Patient monitoring, wearable devices, and the healthcare information ecosystem

Single-center pilot study of remote therapeutic monitoring in patients with operative spinal pathologies

OBJECTIVES

Spine pathology affects a significant portion of the population, leading to neck and back pain, impacting quality of life, and potentially requiring surgical intervention. Current pre- and postoperative monitoring methods rely on patient reported outcome (PRO) measures and lack continuous objective data on patients' recoveries. Remote therapeutic monitoring (RTM) using wearable devices offers a promising solution to bridge this gap, providing real-time physical function data. This study aims to assess the feasibility and correlation between changes in physical function and daily activity levels using RTM for individuals with operative spinal pathologies.

METHODS

A single-center pilot study involving 21 participants with operative spinal pathologies was conducted at an academic hospital. Participants were provided Bluetooth-enabled Fitbit Inspire 2 activity trackers and asked to wear them daily for 100 days. The Healthcare Recovery Solutions (HRS) mobile application facilitated remote administration of the PROMIS - Physical Function Short Form 6b PROs questionnaire at days 1, 30, and 90. Linear regression, Students' paired T tests, and one-way ANOVA were used to analyze collected data.

RESULTS

Average compliance with RTM was found to be 82.4% compared to only 48% for PROMs. Changes in daily steps were moderately positively correlated with changes in PROs at both 30 and 90 days. Participant satisfaction with RTM was high, and responses indicated greater satisfaction with RTM compared to PROMs.

CONCLUSIONS

RTM offers continuous and objective data collection, presenting a potential solution to the limitations of intermittent clinical assessments and self-reported outcomes. The study demonstrated a moderate correlation between changes in activity levels and changes in PROs, suggesting that RTM data could serve as a surrogate for PROs. Participants' high compliance and satisfaction with RTM underscore its feasibility and potential clinical utility. This study lays the groundwork for larger future investigations into the clinical benefits and broader application of RTM in spine care.

Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review

Over the past five years, interest in the literature regarding the security of the Internet of Medical Things (IoMT) has increased. Due to the enhanced interconnectedness of IoMT devices, their susceptibility to cyber-attacks has proportionally escalated. Motivated by the promising potential of AI-related technologies to improve certain cybersecurity measures, we present a comprehensive review of this emerging field. In this review, we attempt to bridge the corresponding literature gap regarding modern cybersecurity technologies that deploy AI techniques to improve their performance and compensate for security and privacy vulnerabilities. In this direction, we have systematically gathered and classified the extensive research on this topic. Our findings highlight the fact that the integration of machine learning (ML) and deep learning (DL) techniques improves both the performance of cybersecurity measures and their speed, reliability, and effectiveness. This may be proven to be useful for improving the security and privacy of IoMT devices. Furthermore, by considering the numerous advantages of AI technologies as opposed to their core cybersecurity counterparts, including blockchain, anomaly detection, homomorphic encryption, differential privacy, federated learning, and so on, we provide a structured overview of the current scientific trends. We conclude with considerations for future research, emphasizing the promising potential of AI-driven cybersecurity in the IoMT landscape, especially in patient data protection and in data-driven healthcare.

Quantifying physiological stability in the general ward using continuous vital signs monitoring: the circadian kernel density estimator

Technological advances seen in recent years have introduced the possibility of changing the way hospitalized patients are monitored by abolishing the traditional track-and-trigger systems and implementing continuous monitoring using wearable biosensors. However, this new monitoring paradigm raise demand for novel ways of analyzing the data streams in real time. The aim of this study was to design a stability index using kernel density estimation (KDE) fitted to observations of physiological stability incorporating the patients' circadian rhythm. Continuous vital sign data was obtained from two observational studies with 491 postoperative patients and 200 patients with acute exacerbation of chronic obstructive pulmonary disease. We defined physiological stability as the last 24 h prior to discharge. We evaluated the model against periods of eight hours prior to events defined either as severe adverse events (SAE) or as a total score in the early warning score (EWS) protocol of ≥ 6,  ≥ 8, or ≥ 10. The results found good discriminative properties between stable physiology and EWS-events (area under the receiver operating characteristics curve (AUROC): 0.772-0.993), but lower for the SAEs (AUROC: 0.594-0.611). The time of early warning for the EWS events were 2.8-5.5 h and 2.5 h for the SAEs. The results showed that for severe deviations in the vital signs, the circadian KDE model can alert multiple hours prior to deviations being noticed by the staff. Furthermore, the model shows good generalizability to another cohort and could be a simple way of continuously assessing patient deterioration in the general ward.

Data Science and Geriatric Anesthesia Research: Opportunity and Challenges

With an increase in geriatric population undergoing surgical procedures, research focused on enhancing their perioperative outcomes is of paramount importance. Currently, most of the evidence-based medicine protocols are driven by studies concentrating on adults encompassing all adult age groups. Given the alterations in physiology with aging, geriatric patients respond differently to anesthetics and, therefore, require specific research initiatives to further expound on the same. Large databases and the development of sophisticated analytic tools can provide meaningful insights into this. Here, we discuss a few research opportunities and challenges that data scientists face when focusing on geriatric perioperative research.

Artificial intelligence and "the Art of Kintsugi" in Anesthesiology: ten influential papers for clinical users

Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the present review we chose ten influential papers from the last five years and through Kintsugi, shed the light on recent evolution of artificial intelligence in anesthesiology. A comprehensive search in in Medline, Embase, Web of Science and Scopus databases was conducted. Each author searched the databases independently and created a list of six articles that influenced their clinical practice during this period, with a focus on their area of competence. During a subsequent step, each researcher presented his own list and most cited papers were selected to create the final collection of ten articles. In recent years purely methodological works with a cryptic technology (black-box) represented by the intact and static vessel, translated to a "modern artificial intelligence" in clinical practice and comprehensibility (glass-box). The purposes of this review are to explore the ten most cited papers about artificial intelligence in anesthesiology and to understand how and when it should be integrated in clinical practice.

Anaesthesia and patient safety in the socio-technical operating theatre: a narrative review spanning a century

We review the development of technology in anaesthesia over the course of the past century, from the invention of the Boyle apparatus to the modern anaesthetic workstation with artificial intelligence assistance. We define the operating theatre as a socio-technical system, being necessarily comprised of human and technological parts, the ongoing development of which has led to a reduction in mortality during anaesthesia by an order of four magnitudes over a century. The remarkable technological advances in anaesthesia have been accompanied by important paradigm shifts in the approach to patient safety, and we describe the inter-relationship between technology and the human work environment in the development of such paradigm shifts, including the systems approach and organisational resilience. A better understanding of emerging technological advances and their effects on patient safety will allow anaesthesia to continue to be a leader in both patient safety and in the design of equipment and workspaces.

Wearable Health Technology for Preoperative Risk Assessment in Elderly Patients: The WELCOME Study

Preoperative identification of high-risk groups has been extensively studied to improve patients' outcomes. Wearable devices, which can track heart rate and physical activity data, are starting to be evaluated for patients' management. We hypothesized that commercial wearable devices (WD) may provide data associated with preoperative evaluation scales and tests, to identify patients with poor functional capacity at increased risk for complications. We conducted a prospective observational study including seventy-year-old patients undergoing two-hour surgeries under general anesthesia. Patients were asked to wear a WD for 7 days before surgery. WD data were compared to preoperatory clinical evaluation scales and with a 6-min walking test (6MWT). We enrolled 31 patients, with a mean age of 76.1 (SD ± 4.9) years. There were 11 (35%) ASA 3-4 patients. 6MWT results averaged 328.9 (SD ± 99.5) m. Daily steps and 𝑉𝑂2𝑚𝑎𝑥 as recorded using WD and were associated with 6MWT performance (R = 0.56, p = 0.001 and r = 0.58, p = 0.006, respectively) and clinical evaluation scales. This is the first study to evaluate WD as preoperative evaluation tools; we found a strong association between 6MWT, preoperative scales, and WD data. Low-cost wearable devices are a promising tool for the evaluation of cardiopulmonary fitness. Further research is needed to validate WD in this setting.

Design of intelligent monitoring of loneliness in the elderly using a serverless architecture with real-time communication API

BACKGROUND

Loneliness and social isolation are recognized as critical public health issues. Older people are at greater risk of loneliness and social isolation as they deal with things like living alone, loss of family or friends, chronic illness, and hearing loss. Loneliness increases a person's risk of premature death from all causes, including dementia, heart disease, and stroke. To address these issues, the inclusion of technological platforms and the use of commercial monitoring devices are vastly increasing in healthcare and elderly care.

OBJECTIVE

The objective of this study is to design and develop a loneliness monitor serverless architecture to obtain real-time data from commercial activity wristbands through an Application Programming Interface.

METHODS

For the design and development of the architecture, the Amazon Web Services platform has been used. To monitor loneliness, the Fitbit Charge 5 bracelet was selected. Through the web Application Programming Interface offered by the AWS Lambda service, the data is obtained and stored in AWS services with an automated frequency thanks to the event bridge.

RESULTS

In the pilot stage in which the system is, it is showing great possibilities in the ease of collecting data and programming the sampling frequency. Once the request is made, the data is automatically analyzed to monitor loneliness.

CONCLUSION

The proposed architecture shows great potential for easy data collection, analysis, security, personalization, real-time inference, and scalability of sensors and actuators in the future. It has powerful benefits to apply in the health sector and reduces cases of depression and loneliness.

Contactless monitoring of respiratory rate (RR) and heart rate (HR) in non-acuity settings: a clinical validity study

OBJECTIVE

Patient monitoring in general wards primarily involves intermittent observation of temperature, heart rate (HR), respiratory rate (RR) and blood pressure performed by the nursing staff. Several hours can lapse between such measurements, and the patient may go unobserved. Despite the growing widespread use of sensors to monitor vital signs and physical activities of healthy individuals, most acutely ill hospitalised patients remain unmonitored, leaving them at an increased risk. We investigated whether a contactless monitoring system could measure vital parameters, such as HR and RR, in a real-world hospital setting.

DESIGN

A cross-sectional prospective study.

SETTING AND PARTICIPANTS

We examined the suitability of employing a non-contact monitoring system in a low-acuity setup at a tertiary care hospital in India. Measurements were performed on 158 subjects, with data acquired through contactless monitoring from the general ward and dialysis unit.

OUTCOME MEASURES

Vital parameters (RR and HR) were measured using a video camera in a non-acuity setting.

RESULTS

Three distinct combinations of contactless monitoring afforded excellent accuracy. Contactless RR monitoring was linearly correlated with Alice NightOne and manual counts, presenting coefficients of determination of 0.88 and 0.90, respectively. Contactless HR monitoring presented a coefficient of determination of 0.91. The mean absolute errors were 0.84 and 2.15 beats per minute for RR and HR, respectively.

CONCLUSIONS

Compared with existing Food and Drug Administration-approved monitors, the findings of the present study revealed that contactless monitoring of RR and HR accurately represented study populations in non-acuity settings. Contactless video monitoring is an unobtrusive and dependable method for monitoring and recording RR and HR. Further research is needed to validate its dependability and utility in other settings, including acute care.

TRIAL REGISTRATION NUMBER

CTRI/2018/11/016246.

Workload involved in vital signs-based monitoring & responding to deteriorating patients: A single site experience from a regional New Zealand hospital

Objective

This study aimed to quantify the workload involved in patient monitoring by vital signs and early warning scores (EWS), and the time spent by a rapid response team locally known as the Patient-at-Risk (PaR) team in responding to deteriorating patients.

Methods

The workload involved in the measurement and the documentation of vital signs and EWS was quantified by time and motion study using electronic stopwatch application in 167 complete sets of vital signs observations taken by nursing staff on general hospital wards at Taranaki Base Hospital, New Plymouth, New Zealand. The workload involved in responding to deteriorating patients was measured by the PaR team in real-time and recorded in an electronic logbook specifically designed for this purpose. Dependent variables were studied using analysis of variance (ANOVA), post hoc Tukey, Kruskal Wallis test, Mann-Whitney test and correlation tests.

Results

The mean time to measure and record a complete set of vital signs including interruptions was 4:18 (95% CI: 4:07–4:28) minutes. After excluding interruptions, the mean time taken to measure and record a set of vital signs was 3:24 (95% CI: 3:15–3:33) minutes. We found no statistical difference between the observer, location of the patient, staff characteristics or experience and patient characteristics. PaR nurses' mean time to provide rapid response was 47:36 (95% CI: 44:57–50:15) minutes. Significantly more time was spent on patients having severe degrees of deterioration (higher EWS) < 0.001. No statistical difference was observed between ward specialty, and nursing shifts.

Conclusions

Patient monitoring and response to deterioration consumed considerable time. Time spent in monitoring was not affected by independent and random factors studied; however, time spent on the response was greater when patients had higher degrees of deterioration.