Salesforce has announced a new library of AI-powered capabilities for industries that offer healthcare-specific tools that organizations can deploy to automate time-consuming tasks.
Salesforce says the new generative AI features available on Health Cloud will integrate with clinician workflows and can help improve the quality and efficiency of patient care.
WHY IT MATTERS
As part of a larger effort to address operational pain points across 15 industries, new AI capabilities are embedded in each of Salesforce’s 15 industry cloud platforms.
Using Einstein Copilot, healthcare organizations will be able to capture natural language patient information summaries using a new set of patient data management features.
“For example, a care coordinator can receive a comprehensive summary of a patient or member including care plans, prescriptions, clinical encounters, prior authorizations, preferences, and more” before an appointment, a Salesforce spokesperson said. Healthcare IT News Tuesday.
According to the Salesforce website, AI-driven patient care services are triggered through Einstein prompts when active within member accounts stored in HealthCloud.
Copilot has leveraged conversational AI to send referrals and schedule appointments, which can help reduce the time and effort required to complete administrative tasks. In terms of privacy and data security, the company says Einstein’s data masking and data-agnostic layers protect patient information as reminders are sent to large language models.
Other healthcare AI features will be available in the new use case library that supports business operations, including validating insurance coverage and determining out-of-pocket costs and eligibility.
These available AI features will be available on Salesforce in October, a spokesperson said. Meanwhile, the company’s website says Industry’s new AI features are priced based on specific deployments.
THE BIGGER TREND
In March, Salesforce launched Einstein AI Copilot on the Einstein Platform 1 to leverage healthcare organizations’ unique data and metadata in its Health Data Cloud.
However, the spokesperson noted that patient services and benefits verification are new capabilities that the company says will help reduce switching between platforms, enable faster approvals, and better support clinicians’ work in pre-examination patient records.
Most organizations lack the time, expertise, and funding to build and train their own AI models. According to Salesforce, developing a trained model alone can cost as much as $100 million.
The rising cost of technology was a sentiment echoed by many digital health leaders last week at the HIMSS Healthcare AI Forum in Boston.
Managing an ever-growing technology footprint, however, is just one of the challenges of AI procurement. In a market flooded with point-source solutions, decision makers must design change management processes within their organizations and address the labor impacts of the new technologies they introduce into their workflows, they said.
Lee Schwamm, chief digital health officer at Yale New Haven Health System, offered this advice at the forum on Friday when asked about the biggest opportunities for AI transformation in the next three to five years: “The easiest things to pursue from a transformation perspective are workflow or office work because they’re not patient-facing.”
“They are very low risk and virtually unregulated.”
ON RECORD
“Organizations of all sizes and budgets can now easily get started with practical AI tools purpose-built to solve their unique challenges,” Jeff Amann, executive vice president and general manager of Salesforce Industries, said in a statement.
Andrea Fox is senior editor of Healthcare IT News.
Email: [email protected]
Healthcare IT News is a publication of HIMSS Media.
