How to Keep Home Healthcare on Target

Home visits have the power to ease the clinical trial process for patients, but complex study design, tight timelines, busy clinical operations teams, and overburdened sites can sometimes make home health feel like yet another moving piece to manage. Individual variables that play a large role in establishing timelines can include factors like: * Amount of protocol-specific training required. * Level of engagement during a visit. * On-site processing requirements. * Drug or sample stability. * Recruitment goals. [...]all training should be to the full satisfaction of the principal investigator overseeing the study. Since sites are still responsible for the conduct of home visits from a regulatory perspective, there is often a concern about how they can remain in control of the progress without overwhelming the already busy study team and staff.

AI and Dynamic Prediction of Deterioration in Covid-19

During the COVID-19 pandemic it became evident that outcome prediction of patients is crucial for triaging, when resources are limited and enable early start or increase of available therapeutic support. COVID-19 demographic risk factors for severe disease and death were rapidly established, including age and sex. Common Clinical Decision Support Systems (CDSS) and Early Warning Systems (EWS) have been used to triage based on demographics, vital signs and laboratory results. However, all of these have limitations, such as dependency of laboratory investigations or set threshold values, were derived from more or less specific cohort studies. Instead, individual illness dynamics and patterns of recovery might be essential characteristics in understanding the critical course of illness.The pandemic has been a game changer for data, and the concept of real-time massive health data has emerged as one of the important tools in battling the pandemic. We here describe the advantages and limitations of established risk scoring systems and show how artificial intelligence applied on dynamic vital parameter changes, may help to predict critical illness, adverse events and death in patients hospitalized with COVID-19.Machine learning assisted dynamic analysis can improve and give patient-specific prediction in Clinical Decision Support systems that have the potential of reducing both morbidity and mortality. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

The Pi-CON Methodology Applied: Operator Errors and Preference Tracking of a Novel Ubiquitous Vital Signs Sensor and Its User Interface

Remote Patient Monitoring has enjoyed strong growth to new heights driven by several factors, such as the COVID-19 pandemic or advances in technology, allowing consumers and patients to continuously record health data by themselves. This does not come without its challenges, however. A literature review was completed and highlights usability gaps when using wearables or home use medical devices in a virtual environment. Based on these findings, the Pi-CON methodology was applied to close these gaps by utilizing a novel sensor that allows the acquisition of vital signs at a distance, without any sensors touching the patient. Pi-CON stands for passive, continuous and non-contact, and describes the ability to acquire vital signs continuously and passively, with limited user interaction. The preference of vital sign acquisition with a newly developed sensor was tested and compared to vital sign tests taken with patient generated health-data devices (ear thermometer, pulse oximeter) measuring heart rate, respiratory rate and body temperature. In addition, the amount of operator errors and the user interfaces were tested and compared. Results show that participants preferred vital signs acquisition with the novel sensor and the developed user interface of the sensor. Results also revealed that participants had a mean error of .85 per vital sign measurement with the patient-generated health data devices and .33 with the developed sensor, confirming the beneficial impact available when using the developed sensor based on the Pi-CON methodology.

IOT based Temperature and Oxygen level monitoring and Data visualization

The Covid-19 pandemic that hit us in 2020 changed our lifestyle in every way. There was tremendous damage to people's lives. It is now predicted that other variants of Coronavirus are affecting people's health throughout the world. We must remain vigilant against upcoming dangers. The Indian health ministry has also advised people to take the necessary precautions. In this paper, we will focus on automating temperature and oxygen monitoring using the Internet of Things. According to our proposed model, data generated by the temperature sensor (MLX90614) and oxygen saturation sensor (MAX30102) will be stored in a relational database. Using this data, future data analyses can be conducted. We are also going to visualize the data by building an interactive dashboard using Power BI. Overall, health monitoring will become much more convenient and speedier. © 2023 IEEE.

Mitigating the impact of COVID-19 on quality and value

The impact of the COVID-19 pandemic on hospital care quality operations and reporting is substantial. The authors experienced it as members of the care and leadership teams directly managing large "surges" of COVID-19 patients in one of the United States' early COVID "hotspots, " the Greater New Orleans area. Leadership, structural, operational, clinical, documentation, and regulatory impacts are described. We also share the mitigation efforts our teams were able to mount to counteract the impact of the COVID surges on patient safety, care team safety, documentation accuracy, and the health of our community. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

The Great Resignation: survival strategies for pediatric practice

The Great Resignation According to surveys from The Physicians Foundation in 2020 and 2021, 8% of medical practices closed, 32% of practices had to reduce staff, and 49% of physician experienced a reduction in income during the pandemic.1,2 Additionally, the US Bureau of Labor Statistics reported that health care workers were leaving the industry at a rate of 500,000 per month during 20223;Elsevier Health reports that 47% of US health care workers plan to leave their positions by 2025.4 Many physicians took government loans or pay cuts to keep practices afloat during the pandemic;when they reopened, patient volume took months to return to prepandemic levels. Over 230,000 physicians, nurse practitioners, and physician assistants quit their jobs by the end of 2021;the health care industry lost 20% of its workforce.6'7 Thus, the "Great Resignation" is considered one of the most significant sequalae of the COVID-19 pandemic. The Great Resignation today As I write this in February 2023, we continue to wear masks in our offices, work with significant clerical and clinical staff reductions, see more patients daily than we did prepandemic, and regularly see patients with mental health issues who need therapy. Many visits, such as for rashes, mental health and atten-tion-deficit/hyperactivity disorder medication checks, weight checks, conjunctivitis, and follow-up illness visits, are appropriate for virtual care.

A Silicon-Based Radio Platform for Integrated Edge Sensing and Communication Toward Sustainable Healthcare

Faced with COVID-19 and the trend of aging, it is demanding to develop an online health metrics sensing solution for sustainable healthcare. An edge radio platform owning the function of integrated sensing and communications is promising to address the challenge. Radar demonstrates the capability for noncontact healthcare with high sensitivity and excellent privacy protection. Beyond conventional radar, this article presents a unique silicon-based radio platform for health status monitoring supported by coherent frequency-modulated continuous-wave (FMCW) radar at Ku-band and communication chip. The radar chip is fabricated by a 65-nm complementary metal–oxide–semiconductor (CMOS) process and demonstrates a 1.5-GHz chirp bandwidth with a 15-GHz center frequency in 220-mW power consumption. A specific small-volume antenna with modified Vivaldi architecture is utilized for emitting and receiving radar beams. Biomedical experiments were implemented based on the radio platform cooperating with the antenna and system-on-chip (SoC) field-programmable gate array (FPGA) edge unit. An industrial, scientific, and medical (ISM)-band frequency-shift keying (FSK) communication chip in 915-MHz center frequency with microwatt-level power consumption is used to attain communications on radar-detected health information. Through unified integration of radar chip, management software, and communication unit, the integrated radio platform featuring −72-dBm sensitivity with a 500-kb/s FSK data rate is exploited to drastically empower sustainable healthcare applications.

Let's talk about death: communication between healthcare professionals and Next-of-Kin

Background or IntroductionThe rise of COVID-19 and subsequent decline in hospital visitation placed increased importance on the quality of communication between healthcare professionals and the next-of-kin at the end of life. Despite this, the general public perceived that sometimes information about their relative was not adequately communicated, and that there was a significant delay in important conversations about changes in management and prognosis. The National Institute for Health and Care Excellence (NICE) describes how prognosis should be discussed ‘as soon as it is recognised that [the patient] may be entering the last days of life' and that prognosis should be clearly documented in the patients' care record to facilitate shared decision making.Method(s)A retrospective case note review and audit was undertaken using data from patients who died in August 2020 in a large tertiary hospital in the West Midlands. Data collected included age, gender, diagnosis, details of admission, any changes in management and prognosis, any communication with relatives documented in the care record, and the presence of relatives in the last days of life.ResultsOf the 67 cases audited, 42% had a clear documentation of prognosis in the case record prior to death. The average time delay between the identification of a significant patient deterioration and when this was communicated to the Next-of-Kin was 3.78 hours, and 3 cases had a delay of over 24 hours. A potential correlation was also identified between those who had the longest delay, and those who were least likely to have Next-of-Kin present in their last days of life.Conclusion(s)Most conversations to notify Next-of-Kin of a significant deterioration were had within 4 hours of the deterioration. However, prognosis is not always clearly documented in the case record which raised potential for standardisation and creation of protocol to aid this process.

Clinical Course of SARS CoV-2 Infection in Hospitalized Pregnant Patients

Objective: To determine the clinical course and outcome of hospitalized pregnant patients with laboratory-confirmed SARS-CoV-2 (COVID-19) infection Study Design: Prospective longitudinal study Place and Duration of Study: Obstetrics Units of Pak Emirates Military Hospital and Combined Military Hospital, Rawalpindi Pakistan, from May to Jun 2020. Methodology: All patients reporting for childbirth were tested for SARS-CoV-2, and those testing positive were included. The primary outcome was virus clearance time and categorization according to the severity of the disease into asymptomatic, mild, moderate, severe and critical. Furthermore, a comparison was made between the presence of comorbid conditions and symptoms in the category of COVID-19. In addition, neonatal sample evaluation for SARS-CoV-2 was done. Results: Out of the 881 women giving birth, 41(4.6%) tested positive for SARS Cov-2. Majority were asymptomatic 28(68.3%) followed by mild 8(19.5%), moderate 4(9.8%) and severe 1(2.4%) category. There was a significant association of the COVID categories with symptoms (p-value<0.005) and comorbid condition (p-value<0.001). The mean virus clearance time was 8.20±1.66 days. During hospital stay 34(82.9%) delivered. All 34(100%) delivered babies had no evidence of vertical transmission. Conclusion: Pregnant women with COVID-19 infection have a nearly similar clinical course to non-COVID women in this study. There is also no evidence of vertical transmission to the neonate.

REDESIGNING THE AMBULATORY ONCOLOGY SUPPORT TEAM

Oncology Nursing Practice The current supportive care team shortages have led to immeasurable negative impacts on the healthcare workforce, compounded by the existing workforce instability generated by the COVID-19 pandemic. To address increasing volume and complexity of patients coming to the ambulatory setting for oncology care amidst the current workforce challenges, it became imperative to re-design the roles and workflows for the ambulatory oncology support team. The Cancer Service Line (CSL) operations leadership team worked with Hospital, Ambulatory, and Laboratory leadership addressing workforce challenges to improve patient flow and satisfaction. The goals of this project were to improve integration, clinical oversight, and care coordination through a phased two-part initiative: 1) Transition Phlebotomists" working in the Cancer Center to align under CSL leadership, and 2) Increase resources and efficiency within Clinic and Lab by cross-training CSL Clinic CSAs and Lab Phlebotomy staff, creating a "one-stop shop" concept, where patients have pre-visit labs and vital signs captured in one location by one staff member. This project aims to improve both patient and provider experience through reduced delays, improved patient flow, and enhanced efficiency. CSL clinical support team expanded to include more role diversity to address the growing numbers of oncology patients amidst the current workforce shortages. Nurse Leaders assembled teams of Registered Nurses, Certified Medical Assistants, Certified Nursing Assistants, Phlebotomists, Licensed Practical Nurses, and Clinical Coordinators. All members of the clinical support team underwent extensive training to cross functional skillsets within the appropriate scope of practice. The cross-functional team received comprehensive didactic training, hands-on training, and competency validation. A new Cancer Center Intake workflow was created to improve patient flow and clinic efficiency. Patients now have their vital signs, intake questionnaires, and phlebotomy services in one location by one staff member, creating a "one-stop shop" for our patient's pre-visit intake needs. The care team redesign and new Intake workflow are being evaluated through the following metrics: patient satisfaction, improved patient flow, provider satisfaction with intake efficiency, increased "economies of scale" through better staffing coverage models, and staff satisfaction and retention through advancement and utilization of new skills. Early results indicate that the care team re-design and Intake workflow has consolidated pre-visit patient stops, streamlined patient flow, and increased efficiency. This low cost, high reward initiative may offer value in supporting oncology care team members through current and future workforce challenges.

AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges

The COVID-19 pandemic has overwhelmed the existing healthcare infrastructure in many parts of the world. Healthcare professionals are not only over-burdened but also at a high risk of nosocomial transmission from COVID-19 patients. Screening and monitoring the health of a large number of susceptible or infected individuals is a challenging task. Although professional medical attention and hospitalization are necessary for high-risk COVID-19 patients, home isolation is an effective strategy for low and medium risk patients as well as for those who are at risk of infection and have been quarantined. However, this necessitates effective techniques for remotely monitoring the patients' symptoms. Recent advances in Machine Learning (ML) and Deep Learning (DL) have strengthened the power of imaging techniques and can be used to remotely perform several tasks that previously required the physical presence of a medical professional. In this work, we study the prospects of vital signs monitoring for COVID-19 infected as well as quarantined individuals by using DL and image/signal-processing techniques, many of which can be deployed using simple cameras and sensors available on a smartphone or a personal computer, without the need of specialized equipment. We demonstrate the potential of ML-enabled workflows for several vital signs such as heart and respiratory rates, cough, blood pressure, and oxygen saturation. We also discuss the challenges involved in implementing ML-enabled techniques. [ABSTRACT FROM AUTHOR] Copyright of Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Personalised Geriatric Health Monitoring: The Need for Process Mining Approach

It is common for an elder person to live alone in today's environment, away from family care, especially during the movement control order due to the COVID-19 pandemic since 2020. This has brought to the concern of this study to justify the need for personalised geriatric health monitoring that adopts the process mining approach. Constant monitoring is deemed required in order to reduce the risk of sudden illness among the elders, as well as to reduce the need to be treated at the hospital when the capacity could be limited during critical time. As part of the findings, this paper presents the process flow of data capture on one of the four vital signs, showing the significance of time, frequency and duration in reading the data for further analysis to understand the pattern in health monitoring. The importance of process mining approach is amplified in terms of the context of time in health monitoring, and the context of personalisation due to the veracity across ageing population. This paper proposes the concept of health monitoring process model, which is produced by collectively analysing the process models of the vital signs. © 2022 IEEE.

Physical Activity and Diet in Older Women: A Narrative Review.

Physical activity and diet are essential for maintaining good health and preventing the development of non-communicable diseases, especially in the older adults. One aspect that is often over-looked is the different response between men and women to exercise and nutrients. The body's response to exercise and to different nutrients as well as the choice of foods is different in the two sexes and is strongly influenced by the different hormonal ages in women. The present narrative review analyzes the effects of gender on nutrition and physical activity in older women. Understanding which components of diet and physical activity affect the health status of older women would help target non-pharmacological but lifestyle-related therapeutic interventions. It is interesting to note that this analysis shows a lack of studies dedicated to older women and a lack of studies dedicated to the interactions between diet and physical activity in women. Gender medicine is a current need that still finds little evidence.

I/Q Channel Selection for 24 GHz CW Radar to Non-Contact Vital Signs Detection for Health Check System

With the increasing improvement of quality of life (QOL), health has become an item of concern. However, owing to Covid-19, most organizations cannot do annual health check-ups because they require contact with people and it is difficult to maintain social distance. Consequently, in an era of increasing epidemics, non-contact methods are paramount. In this paper, we present a non-contact breathing and heart rate measurement system integrated into an application using 24 GHz medical radar to support the health check work. In this system, we solve the problem of imbalance between the two signal channels of the radar to increase the accuracy of the breathing and heart rate extraction. © 2022 IEEE.

COVID-19 prediction using machine learning based on the patient's vital signs: A case for Saudi Arabia

The world faces a rapidly spreading of COVID19 globally, for several countries around the world mitigating the consequences and spread of the pandemic remains a top priority. Researchers work to find a smart and rational solution to limit the spread of this epidemic and its repercussions. The goal of this research is to produce an early and accurate COVID-19 prediction, as well as a comparative analysis of the performance of several machine learning (ML) models based on patient vital signs, dataset balancing, and feature selection. The cases dataset was provided by King Fahad Hospital University in Al-Khobar, Saudi Arabia. The current study used the WEKA 3.8.5 and Python programming language (SKLEARN) to decide which method generated the highest level of accuracy while using fewer features. Random forest with grid search (RF with grid search), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF), J48, XGB Classifier, and XGB Classifier with grid search were the techniques that were compared. The highest level of accuracy obtained with seven features was 84% achieved with the RF using grid search technique, while ANN, SVM, RF, J48, XGB Classifier, and XGB Classifier with grid search obtained 82.85%, 79%, 82.93%, 82.5%,82.21%, and 83.4% accuracy, respectively. © 2022 IEEE.

Technology Empowered Healthcare Response in Pandemics: An IoT-based Covid-19 Test and Isolate System

Detect and isolate was a common strategy that was employed to ensure that the spread of the Covid-19 virus is contained. However, in low-and-middle income countries like Rwanda, upon isolation, there was lack of reliable means for continued real time monitoring of patients' condition. Furthermore, for public screening, most of the technologies that were deployed e.g., at entrances to hospitals and schools had no capability to relay information to authorities for further action. This work presents a low cost IoT-based vital signs monitoring system that can be deployed either in crowded environments or in private homes for real time monitoring of Covid-19 related vital signs. The developed system consists of a display module (i.e., 0.96OLED_4P) for displaying measured parameters, including pulse rate, oxygen saturation and temperature. The Arduino ATMEGA328P-PU is used as the central processing unit and the SIM800L GSM module is included to facilitate emergency level communication. The system was verified and tested on 392 human test subjects. The functionality of the system was compared to the commercially available Wellue FS20F Bluetooth Finger Oximeter. The results show that the developed system is able to achieve the same level of accuracy as commercially available devices. The estimated total cost of the hardware components is USD 61.5. This system has potential to ensure a wider technology deployment to detect suspected cases and monitor Covid-19 patients, especially in low-and-middle income countries. The kit can also be used to monitor other noncommunicable disease that share the same symptoms as Covid-19. © 2022 IEEE.

A Feasibility Study of Remote Non-Contact Vital Signs (NCVS) Monitoring in a Clinic Using a Novel Sensor Realized by Software-Defined Radio (SDR).

The COVID-19 outbreak has caused panic around the world as it is highly infectious and has caused about 5 million deaths globally. A robust wireless non-contact vital signs (NCVS) sensor system that can continuously monitor the respiration rate (RR) and heart rate (HR) of patients clinically and remotely with high accuracy can be very attractive to healthcare workers (HCWs), as such a system can not only avoid HCWs' close contact with people with COVID-19 to reduce the infection rate, but also be used on patients quarantined at home for telemedicine and wireless acute-care. Therefore, we developed a custom Doppler-based NCVS radar sensor system operating at 2.4 GHz using a software-defined radio (SDR) technology, and the novel biosensor system has achieved impressive real-time RR/HR monitoring accuracies within approximately 0.5/3 breath/beat per minute (BPM) on student volunteers tested in our engineering labs. To further test the sensor system's feasibility for clinical use, we applied and obtained an Internal Review Board (IRB) approval from Texas Tech University Health Sciences Center (TTUHSC) and have used this NCVS monitoring system in a doctor's clinic at TTUHSC; following testing on 20 actual patients for a small-scale clinical trial, we have found that the system was still able to achieve good NCVS monitoring accuracies within ~0.5/10 BPM across 20 patients of various weight, height and age. These results suggest our custom-designed NCVS monitoring system may be feasible for future clinical use to help combatting COVID-19 and other infectious diseases.

Cost-Effective Solution of Remote Photoplethysmography Capable of Real-Time, Multi-Subject Monitoring with Social Distancing.

Recent advances in remote-photoplethysmography (rPPG) have enabled the measurement of heart rate (HR), oxygen saturation (SpO2), and blood pressure (BP) in a fully contactless manner. These techniques are increasingly applied clinically given a desire to minimize exposure to individuals with infectious symptoms. However, accurate rPPG estimation often leads to heavy loading in computation that either limits its real-time capacity or results in a costly setup. Additionally, acquiring rPPG while maintaining protective distance would require high resolution cameras to ensure adequate pixels coverage for the region of interest, increasing computational burden. Here, we propose a cost-effective platform capable of the real-time, continuous, multi-subject monitoring while maintaining social distancing. The platform is composed of a centralized computing unit and multiple low-cost wireless cameras. We demonstrate that the central computing unit is able to simultaneously handle continuous rPPG monitoring of five subjects with social distancing without compromising the frame rate and rPPG accuracy.

ReViSe: Remote Vital Signs Measurement Using Smartphone Camera

Remote Photoplethysmography (rPPG) is a fast, effective, inexpensive and convenient method for collecting biometric data as it enables vital signs estimation using face videos. Remote contactless medical service provisioning has proven to be a dire necessity during the COVID-19 pandemic. We propose an end-to-end framework to measure people's vital signs including Heart Rate (HR), Heart Rate Variability (HRV), Oxygen Saturation (SpO2) and Blood Pressure (BP) based on the rPPG methodology from the video of a user's face captured with a smartphone camera. We extract face landmarks with a deep learning-based neural network model in real-time. Multiple face patches also called Regions-of-Interest (RoIs) are extracted by using the predicted face landmarks. Several filters are applied to reduce the noise from the RoIs in the extracted cardiac signals called Blood Volume Pulse (BVP) signal. The measurements of HR, HRV and SpO2 are validated on two public rPPG datasets namely the TokyoTech rPPG and the Pulse Rate Detection (PURE) datasets, on which our models achieved the following Mean Absolute Errors (MAE): a) for HR, 1.73Beats-Per-Minute (bpm) and 3.95bpm respectively;b) for HRV, 18.55ms and 25.03ms respectively, and c) for SpO2, an MAE of 1.64% on the PURE dataset. We validated our end-to-end rPPG framework, ReViSe, in daily living environment, and thereby created the Video-HR dataset. Our HR estimation model achieved an MAE of 2.49bpm on this dataset. Since no publicly available rPPG datasets existed for BP measurement with face videos, we used a dataset with signals from fingertip sensor to train our deep learning-based BP estimation model and also created our own video dataset, Video-BP. On our Video-BP dataset, our BP estimation model achieved an MAE of 6.7mmHg for Systolic Blood Pressure (SBP), and an MAE of 9.6mmHg for Diastolic Blood Pressure (DBP). ReViSe framework has been validated on datasets with videos recorded in daily living environment as opposed to less noisy laboratory environment as reported by most state-of-the-art techniques.

The Association of Acute Signs and Symptoms of COVID-19 and Exacerbation of Depression and Anxiety in Patients With Clinically Mild COVID-19: Retrospective Observational Study.

BACKGROUND: To date, the association between acute signs and symptoms of COVID-19 and the exacerbation of depression and anxiety in patients with clinically mild COVID-19 has not been evaluated. OBJECTIVE: This study was designed to assess the correlation between acute signs and symptoms of COVID-19 and the exacerbation of depression and anxiety in patients with clinically mild COVID-19 at a residential treatment center in South Korea. METHODS: This retrospective study assessed 2671 patients with COVID-19 admitted to 4 residential treatment centers operated by Seoul National University Hospital, South Korea, from March 2020 to April 2022. Depression and anxiety were assessed using the 2-item Patient Health Questionnaire (PHQ-2) and 2-item Generalized Anxiety Disorder (GAD-2) scale, respectively. The exacerbation of depression and anxiety symptoms was identified from the differences in PHQ-2 and GAD-2 scores between admission and discharge, respectively. The patients' clinical characteristics, including acute signs and symptoms of COVID-19, GAD-2 and PHQ-2 scores, were obtained from electronic health records. Demographic characteristics, a summary of vital signs, and COVID-19 symptoms were analyzed and compared between the patient groups with and those without exacerbated PHQ-2 and GAD-2 scores using the chi-square test. We applied logistic regression to identify the association between acute signs and symptoms of COVID-19 and the exacerbation of depression and anxiety. RESULTS: Sleep disorders were associated with exacerbated depression (odds ratio [OR] 1.09, 95% CI 1.05-1.13) and anxiety (OR 1.1, 95% CI 1.06-1.14), and the sore throat symptom was associated with exacerbated anxiety symptoms (OR 1.03, 95% CI 1.00-1.07). Patients with abnormal oxygen saturation during quarantine were more likely to have exacerbated depression (OR 1.27, 95% CI 1.00-1.62), and those with an abnormal body temperature during quarantine were more likely to experience anxiety (OR 1.08, 95% CI 1.01-1.16). As anticipated, patients who experienced psychological symptoms at admission were more likely to experience depression (OR 1.91, 95% CI 1.52-2.41) and anxiety (OR 1.98, 95% CI 1.54-2.53). Meanwhile, the PHQ-2 and GAD-2 scores measured at admission revealed that lower the score, higher the possibility of exacerbation of both depression (OR 0.15, 95% CI 0.11-0.22) and anxiety (OR 0.13, 95% CI 0.10-0.19). CONCLUSIONS: Results from this study suggest the importance of further interventions for patients with abnormal oxygen saturation, abnormal body temperatures, sore throat, and sleep disorder symptoms or initial psychological symptoms to mitigate the exacerbation of depression and anxiety. In addition, this study highlights the usability of short and efficient scales such as the PHQ-2 and GAD-2 in the assessment of the mental health of patients with clinically mild COVID-19 symptoms who were quarantined at home during the pandemic era.