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Objective: To investigate whether the Veterans Health Administration's (VA) 2019 Referral Coordination Initiative (RCI) was associated with changes in the proportion of VA specialty referrals completed by community‐based care (CC) providers and mean appointment waiting times for VA and CC providers. Data Sources/Study Settings: Monthly facility level VA data for 3,097,366 specialty care referrals for eight high‐volume specialties (cardiology, dermatology, gastroenterology, neurology, ophthalmology, orthopedics, physical therapy, and podiatry) from October 1, 2019 to May 30, 2022. Study Design: We employed a staggered difference‐in‐differences approach to evaluate RCI's effects on referral patterns and wait times. Our unit of analysis was facility‐month. We dichotomized facilities into high and low RCI use based on the proportion of total referrals for a specialty. We stratified our analysis by specialty and the staffing model that high RCI users adopted: centralized, decentralized, and hybrid. Data Collection/Extraction Methods: Administrative data on referrals and waiting times were extracted from the VA's corporate data warehouse. Data on staffing models were provided by the VA's Office of Integrated Veteran Care. Principal Findings: We did not reject the null hypotheses that high RCI use do not change CC referral rates or waiting times in any of the care settings for most specialties. For example, high RCI use for physical therapy—the highest volume specialty studied—was associated with −0.054 (95% confidence interval [CI]: −0.114 to 0.006) and 2.0 days (95% CI: −4.8 to 8.8) change in CC referral rate and waiting time at CC providers, respectively, among centralized staffing model adopters. Conclusions: In the initial years of the RCI program, RCI does not have a measurable effect on waiting times or CC referral rates. Our findings do not support concerns that RCI might be impeding Veterans' access to CC providers. Future evaluations should examine whether RCI facilitates Veterans' ability to receive care in their preferred setting. [ABSTRACT FROM AUTHOR]

Objective: To assess whether an online interactive report designed to facilitate interpretation of patients' narrative feedback produces change in ambulatory staff learning, behavior at the individual staff and practice level, and patient experience survey scores. Data Sources and Setting: We studied 22 ambulatory practice sites within an academic medical center using three primary data sources: 333 staff surveys; 20 in‐depth interviews with practice leaders and staff; and 9551 modified CG‐CAHPS patient experience surveys augmented by open‐ended narrative elicitation questions. Study Design: We conducted a cluster quasi‐experimental study, comparing 12 intervention and 10 control sites. At control sites, narratives were delivered free‐form to site administrators via email; at intervention sites, narratives were delivered online with interactive tools for interpretation, accompanied by user training. We assessed control‐versus‐intervention site differences in learning, behavior, and patient experience scores. Data Collection: Staff surveys and interviews were completed at intervention and control sites, 9 months after intervention launch. Patient surveys were collected beginning 4 months pre‐launch through 9 months post‐launch. We used control‐versus‐intervention and difference‐in‐difference analyses for survey data and thematic analysis for interview data. Principal Findings: Interviews suggested that the interface facilitated narrative interpretation and use for improvement. Staff survey analyses indicated enhanced learning from narratives at intervention sites (29% over control sites' mean of 3.19 out of 5 across eight domains, p < 0.001) and greater behavior change at staff and practice levels (31% and 21% over control sites' means of 3.35 and 3.39, p < 0.001, respectively). Patient experience scores for interactions with office staff and wait time information increased significantly at intervention sites, compared to control sites (3.7% and 8.2%, respectively); however, provider listening scores declined 3.3%. Conclusions: Patient narratives presented through structured feedback reporting methods can catalyze positive changes in staff learning, promote behavior change, and increase patient experience scores in domains of non‐clinical interaction. [ABSTRACT FROM AUTHOR]

Objective: To compare the original synthetic control (OSC) method with alternative approaches (Generalized [GSC], Micro [MSC], and Bayesian [BSC] synthetic control methods) and re‐evaluate the impact of a significant restructuring of urgent and emergency care in Northeast England, which included the opening of the UK's first purpose‐built specialist emergency care hospital. Data Sources: Simulations and data from Secondary Uses Service data, a single comprehensive repository for patient‐level health care data in England. Study Design: Hospital use of individuals exposed and unexposed to the restructuring is compared. We estimate the impact using OSC, MSC, BSC, and GSC applied at the general practice level. We contrast the estimation methods' performance in a Monte Carlo simulation study. Data Collection/Extraction Methods: Hospital activity data from Secondary Uses Service for patients aged over 18 years registered at a general practice in England from April 2011 to March 2019. Principal Findings: None of the methods dominated all simulation scenarios. GSC was generally preferred. In contrast to an earlier evaluation that used OSC, GSC reported a smaller impact of the opening of the hospital on Accident and Emergency (A&E) department (also known as emergency department or casualty) visits and no evidence for any impact on the proportion of A&E patients seen within 4 h. Conclusions: The simulation study highlights cases where the considered methods may lead to biased estimates in health policy evaluations. GSC was found to be the most reliable method of those considered. Considering more disaggregated data over a longer time span and applying GSC indicates that the specialist emergency care hospitals in Northumbria had less impact on A&E visits and waiting times than suggested by the original evaluation which applied OSC to more aggregated data. [ABSTRACT FROM AUTHOR]

Objective: To estimate the effect of wait times on patients' choice of provider and simulate changes in choice of provider due to compliance with VA MISSION Act wait time targets. Data Sources: We use nationwide administrative data (2014–2017) on Veterans who are enrolled in Medicare and the Veterans Health Administration (VHA), the Survey of VHA Enrollees, Area Health Resource Files, and other data provided by the Centers for Medicare & Medicaid Services. Study Design: We use an instrumental variables approach to identify the effect of VHA wait times on the proportion of total (Medicare and VHA) services that are paid for by the VHA ("reliance"). We exploit shocks to VHA provider supply to isolate supply‐driven changes in wait times and estimate the effect on VHA reliance. We control for market and time fixed effects and local demand factors. Data Collection/Extraction Methods: We use monthly aggregated data on 140 markets (groups of counties). VHA reliance is computed among patients aged 65 years or older who are dually enrolled in VHA and Medicare. VHA wait times and reliance are calculated for multiple specialties: cardiology, gastroenterology, orthopedics, urology, dermatology, and ophthalmology/optometry. Principal Findings: A 10% increase in the mean wait time (+2.8 days) reduces VHA reliance by 2.3 percentage points (95% CI: 2.3, 2.7), or 7.9% of the sample mean. This implies that meeting the MISSION Act wait time targets may have multi‐billion‐dollar budgetary impacts. Effects vary across specialties. For example, a 10% increase in the mean wait time for cardiology services (+2.0 days) reduces reliance by 1.8 percentage points (95% CI: 1.6, 2.1), or 6.3% of the sample mean for cardiology services. Conclusions: Meeting statutory wait time targets may have substantial unforeseen impacts on federal health care spending as patients sort to providers who have lower wait times. [ABSTRACT FROM AUTHOR]

Objective: To estimate the impact of Medicaid expansion on emergency department (ED) wait times. Data sources We used 2012–2017 hospital‐level secondary data from the CMS Hospital Compare data warehouse. Study design We used a state‐level difference‐in‐differences approach to identify the impact of Medicaid expansion on four measures of ED wait times: time before being seen by a provider; time before being sent home after being seen by a provider; boarding time spent in the ED waiting to be discharged to an inpatient room; and the percentage of patients who left without being seen. We compared outcomes in states that expanded Medicaid with those in states that did not expand Medicaid. Data collection/extraction methods: Our sample included all US acute care hospitals with EDs in states that did not ever expand Medicaid or that fully expanded Medicaid in January of 2014. Principal findings: Medicaid expansion was associated with a 3.1‐min increase (SE: 0.994, baseline mean: 30.8 min) in the time spent waiting to see an ED provider, a relative increase of 10%. Patients who were eventually sent home after being seen by a provider experienced a 7.5‐min increase (SE: 1.8, baseline mean 142.1 min) in wait time. Boarding time rose by 3.8 min (SE 1.9, baseline mean 111.4 min). The percentage of patients who left without being seen rose by 0.3 percentage points (SE: 0.09, baseline mean 2.0), a relative increase of 15.3%. Conclusions: This study provides multistate evidence that Medicaid expansion increased ED wait times for patients, indicating that ED crowding may have worsened post‐expansion. Future work should aim to uncover the mechanisms through which insurance expansion increased ED wait times to provide policy direction. [ABSTRACT FROM AUTHOR]

Objective: To estimate the impact of urgent care centers on emergency department (ED) use.Data Sources: Secondary data from a novel urgent care center database, linked to the Healthcare Cost and Utilization Project State Emergency Department Databases (SEDD) from six states.Study Design: We used a difference-in-differences design to examine ZIP code-level changes in the acuity mix of emergency department visits when local urgent care centers were open versus closed. ZIP codes with no urgent care centers served as a control group. We tested for differential impacts of urgent care centers according to ED wait time and patient insurance status.Data Collection/extraction Methods: Urgent care center daily operating times were determined via the urgent care center database. Emergency department visit acuity was assessed by applying the NYU ED algorithm to the SEDD data. Urgent care locations and nearby emergency department encounters were linked via zip code.Principal Findings: We found that having an open urgent care center in a ZIP code reduced the total number of ED visits by residents in that ZIP code by 17.2% (P < 0.05), due largely to decreases in visits for less emergent conditions. This effect was concentrated among visits to EDs with the longest wait times. We found that urgent care centers reduced the total number of uninsured and Medicaid visits to the ED by 21% (P < 0.05) and 29.1% (P < 0.05), respectively.Conclusions: During the hours they are open, urgent care centers appear to be treating patients who otherwise would have visited the ED. This suggests that urgent care centers have the potential to reduce health care expenditures, though questions remain about their net cost impact. Future work should assess whether urgent care centers can improve health care access among populations that often experience barriers to receiving timely care. [ABSTRACT FROM AUTHOR]

Objective: To examine the effect of Lean primary care redesigns on patient satisfaction with care and timeliness of care received. Data/Setting: We used patient surveys and time‐stamped electronic health record (EHR) data in a large ambulatory care system. Design: Lean‐based changes to clinical spaces and care team workflows were implemented in one pilot site and then scaled to all primary care departments across the system. Redesigns included standardizing equipment and patient education materials in examination rooms, streamlining call management functions, co‐locating physician and medical assistant dyads in a shared workspace, and creating new care team workflows. We used a non‐randomized stepped‐wedge study design and segmented regression with interrupted time series analysis to examine Lean impacts on patient outcomes. Data Collection: We analyzed patient satisfaction ratings and wait times as documented by the EHR. These longitudinal data were collected for 317 physician‐led teams in 46 primary care departments from January 2011 to December 2016. Principal Findings: After implementation of Lean redesigns, patients reported a 44.8 percent increase in satisfaction with the adequacy of time spent with care providers during office visits (P <.05). They also reported 71.6 percent higher satisfaction with their care provider's ability to listen to their concerns, and a 55.4 percent increase in perceived staff helpfulness at the visit (P <.01). Based on monthly EHR data, the amount of time elapsed between a patient request for a routine appointment and the scheduled visit day decreased from baseline by an average 2 percent per month (P <.01). On the day of the visit, patient wait times to be seen also decreased gradually by an average 1.2 percent per month (P <.05). Conclusions: Patient experiences of care after Lean implementations have not been widely studied in primary care settings. We found that Lean redesign yielded improvements that may strengthen clinical operations while enhancing value for patients. [ABSTRACT FROM AUTHOR]

Objectives: To identify consumers' preferences over care settings, such as physicians' offices, emergency rooms (ERs), urgent care centers, retail clinics, and virtual physicians on smartphones, for minor illnesses.Data Sources: A survey conducted between 9/27/16 and 12/7/16 emailed to all University of California, Irvine employees.Study Design: Participants were presented with 10 clinical scenarios and asked to choose the setting in which they wanted to receive care. We estimated multinomial conditional logit regression models, conditioning the choice on out-of-pocket costs, wait time, travel time, and chooser characteristics.Data Collection: 5451 out of 21 037 employees responded.Principal Findings: Out-of-pocket costs and wait time had minimal impact on patient's preference for site of care. Choices were driven primarily by the clinical scenario and patient characteristics. For chronic conditions and children's well-visits, the doctor's office was the preferred choice by a strong majority, but for most acute conditions, either the ER (for high severity) or urgent care clinics (for lower severity) were preferred to the office setting, particularly among younger patients and those with less education.Conclusions: Patients have several alternatives to traditional physicians' offices and ERs. The low impact of out-of-pocket costs suggests that insurers interested in encouraging increased utilization of alternatives would need to consider substantial changes to benefit structure. [ABSTRACT FROM AUTHOR]

Objective Apply quantile regression for a high-resolution analysis of changes in wait time to treatment and assess its applicability to quality improvement data compared with least-squares regression. Data Source Addiction treatment programs participating in the Network for the Improvement of Addiction Treatment. Methods We used quantile regression to estimate wait time changes at 5, 50, and 95 percent and compared the results with mean trends by least-squares regression. Principal Findings Quantile regression analysis found statistically significant changes in the 5 and 95 percent quantiles of wait time that were not identified using least-squares regression. Conclusions Quantile regression enabled estimating changes specific to different percentiles of the wait time distribution. It provided a high-resolution analysis that was more sensitive to changes in quantiles of the wait time distributions. [ABSTRACT FROM AUTHOR]