PiMS: A Pre-ML Labelling Tool

Machine Learning (ML) techniques in clinical decision support systems are scarce due to the limited availability of clinically validated and labelled training data sets. We present a framework to (1) enable quality controls at data submission toward ML appropriate data, (2) provide in-situ algorithm assessments, and (3) prepare dataframes for ML training and robust stochastic analysis. We developed and evaluated PiMS (Pandemic Intervention and Monitoring Systems): a remote monitoring solution for patients that are Covid-positive. The system was trialled at two hospitals in Melbourne, Australia (Alfred Health and Monash Health) involving 109 patients and 15 clinicians.

COVID-19 PANDEMIC AND CHANGING PRACTICES IN SPINAL SURGERY

The novel Coronavirus disease-2019 (COVID-19), which was identified in December 2019, has quickly evolved into a pandemic, thereby forcing spine surgeons to modify their daily practice. Several articles and guidelines have been published on how to manage daily routines during the pandemic. Neurologic deficits, spinal instability and spinal infections are generally considered as emergencies and treated immediately. Every patient who is scheduled to undergo surgery must first be screened for signs and symptoms of the disease, and diagnostic tests must be conducted on suspected and high-risk patients. In addition, precautions must be taken in the operating room to minimise the risk of disease transmission. In regions where the disease has started to decline, a gradual return to the normal routine activities is being considered. Surgeons must be aware of the local circumstances and elective surgeries can only be resumed when the safety of patients and healthcare personnel are no longer at risk. In this review article, we aimed to combine the data with our experience and help spine surgeons in adapting to the current situation. © 2020 Journal of Turkish Spinal Surgery. All rights reserved.

PiMS: A Pre-ML Labelling Tool

Machine Learning (ML) techniques in clinical decision support systems are scarce due to the limited availability of clinically validated and labelled training data sets. We present a framework to (1) enable quality controls at data submission toward ML appropriate data, (2) provide in-situ algorithm assessments, and (3) prepare dataframes for ML training and robust stochastic analysis. We developed and evaluated PiMS (Pandemic Intervention and Monitoring Systems): a remote monitoring solution for patients that are Covid-positive. The system was trialled at two hospitals in Melbourne, Australia (Alfred Health and Monash Health) involving 109 patients and 15 clinicians. © 2022 IEEE.

Virtual triaging in an eye emergency department during the COVID-19 pandemic.

AIM: The aim of this audit was to assess the effect of new guidelines on virtual triage referrals to an Irish eye emergency department (EED) during the COVID-19 pandemic. METHODS: A retrospective phone triage referral and clinical note audit was performed to assess outcomes of phone triaging in October. Guidelines for phone triage were formulated with particular regard to what conditions should be seen in EED, treated over the phone or sent straight to outpatients clinic or minor procedures. A prospective phone triage referral and case note audit was then done to assess outcomes after introduction of the guidelines in November. RESULTS: A total of 1700 patients were referred to the eye emergency department, 861 in October and 839 in November. A total of 577 patients were triaged for in-person EED review in November, compared to 692 prior to implementation of guidelines (p < 0.05). The number of patients referred straight to outpatients (p < 0.05) and treated over the phone (p < 0.05) was also significantly increased. Ultimately, the number of conditions unnecessarily triaged to EED, as per the guidelines implemented, was significantly reduced (p < 0.05). CONCLUSION: This audit addressed the need to reduce footfall during the COVID-19 pandemic, identified suitable avenues of referrals for certain conditions, and demonstrated that these guidelines significantly reduced the number of patients presenting to EED with conditions amenable to phone review or clinic follow-up.

COVID-19 Patients Management and Triaging Using Machine Learning Techniques

COVID-19 is a disease that is caused by a new virus, coronavirus, which first appeared in China and a few months;it spread all over the globe, infecting many people. This disease shows very common symptoms like fever, cough, and tiredness, which makes it more difficult to know if the person is infected or not. There have been a lot of struggles in finding a way to detect the virus in a human body and manage the infected at the same time. There is an immense increase in the number of infected cases, so it becomes difficult to manage patients with proper resources and medical facilities, leading to an increase in casualties. To overcome the difficulty, this study proposes fast and efficient methods for the detection of the virus and proper treatment. COVID-19 patient management and triaging means accurately identifying patients or detecting COVID-19 and categorizing the patients or sorting them accordingly for their proper management. This study aims to help the government and health care system take relevant steps to detect and manage COVID-19 patients. Also, with the details and symptoms of the infected person, we can categorize the person as a mild, critical, or severe case. The proposed methods in the chapter have shown promised results while testing on COVID CT Scan Images and patients’ symptoms dataset. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Triaging of COVID-19 patients using low dose chest CT: Incidence and factor analysis of lung involvement on CT images.

INTRODUCTION: Despite an increase in CT studies to evaluate patients with coronavirus disease 2019 (COVID-19), their indication in triage is not well-established. The purpose was to investigate the incidence of lung involvement and analyzed factors related to lung involvement on CT images for establishment of the indication for CT scans in the triaging of COVID-19 patients. METHODS: Included were 192 COVID-19 patients who had undergone CT scans and blood tests for triaging. Two radiologists reviewed the CT images and recorded the incidence of lung involvement. The prediction model for lung involvement on CT images using clinico-laboratory variables [age, gender, body mass index, oxygen saturation of the peripheral artery (SpO2), comorbidities, symptoms, and blood data] were developed by multivariate logistic regression with cross-validation. RESULTS: In 120 of the 192 patients (62.5%), CT revealed lung involvement. The patient age (odds ratio [OR]; 4.95, 95% confidence interval [CI]; 0.93-26.49), albumin (OR; 4.66, 95%CI; 1.37-15.84), lactate dehydrogenase (OR; 5.79, 95%CI; 1.43-23.38) and C-reactive protein (OR; 8.93, 95%CI; 4.13-19.29) were selected for the final prediction model for lung involvement on CT images. The cross-validated area under the receiver operating characteristics (ROC) curve was 0.83. CONCLUSIONS: The high incidence of lung involvement (62.5%) was confirmed on CT images. The proposed prediction model that includes the patient age, albumin, lactate dehydrogenase, and C-reactive protein may be useful for predicting lung involvement on CT images and may assist in deciding whether triaged COVID-19 patients should undergo CT.

Kloman Metre: An EMD-Based Tool for Triaging Diseases Leading to Lung Infections Including COVID-19

The digital revolution can help developing countries to overcome the problem of limited healthcare infrastructure in developing nations such as India. The COVID-19 pandemic has shown the urgency of integration of digital technologies into healthcare infrastructure. In order to solve the issue of lack of trained healthcare professionals at public health centres (PHCs), researchers are trying to build tools which can help to tag pulmonary ailment within a fraction of second. Such tagging will help the medical community to utilize their time more efficiently. In this work, we have tried to assess the “lung health” of patients suffering from a variety of pulmonary diseases including COVID-19, tuberculosis and pneumonia by applying Earth Mover’s Distance algorithm to the X-ray images of the patients. The lung X-ray images of patients suffering from pneumonia, TB and COVID-19 and healthy persons are pooled together from various datasets. Our preliminary data based upon 100 random images depicting each type of lung disease such as COVID-19, tuberculosis and pneumonia revealed that patients suffering from tuberculosis have the highest severity as per the values obtained from the EMD scale. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Creating symptom-based criteria for diagnostic testing: a case study based on a multivariate analysis of data collected during the first wave of the COVID-19 pandemic in New Zealand.

BACKGROUND: Diagnostic testing using PCR is a fundamental component of COVID-19 pandemic control. Criteria for determining who should be tested by PCR vary between countries, and ultimately depend on resource constraints and public health objectives. Decisions are often based on sets of symptoms in individuals presenting to health services, as well as demographic variables, such as age, and travel history. The objective of this study was to determine the sensitivity and specificity of sets of symptoms used for triaging individuals for confirmatory testing, with the aim of optimising public health decision making under different scenarios. METHODS: Data from the first wave of COVID-19 in New Zealand were analysed; comprising 1153 PCR-confirmed and 4750 symptomatic PCR negative individuals. Data were analysed using Multiple Correspondence Analysis (MCA), automated search algorithms, Bayesian Latent Class Analysis, Decision Tree Analysis and Random Forest (RF) machine learning. RESULTS: Clinical criteria used to guide who should be tested by PCR were based on a set of mostly respiratory symptoms: a new or worsening cough, sore throat, shortness of breath, coryza, anosmia, with or without fever. This set has relatively high sensitivity (> 90%) but low specificity (< 10%), using PCR as a quasi-gold standard. In contrast, a group of mostly non-respiratory symptoms, including weakness, muscle pain, joint pain, headache, anosmia and ageusia, explained more variance in the MCA and were associated with higher specificity, at the cost of reduced sensitivity. Using RF models, the incorporation of 15 common symptoms, age, sex and prioritised ethnicity provided algorithms that were both sensitive and specific (> 85% for both) for predicting PCR outcomes. CONCLUSIONS:  If predominantly respiratory symptoms are used for test-triaging,  a large proportion of the individuals being tested may not have COVID-19. This could overwhelm testing capacity and hinder attempts to trace and eliminate infection. Specificity can be increased using alternative rules based on sets of symptoms informed by multivariate analysis and automated search algorithms, albeit at the cost of sensitivity. Both sensitivity and specificity can be improved through machine learning algorithms, incorporating symptom and demographic data, and hence may provide an alternative approach to test-triaging that can be optimised according to prevailing conditions.

A Systematic Review of Current Teleophthalmology Services in New Zealand Compared to the Four Comparable Countries of the United Kingdom, Australia, United States of America (USA) and Canada.

BACKGROUND: Over 700,000 New Zealanders (NZ), particularly elderly and Maori, live without timely access to specialist ophthalmology services. Teleophthalmology is a widely recognised tool that can assist in overcoming resource and distance barriers. Teleophthalmology gained unprecedented traction in NZ during the COVID-19 pandemic and subsequent lockdown. However, its provision is still limited and there are equity issues. The aim of this study was to conduct a systematic review identifying, describing and contrasting teleophthalmology services in NZ with the comparable countries of Australia, USA, Canada and the United Kingdom. METHODS: The electronic databases Embase, PubMed, Web of Science, Google Scholar and Google were systemically searched using the keywords: telemedicine, ophthalmology, tele-ophthalmology/teleophthalmology. The searches were filtered to the countries above, with no time constraints. An integrative approach was used to synthesise findings. RESULTS: One hundred and thirty-two studies were identified describing 90 discrete teleophthalmology services. Articles spanned from 1997 to 2020. Models were categorised into general eye care (n=21; 16%); emergency/trauma (n=6; 4.5%); school screening (n=25; 19%); artificial intelligence (AI) (n=23; 18%); and disease-specific models of care (MOC) (n=57; 43%). The most common diseases addressed were diabetic retinopathy (n=23; 17%); retinopathy of prematurity (n=9; 7%); and glaucoma (n=8; 6%). Programs were mainly centred in the US (n=72; 54.5%), followed by the UK (n=29; 22%), then Canada (n=16; 12%), Australia (n=13; 10%), with the fewest identified in NZ (n=3; 2%). Models generally involved an ophthalmologist consultative service, remote supervision and triaging. Most models involved local clinicians transmitting fed-forward or live images. CONCLUSION: Teleophthalmology will likely play a crucial role in the future of eye care. COVID-19 has offered a unique opportunity to observe the use of teleophthalmology services globally. Feed-forward and, increasingly, live-based teleophthalmology services have demonstrated feasibility and cost-effectiveness in similar countries internationally. New Zealand's teleophthalmology services, however, are currently limited. Investing in strategic partnerships and technology at a national level can advance health equities in ophthalmic care.

The Impact of COVID-19 on the Service of Emergency Department.

(1) Introduction: the COVID-19 pandemic significantly impacted the number and acuity of emergency departments (ED) patients, specifically those with non-COVID-19-related health problems. However, the exact impact of the COVID-19 pandemic on ED services is the subject of comprehensive debate. (2) Aim: to gain insight into the consequences of the first wave of the COVID-19 pandemic based on non-COVID-19 presentations and patient acuity using the Canadian Triage and Acuity Scale (CTAS). (3) Method: in Phase 1, the ED records of one of the main regional non-COVID-19 hospitals in Saudi Arabia were retrospectively audited from August 2020 to February 2021-after the first wave of COVID-19-then compared to information collected for the same period in previous year. Phase 2 included calculating the waiting time to identify delays and issues that may impact the triage effectiveness. (4) Results: a change across all CTAS levels was observed post the 1st wave of COVID-19 pandemic. Specifically, there was an increase in the number of patients presenting as higher acuity (CTAS 1 and 2) and a decrease in patients presenting as lower acuity (CTAS 4 and 5). Longer waiting times for patients presenting to ED were also reported. Specifically, 83% of patients presenting as higher acuity experienced a delay. (5) Conclusion: further studies are required to investigate association between the 1st wave of COVID-19 and patient presentations and/or acuity or patient demand and ED capacity.

Reducing the risk of infection to patients and staff during gynaecological outpatient and ambulatory appointments.

This chapter explores ways to reduce the risk of severe acute respiratory syndrome coronavirus-2 transmission to women and staff within gynaecology outpatient clinics. The likely routes of transmission are discussed, namely through droplets, aerosols and fomites. Using the 'hierarchy of control' categories, elimination, substitution, engineering, administration and personal protective equipment, practical strategies for modifying virus exposure are presented. The management of specific clinical conditions are reviewed based on advice prepared by the specialist societies in conjunction with each other and the Royal College of Obstetricians and Gynaecologists. The need to maintain at least a minimal level of gynaecological services is recognised and that this should provide safe, equitable and effective care. Ways to reduce clinic attendance are discussed with the substitution of face-to-face with remote consultations and when this is relevant. Current recommendations for ambulatory procedures, which include colposcopy and hysteroscopy, are considered so that best use is made of reduced resources.

Notes from the Field: Dynamic Triaging Using Quality Improvement (QI) Methodology to Prevent the Admission of Asymptomatic COVID Positive Obstetric Patients in New Delhi, India.

A single undiagnosed COVID-19 positive patient admitted in the green zone has the potential to infect many Health Care Workers (HCWs) and other patients at any given time with resultant spread of infection and reduction in the available workforce. Despite the existing triaging strategy at the Obstetric unit of a tertiary hospital in New Delhi, where all COVID-19 suspects obstetric patients were tested and admitted in orange zone and non-suspects in green zone, asymptomatic COVID-19 positive patients were found admitted in the green zone. This was the trigger to undertake a quality improvement (QI) initiative to prevent the admission of asymptomatic COVID-19 positive patients in green zones. The QI project aimed at reducing the admission of COVID-19 positive patients in the green zone of the unit from 20% to 10% in 4 weeks' time starting 13/6/2020 by means of dynamic triaging. A COVID-19 action team was made and after an initial analysis of the problem multiple Plan-Do-Study-Act (PDSA) cycles were run to test the change ideas. The main change ideas were revised testing strategies and creating gray Zones for patients awaiting COVID-19 test results. The admission of unsuspected COVID-19 positive cases in the green zone of the unit reduced from 20% to 0% during the stipulated period. There was a significant reduction in the number of HCWs, posted in the green zone, being quarantined or test positive for COVID-19 infection as well. The authors conclude that Quality Improvement methods have the potential to develop effective strategies to prevent spread of the deadly Corona virus.

Radiographic findings in COVID-19: Comparison between AI and radiologist.

CONTEXT: As the burden of COVID-19 enhances, the need of a fast and reliable screening method is imperative. Chest radiographs plays a pivotal role in rapidly triaging the patients. Unfortunately, in low-resource settings, there is a scarcity of trained radiologists. AIM: This study evaluates and compares the performance of an artificial intelligence (AI) system with a radiologist in detecting chest radiograph findings due to COVID-19. SUBJECTS AND METHODS: The test set consisted of 457 CXR images of patients with suspected COVID-19 pneumonia over a period of three months. The radiographs were evaluated by a radiologist with experience of more than 13 years and by the AI system (NeuraCovid, a web application that pairs with the AI model COVID-NET). Performance of AI system and the radiologist were compared by calculating the sensitivity, specificity and generating a receiver operating characteristic curve. RT-PCR test results were used as the gold standard. RESULTS: The radiologist obtained a sensitivity and specificity of 44.1% and 92.5%, respectively, whereas the AI had a sensitivity and specificity of 41.6% and 60%, respectively. The area under curve for correctly classifying CXR images as COVID-19 pneumonia was 0.48 for the AI system and 0.68 for the radiologist. The radiologist's prediction was found to be superior to that of the AI with a P VALUE of 0.005. CONCLUSION: The specificity and sensitivity of detecting lung involvement in COVID-19, by the radiologist, was found to be superior to that by the AI system.

Triaging algorithm for head & neck oncology follow-up patients in COVID-19 climate.

The current climate is one of uncertainty and immeasurable tragedy for people afflicted by the pandemic of SARS-CoV-2 virus infection. As professionals, we have a duty of care towards all patients especially the vulnerable and those suffering with life-threatening illnesses such as oral cancer. We present a safe & objective triaging method for afflicted with this disease in the prevailing morbid situation.

Critical Ethics: How to Balance Patient Autonomy With Fairness When Patients Refuse Coronavirus Disease 2019 Testing.

OBJECTIVES: A cornerstone of our healthcare system's response to the coronavirus disease 2019 pandemic is widespread testing to facilitate both isolation and early treatment. When patients refuse to undergo coronavirus disease testing, they compromise not only just their own health but also the health of those around them. The primary objective of our review is to identify the most ethical way a given healthcare system may respond to a patient's refusal to undergo coronavirus disease 2019 testing. DATA SOURCES: We apply a systematic approach to a true clinical case scenario to evaluate the ethical merits of four plausible responses to a patient's refusal to undergo coronavirus disease testing. Although our clinical case is anecdotal, it is representative of our experience at our University Tertiary Care Center. DATA EXTRACTION: Each plausible response in the case is rigorously analyzed by examining relevant stakeholders, facts, norms, and ethical weight both with respect to individuals' rights and to the interests of public health. We use the "So Far No Objections" method as the ethical approach of choice because it has been widely used in the Ethics Modules of the Surgical Council on Resident Education Curriculum of the American College of Surgeons. DATA SYNTHESIS: Two ethically viable options may be tailored to individual circumstances depending on the severity of the patient's condition. Although unstable patients must be assumed to be coronavirus disease positive and treated accordingly even in the absence of a test, stable patients who refuse testing may rightfully be asked to seek care elsewhere. CONCLUSIONS: Although patient autonomy is a fundamental principle of our society's medical ethic, during a pandemic we must, in the interest of vulnerable and critically ill patients, draw certain limits to obliging the preferences of noncritically ill patients with decisional capacity.

Clinical Triaging in Cough Clinic Alleviates COVID-19 Overload in Emergency Department in India.

The importance of this study is the efficacy of "symptoms only" approach at a screening clinic for coronavirus disease 2019 (COVID-19) diagnosis in low- and middle-income countries (LMIC) setting. The objective of this study was to assess how efficiently primary care physicians at the screening clinic were able to predict whether a patient had COVID-19 or not, based on their symptom-based assessment alone. The current study is a cross-sectional retrospective observational study. This study was conducted at a single-center, tertiary care setting with a dedicated COVID-19 facility in a metropolitan city in eastern India. Participants are all suspected COVID-19 patients who presented themselves to this center during the outbreak from 1 August 2020 to 30 August 2020. Patients were referred to the Cough Clinic from the various outpatient departments of the hospital or from smaller satellite centers located in different parts of the city and other dependent geographical areas. The main outcome(s) and measure(s) is to study whether outcome of confirmatory test results can be predicted accurately by history taking alone. From 01 August 2020 to 30 Aug 2020, 511 patients with at least one symptom suggestive of COVID-19 reported to screening clinic. Out of these, 65.4% were males and 34.6% were females. Median age was 45 years with range being 01 to 92 years. Fever was seen in 70.4% while cough was present in 22% of cases. Overall positivity for SARS-CoV-2 during this period in this group was 54.21%. At 50% pre-test probability, the sensitivity of trained doctors working at the clinic, in predicting positive cases based on symptoms alone, was approximately 74.7%, and specificity for the same was 58.12%. The positive predictive value of the doctors' assessment was 67.87%, and the negative predictive value was 66.02%. Rapid triaging for confirmatory diagnosis of COVID-19 is feasible at screening clinic based on history taking alone by training of primary care physicians. This is particularly relevant in LMIC with scarce healthcare resources to overcome COVID-19 pandemic.

Application of tele-podiatry in diabetic foot management: A series of illustrative cases.

BACKGROUND AND AIMS: Telemedicine had been proposed as a tool to manage diabetes, but its role in management of diabetic foot ulcer is still evolving. The COVID-19 pandemic and related social restrictions have necessitated the use of telemedicine in the management of diabetic foot disease (tele-podiatry), particularly of patients classified as low-risk. MATERIALS AND METHODS: We present a report of three cases of varied diabetic foot problems assessed during the present pandemic using different forms of telemedicine for triaging, management of low-risk cases and for follow-up. RESULTS: Tele-podiatry was effective in the management of low-risk subjects with diabetic foot ulcer, and also useful in referral of high-risk subjects for hospital/clinic visit, facilitating proper management. It also helped in the follow-up of the cases. CONCLUSION: Telemedicine is a good screening tool for diagnosing and managing low-risk subjects with diabetic foot problems, and also enables a triaging system for deciding on hospital visits and hospitalization. Telemedicine offers several benefits in the management of diabetic foot disease, although it also has some limitations. Based on our experience during the pandemic, we recommend its judicious use in the triaging of patients of diabetic foot disease and management of low-risk cases. Future innovation in technology and artificial intelligence may help in better tele-podiatry care in the time to come.

COVID 19: prioritise autonomy, beneficence and conversations before score-based triage.

At the start of the COVID-19 pandemic, mounting demand overwhelmed critical care surge capacities, triggering implementation of triage protocols to determine ventilator allocation. Relying on triage scores to ration care, while relieving clinicians from making morally distressing decisions under high situational pressure, distracts clinicians from what is essentially deeply humanistic issues entrenched in this protracted public health crisis. Such an approach will become increasingly untenable as countries flatten their epidemic curves. Decisions regarding intensive care unit admission are particularly challenging in older people, who are most likely to require critical care, but for whom benefits are most uncertain. Before applying score-based triage, physicians must first discern if older people will benefit from critical care (beneficence) and second, if he wants critical care (autonomy). When deliberating beneficence, physicians should steer away from solely using age-stratified survival probabilities from epidemiological data. Instead, decisions must be based on individualised risk-stratification that encompasses evidence-based predictors of adverse outcomes specific to older adults. Survival will also need to be weighed against burden of treatment, as well as longer term functional deficits and quality-of-life. By identifying the robust older people who may benefit from critical care, clinicians should proceed to elicit his values and preferences that would determine the treatment most aligned with his best interest. During these dialogues, physicians must truthfully convey the emergent clinical reality, discern the older person's therapeutic goals and discuss the feasibility of achieving them. Given that COVID-19 is here to stay, these conversations aimed at achieving goal-cordant care must become a new clinical norm.

Rapid implementation of an evidence-based remote triaging system for assessment of suspected referrals and patients with head and neck cancer on follow-up after treatment during the COVID-19 pandemic: Model for international collaboration.

BACKGROUND: Outpatient telemedicine consultations are being adopted to triage patients for head and neck cancer. However, there is currently no established structure to frame this consultation. METHODS: For suspected referrals with cancer, we adapted the Head and Neck Cancer Risk Calculator (HaNC-RC)-V.2, generated from 10 244 referrals with the following diagnostic efficacy metrics: 85% sensitivity, 98.6% negative predictive value, and area under the curve of 0.89. For follow-up patients, a symptom inventory generated from 5123 follow-up consultations was used. A customized Excel Data Tool was created, trialed across professional groups and made freely available for download at www.entintegrate.co.uk/entuk2wwtt, alongside a user guide, protocol, and registration link for the project. Stakeholder support was obtained from national bodies. RESULTS: No remote consultations were refused by patients. Preliminary data from 511 triaging episodes at 13 centers show that 77.1% of patients were discharged directly or have had their appointments deferred. DISCUSSION: Significant reduction in footfall can be achieved using a structured triaging system. Further refinement of HaNC-RC-V.2 is feasible and the authors welcome international collaboration.