< + > Leveraging Data Analytics to Improve Charge Capture Accuracy and Financial Performance

It is well understood at this point that data plays a huge role in every single decision we make in healthcare. What is still in talks, though, is the best way to utilize and leverage that data. We reached out to our beautiful Healthcare IT Today Community to ask — how are organizations leveraging data analytics to improve charge capture accuracy and financial performance? Below is what they had to share.

Sunil Konda, Chief Product Officer at SYNERGEN Health
For some, charge capture is a top-five challenge and one of the highest-leakage areas in RCM. With analytics, we’re beginning to see a way to close those gaps. The combination of CDI analytics with retrospective charge auditing creates a feedback loop that can capture and flag missed charges before they result in lost revenue. That data is then used to prevent the same error from occurring further upstream in the workflow. This switches RCM from an offensive, reactive charge recovery to a defensive, proactive charge integrity, driving a direct, quantifiable impact on net revenue.

Yousuf J. Ahmad, President and CEO at AssureCare
Organizations are increasingly using analytics not just for retrospective reporting but as a driver of real-time performance improvement across the revenue cycle.

With detailed visibility into denial codes, reasons, and payer-specific trends, teams can identify systemic issues and take corrective action upstream. This might include improving clinical documentation, refining coding practices, or adjusting workflows to prevent recurring issues before they impact claims.

Analytics also enhances collections performance by enabling more targeted prioritization of follow-up and recovery efforts. The result is stronger charge capture accuracy, reduced revenue leakage, and more consistent financial outcomes. Instead of reacting to missed revenue after the fact, organizations can proactively tighten processes and improve overall performance.

Monte Sandler, Chief Operating Officer at WebPT
Data tells you where the system is breaking. When denials, rejections, and unpaid accounts receivable are analyzed, patterns become visible. From there, the root cause can be addressed and resolved in bulk instead of working on claims reactively one at a time. That is how accuracy improves, and the cost to collect is reduced. The organizations that win are the ones that listen to the data and act on it.

Elevsis Delgadillo, SVP, Customer Success at KeenStack
Organizations are using analytics to improve financial performance by tracking core revenue cycle metrics like collections and denials and identifying where breakdowns occur. By tying analytics to specific workflows, they can measure what changes after new processes or technologies are introduced and better understand what is driving performance.

Firoze Lafeer, SVP of Data Engineering at Revecore
Data analytics is helping organizations move from periodic audits to continuous monitoring by identifying where charges are systematically missed at the service line, provider, or facility level before they become lost revenue. By analyzing patterns across large claim populations, revenue cycle teams can pinpoint the root causes of charge capture failures and fix them upstream rather than chasing discrepancies after the fact. For example, if analytics consistently surfaces a payer systematically underpaying a specific DRG or denial pattern tied to incomplete documentation, that intelligence can drive upstream coding and charge entry corrections before the issue scales across thousands of claims.

Deb Jones, Senior Director, Insights Strategy at Tendo
Organizations are increasingly moving beyond retrospective reporting toward real-time and predictive analytics. On the charge capture side, this means identifying gaps as care is being delivered—not weeks later. By connecting clinical documentation, orders, and billing data, analytics can highlight missing or inconsistent charges, ensure alignment with coding requirements, and surface opportunities for more complete capture.

More broadly, advanced analytics are helping organizations understand the root causes of revenue leakage—whether it’s documentation gaps, workflow breakdowns, or payer-specific trends. Instead of looking at isolated metrics, leading organizations are analyzing performance across the entire revenue lifecycle. The shift is from “What happened?” to “What’s about to happen—and how do we intervene?” That’s where analytics starts to drive meaningful financial improvement.

Jake McCarley, CEO at Alluvium Health
Charge capture begins the moment a patient tries to access care — if referrals leak out of network or scheduling breaks down, there’s no charge to capture.

Advanced analytics platforms consolidate fragmented access data across EMRs, call centers, and digital channels to reveal exactly where revenue is being lost: which referral sources consistently leak, which specialties have untapped capacity, and where in the patient journey demographic collection, insurance verification, and prior authorization break down. Surfacing these gaps earlier — and optimizing how they’re resolved — directly improves charge capture accuracy.

AI-powered insights allow operators to identify these issues quickly and take targeted action, transforming access from reactive troubleshooting into strategic, data-driven performance management. And because the underlying model is purpose-built for this use case, the system compounds in value over time — getting smarter with every interaction.

Kevin Coloton, CEO at HURC
What looks like a payer–provider imbalance is increasingly a breakdown in revenue cycle communication. Hospitals can excel at registration and collections, but when the middle cycle fails, the full value is not achieved. This operational layer not only balances utilization review and denials management but also clinical documentation improvement (CDI) and medical coding.

If there are any gaps in documentation, they lead to denials, extended length-of-stay, and unclear payer communication, which create delays, write-offs, and appeals that never should have occurred. Hospitals are spending nearly $18 billion annually reworking claims and a staggering $43 billion trying to collect payments insurers owe for care already delivered, according to the AHA’s 2026 Cost of Care Report. That is why the middle revenue cycle is where CFOs feel pain so acutely.

Partial fixes such as adding more software, hiring more staff, or outsourcing have not solved the problem. What’s emerging instead is a more effective model: integrated, tech-enabled services that combine technology to handle all the operational components of the middle revenue cycle with experienced operators and embed directly into hospital workflows. This approach enables real-time alignment between clinical intent and payer requirements, driving fewer denials, shorter stays, faster post-acute transitions, and meaningful net revenue improvement, without reducing staff.

Ryan Hungate, DDS, MS, Chief Clinical & Strategy Officer at Henry Schein One
Health IT and AI are finally connecting what has historically been a fragmented process, from patient intake all the way through reimbursement. The biggest impact isn’t just faster claims processing; it’s eliminating the manual handoffs and rework that slow everything down.

When you automate eligibility checks, documentation, coding support, and claim submission in a coordinated way, you reduce errors upstream and improve financial performance downstream. But just as important, it allows staff to step away from the screen and focus on the patient.

The organizations seeing the most success aren’t just adding AI; they’re redesigning workflows so that technology handles the administrative burden and people can focus on care, communication, and decision-making where it matters most.

Thomas Shea, Chief Revenue Officer, AI and Patient Affordability Solutions at Doceree
Making sure every service gets billed correctly sounds straightforward, but a surprising amount slips through — a procedure performed but never coded, a visit billed at a lower level than the work supported, a supply used but not logged. Teams would catch these gaps weeks later in an audit, if at all. That’s changing.

The best organizations now run analytics inside the clinical workflow itself, flagging those gaps in real time — while the provider is still in the note, not after the claim has gone out. In my work embedding analytics into HCP workflows, I’ve seen denial rates drop and clean-claim rates climb noticeably when these checks run at the point of documentation, not in a back-office queue. The biggest wins come from the quiet checks running inside the EHR before the note is even signed.

Scott Schrader, President, Provider Healthcare Solutions at Firstsource
Hospitals lose an estimated 1–3% of net revenue to charge capture problems alone, amounting to millions of dollars annually in legitimately earned revenue that was never billed. Leading organizations are closing this gap by deploying NLP-driven mining of unstructured clinical notes to surface missed billable concepts, computer-assisted coding that can reach 90-95%+ accuracy, and concurrent CDI programs that query physicians on documentation gaps before discharge — identifying significant DRG corrections that compound in impact across a facility each month.

The most sophisticated are building unified analytics across the full revenue cycle that trace a denial back to its origin in registration or documentation rather than treating it as an isolated back-end event, because catching a charge capture problem upstream is exponentially cheaper than appealing a denial downstream.

So many great points to consider here! Huge thank you to everyone who took the time out of their day to submit a quote to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.

How do you think organizations are leveraging data analytics to improve charge capture accuracy and financial performance? Let us know over on social media, we’d love to hear from all of you!

< + > The Semantic Gap in Healthcare: Why Interoperability Alone Won’t Fix What’s Broken

The following is a guest article by Dr. Kaushal Kulkarni, M.D., Physician and Chief Medical Officer at Predoc

In the computer science world, there is a commonly discussed problem known as the semantic gap. It’s the idea that the way computers process information is radically different from the way a human understands information. 

Take an image, for example. To a computer, a photo of a dog is a series of pixels and patterns. A person, on the other hand, not only sees the image, they project memories, emotions, senses, cultural questions and more onto an instantly recognizable image. See the difference?

In healthcare, the semantic gap isn’t just a philosophical mismatch between data and meaning. It’s operational. It shows up every single day in the gap between having data and being able to use it.

On paper, the industry has made enormous progress in creating and moving patient data, particularly in recent years with the 21st Century Cures Act and the more recent CMS Health Tech Ecosystem initiatives. And yet, when a patient is sitting in front of a clinician today, the data is just not there in a usable form. So humans step in. Then they read, interpret, and manually reconstruct the patient’s story so care can move forward. When the full story is not there, they make phone calls, request faxes, chase records, and open PDFs.

That’s the semantic gap in healthcare.

It’s the difference between accessible data and usable information. And today, it is being filled not by technology, but by people.

The Semantic Gap

APIs, the rise of FHIR as a standard for exchange, national networks, and regulatory pressure have all pushed the industry toward greater interoperability. And if data can move, the problem is solved, right?

Except for the fact that healthcare data is still not in any one standardized format. Behind the scenes is an unfathomable amount of information (now measured not in terabytes or even exabytes, but zettabytes) living in faxed documents, scanned PDFs, unstructured notes, and disconnected systems and portals. Even in an increasingly digital ecosystem, critical pieces of the patient story aren’t in a format that is easily searchable and sometimes not really accessible with the plug and play tools available.

If, for example, a patient’s record is in the form of hundreds of physical pages, clinicians are left to track it down, have it faxed over, and sort through page after page to piece together what the data actually matters. A single data point buried deep in the stack could change everything. But getting to that point can take hours, while other patients wait and care is delayed.

What Gets Stuck

When this semantic gap stays open, the effects ripple across the entire healthcare system. 

Clinician burnout, operational inefficiency, delays in care, repeat testing, and poor patient experience all trace back to the same root cause. We’re spending hours tracking down records, reviewing fragmented information, and piecing together an incomplete picture, all before a doctor can even get started on delivering care. Entire teams are built around retrieval and intake just to make the system function. What should take seconds can take days, and when information is missing, the system compensates with duplication and guesswork – in the end, it is the patients who suffer.

The semantic gap explains why so many AI initiatives struggle to move beyond pilots. These systems depend on complete, structured, and trustworthy data. Without that foundation, even the most advanced models fail in real workflows.

There are clear examples of what happens when this is addressed. Organizations that have improved how data is retrieved and prepared have reduced intake times from days to hours and increased patient capacity without adding clinical staff.

Closing the Gap

The healthcare industry is not short on data. It’s not even short on ways to move data.

What it needs is a consistent way to ensure that when data arrives, it is complete, structured, and ready to be acted upon. Until that changes, every gain in interoperability will continue to fall short of its promise.

There will always be administrative work in healthcare. But the current model, where people are required to retrieve, interpret, and reconstruct the patient story before care can begin, is not sustainable. It does not scale with growing data volumes, rising patient demand, or the expectations being placed on the system.

Right now, progress depends on human effort filling in the gaps left by technology. That slows care, limits access, and places a ceiling on what the system can achieve.

The trajectory is clear. More data, more complexity, and more pressure to do more with less. Without a fundamental shift, the gap only widens. 

We can’t build the future of healthcare on data that isn’t usable.

Closing the semantic gap is the prerequisite for everything that comes next.

About Dr. Kaushal Kulkarni

Dr. Kaushal Kulkarni is Co-Founder and Chief Medical Officer at Predoc, where he focuses on making healthcare data more complete, usable, and accessible for clinicians. A board-certified ophthalmologist with subspecialty training in neuro-ophthalmology, he has practiced across academic and community settings and brings firsthand clinical perspective to interoperability and health data workflow challenges. He earned his M.D. from Rutgers Robert Wood Johnson Medical School, completed residency at Georgetown University, and finished fellowship training at the University of Miami’s Bascom Palmer Eye Institute. He is a vocal advocate for usable patient data and clinician-centered innovation.

< + > Verkada Accelerates Physical AI with NVIDIA | XRHealth Acquires Swing Therapeutics

Check out today’s featured companies who have recently completed an M&A deal, and be sure to check out the full list of past healthcare IT M&A.

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Verkada Accelerates Physical AI with NVIDIA

Verkada is Scaling Its Physical AI Platform Across More than 2.4 Million Devices Globally with a New Technical Collaboration and Investment from NVIDIA

Verkada, a leader in AI-powered physical security and operations, today announced a collaboration with NVIDIA to accelerate the development and deployment of physical AI across the built environment. NVIDIA also joins as a new investor in Verkada, following a strategic investment from Alphabet’s CapitalG at the end of last year.

Verkada is applying AI to transform how schools, hospitals, retailers, manufacturers, and other organizations turn real-world operational data into actionable intelligence — helping keep people and places safe.

“Verkada has been building and deploying Physical AI before the term existed. With our footprint of more than 2.4 million devices across 170 countries and 30,000 organizations, we’ve proven that the built environment is one of the largest beneficiaries of AI,” said Filip Kaliszan, Co-Founder and CEO at Verkada. “Working with NVIDIA supercharges what we’ve spent nearly a decade building: AI that keeps students safe in schools, protects workers on factory floors, helps retailers prevent theft, and enables organizations to operate more efficiently.”

Verkada is strengthening the models and data flywheel underpinning its intelligent video analytics through its collaboration with NVIDIA, advancing AI-powered video search, multimodal embeddings and vector retrieval for next-generation semantic search, and synthetic data generation to augment training datasets and improve accuracy.

By leveraging NVIDIA Cosmos world foundation models and NVIDIA Physical AI Data Factory, Verkada has accelerated model training and inference across its rapidly expanding global footprint on NVIDIA accelerated computing. Since the collaboration began, Verkada has improved the mean average precision (mAP) of its AI-powered search by 68% for spatial-temporal understanding, delivering faster, more accurate, and more robust search capabilities…

Full release here, originally announced July 1st, 2026.

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XRHealth Acquires Swing Therapeutics, Expanding Its Platform to Mobile Digital Therapeutics

Acquisition Extends Digital Health Care from Augmented and Virtual Reality Glasses to Smartphone Apps, Smartwatches, and IoT Devices, Combining AI and Licensed Clinicians to Deliver Continuous, Reimbursable Care for Chronic Conditions

XRHealth, the leading platform for therapeutic technologies, today announced the acquisition of Swing Therapeutics, a leading developer of digital therapies and the virtual care platform Swing Care for chronic pain conditions. The acquisition propels XRHealth as the largest extended reality digital health platform for chronic conditions, bringing digital healthcare seamlessly across multiple digital devices, including augmented and virtual reality glasses, smartphone apps, smartwatches, and IoT wearables, combining AI and licensed physicians to treat patients across multiple technologies.

The acquisition adds Swing Therapeutics’ FDA De Novo–authorized Stanza program to XRHealth’s existing behavioral health and chronic pain capabilities, deepening the platform’s reach into two of the most undertreated conditions in the country: fibromyalgia and chronic pain. 

“When we founded Swing, we believed that the future of chronic pain care had to look fundamentally different: more accessible, more personalized, and grounded in evidence,” says Mike Rosenbluth, Founder and CEO at Swing Therapeutics. “Joining XRHealth means we can deliver on that vision at an expanded scale, with clinicians and technology working together through a single platform where patients can access every dimension of their care.”

Swing Therapeutics’ Stanza is the first digital behavioral therapy indicated for treatment of the symptoms of fibromyalgia, a widespread chronic pain condition impacting 10 million people in the United States. In a Phase 3 randomized controlled trial published in The Lancet, Stanza demonstrated significant improvements across symptoms, including fibromyalgia severity, pain intensity, fatigue, sleep, and depression.

The acquisition also brings Swing Care, Swing Therapeutics’ virtual specialty care platform for fibromyalgia and chronic pain, into the XRHealth ecosystem. Developed in collaboration with leading fibromyalgia clinicians, including Medical Director Dr. Andrea Chadwick, a globally recognized expert in the field, Swing Care delivers comprehensive, multidisciplinary care that combines the Stanza digital therapeutic with medication management, mental health support, and personalized coaching.

“The future of therapeutic care is technology. Not as a supplement to medicine, but as the medicine itself—delivered through AI, monitored by licensed clinicians, reimbursed by insurance, and continuously improved by outcomes data,” says Eran Orr, CEO at XRHealth…

Full release here, originally announced July 15th, 2026.

< + > Leading Virtual Nursing Programs

Amid workforce shortages and increasing inpatient volumes, many hospitals are turning to virtual nursing programs that augment in-person care with remote support. Research has shown most virtual nurses are given a dedicated role in the care journey, typically admission/discharge, patient education, or medication reconciliation.

Programs have been shown to succeed when bedside nurses play a role in program development prior to implementation and build relationships with virtual nursing teams. It’s also helpful to avoid duplicative workflows and maintain safe patient-to-nurse staffing ratios even when virtual nursing support is available.

These health systems are using virtual nursing to augment in-person care without creating additional administrative burdens or interrupting clinical workflows.

AdventHealth began with a pilot program in three Florida hospitals, with offsite registered nurses communicating to patients in the ED and inpatient units. One hospital reported a year-over-year drop in RN turnover from 46% to 16%. The health system is also piloting the use of virtual nurses to assist with inpatient admissions at one Colorado hospital.

Akron Children’s uses audio and video equipment to help employed RNs communicate with patients and families, as well as in-room sensors to monitor movement and alert nurses and necessary. Now live across inpatient units, the program builds on a pilot that reduced the time it took for ED orders to arrive in the unit.

Atrium Health assigns virtual nurses to about 10 patients at a time. Nurses monitor safety, assist with charting, and help with admission and discharge. Additional benefits include virtual nursing’s potential to provide emotional support to patients otherwise alone in the hospital room and allow older staff to mentor new hires without needing to be at the bedside.

BayCare began using virtual nurses to monitor ICU patients more than a decade ago. The program recently added virtual nursing at discharge, as the health system found most nurses spent 3 hours per shift on admission, discharge, and patient education tasks. Many nurses divide their time between virtual and bedside shifts – and say it helps them better focus on their work.

Boston Children’s found “virtual nurses provided a critical experience lens,” looking at charts, analyzing data, and otherwise offering procedural support while bedside nurses engage with patients and their families. The program ramped up in 2022 as the hospital hired nurses to meet demand for a new 150-bed facility.

Central Maine Healthcare has similarly focused on using virtual nursing to support staff with less bedside experience. By gathering medical history and other key information as patients transition from the ED to the floor, virtual nurses give bedside nurses the space to build relationships with admitted patients.

Covenant Medical Center served as the virtual nursing pilot for Providence. The model delivered quick results, reducing RN’s first-year turnover rates by 73%. Two keys to success: Virtual nurses take part in daily meetings alongside charge nurses, case managers, and physicians, and support teams remain in place to provide bedside support so floor nurses can focus on assessment.

Emory Healthcare complements employed virtual RNs to handle “hands-off care activities” with LIDAR technology to monitor patients for unexpected movements, proactive alerts of potential fall risks for bedside care teams, and automated voice messages to tell patients to remain in their beds until staff arrive. Eight inpatient units are part of the LIDAR pilot.

Houston Methodist is also building “self-aware” hospital rooms to monitor patient movement (and staff hand hygiene compliance). The hospital’s main focus, though, is virtual nursing for stroke care and psychiatric care, with the former leveraging neurologists from designated stroke centers and the latter using contracted providers.

Lee Health started with virtual observation supported by telehealth carts in acute care and ED settings. The hospital also invested in its staff – virtual observers transitioned from part-time to full-time status – which made it possible to apply virtual nursing support to all patients. Following implementation, the hospital saw a 20% improvement in HCAHPS scores.

Sentara Health virtual nurses provide support to more than 1,700 beds across 73 units. The emphasis is primarily on completing administrative tasks, though patient education – both at discharge and throughout the inpatient stay – has been a bright spot, as virtual nurses tend to have more time to answer patients’ questions.

Upstate University Hospital takes a unique approach, as patients use personal devices to connect to virtual nursing services over a secure network. The model may be different, but results to date have been the same: Nearly 2 hours saved per virtual admission and 44 minutes saved per virtual discharge, along with triage support and remote monitoring of vitals.

Vanderbilt Health started in cardiac care before deploying virtual nursing to inpatient units. Since the pilot, the health system made virtual nursing part of its new virtual care department, which leadership hopes “will enable virtual nursing to develop as a unit and work to meet the growing needs of the patients it cares for.”

Yale New Haven Health is another system that started with telestroke and TeleICU more than a decade ago and saw virtual nursing as a way to provide “vital support” for medical and surgical nursing teams. For example, bedside nurses have more time to address social drivers of health and connect patients to community resources they may need.

< + > Healthcare’s Paper-to-EDI Bridge Was Built on OCR, It’s Time to Replace It With Vision-Language Models

The following is a guest article by Shaurya Sahay, Co-Founder of Hewto.ai, an AI company building AI products for the US healthcare industry

Healthcare standardized on EDI for claim submission decades ago. The X12N 837 is HIPAA-mandated, and the 2025 CAQH Index estimates that nearly 98% of medical claims are submitted electronically — among the highest adoption rates of any HIPAA administrative standard. On paper, the paper-claim problem appears solved.

It isn’t.

At the scale U.S. healthcare operates — billions of claims annually — even the remaining fraction translates into millions of paper claims every year. These claims are concentrated among smaller providers, workers’ compensation and auto medical claims, secondary submissions, and workflows involving attachments.

The fragility of the system became evident when Change Healthcare went offline in February 2024, forcing providers and payers to revert almost overnight to paper, fax, and manual workarounds. The incident exposed how fragile the bridge from paper to EDI really is.

For the last two decades, that bridge has largely been built on OCR: mailroom intake, scanning, OCR extraction, manual review and correction, and finally generation of the 837. The process works — until the document is skewed, photocopied into a low-contrast black-and-white scan, or handwritten, which describes much of what actually arrives in the mailroom.

“0” gets interpreted as “O,” “i” as “l,” and a line gets captured as “1”. Every failed extraction creates a manual touchpoint, and those touchpoints are where the operational cost still resides.

Most health plans rely on outsourcing partners to manage paper claims. These partners optimize extraction for cost efficiency, often with no accuracy guarantees on non-critical fields and limited guarantees even on line-item data. As a result, human review remains deeply embedded in the workflow.

Vision-language models fundamentally change this equation.

Imagine a product powered by a custom-trained vision model that reads paper claims the way an experienced claims processor would — handling skewed scans, poor image quality, low contrast, and mixed handwriting and print, while generating a clean, structured 837 output. The workflow shifts from labor-intensive correction of OCR output to exception-only review for the small percentage of claims that genuinely require human intervention.

A modern paper-to-EDI system typically follows a five-step extraction process:

1. Ingest claim files from the mailroom or scanning systems
2. Detect the claim type — HCFA, UB, or Dental
3. Identify alignment issues and correct skew using feature detection
4. Extract each field using multiple models
5. Flag fields for manual review if validation rules fail or extracted values do not match across models

The reason custom-trained computer vision models outperform off-the-shelf OCR systems is simple: they are trained specifically for healthcare document edge cases.

For example:

• The model understands that it should capture only the handwritten or printed claim data, not the red instructional labels on the form
• It can infer low-ink or faded characters the way a human operator would
• In overlapping-text scenarios, it can determine whether the foreground or background text is the relevant value

In practice, computer vision models are especially effective for claims that are:

• Skewed or poorly aligned
• Low-contrast or low-ink scans
• Handwritten
• Photocopied multiple times
• Mixed-format documents containing both print and handwriting

The operational impact is significant. Custom-trained vision models can reduce paper claim processing times from minutes to under ten seconds per claim, while maintaining high accuracy even on poor-quality and handwritten documents.

However, the quality of the model depends heavily on the quality of the training data. Vision model training requires large, clean datasets consisting of field-level image snippets paired with accurately captured values. The more representative the training set — especially across edge cases — the better the model performs in production.

Healthcare spent the last two decades digitizing claims submission through EDI. The next phase is modernizing the paper-to-EDI bridge itself.

OCR built the first generation of that bridge. Vision-language models will build the next one.

Modern platforms such as Hewto.ai are using custom-trained vision-language models to automate paper-to-EDI conversion even on poor-quality handwritten scans.

< + > This Week’s Health IT Jobs – July 15, 2026

It can be very overwhelming scrolling through job board after job board in search of a position that fits your wants and needs. Let us take that stress away by finding a mix of great health IT jobs for you! We hope you enjoy this look at some of the health IT jobs we saw healthcare organizations trying to fill this week.

Here’s a quick look at some of the health IT jobs we found:

• Clinic Patient Representative – Multi Specialty – CHRISTUS Health
• Community Health Worker – UPMC
• Director, Service Delivery – GroundGame.Health
• Patient Services Specialist 2 – Baylor Scott & White Health
• Data Analyst II – Medical Informaticist – NYU Langone Health
• Medical Director – Care Alliance Health Center
• Billing & Coding Specialist – Lake Country Pediatrics, S.C.
• Senior EPIC Cupid Analyst – Anumana
• Appeals Specialist – MVP Health Care
• ROI Medical Records Specialist – Local Travel – MRO
• Analyst, Data & Analytics (Remote) – Molina Healthcare
• Chief Information Officer – Cytek Biosciences
• Health Information Analyst/HIM Operations – Hartford HealthCare
• Behavioral Health Associate 1 – Acadia Healthcare
• Community Health Worker – Arkansas Children’s
• Chief of Staff to VP, Data Center Engineering & Infrastructure – Oracle
• Sr. Director, GRC, IT Controls & Cyber Culture, Orthopedics – Johnson & Johnson MedTech
• Patient Services Representative Associate – Corewell Health
• Chief Technology Officer – Blooming Health
• Healthcare Expert – Clinical Evaluator – Mercor

If none of these jobs fit your needs, be sure to check out our previous health IT job listings.

Do you have an open health IT position that you are looking to fill? Contact us here with a link to the open position and we’ll be happy to feature it in next week’s article at no charge!

*Note: These jobs are listed by Healthcare IT Today as a free service to the community. Healthcare IT Today does not endorse or vouch for the company or the job posting. We encourage anyone applying to these jobs to do their own due diligence.

< + > The Hidden Cost of Patient No-Shows and How Practices Can Reduce Them

The following is a guest article by Jonah Langer, Founder of AppointmentReminders.com

Nearly every healthcare practice deals with missed appointments. Depending on the specialty and patient population, they can have a significant impact on revenue and scheduling efficiency. A “no-show” is an appointment that is missed without any notification to the provider. No-shows increase provider idle time, extend wait times for other patients, and create additional administrative work as staff contact patients and reschedule appointments. A large study published in BMC Health Services Research found an average no-show rate of 19% with each missed appointment costing about $196.

To understand why the rates are so high, we first need to look at the reasons why appointments are missed. Patients miss appointments for many reasons. Studies have found that work conflicts, transportation challenges, childcare responsibilities, financial concerns, illness, long waits between scheduling and the appointment, and simply forgetting all contribute to no-show rates. While these are understandable reasons for missing an appointment, practices benefit when patients cancel or reschedule in advance so the time can be offered to someone else.

Reducing no-shows starts with understanding that patients’ plans can change. Work schedules, family obligations, transportation issues, or unexpected illnesses can all make it difficult to keep an appointment. Rather than simply reminding patients that an appointment is coming up, practices should make it easy for patients to communicate when something changes. The goal is to encourage patients to engage with the practice before the appointment. If a patient knows they can’t make it, they should have a simple way to cancel or request a new time. Even a day’s notice gives staff the opportunity to contact another patient, fill the opening, and keep the schedule moving.

Technology can make this process much easier. Text message reminders, emails, automated calls, and online scheduling tools all give patients convenient ways to confirm, cancel, or reschedule appointments without waiting on hold or calling during office hours. The easier it is to respond, the more likely patients are to do so instead of simply not showing up.

An effective appointment reminder strategy is very important. Many electronic health record (EHR) systems include basic appointment reminders, but practices should evaluate whether those reminders are actually effective. An effective reminder strategy isn’t just about sending a message, it also includes when reminders are sent, what they say, how patients can respond, and what happens if the reminders are not delivered. For most practices, sending two or three reminders works well. A reminder several days before the appointment gives patients enough time to reschedule if needed, while another reminder 24 hours before helps reduce simple forgetfulness. Same-day reminders can be useful for certain specialties but should be used sparingly to avoid message fatigue.

Keep reminder messages short and clear. Include the appointment date and time, the practice name, and an easy way to confirm, cancel, or request a different appointment. For healthcare providers, it is also important to avoid including unnecessary protected health information and comply with applicable HIPAA laws.

Using multiple communication channels can also improve response rates. If a text message can’t be delivered, automatically following up with a phone call or email helps ensure the patient still receives the reminder. Likewise, patients who don’t respond to an initial reminder may benefit from a second reminder using a different method. The key is to send the right reminder, at the right time, through the channel most likely to reach the patient. Some practices also charge a no-show fee to encourage patients to cancel in advance, though clear communication and easy rescheduling options are often more effective at improving attendance.

Reducing no-shows is an ongoing process. Practices should monitor confirmation, cancellation, rescheduling, and no-show rates to understand what’s working. Small adjustments like changing reminder timing, using a different communication channel, or refining message wording can improve patient engagement over time. The goal isn’t just to send reminders, but to make it easier for patients to communicate with your practice and to be able to change or cancel appointments when necessary. Most practices will always have some no-shows, but many can be prevented. The combination of clear communication, timely reminders, and easy ways for patients to confirm, cancel, or reschedule can significantly improve attendance. It is also important to monitor results and continue refining your reminder strategy over time.

About Jonah Langer

Jonah Langer is the founder of AppointmentReminders.com, a HIPAA Compliant U.S. based reminder service that helps organizations automate appointment reminders through text messages, phone calls, and email. For more than 20 years, he has worked with medical practices of all sizes to improve patient communication, reduce no-show rates, and streamline appointment scheduling. His experience spans healthcare technology, software development, and patient engagement, with a focus on practical solutions that improve both operational efficiency and the patient experience. Jonah lives in Colorado with his family and their samoyed (Dolce).