Literature Review

Which skills will survive the AI onslaught? National Association of Corporate Directors (NACD), Arlington, VA; by Vivek Sharma and David C. Edelman; 12/17/24 ... Boards and CEOs are inundated with reports projecting doomsday scenarios of job displacement due to AI. For example, according to the World Economic Forum's The Future of Jobs Report 2020, automation and AI are expected to displace 85 million jobs globally by 2025, especially in industries such as manufacturing, data entry, and customer service due to the repetitive nature and predictable processes of these fields. AI is already disrupting many roles hitherto considered firmly within the human domain. ... Irreplaceable Human Dimensions ... In analyzing a wide range of articles on the topic, when a job scores high along any of the following three dimensions, it creates significant hurdles for displacement by AI:

Navigating the Generative AI journey: A strategic roadmap for healthcare organizations Perficient; by Felix Bradbury; 12/13/24 The healthcare industry stands at a transformative crossroads with generative AI (GenAI) poised to revolutionize care delivery, operational efficiency, and patient outcomes. Recent MIT Technology Review research indicates that while 88% of organizations are using or experimenting with GenAI, healthcare organizations face unique challenges in implementation. Let’s explore a comprehensive approach to successful GenAI adoption in healthcare. ... The journey to GenAI adoption requires careful consideration of three key dimensions: organizational readiness, use case prioritization, and infrastructure capabilities, [Click on the title's link to continue reading.]

Researcher explores using AI to overcome language barriers with patientsJAMA Network; Yulin Hswen, ScD, MPH; Kate Schweitzer; 12/24In the US, 25 million people with language barriers receive worse health care by nearly every standard than those who speak English fluently. In a recent Viewpoint in JAMA, K. Casey Lion, MD, MPH, an associate professor of pediatrics at the University of Washington School of Medicine, listed the poorer-quality outcomes: inferior comprehension, lower adherence, higher costs, and more frequent serious safety events, to name a few. In this study, we want to understand how it performs under real-world conditions because those are the conditions we would be using it in. I would also encourage other health care systems that are thinking about implementing AI for language access to think about talking to and involving their patients and families in the work to be sure that it’s reflecting what our patients and families actually want rather than what we think they want.

Top places to work in IT: 17 health systems rank in 2025 Becker's Health IT; by Laura Dyrda; 12/10/24 Computerworld published a list of the top places to work in IT for 2025, including several prominent health systems. Foundry, the publisher for Computerworld, examined several companies across the size spectrum on their commitment to in-house IT talent, training, technical and soft skills, as well as culture, diversity and satisfaction. The publication reported 79% of companies surveyed increased the number of IT employees in the last three years, and around half plan to continue growing their IT teams next year. The health systems and provider organizations ranked include:

Applying natural language processing to electronic health record data—From text to triageJAMA Network Open; Grace K. Sun, BS; Andrew P. Ambrosy, MD; 11/24Most information about a patient’s clinical status, disease progression, and response to treatment lies in qualitative clinician documentation in the electronic health record (EHR). The New York Heart Association (NYHA) classification was developed to standardize functional status assessments and treatment decisions ... [but] ... due to inconsistent implementation in routine care, much of the critical information remains in unstructured EHR data that is difficult to capture and analyze. Natural language processing (NLP) is an emerging tool that uses artificial intelligence to process unstructured or semistructured free-text data, such as the embedded assessments of HF symptom status in clinician documentation. NLP, a field of artificial intelligence that focuses on understanding, interpreting, and generating human language, is capable of evaluating these data and providing large-scale insights into patient progress and treatment response, with some limitations. Overall, these findings suggest that deep learning approaches may be used to address meaningful gaps in clinician documentation.

Dallas’ Analog Informatics emerges from stealth to modernize patient engagement in over 150 languages Dallas Innovates, Dallas, TX; by Lance Murray; 12/4/24 AIC aims to "bridge the communication gap" between healthcare providers and patients worldwide by leveraging secure AI and modern communication tools. CEO Philip Lieberman created the platform based on his experiences in caring for his hospitalized mother-in-law during the COVID pandemic. ... Shocked that he knew “more about his Amazon packages” than the state of his loved one, Lieberman vowed to use the experience and resources he’d gained from the previous multinational companies he created “to bring compassionate yet automated continuous communication to every patient and their families.” ... AIC aims to "bridge the communication gap" between healthcare providers and patients worldwide by leveraging secure AI and modern communication tools.  ... AIC said its platform “bridges the communication gap” between healthcare providers and patients, offering continuous automated logistical support in more than 150 languages. 

A people-centric approach powers successful digital transformations in healthcare HFMA - Healthcare Financial Management Association; by Utlimate Kronos Group; 12/2/24 Digital transformation is a high priority for C-suite executives at healthcare organizations, and a dizzying array of new technologies in a growing market is beckoning. But to succeed, leaders must be able to meld the use of technology with a people-first mindset and embrace their people focus in a systematic, measurable manner. ... McKinsey research suggests that almost 90% of health system executives believe that digital and AI transformation is a high or top priority for their organization, though 75% indicated that budget constraints and issues with legacy systems were hampering achievement of technology goals. ... [The discussion includes the following.]

AI can’t worry about patients, and a clinical ethicist says that mattersJAMA; Yulin Hswen, ScD, MPH; Jennifer Abbasi; 11/24This conversation is part of a series of interviews in which JAMA Network editors and expert guests explore issues surrounding the rapidly evolving intersection of artificial intelligence (AI) and medicine. Today, Hull is an associate professor and serves as associate director of the biomedical ethics program at Yale, where her clinical practice focuses on echocardiography and cardiac care of patients with cancer. She spoke about ethical boundaries for using AI in the clinic in a recent conversation with Yulin Hswen, ScD, MPH, an associate editor at JAMA and the newly launched JAMA+ AI and an assistant professor of epidemiology and biostatistics at the University of California, San Francisco. Medicine is, they remind the reader, “as much art as science, as much a moral endeavor as a technical one.”

Aged care nursing in the digital future Atlassian Jira, Australia; by Flinders University; 11/27/24 The results call for nurses to be included when digital innovations are in development, so they can actively shape their implementation and ensure high-quality, compassionate care for residents in their final stages of life. "In Australia and around the world, we have seen a significant increase in digital technologies in the health care sector," says study lead author Dr. Priyanka Vandersman, a Senior Research Fellow at Flinders University's Research Center for Palliative Care, Death and Dying (RePaDD).  "In aged care, these digital approaches can include helping with care planning and assisting with managing medication, but we need to ensure that nurses are equipped with the digital skills needed to use these tools effectively, while continuing to provide high quality, person-centered care."

Artificial intelligence and machine learning in cancer pain: A systematic reviewJournal of Pain and Symptom Management; by Vivian Salama, Brandon Godinich, Yimin Geng, Laia Humbert-Vidan, Laura Maule, Kareem A Wahid, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Clifton D Fuller, Amy C Moreno; 12/24Forty four studies from 2006 to 2023 were included. Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

8 digital healthcare trends for 2025 Perficient; by Lysa Young-Bates; 11/15/24 Our experts are closely monitoring eight healthcare trends that are shaping industry leaders’ strategies in 2025. ... Let’s dive into the eight healthcare trends and pragmatic innovations that our experts are helping HCOs navigate in 2025.

The future of AI in hospice care Hospice News; by Elizabeth Ecker; 11/13/24 As a high-touch, highly human care delivery type, hospice care may seem in a sense removed from the fast-paced, high-tech world of artificial intelligence. Yet in many ways AI is giving rise to exactly what hospice professionals set out to do in the first place: provide patient care. It also supports hospice professionals and patients in several other important ways, from reducing hospitalizations to providing scheduling efficiencies for staff and providing significant time savings. It can even be a key to providing the right care at the right time, and improved accuracy of patient data. ... It also supports hospice professionals and patients in several other important ways, from reducing hospitalizations to providing scheduling efficiencies for staff and providing significant time savings. It can even be a key to providing the right care at the right time, and improved accuracy of patient data.

[UK] Large language models for mental health applications: Systematic reviewZhijun Guo, Alvina Lai, Johan H Thygesen, Joseph Farrington, Thomas Keen, Kezhi Li; 10/24The study identifies several issues: the lack of multilingual datasets annotated by experts, concerns regarding the accuracy and reliability of generated content, challenges in interpretability due to the "black box" nature of LLMs [large language models], and ongoing ethical dilemmas. These ethical concerns include the absence of a clear, benchmarked ethical framework; data privacy issues; and the potential for overreliance on LLMs by both physicians and patients, which could compromise traditional medical practices. As a result, LLMs should not be considered substitutes for professional mental health services. However, the rapid development of LLMs underscores their potential as valuable clinical aids, emphasizing the need for continued research and development in this area.