Scheduled Appointments Research Articles

Urine tenofovir testing for real-time PrEP adherence feedback: a qualitative study involving transgender women in Uganda.

Adherence counselling with point-of-care (POC) drug-level feedback using a novel tenofovir assay may support pre-exposure prophylaxis (PrEP) adherence; however, perceptions of urine testing and its impact on adherence are not well studied. We qualitatively examined how POC tenofovir testing was experienced by transgender women (TGW) in Uganda. Within a cluster randomized trial of peer-delivered HIV self-testing, self-sampling for sexually transmitted infections and PrEP among HIV-negative TGW showing overall low PrEP prevention-effective adherence (NCT04328025), we conducted a nested qualitative sub-study of the urine POC assay among a random sample of 30 TGW (August 2021-February 2022). TGW interviews explored: (1) experiences with POC urine tenofovir testing and (2) perceptions of PrEP adherence counselling with drug-level feedback. We used an inductive content analytic approach for analysis. Median age was 21 years (interquartile range 20-24), and 70% engaged in sex work. Four content categories describe how TGW experienced POC urine tenofovir testing: (1) Urine tenofovir testing was initially met with scepticism: Testing urine to detect PrEP initially induced anxiety, with some perceptions of being intrusive and unwarranted. With counselling, however, participants found POC testing acceptable and beneficial. (2) Alignment of urine test results and adherence behaviours: Drug-level feedback aligned with what TGW knew about their adherence. Concurrence between pill taking and tenofovir detection in urine reinforced confidence in test accuracy. (3) Interpretation of urine tenofovir results: TGW familiar with the interpretation of oral-fluid HIV self-tests knew that two lines on the test device signified positivity (presence of HIV). However, two lines on the urine test strip indicated a positive result for non-adherence (absence of tenofovir), causing confusion. Research nurses explained the difference in test interpretation to participants' satisfaction. (4) White coat dosing: Some TGW deliberately chose not to attend scheduled clinic appointments to avoid detecting their PrEP non-adherence during urine testing. They restarted PrEP before returning to clinic, a behaviour called "white coat dosing." Incorporating POC urine testing into routine PrEP adherence counselling was acceptable and potentially beneficial for TGW but required attention to context. Additional research is needed to identify effective strategies for optimizing adherence monitoring and counselling for this population.

Quality Improvement Initiative Enhances Outpatient Pediatric Pulmonology Follow-up for Premature Infants with Bronchopulmonary Dysplasia

Introduction: Bronchopulmonary dysplasia (BPD) is a chronic lung disorder affecting many premature infants. Infants with BPD have higher hospital readmission rates due to respiratory-related morbidity. We aimed to increase the rates of outpatient pulmonary follow-up and attendance of premature babies with moderate and severe BPD to above 85% within 6 months. Methods: We conducted a quality improvement project at Yale New Haven Children’s Hospital. Key interventions included developing a BPD clinical pathway integrated into the electronic medical record to assist providers in correctly classifying BPD severity, assigning the appropriate International Classification of Diseases, 10th Revision code (P27.1), and providing standardized treatment options. The outcome measures included correct diagnosis and classification of BPD, the percentage of patients with BPD scheduled for pediatric pulmonology appointments within 45 days, and the percentage attending those appointments. Results: There were 226 patients in our study, including 85 in the baseline period. Correct diagnosis of BPD increased from 49% to 95%, the percentage of scheduled appointments increased from 71.9% to 100%, and the percentage of appointments attended increased from 55.6% to 87.1%. Conclusions: Our quality improvement initiative improved the accuracy of diagnosis, severity classification, and outpatient pulmonary follow-up of children with moderate and severe BPD.

INADEQUATE CARE OF HYPERTENSION AND OTHER NON-COMMUNICABLE DISEASES DURING THE COVID-19 PANDEMIC IN SOUTH AFRICA

Objective: In the setting of an already overburdened healthcare system, South Africa has a high burden of hypertension and other non-communicable diseases (NCDs), and the country experienced a high COVID-19 caseload. The aim of this study was to examine the perceptions and experiences of 1) healthcare professionals (HPs) on the preparedness of health systems to provide NCD care and of NCD management during the COVID-19 pandemic, and 2) people living with NCDs (PLWNCDs) on the care they received for hypertension and other NCDs during the COVID-19 pandemic in South Africa. Design and method: Across the country, we recruited a convenience sample of 1) HPs who worked in healthcare management i.e., public health officials and healthcare workers who provided care and engaged with patients in any healthcare capacity, and 2) PLWNCDs with at least 1 NCD. Questionnaires included quantitative and open-ended follow-up questions. Quantitative analyses were conducted using SPSS. The qualitative open-ended questions were evaluated using content analysis with inductive reasoning. Results: There were 31 HPs and 79 PLWNCDs (59 with hypertension) included in this cross-sectional study. HPs perceived COVID-19 care to be adequate, while NCD care was poor during the pandemic. There were service disruptions including for emergency and specialised NCDs care. Strategies for the care of non-COVID-19 illnesses during the pandemic were absent. This would have serious long-term consequences for PLWNCDs and the healthcare system. Perceptions of inadequate hypertension and other NCD care were reiterated by PLWNCDs; 49% felt they received ’very little’ or no adequate care. 18% had a scheduled appointment cancelled. Additionally, many PLWNCDs (52%) felt anxious, lonely, or frightened during the pandemic and 15% felt their mental health had deteriorated. The utility of digital health in future was positively perceived by both HPs and PLWNCDs and may be able to improve health provision during crises. Conclusions: Policies are required 1) so that NCD care will not be overlooked during future crises and 2) to encourage PLWNCDs to timeously access healthcare services during such periods. Such strategies may incorporate digital health apps to improve care and target the mental healthcare needs of PLWNCDs.

Examining Accessibility of Freestanding Abortion Clinics for People With Disabilities [ID 2683401

INTRODUCTION: People with disabilities face numerous barriers to equitable health care but have a fundamental right to safe abortion care. This study reviews the accessibility of freestanding abortion clinics in the United States for those with disabilities. METHODS: A quantitative data analysis was performed. All freestanding abortion clinics registered with the Abortion Care Network were included except those directly affiliated with a university or general gynecology clinic and those exclusively operating online. Two independent reviewers analyzed clinic websites after an interrater reliability test. Independent variables analyzed included Flesch Reading Ease score, presence of a statement affirming dignity for all persons, online appointment scheduling, transparent pricing, and financial assistance information. RESULTS: Seventy-two clinics were included. No clinic explicitly stated that their facilities were accessible to individuals with disabilities. Fifty-four percent had a statement affirming dignity for all persons, most of which were in the Northeast or West Coast. 6% had an accessibility widget, and 63% had online appointment scheduling. Mean Flesch Reading Ease score was 55.37 correlating to a 10th- to 12th-grade reading level. 33% had transparent pricing information, and 71% offered information on financial assistance. CONCLUSION: Several accessibility barriers were identified on these websites. Freestanding abortion clinics should focus on enhancing accessibility by explicitly affirming dignity for all patients, providing clear information on facility accessibility, and implementing an accessibility widget, online scheduling, and transparent pricing. Furthermore, these websites should be written at an easily comprehensible reading level. These simple changes would make these clinics more accessible for those with disabilities.

Tabib: Chatbot for Healthcare Automation with Audio Assistance using Artificial Intelligence

To live a happy and healthy life, people must prioritize their health care. It's not always easy to contact a doctor when you have a health issue, though. The idea is to build a chatbot for healthcare that functions as a virtual assistant using artificial intelligence. The healthcare chatbot system's medical database will be trained using neural networks and natural language processing. The purpose of the healthcare chatbot was to increase access to medical information while reducing overall healthcare costs. There are certain chatbots that function as medical encyclopedias; educating patients about their conditions and helping them take better care of themselves. In the proposed project, a chatbot system converses with patients about their health issues via voice-to-voice and text- to-text and provides a personalized diagnosis based on their symptoms. In addition to text assistance, this chatbot also offers an audio assistance feature that makes it easier for disabled (visually impaired) patients to access the system. This feature eliminates the restrictions that visually impaired patients face with the currently available text-enabled healthcare chat bots. We used the user's current location to locate the specialists nearby and present the information about them. Users can schedule appointments with various physicians in their area. As a result, people will be aware of their health status, receive rapid treatment, and be informed about the best medicines.

Healthcare Virtual Assistant using Generative AI

Abstract: Documentation is a clumsy task for the front- desk staff. They've to fill in patient data, schedule appointments, and attend to patient queries. Even healthcare providers must enter EHR data, which takes a lot of time, and they end up spending lesser time with their patients. In the evolving landscape of healthcare administration, the burden of administrative tasks continues to challenge healthcare professionals and institutions. However, with generative AI, doctors can produce clones of patient data and automate form- filling tasks. It can also be integrated with EHR for documentation work. This project introduces a result — a generative artificial intelligence (AI) system designed to ease administrative burdens and enhance operating efficiency. By employing the power of AI, this web- grounded platform aims to streamline administrative processes, automate routine tasks, and facilitate absolute communication between patients, medical staff, and administrative personnel. The proposed website leverages advanced generative AI approaches to tackle crucial administrative challenges. Through natural language processing (NLP) and image analysis, the system interprets unstructured data such as medical notes, images, and documents. It enables automated documentation generation, medical coding and billing, appointment scheduling, and patient inquiries through a responsive and user-friendly interface

Revolutionizing Healthcare: A Java-Based Approach to Hospital Management

Abstract: In this research paper, we are investigating the development and evaluation of a mobile application designed to streamline Life Care processes. The research explores an ensemble model that integrates functionalities from existing solutions such as appointment scheduling, staff communication, and resource allocation. Through the analysis of real-world data collected during the app's implementation, the study aims to assess its impact on improving workflow efficiency, enhancing communication among healthcare teams, and facilitating better decision-making. The findings suggest that the app has significantly improved hospital management practices and patient care quality, highlighting its potential as a valuable tool in modern healthcare settings.

Smart Health Care System

System is a Java-based web application designed to streamline hospital operations. Its user- friendly interface simplifies administrative complexities, focusing on optimizing patient management, appointment scheduling, and record-keeping. This system enhances coordination among medical staff, ensuring smooth patient care and better outcomes. By automating tasks and organizing crucial information, hospitals can operate more efficiently, delivering faster and more accurate care. This paper introduces a web-based platform facilitating the transition from paper to electronic records, particularly emphasizing the elimination of paper prescriptions through E-Medical Management. This approach enhances patient management efficiency, doctor scheduling, and provides universal access to patient data within the hospital premises.

Compassionate Veterinary Care Platform

Abstract: This paper proposes a digital appointment booking system for pet clinics, aimed at enhancing convenience and efficiency. Through the system's user-friendly interface, pet owners can easily schedule appointments for their beloved friends. Users can choose desired time slots, see which veterinarians are available, and indicate if they are there for an emergency consultation, vaccine, or routine check-up. The system also makes it possible for clinic employees to effectively manage appointments, which lowers wait times and improves efficiency.

Digital Transformation in Healthcare Challenges and Prospects

The following are presented, by the global community, as the main fields of Digital Transformation (DT) in Healthcare: Telemedicine Telemedicine is the remote delivery of healthcare services through Information and Communication Technologies (ICTs). It could be particularly beneficial in a range of services such as medical consultation, diagnosis remote monitoring of patients’ status and educational purposes. Patients could con- nect with healthcare providers at a distance using video calls, messaging platforms, or other communication tools. Telemedicine enhances accessibility to healthcare by overcoming geographical barriers while at the same time provides a transformative approach to healthcare services. Big Data Big data represents vast and diverse health information sets collected from several sources like Electronic Health Records (EHRs), wearable devices and social media. Big data can extract valuable insights and patterns, assisting clinical decision-making, public health management, personalised medicine and disease forecasting. Internet of Things (IoT) The Internet of Things (IoT) in healthcare refers to the interconnected network of medical devices, sensors and sys- tems that collect and exchange health data through the Inter- net. These devices offer real-time exchange of various data monitoring like patient's vital signs, and remote patient’s care, which could eventually result in the improvement of operational efficiency within healthcare systems, and patients' monitoring and care delivery. Virtual Reality (VR) in Health Virtual Reality (VR) employs immersive computer-generated environments into realistic experiences to transform medical training and education, rehabilitation, surgical planning and pain management. Employing VR tools such as headsets, users engage with three-dimensional simulation scenarios for training and skill practice in a safe environment without risking personal and patient safety. In addition, VR offers opportunities to improve outcomes, provide safer experiences to patients, while promoting the evolution of healthcare systems and care delivery. Artificial Intelligence (AI) in Health AI in healthcare is an umbrella term for the applications of Machine Learning (ML) algorithms used in medical contexts. AI algorithms analyse medical data, aiding in diagnostics, drug discovery, personalised medicine, and patient management. Virtual (personal) Assistants in Health Virtual Health Assistants (VHAs) are tools designed to engage patients, provide personalised health information and assist in various aspects of health services administration. Using Natural language Processing (NLP) and ML algorithms, VHAs offer support to remote communication between patients and healthcare providers, understand patients’ inquiries and healthcare-related issues. Also, VHAs could facil- itate appointment scheduling, symptom assessment, medication management, and health coaching, resulting at the enhancement of accessibility in healthcare and the empower- ment of the patient. Blockchain in Health Blockchain is an emerging technology, which utilises decentralised systems to record and manage healthcare information and patient data. By linking data blocks, blockchain can enhance security, interoperability and traceability, and address significant challenges such as data breaches, interoperability issues, and inefficiencies in data sharing, thus providing a stan- dardised and secure platform for data exchange across health- care systems and stakeholders. Medical Wearables Wearables emerge positive outcomes on continuing monitoring, tracking and early detecting of health issues. Wearables are devices wore on the body or putted on clothing allowing constant monitoring of health data and behaviour. They enable individuals to engage a proactive approach to their health and well-being, by empowering them to take control of their health. In addition, wearables have the potential to enhance telemedicine and population health management, while reducing healthcare costs.

Artificial Intelligence for Patient Flow

Why Is This an issue? Inefficient patient flow contributes to the overcrowding of health care settings and negative clinical outcomes and patient experiences downstream. Patient flow management aims to achieve seamless patient movement through the health care system and between acute and long-term settings, ensuring timely access to quality care. What Is the Technology? Artificial intelligence (AI)-based patient flow management tools are interventions designed to forecast and monitor patient movement from admission to discharge as they progress through different care settings. AI-driven tools can leverage big data and digital information systems (e.g., electronic health records) to facilitate effective patient flow. AI-based patient appointment scheduling tools, which can help improve patient flow, are created to automate appointment scheduling and optimize it by minimizing wait times and matching the demand for health services and hospital capacity. What Is the Potential Impact? AI tools for patient flow management can support volume forecasting of patients with various conditions, especially those experiencing chronic conditions that require different types of treatment or care in different settings over a long period of time. These AI tools can predict admissions, patient movement from the emergency department to inpatient beds, discharge, and transfers to different health care settings. Evidence for their effectiveness in patients with emergency admissions and those transferred to tertiary and quaternary care, as well as inpatients from the general, cardiology, and mental health departments, was reported. In addition, evidence suggested that AI tools can optimize appointment scheduling in general outpatient settings and operating rooms. In health care systems in Canada, AI tools are being used or investigated to enhance patient flow by predicting emergency admissions, transfers to alternate levels of care, and general inpatient discharges, as well as optimizing capacity planning for patients receiving oncology care. AI appointment scheduling tools are currently being used in some oncology care settings and operating rooms across Canada. The implementation of AI systems generally requires an upfront investment of time and other resources in addition to the financial cost of the system itself for set-up, integration, and staff training. The goal of these systems is to improve efficiency and save money, time, and human resources in the long run. What Else Do We Need to Know? Patient privacy and data security issues are concerns regarding widespread implementation of AI tools trained on electronic health records systems and patient datasets. AI algorithms trained on datasets lacking adequate representation of all relevant patients may not predict their flow accurately. Training datasets with sufficient data from all relevant patient groups can ensure the inputs and outputs of the algorithms accurately reflect patient care needs and mitigate potential bias. To accurately predict the care needs of local patients, AI algorithms, once deployed, should be retrained on site-specific datasets containing data for the patient populations that are representative of the hospital or health system in which they are being used. Not all institutions have the hardware or computing power available to adequately or efficiently process the large amounts of data that are required by these AI systems, or the infrastructure needed to use big data (e.g., from electronic health records), and may require additional resources for implementation, as well as for ongoing maintenance and updating of the systems.

Using data-driven techniques to predict outpatient ultrasound examination time for the multi-clinic outpatient appointment scheduling problem

Appointment scheduling for hospital patients is becoming increasingly important because it can reduce patient’s waiting time and enhance hospitals’ medical quality. In the case of ultrasound examinations, multiple radiological technologists use multiple rooms to conduct examinations, which involves the sequencing of examination work, time uncertainty, and operational characteristics. To enable hospital managers to effectively evaluate radiological technologists’ effort when carrying out ultrasound examinations, this study uses classification methods in data exploration, including decision tree (DT), logistic regression (LR), and artificial neural network (ANN), to establish a time prediction model for outpatient ultrasound examination. This study found that the DT model (i.e. C4.5) had the highest classification accuracy. After reviewing and removing rules generated from the model with an accuracy below 70%, the overall accuracy of the model was 76.31%. The classification results of the model are a reference for outpatient ultrasound appointment scheduling. The results of the study can effectively predict the examination time needed for ultrasound outpatients requiring different examination positions, thereby fairly allocating the patients to each room for the examination and reducing patient waiting time and radiological technologists’ idle time. Therefore, hospital managers can improve the hospital’s overall medical quality.

Revolutionizing Hospital Operations: A Comprehensive Analysis of Hospital Management System Implementation

Hospital Management System (HMS) technology will transform hospital operations. This summary summarizes HMS implementation's main components, goals, advantages, and drawbacks. Patient registration, appointment scheduling, EMR, billing, inventory management, and analytics are integrated into the HMS. Its main goal is to improve hospital efficiency, automate operations, and centralize information management. Implementing an HMS improves patient care by providing rapid access to precise information, operational efficiency, financial procedures, and data accuracy. The challenges include comprehensive staff training, strong data security, and smooth healthcare system connection. Healthcare organizations are developing digitally, making the hospital administration system essential for patient outcomes and hospital administration

IoT-Based Virtual Primary Clinic: Remote Patient Monitoring and Consultation System

Using Internet of Things technology, the Virtual Primary Clinic project is a cutting-edge healthcare endeavor that enables remote patient monitoring and consultation. The project collects essential health metrics including body temperature, oxygen saturation, heartbeat, and ECG signals by integrating a variety of sensors, including NodeMCU ESP32, SpO2, DHT11, and AD8232 ECG. The information collected by these sensors is sent via API to a specially created website and an online platform called ThingSpeak. With features including patient registration, appointment scheduling, access to medical histories, and doctor-patient contact, the website acts as a comprehensive interface between patients and physicians. Patients can register on the website, provide their medical information, and get medications sent to them remotely based on their conditions. However, without having to see patients in person, doctors can examine patient data, diagnose ailments, and write prescriptions or offer medical advice.The Virtual Primary Clinic project intends to improve healthcare accessible, especially in remote or underserved locations, by enabling remote monitoring and consultation. In addition to providing healthcare providers with an effective platform for remote patient care, it gives patients the ability to take charge of their health. This project is a major step toward using cutting-edge IoT-based technologies to improve patient outcomes and democratize healthcare.

“Patient Care System

Patients can register, enter their information into the system, and schedule medical visits through our project's patient care system. Every patient can receive a unique ID from our program, which also automatically saves all of the patient and staff information. Using the ID, the user can look up a doctor's availability and a patient's data. You can log in to the patient care system with your username and password. An administrator or a receptionist can access it. The database can only contain data that they add. It is simple to retrieve the data. It has an extremely user-friendly interface. The patient care system also has features that allow for easy communication between patients and healthcare providers, such as secure messaging and appointment reminders. Additionally, the system can generate reports on patient demographics, appointment scheduling, and medical history, which can be useful for tracking patient progress and improving overall patient care. Overall, our project's patient care system streamlines the healthcare process and enhances the patient experience. Keyword: Admin Module, Doctor module, health care services, user module.

No Patient Left Behind: A Novel Paradigm to Fulfill Hepatitis C Virus Treatment for Rural Patients.

This study evaluates a novel multidisciplinary program providing expanded access to hepatitis C virus (HCV) treatment for rural Appalachian patients in South Carolina. This program identified patients via an opt-out emergency department screening program, and it aimed to achieve HCV cure by using community paramedics (CPs) to link and monitor patients from treatment initiation through 12-week sustained virologic response (SVR). Patients aged ≥18 years who were HCV RNA positive were eligible for enrollment if they failed to appear for a scheduled HCV appointment or reported barriers to accessing office-based treatment. CPs provided home visits (initial and 4, 12, and 24 weeks) using a mobile Wi-Fi hotspot to support telemedicine appointments (compliant with the Health Insurance Portability and Accountability Act) and perform focused physical assessments, venipuncture, and coordinated home delivery of medications. Statistics described participant characteristics, prevalence of SVR, and patient satisfaction results at 12 weeks posttreatment. Thirty-four patients were eligible for SVR laboratory tests by 31 August 2023; the majority were male (61.7%) and White (64.7%) with an average age of 56 years (SD, 11.7). Twenty-eight (82.4%) completed treatment and achieved 12-week SVR. Six (17.6%) were lost to follow-up. Two-thirds strongly agreed that they were satisfied with the overall care that they received, and half strongly agreed that their overall health had improved. This CP-augmented treatment program demonstrated success curing HCV for rural patients who lacked access to office-based treatment. Other health care systems may consider this novel delivery model to treat hard-to-reach individuals who are HCV positive.

The Construction and Application of an Internet-based Hospital-community-family Home Management Model

Objective: Based on the software, this study aims to develop a hospital-community-home management model and evaluate the impact of nursing interventions provided by home visiting nurses, aligning with national healthcare policies in China. Methods: In the Dongying area, hospitals collaborate closely with local communities and families to facilitate the smooth implementation of Internet + Nursing Services. Patients schedule appointments for a range of medical care services through mobile phone software, with staff accessing orders through the platform to provide professional treatment and nursing care to patients. Results: Currently, there are 214 registrants from 50 departments on the platform. From September 2021 to June 2023, a total of 3529 cases were handled the platform. The top 10 order items included heel blood collection (944 cases), graphic consultation (526 cases), neonatal jaundice measurement (463 cases), neonatal hearing screening (418 cases), gastrostomy tube care (368 cases), catheterization care (192 cases), venous blood sampling (155 cases), postpartum mammary gland unblocking (103 cases), surgical wound care (92 cases), PICC catheterization care (57 cases), and pressure tube placement care (57 cases). Conclusion: The hospital has successfully established a hospital-community-family home nursing model with specialist nurses playing a central role. Through the implementation ofInternet + Nursing Service, medical staff are able to provide quality medical and nursing care to patients who are unable to leave their homes, significantly improving patient convenience and conserving medical resources.

The dynamic stochastic container drayage problem with truck appointment scheduling

The dynamic stochastic container drayage problem with truck appointment scheduling

Telemedicine for Screening and Follow-Up of Glaucoma: A Descriptive Study.

Introduction: Glaucoma is a leading cause of irreversible blindness. It is a prevalent disease worldwide, affecting ∼70 million people and expected to reach up to 112 million by 2040. Purpose: The aim of this study is to describe the implementation and initial experience of a telemedicine program to monitor glaucoma and glaucoma suspect patients in a large, integrated health care system during the COVID-19 pandemic. Methods: A retrospective chart review of established glaucoma or glaucoma suspect patients who participated in a telemedicine evaluation at the ophthalmic center of a large, Colombian health care system between June 2020 and April 2023 was conducted. Clinical and sociodemographic variables were analyzed. Generated clinical orders for additional testing, surgical procedures, follow-ups, and referrals, as well as changes in medical treatment, were evaluated. Results: A total of 11,034 telemedicine consults were included. The mean ± standard deviation age of this group was 63 ± 17.2 years and 67% were female. Of the patients who attended teleconsults, 49% were glaucoma suspects and 38.5% were followed with a diagnosis of open-angle glaucoma. After the consult, 25% of patients were referred to a glaucoma specialist, 40% had additional testing ordered, and 8% had a surgical procedure ordered, mainly laser iridotomy (409 cases). Almost a third of patients returned for subsequent telemedicine visits after the initial encounter. Despite some technical difficulties, 99.8% of patients attended and completed their scheduled telemedicine appointments. Conclusions: A telemedicine program aimed to monitor established glaucoma patients can be successfully implemented. Established patients within an integrated health care system have high adherence to the virtual model. Further research by health care institutions and government agencies will be key to expand coverage to additional populations. Clinical Trial Registration Number: CEIFUS 1026-24.

Teleconsultation for Preoperative Anesthesia Evaluation: Identifying Environmental Potentials by Life Cycle Assessment.

Introduction: Teleconsultations for preoperative evaluation in anesthesiology proved to be feasible during the COVID-19 pandemic. However, widespread implementation of teleconsultations has not yet occurred. Besides time savings and economic benefits, teleconsultations in anesthesia may have the potential to reduce CO2 emissions. Methods: We conducted a life cycle assessment based on prospective surveys to assess the potential environmental benefits of preoperative anesthesia teleconsultations in comparison to the status-quo in-person consultations. Within 1 month, all patients presenting at the preoperative anesthesia clinic at RWTH Aachen University Hospital were asked about the distance traveled and mode of transportation to the hospital. The main outcome measure was the potential environmental benefit resulting from the implementation of teleconsultations. Results: In total, 821 out of 981 patients presenting at the anesthesia clinic participated in the survey. Most patients visited on an outpatient basis (62.9%) and traveled by car (81.7%). The median travel distance was 25 km [interquartile range 12-40]. If patients who came to the hospital solely for the anesthesia appointment had scheduled virtual appointments, the emissions of 3.03-ton CO2 equivalents (CO2-eq) could be avoided in the first month after implementation. The environmental impact associated with the production of teleconsultation equipment is outweighed by the reduction in patient travel. If all outpatient appointments were performed virtually, these savings would triple. Within 10 years, more than 1,300 tons CO2-eq could be avoided. Conclusion: Teleconsultations can mitigate the environmental impact of in-person anesthesia consultations. Further research is essential to leverage teleconsultations for preoperative evaluation also across other medical specialties.