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

Abstract

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 (

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

“…We identified nine studies for the topic of readmissions. Of these nine studies, seven used a retrospective cohort design 243 244 245 246 247 248 249 and two used a prospective cohort design. 244 246 Seven studies primarily used EHR data 243 244 245 246 247 248 250 stored in a data warehouse of the affiliated facility, 243 246 248 249 250 with one in combination with other data sources that included mobile device sensor data 244 and one with a governmental administrative database (Medicare).…”

“…Of these nine studies, seven used a retrospective cohort design 243 244 245 246 247 248 249 and two used a prospective cohort design. 244 246 Seven studies primarily used EHR data 243 244 245 246 247 248 250 stored in a data warehouse of the affiliated facility, 243 246 248 249 250 with one in combination with other data sources that included mobile device sensor data 244 and one with a governmental administrative database (Medicare). 245 Study populations included adults in hospital intensive care, 173 250 those hospitalized with medical conditions, 243 those having had cardiac surgery in a progressive care unit (PCU), 244 and those who utilized Medicare services.…”

“…244 246 Seven studies primarily used EHR data 243 244 245 246 247 248 250 stored in a data warehouse of the affiliated facility, 243 246 248 249 250 with one in combination with other data sources that included mobile device sensor data 244 and one with a governmental administrative database (Medicare). 245 Study populations included adults in hospital intensive care, 173 250 those hospitalized with medical conditions, 243 those having had cardiac surgery in a progressive care unit (PCU), 244 and those who utilized Medicare services. 245 Two studies focused on Medicare patient data, 245 249 and one study of Medicare patients included encounter information from a nonhospital setting (i.e., inpatient rehabilitation, skilled nursing, and home health services).…”

“…245 Two studies focused on Medicare patient data, 245 249 and one study of Medicare patients included encounter information from a nonhospital setting (i.e., inpatient rehabilitation, skilled nursing, and home health services). 245 Sample sizes ranged from 100 patients 244 to over 1 million patients. 249 Data in each study were collected from health care facilities in the United States.…”

Leveraging Remote Physiologic Monitoring in the COVID-19 Pandemic to Improve Care After Cardiovascular Hospitalizations

“…Limited evidence suggests the use of digital health technologies offering frequent monitoring may improve patients’ recovery. 8 Evidence from postoperative monitoring of heart failure patients with implanted cardioverter-defibrillators suggests RPM can safely replace some in-person postoperative visits. 9 Before the COVID-19 pandemic, implementation challenges due to infrastructural (such as integration with electronic health record technologies) and financial barriers impeded adoption of RPM.…”