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\input{cover.tex}


\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 preemptive 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 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}
and in ambulatory care \cite{ehara_effectiveness_2019, burgos-esteban_effectiveness_2022, paganelli_conceptual_2022}.

Two commonly used clinical scores 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 score being used\cite{subbe_validation_2001, noauthor_national_2017}.
For MEWS, each measured physiological parameter is assigned an individual score based on which range it is in.
The ranges for scoring each parameter are shown in Table \ref{mews-table}.
The individual scores are then added together to produce the final MEWS.

\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
        Individual Score & $\mathbf{+3}$ & $\mathbf{+2}$ & $\mathbf{+1}$ & $\mathbf{+0}$ & $\mathbf{+1}$ & $\mathbf{+2}$ & $\mathbf{+3}$ \\
        \hline
        \textbf{Systolic Blood Pressure} [mmHg] & $<70$ & $71-80$ & $81-100$ & $101-199$ &   & $\geq 200$ &   \\
        \hline
        \textbf{Heart Rate} [bpm] &   & $<40$ & $41-50$ & $51-100$ & $101-110$ & $111-129$ & $\geq 130$ \\
        \hline
        \textbf{Respiratory Rate} [bpm] &   & $<9$ &   & $9-14$ & $15-20$ & $21-29$ & $\geq 30$ \\
        \hline
        \textbf{Temperature} [°C] &   & $<35$ &   & $35-38.4$ &   & $\geq 38.5$ &   \\
        \hline
        \textbf{AVPU} &   &   &   & alert & reacting to voice & reacting to pain & unresponsive \\
        \hline
    \end{NiceTabular}
}
\caption{\label{mews-table}MEWS calculation ranges}
\end{table}

Traditionally, doctors and nursing staff perform collection and evaluation of the data manually, often inputting data into an EWS-calculator by hand.
However, as Eisenkraft et al. mentioned in 2023, ``some known setbacks of the NEWS and other scales are the frequency of scoring and
response, integration into practice, a miscalculation by healthcare providers [...]''\cite{eisenkraft_developing_2023}{(p.2)}.

Remote patient monitoring (RPM) can improve deterioration detection\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{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}
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.
Conversely, battery-powered, wireless vitals monitoring devices, such as wearable armbands or smartwatches, can combine several
biometric sensors into one device, allowing for a much higher degree of patient mobility, faster deployment and better
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 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.

In summary, with the current availability of wearable, networked biosensors and the validated 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}.


\newpage
\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.

This will be accomplished by developing and subsequently evaluating a digital system capable of capturing, processing and monitoring patient
vitals data.
The system will consist of a network of smart medical sensors and a centralized web application used to store and process the data.
Patients and, potentially, medical staff can interact with the application to visualize and utilize captured data.
In addition to monitoring individual physiological parameters for abnormalities, the application will calculate the patient's current
MEWS, and send alerts when an increased risk of deterioration is detected.
A visualization depicting the main flow of data in the system is shown in Figure \ref{system-components-macro}.
\begin{center}
    \begin{figure}[h]
        \includegraphics[width=\textwidth]{../figures/components-macro.png}
        \caption{\label{system-components-macro}Data flow of the proposed early warning system}
    \end{figure}
\end{center}

The following vital signs will be captured and processed by the application:
\begin{itemize}
    \item Heart Rate (HR)
    \item Blood Pressure (BP)
    \item Body Temperature (TEMP)
    \item Blood Oxygen Saturation (SPO2)
    \item Respiratory Rate (RR)
        \footnote{
            Determining the respiration rate of a mobile subject accurately using currently available electronic monitoring equipment
            presents a major challenge.
            Leveraging available SPO2 readings alongside asking the subject whether they are experiencing any shortness of breath
            may, however, provide a suitable compromise.
        }
    \item AVPU Score
        \footnote{
            Determining the AVPU score of a patient requires examination by qualified medical staff, but prompting
            the user to answer a simple question coherently to determine whether they are alert or not may be a suitable option.
        }
\end{itemize}
The devices listed in Table \ref{device-table} will be used to measure the patient's vital signs, while
the web application and its alert system prompts the patient periodically to take new measurements.
\begin{table}[!h]
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    \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 (while asleep) \\
        \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
        Patient's phone & Smartphone & AVPU \\
        \hline
    \end{NiceTabular}
}
\caption{\label{device-table}Smart devices used for data capture}
\end{table}

Following the technical implementation of the described system, its day-to-day usability and effectiveness will be evaluated in
a case study.
Over the course of a week, a test subject, representing a patient recently dismissed from an accident and emergency hospital department
(A\&E) will be using the system both at home and while out and about.
While awake, the patient will be prompted by the system via smartphone notifications to take new measurements every two hours. 
The captured data and resulting MEWS records will be periodically reviewed by another person representing medical staff during
this time.

Overall, the proposed research is aimed at answering the following scientific inquiries:
\begin{enumerate}
    \item What are the challenges of developing and utilizing a remote patient monitoring system using smart medical sensors, given the currently available technology?
    \item Can smart medical sensors be used effectively to determine MEWS remotely for patients discharged from A\&E, hospital wards and
        ambulant care?
\end{enumerate}


\newpage
\section{Tasks}

The following milestones are defined for the research project:
\begin{enumerate}
    \item Application design
        \begin{itemize}
            \item Detailed software architecture design, data model design
        \end{itemize}
    \item Application development and unit testing
        \begin{itemize}
            \item Database, API, authentication
            \item MEWS algorithm, alerts
            \item User interface
        \end{itemize}
    \item Application integration and deployment
        \begin{itemize}
            \item SSL certificate installation, deployment to public webserver
        \end{itemize}
    \item Case study data collection
    \item Case study data analysis and interpretation
    \item Written compilation of findings
    \item Reviews and adjustments
\end{enumerate}

The total available time for the project is 12 weeks.
A timeline for each defined milestone is displayed in Figure \ref{gantt}.

\begin{center}
    \begin{figure}[h]
    \includegraphics[width=\textwidth]{../figures/gantt.png}
    \caption{\label{gantt}Project Timeline}
    \end{figure}
\end{center}

\newpage
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