108 lines
7.4 KiB
TeX
108 lines
7.4 KiB
TeX
\documentclass[10pt, a5paper]{article}
|
|
|
|
\usepackage[utf8]{inputenc}
|
|
\usepackage[T1]{fontenc}
|
|
%\usepackage[english, ngerman]{babel}
|
|
\usepackage[english]{babel}
|
|
\usepackage{graphicx}
|
|
\usepackage{parskip}
|
|
\usepackage{caption}
|
|
\usepackage{subcaption}
|
|
\usepackage{fancyhdr}
|
|
\usepackage{blindtext}
|
|
\usepackage[left=1cm, right=1cm, top=1.5cm, bottom=1.5cm]{geometry}
|
|
\usepackage[table]{xcolor}
|
|
\usepackage{color}
|
|
\usepackage[colorlinks]{hyperref}
|
|
\pagestyle{plain}
|
|
|
|
% Citations
|
|
%\usepackage{cite}
|
|
\usepackage[backend=biber, style=vancouver]{biblatex}
|
|
\addbibresource{../bibliography/bibliography.bib}
|
|
|
|
% Colors
|
|
\definecolor{PLRI_Rot}{RGB}{190,30,60}
|
|
\definecolor{grau}{RGB}{120,110,100}
|
|
|
|
\begin{document}
|
|
{\fontfamily{phv}\selectfont}
|
|
|
|
\input{cover.tex}
|
|
|
|
\section{Background}
|
|
|
|
Medical 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.
|
|
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}
|
|
|
|
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 feasibility of using an existing EWS for dismissed patients who are still at risk
|
|
of deterioration.
|
|
design and develop a web application that can capture and process vitals data from a wide range of
|
|
smart medical sensors, and accurately calculate the Modified Early Warning Score (MEWS) based on the captured data.
|
|
The application will be aimed at providing a mobile early warning system for patients at risk of deterioration, by providing real-time
|
|
data and alerts to medical professionals.
|
|
The proposed research will involve development of a robust and user-friendly web application interface.
|
|
The ultimate goal of this research is to provide a tool that can effectively monitor and predict medical deterioration,
|
|
thereby improving patient outcomes and reducing healthcare costs.
|
|
|
|
|
|
\section{Tasks}
|
|
|
|
\newpage
|
|
\printbibliography
|
|
|
|
\end{document}
|