Home-Based Telemedicine for Children with Medical Complexity

Notario, Patricia M.; Gentile, Elise Madeck; Amidon, Matthew; Angst, Denise; Lefaiver, Cheryl; Webster, Kathleen

doi:10.1089/tmj.2018.0186

Cited by 57 publications

(51 citation statements)

References 20 publications

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“…Moreover, those patients with access to telemedicine had fewer hospital days and reduced cost rates. 2 Other studies showed that the adaptation to NIV at home using TM (transmission of data from a home ventilator, night pulse-oximetry, night capnography along with telephone support) was as effective as setting up the NIV in a hospital. In addition, home NIV healthcare costs were lower than in hospitals.…”

Section: Discussionmentioning

confidence: 99%

Telemedicine in children with medical complexity on home ventilation during the COVID‐19 pandemic

Onofri

Pavone

Santis

et al. 2021

Pediatric Pulmonology

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Children with medical complexity (CMC) are patients with one or more complex chronic conditions dependent on medical technologies. In our unit (Pediatric Pulmonology and Respiratory Intermediate Care Unit, Department of Pediatrics, “Bambino Gesù” Children's Hospital and Research Institute), we regularly follow‐up CMC patients, particularly children on long‐term, invasive (IMV) or noninvasive (NIV), ventilation. Children suffering from chronic diseases and with medical complexity have lost the possibility to go to the hospital during the COVID‐19 pandemic. The aim of this article is to describe our experience with telemedicine (teleconsultation [TC] and telemonitoring of ventilator [TM]) in CMC on ventilation. We presented 21 children on long‐term ventilation (NIV or IMV) whose planned hospital admission was postponed due to lockdown. A total of 12 healthcare problems were detected during scheduled TCs. Only one problem was not solved by our remote intervention. Specifically, TM has allowed us to change the ventilator parameters and to monitor patients on ventilation remotely. In conclusion, the use of telemedicine in CMC ventilated patients resulted in a feasible tool to avoid in‐person visits during the pandemic.

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Section: Discussionmentioning

confidence: 99%

Telemedicine in children with medical complexity on home ventilation during the COVID‐19 pandemic

Onofri

Pavone

Santis

et al. 2021

Pediatric Pulmonology

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

“…34,37-39,51,75 Additional research is needed to identify the most effective training techniques for CWT caregivers with LEP including effective provision of translation services. 35 Video interpretation 55 and telehealth 58 may be useful tools for in-hospital training and addressing postdischarge needs of families of CWT with LEP.…”

Section: Discussionmentioning

confidence: 99%

“…17,55,56 Home-based telemedicine has been associated with favorable results among families of CMC, including CWT, and may be an effective method to provide ongoing training, support, and address caregiver needs following discharge. 20,57,58 Home health nursing is one crucial, yet challenging, aspect of caring for CWT. 20,29,51,[59][60][61][62] Although caregivers interviewed felt that home nursing was a critical support, it was fraught with difficulties including concerns regarding standardized nurse training/qualifications, nursing availability, agency accountability, and lifestyle changes ( Table 3).…”

Section: Discussionmentioning

confidence: 99%

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Identifying Caregiver Needs for Children With a Tracheostomy Living at Home

Mai

Davis

Hamilton

et al. 2020

Clin Pediatr (Phila)

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This study sought to understand caregiver needs of children with tracheostomies (CWT) living at home and inform development of standardized tracheostomy simulation training curricula. Long-term goals are decreasing hospital readmissions following tracheostomy placement and improving family experiences while implementing a medical home model. We recruited caregivers of CWT and conducted semistructured interviews, subsequently recorded, transcribed, and analyzed for emerging themes using NVivo. Demographic data were collected via quantitative surveys. Twenty-seven caregivers participated. Emerging themes included the following: (1) caregivers felt overwhelmed, sad, frightened when learning need for tracheostomy; (2) training described as adequate, but individualized training desired; (3) families felt prepared to go home, but transition was difficult; (4) home nursing care fraught with difficulty and yet essential for families of CWT. Families of CWT have specific needs related to discharge training, resources, support, and home nursing. Provider understanding of caregiver needs is essential for child well-being, patient-/family-centered care, and may improve health outcomes.

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“…As an efficient method, using heart sound (HS) analysis is often used to evaluate heart function; this approach has been widely used to diagnose heart disease and evaluate heart functions, such as congenital heart disease classification [1], ventricular septal defect detection [2], blood pressure estimation [3] and congenital heart disease screening [4], for children and adults. A normal HS is primarily composed of two basic sounds: the first sound (S 1 ) which is generated by the closing of aortic valves and the vibrations associated with tensing of the chordae tendineae and the ventricular walls, the second sound (S 2 ) is produced by the closure of the aortic and pulmonic valves at the beginning of is volumetric ventricular relaxation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent System Based on Adjustable Classifier Models for Diagnosing Heart Sounds

Sun¹,

Huang

Zhang

et al. 2021

Preprint

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Background: As an eﬃcient method, heart sounds (HSs) analysis by classifying the features extracted from the four-stage sequence consisting of the ﬁrst heart sound (S1), second heart sound (S2), duration from S1 to S2 and duration from S2 to S1, has been widely used to diagnose heart disease and evaluate heart functions. However, the feature is diﬃcult to be extracted with high accuracy due to the four stages segmented from HSs with low accuracy; the ﬁxed classiﬁers achieved by training the features from the old samples cannot better ﬁt the features from the new ones because they are not adjusted with the incremental features. Thus, a novel intelligent diagnostic system, the innovations of which are primarily reﬂected in the automatic feature extraction and adjustable classiﬁer models, is proposed to realize the diagnosis of heart diseases with higher accuracy. Methods: The three stages of the proposed system are summarized as follows. In stage 1, the short time modiﬁed Hilbert transform (STMHT)-based curve is used to segment and extract the ﬁrst complex sound (CS1) and second complex sound (CS2). In stage 2, the envelopes CS1FE and CS2FE for periods CS1 and CS2 are obtained via a novel method, and the frequency features are automatically extracted from CS1FE and CS2FE by setting diﬀerent threshold value (Thv) lines. Finally, the principal component analysis (PCA)-based ﬁrst three principal components γ1, γ2, and γ3 are determined as the diagnostic features. In stage 3, a Gaussian mixture model (GMM)-based objective function fet(x) is generated. Then, the χ2 distribution for component k is determined by calculating the Mahalanobis distance from x to the class mean µk for component k, and the conﬁdence region of component k is determined by adjusting the optimal conﬁdence level βk and used as the criterion to diagnose HSs. Results: The performance evaluation was validated by sounds from online HS databases and clinical heart databases. The accuracy of the proposed method was compared to the accuracies of other well-known classiﬁers, and the highest classiﬁcation accuracies of 99.43%, 98.93%, 99.13%, 99.85%, 98.62%, 99.67% and 99.91% in the detection of MR, MS, ASD, NM, AS, AR and VSD sounds were achieved by setting βk(k = 1,2,...,7) to 0.87,0.65,0.67,0.65,0.67,0.79 and 0.87, respectively. Conclusions: This proposed intelligent diagnosis system provides an eﬃcient way to diagnose seven types of heart diseases. In addition, methods to manage the sounds (such as some ASD sounds) when CS1 and CS2 cannot be segmented and extracted via the STMHT method will be explored in the future to characterize the physical meanings of the frequency components and to build a model of the secondary curve in the frequency domain.

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Home-Based Telemedicine for Children with Medical Complexity

Cited by 57 publications

References 20 publications

Telemedicine in children with medical complexity on home ventilation during the COVID‐19 pandemic

Telemedicine in children with medical complexity on home ventilation during the COVID‐19 pandemic

Identifying Caregiver Needs for Children With a Tracheostomy Living at Home

A Novel Intelligent System Based on Adjustable Classifier Models for Diagnosing Heart Sounds

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