Healthcare IT product marketer with three decades of experience, focused on go-to-market work for healthcare and B2B SaaS, including revenue cycle management, clearinghouse, and patient access. Charles also writes about making healthcare AI safer through accountability and verification processes.
1.9K followersHealthcare AIBased on 11 posts · latest Aug 2026
Charles T. is most useful for healthcare SaaS AI governance or clinical AI monitoring launches needing product marketing, launch messaging, expert positioning.
Best for
A healthcare SaaS vendor preparing AI governance or clinical AI monitoring launch with revenue-cycle or patient-access buyers.
Not for
Reach-led sponsorship or paid awareness relying on LinkedIn audience scale, or assumed bookable sponsored formats.
Key takeaways
They argue healthcare AI failure is a process gap, not a model gap.
They propose a verification layer, near-miss reporting, feedback loop, and named accountable owner.
They cite BMJ Open 2026 audit: 49.6% of health responses problematic.
Check first: Confirm commercial formats, rates, audience demographics, and comparable post-performance data before contacting them.
Main ideas
Healthcare AI's failure is a process gap, not a model gap · Process first, credentials that prove it second · Where the work sits: revenue cycle, clearinghouse, patient access
Audience
1,870 LinkedIn followers · 0 of 3 Posts that include engagement or reach figures
Charles T. is a healthcare IT product marketer whose posts focus on go-to-market work in healthcare and B2B SaaS, including revenue cycle management, clearinghouse systems, and patient access.
They argue that healthcare AI needs accountable processes for checking errors, reporting near misses, and assigning responsibility—not just better models.
In one post, they connect this view to a “verification layer” for safer deployment.
The available work shows a practical, process-first perspective.
Charles links healthcare AI safety to operational accountability.
The AI post moves from cited safety concerns to specific gaps—near-miss reporting, feedback loops, and clear ownership—then proposes building verification into deployment.
That makes the argument more actionable than a general warning about AI risk.
A different post shows their product-marketing perspective through an endorsement of market-first frameworks and a stated preference for process over credentials alone.
The open-to-work post is a self-description of target roles and skills, not evidence of campaign results.
Follow Charles for arguments about healthcare AI accountability and process-led product marketing.
Charles T. writes about the operational gap between adopting healthcare technology and being able to trust it.
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Across the posts, the recurring argument is that accountability is a process problem, not a technology problem, and process problems need process solutions: near-miss reporting, feedback loops, a named owner, and a verification layer in place before a tool goes live.
A related priority is process-first practice in product marketing: a shared framework and credentials employers ask for by name, used to argue for strategic leadership rather than task execution.
The domain is healthcare's money-and-access side: revenue cycle management, clearinghouse infrastructure, patient access, and front-to-back revenue operations.
The arguments draw on third-party audits, named safety lists, adoption counts, and operational comparisons.
Healthcare AI's failure is a process gap, not a model gap
Charles argues that the accountability gap in healthcare AI is a process problem with process solutions.
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Organizations have built adoption frameworks, deployment timelines, and ROI models, but rarely a systematic way to catch an output that is wrong: no near-miss reporting, no feedback loop, and no one accountable when the tool fails.
The commitment is to build the verification layer that makes a deployment trustworthy rather than chase the next model.
The argument draws on aviation, which asks what happens when a system is wrong before every flight, and nursing, which builds error reporting into training.
The post opens with a number and holds it: a 2026 BMJ Open audit of five AI chatbots found 49.6% of health responses were problematic, and nearly one in five were highly problematic or potentially harmful. Charles pairs that with ECRI's 2026 patient safety concerns list, where AI diagnostic errors ranked first, ahead of cybersecurity and staffing, and asks what happens when the tool goes live and is wrong.
In a post on Pragmatic Marketing certification, Charles argues that process comes before credentials, and that the certification's staying power comes from becoming a shared professional language across organizations rather than a checkbox.
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The framework aims to elevate strategy: teaching marketers to read markets, plan launches, and communicate as leaders—the shift hiring managers are described as looking for.
The post also locates Charles's standing in the field: three decades in healthcare IT and a PMC-II certification that, they say, has not aged.
AI-specific content is now woven through the certification, giving marketers a way to turn AI into practical outcomes without a technical background.
The post cites the Pragmatic Framework as used by more than 8,000 companies and trusted by over 140 Fortune 500 organizations, including Cisco, Dell, and Bright Horizons.
It reports a 50% increase in Net Promoter Scores, a 25% improvement in product sales, and a 25% reduction in time-to-market after adoption.
Those outcome figures are Pragmatic Institute's reported results.
Charles notes that AI-specific material has been added to the certification, making the process-first argument current rather than historical.
Where the work sits: revenue cycle, clearinghouse, patient access
The recurring domain is healthcare's revenue and access side, rather than clinical product marketing in general.
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Charles names revenue cycle management, clearinghouse infrastructure, patient access, and business office or front-to-back revenue operations as focus areas, and describes three decades in healthcare IT spanning RCM, credentialing, clearinghouse infrastructure, and cybersecurity SaaS.
The stated skill set in this domain includes go-to-market strategy, positioning and messaging, product launches, sales enablement, and competitive and market intelligence.
This domain list provides the operating context for the process-first position: in front-to-back revenue operations, an unverified step can turn into a denied claim or delayed payment.
The open-to-work post names target roles as Product Marketing Manager and Director of Product Marketing in healthcare and B2B SaaS.
It lists RCM, clearinghouse, patient access, and business office or front-to-back revenue operations as focus areas, and gives work preferences in order: remote, then hybrid, then on-site in the Dallas–Fort Worth area.
Noetic, Mavenir, and symplr are named as background.
The two AI positions sit differently and are worth reading together.
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On the patient-facing side, the argument is verification before deployment: nearly half of audited chatbot health responses were problematic, and most organizations deploying AI have no process for catching that.
On the marketing side, AI content inside certification is treated as something practitioners should learn so they can turn AI's potential into practical outcomes without a technical background.
In both cases, the test is whether a process exists to catch the tool being wrong; the consequences differ when it fails.
The Pragmatic post says AI-specific content woven through the certification gives marketers the knowledge to turn AI's potential into practical outcomes without requiring a technical background. The healthcare AI post says most organizations deploying AI have no near-miss reporting, no feedback loop, and no one accountable when the output is wrong.
This is the clearest, most self-contained statement of what Charles argues about healthcare AI.
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A reader who wants to know whether they have a distinct position, rather than a general interest in health tech, should start here: the post names a problem, points to support for it, and commits to a specific kind of work.
Charles opens with a number and holds it: a 2026 BMJ Open audit of five AI chatbots found 49.6% of health responses were problematic, with nearly one in five highly problematic or potentially harmful.
The post pairs that with ECRI's 2026 patient safety concerns list, where AI diagnostic errors ranked first, ahead of cybersecurity and staffing.
From there, they argue that the problem is not the model but the missing process around it — no near-miss reporting, no feedback loop, no one accountable when output is wrong — and compare healthcare unfavorably to aviation and nursing, which build that question into training and pre-flight checks.
The piece ends with their stated commitment to building a verification layer rather than chasing the next model, part of a series they say they have been running.
For brands planning a campaign
For a healthcare SaaS company hiring around AI governance, this post is the strongest existing articulation of a "verification layer" position.
Launch messaging could adopt Charles's framing — accountability as a process problem with a process solution — instead of a generic accuracy or compliance claim.
The post also shows how they support the topic: third-party audits and safety lists rather than vendor benchmarks.
Charles states the sectors and systems they have worked in and the certification they hold, which explains why the Pragmatic framework shows up in the other posts.
Charles responds to a 2025 recognition of Pragmatic Marketing as a gold-standard product marketing certification and agrees with it, backing the claim with adoption figures — used by more than 8,000 companies and trusted by over 140 Fortune 500 organizations, with Cisco, Dell, and Bright Horizons named — and reported outcomes of a 50% increase in Net Promoter Scores, 25% improvement in product sales, and 25% reduction in time-to-market.
The post notes that AI-specific content is now woven through the certification.
They close with a personal credential and a chronology: 30 years in healthcare IT across RCM, credentialing, clearinghouse infrastructure, and cybersecurity SaaS, and a PMC-II certification they say has not aged.
The sign-off, "Process first.
Credentials that prove it second," ties the post back to the process-first theme.
For brands planning a campaign
Useful for briefing a hiring or positioning conversation: it shows Charles frames go-to-market as a shared, teachable framework rather than personal instinct, and anchors messaging in named metrics. The credential-first emphasis is a positioning choice to weigh against a customer-outcome-led launch.
This is the plainest statement of what Charles does and where they want to work.
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It answers domain and role questions in one pass, which makes the two more argumentative posts easier to place.
This is a job-search post, not thought leadership.
Charles states the target — Product Marketing Manager and Director of Product Marketing roles in healthcare and B2B SaaS — and narrows the focus to revenue cycle management, clearinghouse, patient access, and business office or front-to-back revenue operations.
Work preference is listed in order: remote, then hybrid, then on-site in the DFW area.
Employers named as background are Noetic, Mavenir, and symplr.
The post closes with a keyword block covering GTM strategy, positioning and messaging, product launches, sales enablement, and competitive and market intelligence, and an explicit request for leads, hiring managers, or recruiters.
For brands planning a campaign
It confirms the domain match for an RCM and healthcare SaaS brief and lists the exact skill set Charles claims — positioning, launches, enablement, competitive intelligence. Because the post is a job search, ask for examples of delivered outcomes; the stronger evidence for how they think sits in the other two posts.
Charles's product-marketing work centers on the money-and-access side of healthcare: revenue cycle management, clearinghouse infrastructure, patient access, and business office operations.
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A job-search post lists targeted roles in product marketing management and director-level marketing for healthcare and B2B SaaS, naming RCM, clearinghouse, patient access, and front-to-back revenue operations as focus areas, alongside core skills in positioning and messaging, product launches, sales enablement, and competitive intelligence.
A separate post describes three decades in healthcare IT spanning RCM, credentialing, clearinghouse infrastructure, and cybersecurity SaaS.
Campaigns aimed at revenue-cycle buyers align closely with the vocabulary and buyer landscape they write about firsthand.
For brands planning a campaign
A strong fit for positioning, launch messaging, and enablement content aimed at revenue-cycle, clearinghouse, or patient-access buyers. Briefs that explain front-to-back revenue operations to provider or billing-office audiences match the detail level Charles already uses.
Healthcare AI accountability and the verification layer
Charles argues that the accountability gap in healthcare AI is a process problem, not a technology problem, and that process problems need process solutions.
Read more
The case rests on figures they cite: a 2026 BMJ Open audit of five AI chatbots found 49.6% of health responses were problematic, with nearly one in five highly problematic or potentially harmful, and ECRI placed AI diagnostic errors at the top of its 2026 patient safety concerns list.
They contrast healthcare with aviation and nursing, which build pre-flight checks and near-miss reporting into practice, and fault most deploying organizations for lacking systematic ways to catch wrong outputs.
Their stated commitment is to build a verification layer that makes deployment trustworthy rather than chase the next model.
Charles builds the argument by pairing published audit and safety-list findings with operational comparisons from aviation and nursing, then identifying a specific gap: no near-miss reporting, no feedback loop, no named accountable owner.
For brands planning a campaign
Useful for a vendor selling AI governance, clinical validation, or monitoring products. The framing—verification layer before go-live, near-miss reporting, named accountability—offers launch messaging and category language that positions the product as process infrastructure rather than another model.
In a post on Pragmatic Marketing certification, Charles argues that process comes first and credentials that prove it come second, and that the framework's staying power comes from serving as a shared professional language.
Read more
The post cites adoption of the Pragmatic Framework by more than 8,000 companies and over 140 Fortune 500 organizations, including Cisco, Dell, and Bright Horizons, and reports that organizations see a 50% increase in Net Promoter Scores, a 25% improvement in product sales, and a 25% reduction in time-to-market after adopting the tools and processes.
On AI, they argue that AI-specific content woven through certification helps marketers turn AI's potential into practical outcomes without a technical background.
They say the framework matters more now because pressure to lead rather than support strategy has never been higher.
The post moves from a market claim about which credentials employers ask for by name to adoption statistics, a strategic-elevation argument, reported outcomes, and the curriculum's evolution around AI.
The adoption counts and the 50% NPS, 25% sales, and 25% time-to-market figures come from Pragmatic Institute material Charles presents. Ask for the underlying measurement details before using them as proof of results.
For brands planning a campaign
A credible voice for content that puts process and measurement ahead of tooling, especially for marketers expected to lead go-to-market strategy rather than execute tasks. Brands selling enablement, frameworks, or marketing-ops products can use the process-first, AI-third framing as a positioning angle.
Beyond publishing, Charles is openly seeking a next role, targeting product marketing manager and director of product marketing positions in healthcare and B2B SaaS.
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The post states a work preference order—remote, then hybrid, then on-site in the Dallas–Fort Worth area—names a background including Noetic, Mavenir, and symplr, and asks hiring managers and recruiters in healthcare SaaS and RCM to comment or make an introduction. The profile is relevant both to readers evaluating them as a publisher or partner and to those considering them as a candidate.
For brands planning a campaign
For a healthcare SaaS company hiring a product marketing leader for AI governance and revenue-cycle go-to-market, this post signals interest in that kind of role and states location preferences. Treat it as a candidacy signal to verify in conversation, not a commercial partnership offer.
The posts mention one credential relationship and three employers listed in a job-search post.
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Charles T. holds the PMC-II certification from Pragmatic Institute and publicly advocates for it; the post discloses no sponsorship or payment. In a separate job-search post, they list Noetic, Mavenir, and symplr as background employers.
Noetic
Employment
Charles lists Noetic among the employers in a background line of a post announcing a search for a product marketing manager or director role in healthcare and B2B SaaS.
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The post does not say when they worked there, in what role, or whether the relationship is current.
Charles cites three decades in healthcare IT spanning revenue cycle management, credentialing, clearinghouse infrastructure, and cybersecurity SaaS, but the post does not tie specific years or titles to symplr.
Charles says the PMC-II certification they earned after 30 years in healthcare IT "hasn't aged," and argues that Pragmatic Institute's certification is the credential product hiring managers request by name.
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They cite the framework's adoption across 8,000 companies and reports of higher Net Promoter Scores, more product sales, and faster time-to-market. The post advocates for the certification; it discloses no sponsorship, affiliate arrangement, or paid partnership.
Charles T. writes about making healthcare technology trustworthy and turning product-marketing practice into a disciplined, market-led process.
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In a post on healthcare AI accountability, they cite a reported audit finding that 49.6% of health responses from five AI chatbots were problematic, then argue that adoption needs verification, near-miss reporting, and clear accountability—not just deployment plans.
The core idea is that AI safety is also a process problem, and someone must own that process.
A post about the roles they are seeking names healthcare and B2B SaaS product marketing, with a focus on revenue cycle management, clearinghouse, patient access, and revenue operations.
Together, these posts connect go-to-market work with the operational realities of healthcare.
For adjacent perspectives, Malcolm Baracho brings first-person revenue-cycle stories, while Charles J.
French focuses on voice security and healthcare cyber risk.
John C. writes about healthcare leadership, governance, and executive learning.
Both address revenue-cycle operations and how errors or manual steps can affect financial performance.
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Baracho’s cited posts make that concrete through a hospital billing-office story and a discussion of preventable write-offs and AI-assisted prioritization.
Baracho favors first-person operational stories and discusses workflow automation; Charles T. connects healthcare operations to product marketing and argues for verification and accountability in AI. Choose Baracho for billing-workflow examples, or them for the process and go-to-market perspective.
Both frame healthcare technology risks as issues that require deliberate safeguards, not just adoption.
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French’s cited posts focus on unwanted and fraudulent calls, voice-channel security, and the potential effects on patient care.
French’s cited posts are closely tied to voice security and a named product; Charles T. focuses on AI verification and product-marketing practice. Choose French for healthcare voice-security coverage, or them for AI accountability and go-to-market ideas.
Both encourage healthcare technology leaders to look beyond implementation and consider the wider responsibilities surrounding technology and organizational decisions.
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John C.’s cited posts discuss governance, risk, strategy, and the need for current cybersecurity knowledge.
John C. writes from an executive-leadership and governance perspective; Charles T. focuses on process accountability for AI and product-marketing practice. Choose John C. for board and CIO leadership topics, or them for AI verification and healthcare go-to-market.
Charles T. has a small, dated LinkedIn following—1,870 followers recorded on 8 October 2026—and three recent posts that show the subjects they address in public.
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The posts cover a product-marketing job search, an argument that healthcare AI needs a verification and accountability layer before deployment, and a case for Pragmatic Marketing certification.
The healthcare AI and certification posts speak most directly to a healthcare SaaS campaign about AI governance and RCM go-to-market; the job-search post shows the roles and markets they are targeting rather than an audience theme.
Published on 19 February 2026 and 23 June 2026, the posts include no engagement, impression, or comment counts, so their relative performance cannot be compared.
Before committing spend on a campaign aimed at RCM and AI-governance decision-makers, ask for recent results on comparable posts: impressions or engagement rates for the AI accountability and RCM-adjacent posts, platform follower demographic data, and any audience survey recording job function or seniority.
Request figures for posts with similar topics and depth to make a like-for-like comparison.
Charles T.'s three LinkedIn posts cover a job search for product marketing roles, an argument about accountability processes for healthcare AI, and a case for Pragmatic Institute certification.
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None describes sponsored placements, paid speaking, consulting engagements, or any other offer a brand could book, and none states terms, deliverables, or rates. Ask them directly whether they accept commercial work and, if so, what formats, deliverables, and rates are available.
Good fit: a healthcare SaaS vendor preparing an AI governance or clinical AI monitoring launch and needing product-marketing strategy, launch messaging, and an expert voice for revenue-cycle and patient-access buyers.
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Charles T. is a strong fit for the expert and positioning work.
The healthcare AI post makes a specific argument — the accountability gap is a process problem, not a technology problem — and supports it with third-party evidence: a 2026 BMJ Open audit of five AI chatbots found 49.6% of health responses problematic and nearly one in five highly problematic or potentially harmful; ECRI's 2026 patient safety concerns list placed AI diagnostic errors first.
The post proposes a verification layer, near-miss reporting, a feedback loop, and a named accountable owner — concrete ingredients for launch messaging.
The other two posts supply buyer vocabulary and background: revenue cycle management, clearinghouse, patient access, front-to-back revenue operations, 30 years in healthcare IT, and a Pragmatic Marketing certification framed as process first, AI third.
Fit rests on subject command, evidence style, and messaging strategy, not audience reach.
Ask for audience composition and post-performance data before making a reach-based decision.
When to choose a specialist
Poor fit: a reach-led sponsorship or paid awareness campaign that depends on Charles T.'s LinkedIn audience to put an AI governance or revenue-cycle offer in front of healthcare SaaS buyers at scale, or assumes bookable sponsored formats such as paid posts, webinars, or speaking slots.
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The posts do not describe sponsorship, consulting, speaking, or other commercial formats; no partnership formats are listed.
Charles T. has 1,870 LinkedIn followers, observed 8 October 2026, and none of the three posts has engagement, impression, or comment figures, so their reach is unclear.
The posts provide no demographic information to show whether followers are revenue-cycle, product marketing, or AI governance decision-makers.
Before considering a reach-led campaign, ask for comparable post-performance data, an audience demographic view, and confirmation of available commercial formats.
This is a poor fit for distribution or sponsored-placement buying based on the information published here, not a claim that Charles T. never accepts paid work.
A worked campaign example
Worked campaign angle: a vendor selling an AI verification layer for provider revenue-cycle and patient-access operations.
Brief: launch a product that helps health systems catch and manage wrong AI outputs before they reach patients or claims.
Audience: product marketing, revenue-cycle operations, clinical informatics, and patient-access leaders at provider organizations.
Core message: accountability is a process problem, not a model problem; buy the verification layer, not another model.
Opening proof: the 2026 BMJ Open audit cited by Charles T. — 49.6% of health responses problematic, nearly one in five highly problematic or potentially harmful — and ECRI's 2026 ranking of AI diagnostic errors as the top patient safety concern.
Campaign arc: (1) start with "Nearly half" to make the gap concrete; (2) contrast healthcare with aviation and nursing, which build near-miss reporting and pre-flight checks into practice; (3) define the verification layer as four process pieces — pre-deployment testing, near-miss triage, feedback into model selection, and a named accountable owner; (4) apply it to revenue-cycle workflows, where wrong AI output can hit claims, coding, prior authorization, or patient billing; (5) close with a 30-day AI verification audit offer and a downloadable checklist.
Reuse Charles T.'s process-first, AI-third language for enablement and webinar framing.
Measure qualified audit requests or brief-completion actions from named target accounts; post reach is not evidenced.
Before spending, ask Charles T. or the hiring vendor for recent post engagement on comparable AI and revenue-cycle topics, an audience demographic view, and confirmation of availability as a paid subject-matter expert or product-marketing contributor.
Treat the Pragmatic adoption and outcome figures in the certification post as reported claims, not independent results.
Choose Malcolm Baracho when the campaign depends on revenue-cycle operations detail rather than AI governance framing.
Read more
The two posts give concrete RCM buyer language: write-offs, coding errors, claim queues, eligibility checks, clean claim rate, denials, cash flow, and agentic AI that spots errors before claims go out, flags at-risk accounts, and guides teams on what to work first. For a launch aimed at hospital CFOs and billing office leaders, Malcolm Baracho can supply the operational before-and-after and revenue-at-risk math, such as recovering 2% on 8M in monthly billing, that a product-marketing brief needs for a revenue-recovery message.
The tradeoff is narrower product-marketing and AI-governance evidence.
Charles T.'s posts say nothing about launch positioning, messaging architecture, competitive intelligence, or the accountability and verification-layer argument.
The AI examples are vendor and automation success stories plus operational prompts, not a governance framework for catching wrong model output before it reaches patients or claims.
If the need is mainly AI governance and product-marketing leadership, Charles T. is a closer fit; if it is mainly RCM workflow pain and AI-assisted revenue recovery, Malcolm Baracho is the stronger alternative.
Choose John C. when the buyer needs board-level governance, CIO credibility, or cybersecurity risk framing for a healthcare AI launch.
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The posts discuss board of directors questions such as strategy clarity, investment priorities, risk management, financial viability, and leadership pipeline, and argue that current cybersecurity knowledge is table stakes for a healthcare CIO.
Listed risk areas include medical device and IoMT risk, identity and access management, Zero Trust, ransomware resilience, third-party and supply-chain risk, incident response, AI-enabled cyber threats, clinical downtime, and patient safety.
For a campaign that must satisfy hospital executives and security committees, John C. can anchor the AI governance message in enterprise risk and board oversight.
The tradeoff is little evidence of product marketing or revenue-cycle go-to-market.
Charles T.'s posts do not discuss positioning, launch messaging, competitive intelligence, RCM, clearinghouse, patient access, or AI verification layers.
John C. is a governance and security-credibility alternative, not a product-marketing leader substitute.
If the need is campaign positioning and launch messaging, Charles T.'s posts offer thinner evidence; if it is executive and board validation of AI risk, John C. is more relevant.
French when the campaign centers on healthcare cybersecurity, voice-channel risk, or patient-experience and reimbursement consequences.
The posts detail nuisance and deceptive calls flooding hospital lines, vishing and AI-powered deepfake voice fraud, credential theft, payment diversion, PHI compromise, and voice firewalls.
They connect unwanted calls to HCAHPS measures such as quietness and responsiveness, CMS Value-Based Purchasing, and patient safety.
For a launch about AI-enabled voice threats or security controls reaching nursing stations and patients, Charles J.
French supplies specific threat mechanics and a hospital-reimbursement angle.
The tradeoff is narrow scope and no product-marketing or RCM evidence.
Charles J.
French's posts focus on voice security and a specific vendor booth and event presence; they do not address AI governance as a process problem, model verification, near-miss reporting, product launches, or revenue-cycle go-to-market.
If the need is AI governance and RCM positioning, Charles J.
French is a specialist alternative for the security-threat portion, not a replacement for the product marketing and verification-layer work in Charles T.'s posts.
Charles T.'s own LinkedIn posts include three examples.
One argues that healthcare AI failures are a process and accountability problem, citing a 2026 BMJ Open audit of five AI chatbots and ECRI's 2026 patient safety concerns list; it refers to a series whose other installments are not included here.
A second makes the case for Pragmatic Marketing certification and process-first product marketing.
The third is a job-search post naming target roles in healthcare and B2B SaaS and listing Noetic, Mavenir, and symplr as background employers.
A LinkedIn profile observed 8 October 2026 shows 1,870 LinkedIn followers.
The posts do not say when they were published or provide engagement or impression figures or audience-demographic data, so how far a post travels and what it reaches is not established.
No articles, talks, transcripts, or other channels appear here, and the posts make no mention of sponsorship, consulting, or speaking format.
Claims about framework adoption and marketing outcomes in the certification post are reported as Pragmatic Institute's figures, not independently verified.
Statements about employers and the PMC-II certification come from Charles T.'s own posts.