Assessment of the Impact of Home-Based Hospitalization on Health Outcomes: An Observational Study.

INTRODUCTION: In Portugal, evidence of clinical outcomes within home-based hospitalization programs remains limited. Despite the adoption of homebased hospitalization services, it is still unclear whether these services represent an effective way to manage patients compared with inpatient hospital care. Therefore, the aim of this study was to evaluate the outcomes of home-based hospitalization compared with conventional hospitalization in a group of patients with a primary diagnosis of infectious, cardiovascular, oncological, or 'other' diseases. METHODS: An observational retrospective study using anonymized administrative data to investigate the outcomes of home-based hospitalization (n = 209) and conventional hospitalization (n = 192) for 401 Portuguese patients admitted to CUF hospitals (Tejo, Cascais, Sintra, Descobertas, and the Unidade de Hospitalização Domiciliária CUF Lisboa). Data on demographics and clinical outcomes, including Barthel index, Braden scale, Morse scale, mortality, and length of hospital stay, were collected. The statistical analysis included comparison tests and logistic regression. RESULTS: The study found no statistically significant differences between patients' admission and discharge for the Barthel index, Braden scale, and Morse scale scores, for both conventional and home-based hospitalizations. In addition, no statistically significant differences were found in the length of stay between conventional and home-based hospitalization, although patients diagnosed with infectious diseases had a longer stay than patients with other conditions. Although the mortality rate was higher in home-based hospitalization compared to conventional hospitalization, the mortality risk index (higher in home-based hospitalization) assessed at admission was a more important predictor of death than the type of hospitalization. CONCLUSION: The study found that there were no significant differences in outcomes between conventional and home-based hospitalization. Home-based hospitalization was found to be a valuable aspect of patient- and family-centered care. However, it is noteworthy that patients with infectious diseases experienced longer hospital stays.

Unlocking human-like conversations: Scoping review of automation techniques for personalized healthcare interventions using conversational agents.

BACKGROUND: Conversational agents (CAs) offer a sustainable approach to deliver personalized interventions and improve health outcomes. OBJECTIVES: To review how human-like communication and automation techniques of CAs in personalized healthcare interventions have been implemented. It is intended for designers and developers, computational scientists, behavior scientists, and biomedical engineers who aim at developing CAs for healthcare interventions. METHODOLOGY: A scoping review was conducted in accordance with PRISMA Extension for Scoping Review. A search was performed in May 2023 in Web of Science, Pubmed, Scopus and IEEE databases. Search results were extracted, duplicates removed, and the remaining results were screened. Studies that contained personalized and automated CAs within the healthcare domain were included. Information regarding study characterization, and human-like communication and automation techniques was extracted from articles that met the eligibility criteria. RESULTS: Twenty-three studies were selected. These articles described the development of CAs designed for patients to either self-manage their diseases (such as diabetes, mental health issues, cancer, asthma, COVID-19, and other chronic conditions) or to enhance healthy habits. The human-like communication characteristics studied encompassed aspects like system flexibility, personalization, and affective characteristics. Seven studies used rule-based models, eleven applied retrieval-based techniques for content delivery, five used AI models, and six integrated affective computing. CONCLUSIONS: The increasing interest in employing CAs for personalized healthcare interventions is noteworthy. The adaptability of dialogue structures and personalization features is still limited. Unlocking human-like conversations may encompass the use of affective computing and generative AI to help improve user engagement. Future research should focus on the integration of holistic methods to describe the end-user, and the safe use of generative models.

Multi-activity 3D printed assistive technology in children: a case study.

Congenital limb defects occur when a limb does not develop normally during pregnancy. The quality of each person's everyday life is significantly impacted by any of these defects and there is no concrete treatment. 3D modeling and printing, enables the creation and customization of precise virtual and/or physical models, including models of the human anatomy. These technologies provide a novel method of producing new devices with optimized design and production time, improving adaptability, and incorporating functionality. To this end, we propose a method of designing and producing 3D printed assistive devices and we also present an example of an assistive device, done in the 3D Printing Center for Health, as well as its impact on the patient's daily life. With this device, the patient became able to play the guitar and hold a knife, thus helping on these two activities.

Effectiveness of the KC@H programme compared with clinic-based rehabilitation in patients recovering from ACL reconstruction: a study protocol for a single-centre, two-arm, single-blinded, randomised controlled superiority trial.

Patients who cannot fully comply with conventional clinic-based rehabilitation (CR) sessions after ACL reconstruction (ACLR) may find additional internet-based sessions beneficial. These remote sessions include therapeutic exercises that can be done at home, potentially extending the reach of rehabilitation services to underserved areas, prolonging the duration of care and providing improved supervision. The study's main purpose is to determine if the Knee Care at Home (KC@H) programme is more effective than conventional CR alone in improving patient-reported, clinician-reported and physical functional performance outcome measures after ACLR. Additionally, the trial assesses the significance of changes in outcome measures for clinical practice. This protocol outlines a randomised controlled trial for postoperative recovery following ACLR. Adult participants of both sexes who meet specific criteria will be randomly assigned to either the CR group or the KC@H group. Only the latter group will receive internet-based sessions of therapeutic exercises at home and CR sessions. A follow-up evaluation will be conducted for both groups 12 weeks after the intervention ends. The trial protocol was approved by the Ethics Committee of the Universidade de Évora and complies with the Code of Ethics of the World Medical Association. All recordings will be stored on a secure server with limited access and deleted as soon as they are no longer needed. The KC@H programme is expected to be superior to conventional CR for patients recovering from ACLR across multiple outcome measures. Also, the programme has the potential to promote superior recovery and extend the reach and duration of care. Trial registration number: NCT05828355.

Predicting post-discharge complications in cardiothoracic surgery: A clinical decision support system to optimize remote patient monitoring resources.

Cardiac surgery patients are highly prone to severe complications post-discharge. Close follow-up through remote patient monitoring can help detect adverse outcomes earlier or prevent them, closing the gap between hospital and home care. However, equipment is limited due to economic and human resource constraints. This issue raises the need for efficient risk estimation to provide clinicians with insights into the potential benefit of remote monitoring for each patient. Standard models, such as the EuroSCORE, predict the mortality risk before the surgery. While these are used and validated in real settings, the models lack information collected during or following the surgery, determinant to predict adverse outcomes occurring further in the future. This paper proposes a Clinical Decision Support System based on Machine Learning to estimate the risk of severe complications within 90 days following cardiothoracic surgery discharge, an innovative objective underexplored in the literature. Health records from a cardiothoracic surgery department regarding 5 045 patients (60.8% male) collected throughout ten years were used to train predictive models. Clinicians' insights contributed to improving data preparation and extending traditional pipeline optimization techniques, addressing medical Artificial Intelligence requirements. Two separate test sets were used to evaluate the generalizability, one derived from a patient-grouped 70/30 split and another including all surgeries from the last available year. The achieved Area Under the Receiver Operating Characteristic curve on these test sets was 69.5% and 65.3%, respectively. Also, additional testing was implemented to simulate a real-world use case considering the weekly distribution of remote patient monitoring resources post-discharge. Compared to the random resource allocation, the selection of patients with respect to the outputs of the proposed model was proven beneficial, as it led to a higher number of high-risk patients receiving remote monitoring equipment.

Scaling-up digital follow-up care services: collaborative development and implementation of Remote Patient Monitoring pilot initiatives to increase access to follow-up care.

Background: COVID-19 increased the demand for Remote Patient Monitoring (RPM) services as a rapid solution for safe patient follow-up in a lockdown context. Time and resource constraints resulted in unplanned scaled-up RPM pilot initiatives posing risks to the access and quality of care. Scalability and rapid implementation of RPM services require social change and active collaboration between stakeholders. Therefore, a participatory action research (PAR) approach is needed to support the collaborative development of the technological component while simultaneously implementing and evaluating the RPM service through critical action-reflection cycles. Objective: This study aims to demonstrate how PAR can be used to guide the scalability design of RPM pilot initiatives and the implementation of RPM-based follow-up services. Methods: Using a case study strategy, we described the PAR team's (nurses, physicians, developers, and researchers) activities within and across the four phases of the research process (problem definition, planning, action, and reflection). Team meetings were analyzed through content analysis and descriptive statistics. The PAR team selected ex-ante pilot initiatives to reflect upon features feedback and participatory level assessment. Pilot initiatives were investigated using semi-structured interviews transcribed and coded into themes following the principles of grounded theory and pilot meetings minutes and reports through content analysis. The PAR team used the MoSCoW prioritization method to define the set of features and descriptive statistics to reflect on the performance of the PAR approach. Results: The approach involved two action-reflection cycles. From the 15 features identified, the team classified 11 as must-haves in the scaled-up version. The participation was similar among researchers (52.9%), developers (47.5%), and physicians (46.7%), who focused on suggesting and planning actions. Nurses with the lowest participation (5.8%) focused on knowledge sharing and generation. The top three meeting outcomes were: improved research and development system (35.0%), socio-technical-economic constraints characterization (25.2%), and understanding of end-user technology utilization (22.0%). Conclusion: The scalability and implementation of RPM services must consider contextual factors, such as individuals' and organizations' interests and needs. The PAR approach supports simultaneously designing, developing, testing, and evaluating the RPM technological features, in a real-world context, with the participation of healthcare professionals, developers, and researchers.

Case management intervention of high users of the emergency department of a Portuguese hospital: a before-after design analysis.

BACKGROUND: Emergency department (ED) High users (HU), defined as having more than ten visits to the ED per year, are a small group of patients that use a significant proportion of ED resources. The High Users Resolution Group (GRHU) identifies and provides care to HU to improve their health conditions and reduce the frequency of ED visits by delivering patient-centered case management integrated care. The main objective of this study was to measure the impact of the GRHU intervention in reducing ED visits, outpatient appointments, and hospitalizations. As secondary objectives, we aimed to compare the GRHU intervention costs against its potential savings or additional costs. Finally, we intend to study the impact of this intervention across different groups of patients. METHODS: We studied the changes triggered by the GRHU program in a retrospective, non-controlled before-after analysis of patients' hospital utilization data on 6 and 12-month windows from the first appointment. RESULTS: A total of 238 ED HU were intervened. A sample of 152 and 88 patients was analyzed during the 6 and 12-month window, respectively. On the 12-month window, GRHU intervention was associated with a statistically significant reduction of 51% in ED visits and hospitalizations and a non-statistically significant increase in the total number of outpatient appointments. Overall costs were reduced by 43.56%. We estimated the intervention costs to be €79,935.34. The net cost saving was €104,305.25. The program's Return on Investment (ROI) was estimated to be €2.3. CONCLUSION: Patient-centered case management for ED HU seems to effectively reduce ED visits and hospitalizations, leading to better use of resources.

Machine Learning Approaches for the Frailty Screening: A Narrative Review.

Frailty characterizes a state of impairments that increases the risk of adverse health outcomes such as physical limitation, lower quality of life, and premature death. Frailty prevention, early screening, and management of potential existing conditions are essential and impact the elderly population positively and on society. Advanced machine learning (ML) processing methods are one of healthcare's fastest developing scientific and technical areas. Although research studies are being conducted in a controlled environment, their translation into the real world (clinical setting, which is often dynamic) is challenging. This paper presents a narrative review of the procedures for the frailty screening applied to the innovative tools, focusing on indicators and ML approaches. It results in six selected studies. Support vector machine was the most often used ML method. These methods apparently can identify several risk factors to predict pre-frail or frailty. Even so, there are some limitations (e.g., quality data), but they have enormous potential to detect frailty early.

Scale-up of Digital Innovations in Health Care: Expert Commentary on Enablers and Barriers.

Health care delivery is undergoing a rapid change from traditional processes toward the use of digital health interventions and personalized medicine. This movement has been accelerated by the COVID-19 crisis as a response to the need to guarantee access to health care services while reducing the risk of contagion. Digital health scale-up is now also vital to achieve population-wide impact: it will only accomplish sustainable effects if and when deployed into regular health care delivery services. The question of how sustainable digital health scale-up can be successfully achieved has, however, not yet been sufficiently resolved. This paper identifies and discusses enablers and barriers for scaling up digital health innovations. The results discussed in this paper were gathered by scientists and representatives of public bodies as well as patient organizations at an international workshop on scaling up digital health innovations. Results are explored in the context of prior research and implications for future work in achieving large-scale implementations that will benefit the population as a whole.

Which outcomes are most important to measure in patients with COVID-19 and how and when should these be measured? Development of an international standard set of outcomes measures for clinical use in patients with COVID-19: a report of the International Consortium for Health Outcomes Measurement (ICHOM) COVID-19 Working Group.

OBJECTIVES: The COVID-19 pandemic has resulted in widespread morbidity and mortality with the consequences expected to be felt for many years. Significant variation exists in the care even of similar patients with COVID-19, including treatment practices within and between institutions. Outcome measures vary among clinical trials on the same therapies. Understanding which therapies are of most value is not possible unless consensus can be reached on which outcomes are most important to measure. Furthermore, consensus on the most important outcomes may enable patients to monitor and track their care, and may help providers to improve the care they offer through quality improvement. To develop a standardised minimum set of outcomes for clinical care, the International Consortium for Health Outcomes Measurement (ICHOM) assembled a working group (WG) of 28 volunteers, including health professionals, patients and patient representatives. DESIGN: A list of outcomes important to patients and professionals was generated from a systematic review of the published literature using the MEDLINE database, from review of outcomes being measured in ongoing clinical trials, from a survey distributed to patients and patient networks, and from previously published ICHOM standard sets in other disease areas. Using an online-modified Delphi process, the WG selected outcomes of greatest importance. RESULTS: The outcomes considered by the WG to be most important were selected and categorised into five domains: (1) functional status and quality of life, (2) mental functioning, (3) social functioning, (4) clinical outcomes and (5) symptoms. The WG identified demographic and clinical variables for use as case-mix risk adjusters. These included baseline demographics, clinical factors and treatment-related factors. CONCLUSION: Implementation of these consensus recommendations could help institutions to monitor, compare and improve the quality and delivery of care to patients with COVID-19. Their consistent definition and collection could also broaden the implementation of more patient-centric clinical outcomes research.

Domains and Methods Used to Assess Home Telemonitoring Scalability: Systematic Review.

BACKGROUND: The COVID-19 pandemic catalyzed the adoption of home telemonitoring to cope with social distancing challenges. Recent research on home telemonitoring demonstrated benefits concerning the capacity, patient empowerment, and treatment commitment of health care systems. Moreover, for some diseases, it revealed significant improvement in clinical outcomes. Nevertheless, when policy makers and practitioners decide whether to scale-up a technology-based health intervention from a research study to mainstream care delivery, it is essential to assess other relevant domains, such as its feasibility to be expanded under real-world conditions. Therefore, scalability assessment is critical, and it encompasses multiple domains to ensure population-wide access to the benefits of the growing technological potential for home telemonitoring services in health care. OBJECTIVE: This systematic review aims to identify the domains and methods used in peer-reviewed research studies that assess the scalability of home telemonitoring-based interventions under real-world conditions. METHODS: The authors followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines and used multiple databases (PubMed, Scopus, Web of Science, and EconLit). An integrative synthesis of the eligible studies was conducted to better explore each intervention and summarize relevant information concerning the target audience, intervention duration and setting, and type of technology. Each study design was classified based on the strength of its evidence. Lastly, the authors conducted narrative and thematic analyses to identify the domains, and qualitative and quantitative methods used to support scalability assessment. RESULTS: This review evaluated 13 articles focusing on the potential of scaling up a home telemonitoring intervention. Most of the studies considered the following domains relevant for scalability assessment: problem (13), intervention (12), effectiveness (13), and costs and benefits (10). Although cost-effectiveness was the most common evaluation method, the authors identified seven additional cost analysis methods to evaluate the costs. Other domains were less considered, such as the sociopolitical context (2), workforce (4), and technological infrastructure (3). Researchers used different methodological approaches to assess the effectiveness, costs and benefits, fidelity, and acceptability. CONCLUSIONS: This systematic review suggests that when assessing scalability, researchers select the domains specifically related to the intervention while ignoring others related to the contextual, technological, and environmental factors, which are also relevant. Additionally, studies report using different methods to evaluate the same domain, which makes comparison difficult. Future work should address research on the minimum required domains to assess the scalability of remote telemonitoring services and suggest methods that allow comparison among studies to provide better support to decision makers during large-scale implementation.

Markers for upper limb dysfunction in Amyotrophic Lateral Sclerosis using analysis of typing activity.

OBJECTIVE: Assistive devices based on keyboard access support communication and control tools for patients with Amyotrophic Lateral Sclerosis (ALS). The aim of this work was to explore movement activity in the use of keyboards and identify markers for upper limb (UL) dysfunction. METHODS: We present a longitudinal study including 19 ALS patients, followed for 2-20 months. Typing activity was recorded with an accelerometer placed on the posterior part of patients' index finger. Participants performed the same 10-word typing task (2-6 assessments). Time and acceleration during keystroke were the main outcomes of this study. Patients were compared with 20 healthy subjects and 6 patients with other neuromuscular disorders. RESULTS: During disease progression, mean time in holding down a key increased and was longer than in control subjects. Acceleration at key press and key release decreased with progression of UL dysfunction. Delay between tapping and pressing down each key increased with UL dysfunction. CONCLUSIONS: Delay in pressing and releasing keys are markers of UL dysfunction in ALS. The decrease in the acceleration of movements related to keystroke can contribute to monitor disease progression. SIGNIFICANCE: Typing activity can be explored to access remotely and continuously to ALS progression by patients who use assistive communication devices.

Quality of life in amyotrophic lateral sclerosis patients and caregivers: Impact of assistive communication from early stages.

INTRODUCTION: In this study we performed a longitudinal investigation to assess the impact of early introduction of assistive communication devices (ACDs) on quality of life (QoL) in amyotrophic lateral sclerosis (ALS) patients and their caregivers. METHODS: Patients were followed for 7-10 months (3 evaluation periods). Bulbar-onset ALS patients (N = 27) and paired caregivers (N = 17) were included. Fifteen randomly selected patients received early support in ACD use. Patients were assessed using the ALS Functional Rating Scale-revised (ALSFRS-R), the McGill QoL (MQoL), the Communication Effectiveness Index (CETI), and performance in writing; and caregivers were assessed with the MQoL and World Health Organization Quality of Life questionnaire (WHOQOL-BREF). RESULTS: Patients with early support had higher MQoL Psychological and MQoL Existential well-being domains; caregivers had higher MQoL Support domain and their MQoL Psychological domain positively associated with patient CETI. Most patients could communicate using a touchscreen keyboard to write, even when handwriting and speech were not possible. CONCLUSION: Early intervention with an ACD seems to have a positive impact on QoL and gives patients the opportunity to improve skills for communication in later disease stages.