Hospitalization Outcomes Among Patients With COVID-19 Undergoing Remote Monitoring

IMPORTANCE

Health care systems have implemented remote patient monitoring (RPM) programs to manage patients with COVID-19 at home, but the associations between participation and outcomes or resource utilization are unclear.

OBJECTIVE

To assess whether an RPM program for COVID-19 is associated with lower or higher likelihood of hospitalization and whether patients who are admitted present earlier or later for hospital care.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective, observational, cohort study of RPM was performed at Froedtert & Medical College of Wisconsin Health Network, an academic health system in southeastern Wisconsin. Participants included patients with internal primary care physicians and a positive SARS-CoV-2 test in the ambulatory setting between March 30, 2020, and December 15, 2020. Data analysis was performed from February 15, 2021, to February 2, 2022.

EXPOSURES

Activation of RPM program.

MAIN OUTCOMES AND MEASURES

Hospitalizations within 2 to 14 days of a positive test. Inverse propensity score weighting was used to account for differences between groups. Sensitivity analyses were performed looking at usage of the RPM among patients who activated the program.

RESULTS

A total of 10 660 COVID-19-positive ambulatory patients were eligible, and 9378 (88.0%) had email or mobile numbers on file and were invited into the RPM program; the mean (SD) age was 46.9 (16.3) years and 5448 patients (58.1%) were women. Patients who activated monitoring (5364 patients [57.2%]) had a mean (SD) of 35.3 (33.0) check-ins and a mean (SD) of 1.27 (2.79) (median [IQR], 0 [0-1]) free-text comments. A total of 878 patients (16.4%) experienced at least 1 alert; 128 of 5364 activated patients (2.4%) and 158 of 4014 inactivated patients (3.9%) were hospitalized (χ21 = 18.65; P < .001). In weighted regression analysis, activation of RPM was associated with a lower odds of hospitalization (odds ratio, 0.68; 95% CI, 0.54-0.86; P = .001) adjusted for demographics, comorbidities, and time period. Monitored patients had a longer mean (SD) time between test and hospitalization (6.67 [3.21] days vs 5.24 [3.03] days), a shorter length of stay (4.44 [4.43] days vs 7.14 [8.63] days), and less intensive care use (15 patients [0.3%] vs 44 patients [1.1%]).

CONCLUSIONS AND RELEVANCE

These findings suggest that activation of an RPM program is associated with lower hospitalization, intensive care use, and length of stay among patients with COVID-19.

Addressing depression and behavioral health needs through a digital program at scale

Depression and anxiety disorders are prevalent mental health conditions; yet they are often unrecognized, under-addressed and/or under-treated, and specialty treatment for these conditions is oftentimes difficult to access. By acting either as a bridge to therapy or as a form of therapy, digital tools, such as those that provide internet-based cognitive behavioral therapy (iCBT), may help clinicians support their patients' mental health needs. At one academic health system, a digital mental health program was deployed in primary care and outpatient behavioral health programs to help patients meet needs identified through screening or clinical visits. Over the first two years of operation, 138 clinicians (40% of eligible clinicians) prescribed the program to 2,228 unique patients, from which 1,117 (48.9%) enrolled. Patients who enrolled tended to be younger and healthier than non-enrollees. On average, enrolled patients spent 114.6 minutes within the iCBT program. Clinical improvement was assessed using pre- and post PHQ-9 and GAD-7 scores for depression and anxiety, respectively. Pre/Post scores were compared using Wilcoxon Rank Sum test. Patients with at least moderate depression had an average 23% reduction in PHQ-9 scores (median change -3(interquartile range 7), p<0.001) and those with at least moderate anxiety had a 26% reduction in GAD-7 scores (-4(7), p<0.001). Improvements were clinically and statistically significant. Future steps include performing a cost analysis to understand whether models utilizing iCBT are net cost-saving for health systems.