A semi-supervised approach to unobtrusively predict abnormality in breathing patterns using hydraulic bed sensor data in older adults aging in place.

Shortness of breath is often considered a repercussion of aging in older adults, as respiratory illnesses like COPD1 or respiratory illnesses due to heart-related issues are often misdiagnosed, under-diagnosed or ignored at early stages. Continuous health monitoring using ambient sensors has the potential to ameliorate this problem for older adults at aging-in-place facilities. In this paper, we leverage continuous respiratory health data collected by using ambient hydraulic bed sensors installed in the apartments of older adults in aging-in-place Americare facilities to find data-adaptive indicators related to shortness of breath. We used unlabeled data collected unobtrusively over the span of three years from a COPD-diagnosed individual and used data mining to label the data. These labeled data are then used to train a predictive model to make future predictions in older adults related to shortness of breath abnormality. To pick the continuous changes in respiratory health we make predictions for shorter time windows (60-s). Hence, to summarize each day's predictions we propose an abnormal breathing index (ABI) in this paper. To showcase the trajectory of the shortness of breath abnormality over time (in terms of days), we also propose trend analysis on the ABI quarterly and incrementally. We have evaluated six individual cases retrospectively to highlight the potential and use cases of our approach.

Tracking personalized functional health in older adults using geriatric assessments.

BACKGROUND: Higher levels of functional health in older adults leads to higher quality of life and improves the ability to age-in-place. Tracking functional health objectively could help clinicians to make decisions for interventions in case of health deterioration. Even though several geriatric assessments capture several aspects of functional health, there is limited research in longitudinally tracking personalized functional health of older adults using a combination of these assessments. METHODS: We used geriatric assessment data collected from 150 older adults to develop and validate a functional health prediction model based on risks associated with falls, hospitalizations, emergency visits, and death. We used mixed effects logistic regression to construct the model. The geriatric assessments included were Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). Construct validators such as fall risks associated with model predictions, and case studies with functional health trajectories were used to validate the model. RESULTS: The model is shown to separate samples with and without adverse health event outcomes with an area under the receiver operating characteristic curve (AUC) of > 0.85. The model could predict emergency visit or hospitalization with an AUC of 0.72 (95% CI 0.65-0.79), fall with an AUC of 0.86 (95% CI 0.83-0.89), fall with hospitalization with an AUC of 0.89 (95% CI 0.85-0.92), and mortality with an AUC of 0.93 (95% CI 0.88-0.97). Multiple comparisons of means using Turkey HSD test show that model prediction means for samples with no adverse health events versus samples with fall, hospitalization, and death were statistically significant (p < 0.001). Case studies for individual residents using predicted functional health trajectories show that changes in model predictions over time correspond to critical health changes in older adults. CONCLUSIONS: The personalized functional health tracking may provide clinicians with a longitudinal view of overall functional health in older adults to help address the early detection of deterioration trends and decide appropriate interventions. It can also help older adults and family members take proactive steps to improve functional health.

Making sense of blood glucose data and self-management in individuals with type 2 diabetes mellitus: A qualitative study.

AIMS AND OBJECTIVES: To describe individuals' with type 2 diabetes mellitus sense-making of blood glucose data and other influences impacting self-management behaviour. BACKGROUND: Type 2 diabetes mellitus prevalence is increasing globally. Adherence to effective diabetes self-management regimens is an ongoing healthcare challenge. Examining individuals' sense-making processes can advance staff knowledge of and improve diabetes self-management behaviour. DESIGN: A qualitative exploratory design examining how individuals make sense of blood glucose data and symptoms, and the influence on self-management decisions. METHODS: Sixteen one-on-one interviews with adults diagnosed with type 2 diabetes mellitus using a semi-structured interview guide were conducted from March-May 2018. An inductive-deductive thematic analysis of data using the Sensemaking Framework for Chronic Disease Self-Management was used. The consolidated criteria for reporting qualitative research (COREQ) checklist were used in completing this paper. RESULTS: Three main themes described participants' type 2 diabetes mellitus sense-making and influences on self-management decisions: classifying blood glucose data, building mental models and making self-management decisions. Participants classified glucose levels based on prior personal experiences. Participants learned about diabetes from classes, personal experience, health information technology and their social network. Seven participants expressed a need for periodic refreshing of diabetes knowledge. CONCLUSION: Individuals use self-monitored glucose values and/or HbA1C values to evaluate glucose control. When using glucose values, they analyse the context in which the value was obtained through the lens of personal parameters and expectations. Understanding how individuals make sense of glycaemic data and influences on diabetes self-management behaviour with periodic reassessment of this understanding can guide the healthcare team in optimising collaborative individualised care plans. RELEVANCE TO CLINICAL PRACTICE: Nurses must assess sense-making processes in self-management decisions. Periodic "refresher" diabetes education may be needed for individuals with type 2 diabetes mellitus.

Using Sensor Signals in the Early Detection of Heart Failure: A Case Study.

Early detection of heart failure in older adults will be a significant issue for the foreseeable future. The current article presents a case study to describe how monitoring ballistocardiogram (BCG) waveforms captured non-invasively using sensors placed under a bed mattress can detect early heart failure changes. Heart and respiratory rates obtained from the bed sensor of a female older adult who was hospitalized with acute mixed congestive heart failure, clinic notes, and data from computer simulations reflecting increasing diastolic dysfunction were analyzed. Mean heart and respiratory rate trends obtained from her bed sensor in the prior 2 months did not indicate heart failure. BCG waveforms resulting from the simulations demonstrated changes associated with decreasing cardiac output as diastolic function worsened. Developing new methods for clinically interpreting BCG waveforms presents a significant opportunity for improving early heart failure detection. [Journal of Gerontological Nursing, 46(7), 41-46.].

Perception of clinical deterioration cues among medical-surgical nurses.

AIM: To examine medical-surgical nurses' capacity and tendency to perceive cues indicating clinical deterioration and nursing characteristics influencing deterioration cue perception. DESIGN: Cross-sectional, explorative study design. METHODS: Data were collected over 10 weeks between September-November 2017. Medical-surgical nurses completed an online survey consisting of a demographic questionnaire, the Occupational Fatigue, Exhaustion Recovery scale and 50 detection trials. Descriptive statistics and statistical tests were used to describe and interpret data. FINDINGS: A significant association was found between nurses' capacity and tendency to perceive deterioration cues. As nurses' capacity to perceive deterioration cues increased, nurses were more likely to classify patient cues as indicators of deterioration. Fatigue, education, and certification were not identified as characteristics associated with deterioration cue perception. However, experience was observed to significantly influence nurses' capacity to perceive deterioration cues based on levels of skills acquisition. CONCLUSION: Study findings imply that future research should be directed at determining whether other individual factors and organizational system dynamics influence deterioration cue perception. IMPACT: To better understand how nurses perceive deterioration cues, this study integrated concepts from the Situation Awareness model and Signal Detection Theory. Novice, advanced beginner and competent nurses were found to have a lower capacity to perceive deterioration cues compared with proficient and expert nurses. With simulation increasingly being used as a primary teaching modality in nursing, the development of a simulation-based signal detection training intervention may be beneficial in enhancing deterioration cue perception.

Automated Deterioration Detection Using Electronic Medical Record Data in Intensive Care Unit Patients: A Systematic Review.

Timely detection of deterioration in status for intensive care unit patients can be problematic due to variation in data availability and the necessity of integrating data from multiple sources. This can lead to opaqueness of clinical trends and failure to rescue. Automated deterioration detection using electronic medical record data can reduce the risk of failure to rescue. This review describes the automated use of electronic medical record data in identifying deterioration in intensive care unit patients. PubMed and Google Scholar were used to retrieve publications between January 1, 2006, and March 31, 2016. Six studies met inclusion criteria: intensive care unit patient focus, description of electronic medical record data use in automated patient deterioration detection, and presence of predictive, sensitivity, and/or specificity values. Detection focused on specific clinical events such as infection; data sources were electronic medical record-populated databases. Detection algorithms incorporated laboratory results, vital signs, medication orders, and respiratory therapy and radiology documentation. Positive and negative predictive values and sensitivity and specificity measures varied across studies. Three systems generated clinician alerts. Automated deterioration detection using electronic medical record data may be an important aid in caring for intensive care unit patients, but its usefulness is limited by variable electronic medical record detection approaches and performance.

The Role of the Electronic Medical Record in the Intensive Care Unit Nurse's Detection of Patient Deterioration: A Qualitative Study.

Failure to detect patient deterioration signals leads to longer stays in the hospital, worse functional outcomes, and higher hospital mortality rates. Surveillance, including ongoing acquisition, interpretation, and synthesis of patient data by the nurse, is essential for early risk detection. Electronic medical records promote accessibility and retrievability of patient data and can support patient surveillance. A secondary analysis was performed on interview data from 24 intensive care unit nurses, collected in a study that examined factors influencing nurse responses to alarms. Six themes describing nurses' use of electronic medical record information to understand the patients' norm and seven themes describing electronic medical record design issues were identified. Further work is needed on electronic medical record design to integrate documentation and information presentation with the nursing workflow. Organizations should involve bedside nurses in the design of handoff formats that provide key information common to all intensive care unit patient populations, as well as population-specific information.

Enhanced registered nurse care coordination with sensor technology: Impact on length of stay and cost in aging in place housing.

BACKGROUND: When planning the Aging in Place Initiative at TigerPlace, it was envisioned that advances in technology research had the potential to enable early intervention in health changes that could assist in proactive management of health for older adults and potentially reduce costs. PURPOSE: The purpose of this study was to compare length of stay (LOS) of residents living with environmentally embedded sensor systems since the development and implementation of automated health alerts at TigerPlace to LOS of those who are not living with sensor systems. Estimate potential savings of living with sensor systems. METHODS: LOS for residents living with and without sensors was measured over a span of 4.8 years since the implementation of sensor-generated health alerts. The group living with sensors (n = 52) had an average LOS of 1,557 days (4.3 years); the comparison group without sensors (n = 81) was 936 days (2.6 years); p = .0006. Groups were comparable based on admission age, gender, number of chronic illnesses, SF12 physical health, SF12 mental health, Geriatric Depression Scale (GDS), activities of daily living, independent activities of daily living, and mini-mental status examination scores. Both groups, all residents living at TigerPlace since the implementation of health alerts, receive registered nurse (RN) care coordination as the standard of care. DISCUSSION: Results indicate that residents living with sensors were able to reside at TigerPlace 1.7 years longer than residents living without sensors, suggesting that proactive use of health alerts facilitates successful aging in place. Health alerts, generated by automated algorithms interpreting environmentally embedded sensor data, may enable care coordinators to assess and intervene on health status changes earlier than is possible in the absence of sensor-generated alerts. Comparison of LOS without sensors TigerPlace (2.6 years) with the national median in residential senior housing (1.8 years) may be attributable to the RN care coordination model at TigerPlace. Cost estimates comparing cost of living at TigerPlace with the sensor technology vs. nursing home reveal potential saving of about $30,000 per person. Potential cost savings to Medicaid funded nursing home (assuming the technology and care coordination were reimbursed) are estimated to be about $87,000 per person. CONCLUSIONS: Early alerts for potential health problems appear to enhance the current RN care coordination care delivery model at TigerPlace, increasing LOS for those living with sensors to nearly twice that of those who did not. Sensor technology with care coordination has cost saving potential for consumers and Medicaid.

Nursing Professional Development in Ambulatory Care: A Phenomenological Study.

Nursing professional development (NPD) practitioners play an important role in ensuring the quality and safety of nursing care and in guiding nurses through practice transitions. Recently, increasing numbers of NPD practitioners have been employed in ambulatory care settings, yet little is known about how the ambulatory practice setting affects or is affected by NPD practice. The aim of this descriptive phenomenology was to describe how the NPD role is experienced in the ambulatory care setting.

Cardiovascular sex-differences: insights via physiology-based modeling and potential for noninvasive sensing via ballistocardiography.

In this study, anatomical and functional differences between men and women in their cardiovascular systems and how these differences manifest in blood circulation are theoretically and experimentally investigated. A validated mathematical model of the cardiovascular system is used as a virtual laboratory to simulate and compare multiple scenarios where parameters associated with sex differences are varied. Cardiovascular model parameters related with women's faster heart rate, stronger ventricular contractility, and smaller blood vessels are used as inputs to quantify the impact (i) on the distribution of blood volume through the cardiovascular system, (ii) on the cardiovascular indexes describing the coupling between ventricles and arteries, and (iii) on the ballistocardiogram (BCG) signal. The model-predicted outputs are found to be consistent with published clinical data. Model simulations suggest that the balance between the contractile function of the left ventricle and the load opposed by the arterial circulation attains similar levels in females and males, but is achieved through different combinations of factors. Additionally, we examine the potential of using the BCG waveform, which is directly related to cardiovascular volumes, as a noninvasive method for monitoring cardiovascular function. Our findings provide valuable insights into the underlying mechanisms of cardiovascular sex differences and may help facilitate the development of effective noninvasive cardiovascular monitoring methods for early diagnosis and prevention of cardiovascular disease in both women and men.

A review of verbal order policies in acute care hospitals.

BACKGROUND: Although verbal and telephone orders (VOs) are commonly used in the patient care process, there has been little examination of the strategies and tactics used to ensure their appropriate use or how to ensure that they are accurately communicated, correctly understood, initially documented, and subsequently transcribed into the medical record and ultimately carried out as intended. A systematic review was conducted of hospital verbal and telephone order policies in acute care settings. METHODS: A stratified random sample of hospital verbal and telephone order policy documents were abstracted from critical access, rural, rural referral, and urban hospitals located in Iowa and Missouri and from academic medical centers from across the United States. FINDINGS: Substantial differences were found across 40 acute care settings in terms of who is authorized to give (including nonlicensed personnel) and take VOs and in terms of time allowed for the prescriber to cosign the VO. When a nonphysician or other licensed prescriber was allowed to communicate VOs, there was no discussion of the process to review the VO before it was communicated in turn to the hospital personnel receiving the order. Policies within several of the same hospitals were inconsistent in terms of the periods specified for prescriber cosignature. Few hospitals required authentication of the identity of the person making telephone VOs, nor the use of practices to improve communication reliability. CONCLUSION: Careful review and updating of hospital VO policies is necessary to ensure that they are internally consistent and optimize patient safety. The implementation of computerized medical records and ordering systems can reduce but not eliminate the need for VOs.

Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric Assessments.

Older adults aged 65 and above are at higher risk of falls. Predicting fall risk early can provide caregivers time to provide interventions, which could reduce the risk, potentially avoiding a possible fall. In this paper, we present an analysis of 6-month fall risk prediction in older adults using geriatric assessments, GAITRite measurements, and fall history. The geriatric assessments included were Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). These geriatric assessments are collected by staff nurses regularly in senior care facilities. From the GAITRite assessments on the residents, we included the Functional Ambulatory Profile (FAP) scores and gait speed to predict fall risk. We used the SHAP (SHapley Additive exPlanations) approach to explain our model predictions to understand which predictor variables contributed to increase or decrease the fall risk for an individual prediction. In case of a high fall risk prediction, predictor variables that contributed the most to elevate the risk could be further examined by the health providers for more personalized health interventions. We used the geriatric assessments, GAITRite measurements, and fall history data collected from 92 older adult residents (age = 86.2 ± 6.4, female = 57) to train machine learning models to predict 6-month fall risk. Our models predicted a 6-month fall with an AUC of 0.80 (95% CI of 0.76-0.85), sensitivity of 0.82 (95% CI of 0.74-0.89), specificity of 0.72 (95% CI of 0.67-0.76), F1 score of 0.76 (95% CI of 0.72-0.79), and accuracy of 0.75 (95% CI of 0.72-0.79). These results show that our early fall risk prediction method performs well in identifying residents who are at higher fall risk, which offers care providers and family members valuable time to perform preventive actions.

Mechanism-Driven Modeling to Aid Non-invasive Monitoring of Cardiac Function via Ballistocardiography.

Left ventricular (LV) catheterization provides LV pressure-volume (P-V) loops and it represents the gold standard for cardiac function monitoring. This technique, however, is invasive and this limits its applicability in clinical and in-home settings. Ballistocardiography (BCG) is a good candidate for non-invasive cardiac monitoring, as it is based on capturing non-invasively the body motion that results from the blood flowing through the cardiovascular system. This work aims at building a mechanistic connection between changes in the BCG signal, changes in the P-V loops and changes in cardiac function. A mechanism-driven model based on cardiovascular physiology has been used as a virtual laboratory to predict how changes in cardiac function will manifest in the BCG waveform. Specifically, model simulations indicate that a decline in LV contractility results in an increase of the relative timing between the ECG and BCG signal and a decrease in BCG amplitude. The predicted changes have subsequently been observed in measurements on three swine serving as pre-clinical models for pre- and post-myocardial infarction conditions. The reproducibility of BCG measurements has been assessed on repeated, consecutive sessions of data acquisitions on three additional swine. Overall, this study provides experimental evidence supporting the utilization of mechanism-driven mathematical modeling as a guide to interpret changes in the BCG signal on the basis of cardiovascular physiology, thereby advancing the BCG technique as an effective method for non-invasive monitoring of cardiac function.

Noninvasive cardiovascular monitoring based on electrocardiography and ballistocardiography: a feasibility study on patients in the surgical intensive care unit.

The time interval between the peaks in the electroccardiogram (ECG) and ballistocardiogram (BCG) waveforms, TEB, has been associated with the pre-ejection period (PEP), which is an important marker of ventricular contractility. However, the applicability of BCG-related markers in clinical practice is limited by the difficulty to obtain a replicable and consistent signal on patients. In this study, we test the feasibility of BCG measurements within a complex clinical setting, by means of an accelerometer under the head pillow of patients admitted to the Surgical Intensive Care Unit (SICU). The proposed technique proved capable of capturing TEB based on the R peaks in the ECG and the BCG in its head-to-toe and dorso- ventral directions. TEB detection was found to be consistent and repeatable both in healthy individuals and SICU patients over multiple data acquisition sessions. This work provides a promising starting point to investigate how TEB changes may relate to the patients' complex health conditions and give additional clinical insight into their care needs.

Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography.

Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters. Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure. Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement. Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing.

Detection of patient risk by nurses: a theoretical framework.

AIM: This paper is a description of a theoretical framework of how nurses detect and interpret patient risk signals in the context of organizational attitudes and procedures related to patient safety. BACKGROUND: The ability to detect when patients are at increased risk for harm is a challenge faced by nurses worldwide. How nurses are able to discriminate patient risk warning signals from background noise is not well understood. Also, the impact of system-level factors on nurses' signal detection capabilities has not been investigated. DATA SOURCES: Computerized database searches were used to identify nursing, organizational science, and cognitive psychology literature from 1964 to 2009 pertinent to the framework. DISCUSSION: The patient risk detection theory synthesizes concepts of signal detection theory and high reliability theory. Signal detection theory explains the decision-making processes of nurses as they scan for signals of potential patient harm. High reliability theory explains how nurses' signal detection capacities are facilitated when healthcare settings operate as high reliability organizations making patient safety the top priority. CONCLUSION: The patient risk detection theory facilitates understanding of both individual and organizational factors that influence nurses' ability to detect risk in complex healthcare settings. It can be used to guide research on interventions to enhance signal detection by nurses and increase patient safety in today's complex care environments. The theory can also be used to guide design of training programmes that permit nurses to develop practical skills in signal detection.

Primary Care Clinic Nurse Activities with a Telehealth Monitoring System.

The purpose of this study was to evaluate differences in the types of nursing activities and communication processes reported in a primary care clinic between patients who used a home-based monitoring system to electronically communicate self-monitored blood glucose and blood pressure values and those who assumed usual care. Data were extracted from electronic medical records from individuals who participated in a randomized controlled trial comparing in-home monitoring and usual care in patients with Type 2 diabetes and hypertension being treated in a primary care clinic. Data about nursing activities initiated by primary care clinic nurses were compared between groups using descriptive statistics and independent t-tests. Significant differences between groups were identified for the direct care nursing activities of providing lifestyle and health education, medication adjustments, and patient follow-up. This study provides evidence of greater nursing activity reported in a primary care clinic in patients who utilized a home-based monitoring system.

Corrections to "Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling" [Oct 19 2906-2917].

Presents a missing funding acknowledgment for Dr. Mihail Popescu in the above named paper.

Factors Influencing How Intensive Care Unit Nurses Allocate Their Time.

Spending time with the patient is essential for intensive care unit (ICU) nurses to detect clinical change. This article reports on an examination of factors influencing nurses' activity time allocation. Data were analyzed from a prospective time and motion study of medical ICU nurses. Nurse demographic data and observation, electronic locator technology, and electronic medical record log data were collected over 12 days from 11 registered nurses. Charlson Co-Morbidity Index and Sequential Organ Failure Assessment scores were calculated for patient assignments. Nurses averaged 78.04 (SD = 47.85) min per patient on activities in the patient room. Years of ICU nursing experience and the patient's Charlson Co-Morbidity Index was significantly associated with time spent in the patient's room. Neither nursing education nor specialty certification was found to influence time spent in a patient's room. Using technology can advance understanding of nurses' time allocation leading to interventions optimizing time spent with the patient.

Monitoring the Relative Blood Pressure Using a Hydraulic Bed Sensor System.

We propose a nonwearable hydraulic bed sensor system that is placed underneath the mattress to estimate the relative systolic blood pressure of a subject, which only differs from the actual blood pressure by a scaling and an offset factor. Two types of features are proposed to obtain the relative blood pressure, one based on the strength and the other on the morphology of the bed sensor ballistocardiogram pulses. The relative blood pressure is related to the actual by a scale and an offset factor that can be obtained through calibration. The proposed system is able to extract the relative blood pressure more accurately with a less sophisticated sensor system compared to those from the literature. We tested the system using a dataset collected from 48 subjects right after active exercises. Comparison with the ground truth obtained from the blood pressure cuff validates the promising performance of the proposed system, where the mean correlation between the estimate and the ground truth is near to 90% for the strength feature and 83% for the morphology feature.