Care Technology Research Articles

Implementation of NCCARE360, a Digital Statewide Closed-Loop Referral Platform to Improve Health and Social Care Coordination: Evidence from the North Carolina COVID-19 Support Services Program.

Efforts to improve population health by being responsive to patients' social and economic conditions will benefit from care models and technologies that assess and address unmet social needs. In 2019, NCCARE360 launched in North Carolina as the first state-wide digital care coordination network to "close the loop" on referrals between community-based organizations (CBOs), health service providers, and social service agencies. The platform creates a shared network for sending and receiving electronic referrals and track client outcomes. As a case study, we compare NCCARE360 resolution rates for community resource referrals originating from a large integrated health system primarily in Durham County from September 1, 2020, through February 28, 2021. In the first year, COVID-19 Support Ser-vices Program (COVID-SSP) funding was available to reimburse associated CBOs for providing food assistance and case management services. We compared this with the same period the following year after funds had been exhausted. We present frontline implementation experiences and highlight opportunities, challenges, and recommendations for NCCARE360 implementation. Multi-level considerations for individual end users, organizations adopting the platform, and policymakers are presented. Addi-tionally, we find that when COVID-SSP funding was available, more referrals were placed (3,220 cases) and referrals were more likely to be resolved (88% resolution rate) when compared to the same time frame when funds were no longer available (860 cases; 30% resolution rate). These results underscore the importance of reimbursement mechanisms and funding. The examination of referral rates is observational and may not generalize to other contexts. The shift to value-based care is an opportunity to embrace structural solutions to health and social care fragmentation. There is also an opportunity to realize the potential of NCCARE360 and efforts like it to contain costs and improve health outcomes and equity.

Patient Reminder System

Overview Patient Health Monitoring on ESP8266 Healthcare technology is very popular in this pandemic situation because of coronavirus. Actually, health care technology is rapidly being revolutionized with the help of the Internet of Things (IoT). Monitoring the health status of a covid patient is a hard task because of our busy schedule and our daily work. Mostly, the elderly covid patients should be monitored periodically. So, I thought to make an innovative system in this lockdown to automate the task. This device uses an ESP8266 webserver to track patient health using this monitoring system. Hence, patient health parameters such as body temperature, heart rate (BPM), blood oxygen levels (Sp02) as well as room temperature and humidity can be monitored from any device (like Smartphone, PC, Laptop, Smart TV,.) That support browsing capabilities. In this project, we will learn how to build an ESP8266 based Patient Health Monitoring System. To measure Heart Rate/Pulse (BPM) and Blood Oxygen Level (SpO2), we use the MAX30100 pulse oximeter sensor. Similarly, to measure body temperature, we use the DS18B20 temperature sensor. Meanwhile, the patient is inside the room. So we need to monitor room temperature and humidity level as well. We should keep them in a room with a certain temperature and humidity level to not feel uncomfortable. Hence, we use the DHT22 Temperature & Humidity sensor. A Smart Patient Health Monitoring System using IoT (Internet of Things) is a technology-driven solution designed to continuously collect, transmit, and analyze health-related data from patients. It provides real-time insights to healthcare professionals, patients, and caregivers to improve patient care, enhance early disease detection, and promote overall well-being. One important component of such a system is the alert system, which notifies relevant parties when certain health parameters or conditions require attention.

A Closed-Loop Digital Health Tool to Improve Depression Care in Multiple Sclerosis: Iterative Design and Cross-Sectional Pilot Randomized Controlled Trial and its Impact on Depression Care.

People living with multiple sclerosis (MS) face a higher likelihood of being diagnosed with a depressive disorder than the general population. Although many low-cost screening tools and evidence-based interventions exist, depression in people living with MS is underreported, underascertained by clinicians, and undertreated. This study aims to design a closed-loop tool to improve depression care for these patients. It would support regular depression screening, tie into the point of care, and support shared decision-making and comprehensive follow-up. After an initial development phase, this study involved a proof-of-concept pilot randomized controlled trial (RCT) validation phase and a detailed human-centered design (HCD) phase. During the initial development phase, the technological infrastructure of a clinician-facing point-of-care clinical dashboard for MS management (BRIDGE) was leveraged to incorporate features that would support depression screening and comprehensive care (Care Technology to Ascertain, Treat, and Engage the Community to Heal Depression in people living with MS [MS CATCH]). This linked a patient survey, in-basket messages, and a clinician dashboard. During the pilot RCT phase, a convenience sample of 50 adults with MS was recruited from a single MS center with 9-item Patient Health Questionnaire scores of 5-19 (mild to moderately severe depression). During the routine MS visit, their clinicians were either asked or not to use MS CATCH to review their scores and care outcomes were collected. During the HCD phase, the MS CATCH components were iteratively modified based on feedback from stakeholders: people living with MS, MS clinicians, and interprofessional experts. MS CATCH links 3 features designed to support mood reporting and ascertainment, comprehensive evidence-based management, and clinician and patient self-management behaviors likely to lead to sustained depression relief. In the pilot RCT (n=50 visits), visits in which the clinician was randomized to use MS CATCH had more notes documenting a discussion of depressive symptoms than those in which MS CATCH was not used (75% vs 34.6%; χ21=8.2; P=.004). During the HCD phase, 45 people living with MS, clinicians, and other experts participated in the design and refinement. The final testing round included 20 people living with MS and 10 clinicians including 5 not affiliated with our health system. Most scoring targets for likeability and usability, including perceived ease of use and perceived effectiveness, were met. Net Promoter Scale was 50 for patients and 40 for clinicians. Created with extensive stakeholder feedback, MS CATCH is a closed-loop system aimed to increase communication about depression between people living with MS and their clinicians, and ultimately improve depression care. The pilot findings showed evidence of enhanced communication. Stakeholders also advised on trial design features of a full year long Department of Defense-funded feasibility and efficacy trial, which is now underway. ClinicalTrials.gov NCT05865405; http://tinyurl.com/4zkvru9x.

COVID-19-related announcements in a continuous disclosure environment: drivers and stock market implications

PurposeThe purpose of this research note is to investigate the drivers and market reaction to firms’ decision to release general COVID-19-related announcements and to withdraw earnings forecasts and dividends during the COVID-19 pandemic in the continuous disclosure environment of Australia.Design/methodology/approachThe authors first tracked the market reaction of all firms in the Australian Securities Exchange All Ordinaries, Top 300, Top 200 and Top 100 indices during the early period of the COVID-19 pandemic between 1 January and 21 September 2020. The authors then focus the investigation on the incidence of firms deciding to withdraw earnings forecasts and dividends and how the market responded to these incidences during that period.FindingsThe market reacted negatively during the March/April 2020 period but then bounced back to the pre-March 2020 level. The market reaction is mainly driven by three industries, including consumer discretionary, health care and utilities. Firms in industry sectors such as consumer discretionary, materials, health care and information technology contribute to the highest percentage of COVID-19 announcements. It is interesting to document that firms issuing COVID-19 announcements and withdrawing earnings forecasts and dividends tend to be larger firms with stronger financial performance and higher financial leverage. Regarding the stock market reaction, while the market generally reacted positively to COVID-19-related announcements, the decision to withdraw earnings forecasts and dividends is significantly regarded as bad news.Originality/valueThe COVID-19 pandemic has provided a unique natural event to examine firms’ disclosure behaviour in the continuous disclosure environment of Australia during this period of extreme uncertainty. The incidences of earnings forecasts and dividend withdrawals are mainly driven by larger, better performing and higher leverage firms in the consumer discretionary, health care, materials and information technology industry sectors. The market generally reacted favourably to COVID-19-related announcements, despite a significant stock price drop during the March/April 2020 period. The findings provide important regulatory and practical implications.

Role of a Nursing Informatics Specialist in a Clinical Simulation Laboratory: The Catalytic Link.

Technology in health care is pervasive and growing exponentially. Simulation in academic settings also continues to expand with the use of advancing technologies. Technology appropriate usage and implementation can become problematic without experts with the right skills in nursing simulation labs. Gaps in technology utilization, simulation best practices, and data analytics now exist. A school of nursing employed a nurse informaticist in its simulation laboratory to address the complexities of technology usage and aid in data analytics. The nurse informaticist was effective at promoting simulation use, increasing faculty and learner usage of the simulation space. The initiation of data capturing was also achieved. Highly trained nurse informaticists are ready to address the unique complexities of advanced simulation environments.

Telematic interview in telepharmacy: consensus document for farmacotherapeutic monitoring and informed drug delivery

Telepharmacy is defined as the practice of remote pharmaceutical care, using information and communication technologies. Given its growing importance in outpatient pharmaceutical care, the Spanish Society of Hospital Pharmacy developed a consensus document, “Guía de entrevista telemática en atención farmacéutica,” as part of its strategy for the development and expansion of telepharmacy, with key recommendations for effective pharmacotherapeutic monitoring and informed dispensing and delivery of medications through telematic interviews.The document was developed by a working group of hospital pharmacists with experience in the field. It highlights the benefits of telematic interviewing for patients, hospital pharmacy professionals, and the healthcare system as a whole, reviews the various tools for conducting telematic interviews, and provides recommendations for each phase of the interview. These recommendations cover aspects such as tool/platform selection, patient selection, obtaining authorization and consent, assessing technological skills, defining objectives and structure, scheduling appointments, reviewing medical records, and ensuring humane treatment.Telematic interview is a valuable complement to face-to-face consultations but its novelty requires a strategic and formal framework that this consensus document aims to cover. The use of appropriate communication tools and compliance with recommended procedures ensure patient safety and satisfaction. By implementing telematic interviews, healthcare institutions can improve patient care, optimize the use of resources and promote continuity of care.

Feasibility and Design Factors for Home-Based Pulmonary Rehabilitation of Patients With Chronic Obstructive Pulmonary Disease and Chronic Lung Diseases Based on a People-Object-Environment Framework: Qualitative Interview Study.

The feasibility of implementing home-based pulmonary rehabilitation (PR) can be assessed from the perspectives of patients with chronic lung disease and health care professionals involved in PR. Through a qualitative inquiry using interviews and the adoption of the people-object-environment framework, this study aims to understand the influences of interpersonal, environmental, and situational factors on the perceptions and considerations of individuals involved in home-based PR for patients with chronic lung disease. One-on-one interviews were conducted with 20 patients with chronic lung disease and 20 health care professionals for investigating their attitudes and opinions based on their experiences regarding home-based PR as well as for identifying the key factors affecting the benefits and drawbacks of such therapies. This study further evaluates the feasibility of using digital tools for medical diagnosis and treatment by examining the technology usage of both parties. The 4 key issues that all participants were the most concerned about were as follows: distance to outpatient medical care, medical efficiency, internet connectivity and equipment, and physical space for diagnosis and treatment. Interviews with patients and health care professionals revealed that the use of technology and internet was perceived differently depending on age and area of residence. Most participants reported that digital tools and internet connectivity had many benefits but still could not solve all the problems; moreover, these same digital tools and network transmission could lead to problems such as information security and digital divide concerns. This study also emphasizes the significant impact of human behavior and thinking on shaping the design of health care interventions and technologies. Understanding user perspectives and experiences is crucial for developing effective solutions for unmet needs. The results of this study indicate that despite the different perspectives of patients and health care professionals, their considerations of the key issues are very similar. Therefore, the implementation of plans related to telemedicine diagnosis, treatment, or rehabilitation should take the suggestions and considerations of both parties into account as crucial factors for telehealth care design.

Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review

Background: Over the past few years, clinical studies have utilized machine learning in telehealth and smart care for disease management, self-management, and managing health issues like pulmonary diseases, heart failure, diabetes screening, and intraoperative risks. However, a systematic review of machine learning’s use in evidence-based telehealth and smart care is lacking, as evidence-based practice aims to eliminate biases and subjective opinions. Methods: The author conducted a mixed methods review to explore machine learning applications in evidence-based telehealth and smart care. A systematic search of the literature was performed during 16 June 2023–27 June 2023 in Google Scholar, PubMed, and the clinical registry platform ClinicalTrials.gov. The author included articles in the review if they were implemented by evidence-based health informatics and concerned with telehealth and smart care technologies. Results: The author identifies 18 key studies (17 clinical trials) from 175 citations found in internet databases and categorizes them using problem-specific groupings, medical/health domains, machine learning models, algorithms, and techniques. Conclusions: Machine learning combined with the application of evidence-based practices in healthcare can enhance telehealth and smart care strategies by improving quality of personalized care, early detection of health-related problems, patient quality of life, patient-physician communication, resource efficiency and cost-effectiveness. However, this requires interdisciplinary expertise and collaboration among stakeholders, including clinicians, informaticians, and policymakers. Therefore, further research using clinicall studies, systematic reviews, analyses, and meta-analyses is required to fully exploit the potential of machine learning in this area.

The Future of Nursing: Navigating the AI Revolution Through Education and Training.

Artificial intelligence is becoming a fundamental technology in health care, nursing, and education. Understanding how to navigate this advanced technology is critically important for clinical nursing staff as well as educators. Basic professional nursing education is increasingly using artificial intelligence solutions for teaching and learning. For experienced clinical staff, new learning is required, and professional development leaders are key to leading nurses in this new future. [J Contin Educ Nurs. 2024;55(3):108-109.].

Development and Validation of Information Technology Scale in Nursing.

The implementation of information technology (IT) in patient care is on the rise. The nursing workforce should be prepared for using such technology to support the delivery of patient-centered care. The integration of informatics into nursing practice has been progressing at a slower rate than the development of advancements and in which areas nurses use IT is still not clear. Our objective was to develop a new instrument to determine the usage of IT in nursing practice. A methodological study was conducted with factor analyses. A total of 498 registered nurses in a university hospital (n = 374) and primary care centers (n = 124) participated in the study. A questionnaire consisting demographic characteristics and an item pool with 50 statements were used to collect data. The validity and reliability of the instrument were statistically tested by computing the Keiser-Meier-Olkin (KMO) and Bartlett tests, an exploratory factor analysis, descriptive statistics, Cronbach's α, and a confirmatory factor analysis. The instrument extracted eight factors comprising 39 items that explained 55% of the variance: professional autonomy(α = 0.82), data sharing/communication(α = 0.80), data management (α = 0.79), professional development (α = 0.71), administration (α = 0.76), research (α = 0.76), informing (α = 0.68), and classification of interventions (α = 0.75). Total reliability was 0.936. KMO index and a measure of sampling adequacy were high (0.936); the Bartlett test of sphericity was significant (p < 0.005). Study provided the evidence for the factor structure, internal consistency, reliability, and responsiveness of the 39-item "The Information Technology Scale in Nursing." Further testing of the developed instrument with a larger number of nurses from various backgrounds and different settings is recommended.

Àgô Sankofa: an overview of the progression of sickle cell disease in Brazil in the past two decades.

Sickle cell disease (SCD) is an emblematic case of historical health neglect in Brazil and reflects how institutional racism produces health inequalities. This article engaged in a historical journey of this disease, showing the delayed implementation of health policies for people with sickle cell disease, often concealed in Public Power's (in)actions and omissions. The lack of commitment to implement the recommendations of the Brazilian Ministry of Health, such as neonatal screening, and the difficulty in incorporating technologies for health care result from this modus operandi. The advances and setbacks in programmatic actions and the constant pressure on several governmental entities have characterized the reported saga in the last twenty years. The present text discusses the policies for people with SCD, appropriating the Sankofa symbol, meaning that building the present is only possible by remembering past mistakes. Thus, we recognize this trajectory and this historical moment in which there is a concrete possibility of moving forward and achieving the longed-for comprehensive care for people with SCD. There is an invitation to glance at a new perspective, one in which hope is the trigger for the movements needed to guarantee the rights of people with SCD.

Primary Care and Diabetes Technologies and Treatments.

Primary Care and Diabetes Technologies and Treatments.

IoT in Healthcare Domain

Abstract: The Internet of Things (IoT) is a system of wireless, interrelated, and connected digital devices that can collect, send, and store data over a network without requiring human-to-human or human-to-computer interaction. Recent advancement in IoT systems has positively impacted the daily activities of humans, from accessing information to the delivery of service in realtime. This has improved healthcare management and services, especially in medical hospitals, for effective and timely access to diagnostic information and treatment of patients. The IoT promises many benefits to streamlining and enhancing health care delivery to proactively predict health issues and diagnose, treat, and monitor patients both in and out of the hospital. Worldwide, government leaders and decision makers are implementing policies to deliver health care services using technology and more so in response to the novel COVID-19 pandemic. It is now becoming increasingly important to understand how established and emerging IoT technologies can support health systems to deliver safe and effective care. The aim of this viewpoint paper is to provide an overview of the current IoT technology in health care, outline how IoT devices are improving health service delivery, and outline how IoT technology can affect and disrupt global health care in the next decade. IoT-based health care has great potential to improve the efficiency of the health system and improve population health.

Patient Victimhood and the Risks of Using Artificial Intelligence Technology in Healthcare

Artificial intelligence technologies are of increasing interest in the field of medicine and are one of the key areas for the digital transformation of healthcare . According to a number of experts, medical professionals and digital technology developers, the use of medical devices equipped with artificial intelligence technologies will raise healthcare to a high level, which will lead to improved clinical decision-making, high-quality analysis of digital images, prediction and control of the correctness of the prescribed treatment .However, failures associated with the use of medical devices equipped with artificial intelligence systems can have serious consequences for both clinical outcomes and patients . These consequences could undermine public confidence in artificial intelligence technologies and health care institutions in general . Given the certain novelty of technological solutions, data on the clinical efficacy and safety of products equipped with artificial intelligence are currently considered insufficient .This publication raises two important issues . The first part of the study describes the main physical, social and mental characteristics (properties) of patients that increase the likelihood that they will be victimized in the event of a crime situation in innovative health care services . The second part of the study identifies the risks of using AI technologies in health care that are of greatest concern to both patients and those exploiting innovative technologies .

A meta-analysis of technology acceptance in healthcare from the consumer’s perspective

Consumer-oriented health care technologies are increasingly available and transforming global health delivery systems. However, there is a paucity of research that systematically investigates health care technology acceptance from the consumer’s perspective. This study conducts a literature review and meta-analysis to examine consumers’ adoption intentions toward health care technologies. The findings suggest that technology acceptance models are transferable to health care technology with modifications, and factors such as perceived risks, technology performance expectancy, consumer trust, and habit significantly correlate with consumers’ adoption intentions. This study provides valuable insights into health care technology management and practical implications for health care service designers, providers, and regulatory authorities.

EmpRess: an eHealth implementation readiness checklist for dementia developed through an interview study of stakeholder needs

Objective This study aimed to create a tool to assess eHealth interventions for dementia by adapting an existing implementation readiness (ImpRess) checklist that assessed manualised interventions. Methods In Part 1, online semi-structured interviews with individual stakeholders (N = 9) with expertise in eHealth and dementia were conducted (response rate 83%). The Nonadoption, Abandonment, and challenges to the Scale-Up, Spread, and Sustainability of Health and care technologies (NASSS) framework was applied, both to guide the construction of the interview guide, as well as to use its subdomains as codes in the deductive qualitative thematic analysis. Respondents were industry professionals (n = 3), researchers (n = 3), policy officers (n = 2), and a clinician (n = 1). In Part 2, the items of the original ImpRess checklist were supplemented by items that covered determinants discussed in the interviews, that were not included in the original checklist. Results The main findings from the interviews included: Participants’ preference for a non-dementia-specific, more general approach to the checklist; the importance of searching for shared values with implementers; and the need for more systematic monitoring of implementation. Conclusions The EmpRess checklist applies an inclusive design approach. The checklist will help evaluate the implementation determinants of eHealth interventions for dementia and provide up-to-date information on what is, and is not, working in eHealth for dementia care.

Nursing Perceptions of Robotic Technology in Healthcare: A Pretest–Posttest-Survey Analysis Using an Educational Video

OCCUPATIONAL APPLICATIONS We used a survey to evaluate the perceptions of nurses and nursing students on robotic technology for nursing care before and after reviewing an educational video that included examples of medical, care, and healthcare service robotic technology. We found that the perception of robotic technology was innately favorable and became more favorable after the video. It is beneficial for engineers to incorporate nurses’ frontline knowledge into the design process from the beginning, while functional changes can be implemented since nurses comprise the largest group of healthcare professionals in hospitals and are the end users of technological devices. Educating nurses in state-of-the-art technology specific to what designers are developing can enable them to provide relevant insight. Designers and engineers can use this insight to create user-friendly, effective technology that improves not only patient care but also nurse job satisfaction.

Nurses’ Experience of Caring amidst Developments in Welfare Technology in Elderly Care

In Norway, the dominant policy understanding of welfare technology sees its development in elderly care as exclusively positive and effective, benefitting both the individual and society at large. However, nurses tend to be viewed as an obstacle to broader use of welfare technology in primary care. This article looks at how nurses experience caring amidst developments in welfare technology in elderly care. The study draws on a psychosocial approach (Olesen, 2020) that enables interpretation of nurses’ expressions of their experiences with caring and welfare technology on the individual level and in the historical, societal and sociocultural context the nurses are situated within. The article illustrates how welfare technology must not be understood one-dimensionally as tools providing specific outcomes and demonstrates how the nurses’ experience of caring amidst developments in welfare technology may be understood as layers of contradictory notions about care, welfare technology and the nursing role.

Insights from CTTACC: immune system reset by cellular therapies for chronic illness after trauma, infection, and burn

Background AimsIn this paper, we present a review of several selected talks presented at the CTTACC conference (Cellular Therapies in Trauma and Critical Care) held in Scottsdale, AZ in May 2023. This conference review highlights the potential for cellular therapies to “reset” the dysregulated immune response and restore physiologic functions to normal. Improvements in medical care systems and technology have increasingly saved lives after major traumatic events. However, many of these patients have complicated post-traumatic sequelae, ranging from short-term multi-organ failure to chronic critical illness. Methods/ResultsPatients with chronic critical illness have been found to have dysregulated immune responses. These abnormal and harmful immune responses persist for years after the initial insult and can potentially be mitigated by treatment with cellular therapies. ConclusionsThe sessions emphasized the need for more research and clinical trials with cellular therapies for the treatment of a multitude of chronic illnesses: post-trauma, radiation injury, COVID-19, burns, traumatic brain injury (TBI) and other chronic infections.

Pragmatic phase II clinical trial to improve depression care in a real-world diverse MS cohort from an academic MS centre in Northern California: MS CATCH study protocol

IntroductionDepression occurs in over 50% of individuals living with multiple sclerosis (MS) and can be treated using many modalities. Yet, it remains: under-reported by patients, under-ascertained by clinicians and under-treated....