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Unrecognized changes in patients' vital signs can result in preventable deaths in hospitalized patients. Few publications or studies instituting routine patient monitoring have described implementation and the setting of alarm parameters for vital signs. We wanted to determine if continuous multi-parameter patient monitoring can be accomplished with an alarm rate that is acceptable to hospital floor nurses and to compare the rate of patient deterioration events to those observed with routine vital sign monitoring. We conducted a prospective, observational, 5-month pilot study in a 26-bed adult, neurological/neurosurgical unit (non-ICU) in an academic medical center. A patient surveillance system employing a wireless body-worn vital signs monitor with automated nursing notification of alarms via smartphones was used to gather data. Data collected included: alarm rates, rapid response team (RRT) calls, intensive care unit (ICU) transfers, and unplanned deaths before and during the pilot study. Average alarm rate for all alarms (SpO, HR, RR, NIBP) was 2.3 alarms/patient/day. The RRT call rate was significantly reduced (p < 0.05) from 189 to 158 per 1000 discharges. ICU transfers per 1000 discharges were insignificantly reduced from 53 to 40 compared to the previous 5-month period in the same unit. Similar measures of comparison units did not change over the same period. Although unplanned patient deaths in the study unit were also reduced during the intervention period, this was not statistically significant. Continual, multi-parameter vital signs monitoring can be customized to reduce a high alarm rates, and may reduce rapid response team calls.

BACKGROUND: Frailty is associated with numerous post-operative adverse outcomes in older adults. Current pre-operative frailty screening tools require additional data collection or objective assessments, adding expense and limiting large-scale implementation. OBJECTIVE: To evaluate the association of an automated measure of frailty integrated within the Electronic Health Record (EHR) with post-operative outcomes for nonemergency surgeries. DESIGN: Retrospective cohort study. SETTING: Academic Medical Center. PARTICIPANTS: Patients 65 years or older that underwent nonemergency surgery with an inpatient stay 24 hours or more between October 8th, 2017 and June 1st, 2019.EXPOSURES: Frailty as measured by a 54-item electronic frailty index (eFI). OUTCOMES AND MEASUREMENTS: Inpatient length of stay, requirements for post-acute care, 30-day readmission, and 6-month all-cause mortality. RESULTS: Of 4,831 unique patients (2,281 females (47.3%); mean (SD) age, 73.2 (5.9) years), 4,143 (85.7%) had sufficient EHR data to calculate the eFI, with 15.1% categorized as frail (eFI > 0.21) and 50.9% pre-frail (0.10 < eFI ≤ 0.21). For all outcomes, there was a generally a gradation of risk with higher eFI scores. For example, adjusting for age, sex, race/ ethnicity, and American Society of Anesthesiologists class, and accounting for variability by service line, patients identified as frail based on the eFI, compared to fit patients, had greater needs for post-acute care (odds ratio (OR) = 1.68; 95% confidence interval (CI) = 1.36-2.08), higher rates of 30-day readmission (hazard ratio (HR) = 2.46; 95%CI = 1.72-3.52) and higher all-cause mortality (HR = 2.86; 95%CI = 1.84-4.44) over 6 months' follow-up. CONCLUSIONS: The eFI, an automated digital marker for frailty integrated within the EHR, can facilitate preoperative frailty screening at scale.

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