Need for clarifying remote physiologic monitoring reimbursement during the COVID-19 pandemic: a respiratory disease case study

Jarrin, Robert; Barrett, Meredith A.; Kaye, Leanne; Sayiner, Sibel; Leer, Amanda von; Johns, Jennifer; Dandrea, Larissa; Nuñez, Carlos M.; Ostrovsky, Andrey

doi:10.1038/s41746-021-00421-8

Cited by 8 publications

(3 citation statements)

References 28 publications

(32 reference statements)

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“…There was a 154% increase in telehealth visits during the last week of March 2020, compared with the same period in 2019 [22]. Remote monitoring is related to but distinct from telemedicine by definition and modality in that it involves the interpretation of medical information without a direct, synchronous interaction between the practitioner and the beneficiary [23]. Remote monitoring can be divided into remote therapeutic monitoring (RTM) and remote physiological monitoring (RPM).…”

Section: Coding and Billingmentioning

confidence: 99%

Digital inhalers for asthma management

Mosnaim,

Rathkopf

2024

Current Opinion in Pulmonary Medicine

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Purpose of review Although digital inhaler systems for asthma management have been commercially available for over a decade, their current use in clinical practice is limited. This review outlines barriers and offers potential solutions to their implementation. Recent findings Digital inhaler systems demonstrate increased controller medication adherence, decreased quick-relief medication use, and improved asthma control. Summary Use of digital inhaler systems is supported by data from large clinical trials demonstrating improved asthma outcomes. Navigating telemedicine during the coronavirus disease 2019 pandemic has led to advances clearing the path for increased adoption of digital inhaler systems. Progress in areas including patient education and onboarding, clinical management, coding and billing, privacy and security have facilitated implementation in clinical practice.

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Section: Coding and Billingmentioning

confidence: 99%

Digital inhalers for asthma management

Mosnaim,

Rathkopf

2024

Current Opinion in Pulmonary Medicine

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“…Meanwhile, the discarded masks containing a high number of microorganisms and viruses would cause the risk of secondary transmission and raise high demand for their safe disposal [6] . Additionally, the COVID-19 pandemic has created an unprecedented need for remote patient respiratory monitoring including tachypnea, dyspnea and even respiratory failure [7] , [8] . According to suggestions of Professor Gattinoni and co-workers, adequate respiratory monitoring plays a key role in the treatment of respiratory failure due to COVID-19 [9] .…”

Section: Introductionmentioning

confidence: 99%

Electrothermal sterilization and self-powered real-time respiratory monitoring of reusable mask based on Ag micro-mesh films

et al. 2023

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“…Traditional SABA data sources have included pharmacy fill records, patient-reported diaries, or non-connected inhaler dose counters, which can require presentation to a clinician for evaluation, time-consuming record-keeping, or time-lagged reports from claims. 8 Digital tools have emerged to capture SABA use objectively and passively. Passive monitoring of SABA use with inhaler sensors provides the date, time, and location of use, and offers a more real-time and objective component of evaluating asthma status remotely in between visits.…”

Section: Introductionmentioning

confidence: 99%

Prediction of short-acting beta-agonist usage in patients with asthma using temporal-convolutional neural networks

Hirons,

Allen,

Matsuyoshi

et al. 2023

JAMIA Open

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Objective Changes in short-acting beta-agonist (SABA) use are an important signal of asthma control and risk of asthma exacerbations. Inhaler sensors passively capture SABA use and may provide longitudinal data to identify at-riskpatients. We evaluate the performance of several ML models in predicting daily SABA use for participants with asthma and determine relevant features for predictive accuracy. Methods Participants with self-reported asthma enrolled in a digital health platform (Propeller Health, WI), which included a smartphone application and inhaler sensors that collected the date and time of SABA use. Linear regression, random forests, and temporal convolutional networks (TCN) were applied to predict expected SABA puffs/person/day from SABA usage and environmental triggers. The models were compared with a simple baseline model using explained variance (R2), as well as using average precision (AP) and area under the receiving operator characteristic curve (ROC AUC) for predicting days with ≥1–10 puffs. Results Data included 1.2 million days of data from 13 202 participants. A TCN outperformed other models in predicting puff count (R2 = 0.562) and day-over-day change in puff count (R2 = 0.344). The TCN predicted days with ≥10 puffs with an ROC AUC score of 0.952 and an AP of 0.762 for predicting a day with ≥1 puffs. SABA use over the preceding 7 days had the highest feature importance, with a smaller but meaningful contribution from air pollutant features. Conclusion Predicted SABA use may serve as a valuable forward-looking signal to inform early clinical intervention and self-management. Further validation with known exacerbation events is needed.

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Need for clarifying remote physiologic monitoring reimbursement during the COVID-19 pandemic: a respiratory disease case study

Cited by 8 publications

References 28 publications

Digital inhalers for asthma management

Digital inhalers for asthma management

Electrothermal sterilization and self-powered real-time respiratory monitoring of reusable mask based on Ag micro-mesh films

Prediction of short-acting beta-agonist usage in patients with asthma using temporal-convolutional neural networks

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