Assessment of Remote Vital Sign Monitoring and Alarms in a Real-World Healthcare at Home Dataset
The importance of vital sign monitoring to detect deterioration increases during healthcare at home. Continuous monitoring with wearables increases assessment frequency but may create information overload for clinicians. The goal of this work was to demonstrate the impact of vital sign observation f...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
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| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/10/1/37 |
| _version_ | 1797445984421150720 |
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| author | Nicole Zahradka Sophie Geoghan Hope Watson Eli Goldberg Adam Wolfberg Matt Wilkes |
| author_facet | Nicole Zahradka Sophie Geoghan Hope Watson Eli Goldberg Adam Wolfberg Matt Wilkes |
| author_sort | Nicole Zahradka |
| collection | DOAJ |
| description | The importance of vital sign monitoring to detect deterioration increases during healthcare at home. Continuous monitoring with wearables increases assessment frequency but may create information overload for clinicians. The goal of this work was to demonstrate the impact of vital sign observation frequency and alarm settings on alarms in a real-world dataset. Vital signs were collected from 76 patients admitted to healthcare at home programs using the Current Health (CH) platform; its wearable continuously measured respiratory rate (RR), pulse rate (PR), and oxygen saturation (SpO<sub>2</sub>). Total alarms, alarm rate, patient rate, and detection time were calculated for three alarm rulesets to detect changes in SpO<sub>2</sub>, PR, and RR under four vital sign observation frequencies and four window sizes for the alarm algorithms’ median filter. Total alarms ranged from 65 to 3113. The alarm rate and early detection increased with the observation frequency for all alarm rulesets. Median filter windows reduced alarms triggered by normal fluctuations in vital signs without compromising the granularity of time between assessments. Frequent assessments enabled with continuous monitoring support early intervention but need to pair with settings that balance sensitivity, specificity, clinical risk, and provider capacity to respond when a patient is home to minimize clinician burden. |
| first_indexed | 2024-03-09T13:34:40Z |
| format | Article |
| id | doaj.art-f7fdf14cc7384138b881e4a5184cd46c |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-09T13:34:40Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-f7fdf14cc7384138b881e4a5184cd46c2023-11-30T21:14:34ZengMDPI AGBioengineering2306-53542022-12-011013710.3390/bioengineering10010037Assessment of Remote Vital Sign Monitoring and Alarms in a Real-World Healthcare at Home DatasetNicole Zahradka0Sophie Geoghan1Hope Watson2Eli Goldberg3Adam Wolfberg4Matt Wilkes5Current Health Inc., Boston, MA 02108, USACurrent Health Inc., Boston, MA 02108, USACurrent Health Inc., Boston, MA 02108, USACurrent Health Inc., Boston, MA 02108, USACurrent Health Inc., Boston, MA 02108, USACurrent Health Ltd., Edinburgh EH1 3EG, UKThe importance of vital sign monitoring to detect deterioration increases during healthcare at home. Continuous monitoring with wearables increases assessment frequency but may create information overload for clinicians. The goal of this work was to demonstrate the impact of vital sign observation frequency and alarm settings on alarms in a real-world dataset. Vital signs were collected from 76 patients admitted to healthcare at home programs using the Current Health (CH) platform; its wearable continuously measured respiratory rate (RR), pulse rate (PR), and oxygen saturation (SpO<sub>2</sub>). Total alarms, alarm rate, patient rate, and detection time were calculated for three alarm rulesets to detect changes in SpO<sub>2</sub>, PR, and RR under four vital sign observation frequencies and four window sizes for the alarm algorithms’ median filter. Total alarms ranged from 65 to 3113. The alarm rate and early detection increased with the observation frequency for all alarm rulesets. Median filter windows reduced alarms triggered by normal fluctuations in vital signs without compromising the granularity of time between assessments. Frequent assessments enabled with continuous monitoring support early intervention but need to pair with settings that balance sensitivity, specificity, clinical risk, and provider capacity to respond when a patient is home to minimize clinician burden.https://www.mdpi.com/2306-5354/10/1/37remote monitoringalarmvital signhospital at homewearable |
| spellingShingle | Nicole Zahradka Sophie Geoghan Hope Watson Eli Goldberg Adam Wolfberg Matt Wilkes Assessment of Remote Vital Sign Monitoring and Alarms in a Real-World Healthcare at Home Dataset Bioengineering remote monitoring alarm vital sign hospital at home wearable |
| title | Assessment of Remote Vital Sign Monitoring and Alarms in a Real-World Healthcare at Home Dataset |
| title_full | Assessment of Remote Vital Sign Monitoring and Alarms in a Real-World Healthcare at Home Dataset |
| title_fullStr | Assessment of Remote Vital Sign Monitoring and Alarms in a Real-World Healthcare at Home Dataset |
| title_full_unstemmed | Assessment of Remote Vital Sign Monitoring and Alarms in a Real-World Healthcare at Home Dataset |
| title_short | Assessment of Remote Vital Sign Monitoring and Alarms in a Real-World Healthcare at Home Dataset |
| title_sort | assessment of remote vital sign monitoring and alarms in a real world healthcare at home dataset |
| topic | remote monitoring alarm vital sign hospital at home wearable |
| url | https://www.mdpi.com/2306-5354/10/1/37 |
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