Informatics Competencies of Students in a Doctor of Nursing Practice Program: A Descriptive Study.

OBJECTIVES: Health systems that apply artificial intelligence (AI) are transforming the roles of healthcare providers, including those of Doctor of Nursing Practice (DNP) providers. These professionals are required to utilize informatics knowledge and skills to deliver quality care, necessitating a high level of informatics competencies, which should be developed through well-structured courses. The purpose of this study is to assess the informatics competency scale scores of DNP students and to provide recommendations for enhancing the informatics curriculum. METHODS: An online informatics course was offered to students enrolled in a Bachelor of Science in Nursing to DNP program, and their informatics competency, which includes three subscales, was evaluated. Online survey data were collected from Fall 2021 to Fall 2022 using the "Self-Assessment of Informatics Competency Scale for Health Professionals." RESULTS: An analysis of 127 student responses revealed that students demonstrated competence in overall informatics competency and in one subscale: "applied computer skills (clinical informatics)." They showed proficiency in the "basic computer skills" and the "role" subscales. However, they reported lower competency in managing data and integrating standard terminology into their practice. CONCLUSIONS: The findings offer detailed insights into the current informatics competencies of DNP students and can inform informatics educators on how to enhance their courses. As healthcare institutions increasingly depend on AI applications, it is imperative for informatics educators to include AI-related content in their curricula.

Empowering Healthcare through Comprehensive Informatics Education: The Status and Future of Biomedical and Health Informatics Education.

OBJECTIVES: Education in biomedical and health informatics is essential for managing complex healthcare systems, bridging the gap between healthcare and information technology, and adapting to the digital requirements of the healthcare industry. This review presents the current status of biomedical and health informatics education domestically and internationally and proposes recommendations for future development. METHODS: We analyzed evidence from reports and papers to explore global trends and international and domestic examples of education. The challenges and future strategies in Korea were also discussed based on the experts' opinions. RESULTS: This review presents international recommendations for establishing education in biomedical and health informatics, as well as global examples at the undergraduate and graduate levels in medical and nursing education. It provides a thorough examination of the best practices, strategies, and competencies in informatics education. The review also assesses the current state of medical informatics and nursing informatics education in Korea. We highlight the challenges faced by academic institutions and conclude with a call to action for educators to enhance the preparation of professionals to effectively utilize technology in any healthcare setting. CONCLUSIONS: To adapt to the digitalization of healthcare, systematic and continuous workforce development is essential. Future education should prioritize curriculum innovations and the establishment of integrated education programs, focusing not only on students but also on educators and all healthcare personnel in the field. Addressing these challenges requires collaboration among educational institutions, academic societies, government agencies, and international bodies dedicated to systematic and continuous workforce development.

Integrating human expertise & automated methods for a dynamic and multi-parametric evaluation of large language models' feasibility in clinical decision-making.

BACKGROUND: Recent enhancements in Large Language Models (LLMs) such as ChatGPT have exponentially increased user adoption. These models are accessible on mobile devices and support multimodal interactions, including conversations, code generation, and patient image uploads, broadening their utility in providing healthcare professionals with real-time support for clinical decision-making. Nevertheless, many authors have highlighted serious risks that may arise from the adoption of LLMs, principally related to safety and alignment with ethical guidelines. OBJECTIVE: To address these challenges, we introduce a novel methodological approach designed to assess the specific feasibility of adopting LLMs within a healthcare area, with a focus on clinical nursing, evaluating their performance and thereby directing their choice. Emphasizing LLMs' adherence to scientific advancements, this approach prioritizes safety and care personalization, according to the "Organization for Economic Co-operation and Development" frameworks for responsible AI. Moreover, its dynamic nature is designed to adapt to future evolutions of LLMs. METHOD: Through integrating advanced multidisciplinary knowledge, including Nursing Informatics, and aided by a prospective literature review, seven key domains and specific evaluation items were identified as follows:A Peer Review by experts in Nursing and AI was performed, ensuring scientific rigor and breadth of insights for an essential, reproducible, and coherent methodological approach. By means of a 7-point Likert scale, thresholds are defined in order to classify LLMs as "unusable", "usable with high caution", and "recommended" categories. Nine state of the art LLMs were evaluated using this methodology in clinical oncology nursing decision-making, producing preliminary results. Gemini Advanced, Anthropic Claude 3 and ChatGPT 4 achieved the minimum score of the State of the Art Alignment & Safety domain for classification as "recommended", being also endorsed across all domains. LLAMA 3 70B and ChatGPT 3.5 were classified as "usable with high caution." Others were classified as unusable in this domain. CONCLUSION: The identification of a recommended LLM for a specific healthcare area, combined with its critical, prudent, and integrative use, can support healthcare professionals in decision-making processes.

Documentation of social determinants of health across individuals from different racial and ethnic groups in home healthcare.

INTRODUCTION: Home healthcare (HHC) enables patients to receive healthcare services within their homes to manage chronic conditions and recover from illnesses. Recent research has identified disparities in HHC based on race or ethnicity. Social determinants of health (SDOH) describe the external factors influencing a patient's health, such as access to care and social support. Individuals from racially or ethnically minoritized communities are known to be disproportionately affected by SDOH. Existing evidence suggests that SDOH are documented in clinical notes. However, no prior study has investigated the documentation of SDOH across individuals from different racial or ethnic backgrounds in the HHC setting. This study aimed to (1) describe frequencies of SDOH documented in clinical notes by race or ethnicity and (2) determine associations between race or ethnicity and SDOH documentation. DESIGN: Retrospective data analysis. METHODS: We conducted a cross-sectional secondary data analysis of 86,866 HHC episodes representing 65,693 unique patients from one large HHC agency in New York collected between January 1, 2015, and December 31, 2017. We reported the frequency of six SDOH (physical environment, social environment, housing and economic circumstances, food insecurity, access to care, and education and literacy) documented in clinical notes across individuals reported as Asian/Pacific Islander, Black, Hispanic, multi-racial, Native American, or White. We analyzed differences in SDOH documentation by race or ethnicity using logistic regression models. RESULTS: Compared to patients reported as White, patients across other racial or ethnic groups had higher frequencies of SDOH documented in their clinical notes. Our results suggest that race or ethnicity is associated with SDOH documentation in HHC. CONCLUSION: As the study of SDOH in HHC continues to evolve, our results provide a foundation to evaluate social information in the HHC setting and understand how it influences the quality of care provided. CLINICAL RELEVANCE: The results of this exploratory study can help clinicians understand the differences in SDOH across individuals from different racial and ethnic groups and serve as a foundation for future research aimed at fostering more inclusive HHC documentation practices.

National survey on understanding nursing academics' perspectives on digital health education.

AIM: This study explored the knowledge and confidence levels of nursing academics in teaching both the theories and practical skills of digital health in undergraduate nursing programs. DESIGN: A cross-sectional study. METHODS: A structured online survey was distributed among nursing academics across Australian universities. The survey included two sections: (1) the participants' demographics and their nursing and digital health teaching experience; (2) likert scales asking the participants to rate their knowledge and confidence to teach the theories and practical skills of four main themes; digital health technologies, information exchange, quality and digital professionalism. RESULTS: One hundred and nineteen nursing academics completed part one, and 97 individuals completed part two of the survey. Only 6% (n = 5) of the participants reported having formal training in digital health. Digital health was mainly taught as a module (n = 57, 45.9%), and assessments of theory or practical application of digital health in the nursing curriculum were uncommon, with 79 (69.9%) responding that there was no digital health assessment in their entry to practice nursing programs. Among the four core digital health themes, the participants rated high on knowledge of digital professionalism (22.4% significant knowledge vs. 5.9% no knowledge) but low on information exchange (30% significant knowledge vs. 28.3% no knowledge). Statistically significant (p < .001) associations were found between different themes of digital health knowledge and the level of confidence in teaching its application. Nursing academics with more than 15 years of teaching experience had a significantly higher level of knowledge and confidence in teaching digital health content compared with those with fewer years of teaching experience. CONCLUSION: There is a significant gap in nursing academics' knowledge and confidence to teach digital health theory and its application in nursing. Nursing academics need to upskill in digital health to prepare the future workforce to be capable in digitally enabled health care settings. IMPLICATIONS FOR THE PROFESSION: Nursing academics have a limited level of digital knowledge and confidence in preparing future nurses to work in increasingly technology-driven health care environments. Addressing this competency gap and providing sufficient support for nursing academics in this regard is essential. IMPACT: What problem did the study address? Level of knowledge and confidence among nursing academics to teach digital health in nursing practice. What were the main findings? There is a significant gap in nursing academics' knowledge and confidence to teach digital health theory and its application in nursing. Where and on whom will the research have an impact? Professional nursing education globally. REPORTING METHOD: The STROBE guideline was used to guide the reporting of the study. PATIENT OR PUBLIC CONTRIBUTION: The call for participation from nursing academics across Australia provided an introductory statement about the project, its aim and scope, and the contact information of the principal researcher. A participant information sheet was shared with the call providing a detailed explanation of participation. Nursing academics across Australia participated in the survey through the link embedded in the participation invite.

Linguistic dissection of nursing handoffs: Implications for patient safety in varied-acuity hospital settings.

AIM: This study examines the intricate language and communication patterns of nurse-to-nurse handoffs across three units with varying patient acuity levels and nurse-patient ratios, seeking to identify linguistic factors that may affect the quality of information transfer and patient outcomes. DESIGN: A mixed-methods cross-sectional design. METHODS: This study used the Nurse-to-Nurse Transition of Care Communication Model to explore the content and meaning of language in nursing handoffs within a large academic medical centre. Data were collected on three units through digital audio recordings of 20 handoffs between June and September 2022, which were transcribed and analysed using the Linguistic Inquiry Word Count programme. Trustworthiness was established by adhering to COREQ and STROBE guidelines for qualitative and quantitative research, respectively. RESULTS: Analysis revealed a preference for casual, narrative language across all units, with ICU nurses demonstrating a higher confidence and leadership in communication. Cognitive processes such as insight and causation were found to be underrepresented, indicating a potential area for miscommunication. Communication motives driven by affiliation were more pronounced in ICU settings, suggesting a strong collaborative nature. No significant differences were observed among the units post multiple testing adjustments. Speech dysfluencies were most pronounced in ICU handoffs, reflecting possible stress and cognitive overload. CONCLUSION: The study highlights the need for improved communication strategies such as interventions to enhance language clarity and incorporating technological tools into handoff processes to mitigate potential miscommunications and errors. The findings advance nursing science by highlighting the critical role of nuanced language in varied-acuity hospital settings and the necessity for structured nurse education in handoff communication and standardized handoff procedures. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: This study underscores the critical role of language in nurse-to-nurse handoffs. It calls for enhanced communication strategies, technology integration and training to reduce medical errors, improving patient outcomes in high-acuity hospital settings. PATIENT OR PUBLIC CONTRIBUTION: Nurses only.

A rapid review on current and potential uses of large language models in nursing.

BACKGROUND: The application of large language models across commercial and consumer contexts has grown exponentially in recent years. However, a gap exists in the literature on how large language models can support nursing practice, education, and research. This study aimed to synthesize the existing literature on current and potential uses of large language models across the nursing profession. METHODS: A rapid review of the literature, guided by Cochrane rapid review methodology and PRISMA reporting standards, was conducted. An expert health librarian assisted in developing broad inclusion criteria to account for the emerging nature of literature related to large language models. Three electronic databases (i.e., PubMed, CINAHL, and Embase) were searched to identify relevant literature in August 2023. Articles that discussed the development, use, and application of large language models within nursing were included for analysis. RESULTS: The literature search identified a total of 2028 articles that met the inclusion criteria. After systematically reviewing abstracts, titles, and full texts, 30 articles were included in the final analysis. Nearly all (93 %; n = 28) of the included articles used ChatGPT as an example, and subsequently discussed the use and value of large language models in nursing education (47 %; n = 14), clinical practice (40 %; n = 12), and research (10 %; n = 3). While the most common assessment of large language models was conducted by human evaluation (26.7 %; n = 8), this analysis also identified common limitations of large language models in nursing, including lack of systematic evaluation, as well as other ethical and legal considerations. DISCUSSION: This is the first review to summarize contemporary literature on current and potential uses of large language models in nursing practice, education, and research. Although there are significant opportunities to apply large language models, the use and adoption of these models within nursing have elicited a series of challenges, such as ethical issues related to bias, misuse, and plagiarism. CONCLUSION: Given the relative novelty of large language models, ongoing efforts to develop and implement meaningful assessments, evaluations, standards, and guidelines for applying large language models in nursing are recommended to ensure appropriate, accurate, and safe use. Future research along with clinical and educational partnerships is needed to enhance understanding and application of large language models in nursing and healthcare.

Educational interventions and their effects on healthcare professionals' digital competence development: A systematic review.

INTRODUCTION: The digitalisation of healthcare requires that healthcare professionals are equipped with adequate digital competencies to be able to deliver high-quality healthcare. Continuing professional education is needed to ensure these competencies. OBJECTIVE: This systematic review aimed to identify and describe the educational interventions that have been developed to improve various aspects of the digital competence of healthcare professionals and the effects of these interventions. METHODS: A systematic literature review following the Joanna Briggs Institute's guidelines for Evidence Synthesis was conducted. Five electronic databases (CINAHL, PubMed, ProQuest, Scopus and Medic) up to November 2023 were searched for studies. Two researchers independently assessed the eligibility of the studies by title, abstract and full text and the methodological quality of the studies. Data tabulation and narrative synthesis analysis of study findings were performed. The PRISMA checklist guided the review process. RESULTS: This review included 20 studies reporting heterogeneous educational interventions to develop the digital competence of healthcare professionals. The participants were mainly nurses and interventions were conducted in various healthcare settings. The length of the education varied from a 20-minute session to a six-month period. Education was offered through traditional contact teaching, using a blended-learning approach and through videoconference. Learning was enhanced through lectures, slide presentations, group work, case studies, discussions and practical exercises or simulations. Educational interventions achieved statistically significant results regarding participants' knowledge, skills, attitudes, perception of resources, self-efficacy or confidence and output quality. CONCLUSIONS: The findings of this review suggest that digital competence education of nurses and allied health professionals would benefit from a multi-method approach. Training should provide knowledge as well as opportunities to interact with peers and instructors. Skills and confidence should be enhanced through practical training. Adequate organisational support, encouragement, and individual, needs-based guidance should be provided.

Nursing informatics competency and its associated factors among palliative care nurses: an online survey in mainland China.

BACKGROUND: Nursing informatics (NI) competency is a required core competency for high-quality care in digitally enabled healthcare environments. Given the increasing reliance on digital health in palliative care settings, it becomes crucial to evaluate the NI competency of nurses to ensure the seamless integration and effective utilization of digital health in their clinical practice. This study aimed to investigate the level of NI competency and explore its associated factors among palliative care nurses in mainland China. METHODS: A cross-sectional design was conducted for this study, involving a total of 409 palliative care nurses from 302 hospitals in mainland China. Anonymous data were collected through a self-designed sociodemographic questionnaire, the Nursing Informatics Competency Scale (NICS) and the Innovative Self-Efficacy Scale. RESULTS: The total score of the NICS was 129.19 ± 22.02, which indicated that Chinese palliative care nurses had a moderate level of NI competency. There was a positive correlation between innovative self-efficacy and NI competency (r = 0.602, P < 0.01). The hospital level and innovative self-efficacy were identified as statistically significant factors influencing nurses' NI competency based on multiple linear regression analysis results. These associated factors could explain 35.1% of the difference in NI competency. CONCLUSIONS: This study found that palliative care nurses in mainland China exhibited moderate levels of NI competency and identified the hospital level and innovative self-efficacy as associated factors of nurses' NI competency. Measures such as developing supported strategies, including targeted NI training programs by nursing education managers of primary-level hospitals and creating a positive culture of innovation by healthcare institutions can be considered to improve the level of NI competency among Chinese palliative care nurses.

Competencies in the Robotics of Care for Nursing Robotics: A Scoping Review.

In parallel with the development and design of different technological advances, competencies in nursing have advanced. With the development of robotics, it is expected that nursing robotic competencies will also increase. The aim of this study is to review the competencies in nursing robotics. A review was conducted between January 2017 and December 2023. The search strategy was carried out in the MEDLINE database (through PubMed). This review explores the developmental competencies in nursing robotics and informatics. The data extraction in this review included an intentional search for competencies and learning outcomes in engineering and robotic programs. A total of 340 competencies and program outcomes were reviewed. The synthesis of the data established a total of 17 developmental competencies in nursing robotics based on this knowledge extraction, which we organized into five categories: assessment, diagnosis, planning, intervention (implementation) and evaluation. This review suggests that nursing robotic competencies for the development of care robotics are still scarce, and there is an opportunity for the development of competencies and the definition of new roles in the area of nursing informatics in order to adapt to the new health care demands of society.

Ethical considerations for artificial intelligence use in nursing informatics.

Artificial intelligence revolutionizes nursing informatics and healthcare by enhancing patient outcomes and healthcare access while streamlining nursing workflow. These advancements, while promising, have sparked debates on traditional nursing ethics like patient data handling and implicit bias. The key to unlocking the next frontier in holistic nursing care lies in nurses navigating the delicate balance between artificial intelligence and the core values of empathy and compassion. Mindful utilization of artificial intelligence coupled with an unwavering ethical commitment by nurses may transform the very essence of nursing.

Informing nursing policy: An exploration of digital health research by nurses in England.

AIMS: Digital health technologies are designed, implemented, and evaluated to support clinical practice, enable patients to self-manage illness, and further public and global health. Nursing and health policies often emphasise the importance of evidence-based digital health services to deliver better care. However, the contribution nurses make to digital health research in many countries is unknown. Hence, this study aims to examine digital health research conducted by nurses in England. DESIGN: A bibliometric analysis. METHODS: The CINAHL, MEDLINE, and Scopus databases were searched between 2000 and 2022, and supplemented with a hand search of nurses' research profiles. Results were screened by title, abstract, and full text against eligibility criteria. Data were extracted and bibliometric analysis used to summarise the findings. RESULTS: Mental health nurses produced the most digital health research in England, followed by nurses working in community care, with several disciplines underrepresented or missing. Web/online health services or information was the most researched technology, followed by mobile health and telehealth. Nurses based in the south-east and north-west of England produced the most digital health research, with other regions less well represented. CONCLUSION: Nurse leaders should support nurses to conduct more digital health research by providing dedicated time, funding, and professional development opportunities, particularly in under researched clinical areas, technologies, and geographic regions to further evidence-based practice and patient care. More digital nursing data is needed to support nurse led research in areas like artificial intelligence and data science. The findings supported the national Philips Ives Review by identifying areas of digital nursing research that need more investment in England.

Evaluating Informatics Competencies Requirements for Hospital Nurse Managers in Japan.

This study classifies the nursing informatics competency requirements for nurses and healthcare leaders in the United States according to each of the four levels listed in the "Management Ladder for Hospital Nurse Managers (JNA version)" published by the Japanese Nursing Association. Computer skills were included in Level I. Fifteen informatics knowledge items and four informatic competency items that could not be classified for the levels of the management ladder for nurse managers in Japan. This list of nursing informatics competencies, categorized according to the management levels of hospital nurse managers, can be used to provide nursing informatics training to them.

Development of a Cloud-Based Electronic Nursing Record System (ENR) for Nursing Student Practice.

The purpose of this study is to develop cloud-based electronic nursing records (ENR) that can be used as Academic-EMR to help students adapt to the clinical field and improve the clarity of nursing records and nursing information capabilities. This research and development are expected to increase the efficiency of nursing work in clinical sites by improving students' access to ENR through the development of various virtual patient contents.

Nursing Informatics: Competency Challenges for Nursing Faculty.

Nursing is the largest workforce in health care with nurses increasingly required to work with digital health technologies. However, despite the adoption of nursing informatics in Australia in the mid-1980s, nursing graduates are not being adequately equipped to use these technologies in a way that benefits the profession and improves patient care. Using a scoping review approach, this paper presents an analysis of contemporary published literature and describes the barriers to faculty engagement with digital health technologies in undergraduate nursing education. Thirty five articles were included and identified faculty lack of understanding of nursing informatics and resistance to technologies, limited infrastructure and expenditure, and limited educational resources and best practice recommendations as significant barriers to the integration of nursing informatics into undergraduate nursing curricula. Recommendations for faculty development will be explored.

Standardizing nursing data extracted from electronic health records for integration into a statewide clinical data research network.

BACKGROUND: Care plans documented by nurses in electronic health records (EHR) are a rich source of data to generate knowledge and measure the impact of nursing care. Unfortunately, there is a lack of integration of these data in clinical data research networks (CDRN) data trusts, due in large part to nursing care being documented with local vocabulary, resulting in non-standardized data. The absence of high-quality nursing care plan data in data trusts limits the investigation of interdisciplinary care aimed at improving patient outcomes. OBJECTIVE: To map local nursing care plan terms for patients' problems and goals in the EHR of one large health system to the standardized nursing terminologies (SNTs), NANDA International (NANDA-I), and Nursing Outcomes Classification (NOC). METHODS: We extracted local problems and goals used by nurses to document care plans from two hospitals. After removing duplicates, the terms were independently mapped to NANDA-I and NOC by five mappers. Four nurses who regularly use the local vocabulary validated the mapping. RESULTS: 83% of local problem terms were mapped to NANDA-I labels and 93% of local goal terms were mapped to NOC labels. The nurses agreed with 95% of the mapping. Local terms not mapped to labels were mapped to the domains or classes of the respective terminologies. CONCLUSION: Mapping local vocabularies used by nurses in EHRs to SNTs is a foundational step to making interoperable nursing data available for research and other secondary purposes in large data trusts. This study is the first phase of a larger project building, for the first time, a pipeline to standardize, harmonize, and integrate nursing care plan data from multiple Florida hospitals into the statewide CDRN OneFlorida+ Clinical Research Network data trust.

A pilot test of an infographic-based health communication intervention to enhance patient education among Latino persons with HIV.

OBJECTIVE: To pilot test an infographic-based health communication intervention that our team rigorously designed and explore whether its implementation leads to better health outcomes among Latino persons with HIV (PWH). MATERIALS AND METHODS: Latino PWH (N = 30) living in New York City received the intervention during health education sessions at 3 study visits that occurred approximately 3 months apart. At each visit, participants completed baseline or follow-up assessments and laboratory data were extracted from patient charts. We assessed 6 outcomes (HIV-related knowledge, self-efficacy to manage HIV, adherence to antiretroviral therapy, CD4 count, viral load, and current and overall health status) selected according to a conceptual model that describes pathways through which communication influences health outcomes. We assessed changes in outcomes over time using quantile and generalized linear regression models controlling for the coronavirus disease 2019 (COVID-19) research pause and new patient status (new/established) at the time of enrollment. RESULTS: Most participants were male (60%) and Spanish-speaking (60%); 40% of participants identified as Mixed Race/Mestizo, 13.3% as Black, 13.3% as White, and 33.3% as "other" race. Outcome measures generally improved after the second intervention exposure. Following the third intervention exposure (after the COVID-19 research pause), only the improvements in HIV-related knowledge and current health status were statistically significant. DISCUSSION AND CONCLUSION: Our infographic-based health communication intervention may lead to better health outcomes among Latino PWH, but larger trials are needed to establish efficacy. From this work, we contribute suggestions for effective infographic use for patient-provider communication to enhance patient education in clinical settings.

Designing an infographic webtool for public health.

OBJECTIVE: To create and evaluate a public health informatics tool, Florence, for communicating information to the public. MATERIALS AND METHODS: This user-centered design study included 3 phases: (1) an interview and survey study with public health practitioners to assess needs for creating infographics; (2) the application of assessment findings and public health-motivated design guidelines to the design and development of a public health-specific infographic design tool; and (3) a feasibility and usability study to evaluate the feasibility and usability of the tool. RESULTS: In phase 1, participants noted the importance of tailoring infographics to an audience and wanted flexible tools along with design guidance to help make fewer design decisions. In phase 2, we developed a prototype tool with: (1) layout and functionality familiar to PH users, (2) quick and intuitive ways to add and modify data in visualizations, and (3) health-focused visual elements. In phase 3, participants found Florence to be usable, providing an intuitive and straightforward experience, and that the focus on public health was useful. DISCUSSION: Based on needs assessments and existing literature, we created Florence along with public health practitioners to address their domain specific needs, ultimately leading to a tool that participants in our study deemed useful. Future research can build on our work to develop user-centered tools to meet their needs. CONCLUSION: Infographics are important for public health communication. Creating user-centered solutions to address the unique needs of public health can support communication efforts.

Shifting Mindsets: The Impact of a Patient Portal on Functioning and Recovery in a Mental Health Setting.

OBJECTIVE: This study aims to understand whether higher use of a patient portal can have an impact on mental health functioning and recovery. METHOD: A mixed methods approach was used for this study. In 2019-2021, patients with mental health diagnoses at outpatient clinics in an academic centre were invited to complete World Health Organization Disability Assessment Scale 12 (WHODAS-12) and Mental Health Recovery Measure surveys at baseline, 3 months, and 6 months after signing up for the portal. At the 3-month time point, patients were invited to a semistructured interview with a member of the team to contextualize the findings obtained from the surveys. Analytics data was also collected from the platform to understand usage patterns on the portal. RESULTS: Overall, 113 participants were included in the analysis. There was no significant change in mental health functioning and recovery scores over the 6-month period. However, suboptimal usage was observed as 46% of participants did not complete any tasks within the portal. Thirty-five participants had low use of the portal (1-9 interactions) and 18 participants had high usage (10+ interactions). There were also no differences in mental health functioning and recovery scores between low and high users of the portal. Qualitative interviews highlighted many opportunities where the portal can support overall functioning and mental health recovery. CONCLUSIONS: Collectively, this study suggests that higher use of a portal had no impact, either positive or negative, on mental health outcomes. While it may offer convenience and improved patient satisfaction, adequate support is needed to fully enable these opportunities for patient care. As the type of interaction with the portal was not specifically addressed, future work should focus on looking at ways to support patient engagement and portal usage throughout their care journey.

Natural Language Processing Applied to Clinical Documentation in Post-acute Care Settings: A Scoping Review.

OBJECTIVES: To determine the scope of the application of natural language processing to free-text clinical notes in post-acute care and provide a foundation for future natural language processing-based research in these settings. DESIGN: Scoping review; reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. SETTING AND PARTICIPANTS: Post-acute care (ie, home health care, long-term care, skilled nursing facilities, and inpatient rehabilitation facilities). METHODS: PubMed, Cumulative Index of Nursing and Allied Health Literature, and Embase were searched in February 2023. Eligible studies had quantitative designs that used natural language processing applied to clinical documentation in post-acute care settings. The quality of each study was appraised. RESULTS: Twenty-one studies were included. Almost all studies were conducted in home health care settings. Most studies extracted data from electronic health records to examine the risk for negative outcomes, including acute care utilization, medication errors, and suicide mortality. About half of the studies did not report age, sex, race, or ethnicity data or use standardized terminologies. Only 8 studies included variables from socio-behavioral domains. Most studies fulfilled all quality appraisal indicators. CONCLUSIONS AND IMPLICATIONS: The application of natural language processing is nascent in post-acute care settings. Future research should apply natural language processing using standardized terminologies to leverage free-text clinical notes in post-acute care to promote timely, comprehensive, and equitable care. Natural language processing could be integrated with predictive models to help identify patients who are at risk of negative outcomes. Future research should incorporate socio-behavioral determinants and diverse samples to improve health equity in informatics tools.