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HCA Healthcare - HCA Healthcare Collaborates With Google Cloud to Bring Generative AI to Hospitals

September 24, 2023

NASHVILLE, Tenn. & SUNNYVALE, Calif.--(BUSINESS WIRE)-- HCA Healthcare, Inc. (NYSE:HCA), one of the nation’s leading healthcare providers, and Google Cloud today announced a new collaboration designed to use generative AI technology to improve workflows on time-consuming tasks, such as clinical documentation, so physicians and nurses can focus more on patient care.

This expanded work with Google Cloud is part of a strategic partnership announced in 2021 that includes safeguards to protect patient privacy and the security of data. HCA Healthcare’s partnership with Google Cloud, as well as its multi-year implementation of MEDITECH Expanse which began in 2022, are key elements of HCA Healthcare’s work to advance its digital transformation.

“We’re on a mission to redesign the way care is delivered, letting clinicians focus on patient care and using technology where it can best support doctors and nurses,” said Michael J. Schlosser, MD, MBA, FAANS, SVP, Care Transformation and Innovation, HCA Healthcare. “Generative AI and other new technologies are helping us transform the ways teams interact, create better workflows, and have the right team, at the right time, empowered with the information they need for our patients.”

As part of a pilot program that began early this year, approximately 75 emergency room physicians at four HCA Healthcare hospitals started using Google’s AI technology to quickly and more easily document key medical information from conversations during patient visits. It is part of a collaboration among HCA Healthcare, Google Cloud, and Augmedix, a healthcare technology company that specializes in ambient medical documentation. According to a 2022 study in JAMA Internal Medicine, 58% of physicians said time spent on documentation limits the amount of time they can spend with patients.

Physicians use an Augmedix app on a hands-free device to create accurate and timely medical notes from clinician-patient conversations. Augmedix's proprietary platform then leverages natural language processing, along with Google Cloud’s generative AI technology and multi-party medical speech-to-text processing, to instantly convert the data into medical notes, which physicians review and finalize before they are transferred in real time to the hospital’s electronic health record (EHR).

Experts from HCA Healthcare’s Care Transformation and Innovation team (CT&I), along with the Google Cloud and Augmedix teams, continue to work closely with physicians to refine the solution, and HCA Healthcare plans to expand its use to more hospitals later this year. While the companies are in the process of collecting measurement data, physicians in the pilot program have reported strong overall satisfaction.

“HCA Healthcare is a leader in care delivery, and our expanded partnership has the potential to benefit the entire healthcare industry,” said Thomas Kurian, CEO, Google Cloud. “Bringing generative AI into solutions that support doctors and nurses can significantly improve their day-to-day experiences and help them focus on what matters most – caring for patients.”

Another opportunity HCA Healthcare has targeted for improvement through generative AI is patient handoffs between nurses. Typically, this important process is manual and time-consuming, and often provides varying levels of detail. HCA Healthcare’s CT&I team built a system using one of Google Cloud’s large language models (LLMs) that helps automatically generate handoff reports and is designed to promote continuity, consistency, patient safety, and clinical quality – while saving nurses significant time and maintaining human oversight.

Prompts were carefully designed to guide the LLM toward prioritized details, such as medication changes, laboratory results, vital sign fluctuations, patient concerns, and overall response to treatment. HCA Healthcare’s team also shaped the model’s outputs to make them intuitive and easy for nurses to read, comprehend, and act upon. During initial beta testing, HCA Healthcare collected nurse feedback to further refine the tool. Currently, the nurse handoff tool is undergoing continued testing at UCF Lake Nona Hospital. After seeing the prototype, nurses were pleased with the speed, accuracy, and relevance of the draft reports the tool is generating and expressed high interest in putting the tool into practice.

Longer term, HCA Healthcare is exploring the use of Google’s medically-tuned Med-PaLM 2 LLM to support caregivers.

“Having an LLM tailored for medical questions and content could be beneficial for certain critical use cases,” said Dr. Schlosser. “We expect Med-PaLM 2 will be especially useful when we’re asking complex medical questions that are grounded on scientific and medical knowledge, while looking for insights in complicated and unstructured medical texts.”

Google Cloud’s approach to data governance and privacy policies are designed for its customers to retain control over their data. In healthcare settings, access and use of patient data is protected through the implementation of Google Cloud’s reliable infrastructure and secure data storage that support HIPAA compliance, along with each customer’s security, privacy controls, and processes. Google Cloud’s responsible approach to generative AI also means customers have access to tools to directly tune large language models and to review model responses for biased or unvalidated content, teaching the model to avoid inappropriate outputs.

For more information, check out the Google Cloud blog on A responsible path to generative AI in healthcare.

About Google Cloud

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

About HCA Healthcare

Nashville-based HCA Healthcare is one of the nation’s leading providers of healthcare services comprising 182 hospitals and approximately 2,300 ambulatory sites of care, including surgery centers, freestanding ERs, urgent care centers, and physician clinics, in 20 states and the United Kingdom. With its founding in 1968, HCA Healthcare created a new model for hospital care in the United States, using combined resources to strengthen hospitals, deliver patient-focused care and improve the practice of medicine. HCA Healthcare has conducted a number of clinical studies, including one that demonstrated that full-term delivery is healthier than early elective delivery of babies and another that identified a clinical protocol that can reduce bloodstream infections in ICU patients by 44%. HCA Healthcare is a learning health system that uses its more than 37 million annual patient encounters to advance science, improve patient care and save lives.

Read More

Google expands generative AI model Med-PaLM to more health customers | Healthcare Dive

September 24, 2023

This audio is auto-generated. Please let us know if you have feedback.

Google is expanding access of its large language model that’s specifically trained on medical information through a preview with Google Cloud customers in the healthcare and life sciences industry next month.

A limited group of customers have been testing the artificial intelligence, called Med-Palm 2, since April, including for-profit hospital giant HCA Healthcare, academic medical system Mayo Clinic and electronic health records vendor Meditech.

Google declined to share how many additional healthcare companies will be using Med-PaLM 2 following the expansion in September, but a spokesperspon said, “there are customers across healthcare sectors that have expressed interest and will be getting access.”

“We’re thrilled to be working with Cloud customers to test Med-PaLM and work to bring it to a place where it exceeds expectations,” Google health AI lead Greg Corrado told reporters during a press briefing on the preview.

Med-PaLM was the first AI system to pass U.S. medical licensing exam style questions. Its second iteration, which Google introduced in March this year, bettered its predecessor’s score by 19%, passing with 86.5% accuracy.

The LLM is not a replacement for doctors, nurses and other medical caregivers, but is instead meant to augment existing workflows and work as an extension of the care team, Corrado said.

However, Med-PaLM faces big questions that have plagued other generative AI in healthcare, including the potential for errors, the complexity of queries it can perform, meeting product excellence standards and a lack of regulation — despite already being piloted in real-world settings.

HCA has been testing Med-PaLM to help doctors and nurses with documentation, as part of the health system’s strategic collaboration with Google Cloud launched in 2021.

The system has been working with health tech company Augmedix and using Google’s LLM to create an ambient listening system that automatically transcribes doctor-patient conversations in the emergency room, according to Michael Schlosser, HCA’s senior vice president of care transformation and innovation.

HCA is currently testing the system in a cohort of 75 doctors in four hospitals, and plans to expand to more hospitals later this year as the automation improves, Schlosser said during the press briefing.

HCA is also piloting using Med-PaLM to generate a transfer summary to help nurses with patient handoffs at UCF Lake Nona Hospital in Orlando.

Meanwhile, Meditech — a major player in the hospital software space — is embedding Google’s natural language processing and LLMs into its EHR’s search and summarization capabilities.

Documentation is an appealing potential use case for generative AI that could cut down on onerous notetaking processes. Along with Google, other tech giants like Amazon and Microsoft have announced or expanded recent AI-enabled clinical documentation plays.

Privacy watchdogs, physician groups and patient advocates have raised concerns around the ethical use of AI and sensitive medical data, including worries about quality, patient consent and privacy, and confidentiality.

In 2019, Google sparked a firestorm of controversy over its use of patient data provided by health system Ascension to develop new product lines without patient knowledge or consent.

Google says that Med-PaLM 2 is not being trained on patient data, and Google Cloud customers retain control over their data as part of the preview. In the case of the the HCA pilot, patients are notified of the ambient listening system when they enter the ER, HCA’s Schlosser said.

Doctors are also leery about ceding control to what is in many cases a black box algorithm for determining information and the right course of patient care.

Schlosser said that the for-profit operator is first building AI into easy-to-accept use cases, like automating handoffs or scheduling, to make doctors and nurses more comfortable with the technology, before eventually implementing AI into additional parts of clinical practice.

“You get into nudging in the workflow around documentation, and then you could slowly step your way up to higher and higher levels of decision support,” Schlosser said. “But I want clinicians to fully embrace AI as a partner that’s making their life easier before we start getting into some of those more controversial areas.”

Read More

Digital divide affecting low-income patients, Reid Health CEO says

September 24, 2023

Craig Kinyon, CEO of Richmond, Ind.-based Reid Health, said the digital divide is disproportionately affecting low-income households in both urban and rural areas. 

In an Aug. 30 LinkedIn post, Mr. Kinyon said that in today's digital world, it is vital to address the issue of digital inequities. 

"Did you know that approximately 19 percent of Americans do not own a smartphone?" he wrote. "Shockingly, 50 percent of households earning less than $30,000 per year have limited access to computers, while around 18 million households in the U.S. lack internet access."

He proposed that healthcare should begin recognizing the impact of social determinants of health and health disparities to assess how it is hindering patients from getting access to care.

"This is why as leaders in the healthcare field, it is imperative for us to collaborate with community organizations, and policymakers in order to bridge this digital divide," he wrote. "By working together harmoniously, innovative solutions can be created that effectively address these challenges."

Read More

IBM trains its LLM to read, rewrite COBOL apps | CIO Dive

September 24, 2023

This audio is auto-generated. Please let us know if you have feedback.
  • IBM trained its watsonx.ai large language model to ingest COBOL code and rescript business applications in Java, the company announced Tuesday.
  • The generative AI solution is designed to ease mainframe modernization, assisting developers in the arduous process of analyzing, refactoring and transforming legacy code and validating the results, Skyla Loomis, VP of IBM Z Software, said during a demonstration.
  • IBM intends to deploy the AI-enabled coding assistant’s new capabilities by the end of the year, the company said.

The specter of technical debt haunts organizations, often leaving critical business functions perched perilously atop layers of arcane code. Despite modernization efforts, businesses still run on-prem applications architected with COBOL, a programming language created in the 1950s.

IBM estimates individual clients at the average enterprise may have tens of millions of COBOL lines running in production. Globally, enterprise production systems run more than 800 billion lines of COBOL daily, according to a Vanson Bourne study commissioned last year by software company Micro Focus.

Several generative AI companies, including Anthropic and OpenAI, recently introduced coding assistants. In February, Microsoft released GitHub Copilot for Business, an AI-enabled developer tool for the enterprise, and saw user headcount double in the first half of the year.

While human language contains nuances and tonal variations that can outwit the best commercially available models, computer code consists of straightforward machine instructions with clearly articulated semantics.

Errors and hallucinations can occur in coding translations, but they are relatively easy to identify and resolve, said Kyle Charlet, IBM fellow and CTO of IBM Z Software.

“Code doesn't lie, so we can immediately highlight any hallucinations that have worked their way into the code and correct them,” said Charlet.

The company trained the LLM on its COBOL data and tested the dataset on IBM's CodeNet, a database of 14 million code samples in more than 55 common programming languages, including C++, Java, Python, FORTRAN and COBOL. IBM used CodeNet to test for accuracy in COBOL to Java translation.

The model was then tuned for two specific use cases: a coding assistant for RedHat’s Ansible automation toolkit; and the new COBOL solution, which has now been trained on more than 80 languages and 1.5 trillion tokens of data, according to the company.

To mitigate risk, all of the model’s training data originated from licensed open source software, Charlet said.

The solution has four functional phases, detailed by Loomis.

  • Auto-discovery, when the tool analyzes the original script, identifies its data dependencies and provides a metadata overview of the application.
  • Refactor, which identifies the application’s business function and suggests modernization updates.
  • Transform, when the user triggers the generative AI’s COBOL-to-Java translation capabilities.
  • Validate, which tests the results to ensure that the new service is semantically and logically equivalent to the original script.

“What we are not doing is a line-by-line COBOL syntax translation to Java,” Loomis said. “When that happens, what you end up with is COBOL syntax expressed in largely unreadable, largely unmaintainable Java.”

Expanding the watsonx toolkit is part of a broader business integration strategy, built around hybrid cloud, mainframe modernization, emerging AI capabilities and IT consulting services.

IBM previously partnered with Microsoft to ease mainframe modernization and deploy enterprise-grade generative AI solutions. The two companies introduced the IBM Z and Cloud Modernization Stack on the Microsoft Azure Marketplace in June and launched a generative AI managed service last week.

Correction: This article has been updated to reflect IBM trained the LLM using its COBOL data. The company used CodeNet to test it for accuracy.

Read More

HCA Healthcare - HCA Healthcare Collaborates With Google Cloud to Bring Generative AI to Hospitals

September 24, 2023

NASHVILLE, Tenn. & SUNNYVALE, Calif.--(BUSINESS WIRE)-- HCA Healthcare, Inc. (NYSE:HCA), one of the nation’s leading healthcare providers, and Google Cloud today announced a new collaboration designed to use generative AI technology to improve workflows on time-consuming tasks, such as clinical documentation, so physicians and nurses can focus more on patient care.

This expanded work with Google Cloud is part of a strategic partnership announced in 2021 that includes safeguards to protect patient privacy and the security of data. HCA Healthcare’s partnership with Google Cloud, as well as its multi-year implementation of MEDITECH Expanse which began in 2022, are key elements of HCA Healthcare’s work to advance its digital transformation.

“We’re on a mission to redesign the way care is delivered, letting clinicians focus on patient care and using technology where it can best support doctors and nurses,” said Michael J. Schlosser, MD, MBA, FAANS, SVP, Care Transformation and Innovation, HCA Healthcare. “Generative AI and other new technologies are helping us transform the ways teams interact, create better workflows, and have the right team, at the right time, empowered with the information they need for our patients.”

As part of a pilot program that began early this year, approximately 75 emergency room physicians at four HCA Healthcare hospitals started using Google’s AI technology to quickly and more easily document key medical information from conversations during patient visits. It is part of a collaboration among HCA Healthcare, Google Cloud, and Augmedix, a healthcare technology company that specializes in ambient medical documentation. According to a 2022 study in JAMA Internal Medicine, 58% of physicians said time spent on documentation limits the amount of time they can spend with patients.

Physicians use an Augmedix app on a hands-free device to create accurate and timely medical notes from clinician-patient conversations. Augmedix's proprietary platform then leverages natural language processing, along with Google Cloud’s generative AI technology and multi-party medical speech-to-text processing, to instantly convert the data into medical notes, which physicians review and finalize before they are transferred in real time to the hospital’s electronic health record (EHR).

Experts from HCA Healthcare’s Care Transformation and Innovation team (CT&I), along with the Google Cloud and Augmedix teams, continue to work closely with physicians to refine the solution, and HCA Healthcare plans to expand its use to more hospitals later this year. While the companies are in the process of collecting measurement data, physicians in the pilot program have reported strong overall satisfaction.

“HCA Healthcare is a leader in care delivery, and our expanded partnership has the potential to benefit the entire healthcare industry,” said Thomas Kurian, CEO, Google Cloud. “Bringing generative AI into solutions that support doctors and nurses can significantly improve their day-to-day experiences and help them focus on what matters most – caring for patients.”

Another opportunity HCA Healthcare has targeted for improvement through generative AI is patient handoffs between nurses. Typically, this important process is manual and time-consuming, and often provides varying levels of detail. HCA Healthcare’s CT&I team built a system using one of Google Cloud’s large language models (LLMs) that helps automatically generate handoff reports and is designed to promote continuity, consistency, patient safety, and clinical quality – while saving nurses significant time and maintaining human oversight.

Prompts were carefully designed to guide the LLM toward prioritized details, such as medication changes, laboratory results, vital sign fluctuations, patient concerns, and overall response to treatment. HCA Healthcare’s team also shaped the model’s outputs to make them intuitive and easy for nurses to read, comprehend, and act upon. During initial beta testing, HCA Healthcare collected nurse feedback to further refine the tool. Currently, the nurse handoff tool is undergoing continued testing at UCF Lake Nona Hospital. After seeing the prototype, nurses were pleased with the speed, accuracy, and relevance of the draft reports the tool is generating and expressed high interest in putting the tool into practice.

Longer term, HCA Healthcare is exploring the use of Google’s medically-tuned Med-PaLM 2 LLM to support caregivers.

“Having an LLM tailored for medical questions and content could be beneficial for certain critical use cases,” said Dr. Schlosser. “We expect Med-PaLM 2 will be especially useful when we’re asking complex medical questions that are grounded on scientific and medical knowledge, while looking for insights in complicated and unstructured medical texts.”

Google Cloud’s approach to data governance and privacy policies are designed for its customers to retain control over their data. In healthcare settings, access and use of patient data is protected through the implementation of Google Cloud’s reliable infrastructure and secure data storage that support HIPAA compliance, along with each customer’s security, privacy controls, and processes. Google Cloud’s responsible approach to generative AI also means customers have access to tools to directly tune large language models and to review model responses for biased or unvalidated content, teaching the model to avoid inappropriate outputs.

For more information, check out the Google Cloud blog on A responsible path to generative AI in healthcare.

About Google Cloud

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

About HCA Healthcare

Nashville-based HCA Healthcare is one of the nation’s leading providers of healthcare services comprising 182 hospitals and approximately 2,300 ambulatory sites of care, including surgery centers, freestanding ERs, urgent care centers, and physician clinics, in 20 states and the United Kingdom. With its founding in 1968, HCA Healthcare created a new model for hospital care in the United States, using combined resources to strengthen hospitals, deliver patient-focused care and improve the practice of medicine. HCA Healthcare has conducted a number of clinical studies, including one that demonstrated that full-term delivery is healthier than early elective delivery of babies and another that identified a clinical protocol that can reduce bloodstream infections in ICU patients by 44%. HCA Healthcare is a learning health system that uses its more than 37 million annual patient encounters to advance science, improve patient care and save lives.

Read More

Google expands generative AI model Med-PaLM to more health customers | Healthcare Dive

September 24, 2023

This audio is auto-generated. Please let us know if you have feedback.

Google is expanding access of its large language model that’s specifically trained on medical information through a preview with Google Cloud customers in the healthcare and life sciences industry next month.

A limited group of customers have been testing the artificial intelligence, called Med-Palm 2, since April, including for-profit hospital giant HCA Healthcare, academic medical system Mayo Clinic and electronic health records vendor Meditech.

Google declined to share how many additional healthcare companies will be using Med-PaLM 2 following the expansion in September, but a spokesperspon said, “there are customers across healthcare sectors that have expressed interest and will be getting access.”

“We’re thrilled to be working with Cloud customers to test Med-PaLM and work to bring it to a place where it exceeds expectations,” Google health AI lead Greg Corrado told reporters during a press briefing on the preview.

Med-PaLM was the first AI system to pass U.S. medical licensing exam style questions. Its second iteration, which Google introduced in March this year, bettered its predecessor’s score by 19%, passing with 86.5% accuracy.

The LLM is not a replacement for doctors, nurses and other medical caregivers, but is instead meant to augment existing workflows and work as an extension of the care team, Corrado said.

However, Med-PaLM faces big questions that have plagued other generative AI in healthcare, including the potential for errors, the complexity of queries it can perform, meeting product excellence standards and a lack of regulation — despite already being piloted in real-world settings.

HCA has been testing Med-PaLM to help doctors and nurses with documentation, as part of the health system’s strategic collaboration with Google Cloud launched in 2021.

The system has been working with health tech company Augmedix and using Google’s LLM to create an ambient listening system that automatically transcribes doctor-patient conversations in the emergency room, according to Michael Schlosser, HCA’s senior vice president of care transformation and innovation.

HCA is currently testing the system in a cohort of 75 doctors in four hospitals, and plans to expand to more hospitals later this year as the automation improves, Schlosser said during the press briefing.

HCA is also piloting using Med-PaLM to generate a transfer summary to help nurses with patient handoffs at UCF Lake Nona Hospital in Orlando.

Meanwhile, Meditech — a major player in the hospital software space — is embedding Google’s natural language processing and LLMs into its EHR’s search and summarization capabilities.

Documentation is an appealing potential use case for generative AI that could cut down on onerous notetaking processes. Along with Google, other tech giants like Amazon and Microsoft have announced or expanded recent AI-enabled clinical documentation plays.

Privacy watchdogs, physician groups and patient advocates have raised concerns around the ethical use of AI and sensitive medical data, including worries about quality, patient consent and privacy, and confidentiality.

In 2019, Google sparked a firestorm of controversy over its use of patient data provided by health system Ascension to develop new product lines without patient knowledge or consent.

Google says that Med-PaLM 2 is not being trained on patient data, and Google Cloud customers retain control over their data as part of the preview. In the case of the the HCA pilot, patients are notified of the ambient listening system when they enter the ER, HCA’s Schlosser said.

Doctors are also leery about ceding control to what is in many cases a black box algorithm for determining information and the right course of patient care.

Schlosser said that the for-profit operator is first building AI into easy-to-accept use cases, like automating handoffs or scheduling, to make doctors and nurses more comfortable with the technology, before eventually implementing AI into additional parts of clinical practice.

“You get into nudging in the workflow around documentation, and then you could slowly step your way up to higher and higher levels of decision support,” Schlosser said. “But I want clinicians to fully embrace AI as a partner that’s making their life easier before we start getting into some of those more controversial areas.”

Read More

Digital divide affecting low-income patients, Reid Health CEO says

September 24, 2023

Craig Kinyon, CEO of Richmond, Ind.-based Reid Health, said the digital divide is disproportionately affecting low-income households in both urban and rural areas. 

In an Aug. 30 LinkedIn post, Mr. Kinyon said that in today's digital world, it is vital to address the issue of digital inequities. 

"Did you know that approximately 19 percent of Americans do not own a smartphone?" he wrote. "Shockingly, 50 percent of households earning less than $30,000 per year have limited access to computers, while around 18 million households in the U.S. lack internet access."

He proposed that healthcare should begin recognizing the impact of social determinants of health and health disparities to assess how it is hindering patients from getting access to care.

"This is why as leaders in the healthcare field, it is imperative for us to collaborate with community organizations, and policymakers in order to bridge this digital divide," he wrote. "By working together harmoniously, innovative solutions can be created that effectively address these challenges."

Read More

IBM trains its LLM to read, rewrite COBOL apps | CIO Dive

September 24, 2023

This audio is auto-generated. Please let us know if you have feedback.
  • IBM trained its watsonx.ai large language model to ingest COBOL code and rescript business applications in Java, the company announced Tuesday.
  • The generative AI solution is designed to ease mainframe modernization, assisting developers in the arduous process of analyzing, refactoring and transforming legacy code and validating the results, Skyla Loomis, VP of IBM Z Software, said during a demonstration.
  • IBM intends to deploy the AI-enabled coding assistant’s new capabilities by the end of the year, the company said.

The specter of technical debt haunts organizations, often leaving critical business functions perched perilously atop layers of arcane code. Despite modernization efforts, businesses still run on-prem applications architected with COBOL, a programming language created in the 1950s.

IBM estimates individual clients at the average enterprise may have tens of millions of COBOL lines running in production. Globally, enterprise production systems run more than 800 billion lines of COBOL daily, according to a Vanson Bourne study commissioned last year by software company Micro Focus.

Several generative AI companies, including Anthropic and OpenAI, recently introduced coding assistants. In February, Microsoft released GitHub Copilot for Business, an AI-enabled developer tool for the enterprise, and saw user headcount double in the first half of the year.

While human language contains nuances and tonal variations that can outwit the best commercially available models, computer code consists of straightforward machine instructions with clearly articulated semantics.

Errors and hallucinations can occur in coding translations, but they are relatively easy to identify and resolve, said Kyle Charlet, IBM fellow and CTO of IBM Z Software.

“Code doesn't lie, so we can immediately highlight any hallucinations that have worked their way into the code and correct them,” said Charlet.

The company trained the LLM on its COBOL data and tested the dataset on IBM's CodeNet, a database of 14 million code samples in more than 55 common programming languages, including C++, Java, Python, FORTRAN and COBOL. IBM used CodeNet to test for accuracy in COBOL to Java translation.

The model was then tuned for two specific use cases: a coding assistant for RedHat’s Ansible automation toolkit; and the new COBOL solution, which has now been trained on more than 80 languages and 1.5 trillion tokens of data, according to the company.

To mitigate risk, all of the model’s training data originated from licensed open source software, Charlet said.

The solution has four functional phases, detailed by Loomis.

  • Auto-discovery, when the tool analyzes the original script, identifies its data dependencies and provides a metadata overview of the application.
  • Refactor, which identifies the application’s business function and suggests modernization updates.
  • Transform, when the user triggers the generative AI’s COBOL-to-Java translation capabilities.
  • Validate, which tests the results to ensure that the new service is semantically and logically equivalent to the original script.

“What we are not doing is a line-by-line COBOL syntax translation to Java,” Loomis said. “When that happens, what you end up with is COBOL syntax expressed in largely unreadable, largely unmaintainable Java.”

Expanding the watsonx toolkit is part of a broader business integration strategy, built around hybrid cloud, mainframe modernization, emerging AI capabilities and IT consulting services.

IBM previously partnered with Microsoft to ease mainframe modernization and deploy enterprise-grade generative AI solutions. The two companies introduced the IBM Z and Cloud Modernization Stack on the Microsoft Azure Marketplace in June and launched a generative AI managed service last week.

Correction: This article has been updated to reflect IBM trained the LLM using its COBOL data. The company used CodeNet to test it for accuracy.

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