the total cost from $52.5 million to $40 million represents a $12.5 million idea. Those two proposals are not really competing anymore. They are answering different questions. Clients increasingly want transformational savings. Incremental labor savings alone are often no longer enough. The question becomes: If this is the total cost of the operation, what can fundamentally change it? That is the shift. The old unit of value was the transaction. The new unit of value is the operating model.

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And once the question changes from, “How cheaply can this task be performed?” to, “Why does the operation cost this much in the first place?” AI becomes a very different conversation. There is an obvious tension for outsourcing organizations. If AI performs more of the work, does the services company ultimately make less money? Potentially. Unless AI also changes how much work the client is willing to give to that partner. Consider a client where Sagility currently performs $6 million worth of work. Another vendor performs another $6 million. And the client retains roughly $13 million internally. The total operating environment is approximately $25 million. Under the traditional model, Sagility is competing largely around its $6 million share. Then AI changes the equation. If technology allows the entire $25 million process to be redesigned with 20 percent productivity improvement, the client may become willing to move substantially more of the operation to a partner. The math is no longer: Six million minus 20 percent. It becomes: Twenty five million minus 20 percent. The process can shrink while the business opportunity grows. It sounds counterintuitive.

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But it may be one of the most important changes taking place in healthcare services. If organizations continue performing exactly the same work in exactly the same way, AI can look like a threat. But when AI enables partners to take on services that clients previously could not economically outsource, AI becomes a growth lever. It is not simply making the old work cheaper. It is changing what work can be handed to a partner at all. There is another complication. Health plans do not necessarily want every partner arriving with its own AI platform. Imagine the challenge from a health plan CIO’s perspective. Every vendor walks through the door with another AI solution. Soon there are 14 AI platforms. Multiple governance frameworks. Different security models. Different data requirements. Different models. Different answers. That is not necessarily transformation. It may simply create another integration problem. One Sagility client conversation illustrated the issue. The discussion began with innovation and AI. But what the client heard was something very different:

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Another black box. Another vendor technology stack that needed to be governed. So the conversation changed. What if the client selected the AI environment? What if the client controlled the governance? The hosting? The guardrails? And Sagility worked inside that approved environment to redesign and operate the process? That reframed the value proposition. Perhaps the scarce capability is not access to AI. Perhaps it is knowing where and how to apply AI inside a complicated healthcare workflow. A health plan may not need another model. It may need someone who deeply understands the business. And that changes the role of people as well. Consider provider data. Historically, an employee might gather information from ten different sources: claims records, provider submissions, websites, databases, and other repositories. The individual compares the information, determines which source is most current, and resolves inconsistencies. Now imagine AI performs 80 percent of the gathering, comparison, and scoring. The human no longer spends the day collecting information. The human makes the judgment. Which source is credible?

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Does the exception make sense? What should happen next? The work shifts from gathering to deciding. From playing the instrument to conducting the orchestra. And that brings the story back to the three percent problem. Imagine a Medicare plan with 200,000 members. Its first pass enrollment rate is 96 percent. Now imagine improving that rate to 99 percent. Three percentage points. That represents 6,000 records. First, those 6,000 records no longer require the same level of remediation. If correcting each problem costs $25, that represents $150,000 in administrative effort. But that is only the beginning. Suppose an enrollment issue delays CMS payment. The member may already be receiving care. The health plan may already be paying claims. But the enrollment has not been fully reconciled. Money that should be arriving from CMS is delayed. Some members may eventually leave before the issue is resolved. The plan may never receive some of that capitation. Bad enrollment data can also travel downstream. Encounters may reject. Risk adjustment processes may be affected.

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Billing can be wrong. Coordination of benefits can be wrong. Perhaps Medicare should have been the secondary payer, but the health plan paid the full claim. Now someone has to recover that money. And recovery itself costs money. The organization is paying to fix a problem it also paid to create. When Sagility has modeled the broader impact in certain situations, differences in enrollment quality can approach approximately $20 per member per year. Twenty dollars sounds insignificant. Until it is multiplied by 200,000 members. That becomes four million dollars. For some Medicare plans, that begins to look a lot like operating margin. So when a health plan says it is operating at 96 percent accuracy, the interesting question is not whether 96 percent sounds good. The interesting question is: What is hiding inside the other four percent? If the financial opportunity can be that significant, it might seem obvious that the first step should be walking into a client meeting with a spreadsheet and showing executives exactly how much money they may be losing. But that is often the wrong first conversation. The first conversation should be about discovery. How fragmented is the environment? What happens during the Annual Enrollment Period?

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Where are the manual handoffs? What is the first pass rate? Where do exceptions appear? What does the underlying technology cost? And most importantly: What is the client actually trying to change? Quantification can come later. There is another reason this matters. The person experiencing the operational problem and the person receiving the financial benefit may be completely different people. The head of enrollment may own an operating budget. That leader might be evaluated on reducing enrollment costs from $20 per member to $16. Meanwhile, fixing the underlying process might create another $20 or $30 of enterprise value somewhere else in the organization. Both can be true. The business case therefore has to work twice. It needs to work for the person operating the process. And it needs to work for the person managing the P&L. The economics become even more interesting for smaller health plans. Consider a Medicare plan with 20,000 members. The organization still needs technology. Security. Infrastructure.

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Licenses. Telephony. Compliance. Potentially the same type of platform used by a health plan with hundreds of thousands of members. If membership doubles, technology costs do not necessarily double. Much of that expense is fixed. That means smaller health plans can carry a disproportionate technology cost per member. For that organization, a conversation focused purely on reducing operations expense may miss the real issue. The CFO may respond: Operations are not the problem. The platform is the problem. Now the conversation is no longer about traditional outsourcing. It is about total cost of ownership. Could multiple smaller plans operate through a shared services model? Could their data remain appropriately separated and secure while infrastructure and operating capacity are shared? Could capacity be managed more efficiently outside the Annual Enrollment Period, when enrollment volume falls? It is the same Medicare lifecycle. But it is a very different economic problem. This evolution also creates a strategic challenge for traditional healthcare services companies. An organization can perform exceptionally well and still lose the work.

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The client may be happy. Service levels may be strong. The operation may have performed successfully for years. Then another company wins the technology platform. The technology moves. And the operations attached to the technology move with it. The services provider did not lose because its operation failed. It lost because the operating model changed. That is part of what makes Sagility Synchrony strategically important. Synchrony is not simply another offering to bring to market. If another organization owns the platform, owns the AI layer, and owns the transformation agenda, eventually that organization may also ask why it should not own the operations. The rug was not pulled away because the service failed. The floor moved. Healthcare services have evolved through several waves. The first wave was labor arbitrage. Take expensive work and perform it somewhere less expensive. The second wave was automation. Robotic process automation. Analytics. Process mining. Continuous improvement. Make the existing workflow more efficient.

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Now another wave is emerging. Platform based. AI enabled. End to end. Outcome oriented. Instead of asking how many people a process requires, ask why the process works the way it does. Instead of optimizing individual steps, redesign the flow. Instead of selling hours, focus on outcomes. Traditional claims outsourcing and call center RFPs have not disappeared. But increasingly, the more interesting opportunities look different. Platform plus operations. AI plus operations. Managed services built around an outcome. Healthcare will not change overnight. Systems are sticky. Contracts are sticky. Organizations have inertia. That is exactly what can make the shift easy to underestimate. One year, everyone is talking about AI. A few years later, organizations may realize the buying model changed while the industry was still talking about the technology. That brings the story back to the Member Lifecycle. It is easy to explain Member Lifecycle with a diagram.

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Benefit design. CMS filing. Enrollment. Billing. Member engagement. All of that is technically correct. But there is a simpler way to understand it. Member Lifecycle is the machinery that turns a person into a member. The opportunity is to make that machinery behave like one connected system rather than a collection of departments, vendors, technologies, reports, and heroic individuals holding everything together. Sometimes that requires a new platform. Sometimes it does not. One approach Sagility describes is transform in place. Keep the client’s technology where it makes sense. Build the workflow around it. Apply automation and AI. Change the operating model. Improve the economics. Technology is a means. The outcome is the point. And once a person successfully becomes a member, another journey begins. Claims. Calls. Utilization management.

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Care management. Medical cost. Member behavior. Quality. That is where capabilities such as CoreIQ begin to matter differently, helping organizations understand what happens once the member is inside the healthcare system. When viewed from far enough away, what initially appears to be a collection of individual healthcare services begins to look like something larger. A connected operating model for healthcare. And that leads to a practical lesson for healthcare growth conversations. Do not begin with the platform. Do not begin with AI. Do not begin with the architecture. Begin with the business problem. Medicare enrollment cost is not simply an operations problem. It is a technology problem. An operations problem. And an outcomes problem. Health plans are dealing with fragmented systems, expensive infrastructure, enrollment errors, manual handoffs, and revenue leakage. The opportunity is to rethink that model. To potentially remove significant cost while improving first pass enrollment rates, member experience, and financial performance.

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The first conversation does not need to sell a Member Lifecycle solution. It needs to make the healthcare executive curious about the economics of the organization’s own operation. And that brings everything back to the apostrophe. One tiny character. One obscure validation rule. Hundreds of exceptions. That is what makes healthcare operations so interesting. The biggest problems often do not look big. They hide inside workflows. Inside handoffs. Inside systems that almost work. Inside a three percent gap that everyone learned to live with. For decades, the industry responded by applying more efficient labor to those gaps. Then automation. Now AI. But perhaps the real breakthrough is not learning how to process the exception faster. Perhaps it is asking why the exception exists at all. Why does the handoff exist? Why are the systems disconnected? Why does the operation cost what it costs? Why did 96 percent become acceptable? And would any of it be built the same way if the organization were starting today? That is the shift. From making the old system cheaper... to imagining a better system. From playing one instrument exceptionally well... to conducting the orchestra. And from looking at three percent and saying, “That is pretty good”... to asking: What is hiding inside the three percent? This is Healthcare Power Ups, where the complexity of healthcare is taken apart so it becomes easier to understand how to put it back together better.