Artificial Intelligence and Case Management: Embracing Transformative Practice

Growth of Remote Therapeutic Monitoring Lands New Opportunities for Case Management

PURPOSE/OBJECTIVES

An increase in the use of remote therapeutic monitoring (RTM) has been spurred by nationwide factors including the COVID-19 pandemic, authorized reimbursement of RTM by the Centers for Medicare & Medicaid Services, and more frequent use of big data analytics in health care delivery. This article discusses the use of RTM by care teams at the point of care and explores the role of the case manager in RTM to address patients' unmet needs.

PRIMARY PRACTICE SETTINGS

Although RTM may be utilized across inpatient and outpatient levels of care, this article focuses on outpatient care such as community clinics, provider groups, and home health care.

FINDINGS/CONCLUSIONS

When implemented along with care management interventions, RTM applications have the potential to improve patient adherence, enhance communication between patients and their providers, streamline resource allocation, and address social determinants of health impacting patient care and outcomes.

IMPLICATIONS FOR CASE MANAGEMENT PRACTICE

RTM reimbursement models are rapidly evolving, utilizing real-world and patient-reported data to identify and initiate timely, individualized solutions that meet the holistic needs of each patient. Use of an RTM system allows the case manager to build rapport with the patient while quickly identifying care gaps and delivering appropriate interventions that can maximize patient outcomes. RTM can drive savings and bring revenue to the system or practice while providing salient documentation of social determinants of health that can be addressed with validation of proven care coordination interventions.

AI and Case Management: From Artificial Intelligence to Generative Intelligence

Conversations about artificial intelligence (AI) are impossible to escape since the inception of ChatGPT in November 2022-whether it is about the end of our jobs or the end of the world! This Editorial talks about AI and its subsets and how this may relate to health care.

Interactive Design Psychology and Artificial Intelligence-Based Innovative Exploration of Anglo-American Traumatic Narrative Literature

The advent of the intelligence age has injected new elements into the development of literature. The synergic modification of Anglo-American (AAL) traumatic narrative (TN) literature by artificial intelligence (AI) technology and interactive design (ID) psychology will produce new possibilities in literary creation. First, by studying natural language processing (NLP) technology, this study proposes a modification language model (LM) based on the double-layered recurrent neural network (RNN) algorithm and constructs an intelligent language modification system based on the improved LM model. The results show that the performance of the proposed model is excellent; only about 30% of the respondents like AAL literature; the lack of common cultural background, appreciation difficulties, and language barriers have become the main reasons for the decline of reading willingness of AAL literature. Finally, AI technology and ID psychology are used to modify a famous TN work respectively and synergically, and the modified work is appreciated by respondents to collect their comments. The results corroborate that 62% of the respondents like original articles, but their likability scores have decreased for individually modified work by AI or ID psychology. In comparison, under the synergic modification efforts of AI and ID psychology, the popularity of the modified work has increased slightly, with 65% of the respondents showing a likability to read. Therefore, it is concluded that literary modification by single ID psychology or AI technology will reduce the reading threshold by trading off the literary value of the original work. The core of literary creation depends on human intelligence, and AI might still not be able to generate high-standard literary works independently because human minds and thoughts cannot be controlled and predicted by machines. The research results provide new ideas and improvement directions for the field of AI-assisted writing.