Telehealth in Times of COVID-19: Spotlight on Austria

Kletečka-Pulker, Maria; Völkl-Kernstock, Sabine; Fassl, Anna; Klager, Elisabeth; Willschke, Harald; Klomfar, Sophie; Wochele-Thoma, Thomas; Schaden, Eva; Atanasov, Atanas G.

doi:10.3390/healthcare9030280

Cited by 8 publications

(6 citation statements)

References 29 publications

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“…In the UK, the higher levels of remote consultations were observed in primary care, and mostly consisted in telephone consultations and text messaging, while video consultations were used to a lesser extent [15]. Similar findings were observed in Austria [11]. On the contrary, video consultations increased in primary care services in the Netherlands during the pandemic, while text messaging and telephone consultations were already diffuse before the coronavirus outbreak [13].…”

Section: Resultssupporting

confidence: 60%

“…The results of the literature review indicated a non-optimal uptake of telemedicine in Austria, Germany, the Netherlands and the UK. However, the adoption rates increased during the COVID-19 pandemic [10][11][12][13]. A major uptake in the German medical practice was observed for telemedicine and teletherapy applications during the pandemic; contrarily for telemonitoring devices for chronic patients [14].…”

Section: Resultsmentioning

confidence: 99%

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Innovative Methods Used in Monitoring COVID-19 in Europe: A Multinational Study

Unim

Schutte

Thißen

et al. 2022

IJERPH

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Several innovative methods have been deployed worldwide to curb the COVID-19 pandemic. The aim of the study is to investigate which innovative methods are used to monitor COVID-19 health issues in Europe and related legislative and ethical aspects. An online questionnaire was administered to European countries’ representatives of the project Population Health Information Research Infrastructure. Additional information was obtained from websites and documents provided by the respondents; an overview of the literature was also performed. Respondents from 14 countries participated in the study. Digital tools are used to monitor the spread of COVID-19 (13/14 countries) and vaccination coverage (12/14); for research, diagnostics, telehealth (14/14); to fight disinformation (11/14) and forecast the pandemic spread (4/14). The level of implementation of telehealth applications was mostly low/medium. Legislative and ethical issues were encountered in many countries, leading to institutional distrust. The COVID-19 pandemic has highlighted the need for timely and accurate health data for research purposes and policy planning. However, the use of innovative methods for population health monitoring and timely data collection has posed challenges to privacy and online security globally. Adequate regulatory oversight, targeted public health interventions, and fight against disinformation could improve the uptake rate and enhance countries’ emergency preparedness.

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Section: Resultssupporting

confidence: 60%

Section: Resultsmentioning

confidence: 99%

Innovative Methods Used in Monitoring COVID-19 in Europe: A Multinational Study

Unim

Schutte

Thißen

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…To this day, when writing this research paper, the world faces the multiple mutants of this virus, which are not much different from its beginnings, as it is almost three years since the beginning of declaring it a pandemic that swept the world, so the need has increased to adopt unconventional means to reduce its effects and deliver health care to its patients. These technological means are numerous from smartphone platforms and applications that provide simultaneous visits that enable video and audio communication and are easy to use for both parties, and doctors while maintaining patient privacy, in addition to applications that perform the functions of measuring temperature and oxygen in the blood, which the patient performs measured by himself [19].…”

Section: Using It Applications In Treating Coronavirus Patientsmentioning

confidence: 99%

The use of information technology applications in the provision of health care to a COVID-19 patients

Hassan

Al-Azzawi

Talal

2022

IJEECS

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At the time of writing this study, the COVID-19 virus continues to spread through its multiple mutants, the latest of which is (Omicron). Therefore, it was necessary to use effective and unconventional treatments to reduce the spread of this virus and to deliver health care to its infected without the need to be exposed to the risk of infection. This research paper presents valuable solutions for communicating with COVID-19 patients through the use of information technology applications. This method contributes to reducing the consumption of medical resources, reducing contact with patients, and helping with social distancing, which contributes to reducing the spread of this virus. The process of using information technology means to provide medical advice and treatment to patients by telemedicine doctors has been clarified. More importantly, the results of this study presented the factors affecting the use of these applications in terms of social, technological, and organizational.

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“…At the same time, a large set of demographic and clinical parameters are being recorded for each patient, and manually analyzing and synthesizing information from all these variables proves to be challenging. In this context, it is imperative to develop a decision support system to assist clinicians in assessing patient risk, providing timely care, and optimizing resource utilization [ 1 , 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit

et al. 2022

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Risk stratification at the time of hospital admission is of paramount significance in triaging the patients and providing timely care. In the present study, we aim at predicting multiple clinical outcomes using the data recorded during admission to a cardiac care unit via an optimized machine learning method. This study involves a total of 11,498 patients admitted to a cardiac care unit over two years. Patient demographics, admission type (emergency or outpatient), patient history, lab tests, and comorbidities were used to predict various outcomes. We employed a fully connected neural network architecture and optimized the models for various subsets of input features. Using 10-fold cross-validation, our optimized machine learning model predicted mortality with a mean area under the receiver operating characteristic curve (AUC) of 0.967 (95% confidence interval (CI): 0.963–0.972), heart failure AUC of 0.838 (CI: 0.825–0.851), ST-segment elevation myocardial infarction AUC of 0.832 (CI: 0.821–0.842), pulmonary embolism AUC of 0.802 (CI: 0.764–0.84), and estimated the duration of stay (DOS) with a mean absolute error of 2.543 days (CI: 2.499–2.586) of data with a mean and median DOS of 6.35 and 5.0 days, respectively. Further, we objectively quantified the importance of each feature and its correlation with the clinical assessment of the corresponding outcome. The proposed method accurately predicts various cardiac outcomes and can be used as a clinical decision support system to provide timely care and optimize hospital resources.

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Telehealth in Times of COVID-19: Spotlight on Austria

Cited by 8 publications

References 29 publications

Innovative Methods Used in Monitoring COVID-19 in Europe: A Multinational Study

Innovative Methods Used in Monitoring COVID-19 in Europe: A Multinational Study

The use of information technology applications in the provision of health care to a COVID-19 patients

An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit

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