Month: October 2024

Do Imaging Costs Scare Patients?

Posted on by Brian Casey

A new study in JACR reveals an uncomfortable reality about medical imaging price transparency: Patients who knew how much they would have to pay for their imaging exam were less likely to complete their study. 

Price transparency has been touted as a patient-friendly tool that can get patients engaged with their care while also helping them avoid nasty billing surprises for out-of-pocket costs. 

  • Price transparency is considered to be so important that CMS in 2021 implemented rules requiring hospitals to disclose their standard charges online, as well as post a user-friendly list of their services that includes prices. 

But given that the rules were implemented relatively recently, not much is known about how they might affect patient behavior, such as compliance with recommended follow-up imaging exams.

  • Indeed, a recent study by some of the same authors found that patients are largely unaware of how much their imaging exams will cost them. 

So researchers analyzed data from two previously published studies of patients who either completed or were scheduled for outpatient imaging exams in Southern California. 

  • Patients were asked if they had been told how much their exam would cost them out-of-pocket when they scheduled it. 

Of the 532 patients who were surveyed, researchers found …

  • Only 15% said they knew about their out-of-pocket costs before their imaging exam. 
  • Fewer patients who completed their exams knew their costs compared to those who canceled (12% vs. 22%).
  • Patients who knew their costs were 67% less likely to complete their appointment than those who didn’t (OR=0.33).

So what’s the solution? The researchers suggested that healthcare providers may need to take a more proactive approach to disclosing price information to patients.

  • One possibility would be to integrate pricing discussions into patient-provider communications when ordering imaging exams, rather than relying on patients to seek pricing information on their own. 

The Takeaway

The findings show that medical imaging price transparency is more complicated than just posting a list of prices online and expecting patients to do the rest of the work. Imaging providers may need to get more involved in pricing discussions – the question is whether many of them are ready for it.

Optimizing Front Office Operations through Integrated Apps and Cloud-Based RIS/PACS

Posted on by Partner Content

Paradox of High Patient Volumes

At first glance, it may appear having more patients should naturally lead to higher revenue. When you consider extra labor costs and the fact that reimbursements are decreasing, increased volume can turn into diminishing returns.

  • Basically, the cost of adding more staff can end up being higher than the value of additional patient volumes.

Optimal management of growing patient volumes requires a new way of working with automation and cloud-based apps that replace the heavy burden of manual processes.

  • By using technology to eliminate processes, medical facilities manage patient loads better without the need for more labor costs. 

This proactive approach not only improves efficiencies but also lets front office staff focus on patient needs instead of getting bogged down with administrative tasks. 

  • Ultimately, shifting towards automation and consolidation of tasks is key to maintaining clinic profitability and keeping high standards of care, especially with increasing medical demands.

How RamSoft Can Help Simplify Front Office Operations 

Achieving workflow excellence starts with a single sign-on into a unified RIS/PACS and providing access to complementary medical imaging apps via a single worklist in the cloud. 

  • By leveraging cloud applications with scalability across facilities, organizations can “build as they grow,” while maintaining control and flexibility.

RamSoft PowerServer and OmegaAI RIS/PACS platforms reduce administrative burdens and costs associated with manual processes. Here’s how…

  • Blume – Patient Portal: Patient access to diagnostic images and reports, imaging sharing with referring clinicians and family, self-scheduling, intake forms, and appointment notifications. These self-service features decrease the number of phone calls, the time needed for patient registration, and the manual process of intake form completion and filing. 
  • pVerify: Batch verification and real-time eligibility (authorization available soon) eliminates the need to call multiple insurance providers, freeing up staff time while reducing denials. 
  • PracticeSuite: An embedded solution including workflow options to accommodate entries from the RIS/PACS worklist or within the billing module. Quickly accesses top billing functions, Payment Ledger for balances and eligibility, and Payment Entry to add payment and print a receipt. 
  • openDoctor: Automated appointment notifications through SMS and email which replaces lists of confirmation calls and reduces missed appointments. 
  • InterFAX by Upland: Integrated digital workflow for inbound (available soon) and outbound faxes, reducing the need for manual acceptance and processing of referral or report faxes. 

Mobile Applications Are Building a Patient-Centric Experience

Protecting patient data is business-critical for all medical practices, as it is for RamSoft. We’re using Microsoft Azure Cloud to ensure all data and applications are secure.

  • Workflow optimization in medical imaging can significantly impact the patient experience, leading to increased loyalty and satisfaction. 

Is Your Practice Operating Optimally?

Explore how RamSoft’s new automation applications, including patient engagement tools, integrated with cloud-based RIS/PACS can improve operations and profitability of your practice. 

Learn more on the company’s website or book a demo at RSNA 2024 for booth #6513 in the North Hall.  

Did Malpractice Risk Kill V/Q Exams?

Posted on by Brian Casey

CT perfusion angiography exams have largely replaced nuclear medicine-based ventilation/perfusion (V/Q) studies for detecting pulmonary embolism. But a new article in Academic Radiology suggests that CT’s rise wasn’t entirely based on clinical efficacy – fears of malpractice risk may have played a role. 

V/Q studies can help diagnose PE by enabling clinicians to visualize lung perfusion, showing defects such as blockages in pulmonary vessels. The scans are typically performed in three phases … 

  1. An albumin injection to show pulmonary vasculature.
  2. A radiopharmaceutical that’s inhaled and imaged with a gamma camera.
  3. A chest radiograph to correlate findings. 

The scans dominated PE imaging in the 1980s, but the rise of CT saw radiology facilities begin to shift.

  • CTPA was seen as having higher spatial resolution and was easier to perform than nuclear medicine exams. 

But the new article suggests that there were other forces at work as well – in particular, fear of malpractice risk from PEs that weren’t adequately followed after inconclusive V/Q exams.

  • The problem originated with clinical guidelines for V/Q reporting that classified some 20% of V/Q studies as “low probability” for PE when they probably would have better been classified as “inconclusive” or “non-diagnostic.”

As a result, a number of “low probability” patients weren’t followed up adequately, with tragic results that later figured into medical malpractice cases …

  • A patient who was diagnosed with pneumonia after an inconclusive V/Q exam, sent home, and died one day later of a “massive” PE.
  • A patient with leg and chest pain who was given heparin after a negative V/Q scan and later suffered internal hemorrhage; fortunately she survived.
  • A patient with “vague symptoms” who had an inconclusive V/Q scan and later died of an undiagnosed PE that some claimed would have been detected on CTPA.

Indeed, the theme of PE malpractice cases began to shift over time, from failure to diagnose V/Q scans to failure to order CTPA exams – which were soon seen as the clinical gold standard.

The Takeaway

Given the fast pace of development in radiology, it’s inevitable that some technologies that were once clinical staples fall by the wayside. But the new article offers a fascinating look at how clinical language can lead to medico-legal concerns that influence physician behavior – often in ways that are impossible to detect as they happen.

Low-Dose CT Confounds CAD in Kids

Posted on by Brian Casey

When it comes to pediatric CT scans, clinicians should make every effort to reduce dose as much as possible. But a new study in AJR indicates that lower CT radiation dose can affect the performance of software tools like computer-aided detection. 

Initiatives like the Image Wisely and Image Gently projects have succeeded in raising awareness of radiation dose and have helped radiologists find ways to reduce it.

But every little bit counts in pediatric dose reduction, especially given that one CT exam can raise the risk of developing cancer by 0.35%. 

  • Imaging tools like AI and CAD could help, but there have been few studies examining the performance of pulmonary CAD software developed for adults in analyzing scans of children.

To address that gap, researchers including radiologists from Cincinnati Children’s Hospital Medical Center investigated the performance of two open-source CAD algorithms trained on adults for detecting lung nodules in 73 patients with a mean age of 14.7 years. 

  • The algorithms included FlyerScan, a CAD developed by the authors, and MONAI, an open-source project for deep learning in medical imaging. 

Scans were acquired at standard-dose (mean effective dose=1.77 mSv) and low-dose (mean effective dose=0.32 mSv) levels, with the results showing that both algorithms turned in lower performance at lower radiation dose for nodules 3-30 mm … 

  • FlyerScan saw its sensitivity decline (77% vs. 67%) and detected fewer 3mm lung nodules (33 vs. 24).
  • MONAI also saw lower sensitivity (68% vs. 62%) and detected fewer 3mm lung nodules (16 vs. 13).
  • Reduced sensitivity was more pronounced for nodules less than 5 mm.

The findings should be taken with a grain of salt, as the open-source algorithms were not originally trained on pediatric data.

  • But the results do underscore the challenge in developing image analysis software optimized for pediatric applications.

The Takeaway

With respect to low radiation dose and high AI accuracy in CT scans of kids, radiologists may not be able to have their cake and eat it too – yet. More work will be needed before AI solutions developed for adults can be used in children.

Mammography AI Predicts Cancer Before It’s Detected

Posted on by Brian Casey

A new study highlights the predictive power of AI for mammography screening – before cancers are even detected. Researchers in a study JAMA Network Open found that risk scores generated by Lunit’s Insight MMG algorithm predicted which women would develop breast cancer – years before radiologists found it on mammograms. 

Mammography image analysis has always been one of the most promising use cases for AI – even dating back to the days of computer-aided detection in the early 2000s. 

  • Most mammography AI developers have focused on helping radiologists identify suspicious lesions on mammograms, or triage low-risk studies so they don’t require extra review.

But a funny thing has happened during clinical use of these algorithms – radiologists found that AI-generated risk scores appeared to predict future breast cancers before they could be seen on mammograms. 

  • Insight MMG marks areas of concern and generates a risk score of 0-100 for the presence of breast cancer (higher numbers are worse). 

Researchers decided to investigate the risk scores’ predictive power by applying Insight MMG to screening mammography exams acquired in the BreastScreen Norway program over three biennial rounds of screening from 2004 to 2018. 

  • They then correlated AI risk scores to clinical outcomes in exams for 116k women for up to six years after the initial screening round.

Major findings of the study included … 

  • AI risk scores were higher for women who later developed cancer, 4-6 years before the cancer was detected.
  • The difference in risk scores increased over three screening rounds, from 21 points in the first round to 79 points in the third round.
  • Risk scores had very high accuracy by the third round (AUC=0.93).
  • AI scores were more accurate than existing risk tools like the Tyrer-Cuzick model.

How could AI risk scores be used in clinical practice? 

  • Women without detectable cancer but with high scores could be directed to shorter screening intervals or screening with supplemental modalities like ultrasound or MRI.

The Takeaway
It’s hard to overstate the significance of the new results. While AI for direct mammography image interpretation still seems to be having trouble catching on (just like CAD did), risk prediction is a use case that could direct more effective breast screening. The study is also a major coup for Lunit, continuing a string of impressive clinical results with the company’s technology.

Breast Cancer Mortality Falls Again

Posted on by Brian Casey

New data from the American Cancer Society highlight the remarkable strides that have been made against breast cancer, with the U.S. death rate falling 44% over the last 33 years – saving over half a million lives. But the statistics also underscore the work that remains to be done, particularly with minority women. 

The fight against breast cancer has been one of public health’s major success stories.

  • High mammography screening uptake has led to early detection of cancers that can then be treated with revolutionary new therapies. 

Much of the credit for this success goes to the women’s health movement, which has conducted effective advocacy campaigns that have led to …

But breast cancer remains the third most common killer of women after heart disease and lung cancer, and there have been disturbing trends even as the overall death rate falls. 

  • Breast cancer incidence has been rising especially in younger women, and major disparities continue to be seen, particularly with survival in Black women.

The American Cancer Society’s new report represents the group’s biennial review of breast cancer statistics, finding … 

  • In 2024 there will be 311k new cases of invasive breast cancer, 56.5k cases of DCIS, and 42.3k deaths. 
  • The breast cancer mortality rate has fallen 44% from 1989 to 2022, from 33 deaths per 100k women to 19 deaths.
  • Some 518k breast cancer deaths have been averted.
  • The mortality rate ranges from 39% higher than average for Black women to 38% lower for Asian American Pacific Islander women. 
  • The mortality rate is slightly higher than average (0.5%) for White women.
  • The average breast cancer incidence rate is 132 per 100k women, but ranges from 5% higher for White women to 21% lower for Hispanic women.
  • Women 50 years and older will account for most invasive cases (84%) and deaths (91%).

The Takeaway

As Breast Cancer Awareness Month begins, women’s health advocates should be heartened by the progress that’s been made overall. But battles remain, from eliminating patient out-of-pocket payments for follow-up studies to addressing race-based disparities in breast cancer mortality. In many ways, the fight is just beginning. 

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