such as severe deterioration, likelihood of intensive care unit (ICU) admission, and mortality, both in hospital wards\cite{subbe_validation_2001, buist_association_2004, paterson_prediction_2006, alam_exploring_2015, bilben_national_2016, brekke_value_2019}
A number of studies have explored RPM combined with automated EWS calculation in hospitals\cite{eisenkraft_developing_2023, filho_iot-based_2021, un_observational_2021, karvounis_hospital_2021}.
With hospitals facing overwhelming patient load during the SARS-CoV-2 pandemic, interest in exploring remote patient monitoring options surged,
and NEWS2 emerged as an effective tool for predicting severe infection outcomes\cite{filho_iot-based_2021, gidari_predictive_2020, otoom_iot-based_2020, carr_evaluation_2021}
%Javanbakht et al. found that continuous vitals monitoring is more cost-effective than intermittent monitoring\cite{javanbakht_cost_2020}, however the findings of this study should be taken lightly due to potential bias reporting.
In order to examine the current state of scientific knowledge about the use of wearable devices for automated EWS monitoring of
patients at home, a comprehensive review of the existing literature was conducted.
By systematically examining and synthesizing the current body of knowledge, this review identified a variety of approaches for
utilizing smart medical devices in post-discharge patient care, as well as existing limitations and challenges in future research
in this rapidly evolving field.
\subsection{Search strategy}
A systematic search strategy was implemented on the Scopus database, aimed to encompass a broad spectrum of literature relevant
to the use of smart medical devices for automated early warning score monitoring of patients dismissed from ambulant or hospital care.
The search focused on topics related to the research area, encompassing the examination of EWS, hospital admission, care escalation,
and medical emergencies in combination with IT automation, medical wearables and Internet of Things (IoT).
The Scopus database was chosen for its extensive coverage of scholarly literature across multiple disciplines.
For the search strategy, the following inclusion and exclusion criteria were employed to select relevant articles:
Inclusion criteria:
\begin{itemize}
\item Articles focusing on the utilization of medical wearable devices for remote patient monitoring
\item Articles addressing the automated calculation of early warning scores
\item Articles discussing the application of early warning scores outside of medical care facilities
\end{itemize}
Exclusion criteria:
\begin{itemize}
\item Non-English language articles
\item Publications for which full-text access was not available
\item Duplicate articles
\item Articles outside of the \enquote{Computer Science} subject area
\end{itemize}
The following Scopus query was used to identify relevant literature:
\begin{tcolorbox}[enhanced, center, width=0.95\linewidth, rounded corners=all, colframe=black!75!white, boxrule=0.5pt, colback=black!5!white]
\begin{lstlisting}[language=SQL]
TITLE-ABS-KEY(("patient" OR "clinical" OR "medical") AND ("deterioration" OR "instability" OR "decompensation" OR "admission" OR "hospitalization" OR "escalation" OR "triage" OR "emergency")) OR ("early warning" OR "early warning score" OR "warning" OR "score*" OR "EWS") AND TITLE-ABS-KEY("system" OR "automat*" OR "smart*" OR "wearable*" OR "internet of thing*" OR "iot" OR "digital" OR "sensor*" OR "signal" OR "intelligen*" OR "predict*" OR "monitor*" OR "sreen*" OR "remote" OR "it" OR "comput*" OR "mobile" OR "5G" OR "network" (("vital*" OR "bio*") AND ("marker*" OR "sign*" OR "monitor*"))) AND TITLE-ABS-KEY("home" OR "domestic" OR "community" OR "remote" OR "longterm" OR "nursing" OR "rehabilitation" OR "outof*hospital" OR "telemedicine" OR "ehealth" OR "mhealth")
Internet of things enabled in-home health monitoring system using early warning score\cite{anzanpour_internet_2015}&
Anzanpour 2015 \\
\hline
2 &
Context-Aware Early Warning System for In-Home Healthcare Using Internet-of-Things\cite{anzanpour_context-aware_2016}&
Anzanpour 2016 \\
\hline
3 &
An IoT based system for remote patient monitoring\cite{archip_iot_2016}&
Archip 2016 \\
\hline
4 &
Wireless sensor network-based smart room system for healthcare monitoring\cite{arnil_wireless_2011}&
Arnil 2011 \\
\hline
5 &
Design and Development of IOT Based Multi-Parameter Patient Monitoring System\cite{athira_design_2020}&
Athira 2020 \\
\hline
6 &
Medical warning system based on Internet of Things using fog computing\cite{azimi_medical_2016}&
Azimi 2016 \\
\hline
7 &
Self-aware early warning score system for IoT-based personalized healthcare\cite{azimi_self-aware_2017}&
Azimi 2017 \\
\hline
8 &
Review on IoT based Healthcare systems\cite{b_v_review_2022}&
Krishna 2022 \\
\hline
9 &
Effectiveness of Early Warning Scores for Early Severity Assessment in Outpatient Emergency Care: A Systematic Review\cite{burgos-esteban_effectiveness_2022}&
Burgos-Esteban 2022 \\
\hline
10 &
A QRS Detection and R Point Recognition Method for Wearable Single-Lead ECG Devices\cite{chen_qrs_2017}&
Chen 2017 \\
\hline
11 &
Adopting the Internet of Things technologies in health care systems\cite{chiuchisan_adopting_2014}&
Chiuchisan 2014 \\
\hline
12 &
An Efficient Wireless Health Monitoring System\cite{chowdary_efficient_2018}&
Chowdary 2018 \\
\hline
13 &
DeepSigns: A predictive model based on Deep Learning for the early detection of patient health deterioration\cite{da_silva_deepsigns_2021}&
da Silva 2021 \\
\hline
14 &
Use of ultra-low cost fitness trackers as clinical monitors in low resource emergency departments\cite{dagan_use_2020}&
Dagan 2020 \\
\hline
15 &
A data fusion algorithm for clinically relevant anomaly detection in remote health monitoring\cite{de_mello_dantas_data_2020}&
de Mello Dantas 2020 \\
\hline
16 &
Patient attitudes towards remote continuous vital signs monitoring on general surgery wards: An interview study\cite{downey_strengths_2017}&
Downey 2018 \\
\hline
17 &
Developing a real-time detection tool and an early warning score using a continuous wearable multi-parameter monitor\cite{eisenkraft_developing_2023}&
Eisenkraft 2023 \\
\hline
18 &
An IoT-Based Healthcare Platform for Patients in ICU Beds During the COVID-19 Outbreak\cite{filho_iot-based_2021}&
Filho 2021 \\
\hline
19 &
Patient Monitoring System Based on Internet of Things\cite{gomez_patient_2016}&
Gomez 2016 \\
\hline
20 &
Continuous monitoring is superior to manual measurements in detecting vital sign deviations in patients with COVID-19\cite{gronbaek_continuous_2023}&
Gronbaek 2023 \\
\hline
21 &
Secure and lightweight privacy preserving Internet of things integration for remote patient monitoring\cite{imtyaz_ahmed_secure_2022}&
Imtyaz 2022 \\
\hline
22 &
Remote Continuous Health Monitoring System for Patients\cite{jagadish_remote_2018}&
Jagadish 2018 \\
\hline
23 &
Cost utility analysis of continuous and intermittent versus intermittent vital signs monitoring in patients admitted to surgical wards\cite{javanbakht_cost_2020}&
Javanbakht 2020 \\
\hline
24 &
Wearable sensors to improve detection of patient deterioration\cite{joshi_wearable_2019}&
A Hospital Healthcare Monitoring System Using Internet of Things Technologies\cite{karvounis_hospital_2021}&
Karvounis 2021 \\
\hline
27 &
All-day mobile healthcare monitoring system based on heterogeneous stretchable sensors for medical emergency\cite{lee_all-day_2020}&
Lee 2020 \\
\hline
28 &
Analysis of the early warning score to detect critical or high-risk patients in the prehospital setting\cite{martin-rodriguez_analysis_2019}&
Martin-Rodriguez 2019 \\
\hline
29 &
An IoT-based framework for early identification and monitoring of COVID-19 cases\cite{otoom_iot-based_2020}&
Otoom 2020 \\
\hline
30 &
A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home\cite{paganelli_conceptual_2022}&
Paganelli 2022 \\
\hline
31 &
Personalized Mobile Health for Elderly Home Care: A Systematic Review of Benefits and Challenges\cite{pahlevanynejad_personalized_2023}&
Pahlevanynejad 2023 \\
\hline
32 &
CuraBand: Health Monitoring and Warning System\cite{phaltankar_curaband_2021}&
Phaltankar 2021 \\
\hline
33 &
Internet of Things in Healthcare, A Literature Review\cite{quraishi_internet_2021}&
Quraishi 2021 \\
\hline
34 &
Vital Sign Monitoring System for Healthcare Through IoT Based Personal Service Application\cite{sahu_vital_2022}&
Sahu 2022 \\
\hline
35 &
Internet-of-Things-Enabled Early Warning Score System for Patient Monitoring\cite{sahu_internet--things-enabled_2022}&
Sahu 2022 \\
\hline
36 &
Cloud-Based Remote Patient Monitoring System with Abnormality Detection and Alert Notification\cite{sahu_cloud-based_2022}&
Sahu 2022 \\
\hline
37 &
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges\cite{shaik_remote_2023}&
Shaik 2023 \\
\hline
38 &
Prototype development of continuous remote monitoring of ICU patients at home\cite{thippeswamy_prototype_2021}&
Thippeswamy 2021 \\
\hline
39 &
IoT based Smart Healthcare Monitoring Systems: A Review\cite{tiwari_iot_2021}&
Tiwari 2021 \\
\hline
40 &
Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients\cite{un_observational_2021}&
Un 2021 \\
\hline
41 &
Adaptive threshold-based alarm strategies for continuous vital signs monitoring\cite{van_rossum_adaptive_2022}&
van Rossum 2022 \\
\hline
42 &
A retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach\cite{wu_predicting_2021}&
Wu 2021 \\
\hline
43 &
IoT based Real Time Health Monitoring\cite{yeri_iot_2020}&
Yeri 2020 \\
\hline
44 &
Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology\cite{youssef_ali_amer_vital_2020}&
Youssef Ali Amer 2020 \\
\hline
45 &
Features of electronic Early Warning systems which impact clinical decision making\cite{zarabzadeh_features_2012}&
Zarabzadeh 2012 \\
\hline
\end{NiceTabular}
}
\caption{\label{inclusion-table}List of included articles}
\end{table}
% TODO for all outcomes, present and compare the findings of each study
\subsection{Discussion}
While the application of EWS in ambulant care facilities and hospitals has been thoroughly investigated,
very little research has been done to assess their practicability for remote monitoring of at-risk patients at home.
Furthermore, it was observed that previous research on the use of IoT-devices for this purpose was largely conducted in
experimental settings, limiting the generalizability of the results.
Some studies have examined monitoring vital signs of at-home-patients for abnormalities,
however in most of them, no automated EWS calculations were made\cite{archip_iot_2016, azimi_medical_2016, chowdary_efficient_2018, yeri_iot_2020, lee_all-day_2020, athira_design_2020, phaltankar_curaband_2021, thippeswamy_prototype_2021}.
In 2015, Anzanpour et al. developed a monitoring system which collects vitals data and calculates EWS, however due to limited or nonexistent
availability of wireless sensors for all relevant vital signs, the work was limited to using a laboratory prototype
and required manual interaction in transferring vitals data\cite{anzanpour_internet_2015}.
Sahu et al. documented their development of an EWS-supported digital early warning system using the PM6750\cite{sahu_internet--things-enabled_2022},
an experimental vitals data monitoring device capable of taking continuous measurements in a laboratory setting\cite{noauthor_pm6750_nodate}.
However, the methodology they used to calculate EWS in real-time with laboratory data is both inconsistent and weak.
Recent studies indicate a growing trend towards investigating automated EWS calculations in real-world scenarios\cite{downey_strengths_2017, karvounis_hospital_2021, b_v_review_2022, dagan_use_2020}.
Notably, the availability of comprehensive, mobile vital signs monitoring equipment has seen a significant increase, especially in the wake of the COVID-19
%Patients appreciate the face-to-face aspect of early warning score monitoring as it allows for reassurance, social interaction, and gives them further opportunity to ask questions about their medical care\cite{downey_patient_2018}.
%Taking continuous measurements is superior to measuring intermittently\cite{gronbaek_continuous_2023, shaik_remote_2023}.
\item What are the challenges of developing and utilizing a remote patient monitoring system using smart medical sensors, given the currently available technology?
