New Study Uncovers Key Insights for Virtual Care Program Staffing and Design
As care-at-home programs continue to expand and evolve, it’s crucial to anticipate the workload that comes with scaling these programs. One key aspect of staffing care-at-home is understanding the trends in the data that is collected. A recent study by the Current Health clinical research team presented at the ATA2023 Annual Conference & Expo examined the admission and discharge patterns, as well as the types and frequency of alarms, for a cohort of 932 patients remotely monitored by four US health systems. The analysis included 228,487 total monitoring hours and 29,280 total alarms.
Temporal Trends in Admission and Discharge Patterns
The study found that the majority of patients were admitted and discharged on weekdays between 8am and 8pm. Patient numbers increased from Monday to Thursday, with most patients being discharged on Fridays. There were significantly fewer patients being monitored on weekends.
Temporal Trends in Alarm Patterns
In terms of alarms, 79% of patients triggered a total of 29,280 alarms, with 74% being vital sign alarms and 26% being adherence alarms. There were significantly more vital sign alarms during the day and significantly more technical adherence alarms overnight.
One surprising finding was that 10% of patients contributed 53% of vital sign alarms and 46% of adherence alarms. This highlights the importance of identifying these high-alarm patients early on and providing them with additional support.
Wearable Adherence and Data Quality
The study also found that wearable adherence was high, with a median adherence rate of 90%. However, prompting patients in the afternoons and on day 6 to charge their wearables and wear them overnight could improve data quality and reduce the number of adherence alarms.
The study’s results suggest that care-at-home programs should be staffed accordingly, with the highest clinical workload being during weekdays “in hours.” Alarm thresholds should also be set to accommodate the physiological challenges of activities of daily living, and future research should focus on understanding and predicting patient trends by clinical condition, demographics, and program goals.
Overall, this study provides valuable insights for care-at-home program administrators and staff, and highlights the importance of understanding temporal trends in the data to effectively staff and design these programs.