Assignment Of Patients Research Articles

HOSPITAL MANAGEMENT SYSTEM BASED ON WEB

Hospital Management Systems (HMS) represent a key advancement in healthcare technology designed to revolutionize operational efficiency, raise standards of patient care and ensure compliance with regulatory protocols. This paper explores the multifaceted functions of HMS, highlighting its role in automating administrative tasks, supporting real-time access to patient data, and facilitating seamless communication between healthcare professionals. Drawing on current research and practical implementations, we delve into the transformative potential of HMS, particularly in the context of recent innovations in telemedicine, data analytics, and remote monitoring systems. In our research, we present SIMRS, a robust hospital management information system developed using the waterfall method, strategically focused on the systematic improvement of hospital operations. By automating key processes such as patient registration, appointment scheduling, payment and inventory management, SIMRS optimizes hospital workflows and ultimately improves patient care delivery. In addition, we examine the empirical evidence that shows the significant impact of management systems and leadership traits on the quality of care in hospital settings, and highlight the indispensable role of effective leadership and management frameworks. Next, we discuss the technical foundations of HMS and highlight the integration of various programming languages and database management systems such as HTML, CSS, JavaScript, PHP, MySQL, and MongoDB. This cross-disciplinary approach underscores the synergy between technological innovation and healthcare management, paving the way for a more efficient patient- centered healthcare ecosystem. In summary, this research illuminates the transformative potential of HMS to revolutionize healthcare operations, improve patient outcomes and optimize resource allocation, advocating for its widespread adoption to shape the future of healthcare delivery. Key Words: JavaScript, MySql, HMS, Electronic Health Records, Telehealth

Optimizing Doctor Availability and Appointment Allocation in Hospitals through Digital Technology and AI Integration

Many patients miss their appointments all around the world and many of them don't even cancel at all or don't do so in time due to several reasons. In order to address the widespread issue of medical no-shows, this paper proposes a solution that involves building a machine learning model utilizing patient datasets that are already available. This model will identify patterns and links between various patient factors and the patients' propensity to miss appointments. As a result, based on their information, it is possible to anticipate the chance of a patient appearing. Based on the Support Vector Machines classification technique, the machine learning model created the solution predictive model. Effective healthcare services are vital in today's fast-paced environment. This strategy aims to reduce the distance between patients and medical professionals by offering a workable and friendly solution. For certain medical institutions, such as clinics and hospitals, this initiative makes it easier for patients and customers to schedule doctor appointments online. Using this technology, patients may easily browse a database of doctors' biographies, specializations, and availability. Even the day and time of their choosing can be chosen for appointments. Each patient's appointment request will be scheduled by this doctor's appointment system and forwarded to the physician. The system administrator will update the list of doctors, including their specialties, personal information, and system access credentials. Patients will look for a physician who specializes in their requirements by exploring the doctor's appointment system online. Before making their request, the patient can browse the doctor's weekly schedule to choose a day and time that works best for them. Following that, the physicians have access to all of their appointments as well as the patients' appointment requests, which are prioritized according to their availability. It gives medical professionals a strong tool for successfully managing the schedules, which reduces administrative strain and ensures a positive patient experience.

Evaluating the implementation of group empowerment and training (GREAT) for diabetes in South Africa: convergent mixed methods

ObjectiveType 2 diabetes is a leading contributor to the burden of disease in South Africa. Primary care is struggling to support self-management and lifestyle change. Group empowerment and training (GREAT)...

Breaking down barriers to bariatric care: a qualitative study on how telemedicine could transform patient experiences in a Swiss monocentric setting

ObjectiveTelemedicine is becoming an increasingly feasible option for patients with chronic diseases due to its convenience, cost-effectiveness and ease of access. While there are certain limitations, the benefits can be...

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.

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.

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

“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.

E016 An audit on patient initiated follow up in rheumatology in Manchester Royal Infirmary

Abstract Background/Aims PIFU is not a new concept in rheumatology. Instead of arranging regular routine appointments for patients with stable diseases, PIFU empowers patients to seek help when needed, enabling them to take control of their care and reduce pressure on rheumatology services. NHS England recommends PIFU for rheumatology patients with stable inflammatory arthritis. They should also have a good understanding of their disease management and be able to contact the service promptly. If patients on PIFU contact the service and require face-to face appointments, they should be seen within 2-4 weeks. Despite existing recommendations, there appears to be wide variation between clinicians and managers on the understanding of PIFU. The aim of this audit is to compare diagnoses of rheumatology patients for PIFU in a single rheumatology department, and the waiting time for review once patients placed on PIFU have made contact, to NHS England standards. Methods A list of rheumatology patients placed on PIFU between October 2022 and July 2023 at Manchester Royal Infirmary was obtained. Data were collected from electronic patient records. Results A total of 130 (1.5%) patients were listed on PIFU by 18 clinicians. The table outlines the demographics and detailed breakdown of diagnoses1. 1 (1%) was put on PIFU after patient declined biologic treatment for active inflammatory arthritis. 3 patients contacted the department whilst on PIFU, 2 of them required face-face review. Median time from start of PIFU to patient-initiated contact was 60 days (IQR 47.5-107.5). One with palindromic rheumatism was seen at 6 weeks and one with osteoarthritis had no appointment date yet 8 weeks after initial contact. Conclusion The audit confirmed that only 22 (17%) patients followed NHS England guidelines and 0% was seen as recommended once the patient contacted the department. Currently, 1.5% of patients in the rheumatology department are placed on PIFU, which is below 5% as per NHS England guidelines. This audit highlights the urgency to educate clinicians and managers on the cohort of patients listed on PIFU and to ensure all these patients are seen within the recommended time. Work is being done in the department to address these issues. Disclosure A. Cheung: None. G. Nowell: None. S. Ling: None. Y. Chiu: None. P. Ho: None.

The relationship between practice ownership and follow-up of comprehensive dental care. A Swedish register study.

The aims of this register study were: 1. To study whether the type of ownership of the dental practice was correlated with the type of dental care provided, that is public versus private ownership and professional (dentist or dental hygienist) versus non-professional ownership. 2. To study the extent of follow-up of patients who have undergone two types of treatments. Material and method: Two types of dental care were defined in the two groups studied, periodontitis/peri-implantitis and comprehensive restorative/rehabilitation. All relevant treatment codes that fall under these definitions are noted when they are performed. Also, the follow-up of each treatment code is noted. Differences in dental and socioeconomic status over time and between regions were adjusted for. A drop-out analysis was performed. Results: Dental practices owned by dentists or dental hygienists schedule follow-up appointments for patients who have undergone comprehensive restorative or rehabilitation dentistry more often than practices with other types of ownership. Dental practices owned by dentists or dental hygienists follow up patients with periodontitis and peri-implantitis less frequently. Type of ownership of a dental business influences the extent to which periodontal, and comprehensive restorative or rehabilitation dentistry were followed up.

Insurance Status and Access to Otolaryngology Care: National Mystery Caller Study in the United States.

To investigate potential differences in new patient appointment wait times for otolaryngology care based on insurance types and explore factors influencing these wait times. A cross-sectional audit study, using a "mystery caller" approach, analyzed with a linear mixed Poisson model to adjust for confounding factors. A total of 612 physicians across 49 states and the District of Columbia, representing 6 otolaryngology subspecialties, were included. Otolaryngology physicians were contacted by mystery callers via telephone with scripted clinical vignettes as patients with either Medicaid or Blue Cross/Blue Shield (BCBS) insurance. Callers requested next available appointment. Wait times for new patient appointments were recorded and analyzed in R using a generalized linear mixed Poisson model. A total of 1183 of 1224 calls reached a representative. Medicaid patients waited 5.73% longer (P < .001) compared to BCBS patients (IRR: 1.06; confidence interval [CI]: 1.03-1.09; P < .001), with respective mean wait times of 36.8 days (SE ± 1.6) and 32.4 days (SE ± 1.6). Longer waiting times were also associated with physicians affiliated with universities (P = .001) and certain subspecialties, such as pediatric otolaryngology (P < .001) and neurotology (P = .008). Regional differences were also observed, with specific AAO-HNS regions showing shorter wait times. The model achieved a conditional R-squared value of 0.947. This study reveals disparities in wait times for otolaryngology care based on insurance type, with extended wait times for Medicaid beneficiaries. The findings highlight a potential access to care disparity, which begets the need for strategies that ensure equitable access to otolaryngology care and further research to understand the underlying reasons for these potential disparities.

Telehealth in a Pelvic Floor Physical Therapy Clinic: A Retrospective Cohort Study.

Telehealth offers advantages to patients with pelvic floor disorders because they face unique barriers to care; however, attendance of telehealth appointments is unknown. The objective of this study was to examine the attendance of telehealth appointments in urogynecology patients receiving pelvic floor physical therapy as compared with in-person visits before and during the COVID-19 pandemic. We retrospectively collected electronic medical record data from patients engaging in pelvic floor physical therapy from 2019, and pre- and post-COVID-19 in 2020. Information included appointment type, attendance, age, primary diagnoses, insurance status, and zip code. Cohort differences were examined using the χ2 test and analyses of variance. Our sample included 359 individuals scheduled for in-person visits in 2019, 57 for telehealth visits in 2020, and 283 for in-person visits in 2020. Patients scheduled for telehealth appointments were younger (39 ± 13 years) than patients in 2019 (45 ± 14 years) or 2020 (42 ± 14 years) in-person cohorts (χ2 (2, 696) = 6.8, P < 0.001). Patients attended telehealth appointments at higher rates (73.7%) than in-person visits in 2019 (56.8%) and 2020 (45.6%; χ2 (2, 699) = 26.2, P < 0.001). Attendance did not differ across primary diagnoses. Proximity based on zip code was not associated with attendance. Pelvic floor physical therapy attendance rates were highest for patients with telehealth visits as compared with in-person visits. Our findings encourage health care providers to continue or begin to offer telehealth visits for pelvic floor physical therapy for the urogynecology patient population.

A Policy Examination of Covid-19 Impact on the Radiology Department Standard Operating Procedures (SOPs): The Malaysian Experience

The purpose of this paper is to review the new management policy in medical imaging of the Covid-19 post-pandemic transition. This paper discussed the Standard Operating Procedure (SOP) introduced by the Ministry of Health (MoH) Malaysia to prevent and control intrahospital transmissions of Covid-19. A conceptual framework is proposed to highlight the key areas in the national SOP for preventing Covid-19 intrahospital transmissions in the radiology department. The key areas were classified into four categories: planned requests (patient appointments), (ii) open-access management (walk-in patient workflow and the triage system), (iii) direct contact (during radiology procedures), and (iv) exit policy and disinfection (post imaging conduct). The paper ends with a summary of diagnostic imaging classifications based on chest radiographs (CXR) and Computed Tomography (CT) images of suspected and confirmed Covid-19 patients. The Covid-19 SOP for the radiology department by the MoH was found to retain most of the patient quarantine and isolation guidelines by the Centre for Disease Control and Prevention (CDC) and incorporated several international policies on patient triage and disinfection of radiological equipment. The majority of the SOP is also sustained, like the SOP during the pandemic, except for the SOP that has been proven to be insignificant by recent research. The Covid-19 SOP for the radiology department plays an important role in reducing the intrahospital spread of Covid-19, with some areas needing improvement. Health workers in the radiology department should continue implementing the Covid-19 SOP and increase their knowledge in identifying Covid-19 signs on radiographic images to help safeguard themselves and the patients from intrahospital transmissions.

Missed Screening Mammography Appointments: Patient Sociodemographic Characteristics and Mammography Completion After 1 Year

ObjectivePatients who miss screening mammogram appointments without notifying the health care system (no-show) risk care delays. We investigate sociodemographic characteristics of patients who experience screening mammogram no-shows at a community health center and whether and when the missed examinations are completed. MethodsWe included patients with screening mammogram appointments at a community health center between January 1, 2021, and December 31, 2021. Language, race, ethnicity, insurance type, residential ZIP code tabulation area (ZCTA) poverty, appointment outcome (no-show, same-day cancelation, completed), and dates of completed screening mammograms after no-show appointments with ≥1-year follow-up were collected. Multivariable analyses were used to assess associations between patient characteristics and appointment outcomes. ResultsOf 6,159 patients, 12.1% (743 of 6,159) experienced no-shows. The no-show group differed from the completed group by language, race and ethnicity, insurance type, and poverty level (all P < .05). Patients with no-shows more often had: primary language other than English (32.0% [238 of 743] versus 26.7% [1,265 of 4,741]), race and ethnicity other than White non-Hispanic (42.3% [314 of 743] versus 33.6% [1,595 of 4,742]), Medicaid or means-tested insurance (62.0% [461 of 743] versus 34.4% [1,629 of 4,742]), and residential ZCTAs with ≥20% poverty (19.5% [145 of 743] versus 14.1% [670 of 4,742]). Independent predictors of no-shows were Black non-Hispanic race and ethnicity (adjusted odds ratio [aOR], 1.52; 95% confidence interval [CI], 1.12-2.07; P = .007), Medicaid or other means-tested insurance (aOR, 2.75; 95% CI, 2.29-3.30; P < .001), and ZCTAs with ≥20% poverty (aOR, 1.76; 95% CI, 1.14-2.72; P = .011). At 1-year follow-up, 40.6% (302 of 743) of patients with no-shows had not completed screening mammogram. DiscussionScreening mammogram no-shows is a health equity issue in which socio-economically disadvantaged and racially and ethnically minoritized patients are more likely to experience missed appointments and continued delays in screening mammogram completion.

Evaluation of a student telehealth rotation at the Columbia University College of Dental Medicine.

This study aimed to assess the impact of a telehealth rotation on the perceived benefits and challenges of telehealth use among third-year dental students at the Columbia University College of Dental Medicine (CDM). A newly implemented weeklong telehealth rotation at CDM engaged all 95 third-year dental students from the 2022 graduating class through assigned readings and virtual interaction with patients. The telehealth rotation involves virtual visits with patients for screening and triage. An ongoingretrospective assessment of the students' post-rotation evaluation was analyzed qualitatively to identify emerging themes and was coded to describe students' attitudes and beliefs about telehealth. Students reported that the greatest benefit of telehealth is logistical convenience, while the greatest challenge to telehealth is related to the use of technology. After the telehealth rotation, students reported improvement in their clinically relevant skills and knowledge, including patient education, facilitating access to care, and communication skills. The skills reported by students as the most important learning takeaway from the telehealth rotation are applicable to many aspects of care delivery, beyond the scope of telehealth. The incorporation of telehealth education into dental curricula allows students to improve essential clinically relevant skills and knowledge beyond the procedural skills emphasized during in-person patient appointments.

Patient characteristics and telehealth are associated with attendance rates in an outpatient rehabilitation infant bridge program.

To describe the characteristics of patients who received outpatient therapy services through an infant bridge program using telehealth mode of service delivery and to identify if attendance rates vary by mode of service delivery. We hypothesized that telehealth visits will increase attendance rates. Retrospective, cross-sectional study. UCSF Benioff Children's Hospital outpatient infant bridge program. Eighty infants with a history of NICU admission and scheduled for a therapy appointment between June 1, 2019 and December 31, 2020 were included in the study. Participants had an average(SD) gestational age of 34.63(4.41) weeks and length of stay was 43.55(56.03) weeks. The majority were English-speaking (96.3%), White (37.5%), and had commercial insurance (72.5%). Descriptive analyses were conducted across the entire group along with service delivery model subgroup analysis. Logistic regression was performed to assess patient characteristics associated with attendance and if service delivery model influences attendance. In the analysis of 596 scheduled visits, there were more completed telehealth sessions than for in-person sessions (90.0% versus 84.1%, p = .011). For in-person sessions, infants (N = 40) with lower birth gestational ages (p = .009), longer length of stay (p = .041), and Medi-Cal insurance (p = .006) were more likely to have ≥2 missed appointments. For the telehealth sessions, infants (N = 40) who had longer length of stay (p = .040) were more likely to have ≥2 missed appointments. There is a higher likelihood of ≥2 missed appointments for patients with a longer length of stay (OR = 1.02, 95% CI [1.01, 1.03]) and for in-person service delivery when compared to telehealth (OR = 6.25, 95% CI [1.37, 28.57]). Telehealth was associated with higher likelihood of attendance, revealing that telehealth has the potential to increase access to early therapy services for certain populations. Future studies with larger sample sizes to determine which populations benefit from telehealth is recommended.

Wait Times for Scheduling Appointments for Prevention of Macrovascular and Microvascular Complications of Diabetes: Cross-Sectional Descriptive Study.

Diabetes is a chronic disease that requires lifelong management and care, affecting around 422 million people worldwide and roughly 37 million in the United States. Patients newly diagnosed with diabetes must work with health care providers to formulate a management plan, including lifestyle modifications and regular office visits, to improve metabolic control, prevent or delay complications, optimize quality of life, and promote well-being. Our aim is to investigate one component of system-wide access to timely health care for people with diabetes in New York City (NYC), namely the length of time for someone with newly diagnosed diabetes to obtain an appointment with 3 diabetes care specialists: a cardiologist, an endocrinologist, and an ophthalmologist, respectively. We contacted the offices of 3 different kinds of specialists: cardiologists, endocrinologists, and ophthalmologists, by telephone, for this descriptive cross-sectional study, to determine the number of days required to schedule an appointment for a new patient with diabetes. The sampling frame included all specialists affiliated with any private or public hospital in NYC. The number of days to obtain an appointment with each specialist was documented, along with "time on hold" when attempting to schedule an appointment and the presence of online booking capabilities. Of the 1639 unique physicians affiliated with (private and public) hospitals in the 3 subspecialties, 1032 (cardiologists, endocrinologists, and ophthalmologists) were in active practice and did not require a referral. The mean wait time for scheduling an appointment was 36 (SD 36.4; IQR 12-51.5) days for cardiologists; 82 (SD 47; IQR 56-101) days for endocrinologists; and 50.4 (SD 56; IQR 10-72) days for ophthalmologists. The median wait time was 27 days for cardiologists, 72 days for endocrinologists, and 30 days for ophthalmologists. The mean time on hold while attempting to schedule an appointment with these specialists was 2.6 (SD 5.5) minutes for cardiologists, 5.4 (SD 4.3) minutes for endocrinologists, and 3.2 (SD 4.8) minutes for ophthalmologists, respectively. Over 46% (158/341) of cardiologists enabled patients to schedule an appointment on the web, and over 55% (128/228) of endocrinologists enabled patients to schedule an appointment on the web. In contrast, only approximately 25% (117/463) of ophthalmologists offered web-based appointment scheduling options. The results indicate considerable variation in wait times between and within the 3 specialties examined for a new patient in NYC. Given the paucity of research on wait times for newly diagnosed people with diabetes to obtain an appointment with different specialists, this study provides preliminary estimates that can serve as an initial reference. Additional research is needed to document the extent to which wait times are associated with complications and the demographic and socio-economic characteristics of people served by different providers.

A Survey Paper on Integrated Hospital Management System

Hospital Management Systems (HMS) are essential tools in modern healthcare, focusing on efficiency and streamlined operations. Key functionalities include patient registration, appointment scheduling, and doctor availability search. Accessible to administrators, doctors, and patients, HMS offers a customizable, user-friendly interface while emphasizing data protection and fast processing. Ongoing advancements promise to enhance the patient experience and diagnostic procedures, solidifying HMS's role in patient centric healthcare delivery.

Abstract 6078: Circulating tumor cell-based early detection of breast cancer

Abstract Although mammograms have been a useful tool since the first half of the 20th century and have an acceptable sensitivity (87%) in the general population, that drops to &amp;lt;50% in women with dense breasts making it an ineffective screening tool in this population. Cell free DNA (cfDNA) has proven to be an inadequate alternative in this population, and while MRI is acceptable, it is expensive. Astrin Biosciences has developed a blood-based assay that detects circulating tumor cells (CTCs) at a high sensitivity to serve as an alternative screening method to mammography in breast cancer early detection, specifically in women with high-density breasts. Astrin’s technology integrates holographic imaging, machine learning, and microfluidics to identify CTCs in the blood stream. Omics analysis can then be performed on the collected cells to further differentiate patients with cancer from matched healthy controls. Astrin has initiated a small cohort study to investigate the sensitivity and specificity of CTC detection in women with early-stage breast cancer. Astrin’s small cohort study, including patients with ductal carcinoma in situ, stage 1, and stage 2 breast cancer patients, demonstrated &amp;gt; 85% sensitivity and specificity. A large cohort study is being conducted to validate these preliminary results. The small cohort study demonstrates the ability of Astrin Bioscience’s platform to detect cancer cells at an early stage, particularly in breast cancer. This technology will drastically increase the effectiveness of breast cancer screening modalities available to patients. Additionally, since it is a blood-based assay, it can be performed at shorter intervals in high-risk populations than mammograms which require a full patient appointment. Citation Format: Justin M. Drake, Kaylee J. Kamalanathan, Catalina Galeano-Garces, Nathaniel Bristow, Nicholas Heller, Mahdi Ahmadi, Olivia Hedeen, Alexa Hesch, Grant Schaap, Joel Hapke, Jeff Miller, Ivo Babris, Tuan Le, Tony Clacko, Jiarong Hong, Badri Konety, Jayant Parthasarathy. Circulating tumor cell-based early detection of breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6078.

Developing a prototype system of computer-aided appointment scheduling: A radiology department case study.

Scheduling patient appointments in hospitals is complicated due to various types of patient examinations, different departments and physicians accessed, and different body parts affected. This study focuses on the radiology scheduling problem, which involves multiple radiological technologists in multiple examination rooms, and then proposes a prototype system of computer-aided appointment scheduling based on information such as the examining radiological technologists, examination departments, the patient's body parts being examined, the patient's gender, and the patient's age. The system incorporated a stepwise multiple regression analysis (SMRA) model to predict the number of examination images and then used the K-Means clustering with a decision tree classification model to classify the patient's examination time within an appropriate time interval. The constructed prototype creates a feasible patient appointment schedule by classifying patient examination times into different categories for different patients according to the four types of body parts, eight hospital departments, and 10 radiological technologists. The proposed patient appointment scheduling system can schedule appointment times for different types of patients according to the type of visit, thereby addressing the challenges associated with diversity and uncertainty in radiological examination services. It can also improve the quality of medical treatment.