Key strategies to gain immediate value from AI technology and blunt burnout
There’s rising pressure to address clinician burnout, and organizations can achieve fast results by considering 4 things when investing in new health IT innovations.
Editor’s Note: Health Data Management’s May cover story investigated the potential of recent innovations to shape healthcare’s future, including combating clinician burnout and supporting value-based care through AI. The following article extends the discussion by examining specific technical and operational considerations for providers looking to invest in AI.
Eight out of 10 healthcare leaders are eyeing investments in artificial intelligence in the next three years, and 37 percent will lean on AI to improve operational efficiency, a recent report found. But gaining optimal value depends on a structured framework for evaluating investments.
Amid the noise of ChatGPT and pressures to determine use cases for generative AI, leaders’ ability to look beyond the “someday potential” of AI and focus on “right now” opportunities could be a competitive differentiator.
Natural language processing and AI technology is already being applied in various care settings to improve information transfer and reduce burnout. This type of realistic focus on immediate improvement, instead of a long-term implementation strategy, positions leaders to relieve pressure on overstretched healthcare teams and increase capacity where it matters most. It also speeds access to actionable intelligence, which strengthens quality of care and satisfaction.
Going beyond the hype
But selecting the right solutions for an AI-powered approach also requires leaders to go beyond the hype. They must carefully assess: What are the foundational AI capabilities needed to improve decision-making and the experience of care — for patients and clinicians — while delivering the best possible outcomes?
Here are four critical considerations for investing in NLP and AI solutions to make an immediate and deep impact in healthcare.
Accuracy of the AI. Today, more than one out of four health systems want tools that support timelier and smarter data sharing. But equipping people to work at a higher level with the help of AI depends on the quality and breadth of the data used to make decisions. In healthcare, where the fax machine is still king, data is often trapped in paper-based documents, including items in handwritten form. To strengthen the accuracy of AI output, health systems need a digital faxing solution combined with innovative technology to transform unstructured text data into a structured format. The structured data then can be intelligently processed and consumed by any EHR system. From there, the data can be used to inform decision-making for faster patient treatment with a higher degree of accuracy and applicability.
Ability to match the organization’s workflows, not the other way around. Nurses believe 42 percent of the time they spend per shift could be reduced by nearly half through technology-enabled processes, including the use of intelligent automation. But too often, healthcare teams must conform to AI-powered workflows to gain the desired outcome. At a time when U.S. healthcare faces an anticipated shortage of 200,000 to 450,000 nurses by 2025, NLP and AI tools that adapt to already established processes are a game changer. The technologies must meet nurses where they are and deliver information directly within the nurses’ workflows, while strengthening efficiency and reducing the risk of burnout. They also must help eliminate critical breakdowns in information transfer that impede the delivery of care.
Replicability. Many different versions of NLP and AI tools are available today that extract information from handwritten text and images — capabilities that are tremendously useful in healthcare. But not all tools can extract information and text no matter what the image looks like. Likewise, their level of accuracy in pulling information or text can vary according to the quality of the image presented. That’s why it’s important to dig deeper, asking for the solutions’ precision percentage, which is measured at the page level. Without a high level of precision, healthcare organizations risk adding unnecessary manual intervention, which creates added stress to already overburdened healthcare teams.
Speed to insight. The handoff of patients from one care setting to another is one of the most difficult challenges that providers face. It’s also the point at which the potential for error dramatically increases. The best NLP and AI solutions speed information transfer and make data meaningful for staff and clinicians. This increases clinicians’ mental capacity for knowledge intake and transfer so they can provide better care with decreased risk of burnout. Advanced solutions also possess the ability to continuously learn from the data they ingest, flagging changes or patterns in patients’ health and prompting staff to make the right interventions for improved outcomes. Realizing that acuity levels can change quickly, intelligent information transfer combined with speed empowers teams to improve health outcomes.
Looking beyond the shiny new tool
We’re at the beginning of the hype cycle for AI in healthcare, and the push to adopt the latest offering — namely, ChatGPT — is strong. But determining the right use cases for large-language models in healthcare is, at best, a work in progress.
Leveraging proven technology like NLP and AI, used in tandem with a powerful integration engine, will ensure that disparate systems communicate accurately in a timely manner. These new technologies will not only strengthen operational efficiency but also can alleviate pressure on overworked staff while minimizing burnout.
The healthcare industry is at a critical juncture as skilled and highly educated employees are becoming harder to find and even more difficult to retain. NLP and AI technology can be used todayto streamline administrative processes, creating significant investment value for both health systems and their valued employees.
John Nebergall is chief operating officer for Consensus Cloud Solutions.