Feature | Radiology Business | October 08, 2019 | April Wilson

Using Big Data and AI to Improve Imaging Workflows and the Revenue Cycle

An illustration of radiology department analytics data showing GE Healthcare's business analytics software.

According to IBM, the world creates 2.5 quintillion bytes of data daily. A large chunk of this data is healthcare information. Medical data continues to expand whether from genomic testing and large imaging studies, to billions of payment transactions. All of this data represents a strong match for advanced systems analysis.

 

Bill Gates previously said, “A breakthrough in machine learning would be worth 10 Microsofts.” Both the front-end of imaging workflows and the back-end of healthcare revenue cycle management (RCM) stand to reap the rewards of big data analytics and artificial intelligence (AI)

 

 

Imaging Workflow Ecosystem Challenges

Imaging clinicians must carefully balance competing time and resource demands. Common challenges include:

   • Increasing study volume per reading radiologist

   • Decreasing misdiagnoses

   • Dealing with large increases in secondary study volumes

 

Healthcare Revenue Cycle Challenges

Today, health systems are also examining how to tackle an expanding amount of imaging data contained in electronic medical records and billing systems. Data issues related to RCM include:

   • Missing or inaccurate charges 
   • Data consolidation across multiple payment vendors
   • Overburdened financial support service staffs
   • Manual data entry errors 
   • Disconnected silos of care and information
   • Data transfer timing delays 
 
Thankfully, machine learning presents a way to determine and analyze data patterns, which in turn can enhance imaging workflows and the healthcare revenue cycle. Hospitals already use AI applications and big data analysis for insurance pre-certifications, denial prediction, and ICD-10 billing code verification. 
 
 
Using AI to Solve Imaging Workflow Challenges
The last challenge in implementing a successful enterprise imaging solution, after leaping connectivity and various interoperability hurdles, is adding intelligence and machine learning applications.  
 
Companies are developing AI intelligent assistants using machine vision, deep learning, computer-aided diagnosis (CAD) and other advanced proprietary algorithms, on the clinician-facing front end, and for imaging workflow and storage needs. AI applications span across multiple disease states from cardiovascular disease monitoring to lung and prostate cancer detection.  
 
For example, studies show that false negative diagnosis rates are at 30 percent and false positive diagnoses are at 2 percent. AI can help proactively prioritize studies that need immediate attention, as well as go back and suggest prior studies that may require re-examination.
 
“Advances in AI imaging tools, and the workflow engines that power them, enable clinicians to make better diagnoses and provide efficacious treatments across a wide array of disease states,” said Beau Jones, COO of DataFirst, developer of the SilverBack workflow engine.
 
 
Big Data Can Help Solve Healthcare Revenue Leakage Issues
It is true that health systems have processes to pinpoint revenue leakage, but many are not efficient. For example, manual audit reviews are cumbersome, time-consuming and can even be inconsistent because of data silos. And, rules-based systems, such as bill scrubbers, often can only find pre-identified errors. 
 
Healthcare systems and imaging centers typically lose 1 to 5 percent of net revenue due to leakage and this will likely grow with ICD-10,  the Accountable Care Act (ACA), and more out-of-pocket payments. To combat revenue leakage, a closer look must be given to analytics-based systems using big data and machine learning.
 
Billing data (such as diagnostics attributes, procedure attributes, billing code attributes, charge code attributes and hospital history) can be synthesized to build a database. Present cases can be aligned with past cases, so any variances can be examined. 
 
 
Tackling Vendor Management
When thinking about using big data, back-end processes, such as billing, cannot be ignored. Most healthcare organizations engage in a complicated receivables process involving multiple vendors. These vendors work with healthcare organizations on boutique processes ranging from payer denials to patient collections.  
 
Big data can help motivate employees, enhance the patient financial experience, and enrich a hospital’s financial health. Data-driven decision models can streamline revenue cycle processes and financial operations to improve patient communications and profits.
 
Vendor management of a variety of providers is challenge because each as its own methodology, interface and data types. How do healthcare organizations ensure that their vendors are identifying incorrect statements and charging the right rates? The answer again is big data.
 
Pulling data from each vendor (instead of utilizing vendor spreadsheets and reports) offers a tremendous benefit allowing hospitals to comb through information about vendors and realize trends. This includes vendor performance data, such as screening rates, cycle times, rejection reason summaries, and in-house application summaries. 
 
This provides an opportunity for health systems to provide vendors with valuable feedback which can lead to corrective actions for improved patient satisfaction ratings. Data science and machine learning in combination with frontline expertise helps receivables operations run smoother and even more profitably.
 
 
Shifting to Value-Based Imaging
Recent advances in front-end imaging workflows, enterprise imaging engines, and back-end revenue cycle process all stand to support the American College of Radiology’s (ACR) Imaging 3.0 Initiative by delivering systems that enable AI applications to work across modalities, optimizing practice management and patient care.
 
 
Editor’s note: Author April Wilson is the vice president of marketing and analytics for RevSpring. The company predicts payment outcomes so they can be improved, transforming complex financial workflows into intuitive financial pathways.
 

Related Content

Gadolinium based contrast dye in brain MRI

Gadolinium contrast agents (GBCAs) are partly retained in the brain, raising safety concerns, as seen in this MRI.

News | Contrast Media | January 17, 2020
January 17, 2020 — Bracco Diagnostics Inc., the U.
Imaging Technology News (ITN) has been acquired by Wainscot Media
News | Imaging Technology News - ITN | January 14, 2020
January 14, 2020 — Park Ridge, N.J.-based publisher Wainscot Medi...
Konica Minolta Business Solutions, U.S.A., Inc. (Konica Minolta) announced its status as a Google Cloud Premier Partner.
News | Archive Cloud Storage | January 14, 2020
January 14, 2020 — ...
Videos | RSNA | January 13, 2020
ITN Editor Dave Fornell takes a tour of some of the most innovative new medical imaging technologies displayed on the
Partners of Collaborative Imaging experience billing collection improvements of over 25 percent while reducing operating costs by 30 percent

Image courtesy of Philips Healthcare

News | Radiology Business | January 08, 2020
January 8, 2020 — The $18 billion radiology industry continues to face a growing threat of consolidation, resulting i
The use of augmented and virtual reality in radiology was the subject of two articles and part of the HIMSS 2019 trends article in the top 25 list. Augmented reality is being looked at as a way to better train radiologists, allow surgeons to use medical imaging in true 3-D to better plan surgeries, and it can allow patients to better understand their conditions compared to use of traditional 2-D medical images. Photo by Dave Fornell.

The use of augmented and virtual reality in radiology was the subject of two articles and part of the HIMSS 2019 trends article in the top 25 list. Augmented reality is being looked at as a way to better train radiologists, allow surgeons to use medical imaging in true 3-D to better plan surgeries, and it can allow patients to better understand their conditions compared to use of traditional 2-D medical images. Photo by Dave Fornell.

Feature | Radiology Imaging | January 03, 2020 | Dave Fornell, Editor
January 3, 2020 — Here is the top 25 radiology articles on the Imaging Technology News (ITN) website from 2019 based
This is artificial intelligence on Fujifilm's mobile digital radiography system to immediately detect pneumothorax (a collapsed lung) and show the location to the technologist and attending physician in a unit before the image is even uploaded to the PACS for a read. AI applications like this that have immediate impact on critical patient care saw a lot of interest at RSNA 2019.

This is work-in-progress artificial intelligence app on Fujifilm's mobile digital radiography system to immediately detect pneumothorax (a collapsed lung), The AI highlights the area of interest to show the location to the technologist and attending physician in a unit before the image is even uploaded to the PACS for a read by a radiologist. The technology also can flag the study for an immediate read in the PACS worklist for confirmation by a human. This technology is from a third-party and will be offered on Fujifilm's REiLI AI platform. Applications like this that have immediate impact on critical patient care saw a lot of interest at RSNA 2019. Photos by ITN Editor Dave Fornell.

Feature | Artificial Intelligence | December 27, 2019 | Siddharth Shah and Srikanth Kompalli, Frost & Sullivan
Radiology artificial intelligence (AI) was again the hottest topic at the 2019...
News | Remote Viewing Systems | December 27, 2019
December 27, 2019 — The Radiological Society of North America (RSNA) and Carequality have developed the Imaging Data