Advance Health Care Research Articles

Advance Healthcare Directives: the Irish Implementation Reality from an Interdisciplinary Group Concept Mapping Study

Abstract Background Advance Healthcare Directives became legal in Ireland on 26th April 2023. While Advance Healthcare Directives are primarily rooted in patients’ legally binding refusal of treatments or interventions, they also encourage patients to set out their care preferences for the future (Government of Ireland 2015). Despite care of the older adult broadly transitioning from acute hospital to community, and a greater than ever focus on keeping older adults living at home for longer, low awareness of, and professional uncertainty around, Advance Healthcare Directives and how to integrate them into the care of the older adult has been widely expressed by Irish hospital physicians (Crowley and Doran 2022). Methods In this implementation study, Group Concept Mapping, using the relatively novel ‘GroupWisdom™’ software, is used to collect and analyse data from multiple healthcare professions to identify contextual issues that affect the implementation of Advance Healthcare Directives. 44 frontline healthcare professionals including Hospital Consultants (n=6), General Practitioners (n=5), Paramedics (n=7), Advance Nurse Practitioners (n=4), Clinical Nurse Specialists (n=4), Public Health Nurses (n=5), Critical Care Nurses (n=3), Nurse Managers (n=2), Healthcare Managers (n=2), Occupational Therapists (n=2) and Social Workers (n=4) participate in this mixed method study. Policymakers and other stakeholders are also involved throughout. Results The main implementation issues for Advance Healthcare Directives in order of importance as outlined by Healthcare Professionals are: 1) Public Education; 2) Homecare Services; 3) Timing and Location; 4) Family Members; 5) Lack of Education for the Healthcare Professional; 6) Complications for the Healthcare Professional; 7) Individual and Cultural Beliefs. Conclusion This study contributes a valuable understanding of where the implementation issues reside for healthcare professionals and policymakers, and how these can be addressed to ensure successful implementation. This implementation study focuses on practical considerations, offering multiple workable solutions to help to resolve these implementation issues on the ground.

Introduction to the Theme "Novel Therapeutics with the Potential to Advance Health Care".

The reviews in Volume 65 of the Annual Review of Pharmacology and Toxicology cover a wide variety of topics in pharmacology and toxicology focused upon the pathway from preclinical studies to clinical trials. Many of these reviews discuss the identification and validation of new therapeutic targets and/or novel therapeutic approaches. Examples include reviews that focus on the treatment of obesity, neuropsychiatric disorders, Parkinson's disease, substance use disorders, liver fibrosis, cardiac arrythmias, chronic intestinal inflammation, prostate cancer, immuno-oncology, sickle cell disease, and snakebite envenoming. Other topics include drug discovery of biologics, microphysiological systems, and human induced pluripotent stem cell-derived organoids and organ-on-chip technology integrated with artificial intelligence methodologies. Together, these and other reviews give new insights into the assessment of aspects of toxicology and provide readers a glimpse of advances in pharmacology and toxicology that we believe will advance health care and environmental safety.

EMPOWERING END-OF-LIFE CONVERSATIONS: The Role of Specialized Nursing Teams in Facilitating Code Status Changes at Discharge.

Objectives: Hospitalized patients may require goals of care (GOC) or Advance Health Care Planning (ACP), which can be time-consuming and emotionally tolling for providers. A nursing team specializing in code status (CODE), GOC, and ACP was developed to provide meaningful support for patients and families and decrease provider burden. Interest in CODE, GOC, ACP, and effectiveness of a nursing team to lead these conversations prompted this study. Methods: A collaborative nursing team was trained to address CODE, GOC, and ACP with patients demonstrating illness or geriatric syndrome. This team conducted 3 visits per patient on average during hospitalization using structured CODE templates to establish longer term goals and document what matters in the healthcare journey. Comprehensive narratives for ACP and GOC were included in charting, syncing the medical team, nursing, patient, and family. Consults were tracked over nine months with data reviewed retrospectively from medical charts. Descriptive analyses of cohort demographics, CODE and outcomes were completed. Results: The study group comprised 3342 patients between October 2022 and June 2023. Patients ranged in age from 18-106 years, with majority (88%) age 65 years and older. Mean length of stay (LOS) was 6.8 days with CODE documented for 91% upon admission. Of the 3166 older adults with known CODE on admission, 946 (30%) changed CODE by discharge, of which 95% were de-escalated. 83% of older patients arriving with limited CODE maintained limitations at discharge, with a small portion converting to comfort (16%). Conclusion: Employing a focused nursing team to conduct CODE, GOC, and ACP conversations may be an effective use of time and resources and result in de-escalation of resuscitation orders for patients demonstrating illness or geriatric syndrome.

Enhancing Spinal Cord Injury Care: Using Wearable Technologies for Physical Activity, Sleep, and Cardiovascular Health

Wearable devices have the potential to advance health care by enabling real-time monitoring of biobehavioral data and facilitating the management of an individual's health conditions. Individuals living with spinal cord injury (SCI) have impaired motor function, which results in deconditioning and worsening cardiovascular health outcomes. Wearable devices may promote physical activity and allow the monitoring of secondary complications associated with SCI, potentially improving motor function, sleep, and cardiovascular health. However, several challenges remain to optimize the application of wearable technologies within this population. One is striking a balance between research-grade and consumer-grade devices in terms of cost, accessibility, and validity. Additionally, limited literature supports the validity and use of wearable technology in monitoring cardio-autonomic and sleep outcomes for individuals with SCI. Future directions include conducting performance evaluations of wearable devices to precisely capture the additional variation in movement and physiological parameters seen in those with SCI. Moreover, efforts to make the devices small, lightweight, and inexpensive for consumer ease of use may affect those with severe motor impairments. Overcoming these challenges holds the potential for wearable devices to help individuals living with SCI receive timely feedback to manage their health conditions and help clinicians gather comprehensive patient health information to aid in diagnosis and treatment.

A Review on Artificial Intelligence in Pharmaceutical Science

Abstract: In recent years, the use of artificial intelligence (AI) in health care has risen steadily, including a wide range of applications in the field of pharmacology. AI is now used throughout the entire continuum of pharmacology research and clinical practice and from early drug discovery to real-world data mining. The types of AI models used range from unsupervised clustering of drugs or patients aimed at identifying potential drug compounds or suitable patient populations, to supervised machine learning approaches to improve therapeutic drug monitoring. Additionally, natural language processing is increasingly used to mine electronic health records to obtain real-world data. In this mini-review, we discuss the basics of AI followed by an outline of its application in pharmacology research and clinical practice. Artificial intelligence is the upcoming technology in advance health care system. Current digitalization of medicine and availability of electronic health records (EHRs) has inspired clinical researchers and healthcare personnel to acquire artificial intelligence (AI) methodologies for big data analytics and for very large scale medical databases. The major advantage of AI is that it reduces the time that is needed for drug development and, in turn, it reduces the costs that are associated with drug development, enhances the returns on investment and may even cause a decrease in cost for the end user. A large number of researches are being carried out to improve the current available AI technology to make the pharmacy profession more efficient. The present article briefly describes the importance of AI in the process of drug development and then looks at the various AI tools that are available at the disposal of a modern-day pharmacist to aid in a more efficient functioning.

Improving Advance Healthcare Planning Documentation in Nursing Home Medical Records

Improving Advance Healthcare Planning Documentation in Nursing Home Medical Records

NIMHD INVESTMENTS IN RESEARCH ON OLDER ADULTS EXPERIENCING HEALTH DISPARITIES: 2018–2022

Abstract The National Institute on Minority Health and Health Disparities (NIMHD) is the nation’s leading institute on the health of populations experiencing health disparities. These populations include individuals with low socioeconomic status (SES), those living in rural areas, sexual and gender minorities (SGM), and racial and ethnic minorities. Despite the need to advance research on these populations, particularly in old age when health disparities may be most salient, there has been no analysis of NIMHD funding on older adults to date. This portfolio analysis synthesized and described NIMHD’s investments in research on older adults using data from fiscal years 2018 through 2022. The Research, Condition, and Disease Categorization system was used to identify funded projects in the area of Aging; manual review confirmed project eligibility. In-depth project characteristics were extracted and topic areas evaluated. Findings demonstrate that NIMHD funded a total of 98 unique research projects focused on older adults. Of these, 47% focused on the etiology of health disparities, 38% on interventions, and 15% on methods and measurement. The most specified population was racial and ethnic minorities (91%), followed by individuals with low SES (21%), rural older adults (11%), and SGM older adults (8%). Projects focused on diagnostic or clinical care (16%), care coordination (6%), caregivers (6%), and shared decision-making (4%) were limited. Findings highlight opportunities for future research to advance health care and reduce health disparities for the growing proportion of older adults from populations experiencing health disparities.

The Factors associated with death in hospitalized patients with severe acute respiratory syndrome by Covid-19: a retrospective longitudinal study in a city in southeastern Brazil

The COVID-19 pandemic represents a challenge for health systems. Considering the importance of determining possible risk groups, this study aimed to describe the epidemiological profile and to evaluate the factors associated with death in hospitalized patients with Severe Acute Respiratory Syndrome caused by SARS-Cov-2 (COVID-19), in Contagem, Minas Gerais, during the year of 2020. A bivariate logistic regression was performed for each explanatory variable, followed by step-by-step backward selection. Only adjusted variables showing a significant association remained in the final model. This study demonstrates that the chances of death in hospitalized patients were higher in men; people aged 60 years or older; that presented respiratory distress as a symptom; that were obese or immunocompromised; that had cardiac, hepatic, renal, pulmonary or neurologic chronic diseases; and that required intensive care unit admission or invasive respiratory support. We conclude that COVID-19 represents an enormous challenge for health systems, demanding the need for specialized hospital services. Therefore, knowing the risk profile is a necessary tool to optimize treatment and to prevent worse outcomes. KEY WORDS: Severe acute respiratory syndrome; coronavirus infection; advance health care planning; health correlates; risk factor; covid-19.

Intervening in the Cancer Care System: An Analysis of Equity-Focused Nurse Navigation and Patient-Reported Outcomes.

Nurse navigation can improve quality of cancer care and reduce racial disparities in care outcomes. Addressing persistent structurally-rooted disparities requires research on strategies that support patients by prompting structural changes to systems of care. We applied a novel conceptualization of social support to an analysis of racial equity-focused navigation and patient-reported outcomes. We applied an antiracism lens to create a theory-informed definition of system-facing social support: intervening in a care system on a patient's behalf. Participants were adults with early-stage breast or lung cancer, who racially identified as Black or White, and received specialized nurse navigation (n = 155). We coded navigators' clinical notes (n = 3,251) to identify instances of system-facing support. We then estimated models to examine system-facing support in relation to race, perceived racism in health care settings, and mental health. Twelve percent of navigators' clinical notes documented system-facing support. Black participants received more system-facing support than White participants, on average (b = 0.78, 95% confidence interval [CI]: [0.25, 1.31]). The interaction of race*system-facing support was significant in a model predicting perceived racism in health care settings at the end of the study controlling for baseline scores (b = 0.05, 95% CI [0.01, 0.09]). Trends in simple slopes indicated that among Black participants, more system-facing support was associated with slightly more perceived racism; no association among White participants. The term system-facing support highlights navigators' role in advocating for patients within the care system. More research is needed to validate the construct system-facing support and examine its utility in interventions to advance health care equity.

Prediction of incident heart failure through deep learning-based analysis of 1-lead ECGs

Abstract Background/Introduction Identification of individuals at risk for heart failure (HF) can lead to early intervention. However, large scale screening for incident HF in the general population is insufficient, partly due to the limited capacities in health care systems and number of health care specialists. One-lead ECGs, e.g. from wearables capable of deriving this biomarker, are becoming more popular in the population. This holds the potential to identify individuals at risk who could benefit from a referral to a HF specialist. Purpose We examined the ability to predict three-year HF incidence from 1-lead ECGs using a deep learning (DL) model that was trained on data from the UK Biobank (UKB). Methods The UKB was established in 2006 and contains data from 500,000 participants from the British general population. N = 100,551 1-lead ECGs from 91,898 unique participants were used for this study. This includes 57,897 3-lead resting ECGs written during ergometry exams between 2006 and 2010 (Exam 1; median age 56.7 years, 46.2% men) and at the first follow-up from 2010-2013 (Exam 2; median age 61.3 years, 49% men) and 32,828 12-lead resting ECGs which were acquired between 2014 and 2020 (Exam 3; median age 63.5 years, 48.4% men). Lead I was extracted from the ECGs for all analyses. A 1D-Convolutional Neural Network was used for prediction of the 3-year risk of incident HF. A comparison with the CHARGE-AF score was performed using a nested 6-fold cross-validation scheme. The models were evaluated using the Area Under the Curve (AUC). Results In all three exams, the DL model was able to reliably detect 3-year incident HF (AUC; Exam 1: 0.738, Exam 2: 0.822, Exam 3: 0.738) while the CHARGE-AF score provided slightly better predictions at Exam 1 and Exam 3 (AUC 0.766, 0.748), but lower results in Exam 2 (AUC 0.736). Combining both models led to increased discriminatory properties across all exams (AUC; Exam 1: 0.812, Exam 2: 0.832, Exam 3: 0.788). Conclusion The derived DL model shows high discriminatory ability in the prediction of incident HF from 1-lead ECGs. The predictive ability can be further increased when combined with simple clinical variables from the CHARGE-AF score. This study represents another step towards the application of DL-based analysis of the ECG to advance health care.

Measuring Progress Toward Equity in Health Care

Abstract: In this commentary, we consider the need for measurement approaches that support efforts to advance health care equity nationwide in a meaningful and sustained way. The authors of this piece are co-chairs of the Measurement Steering Committee of Rise to Health: A National Coalition for Equity in Health Care (www.risetohealthequity.org), and our writing is directly informed by our work in the Coalition. We present three foundational questions and our recommendations for addressing them: 1.) Tracking progress: Will we be able to see if change occurs? 2.) Ensuring accountability: Are we measuring only what is easy to measure or what is meaningful? 3.) Inspiring and sustaining collective action: What will hold a diverse set of institutions together?

A CT Scan of Our Earth: The Radiology Department’s How-To Guide for Addressing Planetary Health

The planet has a fever, and it is getting worse. Climate change manifests through mechanisms such as extreme weather, shifting disease burden, wildfires, and drought, which all have negative implications on human health. Simultaneously, the health care sector is responsible for 4.6% of global greenhouse gas emissions. As users of some of the hospital’s most energy-intensive equipment, radiology departments are key stakeholders in the transition to clean energy. The authors propose a framework to guide radiology departments to advance health care sustainability. The approach outlines how a radiology department can reduce its environmental footprint through appointing a sustainability officer, forming a dedicated green team, incorporating sustainability into the departmental strategic plan, quantifying total greenhouse gas emissions, committing to education, and advocating for systemic change. By delineating a structured path, the authors hope to encourage the transition toward environmentally friendly practices in all radiology practice settings.

Teleconsultation Platforms Versus WhatsApp Consultations: Evaluating Patients and Physicians' Attitudes.

Background: Since the 1990s, society has witnessed a surge in information and communication technologies that extended to the health care system. Teleconsultation platforms have become widely established tools that provide an opportunity for emerging countries such as Lebanon to expand and advance health care services. However, their use in Lebanon remains limited considering many people's preference to resort to nonmedical platforms such as WhatsApp for consultations. Methods: The purpose of this mixed-method study is to evaluate patients' and physicians' attitudes toward teleconsultation platforms versus WhatsApp consultations in Lebanon. In the quantitative component, 1,467 patients were administered a self-developed questionnaire, while in the qualitative component, a semistructured interview was conducted with 14 physicians practicing in Lebanon. Results: Results revealed that teleconsultation platforms and WhatsApp are not interchangeable. Patients' and physicians' experiences, perceptions, and satisfaction with teleconsultation platforms and WhatsApp are discussed. Conclusions: Individual and governmental recommendations for safer, more controlled, and more ethical online consultation practices are provided for both physicians and patients, through both modalities.

An exploration of faculty perspectives towards interprofessional education and collaborative practice during international electives in health professions training institutions in Africa

Background Faculty perspectives are key drivers of learning during international electives (IEs). However, in Africa, there is limited literature on these perspectives on interprofessional education and collaborative practice (IPECP) during IEs. Therefore, this study explored faculty perspectives toward IPECP within an international elective context from selected African institutions. Methods This was an exploratory qualitative study. The study participants included faculty from four health professional training universities in Africa. Thematic analysis was used to analyze the data. Common codes were identified and grouped to create subthemes and major themes. Results Four themes were identified: (i) The importance of IPECP during IEs, (ii) Approaches to IPECP during IEs, (iii) Perceived key barriers, and (iv) Perceived institutional support for IPECP during IEs. Most of the participants perceived IEs as a platform that can be used to cultivate IPECP with a multicultural perspective while promoting cross-border partnerships to advance health care. Suggested teaching approaches included case studies, joint clinical ward rounds, and simulations, among others, with an openness to having an online, blended, or physical mode of delivery. A framework to guide the implementation of IPECP during IEs was considered important, and most participants thought that their institutions had the infrastructure to support IPECP-IEs. Conclusions Faculty reported enabling perspectives of IPECP during IEs, emphasizing the need for a framework to guide and structure implementation. IPECP-IEs were perceived as a unique platform to cultivate IPECP skills with a multicultural component.

Shortage of Mental Health Professionals Doing Research: A Cause for Concern.

Research plays a crucial role in advancing health care. Physician scientists, who bring together clinical and scientific perspectives, are integral to this effort. However, the pursuit of research careers among physicians has declined due to several obstacles, including inadequate support, training, time, and financial constraints. Emergency medicine and psychiatric specialties receive less support than other specialties, and COVID-19 has worsened the challenges. The COVID-19 has made things harder for physician scientists, increasing clinical demands and causing research delays. To tackle this, future physician scientists should receive organized training that includes mandatory research activities during their training. Large-scale studies should identify barriers to research and offer mentoring for young physician scientists. Encouraging female researchers and involving research faculty in residency training is crucial. Collaboration between physician and PhD researchers is essential for the future of medical research. It is vital to address these to advance health care and improve mental health outcomes.

Disparities in primary and emergency health care among "off-reserve" Indigenous females compared with non-Indigenous females aged 15-55 years in Canada.

Access to primary care protects the reproductive and non-reproductive health of females. We aimed to quantify health care disparities among "off-reserve" First Nations, Métis and Inuit females, compared with non-Indigenous females of reproductive age. We used population-based data from cross-sectional cycles of the Canadian Community Health Survey (2015-2020), including 4 months during the COVID-19 pandemic. We included all females aged 15-55 years. We measured health care access, use and unmet needs, and quantified disparities through weighted and age-standardized absolute prevalence differences compared with non-Indigenous females. We included 2902 First Nations, 2345 Métis, 742 Inuit and 74 760 non-Indigenous females of reproductive age, weighted to represent 9.7 million people. Compared with non-Indigenous females, Indigenous females reported poorer health and higher morbidity, yet 4.2% (95% confidence interval [CI] 1.8% to 6.6%) fewer First Nations females and 40.7% (95% CI 34.3% to 47.1%) fewer Inuit females had access to a regular health care provider. Indigenous females waited longer for primary care, more used hospital services for nonurgent care, and fewer had consultations with dental professionals. Accordingly, 3.2% (95% CI 0.3% to 6.1%) more First Nations females and 4.0% (95% CI 0.7% to 7.3%) more Métis females reported unmet needs, especially for mental health (data for Inuit females not reported owing to high variability). During reproductive age, Indigenous females in Canada face many disparities in health care access, use and unmet needs. Solutions aimed at increasing access to primary care are urgently needed to advance health care reconciliation.

Who Bears Responsibility for the Post-Acute Older Adult: Patient, Family or State?

This study explores the perceptions of hospital discharge coordinators on the issues raised for the post-acute older adult and their family, using the lens of assisted decision-making and advance directives. New legislation has been developed in Ireland to support assisted decision-making and advance directives that is largely consistent with other countries around the world. The Assisted Decision-Making Act of 2015 was fully commeced in April 2023, following lengthy debate. However, there is a lot of professional uncertainty regarding how to support and integrate Advance Healthcare Directives and assisted decision-making into the care of adults and into the role of nurses working in the community. Utilising a qualitative approach, this study conducted a series of five focus groups, with 23 participants, across the South-East of Ireland. Thematic analysis was used to interpret results. The 32-item consolidated criteria for reporting qualitative research checklist was utilised. The central finding in this study concerned the locus of control, as well as findings on burden of care and the role of the nurse. A stark incongruence of beliefs among patient, family and State was identified. Nurses must learn to circumnavigate the complex terrain involved in supporting the older adult's advance decision-making and advance healthcare directives.

Implementing a chatbot on Facebook to reach and collect data from thousands of health care providers: PharmindBot as a case

BackgroundThe world is moving fast toward digital transformation as we live in the artificial intelligence (AI) era. The COVID-19 pandemic accelerates this movement. Chatbots were used successfully to help researchers collect data for research purposes. ObjectiveTo implement a chatbot on the Facebook platform to establish connections with health care professionals who had subscribed to the chatbot, provide medical and pharmaceutical educational content, and collect data for online pharmacy research projects. Facebook was chosen because it has billions of daily active users, which offers a massive potential audience for research projects. Practice descriptionThe chatbot was successfully implemented on the Facebook platform following 3 consecutive steps. Firstly, the ChatPion script was installed on the Pharmind website to establish the chatbot system. Secondly, the PharmindBot application was developed on Facebook. Finally, the PharmindBot app was integrated with the chatbot system. Practice innovationThe chatbot responds automatically to public comments and sends subscribers private responses using AI. The chatbot collected quantitative and qualitative data with minimal costs. Evaluation methodsThe chatbot's auto-reply function was tested using a post published on a specific page on Facebook. Testers were asked to leave predefined keywords to test its functionality. The chatbot's ability to collect and save data was tested by asking testers to fill out an online survey within Facebook Messenger for quantitative data and answer predefined questions for qualitative data. ResultsThe chatbot was tested on 1000 subscribers who interacted with it. Almost all testers (n = 990, 99%) obtained a successful private reply from the chatbot after sending a predefined keyword. Also, the chatbot replied privately to almost all public comments (n = 985, 98.5%) which helped to increase the organic reach and to establish a connection with the chatbot subscribers. No missing data were found when the chatbot was used to collect quantitative and qualitative data. ConclusionsThe chatbot reached thousands of health care professionals and provided them with automated responses. At a low cost, the chatbot was able to gather both qualitative and quantitative data without relying on Facebook ads to reach the intended audience. The data collection was efficient and effective. Using chatbots by pharmacy and medical researchers will help do more feasible online studies using AI to advance health care research.

Leveraging physiology and artificial intelligence to deliver advancements in health care.

Artificial intelligence in health care has experienced remarkable innovation and progress in the last decade. Significant advancements can be attributed to the utilization of artificial intelligence to transform physiology data to advance health care. In this review, we explore how past work has shaped the field and defined future challenges and directions. In particular, we focus on three areas of development. First, we give an overview of artificial intelligence, with special attention to the most relevant artificial intelligence models. We then detail how physiology data have been harnessed by artificial intelligence to advance the main areas of health care: automating existing health care tasks, increasing access to care, and augmenting health care capabilities. Finally, we discuss emerging concerns surrounding the use of individual physiology data and detail an increasingly important consideration for the field, namely the challenges of deploying artificial intelligence models to achieve meaningful clinical impact.

Heart Diseases Prediction using Yolo Algorithm

The Development of Advance Healthcare System is developing rapidly, lots of patient data are nowadays available (i.e. Big Data in Electronic Health Record System) which can be used for designing predictive models for Cardiovascular diseases. Data mining or machine learning is a discovery method for analyzing big data from an assorted perspective and encapsulating it into useful information. “Data Mining is a non-trivial extraction of implicit, previously unknown and potentially useful information about data”.