Using a Real-Time Location System for Assessment of Patient Ambulation in a Hospital Setting

Graph Trilateration for Indoor Localization in Sparsely Distributed Edge Computing Devices in Complex Environments Using Bluetooth Technology

Spatial navigation patterns in indoor space usage can reveal important cues about the cognitive health of participants. In this work, we present a low-cost, scalable, open-source edge computing system using Bluetooth low energy (BLE) beacons for tracking indoor movements in a large, 1700 m2 facility used to carry out therapeutic activities for participants with mild cognitive impairment (MCI). The facility is instrumented with 39 edge computing systems, along with an on-premise fog server. The participants carry a BLE beacon, in which BLE signals are received and analyzed by the edge computing systems. Edge computing systems are sparsely distributed in the wide, complex indoor space, challenging the standard trilateration technique for localizing subjects, which assumes a dense installation of BLE beacons. We propose a graph trilateration approach that considers the temporal density of hits from the BLE beacon to surrounding edge devices to handle the inconsistent coverage of edge devices. This proposed method helps us tackle the varying signal strength, which leads to intermittent detection of beacons. The proposed method can pinpoint the positions of multiple participants with an average error of 4.4 m and over 85% accuracy in region-level localization across the entire study area. Our experimental results, evaluated in a clinical environment, suggest that an ordinary medical facility can be transformed into a smart space that enables automatic assessment of individuals' movements, which may reflect health status or response to treatment.

Ambulatory Behavior Assessment Using Deep Learning

This work leverages a custom implementation of a deep neural network-based object detection algorithm to detect people and a set of assistive devices relevant to clinical environments. The object detections form the basis for the quantification of different ambulatory activities and related behaviors. Using features extracted from detected people and objects as input to machine learning models, we quantify how a person ambulates and the mode of ambulation being used.Clinical relevance- This system provides the data required for clinicians and hospitalized patients to work together in the creation, monitoring, and adjustment of ambulatory goals.

Indoor Location Data for Tracking Human Behaviours: A Scoping Review

Real-time location systems (RTLS) record locations of individuals over time and are valuable sources of spatiotemporal data that can be used to understand patterns of human behaviour. Location data are used in a wide breadth of applications, from locating individuals to contact tracing or monitoring health markers. To support the use of RTLS in many applications, the varied ways location data can describe patterns of human behaviour should be examined. The objective of this review is to investigate behaviours described using indoor location data, and particularly the types of features extracted from RTLS data to describe behaviours. Four major applications were identified: health status monitoring, consumer behaviours, developmental behaviour, and workplace safety/efficiency. RTLS data features used to analyse behaviours were categorized into four groups: dwell time, activity level, trajectory, and proximity. Passive sensors that provide non-uniform data streams and features with lower complexity were common. Few studies analysed social behaviours between more than one individual at once. Less than half the health status monitoring studies examined clinical validity against gold-standard measures. Overall, spatiotemporal data from RTLS technologies are useful to identify behaviour patterns, provided there is sufficient richness in location data, the behaviour of interest is well-characterized, and a detailed feature analysis is undertaken.

Extended mobility scale (AMEXO) for assessing mobilization and setting goals after gastrointestinal and oncological surgery: a before-after study

Background

Early structured mobilization has become a key element of Enhanced Recovery After Surgery programs to improve patient outcomes and decrease length of hospital stay. With the intention to assess and improve early mobilization levels, the 8-point ordinal John Hopkins Highest Level of Mobility (JH-HLM) scale was implemented at two gastrointestinal and oncological surgery wards in the Netherlands. After the implementation, however, healthcare professionals perceived a ceiling effect in assessing mobilization after gastrointestinal and oncological surgery. This study aimed to quantify this perceived ceiling effect, and aimed to determine if extending the JH-HLM scale with four additional response categories into the AMsterdam UMC EXtension of the JOhn HOpkins Highest Level of mObility (AMEXO) scale reduced this ceiling effect.

Methods

All patients who underwent gastrointestinal and oncological surgery and had a mobility score on the first postoperative day before (July–December 2018) or after (July–December 2019) extending the JH-HLM into the AMEXO scale were included. The primary outcome was the before-after difference in the percentage of ceiling effects on the first three postoperative days. Furthermore, the before-after changes and distributions in mobility scores were evaluated. Univariable and multivariable logistic regression analysis were used to assess these differences.

Results

Overall, 373 patients were included (JH-HLM n = 135; AMEXO n = 238). On the first postoperative day, 61 (45.2%) patients scored the highest possible mobility score before extending the JH-HLM into the AMEXO as compared to 4 (1.7%) patients after (OR = 0.021, CI = 0.007–0.059, p < 0.001). During the first three postoperative days, 118 (87.4%) patients scored the highest possible mobility score before compared to 40 (16.8%) patients after (OR = 0.028, CI = 0.013–0.060, p < 0.001). A change in mobility was observed in 88 (65.2%) patients before as compared to 225 (94.5%) patients after (OR = 9.101, CI = 4.046–20.476, p < 0.001). Of these 225 patients, the four additional response categories were used in 165 (73.3%) patients.

Conclusions

A substantial ceiling effect was present in assessing early mobilization in patients after gastrointestinal and oncological surgery using the JH-HLM. Extending the JH-HLM into the AMEXO scale decreased the ceiling effect significantly, making the tool more appropriate to assess early mobilization and set daily mobilization goals after gastrointestinal and oncological surgery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12893-021-01445-3.

A novel early mobility bundle improves length of stay and rates of readmission among hospitalized general medicine patients

Inpatient early mobility initiatives are effective therapeutic interventions for improving patient outcomes and decreasing use of hospital resources among adult ICU and general medicine patients. To establish and demonstrate guidelines for early patient ambulation, we developed and implemented a novel multidisciplinary mobility bundle utilizing the JH-HLM (Johns Hopkins Highest Level of Mobility) scale for mobility classification, on a single adult general medicine unit of a community hospital. Our results show that patients admitted to the unit after implementation of the mobility bundle had improved mobility scores, reduced rates of 30-day hospital readmission, and a shortened length of hospital stay. This study emphasizes the importance of measuring mobility using a systematic method, easing workflow among unit practitioners, and allowing mobility initiatives to be jointly driven by nursing, physical therapy, and physicians.

Real-Time Person Identification in a Hospital Setting: A Systematic Review

In the critical setting of a trauma team activation, team composition is crucial information that should be accessible at a glance. This calls for a technological solution, which are widely available, that allows access to the whereabouts of personnel. This diversity presents decision makers and users with many choices and considerations. The aim of this review is to give a comprehensive overview of available real-time person identification techniques and their respective characteristics. A systematic literature review was performed to create an overview of identification techniques that have been tested in medical settings or already have been implemented in clinical practice. These techniques have been investigated on a total of seven characteristics: costs, usability, accuracy, response time, hygiene, privacy, and user safety. The search was performed on 11 May 2020 in PubMed and the Web of Science Core Collection. PubMed and Web of Science yielded a total n = 265 and n = 228 records, respectively. The review process resulted in n = 23 included records. A total of seven techniques were identified: (a) active and (b) passive Radio-Frequency Identification (RFID) based systems, (c) fingerprint, (d) iris, and (e) facial identification systems and infrared (IR) (f) and ultrasound (US) (g) based systems. Active RFID was largely documented in the included literature. Only a few could be found about the passive systems. Biometric (c, d, and e) technologies were described in a variety of applications. IR and US techniques appeared to be a niche, as they were only spoken of in few (n = 3) studies.

Assessment of Patient Ambulation Profiles to Predict Hospital Readmission, Discharge Location, and Length of Stay in a Cardiac Surgery Progressive Care Unit

IMPORTANCE

Promoting patient mobility during hospitalization is associated with improved outcomes and reduced risk of hospitalization-associated functional decline. Therefore, accurate measurement of mobility with high-information content data may be key to improved risk prediction models, identification of at-risk patients, and the development of interventions to improve outcomes. Remote monitoring enables measurement of multiple ambulation metrics incorporating both distance and speed.

OBJECTIVE

To evaluate novel ambulation metrics in predicting 30-day readmission rates, discharge location, and length of stay using a real-time location system to continuously monitor the voluntary ambulations of postoperative cardiac surgery patients.

DESIGN, SETTING, AND PARTICIPANTS

This prognostic cohort study of the mobility of 100 patients after cardiac surgery in a progressive care unit at Johns Hopkins Hospital was performed using a real-time location system. Enrollment occurred between August 29, 2016, and April 4, 2018. Data analysis was performed from June 2018 to December 2019.

MAIN OUTCOMES AND MEASURES

Outcome measures included 30-day readmission, discharge location, and length of stay. Digital records of all voluntary ambulations were created where each ambulation consisted of multiple segments defined by distance and speed. Ambulation profiles consisted of 19 parameters derived from the digital ambulation records.

RESULTS

A total of 100 patients (81 men [81%]; mean [SD] age, 63.1 [11.6] years) were evaluated. Distance and speed were recorded for more than 14 000 segments in 840 voluntary ambulations, corresponding to a total of 127.8 km (79.4 miles) using a real-time location system. Patient ambulation profiles were predictive of 30-day readmission (sensitivity, 86.7%; specificity, 88.2%; C statistic, 0.925 [95% CI, 0.836-1.000]), discharge to acute rehabilitation (sensitivity, 84.6%; specificity, 86.4%; C statistic, 0.930 [95% CI, 0.855-1.000]), and length of stay (correlation coefficient, 0.927).

CONCLUSIONS AND RELEVANCE

Remote monitoring provides a high-information content description of mobility, incorporating elements of step count (ambulation distance and related parameters), gait speed (ambulation speed and related parameters), frequency of ambulation, and changes in parameters on successive ambulations. Ambulation profiles incorporating multiple aspects of mobility enables accurate prediction of clinically relevant outcomes.

Wearable Devices for Precision Medicine and Health State Monitoring

Wearable technologies will play an important role in advancing precision medicine by enabling measurement of clinically-relevant parameters describing an individual's health state. The lifestyle and fitness markets have provided the driving force for the development of a broad range of wearable technologies that can be adapted for use in healthcare. Here we review existing technologies currently used for measurement of the four primary vital signs: temperature, heart rate, respiration rate, and blood pressure, along with physical activity, sweat, and emotion. We review the relevant physiology that defines the measurement needs and evaluate the different methods of signal transduction and measurement modalities for the use of wearables in healthcare.

Balance ability and cognitive impairment influence sustained walking in an assisted living facility

PURPOSE OF STUDY

The purpose of this study was to determine the influence of cognitive impairment (CI),¹ gait quality, and balance ability on walking distance and speed in an assisted living facility.

MATERIALS AND METHODS

This was a longitudinal cohort study of institutionalized older adults (N = 26; 555 observations) followed for up to 8 months. Hierarchical linear modeling statistical techniques were used to examine the effects of gait quality and balance ability (using the Tinetti Gait and Balance Test) and cognitive status (using the Montreal Cognitive Assessment) on walking activity (distance, sustained distance, sustained speed). The latter were measured objectively and continuously by a real-time locating system (RTLS).

RESULTS

A one-point increase in balance ability was associated with an 8% increase in sustained walking distance (p = 0.03) and a 4% increase in sustained gait speed (p = 0.00). Gait quality was associated with decreased sustained gait speed (p = 0.03). Residents with moderate (ERR = 2.34;p = 0.01) or severe CI (trend with an ERR = 1.62; p = 0.06) had longer sustained walking distances at slower speeds when compared to residents with no CI.

CONCLUSIONS

After accounting for cognitive status, it was balance ability, not gait quality, that was a determinant of sustained walking distances and speeds. Therefore, balance interventions for older adults in assisted living may enable sustained walking activity. Given that CI was associated with more sustained walking, limiting sustained walking in the form of wandering behavior, especially for those with balance impairments, may prevent adverse events, including fall-related injury.