Wait times for scheduling appointments with hospital affiliated dermatologists in New York City.

Patients' experience accessing dermatologic care is understudied. The purpose of this cross-sectional study was to examine current wait times for new patients to receive dermatological care in NYC. Websites at 58 accredited private and public hospitals in the five boroughs of NYC were reviewed to identify dermatology practices. Office telephone numbers listed on each website were called to collect information pertaining to whether the physician was accepting new patients, type of insurance accepted (public, private, both, or none), and the number of days until a new patient could be seen for an appointment. Data pertaining to the time kept on hold and availability of web-based booking were also collected. Mean waiting time for an appointment was 50 days [standard deviation, SD 66] - nearly 2 months, but the distribution was considerably skewed. The median waiting time was 19.5 days [Interquartile range, IQR 4-60]. The time kept on hold to make the appointment was negligible at about 1 min (63 s, SD = 77) but could take up to ~ 7 min. Two-thirds of dermatologists accepted private, Medicare, and Medicaid insurance (n = 228, 66%); a small number accepted only private insurance (n = 12, 4%) or no insurance at all (n = 16, 5%). The median waiting time for an appointment for the 228 providers that accepted Medicaid was 30.5 days (IQR = 5.0-73.25) while for providers who did not accept Medicaid (n = 116) the median wait time for an appointment was 13.0 days (IQR = 3.0-38.0). Just over half (56%) of the dermatologists allowed for appointments to be booked on their website (n = 193). This research highlights the necessity of incorporating new strategies into routine dermatology appointments in order to increase treatment availability and decrease healthcare inequality.

Patient Opportunities to Self-Schedule in a Large Multisite, Multispecialty Medical Practice: Program Description and Uptake of 7 Unique Processes for Patients to Successfully Self-Schedule (and Reschedule) Their Medical Appointments.

Introduction: Patient self-scheduling of medical appointments is becoming more common in many medical institutions. However, the complexity of scheduling multiple specialties, following scheduling guidelines, and managing appointment access requires a variety of processes for a diverse inventory of self-schedulable appointment types. Methods: From 7 unique patient self-scheduling methods, we captured counts of successfully self-scheduled and completed appointments. A process map was created to show the paths of 5 different primary self-scheduling processes (new appointment self-scheduling) and 2 secondary self-scheduling processes (existing appointment self-rescheduling). Results: There were 7 unique processes that led to 733,651 successfully self-scheduled completed visits from January 1 to December 31, 2023 at a multisite, multispecialty clinic. The self-scheduling processes consisted of the following: (1) Ticket offer (appointment "ticket" offers for specific visits generated by a provider order or system rules), the software "ticket" sent to the patient permits "admission" to self-schedule calendar templates (341,591 uses, 46.6%); (2) direct self-scheduled visit for prequalified visit types (203,593 uses, 27.6%); (3) self-reschedule option (patient option to reschedule existing appointment, 79,706 uses, 10.9%); (4) new patient self-scheduled visit via clinic website (does not require portal access, 54,367 uses, 7.4%). (5) automated waitlist self-rescheduled visit (38,649 uses, 5.3%); (6) automated waitlist self-scheduled visit of previously unscheduled visit (10,939 uses, 1.5%); and (7) self-triage self-scheduled visit (4806 uses, 0.7%). Conclusion: The processes for self-scheduling are expanding. Our multispecialty clinic has implemented 7 different processes to help patients successfully self-schedule medical appointments. Some of the processes occur before initial scheduling (such as self-triage), and some are implemented after successful scheduling has already occurred (self-rescheduling option and self-rescheduling aided by an automated waitlist). Continued research is needed to look for measures of success beyond the ability to complete a self-scheduled visit, including the accuracy of the booking (right provider, location, and length of visit).

Appointment Blitz: Optimizing Patient-Therapist Rehab Scheduling.

Taiwan has a well-structured healthcare insurance system that offers accessible medical resources to the public through nominal health insurance fees. Consequently, individuals in need of care willingly pay nominal charges for medical services, including rehabilitation treatment. This study delves into the rehabilitation department of a medical center in southern Taiwan. Despite offering comprehensive traditional rehabilitation services covering neurological, musculoskeletal, pediatric, cardiopulmonary, communication, and swallow disorders, the demand for appointments significantly surpasses the number of available therapists. Therefore, this paper proposes an efficiently method to optimize patient-therapist appointment. With a Complex Conditional Logic that we have designed in this paper, we aim to simplify the scheduling processing for patient seeking appointment either online or via phone calls. More than 50,000 cases have been treated since the system's launch within a year, facilitates hospital resource allocation and enhancing patient medical experiences.

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

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

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

BACKGROUND: Scheduling patient appointments in hospitals is complicated due to various types of patient examinations, different departments and physicians accessed, and different body parts affected. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Intraday dynamic rescheduling under patient no-shows.

Patient no-shows are a major source of uncertainty for outpatient clinics. A common approach to hedge against the effect of no-shows is to overbook. The trade-off between patient's waiting costs and provider idling/overtime costs determines the optimal level of overbooking. Existing work on appointment scheduling assumes that appointment times cannot be updated once they have been assigned. However, advances in communication technology and the adoption of online (as opposed to in-person) appointments make it possible for appointments to be flexible. In this paper, we describe an intraday dynamic rescheduling model that adjusts upcoming appointments based on observed no-shows. We formulate the problem as a Markov Decision Process in order to compute the optimal pre-day schedule and the optimal policy to update the schedule for every scenario of no-shows. We also propose an alternative formulation based on the idea of 'atomic' actions that allows us to apply a shortest path algorithm to solve for the optimal policy more efficiently. Based on a numerical study using parameter estimates from existing literature, we find that intraday dynamic rescheduling can reduce expected cost by 15% compared to static scheduling.

Fast and Frictionless: A Novel Approach to Radiology Appointment Scheduling Using a Mobile App and Recommendation Engine.

Many outpatient radiology orders are never scheduled, which can result in adverse outcomes. Digital appointment self-scheduling provides convenience, but utilization has been low. The purpose of this study was to develop a "frictionless" scheduling tool and evaluate the impact on utilization. The existing institutional radiology scheduling app was configured to accommodate a frictionless workflow. A recommendation engine used patient residence, past and future appointment data to generate three optimal appointment suggestions. For eligible frictionless orders, recommendations were sent in a text message. Other orders received either a text message for the non-frictionless app scheduling approach or a call-to-schedule text. Scheduling rates by type of text message and scheduling workflow were analyzed. Baseline data for a 3-month period prior to the launch of frictionless scheduling showed that 17% of orders that received an order notification text were scheduled using the app. In an 11-month period after the launch of frictionless scheduling, the rate of app scheduling was greater for orders that received a text message with recommendations (frictionless approach) versus app schedulable orders that received a text without recommendations (29% vs. 14%, p < 0.01). Thirty-nine percent of the orders that received a frictionless text and scheduled using the app used a recommendation. The most common recommendation rules chosen for scheduling included location preference of prior appointments (52%). Among appointments that were scheduled using a day or time preference, 64% were based on a rule using the time of the day. This study showed that frictionless scheduling was associated with an increased rate of app scheduling.

Towards the Definition of an Intelligent System for Organizing Medical Visits and Collecting Medical Data.

Appointment Scheduling (AS), typically serves as the basis for the majority of non-urgent healthcare services and is a fundamental healthcare-related procedure which, if done correctly and effectively, can lead to significant benefits for the healthcare facility. The main objective of this work is to present ClinApp, an intelligent system able to schedule and manage medical appointments and collect medical data directly from patients.

Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning.

In this work, we examine magnetic resonance imaging (MRI) and ultrasound (US) appointments at the Diagnostic Imaging (DI) department of a pediatric hospital to discover possible relationships between selected patient features and no-show or long waiting room time endpoints. The chosen features include age, sex, income, distance from the hospital, percentage of non-English speakers in a postal code, percentage of single caregivers in a postal code, appointment time slot (morning, afternoon, evening), and day of the week (Monday to Sunday). We trained univariate Logistic Regression (LR) models using the training sets and identified predictive (significant) features that remained significant in the test sets. We also implemented multivariate Random Forest (RF) models to predict the endpoints. We achieved Area Under the Receiver Operating Characteristic Curve (AUC) of 0.82 and 0.73 for predicting no-show and long waiting room time endpoints, respectively. The univariate LR analysis on DI appointments uncovered the effect of the time of appointment during the day/week, and patients' demographics such as income and the number of caregivers on the no-shows and long waiting room time endpoints. For predicting no-show, we found age, time slot, and percentage of single caregiver to be the most critical contributors. Age, distance, and percentage of non-English speakers were the most important features for our long waiting room time prediction models. We found no sex discrimination among the scheduled pediatric DI appointments. Nonetheless, inequities based on patient features such as low income and language barrier did exist.

Small Changes in Patient Arrival and Consultation Times Have Large Effects on Patients' Waiting Times: Simulation Analyses for Primary Care.

(1) Background: Workflows are a daily challenge in general practices. The desired smooth work processes and patient flows are not easy to achieve. This study uses an operational research approach to illustrate the general effects of patient arrival and consultation times on waiting times. (2) Methods: Stochastic simulations were used to model complex daily workflows of general practice. Following classical queuing models, patient arrivals, queuing discipline, and physician consultation times are three key factors influencing work processes. (3) Results: In the first scenario, with patients arriving every 7.6 min and random consultation times, the individual patients' maximum waiting time increased to more than 200 min. The second scenario with random patient arrivals and random consultation times increased the average waiting time by up to 30 min compared to patients arriving on schedule. A busy morning session based on the second scenario was investigated to compare two alternative intervention strategies to reduce subsequent waiting times. Both could reduce waiting times by a multiple for each minute of reduced consultation time. (4) Conclusions: Aiming to improve family physicians' awareness of strategies for improving workflows, this simulation study illustrates the effects of strategies that address consultation times and patient arrivals.

Patient Unpunctuality's Effect on Appointment Scheduling: A Scenario-Based Analysis.

This study examined patient unpunctuality's effect on patient appointment scheduling in the ultrasound department of a hospital. The study created a simulation system incorporating the formulated F3 distribution to describe patient unpunctuality. After the simulation model passed verification and validation processes, what-if scenarios were conducted under two policies: The preempt policy and the wait policy. A comparison of the total cost of each policy showed that the preempt policy performed better than the wait policy in the presence of unpunctuality. The study used sensitivity analyses to identify the different effects of patient unpunctuality on the system. The weights of the cost coefficient of both radiological technician's idle time and patient waiting time must be equal in order to achieve a lower cost. The patient's inter-arrival time must be close to the average total time in the system to achieve lower costs. Moreover, utilization decreases as the patient's inter-arrival increases. Therefore, the patient's inter-arrival time should be higher than, but close to, the service time to ensure less radiological technician's idle time and patient waiting time.

A pull-strategy for the appointment scheduling of surgical patients in a hospital-integrated facility.

This paper addresses the daily appointment scheduling (AS) of patients in a hospital-integrated facility where outpatients and inpatients are treated simultaneously and share critical resources. We propose a lean approach based on the pull-strategy "Constant Work in Process" (ConWIP) to develop robust and easy-to-implement AS rules. Our objective is to reduce patients' waiting time and maximise the use rate of resources while considering the global surgical process and stochastic service times. The AS rules based on ConWIP are evaluated using a Discrete-Event-Simulation model. Numerical experiments based on a real-life case study are carried out to assess the proposed appointment rules' performance and compare them to AS rules developed in the literature. The results highlight the robustness of our approach and demonstrate its usefulness in practice.

Characteristics of Walk-In Patients and Related Factors in a Dental University Hospital.

This study aimed to identify the characteristics of walk-in patients who visited a dental university hospital more than once (returning patient). The factors affecting walk-in visits were analyzed in relation to demographic, appointment, and treatment characteristics among 146,567 cases treated between 1 March 2019 and 29 February 2020. Multiple logistic regression was used to assess factors influencing walk-in visits. The walk-in rate was 14.1%. The likelihood of walk-in visits was higher in men, patients aged 20-39 years, patients residing in Seoul, and hospital employees or their family members. Walk-in visits were more likely to take place from 8:00 to 9:59 and on Saturdays and Mondays. The walk-in odds ratios differed depending on the treatment department and diagnosis. Return patients had higher odds of walk-in visits for treatments not covered by insurance. The probability of being a walk-in patient was lower among patients who also received treatment in other departments on the same day than among those who did not. These results indicate that each examined factor has a predictable pattern. The findings also suggest a relatively high percentage of walk-in cases in dental university hospitals and that walk-in patients differ in their characteristics from patients with appointments.

Strengthening Saudi Arabia's Primary Health Care through an e-Referral System: A Case Study.

Health systems are becoming more complex, regulatory bodies are increasing their vigilance, and reimbursement practices are shifting toward value, making closing the referral loop an imperative for patient safety, regulatory oversight, and financial viability. The aim of this study was to examine the referral pattern in PHC services and whether a significant variation exists among them based on geographic accessibility to a referred hospital. This was a cross-sectional retrospective study that included all sequentially referred patients between 1 January 2019 and 30 December 2021. A pre-initiative comparison could not be performed, as previous data on the traditional referral system could not be collected. The primary outcome measures considered in this study were the referral rate, and the proportion of the documented appointment date. The healthcare facilities' geographic locations and data of the hospital departments to which the patients were referred were also available. Between 2019 and 2021, the hospital received 52,143 referrals from the 9 designated PHC centres covering 34 districts. In the PHC centres located within the ≤13 km zone, 1 in every 14 patients were referred to the hospital, whereas 1 in every 20 patients visited PHC centres outside this zone. Since the introduction of the Ehalati e-referral system, the number of documented appointment schedules of the referred patients has improved over time by 16.1% (from 79.6% to 95.7%, p < 0.001). Ophthalmologic (17.1%) and dental services (15.4%) received the most referrals among all other specialties, whereas the referral rate for cardiology services was the lowest (2.5%). The documented appointment scheduling record of referred patients has improved significantly since the introduction of the Ehalati e-referral system. However, the results of this study indicate that the proximity of PHC centres to specialised hospitals is more likely associated with higher referral and documented appointment scheduling rates. Strategies that improve scheduling, decrease variation among clinics, and improve patient access will likely improve the closing rates of the referral loop.

Smart Scheduling (SMASCH): multi-appointment scheduling system for longitudinal clinical research studies.

Objective: Facilitate the multi-appointment scheduling problems (MASPs) characteristic of longitudinal clinical research studies. Additional goals include: reducing management time, optimizing clinical resources, and securing personally identifiable information. Materials and methods: Following a model view controller architecture, we developed a web-based tool written in Python 3. Results: Smart Scheduling (SMASCH) system facilitates clinical research and integrated care programs in Luxembourg, providing features to better manage MASPs and speed up management tasks. It is available both as a Linux package and Docker image (https://smasch.pages.uni.lu). Discussion: The long-term requirements of longitudinal clinical research studies justify the employment of flexible and well-maintained frameworks and libraries through an iterative software life-cycle suited to respond to rapidly changing scenarios. Conclusions: SMASCH is a free and open-source scheduling system for clinical studies able to satisfy recent data regulations providing features for better data accountability. Better scheduling systems can help optimize several metrics that ultimately affect the success of clinical studies.

Factors Affecting the Patient Journey in Scheduling a Specialist Appointment in a Public Healthcare System.

A questionnaire was developed to evaluate the journey experienced by patients from identifying a need to see a community specialist in Israel's public healthcare system, through scheduling an appointment and attending. A telephone survey was conducted with a nationally representative group of 3751 adults, in 2019 to 2020. Fifty-seven percent needed to see a specialist in the last 6 months; among those, 82%, visited a specialist. Among the 3% who did not make an appointment, in 41 of 52 (79%) cases this was due to long waiting time. Younger and more educated patients were more likely to try to get an earlier appointment. Timeliness (55%) and wanting a specific physician (43%) were major considerations in scheduling. Reported need was greater in females, Jewish versus Arab respondents, more educated and those with chronic illness. Those who did not make an appointment sought private care, emergency treatment, or went untreated. Although a large percentage of respondents did eventually get an appointment, vulnerable patients may have more difficulty navigating the system. Following the patient journey can provide insights to help design services better suited to patients' needs.

Effects of Behavioral Economics-Based Messaging on Appointment Scheduling Through Patient Portals and Appointment Completion: Observational Study.

BACKGROUND: Behavioral economics-based techniques have been an increasingly utilized method in health care to influence behavior change by modifying language in patient communication (through choice architecture and the framing of words). Patient portals are a key tool for facilitating patient engagement in their health, and interventions deployed via patient portals have been effective in improving utilization of preventive health services. OBJECTIVE: We examined the impacts of behavioral economics-based nudge health maintenance reminders on appointment scheduling through a patient portal and appointment completion for 2 preventive services: Medicare wellness visits and Pap smear. METHODS: We conducted a retrospective observational study using electronic health record data from an integrated health care system in Northern California. Nudge health maintenance reminders with behavioral economics-based language were implemented for all sites in November 2017 for Medicare wellness visits and for selected sites in February 2018 for Pap smears. We analyzed 125,369 health maintenance reminders for Medicare wellness visits, and 585,358 health maintenance reminders for Pap smear sent between January 2017 and February 2020. The primary outcomes were rate of appointments scheduled through the patient portal and appointment completion rate. We compared the outcomes between those who received the new, behavioral economics-based health maintenance reminders (the nudge group) and those who received the original, standard health maintenance reminders (the control group). We used segmented regression with interrupted time series to assess the immediate and gradual effect of the nudge for Medicare wellness visits, and we used logistic regression to assess the association of nudge health maintenance reminders, adjusting for the propensity to receive a nudge health maintenance reminder, for Pap smear. RESULTS: The rates of appointments scheduled through the patient portal were higher for nudge health maintenance reminder recipients than those for control health maintenance reminder recipients (Medicare wellness visits-nudge: 12,537/96,839, 13.0%; control: 2,769/28,530, 9.7%, P<.001; Pap smear-nudge: 8,239/287,149, 2.9%; control: 1,868/120,047, 1.6%; P<.001). Rates of appointment completion were higher for nudge health maintenance reminders for Pap smear (nudge: 67,399/287,149, 23.5% control: 20,393/120,047, 17.0%; P<.001) but were comparable for Medicare wellness visits (nudge: 49,835/96,839, 51.5% control: 14,781/28,530, 51.8%; P=.30). There was a marginally gradual effect of nudge on number of appointments scheduled through the patient portal for the overall Medicare wellness visits sample (at a monthly rate of 0.26%, P=.09), and a significant gradual effect among scheduled appointments (at a monthly rate of 0.46%, P=.04). For Pap smear, nudge health maintenance reminders were positively associated with number of appointments scheduled through the patient portal (overall sample: propensity adjusted odds ratio [OR] 1.62; 95% CI 1.50-1.74; among scheduled appointments: propensity adjusted OR 1.61, 95% CI 1.47-1.76) and with appointment completion (propensity adjusted OR 1.07; 1.04-1.10). CONCLUSIONS: Nudges, a behavioral economics-based approach to providing health maintenance reminders, increased the number of appointments scheduled through the patient portal for Medicare wellness visits and Pap smear. Our study demonstrates that a simple approach-framing and modifying language in an electronic message-can have a significant and long-term impact on patient engagement and access to care.

[Integrated care concepts and multidisciplinary process chains in a radiological context]. / Integrierte Behandlungskonzepte und multidisziplinär organisierte Prozessketten im radiologischen Kontext.

Modern patient-centered and cost-efficient care concepts in hospitals require the mapping of multidisciplinary process chains into clinical pathways. Clinical decision support systems and operations research methods use algorithms to classify patients into homogeneous groups and to model a complete clinical pathway for scheduling individual procedures. An improvement of the economic situation of the care facility can be achieved through improved resource utilization, reduced patient waiting times and a shortening of the length of stay. The interdisciplinary use of centrally stored interoperable information and comprehensive care management via information technology (IT) services lay the foundation for the dissolution of traditional IT system architectures in medicine and the development of flexibly integrable modern system platforms. New IT approaches such as the semantically standardized definition of procedures and resource properties, the use of clinical decision support systems and the use of service-oriented system architectures form the basis for the deep integration of radiology services into comprehensive interdisciplinary care concepts.

Using Simulation Optimization to Solve Patient Appointment Scheduling and Examination Room Assignment Problems for Patients Undergoing Ultrasound Examination.

This study investigates patient appointment scheduling and examination room assignment problems involving patients who undergo ultrasound examination with considerations of multiple examination rooms, multiple types of patients, multiple body parts to be examined, and special restrictions. Following are the recommended time intervals based on the findings of three scenarios in this study: In Scenario 1, the time interval recommended for patients' arrival at the radiology department on the day of the examination is 18 min. In Scenario 2, it is best to assign patients to examination rooms based on weighted cumulative examination points. In Scenario 3, we recommend that three outpatients come to the radiology department every 18 min to undergo ultrasound examinations; the number of inpatients and emergency patients arriving for ultrasound examination is consistent with the original time interval distribution. Simulation optimization may provide solutions to the problems of appointment scheduling and examination room assignment problems to balance the workload of radiological technologists, maintain high equipment utilization rates, and reduce waiting times for patients undergoing ultrasound examination.