Blake Madden writes and hosts about healthcare technology and health-system operations, including AI in revenue-cycle work and care delivery. The posts point readers to deep dives and episodes of the Claims Denied podcast.
AKASA post explains inpatient coding complexity, AI intended workflow, and third-party accuracy measures.
OhioHealth post reports hospital days saved, cost savings, and staff hours automated.
R1 post focuses on catching revenue-cycle risk earlier while keeping physician expertise central.
Check first: Confirm audience job titles, seniority, geography, and organization type before a sponsored partnership.
Main ideas
Buy systems that act, while keeping control with the health system · Vendors should bring receipts; guaranteed ROI is becoming the bar · Move revenue-cycle work upstream to catch risk before it becomes a denial
Blake Madden writes about healthcare technology and health-system operations and hosts the Claims Denied podcast.
Their work helps health-system readers assess how new technology and policy could change practice: how care or administrative work gets done, where risks arise, and which results matter.
They pair explanations of products and workflows with broader arguments about issues such as patient access and value-based care.
Madden makes healthcare technology easier to evaluate by connecting product claims to operational details and reported outcomes.
In the AKASA coding post, they explain why inpatient coding is complex, how the AI is intended to work, and which accuracy measures third-party evaluations assessed.
The OhioHealth post leads with reported results—hospital days saved, cost savings, and staff hours automated—and makes the case for measuring AI by its operational impact.
They also develop strategic arguments: the Lumeris post frames patient access as a way for health systems to differentiate, while the R1 post focuses on catching revenue-cycle risk earlier while keeping physician expertise central.
The mix of workflow detail, outcome-focused case examples, and industry analysis gives readers practical insight and a clear point of view.
Blake Madden argues that health systems should spend on software that changes what happens next, not just software that records what happened—and that vendors should arrive with measured results.
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Recurring priorities include moving revenue-cycle work upstream to catch risk before a denial forms, letting automation handle routine work while clinical experts take cases requiring judgment, and treating access—not the electronic health record—as a differentiation lever health systems still control. The posts pair vendors’ or guests’ claims with operational detail, and sponsored posts disclose that in the text.
Buy systems that act, while keeping control with the health system
Madden distinguishes a system of record from a system of action, and argues that the latter is worth paying for.
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Autonomy, they say, is configured by the health system, not switched on by the vendor: health systems set the rules for which encounters proceed without human review and which go to an expert. Automated coding decisions also carry evidence back to the source documentation.
At OhioHealth, one AI deployment reportedly saved 13,000 excess hospital days, $5.1M in costs and nearly 2,900 hours of frontline staff administrative work.
The health system responded by expanding into two more care settings.
The AKASA launch describes a platform designed to code complex inpatient cases fully autonomously, while letting health systems decide which encounters move without human review.
It takes roughly 90 seconds per encounter after discharge.
Vendors should bring receipts; guaranteed ROI is becoming the bar
The posts treat measurement as part of the story.
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Accuracy is tied to the evaluation design, while a health system’s decision to expand a deployment is offered as stronger proof.
Madden also argues that AI vendors should be thinking about guaranteed ROI, raising the burden on vendors rather than buyers.
The accuracy and scale figures are vendor-attributed.
The AKASA post cites blinded, third-party evaluations in which the system matched or exceeded expert coders on MS-DRG assignment, principal diagnosis and present-on-admission status.
It also cites a customer base representing more than $180 billion in aggregate net patient revenue and roughly one in ten U.S. inpatient discharges.
The OhioHealth post makes the case from the buyer’s side: the system had the receipts, and the response was to buy more, not declare victory.
Move revenue-cycle work upstream to catch risk before it becomes a denial
The post argues that utilization review should happen continuously and earlier, intercepting problems before they become appeals.
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Its position on labor is specific: clinical judgment stays central, while routine review work is removed. That combination is aimed at hospital finance and utilization-management readers.
The line comes from Melissa Hannan, VP of Product at R1 RCM: "Shift as much left as you can."
The post describes R1’s Phare Utilization Management as AI-driven case review across 100% of encounters, continuous chart analysis and payor-specific denial likelihood.
It says the physician advisor’s expertise stays central while routine work is stripped out.
Access is a differentiation lever health systems still control—and often give away
After following Lumeris and its AI-native primary care platform, Tom, for 18 months, Madden argues that every health system needs to differentiate away from its EHR—and that most leaders do not yet believe it.
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Ambient documentation and autonomous coding are table stakes, they say; access is the one lever health systems still control, yet often cede through MyChart. The argument pairs an analogy with a promised financial model.
The post casts access as healthcare’s ghost kitchen problem: the health system keeps the clinicians, buildings and capital stack, while somebody else owns the customer. It points to napkin math on what 20% panel growth is worth to a health system’s bottom line, and traces Tom from a conference-room pitch to a collaboration with Ochsner Health.
Value-based care policy and the physician enterprise belong in the same conversation as product news
The podcast coverage extends beyond vendor mechanics to regulation and workforce questions; the positions in these episodes belong to the guest, not Madden.
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Topics include the end of voluntary participation, the first weeks of a mandatory bundle and the role of specialists. AI’s effect on burnout is framed as broader than ambient scribes, tying the technology discussion to operations rather than novelty.
Navvis CEO Tim Elliott returns to the Claims Denied podcast to discuss why CMS dropped its "voluntary" posture on value-based care, what happens in a health system’s first 60 days under a mandatory bundle, why specialists as well as primary care are in CMS’s crosshairs, physician enterprise 2.0, and what health systems get wrong heading into 2027. The post links the episode on Apple and Spotify and points to the guest’s earlier appearance in June 2026.
This is the clearest example of how Madden covers a vendor announcement: the product claims are tied to the revenue-cycle work they replace, not left as marketing language.
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A newcomer sees both the mechanics of inpatient coding and the supporting information a vendor is asked to bring.
Blake Madden breaks down a product launch in two moves.
First, the operational problem: one inpatient stay can span dozens of documents, a coder typically spends 30 to 60 minutes on a single encounter, and those codes drive reimbursement, quality reporting, and when a claim can be billed.
Then, the product: a platform that reads the full record and assigns codes tied to source documentation, aims to code complex inpatient cases with no human intervention, and lets health systems set rules for which encounters move autonomously and which get expert review.
The post reports that, in blinded third-party evaluations, the system matched or exceeded expert coders on MS-DRG assignment, principal diagnosis, and present-on-admission status, and can code an encounter in roughly 90 seconds after discharge.
It states the post was written in partnership with AKASA.
For brands planning a campaign
A vendor with a technical product can expect a piece that pairs launch features with throughput and capacity, third-party accuracy evidence, and a route to a demo. The format suits a sponsored deep dive or announcement post where the operational stakes are the story.
This shows the opinion-driven side of the work: how a strategic argument, analogy, and quantified business case come together, with the sponsored relationship labeled in the text.
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Here Madden makes an argument rather than summarizing a launch: every health system needs to differentiate away from its EHR, and most leadership does not believe it yet.
The illustration is the ghost kitchen problem of healthcare: a health system keeps the clinicians, buildings, and capital stack while somebody else owns the customer.
Ambient documentation and autonomous coding are called table stakes; access is framed as the differentiation lever still under the health system's control, but often ceded to MyChart.
The analysis draws on 18 months following Lumeris and its AI-native primary care platform, Tom, from a conference-room pitch to a collaboration with Ochsner Health.
Madden also points to napkin math on what a 20% panel growth is worth to a health system.
The post is labeled a partnership with Lumeris and states the thoughts are Madden's own.
For brands planning a campaign
A positioning-led campaign can use this approach: the creator builds an argument and analogy, quantifies the payoff, and discloses the partnership. It fits a narrative or category-creation brief better than a straightforward product walkthrough.
This is the best first stop for anyone who wants to see how Madden writes about results.
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The numbers come before the argument, so readers can judge whether they support the claim.
This post opens with outcomes and works backward: 13,000 excess hospital days saved, $5.1M in cost savings, and nearly 2,900 hours of administrative work automated off frontline staff, all from one AI deployment at OhioHealth.
Madden's point is that the health system responded by expanding the partnership into two more care settings rather than declaring victory.
The post returns to the distinction between a system of record and a system of action.
It ties the results to a longer write-up, argues that AI vendors need to think about guaranteed ROI, and calls automated care operations a category heading toward the same table-stakes status as an EHR.
The post states it was written in partnership with Qventus.
For brands planning a campaign
A proof-point-led campaign fits this pattern: give the creator documented outcomes and a customer who expanded the deployment, and expect a piece built around the receipts and a category claim. Vendors without published results should expect the argument to lean more on positioning than numbers.
A short, interview-based item showing how a named executive and a vivid quote can carry a post.
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It offers a look at formats beyond the long product breakdowns.
The post centers on a single quoted line from Melissa Hannan, VP of Product at R1 RCM: "Shift as much left as you can."
Madden describes Phare Utilization Management as an extension of R1's revenue-cycle operating system: AI-driven case review across 100% of encounters, continuous chart analysis, and payor-specific denial likelihood, all designed to catch risk upstream before it becomes a denial.
The discussion covers how clinical judgment and payor intelligence combine, and why the physician advisor's expertise stays central while routine work is removed.
The post links to a video clip and the full conversation, and states it was produced in partnership with R1.
For brands planning a campaign
For a brand that wants a named spokesperson featured, expect a quote-led post, a plain-language description of the mechanism, and links to a clip and full video. It favors vendors with an executive willing to speak on camera.
This is the recurring platform behind the writing, and the post shows how episodes are framed for a professional audience.
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Readers can see the topics Madden returns to and decide whether the podcast is the right entry point.
This post promotes an episode of the Claims Denied podcast featuring returning guest Navvis CEO Tim Elliott.
Topics include why CMS dropped its "voluntary" posture on value-based care; what happens in a health system's first 60 days under a mandatory bundle; why specialists, not just primary care, are now in CMS's crosshairs; physician enterprise 2.0; where AI is moving the needle on burnout beyond ambient scribes; and the guest's read on what health systems get wrong heading into 2027.
The post links to Apple and Spotify and to the guest's earlier appearance in June 2026, placing this episode at or after that date.
For brands planning a campaign
A podcast segment or sponsored episode offers a distinct alternative to a written deep dive.
The topic list shows the regulatory and operational territory these episodes cover.
A vendor brief tied to CMS policy or value-based care contracting fits this format better than a pure product launch.
Formats: short posts that point to deep dives, episodes, and demos5
Autonomous coding and the mid-cycle revenue cycle2
Vendor evidence: third-party evaluations, receipts, and guaranteed ROI2
Shifting revenue-cycle work upstream to prevent denials1
Access, EHR differentiation, and health-system strategy1
Value-based care policy and the physician enterprise1
Autonomous coding and the mid-cycle revenue cycle
These posts focus on a specific part of the revenue cycle: the middle, where clinical records become codes, and where AI is being tested hardest.
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A post on AKASA's launch describes a single inpatient stay spanning dozens of documents and a coder spending 30 to 60 minutes on one encounter.
It then lists what the platform does: reads the full record, assigns codes with supporting evidence tied back to source documentation, targets fully autonomous coding of complex inpatient cases across specialties, and lets health systems set which encounters move autonomously and which get expert review.
The post reports roughly 90 seconds of processing per encounter after discharge.
A separate post on a Qventus deployment at OhioHealth reports 13,000 excess hospital days saved, $5.1M in cost savings, and nearly 2,900 hours of administrative work automated off frontline staff.
It also draws a distinction between a system of record and a system of action.
Blake Madden names the product, breaks the process into steps, then lists the operational changes the vendor claims, giving readers enough detail to compare with their own process.
For brands planning a campaign
A vendor in this category should expect to be judged on turnaround per encounter, which cases run without human review, who reviews the rest, and measured savings—not on general claims about AI in healthcare.
Shifting revenue-cycle work upstream to prevent denials
A post about R1 RCM's Phare Utilization Management is built around a line from Melissa Hannan, VP of Product: "Shift as much left as you can."
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It describes AI-driven case review across 100% of encounters, continuous chart analysis, and payor-specific denial likelihood, all aimed at catching risk before it becomes a denial rather than after. It also takes a position on labor: the physician advisor's expertise stays central while routine review work is stripped out.
For brands planning a campaign
An argument for prevention before the claim, with a clear role for clinical experts, fits how this material speaks to hospital finance and utilization-management audiences. A recovery-focused story would take the opposite direction.
Vendor evidence: third-party evaluations, receipts, and guaranteed ROI
The posts distinguish between vendors that assert accuracy and vendors that show how it was measured.
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The AKASA post cites blinded, third-party evaluations where the system matched or exceeded expert coders on MS-DRG assignment, principal diagnosis, and present-on-admission status.
It also cites a customer base representing more than $180 billion in aggregate net patient revenue and roughly one in ten U.S. inpatient discharges.
The OhioHealth post calls out "guaranteed ROI" as something AI vendors need to be thinking about, and frames the health system's decision as expanding a partnership into two more care settings rather than declaring victory on one deployment.
The accuracy results and customer-scale figures come from vendor announcements. Ask for independent validation before treating them as verified results.
For brands planning a campaign
Bring evaluation design and outcome baselines to the conversation; these posts use that level of proof to justify a purchase or expansion decision.
Access, EHR differentiation, and health-system strategy
Drawing on 18 months following Lumeris and its AI-native primary care platform Tom, a post argues that every health system needs to differentiate away from its EHR, and that most leadership does not believe it yet.
Read more
It introduces the line that access is "the ghost kitchen problem of healthcare": the clinicians, buildings, and capital stack stay with the system, but someone else owns the customer. The post calls ambient documentation and autonomous coding table stakes, treats access as the one differentiation lever health systems still control, and promises napkin math on what 20% panel growth is worth to a health system's bottom line.
The post opens with a strategic claim, uses one memorable phrase to carry it, then points to a longer analysis where the financial model is worked out.
For brands planning a campaign
Positioning a product as an access or differentiation play, with a quantified financial model attached, matches the strategic framing used here; a narrow feature pitch would not.
Value-based care policy and the physician enterprise
A podcast post featuring Navvis CEO Tim Elliott covers CMS dropping its "voluntary" posture on value-based care, what actually happens in the first 60 days when a mandatory bundle lands on a health system, and why specialists as well as primary care are now in CMS's crosshairs.
Read more
The same episode covers physician enterprise 2.0, where AI is moving the needle on burnout beyond ambient scribes, and what health systems get wrong heading into 2027. The post links the full episode on Apple and Spotify and points back to the guest's earlier appearance in June 2026.
For brands planning a campaign
Policy-driven subjects—mandatory bundles, specialist exposure, workforce burnout—reach a leadership audience alongside product topics, giving vendors with a regulatory or reimbursement angle a route into the podcast.
Formats: short posts that point to deep dives, episodes, and demos
The material uses a few recurring formats.
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One post points to a written deep dive and states it was written in partnership with a vendor but is "100% my thoughts."
Two posts announce a platform launch or product extension with a partnership disclosure and a demo link.
One post announces a podcast episode with listening links.
One promotes a video conversation clip with a link to the full video.
Several posts close with a direct link to the longer work—the deep dive, the episode, or the demo—so the post itself functions as the hook rather than the analysis.
The posts disclose partnerships but do not specify what a paid arrangement includes. Ask what the partnership covers.
For brands planning a campaign
Plan for a short post that drives traffic to longer sponsored work, with the deep dive or episode carrying the technical argument and the post carrying the hook.
Four of the five posts are labeled as partnerships with healthcare technology companies: Qventus, R1 RCM, AKASA, and Lumeris.
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Each has its own disclosure line.
Navvis appears once as a podcast guest, not as a disclosed commercial partner.
Ask for contract terms, duration, and whether any arrangement continued beyond the post.
Qventus
Disclosed partnership
Madden broke down a Qventus deployment at OhioHealth, citing 13,000 excess hospital days saved, $5.1M in cost savings, and about 2,900 hours of administrative work removed.
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They argued that AI vendors need to think about guaranteed ROI. The post links to the full story and is labeled a partnership.
Original disclosure
Written in partnership with Qventus, Inc., but, as always, 100% my thoughts.
Madden interviewed R1 RCM VP of Product Melissa Hannan about Phare Utilization Management, an extension to R1's Phare revenue-cycle operating system.
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The post describes AI-driven case review across all encounters and payor-specific denial likelihood, and links to the full video clip. It is labeled a partnership.
Madden covered AKASA's launch of an autonomous AI platform for inpatient medical coding and clinical documentation integrity, listing claimed results from blinded third-party evaluations and a roughly 90-second coding time per inpatient encounter.
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The post is labeled a partnership and links to a product demonstration.
Madden says they spent 18 months following Lumeris and its AI-native primary care platform, Tom, from an initial pitch to a collaboration with Ochsner Health.
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They published a deep dive that includes estimates of what a 20% panel growth is worth to a health system. The post is labeled a partnership and links to the deep dive.
Original disclosure
Written in partnership with Lumeris but, as always, 100% my thoughts.
Navvis CEO Tim Elliott returned as a guest on Madden's Claims Denied podcast to discuss CMS's shift on value-based care, the first 60 days after a mandatory bundle lands at a health system, and physician enterprise strategy.
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The post does not disclose a commercial relationship; it presents Elliott as a podcast guest.
Blake Madden writes about healthcare AI and the business side of health-system operations, mostly through short LinkedIn posts that point readers to a longer piece: a written deep dive, a product demo, or an episode of the Claims Denied podcast.
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The through-line is money and workflow — autonomous inpatient coding, utilization review moved earlier in the process, care operations that produce measurable savings, access as a competitive lever against the electronic health record, and value-based care policy.
Sponsored work is labeled in the post, usually with a note that the thinking is their own.
To explore, start with the podcast for regulation and leadership conversations, and the deep dives for product-by-product breakdowns of the specifics — turnaround per encounter, which cases run without human review, and how accuracy was tested.
Healthcare AIRevenue cycle managementAutonomous medical codingPatient accessValue-based carePhysician burnoutHealth system strategyPodcastVendor deep divesHospital finance leadersHealth system executives
Nicols makes the same argument as Madden about where AI pays off in the revenue cycle: the value sits in high-volume, rules-heavy shared services such as revenue cycle and finance, and automating a broken workflow just produces a faster broken workflow.
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Both push the goal from processing denials to preventing them, and both treat measurement and operating discipline, not tool count, as what separates health systems that get value from AI from those that do not.
Nicols argues from published research and a broad estimate of productivity gains across shared services, without naming vendor platforms or partnerships. Madden takes the opposite approach, breaking down a specific product or deployment, naming the partner, and pointing to a deep dive or podcast episode for the rest.
Omar looks at healthcare AI through the same incentive lens as Madden: which AI a health system will actually buy is the one that makes it more money, and the fastest return comes from taking administrative work off staff rather than from clinical models.
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Both build arguments around hard operating numbers — denial risk, staff cost per hour, the cost of replacing a physician — rather than technology claims.
Omar's posts are short and argument-first, often built around a quoted line from an industry figure or a vendor's per-hour price, and written to land with hospital leaders in a few sentences. Madden's are longer, more specific about how a system works and how its accuracy was evaluated, and usually route the reader to a linked deep dive or episode.
Belkin and Madden both tie workforce strain to health-system finances and treat the problem as structural rather than as a story about individual stress.
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Belkin links patient access and revenue stability to how well an organization plans for clinician supply; Madden links documentation and coding capacity to time to bill and cost to collect. Both argue that waiting to react is the expensive choice.
Belkin writes from inside physician staffing, recruiting, and workforce strategy, drawing on client work and burnout data across specialties, and covers flexible staffing models such as locums and PRN. Madden stays focused on software, AI products, coding, and care operations, with staffing as context rather than subject.
Blake Madden is a plausible topical fit for a healthcare IT vendor targeting revenue-cycle and health-system AI buyers.
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Recent posts cover autonomous inpatient coding, utilization management, value-based care, and AI care operations; they have also published labeled partnership deep dives with AKASA, R1 RCM, Lumeris, and Qventus.
Their LinkedIn following was 28,797 on October 8, 2026.
Before committing to a sponsored deep dive or podcast segment, ask for recent audience survey results or platform demographic data on job titles, seniority, geography, and organization type for the same healthcare IT and revenue-cycle topics.
If that data is unavailable, treat the partnership as a topical reach play rather than a verified buyer-audience buy.
Blake Madden's posts show disclosed commercial partnerships with four healthcare technology companies—Qventus, Lumeris, AKASA, and R1 RCM—across three recurring formats: a written deep dive on a partner's product or strategy, a recorded executive conversation, and a structured product-launch breakdown.
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Each post carries an explicit disclosure, such as "Written in partnership with Qventus, Inc, but as always, 100% my thoughts" or "(in partnership with R1)," showing how sponsored work is labeled and framed.
The posts also feature Claims Denied, an owned podcast where Blake interviews health-system executives.
Those episodes are not labeled as partnerships, so buyers should confirm sponsorship availability and terms directly.
The posts show no official partnership policy, rate card, or inquiry page; buyers should ask directly about pricing and availability.
Sponsored written deep dive
Buyer requirement
A post or linked article analyzing a partner's product, market position, or strategy in Blake Madden's voice, with a named disclosure.
Two examples are documented: a Qventus piece on an OhioHealth AI deployment that reports 13,000 excess hospital days saved, $5.1M in cost savings, and roughly 2,900 hours of administrative work automated; and a Lumeris piece on the Tom primary care platform and its work with Ochsner Health.
The buyer should supply product and strategy details, customer outcome figures the piece can cite, and any internal numbers the analysis will use.
The disclosure and Blake's framing remain in the published text.
Sponsored executive video conversation
Buyer requirement
A recorded discussion with a named partner executive, distributed as a short clip plus a link to the full video.
One example is R1 RCM: a conversation with Melissa Hannan, VP of Product, about Phare Utilization Management, covering AI-driven case review across encounters, continuous chart analysis, and payor-specific denial likelihood.
The buyer should make a product executive available for recording, agree on product claims in advance, and prepare for a discussion focused on a specific workflow problem—in this case, catching risk before it becomes a denial—rather than a general company overview.
Sponsored product launch breakdown
Buyer requirement
A launch-day post listing what's new about a platform and linking to a product demo.
The documented example is an AKASA post announcing an autonomous mid-cycle platform for inpatient coding.
It details the expansion from prebill review into inpatient coding and clinical documentation integrity, the roughly 90-second coding time per encounter after discharge, and results from blinded third-party evaluations.
The buyer should provide launch specifics, quantified performance claims, the basis for those claims, and a working demo link; the post attributes accuracy results to third-party testing.
Owned podcast episode
Buyer requirement
Claims Denied, Blake Madden's interview show, is distributed through Apple and Spotify and features health-system executives.
A documented episode features Navvis CEO Tim Elliott returning to discuss mandatory value-based care bundles, the first 60 days after a bundle lands, and physician enterprise priorities.
The episodes in this material carry no partnership disclosure.
A buyer interested in a sponsored segment should confirm sponsorship terms, placement, and labeling directly, and provide an executive guest and an episode topic tied to a current policy or market shift.
Campaign need: A healthcare IT vendor selling autonomous inpatient coding, clinical documentation integrity, or utilization-management software to hospital finance, revenue-cycle, and innovation leaders wants a sponsored written deep dive or podcast segment on autonomous coding and care operations.
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Decision: strong fit.
Madden's posts show repeated command of the buyer's world: the mid-cycle revenue cycle, where one inpatient stay spans dozens of documents and a coder spends 30 to 60 minutes per encounter; the shift-left argument for catching denial risk before it becomes a denial; and the distinction between a system of record and a system of action.
The AKASA launch post pairs product mechanics with blinded third-party accuracy claims and a 90-second post-discharge coding time.
The Qventus post leads with OhioHealth outcomes — 13,000 excess hospital days saved, $5.1M in cost savings, and roughly 2,900 hours automated — and treats one deployment as a reason to expand, not a finish line.
The R1 RCM post centers on a named product executive and one workflow problem, a usable template for a sponsor that can supply a spokesperson.
Madden also discloses partnerships in the post text, making sponsored work easy to identify.
Two checks matter before launch.
The observed LinkedIn following was 28,797 on October 8, 2026, but the data does not show job titles or buyer seniority; request demographic data or build the campaign around topical reach.
The Claims Denied episode carries no partnership disclosure, so confirm podcast sponsorship terms, labeling, and placement directly.
When to choose a specialist
Campaign need: A consumer wellness or patient-facing health app wants short-form lifestyle videos and direct-to-consumer product reviews from a healthcare creator to drive app installs among patients.
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Decision: poor fit.
Madden's posts contain no consumer-facing health, wellness, or patient-education content.
They address hospital and vendor decision-makers: revenue-cycle operations, coding accuracy, utilization management, value-based care policy, and health-system strategy.
The commercial formats include a sponsored written deep dive, a recorded executive video conversation, a product-launch breakdown, and an owned interview podcast.
There are no product-review videos, consumer-style demonstrations, or patient acquisition campaigns.
The only video is an executive conversation clip tied to a vendor partnership, not a consumer format.
The available audience data is a follower count only; it does not establish that the following consists of patients or consumers.
Madden's posts provide no install, purchase, or consumer-intent data.
Reject this campaign or reframe it for hospital or health-system buyers.
A worked campaign example
Worked campaign angle for a hospital or health-system buyer campaign.
Objective: position a revenue-cycle AI vendor as the credible choice for autonomous inpatient coding and upstream denial prevention.
Format: a Claims Denied podcast segment with a vendor executive, supported by a sponsored written deep dive and one LinkedIn post that drives to the deep dive.
Angle: 'From prebill review to autonomy: what a health system should see before letting coding run without human review.' The episode can follow the R1 RCM pattern — one named executive, one workflow problem, one clear line such as shifting risk left — while the deep dive can follow the AKASA pattern — name the product, show the work being replaced, list what runs autonomously and what stays with human experts, and state how accuracy was evaluated.
Required vendor inputs: product and strategy detail; which encounters are eligible for autonomous coding; who reviews the rest; the evaluation design behind any accuracy claim; customer outcome figures with permission to cite; a working demo link; and an executive available for recording.
The written post should carry a plain disclosure, following the pattern visible in Madden's sponsored work.
Success measures should be buyer-side: qualified visits to the deep dive, demo requests from hospital finance and revenue-cycle titles, and podcast completion or clip views.
Do not promise verified buyer demographics, guaranteed lead volume, or clinical outcomes beyond the vendor's claims.
Before launch, confirm podcast sponsorship terms and labeling, because the episode is not labeled as a partnership, and request audience demographic data or state the reach limitation in the campaign plan.
Choose Nicols when the campaign's spine is the strategic argument rather than a product walkthrough.
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Nicols makes the case a revenue-cycle buyer has to defend upward: AI's clearest return right now sits in shared services such as the revenue cycle and finance, where work is high volume, rules heavy, and overdue for change.
A McKinsey figure puts potential productivity gains in those functions at up to 50%.
Nicols's sharper point is that revenue cycle is automating inefficiency faster than it is eliminating it, so a faster broken workflow is still broken.
A vendor whose differentiator is removing unnecessary work before automating what remains has a ready-made argument here.
A July 27, 2026 post extends that point to AI adoption generally, drawing on a Stanford and Harvard report: the hard problem is no longer access to capability but proving in live settings when the technology helps.
Accuracy is not the same as outcomes, and documentation, coding, and data management carry the clearest early return.
That makes Nicols a fit when the sponsor wants a sober, current, strategy-level voice for hospital finance and innovation readers.
Nicols does not offer close readings of named vendor deployments, with customer outcomes, product mechanics, or accuracy evaluations.
The tradeoff is argument credibility without demonstration.
If a campaign needs readers to see how the product works and hear a named executive explain it, ask for a deployment example with outcome figures and an executive interview; Madden or a vendor spokesperson carries more of that load.
Choose Omar when the campaign should land as arithmetic for a hospital finance leader.
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Omar writes prior authorization as a cost line: clinical staff spend 7 to 14 days on each authorization across thousands a month; every one of those hours is paid at clinical wages for clerical work; prior authorization is now a leading reason physicians leave; and a burned-out physician costs roughly $500K to replace.
The framing that the cost problem and morale problem are the same problem is a line a revenue-cycle or utilization-management vendor can build a segment around.
Omar also states the buying reality bluntly, quoting Taha Kass-Hout: hospitals will adopt the AI that makes them more money, and the AI that saves society money is a different product, because avoiding unnecessary procedures does not get rewarded when payment runs per procedure.
That quotable framing suits a brief about denial prevention or administrative staffing where the sponsor wants the economic argument stated without hedging.
Omar's posts focus narrowly on prior authorization, administrative labor, and staffing cost, with no material on inpatient coding, clinical documentation integrity, care operations, or product performance in coding or utilization management.
One post rests on a direct vendor comparison—a Sully.ai employee at about $2 an hour against $41 for an administrative worker—which reads closer to product messaging than independent analysis.
Ask for sponsor substantiation before reusing that comparison.
The tradeoff is a sharper dollar argument in a smaller subject area, with no comparable template for Madden's executive interview or disclosed partnership format; the sponsor would need to create one.
Choose Belkin when the campaign shifts from coding and revenue-cycle software to clinician supply.
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Belkin writes physician workforce planning as an operational discipline: forecasting demand by service line, tracking retirement and attrition, and building pipelines before coverage gaps become urgent.
They claim that 80% of clients over 22 years fell into a reactive trap.
An April 28, 2026 post turns to burnout data, citing roughly 50% of physicians reporting burnout in 2024 to 2025, with emergency medicine above 60% and OBGYN, oncology, anesthesia, and radiology around 50%.
They argue that the structural drivers are unpredictable schedules, high acuity, rising demand against limited capacity, and administrative burden.
Their conclusion—that flexible models such as locums, PRN, rapid-response teams, and fractional coverage need to be structural rather than optional—is directly usable by a staffing or workforce-technology sponsor.
This is the right alternative when a vendor's story runs through clinical capacity and retention rather than the revenue cycle.
Belkin's posts focus on workforce strategy and services, including the 22-year client reference, rather than revenue-cycle, coding, or autonomous care operations.
They offer no AI product evaluation, accuracy or deployment information, or sponsored deep-dive example.
The tradeoff is relevance in the wrong function: strong, current language on physician burnout and staffing that could carry a workforce campaign, but little to help a hospital finance audience assess coding software.
