Literature Review

From explainable to interpretable deep learning for natural language processing in healthcare: How far from reality?Computational and Structural Biotechnology Journal; by Guangming Huang, Yingya Li, Shoaib Jameel, Yunfei Long, Giorgos Papanastasiou;12/24Deep learning (DL) has substantially enhanced natural language processing (NLP) in healthcare research. However, the increasing complexity of DL-based NLP necessitates transparent model interpretability, or at least explainability, for reliable decision-making. This work presents a thorough scoping review of explainable and interpretable DL in healthcare NLP.

Critical components of the digital operating model in health bounteous x Accolite; by Abby Matchett; 12/4/24 ... To determine how to shape your resources and structure an operating model, you must first assess and evaluate several critical inputs, starting with the overall digital transformation strategy. 

Creach Family Holdings announces acquisition of HealthRev Partners EIN Presswire, Stuart, FL; by Careficient, Inc.; 12/3/24 Creach Family Holdings announces acquisition of HealthRev Partners Expanding Healthcare Technology Solutions for Home Health and Hospice Providers. Creach Family Holdings, a privately held investment firm focused on healthcare technology and tech-enabled services, announces the acquisition of HealthRev Partners, a provider of technology-driven revenue cycle management (RCM) solutions serving the home health and hospice market. This acquisition complements Creach Family Holdings’ portfolio, including Careficient, a leading electronic medical record (EMR) provider for home health and hospice agencies. By combining HealthRev Partners’ RCM expertise with Careficient’s EMR platform, Creach Family Holdings aims to provide an integrated suite of solutions that streamline operations, optimize revenue cycles, and improve patient care. 

Nursing homes must break cycle of patient rehospitalization Special to the USA TODAY Network, republished by The Record, Bergen County; by Mary Holden Jones; 11/28/24 One of the most severe issues facing long-term care patients is rehospitalization. Over 20% of nursing home residents are readmitted to the hospital within 30 days of discharge, a cycle that often worsens health outcomes and creates significant emotional, financial and logistical burdens for families, caregivers and the health care system. ... While hospital stays are sometimes necessary, they create added risks of infections and the constant back and forth can add to confusion and cognitive decline — both of which further reduce quality of life and prolong recovery times. ... The emotional toll on families is equally significant. ... Fortunately, many rehospitalizations are preventable with more proactive, coordinated care. Technology-driven health care solutions offer a promising way to monitor patients’ health in real time, alerting caregivers and health care providers to early signs of trouble before a minor issue becomes a major problem.

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."

Hologram technology wows rural care patients Becker's ASC Review; by Francesca Mathewes; 11/25/24 Cancer patients in rural Tennessee are testing virtual visits from hologram health consultants, Fox 26 News reported Nov. 23. The technology allows healthcare providers, in this case a physician, to consult with patients via a lifelike hologram. Clay Jackson, MD, a palliative care specialist in Germantown, Tenn., said that the technology has expanded access to care in the rural area, where patients might otherwise have to travel more than 100 miles for in-person care. "Two words: blown away. My patients can't believe how great the technology is," said Dr. Jackson in the report. "They may be a one-car family, it may be patients for whom additional travel would cause additional pain or discomfort or stress, especially those who are undergoing active treatment for their cancer," he added. The device and AI-powered spatial computing platform cost $29,000. The technology has been used in the entertainment industry and in professional sports, but this marks the first time the technology has been used in health consultations.

Machine learning for targeted advance care planning in cancer patients: A quality improvement studyJournal of Pain and Symptom Management; by Mihir N Patel, Alexandria Mara, Yvonne Acker, Jamie Gollon, Noppon Setji, Jonathan Walter, Steven Wolf, S Yousuf Zafar, Suresh Balu, Michael Gao, Mark Sendak, David Casarett, Thomas W LeBlanc, Jessica Ma; 12/24Objectives: Examine a quality improvement mortality prediction algorithm intervention's impact on ACP documentation and EOL care. Conclusion: Identifying patients with cancer and high mortality risk via machine learning elicited a substantial increase in documented ACP conversations but did not impact EOL care. Our intervention showed promise in changing clinician behavior. Further integration of this model in clinical practice is ongoing.

Large language model influence on diagnostic reasoning-A randomized clinical trialJAMA Network Open; Ethan Goh, MBBS, MS; Robert Gallo, MD; Jason Hom, MD; Eric Strong, MD; Yingjie Weng, MHS; Hannah Kerman, MD; Joséphine A. Cool, MD; Zahir Kanjee, MD, MPH; Andrew S. Parsons, MD, MPH; Neera Ahuja, MD; Eric Horvitz, MD, PhD; Daniel Yang, MD; Arnold Milstein, MD; Andrew P. J. Olson, MD; Adam Rodman, MD, MPH; Jonathan H. Chen, MD, PhD; 10/24Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves physician diagnostic reasoning. In this trial, the availability of an LLM to physicians as a diagnostic aid did not significantly improve clinical reasoning compared with conventional resources. The LLM alone demonstrated higher performance than both physician groups, indicating the need for technology and workforce development to realize the potential of physician-artificial intelligence collaboration in clinical practice.

With the rise of AI, is a Chief Automation Officer necessary? HR Daily Advisor Newsletter - Technology; by Lou Bachenheimer; 11/11/24 In the age of digital transformation and advanced technology like AI, automation is a critical element for organizations to stay competitive. That’s why many C-suites are considering incorporating a Chief Automation Officer (CAO) into the fabric of their organization to provide senior executive oversight of automation initiatives. But is a whole new role in the c-suite needed and necessary to successfully implement AI and automation into your organization? ... Instead of pouring new resources and energy into new roles that are created off the back of hype, what is needed is executive sponsorship and leadership of advanced automation programs at the highest and most influential levels, underpinned by the appropriate business knowledge and network to be able to drive real change.

Adopting tomorrow’s tech: 5 steps to effective AI training in hospice Hospice News; by Mick Stahlberg; 11/7/24 In today’s hospice environment, providers are continually asked to do more with less. Staffing shortages, rising costs and growing patient demands make efficiency a critical component of success. Enter artificial intelligence (AI). From automating administrative tasks to improving scheduling and optimizing resource allocation, AI is poised to significantly enhance operational efficiency at every level. In turn, proper training will play a monumental role in helping teams get comfortable incorporating this technology into their clinical and administrative workflows from day one. Providers will be best served to follow 5 key steps to effective AI training as they work with their technology partners to articulate a comprehensive strategy based on their organization and its unique needs. These steps incorporate:

Top 10 Governance, Risk & Compliance (GRC) tool eSecurity Planet; by Claire dela Luna; 10/28/24 In today’s global economy, even small businesses operate across multiple regions and markets, each with its own regulatory landscape. Without effective GRC tools, organizations can quickly fall out of compliance, exposing themselves to security risks and operational disruptions. GRC software solutions enable businesses to manage these risks by automating policies, tracking controls, and providing real-time compliance monitoring across international borders. .. Here’s a guide to help you make the best choice.

Change Healthcare cyberattack impacts 100 million people Becker's Health IT; by Naomi Diaz; 10/25/24 The Feb. 21 ransomware attack on UnitedHealth Group subsidiary Change Healthcare has impacted 100 million individuals. The number of impacted individuals was posted on the Office for Civil Rights Breach Portal, which is used for reporting breaches of unsecured protected health information under HIPAA. Previously, UnitedHealth said that the data stolen by hackers likely covered a "substantial proportion of people in America." The cyberattack crippled financial operations for hospitals, insurers, pharmacies and medical groups nationwide. In July, the organization began sending out breach notification letters to individuals affected by the attack.  

Perspectives on artificial intelligence–generated responses to patient messagesJAMA Network Open; Jiyeong Kim, PhD, MPH; Michael L. Chen, BA; Shawheen J. Rezaei, MPhil; April S. Liang, MD; Susan M. Seav, MD; Sonia Onyeka, MD; Julie J. Lee, MD, MPH; Shivam C. Vedak, MD, MBA; David Mui, MD, MBA; Rayhan A. Lal, MD; Michael A. Pfeffer, MD; Christopher Sharp, MD; Natalie M. Pageler, MD, MEd; Steven M. Asch, MD, MPH; Eleni Linos, MD, DrPH; 10/24Generative artificial intelligence (AI) has the potential to assist clinicians in responding to patients’ messages. Satisfaction was consistently higher with AI-generated responses than with clinicians overall and by specialty. However, satisfaction was not necessarily concordant with the clinician-determined information quality and empathy. For example, satisfaction was highest with AI responses to cardiology questions while information quality and empathy were highest in endocrinology questions. Interestingly, clinicians’ response length was associated with satisfaction while AI’s response length was not. The findings suggest that the extreme brevity of responses could be a factor that lowers satisfaction in patient-clinician communication in EHR.