Commonly, essential yet routine and time-consuming tasks, such as answering phone calls, scheduling appointments, or other administrative work, prevent health care workers from focusing on more value-added and enjoyable job functions.
The health care industry faces new and complex challenges, from ongoing burnout and turnover to planning accordingly for a steadily aging population. At the same time, teams must also prioritize patient care outcomes while managing their practices in a way that maintains compliance with regulatory obligations. In light of these ever-exacerbating issues, medical organizations and hospitals should look to the latest technology solutions—such as generative AI—to help overcome these challenges and foster greater workplace efficiency.
Popularized by ChatGPT, generative AI synthesizes the answer to a question based on similar statistical analysis by leveraging large language models. People are likely familiar with the technology’s ability to craft written responses; however, it has many other modalities, such as images, video, code, etc.
Text, for example, includes various adjustable elements such as transcription, translation, tone, sentiment, vocabulary level, length, and style. Although work is underway to refine the technology, generative AI can enable health care workers to automate routine tasks, personalize experiences, and improve the patient journey.
One of the most significant barriers inhibiting medical professionals from improving the patient experience is time. Commonly, essential yet routine and time-consuming tasks, such as answering phone calls, scheduling appointments, or other administrative work, prevent health care workers from focusing on more value-added and enjoyable job functions.
Alternatively, hospitals and health care practices can use generative AI-enabled solutions to automate many of these mundane objectives, whether summarizing meeting notes, creating web content, or answering patient questions, thereby allowing teams to devote more of their time to enhancing the quality of patient care. Moreover, health care organizations can decrease errors and improve general efficiencies by automating repetitive tasks such as coding, documentation, billing, collections, etc.
Generative AI can also enrich communications between medical experts and their patients—notably, long-standing language barriers. In the past, if a patient did not speak the same language as their physician, another person would need to be present to translate; however, this method is not totally perfect, and nuance or facts can become lost in the translation process.
Currently, there are AI systems that know 30 different languages and can translate and transcribe a physician’s instructions in real time. An equally significant language barrier is that of expertise.
Some high-level medical terminology may sound no different than a physician speaking another language to the patient. But, with generative AI, health care workers can transcribe jargon-heavy conversations and complex notes into the everyday vernacular, making the information much more digestible for patients.
Patient expectations, particularly for personalized care and attention, are another challenge that continues to place pressure on understaffed health care teams. Consider that when someone goes to their family physician, they usually receive a personalized experience due to their long history and familiarity with that medical professional.
Typically, it is much more difficult to replicate that same level of personalization in a larger setting. However, with generative AI solutions, large practices and hospitals can analyze the history of a patient’s medical files to more closely emulate the same personalized experience of a family physician.
Although a knowledgeable physician could comb through a backlog of medical data and come to the same conclusion concerning a patient as a generative AI solution, a human wouldn’t be as efficient as the machine. Besides, a physician’s time is better off doing other tasks, pouring over decades of data to build a more personalized treatment plan (when an AI can do it much faster) is not an ideal use of a professional’s limited time.
Of course, an issue arises if these medical records are half digital and half paper. Such a scenario would require scanning and uploading these files into a format the AI could decipher. Much of this is hypothetical but likely inevitable due to the continued progression of the technology.
In addition to helping health care workers deliver personalized care, generative AI can help medical teams tailor and individualize the patient journey. Specifically, medical teams can personalize patient communications by pairing generative AI with a communications automation platform. For example, health care facilities could automatically send patients curated emails, SMS messages, and other relevant content rather than generic marketing material.
Although generative AI is an impressive technology and will transform health care and many other industries, medical practices must implement the necessary parameters around their deployments. Consider that a generative AI solution might use an untrustworthy source or say something emotionally insensitive to a patient.
Ideally, health care professionals should create systems in which employees vet the content generated by such solutions. And, for situations in which AI suggests a possible course of action for various procedures, human intuition should always have the final say.
About the Author
Matt Edic is Chief eXperience Officer at IntelePeer.