Hospital at Home for COPD Patients – New Possibilities

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Press release from Medsensio

With the healthcare system on the path of recovery after a global pandemic, it is important to evaluate the learnings from the past years, specifically the digital and remote shift in medicine. Despite successful societal changes reducing risk factors such as smoking and occupational exposure, patients with COPD are still the third leading cause of hospitalizations annually and represent a significant burden for the healthcare system [1]. This is reflected in patients themselves saying that they spend too much of their time in hospitals [2]. Moreover, the COVID pandemic has further highlighted the need for more remote care options, as a way to move some of the therapeutic process from hospitals to home settings [3-4].

One such option is Hospital at Home (HAH), which allows patients to receive treatment for conditions that would otherwise require hospital admission, from the comfort of their own homes. Studies dating back to the early 2000s have shown the benefits of HAH, including lower costs and improved quality of life [5-9].

With advancements in Artificial Intelligence (AI) and technology, it is now possible to use specialized mobile applications, such as those offered by Medsensio, to remotely monitor patients’ health by collecting and analyzing medical data based on vitals measurements and self-reported symptoms. Additionally, Digital Stethoscopes (DS) have enabled patients themselves to perform auscultations at home, improving the quality of data available for remote decision makers. These recordings provide additional information for AI-based analysis of lung and heart disease, which have been found to be especially useful in detecting exacerbations of COPD and other respiratory and cardiovascular diseases[10-11].

The introduction of such HAH can be particularly beneficial for patients with limited mobility, such as those residing in nursing facilities, or reduced access to healthcare due to living in rural areas. Furthermore, AI-based analysis is characterized by greater objectivity and independence from examiner bias compared to analogue auscultation, and can analyze data from multiple perspectives beyond just the audible sound [11-13]. Studies have even shown that AI-based algorithms can achieve higher crackles and wheezes recognition performance than experienced human auscultators [14-15].

In practical terms, patients with COPD exacerbations could be assessed remotely and the need for hospitalization might be assessed based on clinical parameters such as the DECAF score [16]. If the exacerbation is evaluated as low-risk, then HAH with telemonitoring including vital measurements and lung sound auscultations can be offered as an alternative to hospitalization. Similarly, COPD patients could be offered the same telemonitoring solution on discharge, reducing the risk of readmission.

In conclusion, telemedical solutions that provide data on vitals, lung auscultations and self-reported symptoms can enable more COPD patients to be treated from the comfort of their own homes, instead of being hospitalized. As the general population continues to age, healthcare providers must consider adopting more digital solutions in order to provide sustainable healthcare.

  1. Roberto Rodriguez-Roisin, Toward a Consensus Definition for COPD Exacerbations, Chest, Volume 117, Issue 5, Supplement 2, 2000, Pages 398S -401S, https://doi.org/10.1378/chest.117.5_suppl_2.398S. https://www.sciencedirect.com/science/article/pii/S0012369215328592?via%3Dihub#bib4

  2. Home treatment of COPD exacerbation selected by DECAF score: a non-inferiority, randomised controlled trial and economic evaluation

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  10. Liu WT, Huang CD, Wang CH, Lee KY, Lin SM, Kuo HP. A mobile telephone-based interactive self-care system improves asthma control. Eur Respir J. 2011 Feb;37(2):310-7. doi: 10.1183/09031936.00000810. Epub 2010 Jun 18. PMID: 20562122. https://pubmed.ncbi.nlm.nih.gov/20562122/

  11. Kim Y, Hyon Y, Lee S, Woo SD, Ha T, Chung C. The coming era of a new auscultation system for analyzing respiratory sounds. BMC Pulm Med. 2022 Mar 31;22(1):119. doi: 10.1186/s12890-022-01896-1. PMID: 35361176; PMCID: PMC8969404.

  12. Fernandez-Granero MA, Sanchez-Morillo D, Leon-Jimenez A. Computerised analysis of telemonitored respiratory sounds for predicting acute exacerbations of COPD. Sensors (Basel) 2015;15(10):26978–26996. [PMC free article] [PubMed] [Google Scholar]

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Originally published on 12 January.

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