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.

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.

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.].

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.

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.

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.

Factors influencing when intensive care unit nurses go to the bedside to investigate patient related alarms: A descriptive qualitative study.

OBJECTIVE: This study examines what prompts the intensive care unit (ICU) nurse to go to the patient's bedside to investigate an alarm and the influences on the nurse's determination regarding how quickly this needs to occur. METHOD: A qualitative descriptive design guided data collection and analysis. Individual semi-structured interviews were conducted. Thematic analysis guided by the Patient Risk Detection Theoretical Framework was applied to the data. SETTING: Four specialty intensive care units in an academic medical center. RESULTS: ICU nurses go the patient's bedside in response to an alarm to catch patient deterioration and avert harm. Their determination of the immediacy of patient risk and their desire to prioritize their bedside investigations to true alarms influences how quickly they proceed to the bedside. CONCLUSION: Ready visual access to physiological data and waveform configurations, experience, teamwork, and false alarms are important determinants in the timing of ICU nurses' bedside alarm investigations.

Automated Detection of Sepsis Using Electronic Medical Record Data: A Systematic Review.

Severe sepsis and septic shock are global issues with high mortality rates. Early recognition and intervention are essential to optimize patient outcomes. Automated detection using electronic medical record (EMR) data can assist this process. This review describes automated sepsis detection using EMR data. PubMed retrieved publications between January 1, 2005 and January 31, 2015. Thirteen studies met study criteria: described an automated detection approach with the potential to detect sepsis or sepsis-related deterioration in real or near-real time; focused on emergency department and hospitalized neonatal, pediatric, or adult patients; and provided performance measures or results indicating the impact of automated sepsis detection. Detection algorithms incorporated systemic inflammatory response and organ dysfunction criteria. Systems in nine studies generated study or care team alerts. Care team alerts did not consistently lead to earlier interventions. Earlier interventions did not consistently translate to improved patient outcomes. Performance measures were inconsistent. Automated sepsis detection is potentially a means to enable early sepsis-related therapy but current performance variability highlights the need for further research.

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.

Organizational and individual attributes influencing patient risk detection.

This study examined organizational and individual variables impacting patient risk detection by Intensive Care Unit nurses and their decision to reduce the risk of failure to rescue. Thirty-four nurses were randomly assigned to two groups. A video of a manager and staff nurse patient safety discussion was used to prime one group to prioritize patient safety. Participants provided demographic information, received end-of-shift report on two fictional patients, experienced 52 alarm trials during a medication preparation scenario, and completed the Safety Attitude Questionnaire. No difference existed in risk detection; however, nurses who perceived their work environment quality to be good correctly ignored a clinically irrelevant alarm more often and were more apt to classify an alarm as irrelevant. They chose to reduce the risk of medication error rather than that of failure to rescue. This information can assist nurses to balance disregarding distractions with responding to potential patient risk signals.

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.