Patient Wait Research Articles

Analysis of Density and Patient Wait Times in Terms of System Management in Turkish Hospitals: Setting A Pattern By Days and Hours of the Week

The time spent waiting is an important problem regarding patient satisfaction and hospital efficiency, and increases the need for evidence-based information for management to make decisions towards a solution. This study aims to examine the process flows in hospitals based on actual data with the intent to design a better service delivery system and set a pattern in terms of determining the congestion in the process and planning the necessary improvements. For this purpose, the data sets for 2016 pertaining to secondary and tertiary level hospitals in Turkey, which are stored in the central physician appointment system (CPAS), are investigated. The data is analyzed through pre-processing, cleaning and transformation stages. Congestion patterns are determined by days and hours spent in hospitals. Monday is found to be the day with the highest patient density and the longest wait time in Turkish hospitals. It is observed that wait times are shorter on Wednesday in the middle of the week, while they are longer on Friday, the last working day. Additionally, when analyzed by working hours, it is determined that the first 2 hours in the morning (9.00 a.m. to 11.00 a.m.) is the period when most patients are examined. The lunchtime (between 12.00 p.m. - 1.00 p.m.) and the afternoon from 4.00 p.m. to 5.00 p.m. are the times when patient density is the lowest, but average wait time is the longest. Turkish hospitals are found to be particularly congested on some days and during some hours regarding patient wait times. Thus, policy recommendations can be developed specifically to the days and times when congestion patterns are identified rather than suggesting a general policy. This study is the most comprehensive study conducted in Turkey through process data. The working model is reproducible in different countries and regions.

Analysis of patient demographics and attendance at a Far North Queensland public dermatology outpatient clinic.

There is limited literature focussing on the demographics of patients presenting to public dermatology outpatient clinics, with an even smaller amount focussing on clinics in regional Australia. This study presents the first patient demographic analysis for the Cairns Hospital's Dermatology Outpatient Department and analyses the cohort of patients who did not attend their appointments. In doing so, it recommends potential strategies that should be considered to address the issues of patient absenteeism and wait times in a regional setting, while also suggesting future data points that should be collected for analysis. A 4-year retrospective cohort study using demographic data from all referrals with medical officers (N = 10,333) from 1 January 2018 to 31 December 2021 at the Cairns Hospital Dermatology Outpatient Department. The hospital is the only facility with a dermatology service within the Cairns and Hinterland Hospital and Health Service. Data were extracted from the Cairns Hinterland Analytical Intelligence (CHAI) system. Data pertaining to patient demographics, attendance of appointments, triage categories and wait times were collected and reviewed for patients referred during the study period. The Dermatology Outpatient Department services an ever-growing and diverse patient cohort. Barriers to access and long wait times exist for patients referred to the Department. Strategies to combat these issues, such as an increase in funding and resourcing, should be considered to better optimise patient care and the utilisation of health resources.

Improving Safety and Quality With an Emergency Department Overcrowding Plan

IntroductionEmergency department overcrowding is a concern that predates the recent coronavirus disease pandemic. Overcrowding in the emergency department continues to worsen internationally. There are multiple combined strategies that help to maintain quality and safety by reducing patient wait times, left-without-being-seen rates, and the length of time a patient stays in the emergency department. The objective of the project was to use an interdisciplinary team to strengthen and revise the emergency department overcrowding plan to reduce the patient wait times, length of stay, and the left-without-being-seen rates. MethodsThe quality improvement team used interprofessional collaboration to focus on 3 areas of the emergency response plan. The team automated an instrument to measure overcrowding in the emergency department, developed a tiered response plan to overcrowding, and implemented a standardized multidisciplinary paging protocol. ResultsThe emergency department overcrowding plan resulted in a 2.7% decrease in the left-without-being-seen rates, a 42-minute (14.5%) decrease in median emergency department length of stay, and a 3.56-hour (33.3%) decrease in daily overcrowding. DiscussionEmergency department overcrowding is influenced by a multitude of factors. The development and implementation of an efficient and effective overcrowding plan have significant value for patient quality and safety as well as health system planning. An effective response to emergency department overcrowding is a pre-established plan that incrementally uses system-wide resources to support emergency department functions as the census and patient acuity fluctuate.

Anecdotes Drive Attitudes, Data Drives Decisions: Optimizing the Emergency Department Workup Prior to Surgical Consultations

Emergency department (ED) wait times and ED length of stay (LOS) have a significant impact on patient morbidity and mortality and patient satisfaction. Consultation-to-decision time can contribute to increased wait times and LOS in the ED. Up to 40% of patients presenting to the ED require consultation from subspecialty services. We hypothesize that in surgical patients, completion of workup prior to consultation will decrease consultation-to-decision time, ED wait times, and LOS in the ED. A retrospective review was conducted at a single site including all overnight general surgery consultations from the ED over 2-months. Data collected included wait times, LOS, and workup completed prior to consultation. Summary statistics were calculated and bivariate tests were performed using t-tests for continuous variables. Time to final surgical plan and LOS in ED were evaluated for 137 patients comparing "complete" and "incomplete" workups at time of consultation. It was considered a "complete" workup if labs and imaging were resulted prior to time of consult. If any baseline tests were not ordered prior to time of consult, it was considered an "incomplete" workup. Analysis demonstrated an average time of 4.9 and 2.5 hours for consultation-to-decision time for "incomplete" and "complete" workups respectively (p < 0.0001). For LOS in ED, there was an average of 11.4 and 7.9 hours for "incomplete" and "complete" workups respectively (p < 0.0001). There is a significant difference in consultation-to-decision time and LOS in the ED when consultation is performed following a complete versus incomplete workup for surgical patients independent of inherent wait times for testing to result and the need for additional testing requested from consulting services. Developing strategies to optimize workups from the ED prior to surgical consultation, including the development of care pathways, could significantly decrease patient wait times and LOS in the ED.

Patient Waiting Time at Primary Healthcare Centers in Riyadh Region, Saudi Arabia: A Cross-sectional Study

BackgroundPatient waiting time can adversely affect health services utilization and continuity of care, impacting patient satisfaction. In Saudi Arabia, there is limited information about the average waiting time at primary healthcare centers. Therefore, this study quantifies patient waiting time for services offered in Ministry of Health primary healthcare centers in Saudi Arabia.Materials and methodsA cross-sectional study utilizing secondary data from the Health Information System at primary healthcare centers in Riyadh was conducted in December 2020. Ten primary healthcare centers were selected. All patients visiting the selected centers during the study period were included, except those who needed emergency care or came for services without seeing the physician. Data regarding patient waiting time in minutes for different stages of primary healthcare services were collected.ResultsThe median total waiting time was 23 min. The median waiting time for pre-consultation and consultation were 6.00 and 6.78 min, respectively. The waiting time for pharmacies in rural primary healthcare centers was 22.1 min, while there was no recorded information regarding pharmacy waiting time in urban primary healthcare centers. The total waiting time at urban primary healthcare centers was longer than at rural primary healthcare centers, with a significant difference between both groups (t = − 15.5, P < 0.001). Patient age, marital status, educational level, occupation, and weekdays were significant factors influencing waiting time (P < 0.05).ConclusionThis study reveals positive outcomes regarding the average waiting time in primary healthcare centers in the Riyadh region. Further primary research is recommended.

Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients.

Early bed assignments of elective surgical patients can be a useful planning tool for hospital staff; they provide certainty in patient placement and allow nursing staff to prepare for patients' arrivals to the unit. However, given the variability in the surgical schedule, they can also result in timing mismatches-beds remain empty while their assigned patients are still in surgery, while other ready-to-move patients are waiting for their beds to become available. In this study, we used data from four surgical units in a large academic medical center to build a discrete-event simulation with which we show how a Just-In-Time (JIT) bed assignment, in which ready-to-move patients are assigned to ready-beds, would decrease bed idle time and increase access to general care beds for all surgical patients. Additionally, our simulation demonstrates the potential synergistic effects of combining the JIT assignment policy with a strategy that co-locates short-stay surgical patients out of inpatient beds, increasing the bed supply. The simulation results motivated hospital leadership to implement both strategies across these four surgical inpatient units in early 2017. In the several months post-implementation, the average patient wait time decreased 25.0% overall, driven by decreases of 32.9% for ED-to-floor transfers (from 3.66 to 2.45 hours on average) and 37.4% for PACU-to-floor transfers (from 2.36 to 1.48 hours), the two major sources of admissions to the surgical floors, without adding additional capacity.

An enhanced grey wolf optimizer boosted machine learning prediction model for patient-flow prediction

Large and medium-sized general hospitals have adopted artificial intelligence big data systems to optimize the management of medical resources to improve the quality of hospital outpatient services and decrease patient wait times in recent years as a result of the development of medical information technology and the rise of big medical data. However, owing to the impact of several elements, including the physical environment, patient, and physician behaviours, the real optimum treatment effect does not meet expectations. In order to promote orderly patient access, this work provides a patient-flow prediction model that takes into account shifting dynamics and objective rules of patient-flow to handle this issue and forecast patients' medical requirements. First, we propose a high-performance optimization method (SRXGWO) and integrate the Sobol sequence, Cauchy random replacement strategy, and directional mutation mechanism into the grey wolf optimization (GWO) algorithm. The patient-flow prediction model (SRXGWO-SVR) is then proposed using SRXGWO to optimize the parameters of support vector regression (SVR). Twelve high-performance algorithms are examined in the benchmark function experiments' ablation and peer algorithm comparison tests, which are intended to validate SRXGWO's optimization performance. In order to forecast independently in the patient-flow prediction trials, the data set is split into training and test sets. The findings demonstrated that SRXGWO-SVR outperformed the other seven peer models in terms of prediction accuracy and error. As a result, SRXGWO-SVR is anticipated to be a reliable and efficient patient-flow forecast system that may help hospitals manage medical resources as effectively as possible.

Access to Total Knee Arthroplasty for Military Insured Patients.

Rigorous training may lead to increased rates of knee osteoarthritis and arthroplasties in military service members. Given the large numbers of arthritis and the increasing need for total joint replacements, access to appropriate care can be difficult for this population based on insurance restrictions. The aim of this study was to evaluate access to total knee arthroplasty for TRICARE patients in contracted civilian medical facilities. Orthopedic surgeons contracted to perform total knee replacements in the state of Florida were identified via TRICARE's website. Investigators used a secret shopper methodology with a standardized script to request an appointment for their family member for a total knee arthroplasty using either TRICARE Select or BlueCross preferred provider organization. The appointment acceptance rates, wait times, call duration, and accuracy of the physician listing were collected. A total of 228 offices that perform total knee arthroplasties in Florida were successfully contacted. Overall, 43.1% of the clinics had an inaccurate online listing, and 207 (91%) were able to schedule an appointment with TRICARE, compared to 93% for BlueCross Blue Shield (P= .06). The average wait for TRICARE patients was 24 days and 18 days for BlueCross (P < .01). Call times for TRICARE patients averaged 7.2 minutes, compared to 5.2 minutes for BlueCross (P < .01). TRICARE patients encountered longer waiting periods and inaccurate provider listings when accessing orthopedic care. Our results suggest a disparity in healthcare access for patients using TRICARE, which may result in negative health outcomes from receiving delayed care.

(513) Search Trends Signal Increased Vasectomy Interest in States with Sparsity of Urologists After Overrule of Roe vs. Wade

Abstract Introduction The Supreme Court of the United States’ (SCOTUS) decision to overrule Roe vs. Wade has rendered abortion illegal across a multitude of states. This decision, which directly impacts the management of women’s reproductive health, may also impact urologic practice. Objective Over the last decade, the number of vasectomies performed in the United States has steadily declined across all age groups. With the anticipated lack of reliable access to medical contraception and abortions, we may see a sudden reversal of this trend. Herein, we aim to provide a trend forecast using Google search trends as a surrogate for public interest in vasectomy. Methods To establish a baseline and quantify changes in search trends, Google search trends (https://trends.google.com/trends/?geo=US) data from three months prior to and three days after the SCOTUS decision (03/25/2022 – 06/29/2022) were queried for “Vasectomy.” Search trend results are reported using Google’s relative search volume (RSV) metric, which normalizes the data to the time and location of a query. RSV ranges from 0 – 100 and is stratified by state. For each state, we calculated the ratio of licensed Urologists to adult men (ages 18+) using data from the American Urological Association 2021 census and the United States 2020 census. Lastly, we referenced The New York Times to sort states based on legal status of abortion2. States were categorized as either Prohibited (abortion banned or expected to be), Restricted (gestational limit in effect or expected to be), Uncertain (decision based on 2022 ballot), or Legal. Spearman’s correlation was conducted to assess the relationship between RSV and Urologist ratio. ANOVA was used to calculate differences between legal status categories. All analyses were conducted using SPSS Version 27 and statistical significance was set at P-value &amp;lt; 0.05. Results We found that RSV was highest in states with the lowest ratio of urologists to adult men (R = -0.36, P=0.017; Table 1). Figure 1 represents the inverse relationship between RSV and urologist-to-adult-men ratio. Following the SCOTUS’ decision, interest in vasectomies was greatest in Oklahoma (RSV = 100), Utah (96), and Idaho (92) and lowest in New York (45), Delaware (47), and New Jersey (50). The ratio of Urologist-to-adult-men (per 100,000) was highest in New York (14.73), Massachusetts (14.20), and New Hampshire (13.52), and lowest in Nevada (7.01), New Mexico (7.33), and Idaho (7.82). ANOVA analysis revealed a significant difference in mean RSV based on legal status of abortion. The difference was greatest between Prohibited states and Legal states (mean RSV 78.5 vs. 64.2, P&amp;lt;0.001). Conclusions The field of Urology continues to face a shortage of physicians in the US. This shortage is most palpable in states where recent public interest for vasectomies is highest (and abortion is currently prohibited). The amalgam of high public interest in vasectomies, low urologist-to-adult-male ratio, and abortion prohibition is anticipated to put a strain on the urologic workforce and subsequently increase patient wait times; particularly in areas where the density of Urologists is lowest. Disclosure No

Comparison of Resuspension Techniques on the Cellular and Cytokine Characteristics of Platelet Rich Plasma: A Pilot Study

Background: Platelet Rich Plasma (PRP) is increasingly used for a variety of musculoskeletal ailments such as tendinopathy and arthritis. There are numerous protocols for preparing PRP, but to our knowledge there have been no studies to assess the impact that re-suspension techniques may have on the cellular characteristics and growth factor expression of the final PRP product. Hypothesis: The type of re-suspension technique used will alter the platelet characteristics and growth factor expression of PRP. Study Design: Descriptive laboratory study. Methods: Blood was collected from nine healthy volunteer donors between ages 26-60. Informed consent and ethics approval was obtained. A double spin technique was used resulting in a concentrated PRP product. Three commonly employed re-suspension techniques were utilized to re-suspend the final PRP product and the cellular characteristics and growth factor expression were analyzed using a reproducible protocol. This study was completed over a single day. Results: All three re-suspension techniques resulted in a 4.6-5.2-fold increase in platelet concentration with no difference between groups (p=0.31). This is consistent with the recommended PRP concentration in published literature. Each re-suspension technique also demonstrated in increase in concentration of PDGF and VEGF with no difference between groups (p=0.81 and p=0.97). Conclusion: This pilot study suggests that the re-suspension techniques may be equivalent and do not result in altered cellular or growth factor characteristics within the study limitations. Clinical Relevance: The mixing method can be labor and time intensive and prolong patient wait times. The vortex method can shorten the overall patient encounter time while still providing an optimal PRP product. Previously, there were only theoretical assumptions regarding how the re-suspension techniques may modify PRP characteristics.

Unanswered Questions Pertaining to the Economic and Workforce Issues in Pediatric Ophthalmology

To create a survey that examines remaining unanswered questions related to the economic and workforce issues in pediatric ophthalmology. A 12-question survey was distributed to U.S.-based pediatric ophthalmologists on the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) discussion board and various other social media forums. Statistical analysis was performed on the basis of practice setting groups. Two hundred eighty-seven pediatric ophthalmologists completed the survey. Of all respondents, 150 (52.3%) believed pediatric ophthalmology is a prestigious subspecialty, 137 (47.7%) thought that a hybrid fellowship combining pediatric ophthalmology and adult cataract surgery would increase the number of practicing pediatric ophthalmologists, 189 (65.9%) felt that early exposure during medical school would increase the number of residents who ultimately apply to pediatric ophthalmology fellowship, and 81 (28.2%) believed that the lobbyists hired by the AAPOS will be successful in preventing further Centers for Medicare and Medicaid Services reimbursement cuts. Medicaid patients comprised more than 25% of the total practice for 156 (54.4%) respondents. New patient, follow-up, and surgery wait times were greater than 2 months in 50.9%, 46.7%, and 38.7% of respondents, respectively. As the economic crisis in pediatric ophthalmology continues to worsen, questions related to increasing interest in the field through early exposure, hybrid fellowship, prestige, appointment wait times, and belief in the success of AAPOS lobbyists remain. Exceptional measures are required to revitalize the field and improve access to care. [J Pediatr Ophthalmol Strabismus. 2023;60(3):163-169.].

The Medicaid Dilemma and Its Profound Effect on the Economic and Workforce Issues in Pediatric Ophthalmology and Ultimately on Access to Pediatric Eye Care.

To identify new patient, follow-up, and surgery appointment wait times for children seeking care at academic eye centers/children's hospitals (AEC/CHs) in the setting of very low Medicaid reimbursements and workforce issues in pediatric ophthalmology. Each state in the United States with AEC/CHs was contacted by phone or email. Using a standardized script, the authors inquired about approximate wait times for non-emergency new patient, follow-up, and surgery appointment times, as well as percentage of patients insured by Medicaid seen at the AEC/CH. Medicaid reimbursements for new patient visits, follow-up visits, sensorimotor examinations, and one-muscle strabismus surgery were examined for each state. The mean percentage of children enrolled in Medicaid across the United States was 38.0%. Appointment and Medicaid inquiry calls were conducted across 41 states. Mean wait times across the United States for new patient, follow-up, and surgery appointments were 3.9, 3.4, and 2.7 months, respectively. Mean percentage of patients insured by Medicaid at AEC/CHs was 57.1%. High Medicaid-insured states offered longer wait times for new patient appointments than low Medicaid-insured states (4.4 versus 3.5 months; P = .20). The lowest Medicaid reimbursements for new patient visit, follow-up visit, sensorimotor examination, and one-muscle strabismus surgery occurred in Illinois, California, Ohio, and Michigan, respectively. Across the United States, states with higher percentages of patients insured by Medicaid exhibited longer new patient appointment wait times, suggesting that challenges related to Medicaid reimbursements directly contribute to disparities in access to pediatric eye care. [J Pediatr Ophthalmol Strabismus. 2023;60(3):170-177.].

Meniscus Size Differs Between Patient and Donor Populations for Meniscus Allograft Transplantation

To determine the extent of variability in meniscus size and anthropometric data between donors (supply) and patients (demand), to evaluate potential factors that may contribute to size discrepancies, and to determine whether the discrepancies lead to longer patient wait times. Lateral and medial meniscal measurements, anthropometric data, and time to match a donor graft were extracted from a tissue supplier database. The frequency and distribution of meniscus size were analyzed. Body mass index (BMI), relative meniscus area, body mass to meniscus area index, and height to meniscus area index were compared between patient and donor pools via χ2 tests and independent samples t-test. The effect of size on time to match was analyzed using analysis of variance and post-hoc Tukey test. The lateral meniscus patient population showed a greater frequency of larger size requirements compared to the donor population (P < .001) and the medial meniscus patient population showed a higher frequency of smaller meniscus size requirements (P < .001). The medial meniscus analysis showed significantly smaller meniscus areas (P < .001) in the patient population contributing to the observed trend of an increased body mass to meniscus area index and height to meniscus area index. The time to match a donor meniscus was affected by the patient meniscus size. This analysis demonstrates variations in frequency of meniscus sizes between donor and patient populations. This variation is attributed to differences in anthropometric data between patient and donor populations. This work identifies a mismatch between demand and supply for certain patient sizes contributing to longer times to match. This work associated donor and patient mismatches with longer wait times. This can be useful for patient counseling as well as provide a framework to determine whether there are solutions within the current meniscus donor pool that can be used to meet this clinical need.

The General Population's Perspectives on Implementation of Artificial Intelligence in Radiology in the Western Region of Saudi Arabia.

Background Artificial intelligence (AI) is a broad spectrum of computer-executed operations that mimics the human intellect. It is expected to improve healthcare practice in general and radiology in particular by enhancing image acquisition, image analysis, and processing speed. Despite the rapid development of AI systems, successful application in radiology requires analysis of social factors such as the public's perspectives toward the technology. Objectives The current study aims to investigate the general population's perspectives on AI implementation in radiology in the Western region of Saudi Arabia. Methods A cross-sectional study was conducted from November 2022 and July 2023 utilizing a self-administrative online survey distributed via social media platforms. A convenience sampling technique was used to recruit the study participants. After obtaining Institutional Review Board approval, data were collected from citizens and residents of the western region of Saudi Arabia aged 18 years or older. Results A total of 1,024 participants were included in the present study, with the mean age of respondents being 29.6 ± 11.3. Of them, 49.9% (511) were men, and 50.1% (513) were women. The comprehensive mean score of the first four domains among our participants was 3.93 out of 5.00. Higher mean scores suggest being more negative regarding AI in radiology, except for the fifth domain. Respondents had less trust in AI utilization in radiology, as evidenced by their overall distrust and accountability domain mean score of 3.52 out of 5. The majority of respondents agreed that it is essential to understand every step of the diagnostic process, and the mean score for the procedural knowledge domain was 4.34 out of 5. The mean score for the personal interaction domain was 4.31 out of 5, indicating that the participants agreed on the value of direct communication between the patient and the radiologist for discussing test results and asking questions. Our data show that people think AI is more effective than human doctors in making accurate diagnoses and decreasing patient wait times, with an overall mean score of the efficiency domain of 3.56 out of 5. Finally, the fifth domain, "being informed," had a mean score of 3.91 out of 5. Conclusion The application of AI in radiologic assessment and interpretation is generally viewed negatively. Even though people think AI is more efficient and accurate at diagnosing than humans, they still think that computers will never be able to match a specialist doctor's years of training.

Optimizing Outpatient Oral Contrast Use in Abdominal CT-A Radiology Pandemic Response Initiative to Reduce Patient Time in the Waiting Room and Reduce Costs, While Improving Patient Experience.

Purpose: The aim was to reduce outpatient wait time and improve patient experience by optimising oral contrast use. Methods: Our multidisciplinary stakeholder collaboration implemented two simultaneous interventions: (1) Creation of 'oral contrast policy', limiting recommended indications. (2) Creation of a new shorter oral contrast regime (30 vs 60min). We conducted a retrospective service evaluation of oral contrast use in outpatient (OP) abdominal CT at baseline and post-intervention. Patient wait times were measured and per-patient cost-savings were reported. An image quality review was performed by 2 blinded abdominal radiologists. Patient experience was evaluated with a standard voluntary survey. Statistical analysis was performed comparing baseline and evaluation outcomes using Chi-square or Fisher Exact test for categorical variables and Student's t-test or ANOVA for continuous data. Results: Over 1-month periods, OP CT scans were assessed in baseline (pre-pandemic) n = 575, baseline (pandemic) n = 495 and post-intervention n = 545 groups. Oral contrast use reduced from 420/575, 73.0% at baseline to 178/545, 32.7% post intervention. The turn-around time reduced by 15.8minutes per patient from 70.3 to 54.5minutes, P < .001 (Interventions 1 and 2). The diagnostic quality did not differ between the oral contrast regimes (Intervention 2, P = 1.0, P = .08). No repeat CTs were needed due to lack of oral contrast (Intervention 1) or poor opacification (Intervention 2). There was oral contrast cost reductions of 69.1-78.4% (P < .001). Patients reported their overall experience was improved post-intervention (Interventions 1 and 2). Conclusions: Optimising the CT oral contrast service through judicious use and a shorter regime, reduced patient wait times, improved patient experience and preserved diagnostic quality.

Watch and wait after neoadjuvant treatment in rectal cancer: comparison of outcomes in patients with and without a complete response at first reassessment in the International Watch & Wait Database (IWWD).

In rectal cancer, watch and wait for patients with a cCR after neoadjuvant treatment has an established evidence base. However, there is a lack of consensus on the definition and management of a near-cCR. This study aimed to compare outcomes in patients who achieved a cCR at first reassessment versus later reassessment. This registry study included patients from the International Watch & Wait Database. Patients were categorized as having a cCR at first reassessment or at later reassessment (that is near-cCR at first reassessment) based on MRI and endoscopy. Organ preservation, distant metastasis-free survival, and overall survival rates were calculated. Subgroup analyses were done for near-cCR groups based on the response evaluation according to modality. A total of 1010 patients were identified. At first reassessment, 608 patients had a cCR; 402 had a cCR at later reassessment. Median follow-up was 2.6 years for patients with a cCR at first reassessment and 2.9 years for those with a cCR at later reassessment. The 2-year organ preservation rate was 77.8 (95 per cent c.i. 74.2 to 81.5) and 79.3 (75.1 to 83.7) per cent respectively (P = 0.499). Similarly, no differences were found between groups in distant metastasis-free survival or overall survival rate. Subgroup analyses showed a higher organ preservation rate in the group with a near-cCR categorized exclusively by MRI. Oncological outcomes for patients with a cCR at later reassessment are no worse than those of patients with a cCR at first reassessment.

Michigan Market Referral Coordination Initiative: a Regional Market Approach to VA Specialty Care

BackgroundThe Maintaining Internal Systems and Integrated Outside Networks (MISSION) Act of 2018 was created in response to reports of prolonged wait times for veterans accessing health care within the Veterans Affairs (VA) system. In Michigan, the MISSION Act Community Care Program led to an increased number of veterans receiving specialty care outside the VA system, in part due to the complicated process of coordinating specialty care within the VA system. From 2018 to 2020, the percentage of veterans referred to the VA Ann Arbor Healthcare System (AA) for specialty care from its two referring facilities, Battle Creek VA Medical Center (BC) and Saginaw VA Healthcare System (SAG), decreased from 54.4 to 27%.ObjectiveImprove the number of Michigan veterans choosing VA specialty care.InterventionIn 2021, three VA facilities in Michigan (AA, BC, and SAG) created a market-level referral system named the Michigan Market Referral Initiative (MMRCI). This unique approach used a centralized nurse-driven team to manage specialty referrals, working directly with the veteran to explore both VA and community care (CC) options.Main MeasuresReferrals triaged and acceptance rates for VA care were tracked. The localized Standard Episode of Care model was used to estimate cost savings. Post-intervention AA patient wait times were compared to local CC wait times.Key ResultsIn the 14 months after implementation of the MMRCI, the rate of veteran retention increased by 32.4%. The estimated dollars retained within the VA by MMRCI efforts was $24,105,251 as of 7/1/2022. Post-intervention AA wait times were superior to community care except in 3 specialties.ConclusionsThis multifacility effort is an example of a highly coordinated, veteran-centered collaboration that has led to successful retention of veterans within the VA system with resultant large-scale cost avoidance and comparable clinic wait times. Focusing on central care coordination and veteran engagement in the referral process are keys to its success, along with leveraging existing referral patterns between nearby VA facilities. This model could be extrapolated to other VA markets throughout the country where similar relationships exist.

Medicaid coverage and access to obstetrics and gynecology subspecialists: findings from a national mystery caller study in the United States

Previous research suggests that access to healthcare may influence the diagnosis and treatment of obstetrical and gynecologic pathologies. Audit studies, a single-blinded and patient-centered design, have been employed to measure access to care for health services. To date, no study has assessed the dimensions of access to obstetrics and gynecologic subspecialty care based on insurance type (Medicaid vs commercial). This study aimed to evaluate the mean appointment wait time for a new patient visit to female pelvic medicine and reconstructive surgery, gynecologic oncology, maternal-fetal medicine, and reproductive endocrinology and infertility when presenting with Medicaid vs commercial insurance. Each subspecialty medical society has a patient-facing physician directory of physicians across the United States. Of note, 800 unique physicians were randomly selected from the directories (200 per subspecialty). Of the 800 physicians, each physician was called twice. The caller presented with Medicaid or, in a separate call, with Blue Cross Blue Shield. The order in which the calls were placed was randomized. The caller asked for the soonest appointment available for respective medical conditions based on subspecialty: stress urinary incontinence, new-onset pelvic mass, preconceptual counseling after an autologous kidney transplant, and primary infertility. From 800 physicians initially contacted, 477 responded to at least 1 call in 49 states plus the District of Columbia. The mean appointment wait time was 20.3 business days (standard deviation, ±18.6). A significant difference was found in new patient appointment wait times by type of insurance, with 44% longer wait time for Medicaid (ratio, 1.44; 95% confidence interval, 1.34-1.54; P<.001). When the interaction between insurance type and subspecialty was added to the model, it was also highly significant (P<.01). More specifically, Medicaid patients in female pelvic medicine and reconstructive surgery had a longer wait time than commercially insured patients. Patients seeking care in maternal-fetal medicine had the least difference, but Medicaid-insured patient wait times were still longer than commercial-insured patient wait times. Typically, a patient can expect to wait 20.3 days for a new patient appointment with a board-certified obstetrics and gynecology subspecialist. Callers presenting with Medicaid insurance experienced significantly longer new patient appointment wait times than callers with commercial insurance.

Using AI to build the foundation of better capacity management

Operating at optimal capacity is more critical than ever for hospitals and health systems. Sophisticated capacity management allows healthcare organisations to create the right availability in intensive care units (ICU) and floor units, meet demand for elective surgeries and infusion treatments in a timely way, and adjust staff and nursing rosters nimbly to avoid burnout. But why are health systems so challenged to address these issues now, let alone during crises? In healthcare, the foundational math of capacity management, matching supply with demand and linking multiple services, is largely broken. This paper discusses how the healthcare industry can learn much from the successes other industries have achieved in these areas. It will, however, require fundamentally revamping existing tools and processes for health systems not only to overcome capacity shocks like future COVID-19 surges but also to maximise the use of existing assets and create value for patients and the organisation. Essential to delivering value-based care in today’s dynamic environment is deploying proven and scalable AI-driven, virtual and distributed systems and creating an organisational culture of innovation that embraces the change. Hundreds of health systems across the USA have already adopted digital transformation solutions that empower them to make real-time capacity optimisation decisions. Results include an 8 per cent decrease in opportunity days (difference between Med/Surg LOS &amp; CMS LOS), US$500,000 more revenue earned per operating room (OR) per year, and significantly decreased patient wait times and increased patient volumes for infusion centres.

Assessing Primary Care Clinic Operational Strategies via EMR Data Mining.

Understanding how operational strategies impact critical performance metrics is critical to the clinic's ability to provide a value-based service to patients. This study investigated the utility of electronic medical record (EMR) audit file data in assessing operational strategies. EMR data were used to assess patient appointment lengths and conclude that shorter scheduled patient visit lengths, which resulted from one operational strategy (physician choice of visit lengths), had a negative impact on a second operational strategy (minimizing patient wait times). Patients with 15-minute appointments had a higher total mean wait time and shorter provider care or contact time.