Wearable devices for remote vital signs monitoring in the outpatient setting: an overview of the field

Early detection of physiological deterioration has been shown to improve patient outcomes. Due to recent improvements in technology, comprehensive outpatient vital signs monitoring is now possible. This is the first review to collate information on all wearable devices on the market for outpatient physiological monitoring. A scoping review was undertaken. The monitors reviewed were limited to those that can function in the outpatient setting with minimal restrictions on the patient’s normal lifestyle, while measuring any or all of the vital signs: heart rate, ECG, oxygen saturation, respiration rate, blood pressure and temperature. A total of 270 papers were included in the review. Thirty wearable monitors were examined: 6 patches, 3 clothing-based monitors, 4 chest straps, 2 upper arm bands and 15 wristbands. The monitoring of vital signs in the outpatient setting is a developing field with differing levels of evidence for each monitor. The most common clinical application was heart rate monitoring. Blood pressure and oxygen saturation measurements were the least common applications. There is a need for clinical validation studies in the outpatient setting to prove the potential of many of the monitors identified. Research in this area is in its infancy. Future research should look at aggregating the results of validity and reliability and patient outcome studies for each monitor and between different devices. This would provide a more holistic overview of the potential for the clinical use of each device

Continuous monitoring of health and mobility indicators in patients with cardiovascular disease: a review of recent technologies

Cardiovascular diseases kill 18 million people each year. Currently, a patient’s health is assessed only during clinical visits, which are often infrequent and provide little information on the person’s health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring

Monitoring outpatients in palliative care through wearable devices

Patients in palliative care suffer from a life-threatening disease. Holistic treatment includes control of symptoms (e. g., pain, nausea, sleeplessness) as well as psychosocial and spiritual help which is also extended to the relatives of a patient. For advanced cancer patients in palliative care, a crucial phase is the transition from palliative care in the hospital to the home setting, where care around the clock is not guaranteed any more, leads to an increased number of unplanned hospital re-admissions and emergency visits. Physicians aim to fill this care gap by monitoring physical and social activities as well as vital signs. Daily monitoring data, provided to caregivers, could enable caregivers to timely intervene when symptoms of a patient deteriorate. Besides patients in palliative care, also cancer survivors suffering from cancer-related fatigue could benefit from activity monitoring. Up to now, the remedies and effective treatments for cancer-related fatigue are limited. Research still has to unveil the underlying mechanisms that lead to a state of chronic exhaustedness. Measures that help healthy people like regenerative sleep show no or little effect in fatigued patients. Besides psycho-stimulants that come with the risk of addiction, cognitive behavioural therapy and moderate physical exercise have been shown to be effective. However, research still has to investigate timing, frequency and intensity of physical activity and researchers need a better understanding how the fatigue evolves during the day and in long-term. This thesis investigates the possibilities and limitations of activity monitoring using wearable devices such as smartphones and an armworn devices that is capable of measuring vital signs such as heart rate. Three studies involving cancer patients are conducted: - An interview study including 12 cancer patients enabled a patient-centric design for an Android activity monitoring app for smartphones. - Only using the smartphone as monitoring device, a study with 7 cancer survivors suffering from cancer-related fatigue was conducted as a pre-study in order to gain first experiences and to explore the possible knowledge gain about cancer-related fatigue through activity monitoring. - During a planned study period of 12 weeks per patient, 30 patients in ambulatory palliative care were wearing a smartphone and the arm-worn sensor as monitoring devices. The age range of the study participants was 39 to 85 years. In weekly interviews, patients were asked about their experiences with the devices and their quality of life. The aim of the study was to evaluate feasibility and acceptance of activity monitoring in this patient group. Furthermore, exploratory data analysis investigated the possibilities and limitations of unsupervised methods on this real-world data set. The two data sets, collected during the fatigue study and during the palliative care study, were pre-processed including cleaning steps, classification and clustering methods to add higher level information such as visited locations (anonymized). From these prepared data sets, features were extracted such as number of places visited per day. On the resulting datasets of features, statistical methods were applied to explore relations between sensor data, self-reports and, in case of the palliative care study, emergency visits to the hospital. For the latter analysis, patients who experienced an emergency room visit and those who did not were compared by means of hypothesis testing. For each feature, the underlying alternative hypothesis was that the change of a feature between the first week of study participation at home and the week before an emergency visit (or the last week of study participation for the patients without an emergency visit), differs in the two patient groups. The rate of change was defined by the ratio of the medians of the two weeks. Changes of three features, namely resting heart rate, resting heart rate variability and step speed were identified to have significant group differences: - The resting heart rate had an increasing trend in the group with emergency visits (median=1.01, interquartile range [0.96, 1.12]) and a decreasing trend in the group without an emergency visit (median=0.9, interquartile range [0.89, 0.99]) with a nominal significance of p=.021 and a medium effect size r=.46. - The resting heart rate variability had a decreasing trend in the group with emergency visits (mean=0.81, standard deviation=0.14) and an increasing trend in the group without an emergency visit (mean=1.17, standard deviation=0.46) with a nominal significance of p=.011 and a large effect size r=.53. - The step speed had an increasing trend in the group with emergency visits (median=1.1, interquartile range [1.08, 1.13]) and a decreasing trend in the group without an emergency visit (median=0.99, interquartile range [0.96, 1.04]) with a nominal significance of p=.003 and a large effect size r=.61. In contrast, hypothesis testing for features based on patients’ subjective self-reports for pain, distres and global quality of life did not reveil any significant differences. Hence, activity monitoring of vital signs and physical activity outperformed patients’ self-reports. However, a power analysis based on the three nominally significant results would recommend an independent study with 84 patients to confirm the results of this study. Furthermore, a set of recommendations for future research was concluded from the experiences gained through conducting these studies

Nursing-Relevant Patient Outcomes and Clinical Processes in Data Science Literature: 2019 Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this paper, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (e.g., natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope the studies described in this paper help readers: (a) understand the breadth and depth of data science’s ability to improve clinical processes and patient outcomes that are relevant to nurses and (b) identify gaps in the literature that are in need of exploration

Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond

The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well‐being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team‐based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit

Towards a tricorder: clinical, health economic, and ethical investigation of point-of-care artificial intelligence electrocardiogram for heart failure

Heart failure (HF) is an international public health priority and a focus of the NHS Long Term Plan. There is a particular need in primary care for screening and early detection of heart failure with reduced ejection fraction (HFrEF) – the most common and serious HF subtype, and the only one with an abundant evidence base for effective therapies. Digital health technologies (DHTs) integrating artificial intelligence (AI) could improve diagnosis of HFrEF. Specifically, through a convergence of DHTs and AI, a single-lead electrocardiogram (ECG) can be recorded by a smart stethoscope and interrogated by AI (AI-ECG) to potentially serve as a point-of-care HFrEF test. However, there are concerning evidence gaps for such DHTs applying AI; across intersecting clinical, health economic, and ethical considerations. My thesis therefore investigates hypotheses that AI-ECG is 1.) Reliable, accurate, unbiased, and can be patient self-administered, 2.) Of justifiable health economic impact for primary care deployment, and 3.) Appropriate across ethical domains for deployment as a tool for patient self-administered screening. The theoretical basis for this work is presented in the Introduction (Chapter 1). Chapter 2 describes the first large-scale, multi-centre independent external validation study of AI-ECG, prospectively recruiting 1,050 patients and highlighting impressive performance: area under the curve, sensitivity, and specificity up to 0·91 (95% confidence interval: 0·88–0·95), 91·9% (78·1–98·3), and 80·2% (75·5–84·3) respectively; and absence of bias by age, sex, and ethnicity. Performance was independent of operator, and usability of the tool extended to patients being able to self-examine. Chapter 3 presents a clinical and health economic outcomes analysis using a contemporary digital repository of 2.5 million NHS patient records. A propensity-matched cohort was derived using all patients diagnosed with HF from 2015-2020 (n = 34,208). Novel findings included the unacceptable reality that 70% of index HF diagnoses are made through hospitalisation; where index diagnosis through primary care conferred a medium-term survival advantage and long-term cost saving (£2,500 per patient). This underpins a health economic model for the deployment of AI-ECG across primary care. Chapter 4 approaches a normative ethical analysis focusing on equity, agency, data rights, and responsibility for safe, effective, and trustworthy implementation of an unprecedented at-home patient self-administered AI-ECG screening programme. I propose approaches to mitigating any potential harms, towards preserving and promoting trust, patient engagement, and public health. Collectively, this thesis marks novel work highlighting AI-ECG as tool with the potential to address major cardiovascular public health priorities. Scrutiny through complimentary clinical, health economic, and ethical considerations can directly serve patients and health systems by blueprinting best-practice for the evaluation and implementation of DHTs integrating AI – building the conviction needed to realise the full potential of such technologies.Open Acces

Multimodal machine learning in medical screenings

The healthcare industry, with its high demand and standards, has long been considered a crucial area for technology-based innovation. However, the medical field often relies on experience-based evaluation. Limited resources, overloading capacity, and a lack of accessibility can hinder timely medical care and diagnosis delivery. In light of these challenges, automated medical screening as a decision-making aid is highly recommended. With the increasing availability of data and the need to explore the complementary effect among modalities, multimodal machine learning has emerged as a potential area of technology. Its impact has been witnessed across a wide range of domains, prompting the question of how far machine learning can be leveraged to automate processes in even more complex and high-risk sectors. This paper delves into the realm of multimodal machine learning in the field of automated medical screening and evaluates the potential of this area of study in mental disorder detection, a highly important area of healthcare. First, we conduct a scoping review targeted at high-impact papers to highlight the trends and directions of multimodal machine learning in screening prevalent mental disorders such as depression, stress, and bipolar disorder. The review provides a comprehensive list of popular datasets and extensively studied modalities. The review also proposes an end-to-end pipeline for multimodal machine learning applications, covering essential steps from preprocessing, representation, and fusion, to modelling and evaluation. While cross-modality interaction has been considered a promising factor to leverage fusion among multimodalities, the number of existing multimodal fusion methods employing this mechanism is rather limited. This study investigates multimodal fusion in more detail through the proposal of Autofusion, an autoencoder-infused fusion technique that harnesses the cross-modality interaction among different modalities. The technique is evaluated on DementiaBank’s Pitt corpus to detect Alzheimer’s disease, leveraging the power of cross-modality interaction. Autofusion achieves a promising performance of 79.89% in accuracy, 83.85% in recall, 81.72% in precision, and 82.47% in F1. The technique consistently outperforms all unimodal methods by an average of 5.24% across all metrics. Our method consistently outperforms early fusion and late fusion. Especially against the late fusion hard-voting technique, our method outperforms by an average of 20% across all metrics. Further, empirical results show that the cross-modality interaction term enhances the model performance by 2-3% across metrics. This research highlights the promising impact of cross-modality interaction in multimodal machine learning and calls for further research to unlock its full potential