N. Capp, V. Fauveau, Y. Au, P. Glasser, T. Muqeem, G. Hassen, A. Cardenas
{"title":"Strados Labs: An Efficient Process to Acquire and Characterize Clinically Validated Respiratory System Information Using a Non-Invasive Bio-Sensor","authors":"N. Capp, V. Fauveau, Y. Au, P. Glasser, T. Muqeem, G. Hassen, A. Cardenas","doi":"10.1109/SPMB47826.2019.9037836","DOIUrl":null,"url":null,"abstract":"Patients with respiratory diseases are often rushed to the emergency room with acute decompensation. If not managed properly, chronic respiratory disease prolongs the episode of care or leads to hospital readmissions that are costly and burdensome to the patient. The current standard of care, in an inpatient setting, relies on labor-intensive, eSpisodic clinical assessments to detect signs of worsening disease progression. In the outpatient setting, disease monitoring relies solely on self-reporting by patients. Occasionally, patients have the aid of an instrument, such as a peak flow meter, but these aids are prone to user error and cannot always accurately report critical data 0. Additionally, patients with COPD (Chronic Obstructive Pulmonary Disease) and asthma often receive inadequate treatment due to poor communication between the patient and clinician [2] – [3] , poor disease status assessment by the clinician, inconsistent use of medication [4] – [5] , or the unreliability of peak flow measurements 0. A system capable of continuously and remotely monitoring a patient’s respiratory health could address this disconnect in patient care. Utilizing an intelligent patient monitoring system could improve patient care triage, reduce the length of hospital stay, lower the healthcare costs incurred by expensive pulmonary complications, and standardize the objective assessment of a patient’s respiratory health.","PeriodicalId":143197,"journal":{"name":"2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPMB47826.2019.9037836","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Patients with respiratory diseases are often rushed to the emergency room with acute decompensation. If not managed properly, chronic respiratory disease prolongs the episode of care or leads to hospital readmissions that are costly and burdensome to the patient. The current standard of care, in an inpatient setting, relies on labor-intensive, eSpisodic clinical assessments to detect signs of worsening disease progression. In the outpatient setting, disease monitoring relies solely on self-reporting by patients. Occasionally, patients have the aid of an instrument, such as a peak flow meter, but these aids are prone to user error and cannot always accurately report critical data 0. Additionally, patients with COPD (Chronic Obstructive Pulmonary Disease) and asthma often receive inadequate treatment due to poor communication between the patient and clinician [2] – [3] , poor disease status assessment by the clinician, inconsistent use of medication [4] – [5] , or the unreliability of peak flow measurements 0. A system capable of continuously and remotely monitoring a patient’s respiratory health could address this disconnect in patient care. Utilizing an intelligent patient monitoring system could improve patient care triage, reduce the length of hospital stay, lower the healthcare costs incurred by expensive pulmonary complications, and standardize the objective assessment of a patient’s respiratory health.