Personalized Care Plan Research Articles

From Diagnosis to Recovery: The Life-Changing Benefits of Exercise for Cancer Patients

Physical activity has emerged as a crucial component in the comprehensive care of oncological patients, offering significant benefits across the cancer continuum—from prevention to treatment, recovery, and survivorship. This article provides an in-depth exploration of the role of exercise in cancer care, highlighting its potential to improve physical function, reduce treatment-related side effects, enhance mental well-being, and improve overall quality of life. Epidemiological evidence supports the protective effect of regular physical activity in reducing the risk of developing certain cancers, such as breast, colorectal, and endometrial cancer, through mechanisms like hormonal regulation, inflammation reduction, and immune function enhancement. During active cancer treatment, exercise helps combat common side effects such as fatigue, muscle wasting, and cardiovascular decline, while also offering psychological benefits like reduced anxiety and depression. Tailored exercise programs, developed in collaboration with healthcare professionals, are essential for addressing the unique needs of cancer patients, considering factors like cancer type, treatment stage, and comorbidities. Post-treatment, physical activity continues to play a vital role in rehabilitation, reducing the risk of recurrence, and supporting long-term survivorship. Despite the clear benefits, barriers such as physical limitations, psychological distress, and lack of access to resources can hinder patients' ability to engage in regular exercise. Addressing these challenges through personalized care plans, patient education, and supportive interventions is crucial. As research in exercise oncology advances, integrating physical activity into routine cancer care holds promise for improving outcomes and enhancing the overall well-being of cancer patients.

Chemotherapy and Metabolic Syndrome: A Comprehensive Review of Molecular Pathways and Clinical Outcomes.

Cancer therapies, notably chemotherapy, have significantly improved survival rates and quality of life for many patients. However, chemotherapy's cytotoxic effects also impact normal cells, leading to adverse effects, including metabolic disturbances. This paper explores the link between chemotherapy and metabolic syndrome, a cluster of metabolic abnormalities that increase the risk of cardiovascular diseases and type 2 diabetes. Understanding the predictors, such as specific chemotherapy regimens, patient characteristics, comorbid conditions, lifestyle factors, and genetic variations, is crucial for formulating personalized care plans and preventive strategies. Research indicates that older age, female gender, pre-existing diabetes, and baseline obesity are significant predictors of metabolic syndrome in cancer patients. Chemotherapy-induced molecular changes, including insulin resistance, dyslipidemia, chronic inflammation, oxidative stress, and tissue fibrosis, contribute to the development of this syndrome. Effective management strategies require a multidisciplinary approach, incorporating lifestyle interventions, pharmacological treatments, and regular monitoring. This paper underscores the importance of personalized medicine in mitigating the risks associated with metabolic syndrome and improving long-term health outcomes for cancer survivors. Future research directions include longitudinal studies to track metabolic health over time, mechanistic studies to uncover the molecular pathways involved, and the development of integrative therapies. By adopting comprehensive care models, healthcare providers can enhance the overall quality of life for cancer survivors, addressing both cancer and metabolic health challenges.

Advancing social prescribing in Singapore: an update on progress

In 2019, SingHealth Community Hospitals (SCH) introduced Social Prescribing (SP) program to support patients in transitioning back to the community after hospitalization, which involves personalized care plans developed by Wellbeing Coordinators (WBCs) to connect patients with relevant community resources. With the recent launch of the nation-wide 'Healthier SG' initiative, a population health strategy in Singapore aimed at enabling individuals to prevent and manage chronic diseases, it is important to provide an update on our program's recent developments. This includes creating a living asset map, updating outcome assessment tools, organizing training sessions to enhance the skills SP practitioners, and establishing the Singapore Community of Practice in Social Prescribing (SCOMP).

Leveraging open data analytics and machine learning to improve mental health research and innovation: 2024 Inter-University Big Data Challenge ProceedingsIn partnership with Canadian Science Publishing, the Canadian Personalized Healthcare Innovation Network, and Underline.io

The National Inter-University Big Data and AI Challenge is an interdisciplinary, agile educational environment that bridges the gap between traditional coursework and real-world data science applications. The challenge provides undergraduate and graduate researchers with the unique opportunity to explore the intersection of Open Data and applied scientific research by developing and presenting their research amongst peers and professionals from academia and industry. By participating, students gain skills in uncovering hidden patterns and trends in structured and unstructured data using a wide range of data analytics tools and programming languages including Python, R, and various machine learning frameworks. They also gain extensive experience with scientific communication by compiling abstracts, manuscripts, posters, and videos which ultimately culminate in a presentation on Big Data Day. The guiding theme for the 2024 National Inter-University Big Data and AI Challenge was “Leveraging Open Data Analytics and Machine Learning to Improve Mental Health Research and Innovation”. During the 2024 event, we saw over 134 participants from over 15 different institutions and 15 different degree programs learn and utilize important techniques in analytics, artificial intelligence (AI), and machine learning (ML) in order to delve into the complexities of mental health diseases and treatments. These future leaders were tasked with using Open Data to enhance our understanding of mental health and explore areas for innovation regarding mental health practice and research, gaining critical insights into this field. Various topics were investigated, ranging from the socioeconomic determinants of mental health to differential outcomes in treatment efficacy across geographic boundaries. By applying computational thinking, students explored the interplay between mental health and external factors, ultimately contributing to the development of tailored interventions and personalized care plans. We are privileged to witness the analytical capabilities of this talented generation of students, and we are confident they will demonstrate excellence throughout their academic and professional careers. The 2024 Big Data Day, the culmination of our participants’ trailblazing research, was held at the Microsoft Headquarters in Toronto. On behalf of STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge and wish them success in their future endeavours. We also want to express our appreciation to all the STEM Fellowship volunteers who support this program. We greatly appreciate the valuable collaboration and support of our partners: Research Canada, Canadian Science Publishing, CPHIN, Underline, JMIR Publications, Overleaf.

Applications of Artificial Intelligence in Psychiatric Nursing: A Scope Review.

Rapid advances in artificial intelligence (AI) have reshaped healthcare, including psychiatric nursing, to address the limitations of traditional approaches and meet escalating mental health challenges. A scoping review analyzed 48 articles examining the application of AI in psychiatric nursing across different technologies and topics, noting trends in publications and countries involved. The articles covered different aspects of mental health using AI technologies such as machine learning and robotics, and primarily explored AI applications in mental health, specifically dementia, autism and schizophrenia. These studies highlighted the role of AI in personalized care plans, symptom monitoring and risk assessment. AI is promising, but faces challenges such as data bias and ethical concerns. Future research needs to focus on long-term studies, diverse populations, patient interaction and personalized treatments for practical integration into psychiatric nursing.

Advancements in health informatics for monitoring perioperative patient data

Advancements in health informatics are revolutionizing perioperative care by enhancing patient monitoring, improving clinical decision-making, and promoting patient safety. The integration of electronic health records (EHR) facilitates seamless information flow across all perioperative stages, ensuring up-to-date patient data is accessible to the entire healthcare team. This accessibility reduces errors, improves coordination, and enhances overall patient outcomes. EHR systems also support standardized documentation, reducing variability in record-keeping and ensuring comprehensive patient information is consistently captured. Real-time data analytics and predictive modeling are powerful tools in perioperative settings, enabling clinicians to anticipate and manage potential complications proactively. By analyzing vast datasets, predictive models identify patterns and risk factors associated with adverse outcomes, allowing for targeted interventions and personalized care plans. Real-time analytics provide continuous monitoring of patient status during surgery, offering immediate feedback and enabling prompt adjustments to ensure patient stability. Wearable devices and remote monitoring systems further augment real-time data analytics by continuously tracking physiological parameters throughout the surgical process and into the postoperative period. This continuous data stream allows for ongoing assessment of patient status and early detection of complications, enhancing patient safety and outcomes. Health informatics solutions, including automated alerts and decision support tools, minimize human error and ensure adherence to clinical guidelines. Automated reminders for critical actions, such as timely antibiotic administration, significantly reduce the incidence of surgical site infections. Decision support tools provide real-time, evidence-based recommendations at the point of care, supporting clinicians in making informed decisions and improving patient outcomes. Additionally, health informatics facilitates continuous quality improvement by enabling the collection and analysis of performance data, informing targeted interventions and process improvements. Overall, advancements in health informatics are transforming perioperative care, offering numerous benefits in terms of efficiency, accuracy, and patient safety, ultimately leading to better patient outcomes and higher quality of care. Continued innovation in this field will be essential for maintaining and improving the standards of perioperative care.

Breaking the cycle: Exploring the relationship of metacognition beliefs, obsessive-compulsive symptoms, and psychosocial performance among individuals diagnosed with schizophrenia.

This research paper explores the intricate relationship between metacognitive dysfunctional beliefs, obsessive-compulsive symptoms, and psychosocial performance in patients diagnosed with schizophrenia. Understanding these dynamics can help mental health nurses identify and address each patient's needs more effectively. It can guide them in devising personalized care plans that not only manage the symptoms but also improve the underlying mechanism that exacerbates the psychotic symptoms and social functioning and the overall quality of life. Moreover, the findings of this research can contribute to developing training programs for mental health nurses, equipping them with the necessary skills and knowledge to provide optimal care. What the paper adds to existing knowledge? • This study provides empirical evidence of the significant positive correlation between OCS and metacognitive dimensions in individuals with schizophrenia. • It highlights the role of certain demographic factors, such as younger age and single marital status, in increasing the likelihood of elevated OCS. • It underscores the inverse relationship between higher metacognitive dysfunctional beliefs and lower levels of psychosocial functioning. • It identifies age and metacognitive scores as crucial predictors of psychosocial functioning across various domains. What are the implications for practice? • The findings suggest that therapeutic nursing interventions for individuals diagnosed with schizophrenia should address metacognitive dysfunctional beliefs to improve overall functioning and well-being. • Clinicians, including psychiatrists and psychiatric nurses, should consider the patient's age, marital status, and metacognitive scores when assessing the risk of elevated OCS and devising treatment plans. • The study emphasizes the need for comprehensive psychiatric nursing assessment, including metacognitive dysfunction and OCS evaluation. What are the implications for future research? • Future research could explore the causal relationships between metacognitive dysfunctional beliefs, OCS, and psychosocial functioning in schizophrenia. • Longitudinal studies could provide insights into the progression of these relationships over time and the impact of therapeutic interventions. • Further research could also investigate the effectiveness of specific therapeutic strategies such as Metacognitive Therapy (MCT), Schema Therapy (ST), Cognitive Enhancement Therapy (CET), and Cognitive Behaviour Therapy (CBT) to address this population's metacognitive dysfunctional beliefs. BACKGROUND: Schizophrenia is a chronic mental health disorder that significantly impacts an individual's cognitive, emotional and social functioning. Recent research has highlighted the role of metacognitive beliefs and obsessive-compulsive symptoms (OCS) in the psychosocial performance of individuals diagnosed with schizophrenia. Understanding these relationships could provide valuable insights for developing more effective nursing interventions. This study aimed to investigate the relationship between metacognitive beliefs, OCS and psychosocial performance among individuals diagnosed with schizophrenia. A cross-sectional survey was conducted involving 174 purposively selected participants diagnosed with schizophrenia. The Meta-Cognitions Questionnaire-30, Young Adult Self-Report Scale for OCS and Specific Level of Functioning Scale were used to gather the necessary data. The study found a significant positive correlation between OCS and metacognitive dimensions. Age was a significant predictor with an Oddis Ratio of 2.471. The metacognitive dysfunction was a highly significant predictor in univariate and multivariate analyses, with Oddis Ratios of 1.087 and 1.106, respectively. The study also discovered that higher levels of metacognitive dysfunctional beliefs were associated with lower levels of psychosocial functioning. Age and the metacognitive dysfunction score were significant predictors of psychosocial functioning scores, accounting for 26.8% of the variance in these scores. The study reveals a compelling inverse relationship between higher metacognitive dysfunctional beliefs and lower levels of psychosocial functioning in individuals diagnosed with schizophrenia. It also identifies certain demographic factors, such as younger age, as significant contributors to elevated OCS. Importantly, metacognitive dysfunction emerged as a critical predictor of psychosocial functioning across various domains. These findings underscore the potential of incorporating metacognitive-focused interventions in the treatment plans for schizophrenia patients. By addressing these cognitive patterns, healthcare professionals can enhance overall functioning and well-being in individuals diagnosed with schizophrenia.

Evaluating a model of best practice in primary care led post-diagnostic dementia care: feasibility and acceptability findings from the PriDem study

ObjectivesTo evaluate the feasibility and acceptability of a primary care-based intervention for improving post-diagnostic dementia care and support (PriDem), and implementation study procedures.DesignA non-randomised, mixed methods, feasibility study.SettingSeven general practices...

Diagnosis and management of frailty

Background: Frailty, a syndrome characterized by reduced physiological reserve and heightened susceptibility to stressors, impairs homeostasis across physical, psychological, and physiological domains in older adults. Approximately 50% of older Korean adults are frail or pre-frail, which highlights the need for targeted interventions in community healthcare and primary care settings.Current Concepts: Various methods, including use of physical and biological markers and health deficit analysis are available for frailty assessment; the Frailty Phenotype and Frailty Index are widely used screening tools. Following advances in technology, electronic health records and big data play a key role in identifying potential biomarkers. Considering the multifaceted nature of frailty, comprehensive geriatric assessments are recommended for holistic evaluation and management, based on a patient’s medical, psychological, and functional capabilities. Effective management can reverse frailty, which emphasizes the importance of early detection and multidisciplinary interventions. International and national guidelines propose a comprehensive approach focusing on modifiable risk factors, physical activity, a balanced diet, chronic disease management, social participation, and regular review of medication regimens. Personalized care plans based on comprehensive assessments are essential to enhance or maintain the residual function of frail older adults to improve their quality of life and reduce the need for long-term care.Discussion and Conclusion: Timely and effective management of potentially reversible frailty highlights the importance of prompt detection and a multidisciplinary approach to care. International and national guidelines recommend a holistic management strategy. Tailored care plans based on comprehensive geriatric assessments are pivotal in improving or maintaining the functional capabilities of frail elderly individuals to enhance their quality of life.

A New Paradigm in the Management of Knee Osteoarthritis and Arthroplasty with Dynamic Patient-source Outcome Measures: Comprehensive Clinical Review of the Knee Kinesiography Exam with the KneeKG® System

While knee disorders, and more specifically knee osteoarthritis, account for a significant portion of the orthopedic visits to health care providers, there are still clinical gaps that need to be addressed to achieve effective management of this disease. The absence of functional tools to objectively assess knee function in motion and weight-bearing conditions poses a challenge for clinicians to hindering their ability to design individualized conservative and surgical treatment plans aimed at achieving better patient outcomes. The Knee Kinesiography exam with the KneeKG® system provides accurate, reliable, three-dimensional, and real-time information about dynamic knee malalignment and joint dysfunction during weight-bearing activities through the assessment of kinematic biomechanical markers. This work presents a comprehensive clinical review of the materials related to this exam which have been published since its validation in 2012. The aim is to summarize literature evidence on how the Knee Kinesiography exam may enhance both non-surgical and surgical management of knee osteoarthritis and arthroplasty. The access to objective patient-source outcome measures from this exam, directly associated with both patient-reported outcomes and osteoarthritis onset and progression before and after total knee arthroplasty, can allow clinicians to identify mechanisms behind knee pain, dysfunction, and dissatisfaction. In the era of orthopedic value-based care, such patient-source data may help clinicians better understand the condition of the knee and thus assist them in their decision-making to establish the best personalized care plan possible throughout the continuum of care, from diagnosis to post-surgery management.

Falls prevention in older people and the role of nursing.

Falls among older individuals pose a significant public health challenge globally, impacting both individual wellbeing and healthcare systems. This article examines the importance of falls prevention in older people and the pivotal role of nursing in this domain. It presents statistics indicating the high prevalence of falls among older adults, highlighting their substantial impact on morbidity, mortality and healthcare costs. Furthermore, it discusses the multifactorial nature of fall risk factors, including age-related changes, chronic health conditions, medication use, impaired mobility, sensory deficits and environmental hazards. Nursing interventions encompass comprehensive assessments, personalised care plans, patient education and advocacy efforts aimed at reducing fall risks and enhancing safety. By addressing intrinsic and extrinsic factors contributing to falls, nurses contribute significantly to improving the quality of life for older adults and reducing the economic burden associated with fall-related injuries.

Access to personalised dementia care planning in primary care: a mixed methods evaluation of the PriDem intervention

ObjectivesProvision of personalised care planning is a national priority for people with dementia. Research suggests a lack of quality and consistency of care plans and reviews. The PriDem model of...

Design, implementation and usability analysis of patient empowerment in ADLIFE project via patient reported outcome measures and shared decision making

IntroductionThis paper outlines the design, implementation, and usability study results of the patient empowerment process for chronic disease management, using Patient Reported Outcome Measurements and Shared Decision-Making Processes.BackgroundThe ADLIFE project aims to develop innovative, digital health solutions to support personalized, integrated care for patients with severe long-term conditions such as Chronic Obstructive Pulmonary Disease, and/or Chronic Heart Failure. Successful long-term management of patients with chronic conditions requires active patient self-management and a proactive involvement of patients in their healthcare and treatment. This calls for a patient-provider partnership within an integrated system of collaborative care, supporting self-management, shared-decision making, collection of patient reported outcome measures, education, and follow-up.MethodsADLIFE follows an outcome-based and patient-centered approach where PROMs represent an especially valuable tool to evaluate the outcomes of the care delivered. We have selected 11 standardized PROMs for evaluating the most recent patients’ clinical context, enabling the decision-making process, and personalized care planning. The ADLIFE project implements the "SHARE approach’ for enabling shared decision-making via two digital platforms for healthcare professionals and patients. We have successfully integrated PROMs and shared decision-making processes into our digital toolbox, based on an international interoperability standard, namely HL7 FHIR. A usability study was conducted with 3 clinical sites with 20 users in total to gather feedback and to subsequently prioritize updates to the ADLIFE toolbox.ResultsUser satisfaction is measured in the QUIS7 questionnaire on a 9-point scale in the following aspects: overall reaction, screen, terminology and tool feedback, learning, multimedia, training material and system capabilities. With all the average scores above 6 in all categories, most respondents have a positive reaction to the ADLIFE PEP platform and find it easy to use. We have identified shortcomings and have prioritized updates to the platform before clinical pilot studies are initiated.ConclusionsHaving finalized design, implementation, and pre-deployment usability studies, and updated the tool based on further feedback, our patient empowerment mechanisms enabled via PROMs and shared decision-making processes are ready to be piloted in clinal settings. Clinical studies will be conducted based at six healthcare settings across Spain, UK, Germany, Denmark, and Israel.

Implementation of a Comprehensive and Personalised Approach for Older People with Psychosocial Frailty in Valencia (Spain): Study Protocol for a Pre-Post Controlled Trial.

With ageing, the risk of frailty increases, becoming a common condition that exposes older people to an increased risk of multiple adverse health outcomes. In Valencia (Spain), the ValueCare project develops and applies a value-based care approach that addresses the multidimensional nature of frailty by implementing integrated and personalized care to tackle psychosocial frailty. A pre-post controlled design with a baseline measurement at inclusion, at the end of implementation and a follow-up measurement after 6 months of intervention. In Valencia (Spain), 120 participants over 65 years of age are recruited from primary care centres to receive the ValueCare comprehensive and personalised care plan according to the results and are compared with 120 participants receiving "usual care". An assessment questionnaire is designed using validated instruments, and a personalised care plan is developed specifically for each participant based on the results obtained. The study protocol has been registered under the ISRCTN registration number ISRCTN25089186. Addressing frailty as a multidimensional and multifactorial risk condition requires the development and implementation of comprehensive assessments and care. In this context, this study will provide new insights into the feasibility and effectiveness of a value-based methodology for integrated care supported by ICT for older people experiencing frailty.

Human Action Recognition System Using LRCN

Abstract: The importance of human action recognition is significant across various domains, driving advancements in safety, healthcare, sports analytics, and interactive technologies. Leveraging machine learning techniques like Long Recurrent Convolutional Neural Networks (LRCN) trained on datasets such as UCF50, our project focuses on action prediction from YouTube videos. This technology plays a pivotal role in enhancingsafety and security by enabling the detection of anomalies and suspicious behaviours in surveillance systems. In healthcare, it supports remote patient monitoring, rehabilitation assessment, and personalized care plans based on observed actions. Addi- tionally, in sports analytics and entertainment, human action recognition informs performance evaluation, content creation, and immersive experiences. Our integration of a pre-trained LRCN model into a Flask web application signifies the tangible impact of machine learning on human action recognition, with ongoing efforts to optimize functionality through input validation, error handling, user feedback, security measures, and performance enhancements, illustrating the practical application and societal benefits of this innovative technology

What every clinician needs to know about chronic kidney disease: Detection, classification and epidemiology.

Chronic kidney disease (CKD) is a major healthcare challenge, affecting >800 million people worldwide. Implications for population health result from the strong associations of CKD with increased rates of cardiovascular disease, heart failure, progressive CKD leading to kidney failure, acute kidney injury (AKI), and mortality. In addition to a single disease perspective, CKD commonly coexists alongside other long-term conditions, in particular type 2 diabetes and cardiovascular disease. CKD is therefore an important component of multimorbidity that influences individual management and impacts prognosis. CKD is defined by abnormalities of kidney structure or function of any cause with implications for health that are present for longer than 3 months. The diagnosis is usually made on the basis of an abnormal glomerular filtration rate (GFR < 60 mL/min/1.73 m2) and/or the presence of proteinuria (urine albumin to creatinine ratio > 30 mg/g or >3 mg/mmol). GFR is usually estimated from serum creatinine concentration using a variety of validated equations. However, serum creatinine is closely related to muscle mass and may therefore not be an accurate marker of GFR in people with high or low muscle mass (sarcopaenia). Cystatin C is an alternative endogenous marker of GFR that is increasingly being used but also has limitations. An estimate of GFR based on both creatinine and cystatin C is the most accurate. Diagnosis should be followed by classification and risk stratification to guide the development of a risk-based, personalized care plan. Improved detection and widespread implementation of optimal CKD management has the potential to bring major benefits to population health.

Transforming evidence-based clinical guidelines into implementable clinical decision support services: the CAREPATH study for multimorbidity management.

The CAREPATH Project aims to develop a patient-centered integrated care platform tailored to older adults with multimorbidity, including mild cognitive impairment (MCI) or mild dementia. Our goal is to empower multidisciplinary care teams to craft personalized holistic care plans while adhering to evidence-based guidelines. This necessitates the creation of clear specifications for clinical decision support (CDS) services, consolidating guidance from multiple evidence-based clinical guidelines. Thus, a co-creation approach involving both clinical and technical experts is essential. This paper outlines a robust methodology for generating implementable specifications for CDS services to automate clinical guidelines. We have established a co-creation framework to facilitate collaborative exploration of clinical guidelines between clinical experts and software engineers. We have proposed an open, repeatable, and traceable method for translating evidence-based guideline narratives into implementable specifications of CDS services. Our approach, based on international standards such as CDS-Hooks and HL7 FHIR, enhances interoperability and potential adoption of CDS services across diverse healthcare systems. This methodology has been followed to create implementable specifications for 65 CDS services, automating CAREPATH consensus guideline consolidating guidance from 25 selected evidence-based guidelines. A total of 296 CDS rules have been formally defined, with input parameters defined as clinical concepts bound to FHIR resources and international code systems. Outputs include 346 well-defined CDS Cards, offering clear guidance for care plan activities and goal suggestions. These specifications have led to the implementation of 65 CDS services integrated into the CAREPATH Adaptive Integrated Care Platform. Our methodology offers a systematic, replicable process for generating CDS specifications, ensuring consistency and reliability across implementation. By fostering collaboration between clinical expertise and technical proficiency, we enhance the quality and relevance of generated specifications. Clear traceability enables stakeholders to track the development process and ensure adherence to guideline recommendations.

A digital twin model incorporating generalized metabolic fluxes to identify and predict chronic kidney disease in type 2 diabetes mellitus

We have developed a digital twin-based CKD identification and prediction model that leverages generalized metabolic fluxes (GMF) for patients with Type 2 Diabetes Mellitus (T2DM). GMF digital twins utilized basic clinical and physiological biomarkers as inputs for identification and prediction of CKD. We employed four diverse multi-ethnic cohorts (n = 7072): a Singaporean cohort (EVAS, n = 289) and a North American cohort (NHANES, n = 1044) for baseline CKD identification, and two multi-center Singaporean cohorts (CDMD, n = 2119 and SDR, n = 3627) for 3-year CKD prediction and risk stratification. We subsequently conducted a comprehensive study utilizing a single dataset to evaluate the clinical utility of GMF for CKD prediction. The GMF-based identification model performed strongly, achieving an AUC between 0.80 and 0.82. In prediction, the GMF generated with complete parameters attained high performance with an AUC of 0.86, while with incomplete parameters, it achieved an AUC of 0.75. The GMF-based prediction model utilizing complete inputs is the standard implementation of our algorithm: HealthVector Diabetes®. We have established the GMF digital twin-based model as a robust clinical tool capable of predicting and stratifying the risk of future CKD within a 3-year time horizon. We report the correlation of GMF with basic input parameters, their ability to differentiate between future health states and medication status at baseline, and their capability to quantify CKD progression rates. This holistic methodology provides insights into patients’ health states and CKD progression rates based on GMF metabolic profile differences, enabling personalized care plans.

The Montana Postural Care Project: A pilot study implementing posture care management in a rural, low-resource region

BackgroundMobility impairment limits control of posture and body alignment. This leads to altered body shapes, co-occurring problems with pain and sleep, cardiopulmonary concerns, digestive health issues, and emergent health outcomes, which further complicate functions of daily living. 24-hour posture care management was developed to remedy these challenges by restoring body symmetry. ObjectiveTo determine the feasibility of introducing posture care management to a rural-based, medically complex patient population, evaluate response of body symmetry, and examine its impact on pain and sleep quality. MethodsThis pilot study employed a longitudinal, quasi-experimental study design from March 2016 to September 2018. The posture care management intervention introduced positioning support for use when lying down, a personalized training workshop for caregiver teams, and in-home initial and follow-up assessments to provide materials and collaboratively develop a personalized care plan. Participants were followed pre-post for 6–9 months. ResultsA total of 73 participants enrolled in the study; 55 (75 %) completed. The majority were male (55 %) with a median age of 11. Most caregivers were immediate family members, and most participants had 1+ diagnosis characterized as a neurodevelopmental disorder. A majority of participants improved body symmetry (56–76 %), and 53 % with comparable information saw improvement in body symmetry with no worsening of pain or sleep quality. ConclusionThis study established the feasibility of administering posture care management in North America. These findings provide preliminary evidence of improvements in body symmetry and address concerns that posture care management can interfere with pain and sleep. Future research should consider levels of caregiver engagement and explore remote-monitored options of a posture care management intervention.

A balance of unsafe care incidents and interactive cooperative care: A constructivist grounded theory study of safe care ecosystem for older inpatients.

To explore older inpatients and their caregivers' care experience and how this relates to the gerontology care practice. Research interest in the conceptualization of safe care for older inpatients was growing, and these studies were predominantly reported from a single or healthcare perspective. There is a shortage of literature on how patients and their caregivers conceptualise safe care. Constructivist grounded theory. Stage 1 included semi-structured interviews with inpatients. Stage 2 included semi-structured interviews with caregivers and six field notes. Purposive and theoretical sampling were used to recruit 61 participants across six healthcare institutions. Data analysis included initial coding, focused coding, and theoretical coding using constant comparative, field notes and memo writing. The substantive theory to emerge from the data was A balance of unsafe care incidents and interactive cooperative care. This core concept was informed by four categories: unsafe care incidents, interactive cooperative care, person-centred care, contextual conditions, and one care outcome. The relationships between these categories constituted a balance in which patient-centred care was the core, unsafe care incidents were the barriers, interactive cooperative care was the facilitator, and the result of the balance was the care outcome. The balance constituted a safe care ecosystem under the interaction of contextual conditions. Interactive cooperative care is vital in enabling care stakeholders to reduce unsafe care incidents, which facilitates them in achieving safe care and further constructing a healthy care ecosystem. This theory identifies barriers and facilitators encountered by care stakeholders to cope with everyday problems and guides them in developing personalised care plans to ensure patient safety.