Triage Of Patients Research Articles

Health Pods for Automated Triage Improve Efficiency and Satisfaction in Nurses and Patients

Emergency department (ED) overcrowding and limited staff availability pose ongoing challenges to healthcare efficiency. Recent advancements in automated health technologies, such as the health pod, aim to alleviate these pressures by automating vital sign measurements for low-risk patients. Over three months, the CAPSULA Health Pod was implemented and used in a paired setting with normal triage procedures in an urban hospital ED; it demonstrated improvements in triage efficiency and patient satisfaction, aligning with evidence that supports automation as a solution in high-demand healthcare settings. With 1342 assessments across 404 patients, despite some challenges with elderly patient engagement, CAPSULA achieved excellent measurement accuracy and relevant efficiency for the first assessment of patients in crowded situations and for reassessment. The findings indicate CAPSULA’s potential to reduce patient wait times, improve workflow efficiency, and support resource-limited EDs. Although the main limitation remains IT integration, the system demonstrates scalability and potential for broader adoption.

Compassionomics in Action: Professional and Ethical Issues in Home Health Physical Therapy During the COVID-19 Pandemic

Home health physical therapy plays a crucial role in patient recovery and return to the home environment following hospitalization. Despite the significant presence of physical therapists and physical therapist assistants in home health, there has been limited research on the professional and ethical challenges they face. The COVID-19 pandemic exacerbated these challenges, including triaging patients, balancing patient rights with public safety, resource allocation, moral distress, lack of protective equipment, burnout, and organizational challenges. The purpose of this study was to explore the professional and ethical issues encountered by PTs and PTAs in home health care settings during the COVID-19 pandemic. This qualitative study employed virtual semi-structured interviews with 15 PTs (n = 12) and PTAs (n = 3) to explore these issues. Reflexive thematic analysis produced 4 major themes: the silo effect, caring for patients, pouring from an empty cup, and constant moral conflicts for individuals and organizations. Results demonstrated that the COVID-19 pandemic created new ethical and professional issues as well as magnifying pre-existing issues. This study revealed experiences similar to other physical therapy settings during the COVID-19 pandemic, as well as challenges unique to the home health setting such as the invisibility of the patient and those involved in their care. The study highlights the importance of context in shaping ethical issues and suggests the need for further research, education, and advocacy to address these challenges. Despite these challenges, the dedication and professional duty exhibited by physical therapists and physical therapist assistants during the pandemic were commendable.

Factors affecting communication time in an emergency medical communication centers

BackgroundEmergency Medical Communication Centres (EMCCs) play a crucial role in emergency care by ensuring timely responses through telephone triage. However, extended communication times can impede accessibility, patient triage, and decision-making. Identifying the factors influencing communication duration is essential for improving EMCC efficiency.ObjectiveThis study aims to identify temporal, human, and contextual factors associated with prolonged communication times in an EMCC where decision-making is conducted by physicians.MethodsWe conducted a retrospective observational study of all calls received at a French EMCC between March 1 and December 31, 2019. A total of 108,548 patient medical files were analyzed, excluding calls from medical personnel or hospitals. We examined the total communication time (from call initiation to decision) and the medical communication time (physician involvement). Bivariate and multivariate logistic regressions were used to identify factors associated with prolonged communication times.ResultsThe median total communication time was 7 min [IQR 5–11], and the median medical communication time was 3 min [IQR 2–4]. Psychiatric reasons for calling (OR = 1.75) and elderly patients (OR = 1.58) were associated with longer communication times. Calls leading to medical advice (OR = 1.48) and calls during weekends or nighttime were also significant factors. Conversely, calls for trauma or from nursing homes, and those handled by emergency physicians, were associated with shorter durations.ConclusionSeveral factors influence communication times in EMCCs, including patient demographics, reason for the call, and time of day.

Addressing the overuse of hospital emergency departments in the Portuguese NHS: a new paradigm

The escalating trend of inappropriate visits to Emergency Departments (ED) has led to significant concerns, including resource misallocation, compromised patient care, and an increased burden on healthcare workers. Portugal faces a notable challenge, reporting one of the highest per capita ED visit rates, with an annual average of approximately 6 million ED visits from 2013 to 2023. In response, the “Call First, Save Lives” pilot project was launched by the Portuguese NHS Executive Board, in 2023, at the Local Health Unit (LHU) of Póvoa de Varzim/Vila do Conde. This project leverages the SNS 24 telephone triage system to direct patients to the most appropriate care settings, alleviating pressure on ED, and optimizing healthcare resources. The project introduced several strategic enhancements, including optimizations of the SNS 24 system and inter-institutional initiatives to promote interoperability, hospital follow-ups for chronic conditions, and a “reverse referral” system directing non-urgent ED cases to Primary Care Services (PCS). In PCS, the project expanded acute care consultations and introduced complementary health services. The initiative also emphasized communication to improve health literacy and appropriate utilization of the NHS, supported by legal framework development. Since the beginning of the project, the number of calls received by SNS 24 showed an upward trend, both at the local, 27.6%, and national level, 44.5%, having already expanded to other LHU. Nonetheless, challenges such as the “reverse referral” process complexity, healthcare information systems integration, and procedural standardization need addressing, with future plans including satisfaction surveys and patient triage protocol refinements to improve service delivery and patient adherence.

The effects of patient triage management strategies on clinical outcomes and risk management in emergency departments: a prospective comparative study

Aims: The aim of this study is to compare the impact of the emergency severity index (ESI) and the Manchester triage system (MTS) on clinical outcomes, patients' satisfaction, and risk management using a prospective approach. Methods: In this study, which includes 12,000 patients who visited the emergency department of Esenyurt Necmi Kadıoğlu State Hospital between September 1, 2024, 2024, and October 15, 2024, 6,000 patients were triaged using the ESI, and 6,000 patients were triaged using the MTS. The study was conducted by randomly selecting patients between the ages of 18-85 who were visiting the emergency department for the first time. Data were collected using the observations of healthcare personnel, electronic health system data, and patient admission records. The primary variables include treatment times, clinical outcomes, resource utilization in the emergency department, and patient waiting times. Surveys were used to measure patients' satisfaction, and medical inaccuracies were evaluated based on error reports and inconsistencies in medical records. Statistical analyses were performed using SPSS software, applying independent sample t-tests, chi-square tests, and logistic regression analyses. Results: It was found that patients triaged using the ESI had significantly lower waiting times compared to those triaged using the MTS (OR: 0.65, 95% CI: 0.50-0.85, p

An Innovative Artificial Intelligence Based Decision Making System for Public Health Crisis Virtual Reality Rehabilitation

The COVID-19 disease caused by the SARS-CoV-2 virus was declared by the World Health Organization (WHO) as a spreadable viral disease. During the COVID pandemic, there was difficulty in notifying the Decision-Making System (DMS) about the rapid and precise triage of patients admitted to the emergency wards. As a method to achieve the aim and develop digital healthcare revolutions in data and analytics, digital healthcare information was established. Artificial Intelligence (AI) is a robust automation tool for sustainability in the context of the COVID-19 health crisis on big datasets. Besides, the gap between AI investment and commercial real-time application, which are the initial digital technology development curves, has been identified. It was discovered that AI’s new applications are grounded in Digital Transformation Mapping (DTM) for the DMS of Health Crises. The fast inventions in AI and Machine Learning (ML) have implications for amazingly preventive and clinical healthcare, and for the association, ML was developed as a predictable attention. Billions of smartphones, massive online datasets, linked wireless wearable devices, comparatively cost-effective computing resources and improved ML and Nural Language Processing (NLP) are leveraged by these rapid responses, with the trained dataset of 65% and evaluated in the other 35%, the renowned ML models for structured data like Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), Logistic Regressive Tree (LRT), Decision Tree (DT), Stochastic Gradient Booster (SGB), and Random Forest (RF) are used for simulating new unidentified data. AI-DTM challenges DMS of Health Crises (COVID-19) and the drawbacks of critically contributing risk factors to healthcare diseases. Meanwhile, a comprehensive collection of healthcare datasets over what is spreadable would be required to save human lives, train AI, and limit cost-effective health risks.

Age-dependent differences in FIB-4 predictions of fibrosis in patients with MASLD referred from primary care.

Fibrosis 4 (FIB-4) is widely used to triage patients with metabolic dysfunction-associated steatotic liver disease. Given that age is part of FIB-4, higher scores may be expected in the elderly population. This led to the proposal of using a higher threshold of FIB-4 to triage patients aged ≥65. Our main objective is to evaluate how age modifies the association between the FIB-4 index and disease severity based on the vibration-controlled transient elastography (VCTE) "rule of 5s." In this cross-sectional study, we prospectively analyzed data from a primary care referral pathway. We used liver stiffness measurement by VCTE as a reference standard for liver risk. We modeled with ordinal regression the exceedance probabilities of finding different liver stiffness measurement thresholds according to FIB-4, and how age modifies FIB-4 predictions. Nine hundred eighty-five participants with complete data were used for modeling. Participants aged ≥65 had a higher prevalence of advanced liver disease estimated by VCTE and higher FIB-4 values than those <65 (85.9% vs. 20.2% for FIB-4 ≥1.3, and 46.5% vs. 6.5% for FIB-4 ≥2.0). In participants age ≥65, the negative predictive value for VCTE ≥10kPa of FIB-4 <1.3 was 100% versus FIB-4 <2.0 was 83%. Age significantly modified FIB-4-based prediction of fibrosis, but predictions at a threshold of 1.3 or 2 were only minimally altered. For higher FIB-4 threshold (ie, 2.7), age strongly modified FIB-4 predictions of liver stiffness measurement. Age does not relevantly modify FIB-4 predictions when using the common threshold of 1.3. Our data suggest no rationale for increasing the FIB-4 threshold to 2 for undergoing further testing in patients aged ≥65. However, the meaning of a FIB-4 of 2.7 strongly changes with age. This cutoff for ages over 65 is not enough to define high-risk and would not warrant direct referral.

Fortifying defenses: Tactical safety protocols for COVID-19 sub-variant JN.1 in healthcare and laboratory settings

ABSTRACT Introduction: Primary care physicians are crucial in fighting COVID-19, especially with the emergence of the new JN.1 sub-variant. Measures to Reduce Risk: Given your direct exposure to infected patients, it is imperative to establish a protocol for triaging patients with respiratory symptoms and to uphold a minimum distance of 2 meters between patients and primary care physicians. Patients suspected or diagnosed with the JN.1 sub-variant should be advised to wear surgical masks for their protection and others protection. Primary care physicians must also use personal protective equipment (PPE) and maintain strict hand hygiene practices when dealing with these patients. Patient samples should be treated as high risk for contamination, and laboratory procedures should be meticulously evaluated for potential hazards. PPE should be tailored to the procedure. Conclusion: To protect the health and well-being of primary care physicians, who play a critical role in addressing the challenges, it is essential to strictly adhere to infection control measures.

Penumbral Imaging to Guide Endovascular Treatment for M2 Middle Cerebral Artery Stroke.

A potential benefit of mechanical thrombectomy for patients with distal medium vessel occlusions is currently being investigated in randomized trials. Computed tomography perfusion imaging has not yet been tested as a method to guide mechanical thrombectomy for distal medium vessel occlusions. The purpose of this study was to assess penumbral imaging as an imaging-based method for triaging patients with ischemic stroke and acute M2-middle cerebral artery occlusion. This observational retrospective study of M2-middle cerebral artery patients with ischemic stroke triaged by multimodal computed tomography undergoing mechanical thrombectomy at a high-volume stroke center between January 2015 and January 2023. The effect of recanalization was analyzed according to computed tomography perfusion-derived lesion volumes (defined using relative cerebral blood flow <30% and Tmax >6 seconds) using logistic regression analysis, and interaction terms between the independent variables and recanalization were tested. The primary end point was functional independence at day 90, defined using modified Rankin Scale scores of 0 to 2. A total of 140 patients with M2-middle cerebral artery occlusion were included. In multivariable logistic regression analysis, recanalization was not associated with better functional outcome (adjusted odds ratio, 1.85 [95% CI, 0.87-3.90]; P=0.11). After including interaction terms, a significant treatment effect between recanalization and computed tomography perfusion-derived lesion volumes was observed in patients with >150 mL hypoperfusion volume (adjusted odds ratio, 1.02 [95% CI, 1.00-1.03]; P=0.007) or >125 mL penumbral volumes (adjusted odds ratio, 1.02 [95% CI, 1.01-1.03]; P=0.005), as well as for baseline ischemic core volume within the range of 15 to 40 mL (adjusted odds ratio, 1.11 [95% CI, 1.01-1.22]; P=0.03). Penumbral imaging might serve as a useful tool for treatment decision-making in distal medium vessel occlusions, particularly in cases of suspected non- or codominant M2-middle cerebral artery vessel occlusions. A hypoperfusion volume threshold of >150 mL emphasizes the potential value of computed tomography perfusion as a standardized tool directly showing the volumetric relevance in distal medium vessel occlusion cases.

Harnessing machine learning in diagnosing complex hoarseness cases

PurposeTraditional vocal fold pathology recognition typically requires expertise of laryngologists and advanced instruments, primarily through direct visualization. This study aims to augment this conventional paradigm by introducing a parallel diagnostic procedure. Our objective is to harness a machine-learning algorithm designed to discern intricate patterns within patients' voice recordings to distinguish not only between healthy and hoarse voices but also among various specific disorders. Materials and methodsWe employed a machine-learning algorithm, utilizing transfer learning on the HuBERT model with Saarbruecken Voice Database samples. The study was conducted in two stages: a binary classifier distinguishes healthy and hoarse voices, while a subsequent multi-class classifier identifies specific voice disorders. Data from 2103 sessions, including over 25,000 components, representing diverse pathologies and healthy individuals, was analyzed. The models were trained, validated, and tested with a focus on robustness and accuracy in diagnosis. ResultsThe binary classifier achieved 82 % accuracy in distinguishing healthy from pathological voices. The multi-class algorithm which aims to identify specific laryngeal disorders obtained the highest accuracy (>93 %) for Laryngeal Dystonia. Noteworthy is the persistent challenge posed by Laryngeal Dystonia, a condition lacking a definitive diagnostic modality. ConclusionsOur findings demonstrate the feasibility of utilizing machine-learning algorithms to process voice samples, categorizing them into distinct pathologies. This approach holds potential for enhance patient triage, streamline diagnostics, and elevate overall patient care. Particularly valuable for challenging diagnoses, such as Laryngeal Dystonia, this method underscores the transformative role of machine learning in optimizing healthcare practices.

Proposed computed tomography severity index for the evaluation of invasive fungal sinusitis: Preliminary results.

Invasive fungal sinusitis (IFS) can present as a mild disease to life-threatening infection. A recent surge in cases was seen due to the coronavirus disease 2019 (COVID-19) pandemic. Many patients require surgical debridement and hence imaging [contrast-enhanced computed tomography (CECT) of the paranasal sinuses (PNS)] to document the extent of the disease. However, there was no scoring system using CECT to describe the severity of IFS. This study proposes a computed tomography (CT) severity index (CTSI) to describe the severity of rhino-orbital-cerebral involvement in symptomatic COVID-19 patients and hypothesizes that higher CTSI correlates with disease severity and thus slow response/non-response to treatment. To propose a scoring system using CECT to describe the severity of IFS and correlate it with clinical outcomes. A prospective study on 66 COVID-19 positive patients with CECT PNS done for IFS was performed. Split-bolus single-phase CT technique was used. Based on the extent of involvement, a CTSI was designed. Disease in four major subsite areas was assessed. Each subsite involvement was given points according to this model and then summated. Based on the final summated CTSI, the disease was classified as mild, moderate, or severe. Two subsets were subsequently analyzed including survival and death; and responders and non-responders. The study cohort was 66 COVID-19-positive patients with suspected IFS with a median age of 48.5 years. Mild disease was noted in 34 (51.52%), moderate in 28 (42.42%), and severe disease in 4 (6.06%) patients. There was a significant association of mortality and poor clinical response (P = 0.02) with disease bilaterality. Laterality and CTSI were significant predictors of response to treatment. The mean CTSI of responders was 6.3, of non-responders was 12.9 and the response to treatment was significantly associated with CTSI (t-test, P < 0.001). Receiver operating characteristic curve analysis (Liu method) to distinguish between responders and non-responders showed that the cut-off value for CTSI of 11 had a sensitivity of 78.26% and a specificity of 95.35% to predict response assessment. CTSI can help in quantification of the disease burden, mapping out disease extent, triaging patients, and response assessment; especially patients with underlying comorbidities. A higher score would alert the clinician to initiate aggressive treatment, as severe disease correlates with slow response/non-response to the treatment.

Developing and integrating a destination decision support algorithm into an innovative electronic communication platform to improve injury care service coordination in Rwanda: The Rwanda912 study protocol.

Delays in getting injured patients to hospital in a timely manner can increase avoidable death and disability. Like many low- or middle-income countries (LMICs), Rwanda experiences delays related to lack of efficient prehospital communication and formal guidelines to triage patients for hospital care. This paper describes the protocol to develop, roll out, and evaluate the effectiveness of a Destination Decision Support Algorithm (DDSA) integrated in an electronic communication platform, '912Rwanda'. The DDSA will facilitate the linkage of patients to health facilities able to treat their condition(s). Work will be conducted in three phases: i. development, ii. training and roll-out, and iii. evaluation. Work will be conducted in the pre-hospital emergency service "SAMU - Service d'Aide Medicale Urgente" and health facilities in Kigali City and Musanze District, which serve predominately urban and rural populations, respectively. We will develop interfaces to capture facility and patient-relevant data, which feed into a guideline-based electronic DDSA to match patients to hospitals. We will assess existing trauma care processes using qualitative and quantitative methodologies. This will be followed by a series of consensus workshops to develop at-scene triage guidelines and agree variables to capture in the interfaces. The DDSA will be developed based on outputs from these workshops and tested against historical ambulance data and expert opinion until acceptable thresholds of performance are passed. User interfaces will be developed and tested using human computer interface design principles. The combined collaborative approach of bringing together experts and software developers, and with deep engagement of Rwandan stakeholders, including leadership of Rwanda Ministry of health through its technical arm, the Rwanda Biomedical Center, should lead to an ambulance communication system which is utilized, sustained, and effective.

Infectious Risk in Pediatric Emergency Departments in Italy: A Survey by the Italian Society for Pediatric Emergency and Urgent Medicine (SIMEUP) on Available Preventive and Diagnostic Tools.

Background/Objectives: The COVID-19 pandemic has emphasized the importance of preparedness in preventing the spread of infectious diseases, especially in Emergency Departments (EDs), where initial patient assessments and triage occur. This study aims to evaluate the current practices and available tools for infection control in Pediatric EDs across Italy, focusing on the differences between various hospital types and regional settings. Methods: A cross-sectional national survey was conducted in February 2022, targeting healthcare workers in Pediatric EDs across Italy. The survey, distributed via the Italian Society for Pediatric Emergency and Urgent Medicine (SIMEUP) mailing list, collected data on infection control measures, including the availability of hand hygiene stations, personal protective equipment, disinfection protocols, and the use of rapid diagnostic tests. Results: A total of 80 questionnaires were completed from 119 (67.2%) different ERs. The majority of respondents were from Northern Italy (47.5%) and worked in hospitals with 24 h pediatric assistance (48.8%). Less than half of non-pediatric hospitals had separate access for children, potentially exposing them to adult pathogens. Across all settings, basic infection control measures, such as providing masks and hand gel, were widely implemented. However, significant differences were observed in the availability of social distancing, informational materials, and dedicated pediatric pathways, with I level assistance hospitals less likely to have these resources. Rapid diagnostic tests were available in most settings, but the focus was predominantly on SARS-CoV-2, despite other respiratory pathogens' relevance in pediatric care. Conclusions: Strengthening preparations for future pandemics will be crucial in enhancing the resilience of healthcare systems and ensuring the safety of both patients and healthcare workers in the face of emerging infectious threats.

Incidence and Prevalence of Alzheimer's Disease in Medicare Beneficiaries.

The availability of anti-amyloid therapy for mild cognitive impairment (MCI) due to Alzheimer's disease and mild Alzheimer's dementia (AD) has underscored the need for realistic estimates of the population with AD/MCI within the healthcare system to assure adequate preparedness. We hypothesize that administrative databases can provide real-world epidemiologic estimates reflecting the population with diagnosed (known) MCI and AD. This study was conducted to estimate diagnostic incidence and prevalence of AD and all-cause MCI among the Medicare fee-for-service (FFS) and Medicare Advantage (MA) beneficiaries in the United States. This was a retrospective analysis of Medicare beneficiaries (aged 65 and older) with identified diagnoses of AD/MCI based on ≥2 diagnostic codes ≥30 days apart. Incidence/prevalence estimates were reported per 10,000 person-years. In FFS, AD incidence (2008-2018) decreased (138 to 104); MCI incidence increased (8 to 47), but the sum (MCI+AD) was relatively stable (146 to 151). Prevalence (2008-2017) increased for AD (318 to 354), and MCI (13 to 99). In MA (2016) epidemiological estimates were consistent with FFS. In 2017, older age, female sex and the Northeastern region were consistently associated with higher AD/MCI prevalence among FFS beneficiaries. In FFS, AD/MCI diagnostic prevalence increased over 10 years, especially for MCI; prevalence estimates in MA (2016) were comparable. Diagnostic prevalence in 2016 (FFS+MA) was 3.4% for AD and 0.85% for MCI. Our findings address the reality of Alzheimer's disease in clinical practice in the United States that is confronted by healthcare professionals, payors, healthcare decision-makers, patients, and caregivers, and may offer a realistic gauge for patient triage for treatment, healthcare resource allocation, and health-systems' operational prioritization. With the availability of anti-amyloid treatments, we anticipate that the population with diagnosed MCI/AD within the Medicare database may rise over time; therefore, periodic updates of incidence/prevalence estimates may provide support for timely healthcare decision-making.

Empowering cancer prevention with AI: unlocking new frontiers in prediction, diagnosis, and intervention.

Artificial intelligence is rapidly changing our world at an exponential rate and its transformative power has extensively reached important sectors like healthcare. In the fight against cancer, AI proved to be a novel and powerful tool, offering new hope for prevention and early detection. In this review, we will comprehensively explore the medical applications of AI, including early cancer detection through pathological and imaging analysis, risk stratification, patient triage, and the development of personalized prevention approaches. However, despite the successful impact AI has contributed to, we will also discuss the myriad of challenges that we have faced so far toward optimal AI implementation. There are problems when it comes to the best way in which we can use AI systemically. Having the correct data that can be understood easily must remain one of the most significant concerns in all its uses including sharing information. Another challenge that exists is how to interpret AI models because they are too complicated for people to follow through examples used in their developments which may affect trust, especially among medical professionals. Other considerations like data privacy, algorithm bias, and equitable access to AI tools have also arisen. Finally, we will evaluate possible future directions for this promising field that highlight AI's capacity to transform preventative cancer care.

The Use of Artificial Intelligence in Patient Triage in Emergency Departments: an Integrative Review

Objective: This study aims to explore how AI has been applied in patient triage in emergency services, investigating its contributions to the efficiency of care, the challenges faced in implementation, and the opportunities for response time optimization and resource allocation. Method: An integrative literature review was carried out with studies published between 2020 and 2024. The research was guided by the PICo (Population, Interest and Context) model, focusing on patients treated in emergency services (P), the application of AI for triage (I) and the hospital and emergency context (Co). The search was carried out in databases such as PubMed, LILACS, SciELO and Scopus, applying the Boolean code "Artificial Intelligence" AND "Triage" AND "Emergency Services". After screening 214 initial articles, 12 studies were selected for final analysis, based on the PRISMA guidelines. Results and Discussion: The review pointed out that AI significantly improves response time and reduces the margin of error in patient classification, especially in high-demand situations. The use of AI stood out in identifying critical outcomes, such as the need for immediate care. During the COVID-19 pandemic, AI has proven to be essential in the remote triage of high-risk patients, ensuring efficient use of resources in overcrowded settings. However, implementation faces challenges, such as resistance from health professionals and the need for integration with existing health systems. Conclusion: In summary, the use of AI in emergency services has brought significant benefits, such as increased triage efficiency, improved diagnostic accuracy, and improved resource management. However, overcoming cultural and operational barriers and setting clear ethical guidelines are essential. The careful integration of AI, with continuous training and periodic assessments, is critical to transforming care and ensuring more effective and safer patient care.

Evaluating emergency nurses' use of and satisfaction with a web-based patient triage app.

Evidence suggests that using a standardised triage system that categorises patients into levels of severity or acuity to prioritise their care can provide the greatest levels of patient safety. The use of a web-based app to record patient triage data can support operational decision-making, reduce the risk of errors and standardise the time it takes to record the data required for accurate triage. This article details one aspect of a three-stage research project undertaken in Thailand which aimed to improve the quality of nurse triage, reduce errors in triage decisions and decrease the time spent on triage through implementation of a patient triage app. The patient information input into the app at initial triage is automatically uploaded into the electronic patient record. The authors report the results of the third stage of the project, which aimed to analyse the feasibility of using the app by evaluating completion of patient records and user satisfaction levels.

Evaluating Telehealth Diagnostic Accuracy in Oral and Maxillofacial Diseases: A Comparative Study.

Objectives: This study evaluated the accuracy of diagnosing oral and maxillofacial diseases using telehealth. Methods: We recruited 100 patients from the Oral Health Centre of Western Australia. They were either new patients or existing patients with a condition not previously diagnosed. The patients initially underwent a telehealth consultation without administrative or clinical staff assistance. On the same day, they also received a traditional in-office (face-to-face) consultation with an Oral Medicine registrar and/or consultant. Results: In this study, 70 patients were consulted via telehealth for a mucosal condition, while 30 were consulted for orofacial pain. When comparing telehealth diagnoses to in-person diagnoses, 65.7% of mucosal cases and 70% of orofacial pain cases had the same diagnosis. Furthermore, regarding the diagnoses within the chief complaint's telehealth differential diagnosis list, 87.1% were similar in the mucosal cases, and 96.7% were similar in orofacial pain cases compared to the in-office consultation. Conclusions: Our study's findings demonstrate that telehealth is less reliable than the gold standard in-person consultation for diagnosing oral diseases. However, it shows promise as an adjunctive service for screening, triaging, and monitoring patients. Further studies with guidelines for patients undergoing telehealth consultations to improve the diagnostic accuracy of oral and maxillofacial diseases are necessary.

Value of Platelet-to-Lymphocyte and Monocyte-to-Lymphocyte Ratios in Differentiating Idiopathic Orbital Inflammation From Orbital Infectious Disease.

Monocyte-lymphocyte ratio (MLR) and platelet-lymphocyte ratio (PLR) are emerging biomarkers; they have not been widely utilized in orbital disease. This study investigated the role of these ratios in distinguishing orbital inflammation from infection. A retrospective review of medical records was conducted to identify adult patients who presented acutely to a single emergency department at an academic medical center and were diagnosed with serologically and biopsy-proven idiopathic orbital inflammation (IOI, n = 9), orbital cellulitis (OC, n = 14), or necrotizing fasciitis (NF, n = 12). MLR and PLR were calculated from the first blood draw on presentation to the emergency department. Statistical analysis was performed via the Mann-Whitney test with a dedicated computerized software package (GraphPad Prism, La Jolla, CA) with p values <0.05 considered statistically significant. Mean PLRs were 194.90 (SD = 118.01), 304.21 (SD = 341.39), and 203.38 (SD = 196.32) for IOI, NF, and OC, respectively. Mean MLRs were 0.40 (SD = 0.24), 1.74 (SD = 2.41), and 0.75 (SD = 0.40) for IOI, NF, and OC, respectively. MLR was significantly lower in the IOI group compared to the NF group (p = 0.018) and OC group (p = 0.020). Differences in MLR between OC and NF groups were not statistically significant (p = 0.43). While MLR showed no significant difference between different infectious etiologies, it was significantly higher in infectious processes than in IOI, distinguishing inflammation from infection. MLR could be a valuable addition to the diagnostic toolkit for triaging patients in the emergency department and initiating prompt, focused therapy.

Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments.

Longitudinal tracking of multiple sclerosis (MS) symptoms in an individual's own environment may improve self-monitoring and clinical management for people with MS (pwMS). We present a machine learning approach that enables longitudinal monitoring of clinically relevant patient-reported symptoms for pwMS by harnessing passively collected data from sensors in smartphones and fitness trackers. We divide the collected data into discrete periods for each patient. For each prediction period, we first extract patient-level behavioral features from the current period (action features) and the previous period (context features). Then, we apply a machine learning (ML) approach based on Support Vector Machine with Radial Bias Function Kernel and AdaBoost to predict the presence of depressive symptoms (every two weeks) and high global MS symptom burden, severe fatigue, and poor sleep quality (every four weeks). Between November 16, 2019, and January 24, 2021, 104 pwMS (84.6% women, 93.3% non-Hispanic White, 44.0±11.8 years mean±SD age) from a clinic-based MS cohort completed 12-weeks of data collection, including a subset of 44 pwMS (88.6% women, 95.5% non-Hispanic White, 45.7±11.2 years) who completed 24-weeks of data collection. In total, we collected approximately 12,500 days of passive sensor and behavioral health data from the participants. Among the best-performing models with the least sensor data requirement, ML algorithm predicts depressive symptoms with an accuracy of 80.6% (35.5% improvement over baseline; F1-score: 0.76), high global MS symptom burden with an accuracy of 77.3% (51.3% improvement over baseline; F1-score: 0.77), severe fatigue with an accuracy of 73.8% (45.0% improvement over baseline; F1-score: 0.74), and poor sleep quality with an accuracy of 72.0% (28.1% improvement over baseline; F1-score: 0.70). Further, sensor data were largely sufficient for predicting symptom severity, while the prediction of depressive symptoms benefited from minimal active patient input in the form of response to two brief questions on the day before the prediction point. Our digital phenotyping approach using passive sensors on smartphones and fitness trackers may help patients with real-world, continuous, self-monitoring of common symptoms in their own environment and assist clinicians with better triage of patient needs for timely interventions in MS (and potentially other chronic neurological disorders).