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\section{Background}

Clinical deterioration is a critical concern in healthcare, particularly for vulnerable populations such as the elderly and chronically
ill patients. It refers to a decline in a patient's health status and may lead to adverse outcomes, including hospitalization,
longer stays in intensive care units, and increased healthcare costs.
Early warning scores (EWS) have been widely adopted internationally for early detection of deteriorating patients\cite{downey_strengths_2017}.
A large body of scientific evidence validates the effectiveness of EWS in assessing severity of illness, and in predicting adverse clinical events,
such as severe deterioration, likelihood of ICU admission, and mortality, both on hospital wards\cite{subbe_validation_2001, buist_association_2004, paterson_prediction_2006, alam_exploring_2015, bilben_national_2016, brekke_value_2019}
and in ambulatory care \cite{ehara_effectiveness_2019, burgos-esteban_effectiveness_2022, paganelli_conceptual_2022}.

Two common implementations are the \textit{National Early Warning Score 2} (NEWS2) and the
\textit{Modified Early Warning Score} (MEWS)\cite{burgos-esteban_effectiveness_2022}.
Both are calculated by capturing various vital parameters from the patient at a specific point in time, followed by numerical aggregation of the
captured data according to the specifically used score.
For MEWS, each type of vitals parameter is assigned an individual score based on which range it is in.
The individual scores are then added together to produce the final MEWS.
The ranges for individual scores for each type of vital parameter is shown in table \ref{mews-table}.

\begin{table}[!h]
\noindent\adjustbox{max width=\textwidth}{
    \begin{NiceTabular}{l>{\columncolor{red!15}}c>{\columncolor{orange!15}}c>{\columncolor{yellow!15}}c>{\columncolor{green!15}}c>{\columncolor{yellow!15}}c>{\columncolor{orange!15}}c>{\columncolor{red!15}}c}[hvlines,colortbl-like]
        \hline
        & $\mathbf{+3}$ & $\mathbf{+2}$ & $\mathbf{+1}$ & $\mathbf{+0}$ & $\mathbf{+1}$ & $\mathbf{+2}$ & $\mathbf{+3}$ \\
        \hline
        Systolic Blood Pressure [mmHg] & $<70$ & $71-80$ & $81-100$ & $101-199$ &   & $\geq 200$ &   \\
        \hline
        Heart Rate [bpm] &   & $<40$ & $41-50$ & $51-100$ & $101-110$ & $111-129$ & $\geq 130$ \\
        \hline
        Respiratory Rate [bpm] &   & $<9$ &   & $9-14$ & $15-20$ & $21-29$ & $\geq 30$ \\
        \hline
        Temperature [°C] &   & $<35$ &   & $35-38.4$ &   & $\geq 38.5$ &   \\
        \hline
        AVPU score &   &   &   & alert & reacting to voice & reacting to pain & unresponsive \\
        \hline
    \end{NiceTabular}
}
\caption{\label{mews-table}MEWS calculation thresholds}
\end{table}

Traditionally, doctors and nursing staff perform collection and evaluation of the data manually, inputting data into an EWS-calculator by hand.
Low frequency of scoring, miscalculations and practical integration are known setbacks of NEWS2 and other scores\cite{eisenkraft_developing_2023}.

Remote patient monitoring (RPM) can improve detection of deterioration\cite{shaik_remote_2023} by greatly reducing the
amount of human interaction required to take measurements and perform EWS calculations.
A number of studies have explored RPM combined with automated EWS calculation in hospitals\cite{filho_iot-based_2021, un_observational_2021, karvounis_hospital_2021, eisenkraft_developing_2023}.
With hospitals facing critical patient demand 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{gidari_predictive_2020, otoom_iot-based_2020, filho_iot-based_2021, carr_evaluation_2021},
while reducing person-to-person contact during patient monitoring.
Since then, a variety of wearable medical sensors capable of continuously recording vital parameters have been developed and are
commercially available\cite{noauthor_visi_nodate, noauthor_equivital_nodate, noauthor_vitls_nodate, noauthor_caretaker_nodate, noauthor_medtronic_nodate}.

%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.


\section{Motivation}

% TODO EWS makes prediction value better than monitoring abnormalities in single vital signs
Installing and operating traditional continuous monitoring systems, like the vital sign monitors used in medical facilities, demands
specialized equipment and technical expertise.
Furthermore, these systems are cumbersome for patients, as they involve connecting patient and sensor device with numerous electrodes
and cables, restricting patient mobility to the bed area, and physically tying the monitoring equipment
to a single location.
In contrast, battery-powered, wireless vitals monitoring devices, such as wearable arm- or wristbands, can incorporate multiple biosensors in a single device in a
much smaller form-factor, and allow for a much higher degree of patient mobility, rapid deployment and scalability\cite{un_observational_2021}.
Therefore, utilizing such devices for RPM is a suitable approach.

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.
Some studies have examined monitoring vital signs of at-home-patients for abnormalities in an experimental setting,
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 vital signs relevant to EWS, 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, their methodology of real-time EWS calculation using data gathered in the laboratory is inconsistent and was not demonstrated.

In summary, with the current availability of wearable, networked biosensors and the effectiveness of EWS in medical facilities, combining
both aspects presents an important and interesting research opportunity which could help reduce mortality and improve clinical outcomes
for patients at risk of deterioration, both in their homes and on the go.

%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}.


\section{Objectives}

The objective of this research is to explore the practical feasibility of using an existing, clinically validated EWS to remotely monitor
patients who are still at risk of deterioration after having been dismissed from medical care facilities,
utilizing smart medical sensor devices.
Taking measurements using the devices should be as easy and unintrusive as possible for the patient, enabling them to take
vital sign readings easily from the comfort of their home or while out of the house.

MEWS will be used as an EWS for deterioration monitoring.
Furthermore, individual vital signs will be monitored for abnormalities.
The following vital signs will be captured:
\begin{itemize}
    \item Heart Rate (HR)
    \item Blood Pressure (BP)
    \item Respiratory Rate (RR)
    \item Body Temperature (TEMP)
    \item Blood Oxygen Saturation (SPO2)
    \item AVPU Score
\end{itemize}
The following smart medical devices will be used to take vital sign measurements:

\begin{table}[!h]
\noindent\adjustbox{max width=\textwidth}{
    \begin{NiceTabular}{lll}[hvlines,colortbl-like]
        \hline
        \textbf{Device Name} & \textbf{Device Type} & \textbf{Captured Vitals Parameter} \\
        \hline
        \href{https://www.withings.com/de/en/scanwatch}{Withings Scanwatch} & Wearable Smartwatch & HR, SPO2, RR (sleeping) \\
        \hline
        \href{https://www.withings.com/de/en/thermo}{Withings Thermo} & Handheld Smart Thermometer & TEMP \\
        \hline
        \href{https://www.withings.com/de/en/bpm-core}{Withings BPM Core} & Smart Blood Pressure Cuff & BP, HR \\
        \hline
    \end{NiceTabular}
}
\caption{\label{device-table}Smart Devices used for data capture}
\end{table}

This will be accomplished by designing and developing a web application that can capture and process vitals data from a wide range of
smart medical sensors, and accurately calculates the MEWS based on the captured data.
If the calculated value lies outside of the acceptable MEWS threshold, both the patient and medical staff can be alerted,
allowing preemptive action to be taken.


\section{Tasks}

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