Physical Activity Behavior of Patients at a Skilled Nursing Facility: Longitudinal Cohort Study.

BACKGROUND: On-body wearable sensors have been used to predict adverse outcomes such as hospitalizations or fall, thereby enabling clinicians to develop better intervention guidelines and personalized models of care to prevent harmful outcomes. In our previous work, we introduced a generic remote patient monitoring framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and the extraction of indoor localization using Bluetooth low energy beacons, in concert. Using the same framework, this paper addresses the longitudinal analyses of a group of patients in a skilled nursing facility. We try to investigate if the metrics derived from a remote patient monitoring system comprised of physical activity and indoor localization sensors, as well as their association with therapist assessments, provide additional insight into the recovery process of patients receiving rehabilitation. OBJECTIVE: The aim of this paper is twofold: (1) to observe longitudinal changes of sensor-based physical activity and indoor localization features of patients receiving rehabilitation at a skilled nursing facility and (2) to investigate if the sensor-based longitudinal changes can complement patients' changes captured by therapist assessments over the course of rehabilitation in the skilled nursing facility. METHODS: From June 2016 to November 2017, patients were recruited after admission to a subacute rehabilitation center in Los Angeles, CA. Longitudinal cohort study of patients at a skilled nursing facility was followed over the course of 21 days. At the time of discharge from the skilled nursing facility, the patients were either readmitted to the hospital for continued care or discharged to a community setting. A longitudinal study of the physical therapy, occupational therapy, and sensor-based data assessments was performed. A generalized linear mixed model was used to find associations between functional measures with sensor-based features. Occupational therapy and physical therapy assessments were performed at the time of admission and once a week during the skilled nursing facility admission. RESULTS: Of the 110 individuals in the analytic sample with mean age of 79.4 (SD 5.9) years, 79 (72%) were female and 31 (28%) were male participants. The energy intensity of an individual while in the therapy area was positively associated with transfer activities (ß=.22; SE 0.08; P=.02). Sitting energy intensity showed positive association with transfer activities (ß=.16; SE 0.07; P=.02). Lying down energy intensity was negatively associated with hygiene activities (ß=-.27; SE 0.14; P=.04). The interaction of sitting energy intensity with time (ß=-.13; SE 0.06; P=.04) was associated with toileting activities. CONCLUSIONS: This study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features, a subset of which can provide crucial information to the story line of daily and longitudinal activity patterns of patients receiving rehabilitation at a skilled nursing facility. The findings suggest that detecting physical activity changes within locations may offer some insight into better characterizing patients' progress or decline.

Seroprevalence of Severe Acute Respiratory Syndrome Coronavirus 2 Among Skilled Nursing Facility Residents and Staff Members-Los Angeles County, August-September 2020.

BACKGROUND: The prevalence of current or past coronavirus disease 2019 in skilled nursing facility (SNF) residents is unknown because of asymptomatic infection and constrained testing capacity early in the pandemic. We conducted a seroprevalence survey to determine a more comprehensive prevalence of past coronavirus disease 2019 in Los Angeles County SNF residents and staff members. METHODS: We recruited participants from 24 facilities; participants were requested to submit a nasopharyngeal swab sample for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) polymerase chain reaction (PCR) testing and a serum sample for detection of SARS-CoV-2 antibodies. All participants were cross-referenced with our surveillance database to identify persons with prior positive SARS-CoV-2 results. RESULTS: From 18 August to 24 September 2020, we enrolled 3305 participants (1340 residents and 1965 staff members). Among 856 residents providing serum samples, 362 (42%) had current or past SARS-CoV-2 infection. Of the 346 serology-positive residents, 199 (58%) did not have a documented prior positive SARS-CoV-2 PCR result. Among 1806 staff members providing serum, 454 (25%) had current or past SARS-CoV-2 infection. Of the 447 serology-positive staff members, 353 (79%) did not have a documented prior positive SARS-CoV-2 PCR result. CONCLUSIONS: Past testing practices and policies missed a substantial number of SARS-CoV-2 infections in SNF residents and staff members.

A Combination of Indoor Localization and Wearable Sensor-Based Physical Activity Recognition to Assess Older Patients Undergoing Subacute Rehabilitation: Baseline Study Results.

BACKGROUND: Health care, in recent years, has made great leaps in integrating wireless technology into traditional models of care. The availability of ubiquitous devices such as wearable sensors has enabled researchers to collect voluminous datasets and harness them in a wide range of health care topics. One of the goals of using on-body wearable sensors has been to study and analyze human activity and functional patterns, thereby predicting harmful outcomes such as falls. It can also be used to track precise individual movements to form personalized behavioral patterns, to standardize the concept of frailty, well-being/independence, etc. Most wearable devices such as activity trackers and smartwatches are equipped with low-cost embedded sensors that can provide users with health statistics. In addition to wearable devices, Bluetooth low-energy sensors known as BLE beacons have gained traction among researchers in ambient intelligence domain. The low cost and durability of newer versions have made BLE beacons feasible gadgets to yield indoor localization data, an adjunct feature in human activity recognition. In the studies by Moatamed et al and the patent application by Ramezani et al, we introduced a generic framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and extracting indoor localization using BLE beacons, in concert. OBJECTIVE: The study aimed to examine the ability of combination of physical activity and indoor location features, extracted at baseline, on a cohort of 154 rehabilitation-dwelling patients to discriminate between subacute care patients who are re-admitted to the hospital versus the patients who are able to stay in a community setting. METHODS: We analyzed physical activity sensor features to assess activity time and intensity. We also analyzed activities with regard to indoor localization. Chi-square and Kruskal-Wallis tests were used to compare demographic variables and sensor feature variables in outcome groups. Random forests were used to build predictive models based on the most significant features. RESULTS: Standing time percentage (P<.001, d=1.51), laying down time percentage (P<.001, d=1.35), resident room energy intensity (P<.001, d=1.25), resident bed energy intensity (P<.001, d=1.23), and energy percentage of active state (P=.001, d=1.24) are the 5 most statistically significant features in distinguishing outcome groups at baseline. The energy intensity of the resident room (P<.001, d=1.25) was achieved by capturing indoor localization information. Random forests revealed that the energy intensity of the resident room, as a standalone attribute, is the most sensitive parameter in the identification of outcome groups (area under the curve=0.84). CONCLUSIONS: This study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features at baseline, a subset of which can better distinguish between at-risk patients that can gain independence versus the patients that are rehospitalized.

Do internal medicine residents know enough about skilled nursing facilities to orchestrate a good care transition?

BACKGROUND: Although many older adults require skilled nursing facility (SNF) care after acute hospitalization, it is unclear whether internal medicine residents have sufficient knowledge of the care that can be provided at this site. METHODS: We developed a 10-item multiple choice pre-test that assessed knowledge of the definition of a SNF, SNF staffing requirements, and SNF services provided on-site. The test was administered to trainees on the first day of a mandatory SNF rotation that occurred during their first, second or third year of training. RESULTS: Sixty-seven internal medicine residents [41 postgraduate year (PGY)-1, 11 PGY-2, and 15 PGY-3] were assessed with the test. The mean number of questions answered correctly was 4.9, with a standard deviation of 1.6. Regardless of their level of training, residents had a poor baseline knowledge of SNF care (mean scores 4.2 for PGY-1, 5.3 for PGY-2, and 6.3 for PGY-3) (P < .0001). Performance on some questions improved with increased level of training but others did not. CONCLUSIONS: Medical residents have insufficient knowledge about the type of care that can be provided at a SNF and efforts to improve this knowledge are needed to assure proper triage of patients and safe transitions to the SNF.

Transitions of care and rehabilitation after fragility fractures.

Transitions in care are a vulnerable time period for patients during which unintended errors may occur. This article discusses potential risks that could occur during care transitions, suggested improvements, and the transition from hospital to skilled nursing facilities for patients needing rehabilitation after their discharge from the hospital. Different rehabilitation settings and their reimbursement are reviewed. Common potential medical conditions arising in patients undergoing rehabilitation, rehabilitation goals, and secondary prevention also are discussed.

Addressing delays in medication administration for patients transferred from the hospital to the nursing home: a pilot quality improvement project.

BACKGROUND: Patients being transferred to a nursing home (NH) after an acute hospitalization are subject to adverse effects, including medication errors, related to poor coordination of care across settings. OBJECTIVE: The goal of this study was to develop, implement, and evaluate the impact of a pilot intervention to improve patient safety by reducing delays in administration and omission of medications among patients discharged from the hospital to the NH. METHODS: An expedited discharge protocol was developed in collaboration with hospital physician residents, hospital discharge planners, and NH staff (administrators, directors of nursing services, and licensed nurses). The intervention included education of the involved health care professionals and implementation of the expedited protocol to ensure that medication orders were transmitted to the NH-contracted pharmacy before patients' arrival at the NH. The intervention protocol was compared with a standard discharge protocol among patients aged > or =65 years being discharged from 2 university-affiliated hospitals to a single proprietary NH. The primary outcomes were the time between arrival at the NH and administration of first dose of an ordered medication; the number of omitted medications; the proportion of patients experiencing medication omissions; and the proportion of patients with omitted medications that had a low, medium, and high potential for negative consequences. RESULTS: The study involved 10 patients discharged from each of the 2 hospitals and transferred to the NH. Although several components of the intervention were successfully implemented, none of the medication orders were transmitted to the NH-ccontracted pharmacy before patients' arrival at the NH. All 17 patients with medications ordered to be administered in the evening had > or =1 dose of a medication omitted after their arrival at the NH. The mean (SD) delay from arrival at the NH to administration of the first dose of an ordered medication was 12.55 (7.45) hours. The mean number of doses of different medications omitted per patient was 3.4 (2.60). Sixty-seven doses of medications were omitted; 53 of these omissions involved only 1 dose of a medication. Thirty-three percent of omitted doses involved medications with the highest potential for resulting in a negative consequence. CONCLUSIONS: The intervention to improve patient safety by reducing medication delays for patients making the transition from the hospital to the NH was not successfully implemented, as medication orders were not transmitted to the NH-contracted pharmacies before patients' arrival at the NH. All patients making the transition from hospital to NH experienced a >12-hour delay in medication administration, and the mean number of missed doses of medications was >3. There is a need for further exploration of the reasons for and possible solutions to delays in medication administration during the transition to the NH, as well as of the impact of such delays on patient outcomes, including adverse drug events, emergency department visits, and rehospitalizations.