Every few months, another health system announces it has deployed artificial intelligence across its revenue cycle. The press release follows a familiar script: reduced denials, fastero authorizations, staff hours reclaimed, efficiency unlocked. What almost never appears in that announcement is a second document, the one that defines how the organization will know, 12 months from now, whether any of that is actually true.
That absence is not an accident. It reflects something deeper about how healthcare has historically treated its administrative infrastructure: as a problem to manage rather than a system to understand. And now, as AI tools move from pilot programs into operational deployment at scale, that gap is now creating real operational risk as AI moves into live production environments.
I have spent more than twelve years working alongside revenue cycle teams, coders, billers, authorization specialists, and CFOs, and I can say with some confidence that most of the people closest to this work are deeply skeptical of headlines. They have seen technology promises before. They remember the EHR implementations that were supposed to streamline documentation and instead added hours to the physician workday. They remember the clearinghouse upgrades that reduced one bottleneck and created three others downstream. They are not cynics. They are people who have learned, through experience, that what a system claims to do and what it actually does inside a live operational environment are often very different things.
That skepticism is not resistance to change. It is exactly the kind of operational discipline that should shape how AI gets evaluated and deployed.
The challenge right now is that the industry has skipped that step. Conference stages are crowded with transformation narratives. Health systems facing tight margins and persistent staffing shortages feel genuine urgency to find operational relief. All of that is understandable. But urgency without accountability is how you end up automating broken processes rather than fixing them. And in the revenue cycle, broken processes do not just affect the balance sheet. They affect whether a patient gets a procedure approved on time. They affect whether a physician burns another hour on paperwork that should have taken ten minutes. They affect the trust that providers, payers, and patients depend on to make the system function.
What I find missing in most AI deployment conversations is a straightforward commitment to answering a basic question before the contract is signed: what does success look like, and how will we measure it independently? Through clean, pre-specified performance benchmarks, first-pass resolution rates, authorization turnaround times, denial overturn rates, measured against a documented baseline and evaluated at regular intervals by people inside the organization who are empowered to say when something is not working.
Part of the reason is structural. Revenue cycle operations in most health systems sit in a complicated organizational space, accountable to finance, connected to clinical operations, dependent on technology infrastructure managed by IT, and constrained by payer relationships that nobody controls entirely. That diffusion of accountability makes it genuinely difficult to assign ownership over AI performance. When a denial rate creeps up six months after an AI tool goes live, the question of who is responsible for diagnosing why, whether the technology team, the RCM leadership, or the vendor, rarely has a clean answer. So the question often goes unasked, or gets absorbed into the background noise of operational management.
The other part is cultural. Healthcare administration has a long tradition of accepting complexity as inherent rather than examining it as designed. Prior authorization, to take the most visible example, has become so procedurally dense that many organizations have simply built workforces around navigating it rather than questioning whether the navigation itself can be fundamentally restructured.
The scale of that problem is not abstract: according to CMS, more than 53 million prior authorization requests were submitted to Medicare Advantage insurers in 2024 alone, and of the denials that were appealed, more than 80% were ultimately overturned. AI can reduce the friction of that navigation. But if the underlying logic of the process remains unchanged, if the criteria are still opaque, the payer responses still inconsistent, the documentation requirements still disconnected from clinical reality, then automation speeds up a broken system without healing it. That is a meaningful difference, and it is one that outcome measurement frameworks need to be designed to capture.
What better practice looks like, in my view, is fairly concrete. It starts with a pre-deployment audit with a clear-eyed inventory of where the revenue cycle is actually failing, not where it looks like it might benefit from technology. It requires that AI tools be evaluated against those specific failure points, with defined thresholds for what improvement looks like at thirty, ninety, and one hundred eighty days.
It demands that operational staff, the people who work inside these processes daily, have a formal mechanism to surface when a tool is creating new problems, not just solving old ones. And it insists that model performance be reviewed on a scheduled basis, because the payer landscape does not hold still, and a model trained on last year’s coverage criteria may be quietly degrading against this year’s.
None of this is technologically complicated. It is organizationally disciplined. And that distinction matters, because the conversations health systems need to have about AI accountability are not primarily conversations with vendors. They are internal conversations about how seriously the organization intends to govern its own operations.
Policymakers have a parallel responsibility. As federal and state attention increasingly focuses on prior authorization reform and payer transparency, there is an opportunity to embed outcome reporting requirements into any regulatory framework that governs automated administrative decision-making. An AI system that accelerates a payer’s denial process without improving clinical appropriateness is not a healthcare innovation. It is an efficiency tool for the payer, not an improvement in care decision-making. Regulators should require that distinction to be measurable and reported, not left to vendor interpretation.
The potential here is real. The revenue cycle absorbs an extraordinary share of healthcare resources, resources that could otherwise support direct patient care, workforce retention, or capital investment in underserved communities. Thoughtful AI deployment, governed by rigorous measurement, can free up meaningful capacity across the system. I have seen it work in contained, well-designed implementations. The problem is not that the technology cannot deliver. The problem is that without accountability frameworks, we will not actually know when it does, and we will not catch it when it does not.
Healthcare has spent years debating what AI can do. It is past time to build the infrastructure to find out what it is doing.
By Jaylee Hilliard, MSN, RN, NEA-BC, CPXP, Vice President of Clinical Strategy, AngelEye Health.
In many neonatal intensive care units (NICUs), bedside cameras are still treated like an optional amenity. That framing no longer aligns with the reality of modern neonatal care or with what families experience during prolonged separation.
In one study, only 22% of parents with bedside camera access described separation from their infant as extremely stressful, compared to 63% of parents without access. The question is no longer whether families value virtual visibility, but why access remains inconsistent across units and health systems.
For many families, separation from their child is an unavoidable part of the NICU experience—not from lack of desire, but because medical acuity, postpartum recovery, distance, work, and caregiving responsibilities make around-the-clock presence nearly impossible. Even in units with open visitation, most units are not structured with private rooms or accommodations that allow families to remain at the bedside overnight. Parents must balance learning their infant’s care plan, participating in bedside rounds, and preparing for discharge during the limited hours they can be there.
When access to the bedside becomes inconsistent, stress rises. The NICU experience is emotionally intense for many families, and prolonged separation often intensifies that strain. Beyond physical distance, uncertainty between clinical updates or overnight when parents cannot be present can compound anxiety and make an already difficult experience feel overwhelming.
Visual access is not a substitute for bedside participation, but it can reduce uncertainty during unavoidable separation. When hospitals treat bedside cameras as essential infrastructure, they acknowledge how NICU stress truly manifests and can address it with tools that protect privacy, support clinicians, and integrate seamlessly into care delivery.
Visual Access as a Component of Family Integrated Care Family integrated care has long shown that meaningful parent participation in care and decision-making supports better outcomes for both infants and parents. Bedside cameras do not replace hands-on caregiving, skin-to-skin time, or bedside teaching. Rather, they serve a complementary function: maintaining connection when physical presence is not possible.
Studies have linked real-time video access to improved parental well-being, a stronger sense of involvement, and greater trust in the care team. That connection matters clinically as distress can affect how families process information, stay engaged over long hospitalizations, and build confidence for discharge.
The question, then, is not whether cameras offer value, but whether they’re implemented well and equitably.
Operational Integration: Designing Cameras That Support Families and Care Teams
When bedside cameras are deployed with clear privacy standards and defined workflows, they stop functioning as isolated tools and begin operating within the care model itself. In practice, families use visual access during the hours they cannot be physically present and when bedside participation isn’t feasible. Overnight, during recovery, or while balancing work and other responsibilities, cameras extend connection beyond hospital walls in a structured way when other real-world constraints prevent physical presence.
For care teams, the difference is operational. Despite concerns of adding another technology to manage or another tool to navigate, when implemented with clear governance, cameras can support continuity without adding burden. Cameras Support communication consistency across shifts, reduce information gaps between updates, and align with broader efforts to make family-integrated care reliable rather than dependent on individual practice styles. The real distinction is not whether a unit simply has cameras, but whether that visual access is intentionally designed into the clinical environment.
Without defined expectations, visual access can feel informal and unpredictable. With governance structures such as one-way video, pausing during hands-on care, and consistent communication norms, expectations become standardized. Families understand what they will see, when it may pause, and how to escalate questions appropriately. This clarifies boundaries for both clinical staff and parents.
What Standardized, Scalable NICU Camera Programs Get Right
Inconsistent enrollment and informal workflows can unintentionally create access gaps. When visual access depends solely on staff reminders, language availability, or passive opt-in processes, some families receive full support while others are left navigating the system on their own. Standardization prevents that variability. A standard-of-care approach is not “install cameras and hope for the best;” it’s a deliberately designed, burden-light program with clear operating norms:
One-way team-to-family micro-updates (photo/video/text) that are brief, consistent, and easy to standardize across shifts.
Proactive enrollment at admission, integrated with existing workflows (not passive opt-in) and supported by plain-language, translated instructions.
Privacy-protective operations by default, including pausing during hands-on care and no audio.
Language access built into the platform rather than added later.
Ongoing monitoring of activation and use by language, insurance, and distance, with targeted support when gaps appear.
Designing and operationalizing NICU camera programs is practical and cross-functional, requiring coordination among clinical leadership, patient experience, IT/security, and NICU operations to support safety, trust, communication, workforce experience, and equity.
Moving the Frame From “Amenity” to Expected Care
NICUs standardize practices to reduce harm and improve outcomes. NICUs standardize practices to reduce harm and improve outcomes. Over time, interventions such as standardized handoffs, barcode-based safety checks, and structured discharge-readiness workflows have shifted from “nice to have” to expected care. Bedside cameras—implemented with privacy safeguards and equitable access—fit that same evolution.
Bedside cameras, when implemented with strong privacy safeguards and equitable access, align with that same purpose. They support connection during unavoidable separation, reinforce trust, reduce unnecessary communication strain, and help extend family participation beyond visiting hours.
Treating visual access as a dependable component of care, rather than a discretionary add-on, requires planning and governance, not improvisation. Clear expectations for families, consistent staff workflows, privacy-first controls, and active monitoring for equitable access are what distinguish a technology feature from care infrastructure.
In NICU care, “must-have” capabilities are those that standardize safety and reduce variation over time and across staff. With privacy-first governance and workflow integration, bedside cameras meet that bar—shifting from an optional feature to dependable infrastructure.
The real question is not whether cameras feel family-centered, but whether they function like other NICU-critical systems: standardized, governed, equitable, and reliable across every shift.
Relationships between healthcare organizations and insurers play an important role in providing proficient care. Yet, administrative circumstances, often beyond a medical group’s control, strain payer-provider relations.
Operational structures often leave healthcare personnel navigating disconnected communication channels, inconsistent formats, and slow reimbursement timelines, creating friction between medical teams and those providing payment for their services. The Healthcare Financial Management Association (HFMA) reports that nearly 87% of provider CFOs believe strained payer relationships impact their ability to provide optimal care.
Staff also feel the toll of redundant reconciliation work, which contributes to burnout and frustration with clunky administrative systems. An occupational health survey by the Public Health Reviews journal found that 70% of respondents reported burnout symptoms, with dissatisfaction tied to administrative processes among the factors. These burdens aggravate staff wellbeing and the tense interactions between medical systems and payers.
To lessen administrative exhaustion and the breakdown of trust between the industry and carriers we work with, systems and practices alike will benefit from understanding the financial pain points and how adopting modern payment strategies can reduce monetary burdens.
The Disconnect: Banks and Providers
Banks and other financial institutions are not HIPAA-covered entities, meaning they are limited in how they handle patient-specific remittance data, further fueling the disconnect between payers and providers. This limitation, further worsened by legacy methods such as paper checks, ACH transfers, and standalone 835 files, causes healthcare professionals to juggle between parallel systems to transfer necessary information to the proper recipient, resulting in additional costs and errors.
Reconciling installments using these labor-intensive approaches can contribute to higher claim denial rates. The American Hospital Association found that private payers denied nearly 15% of all claims at initial submission. With rising healthcare costs pressuring affordability, patients often struggle with delays caused by inconsistent claim resolutions. When claims go unresolved, trust between medical groups and insurance providers becomes significantly more important to positive patient outcomes.
Simplifying how claims are returned and processed helps with these delays. Accelerated claim adjudication offers a practical path to help rebuild trust in the reimbursement process. To accomplish this task, our sector must empower banks with the necessary information to address repayment concerns quickly.
Cutting Administrative Drag in Reimbursement
Healthcare facilities can reduce manual work by embedding HIPAA-compliant data directly into each financial transaction. When payment and remittance data travel together, staff spend less time re-associating settlements with separate 835 files after processing, reducing errors associated with fragmented formats.
Any modernization effort must also protect sensitive information. Accomplishing this requires building encrypted, PCI- and HIPAA-compliant data paths that safeguard patient privacy. By unifying financial and clinical information into a single system, with secure guardrails, providers eliminate the need for multiple platforms.
Modern payment software supports this approach by unifying reimbursement and remittance into a single, automated flow. These platforms can securely post payments in real time to practice management systems (PMS), electronic medical records (EMR), and electronic health records (EHR).
Practices that integrate automated payer reimbursement platforms into their existing systems can standardize explanation of payments (EOP), permitting the replacement of inconsistent layouts. This digitization enables one-click posting, supporting accuracy and reinforcing audit readiness for large and small healthcare organizations.
Including encrypted data in payment transactions reduces errors and administrative redundancies across all stages of the reimbursement cycle, helping fuel smoother fiscal experiences between coverage organizations and providers.
A Smoother Path From Claim to Payment
Reducing errors helps ensure minimal denials and faster claim turnaround times. This diminishes accounts receivable, cuts reimbursement processing time, and improves cash flow. Practices that minimize payment inefficiencies free practitioners to focus on patient-facing care, reducing burnout tied to administrative systems, and restoring attention to critical healthcare priorities.
With minimal payment inefficiencies, the disconnect between insurers and medical groups will lessen, further strengthening payer-provider relationships. This will, in turn, allow care recipients to divert their attention from the burden of denied financial claims to what’s most important: recovery.
As healthcare finance keeps shifting, organizations that implement strategies to strengthen financial relationships will be better suited to help payers, providers, and patients thrive.
Marchex, which harnesses the power of AI and conversation intelligence to provide actionable insights derived from prescriptive vertical-market data analytics, today announced the launch of a new integration with Freshpaint, a platform built for healthcare marketers to deliver measurable performance while safeguarding patient trust in a complex regulatory environment.
Freshpaint’s data platform enables healthcare organizations to securely connect and activate data across channels, so that they can link marketing spend to outcomes while safeguarding patient privacy and complying with industry regulations.
Empowering Healthcare Marketers with Stronger Outcomes
The Marchex-Freshpaint integration allows healthcare marketers to seamlessly connect rich inbound call and conversation data with downstream marketing platforms, so that they can easily associate digital marketing activity with phone-based patient scheduling, without putting protected health information at risk. By leveraging advanced data capture and attribution, healthcare organizations can drive stronger marketing outcomes while operating compliantly within highly regulated healthcare environments, unlocking new opportunities for campaign optimization, patient segmentation, and patient journey management.
“Integrating Marchex’s industry-specific conversation intelligence with Freshpaint’s leading privacy-by-design infrastructure gives marketers clearer visibility into the patient interactions that shape demand,” said Troy Hartless, President and CRO at Marchex. “Together, we’re enabling organizations to activate conversation data with precision, tie marketing efforts directly to booked appointments, and unlock more efficient growth across the patient journey.”
“Healthcare marketers are under pressure to drive measurable growth on fixed budgets, and they shouldn’t have to choose between performance and compliance,” said Ray Mina, CEO at Freshpaint. “By combining Marchex’s conversation intelligence with Freshpaint’s privacy-first infrastructure, we’re helping teams turn privacy into a strategic advantage — so they can connect data to real appointment outcomes, prove what works, and optimize campaigns with confidence.”
Through Marchex’s AI-powered conversation intelligence solution, integrated with Freshpaint, healthcare marketers gain deeper insights into patient access, appointment scheduling outcomes, and patients’ call-driven care navigation as they manage their care.
Driving Innovation and Differentiation
Uniting Marchex’s conversation intelligence with Freshpaint’s compliant data infrastructure, healthcare marketers can elevate appointment volumes and patient acquisition through campaign optimization and improved automated bidding that attract more targeted, high-value leads.
Integration Features and Benefits
Advanced Data Capture – Marchex captures and securely transmits call events to Freshpaint, including session data, attribution details, and rich conversation signal data, such as sentiment, patient type, lead outcome, and topics, fueling better patient acquisition outcomes through more precise targeting, attribution, and journey insights.
Comprehensive Marketing Capabilities – The integrated solution enables clients to optimize campaigns, segment audiences, track patient journeys, and attribute marketing performance to scheduled appointments or visits, while complying with healthcare privacy regulations.
Patient Journey Completeness – The integration supports both session-based and offline calls, giving teams more comprehensive insights into the patient journey and the touchpoints that drive appointments and visits.
Electronic health record implementations are often framed as technology projects. In reality, they are operational transformations that affect nearly every function within a healthcare organization.
When health systems plan a new EHR rollout, the focus typically centers on the technical build, data migration, and integration architecture. Those elements are critical, but they are only part of the equation. The success or failure of an implementation is usually determined by how well operational workflows across the organization adapt to the system.
In multi-clinic environments, this complexity multiplies quickly. Each location may have slightly different intake procedures, clinical documentation habits, scheduling workflows, or billing processes. When those differences are not reconciled before implementation, organizations often find themselves trying to force technology to accommodate inconsistent operational practices.
One of the most common challenges appears during workflow mapping. Teams often underestimate the number of variations that exist between clinics performing the same functions. Front desk staff may collect demographic data differently. Clinical teams may document diagnoses using different conventions. Billing departments may rely on legacy processes that are not compatible with the new system.
Without operational leadership involved early in the implementation process, these inconsistencies frequently surface only after go-live. At that point, correcting them becomes significantly more disruptive and expensive.
Another critical factor is cross-functional alignment. EHR implementations require coordination between clinical teams, revenue cycle departments, IT, compliance, and administrative leadership. Each group interacts with the system differently, and each has its own operational priorities. When these groups are not aligned during system design, organizations risk creating workflows that work well for one department but create friction for another.
Operational leaders play an important role in bridging those perspectives. By facilitating discussions across departments and ensuring that workflows are standardized before configuration begins, they help reduce downstream issues during training and deployment.
Training is another area where operational considerations often determine success. Technical system training alone is rarely sufficient. Staff members need to understand not only how to navigate the system but also how their daily workflows will change. When teams are not prepared for those operational changes, productivity drops can persist for months after implementation.
Data migration also requires close collaboration between technical teams and operational leaders. While IT teams manage the mechanics of transferring data, operational teams are best positioned to validate whether the data being migrated reflects real clinical and business workflows. Incorrect mappings or inconsistencies in historical records can create problems that extend well beyond the initial go-live period.
Healthcare organizations increasingly recognize that successful EHR implementations require more than strong technical execution. They require operational discipline, leadership alignment, and a clear understanding of how work actually happens across the enterprise.
Technology can enable transformation, but it cannot define the operating model on its own. That work belongs to the leaders who understand the day-to-day realities of patient access, clinical care, documentation, billing, and compliance.
When operational leadership is treated as secondary, organizations often end up solving avoidable problems after go-live. When it is included from the beginning, implementation teams are better equipped to standardize workflows, prepare staff, and build a system that supports both clinical and business performance.
In the end, EHR success is not just about whether the system works. It is about whether the organization works effectively within it.
Melissa Corneal, MBA is a healthcare operations and program management leader known for executing enterprise transformation across complex, multi-site organizations. She has played a key role in the evolution of Flagler Hospital and First Coast Health Alliance, supporting the transition from a network of more than 250 physicians across three hospitals to an integrated system of 49 clinics aligned with MSO and ACO operating models.
When you hear the word zombie, you probably think of something that’s dead, but still walking around, looking disturbingly alive. In the digital world, zombie phishing works the same way: attackers resurrect old email threads to spread malware or steal credentials, hiding danger inside something that looks completely normal.
These malicious “undead” email messages nudge you to “click here to view the full update” or open an attachment. Why not? It’s part of a familiar conversation, from a trusted contact. But behind that link or file is malicious content that can compromise your organization’s defenses.
Zombie phishing is an ever-growing menace that exploits trust in ways traditional security tools struggle to catch.
What Is Zombie Phishing?
Zombie is a stealthy type of phishing attack that hides amongst your many emails like a wolf in sheep’s clothing. Here’s how it works:
The phisher compromises a real email account, usually through phishing, weak passwords, or lack of MFA. Now they control a legit, trusted account.
Then they scan old emails, looking for existing threads, especially ones with multiple people or unfinished business.
They revive an email thread by replying to a real message with something like: “See the attached update” or “Please review this doc.” The email looks normal because it’s part of a familiar conversation.
They add a malicious payload, which might include a link to a fake login page or a malicious attachment. Since the message is sent from a real account, it bypasses most security filters.
The victim falls for it because they recognize the sender and click. Here they might enter their credentials, download malware, or open a path into the organization.
The attack spreads, and new victims may have their accounts compromised too. The attacker keeps reusing threads, creating new “zombies” to spread the attack further.
Who Should Be Worried?
No one’s immune, but some are in the crosshairs more so than others. Small and medium-sized businesses (SMBs) often lack the robust security budgets of enterprises, making them prime targets. The Cybersecurity and Infrastructure Security Agency warns that SMBs account for 43% of cyberattack victims, with email as the top vector.
Larger organizations aren’t safe either, especially those in finance, healthcare, and manufacturing, where supply chain relationships and high-value transactions create juicy opportunities. Employees at all levels, from receptionists to C-suite executives, must stay vigilant, but finance and HR teams, gatekeepers of funds and sensitive data, are significant targets.
Steps to Fight Back
You can’t just hope your employees will spot every zombie in your inbox, and prevention demands a layered approach, technical, procedural, and human. Here’s how to start:
Lock Down Accounts with Multi-Factor Authentication (MFA):CISA reportsthat MFA blocks 99.9% of account takeover attempts. Make it mandatory for every email login, with no exceptions. A second verification step can stop attacks, even if they snag passwords.
Train the Human Firewall: Awareness is your best defense. Regular training (quarterly refreshers, for example) should teach staff to spot red flags: sudden urgency, odd tone shifts, or unexpected links in old threads. Security awareness training focuses on familiarizing employees with various cyber threats, such as phishing scams, malware, ransomware, and social engineering tactics, aiming to instill a culture of security mindfulness among staff.
Verify Before You Act: Establish a golden rule: no wire transfers or data shares without voice or face-to-face confirmation. The FBI’s IC3emphasizes that this simple step could’ve thwarted countless business email compromise (BEC) scams. Email alone isn’t enough.
Monitor and Audit Email Activity: Establish alerts for unusual logins or email forwarding rules, which are common indicators of a compromised account. Implement logging and alert features to detect suspicious logins, unauthorized forwarding rules, and unusual email activity. These measures ensure that potential threats are identified and investigated promptly, preventing significant harm. Email security solutions, such as Secure Email Gateways (SEG) and Integrated Email Security (IES) applications, are crucial for businesses to combat these attacks. These tools offer real-time monitoring and alerting for suspicious activities, enabling the early detection of compromise before attackers can inflict substantial damage.
Up-to-Date Patches and Update Relentlessly: Keep email platforms and endpoints patched and current. Attackers exploit gaps in unpatched systems to plant malware or harvest credentials. Up-to-date patches are critical to robust security, so vulnerabilities are tackled while reducing malware infections and credential theft. Endpoint Detection & Response (EDR) solutions also provide comprehensive reporting features.
The Road Ahead
Zombie phishing isn’t going away, it’s evolving. With AI now powering 43% of phishing attacks, expect more convincing fakes than ever. VIPRE’s latest threat intelligence shows a 74% rise in non-signature-based threats and a 10% increase in BEC attacks, signaling that cybercriminals are getting smarter and stealthier.
You must adapt and blend defenses with a culture of caution, regular security awareness training, and patch management to ensure vulnerabilities are addressed proactively. Security solutions that deliver real-time insights into emerging threats and integrate email security tools add another layer, monitoring email environments for suspicious logins, unauthorized forwarding rules, and unusual activity. These log and alert features allow teams to investigate potential threats before they escalate into breaches.
It’s not just about protecting data or dollars; it’s about preserving trust in the tools we rely on every day.
Zombie email defense requires preserving trust in the tools we rely on daily. The zombies are out there, potentially lurking in your inbox. The question remains: Are you ready to fight back?
Private practices and outpatient clinics are under sustained pressure to control operating costs while meeting growing expectations around patient access, data security, and regulatory standards. Much of that focus centers on staffing models, revenue cycle performance, and electronic health record optimization. One operational function, however, continues to receive relatively little scrutiny: printing.
Despite widespread EHR adoption, paper remains deeply embedded in daily clinical workflows. Intake packets, consent forms, prescriptions, referrals, insurance documentation, and patient statements are still printed in ambulatory settings. A dispersed practice across departments and devices, the actual cost is often underestimated or unmeasured.
Print cost recovery and analytics are emerging as ways to bring visibility to an overlooked expense and to better manage operational risk.
Printing Continues Throughout the Patient Journey
Paper use begins before a patient ever sees a clinician. Front desk staff routinely print intake forms, privacy notices, and consent documents. Medical assistants and clinicians print treatment summaries, prescriptions, and referral paperwork. Billing teams generate insurance forms, explanations of benefits, and patient invoices.
Individually, these print jobs appear routine. Collectively, they add up to a steady stream of spending on paper, toner, device maintenance, and staff time. Smaller practices frequently depend on older printers that are expensive to maintain and lack basic security features, further increasing costs.
Because printing expenses are typically rolled into general office overhead, many practices lack a clear picture of how much they spend on printing or which workflows drive the highest volume.
Measuring Print as Part of Care Delivery
Print cost recovery systems allow practices to track usage by device, department, user, or document type. When paired with practice management or billing platforms, print activity can be analyzed alongside patient visits and procedures.
That level of detail helps clinics understand where printing supports care delivery and where it reflects habit rather than necessity. For example, a practice may discover that the same forms are printed multiple times per visit or that certain departments generate significantly more paper than others performing similar functions.
This insight supports more accurate cost accounting and helps leaders assess whether print-related overhead is aligned with patient volume and service mix.
Compliance Risks Are Often Overlooked
Cost is not the only concern. Printed documents frequently contain protected health information, making them a potential HIPAA exposure if mishandled.
In busy outpatient environments, documents can be left unattended on printers, picked up by the wrong staff member, or misfiled. These occurrences typically go unnoticed until a compliance review or patient complaint brings them to light.
Print management platforms increasingly include features such as user authentication, secure print release, and detailed audit records. These tools limit access to sensitive documents and create records that show who printed what and when. For clinics subject to audits or internal compliance reviews, that documentation can be critical.
Using Data to Support Digital Workflows
Print analytics also help clinics identify opportunities to reduce paper use without disrupting care. High-volume documents such as appointment reminders, standard consent forms, and insurance verifications are often well-suited for digital delivery through patient portals, secure email, or electronic signature platforms.
By calculating the cost of printing these documents, practices can make more data-driven decisions about allocating funds to digital alternatives. The conversation shifts from preference to evidence, helping leadership teams prioritize technology upgrades that deliver measurable returns.
Accountability Shapes Behavior
When printing costs are invisible, usage tends to grow unchecked. Once practices begin tracking print activity, patterns become harder to ignore.
Some clinics allocate print costs internally by department to encourage awareness of usage levels. Others rely solely on reporting to guide policy conversations and set reasonable expectations. In both cases, the goal is not to eliminate printing but to ensure it is purposeful and appropriate.
Clinics that adopt this method frequently see gradual reductions in unnecessary printing without imposing strict controls that frustrate staff or interfere with patient care.
Data Informs Equipment Decisions
Print cost data can also guide decisions about hardware upgrades. Older printing devices commonly require frequent service, consume more supplies, and lack security features that are now considered standard.
When leaders can point to usage data showing where print demand is highest, they can make a clearer case for replacing inefficient devices with newer, more secure models. That evidence-based approach is critical as capital budgets tighten and technology purchases face greater scrutiny.
A Clearer View of an Overlooked Expense
Print cost recovery is not about shifting costs to patients or penalizing staff. It is about understanding an operational function that has long operated in the background.
For private practices managing thin margins, evolving compliance requirements, and ongoing digital transformation, even modest improvements in visibility can support better decision-making. Printing may never disappear from healthcare, but with the right data, clinics can manage it more deliberately and align it more closely with patient care priorities.
A patient wakes up with knee pain. Instead of calling their primary care doctor, they open ChatGPT, Claude, or Google and type a question. From there, these AI tools pull from what they already know: your reviews, your directory listings, what patients have said about you in forums, and return a short list of recommendations.
You weren’t consulted. You didn’t get a chance to make your case. And you probably have no idea what it said. For the patient, the process feels simple. For healthcare organizations, it raises a new question: what information are these AI tools using to describe your practice?
The Referral Network You’re Not Part Of
For decades, patient acquisition followed a predictable pattern. Another doctor made a referral, the patient had a friend or neighbor who recommended their surgeon, or perhaps a coworker or family member vouched for a nearby specialist. These were human conversations built on relationships, and practices could influence them by delivering great care and building strong professional networks.
Today, the process often begins somewhere else: the search bar. Increasingly, that search leads to an AI-generated summary from tools like Google’s AI Overviews, ChatGPT, or Gemini. Instead of scrolling through links, patients get one synthesized answer. Part of the reason is structural. Younger patients, for example, are less likely to enter the healthcare system through a traditional referral.
According to a national survey from the Cleveland Clinic, nearly two in five Gen Z adults do not have a primary care provider. At the same time, 45% of Gen Zers are enrolled in high-deductible health plans, which typically do not require referrals to see a specialist. Without a PCP guiding the process, many patients start their search online.
When these AI models recommend one provider over another, they influence which practices prospective patients investigate first, and which ones they never see.
AI Is Looking Beyond Your Practice’s Website
Many healthcare organizations assume that if their website is accurate and up to date, they are in good shape. In reality, AI tools pull from a much wider range of sources. They analyze Google reviews and listings on sites like Healthgrades, Vitals, and WebMD, and they scan patient discussions in online forums like Reddit, Quora, and local Facebook groups. Some AI models even incorporate employee feedback from sites like Glassdoor.
Together, these sources form a holistic picture that AI systems use to describe your practice. This means that information a practice rarely monitors, such as an outdated directory listing, an old review thread, or a frustrated patient comment about their parking experience, can influence how AI is summarizing that organization to prospective patients.
How to Show Up Where Patients Are Searching
The first step is surprisingly simple: search the way your patients would. Ask AI tools the questions a prospective patient might ask: “who is the best orthopedic surgeon near me,” “who is the top dermatologist in Phoenix,” “which cardiologist in Dallas has the best reviews?” Then, review the responses carefully. Is the description accurate? Are competitors appearing instead? From there, organizations can begin tracing where those answers are coming from.
When organizations begin running these searches, they often uncover a pattern: certain providers appear frequently, while others are missing entirely. One of the most common reasons is the “silent profile.” Many providers, especially newer physicians or specialists in smaller service lines, simply do not have enough recent reviews or online activity for AI models to confidently recommend them. Even highly respected providers can become invisible in AI-generated answers if their profiles appear inactive or outdated. Maintaining a steady flow of fresh patient reviews and ensuring provider profiles remain active across all platforms like Google, Healthgrades, Vitals, and WebMD can help close that gap.
Your reputation has always been shaped by what patients say about you. What’s changing is how that information gets interpreted. That makes the information surrounding your practice across review sites, directories, and community conversations more important than ever. Healthcare organizations don’t need to become experts in AI, but they do need to understand how patients are searching today, and how those tools are describing them when they do.
