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Health equity has direct impacts on pediatric cardiac patient outcomes in hospital-settings, yet there is no clear or quantifiable way to measure it. Our aim was to determine the best metrics to measure patient outcomes and develop a health equity analysis tool to measure impact of future projects. This was done through a three-step process. First, we surveyed providers, administrators, and other stakeholders to generate metric ideas. Next, we utilized PAC3 and PC4 Arbormetrics databases to note comparisons across institutions for these metrics. Finally, we used Lucile Packard Children’s Hospital (LPCH) cardiac surgical data from 2017-2022 to determine metrics with statistical significance between groups of various demographic factors with standardization by STAT score and procedure type. Final metrics included postoperative length of stay, 30-day readmission rate, failure-to-rescue rate, and hospital acquired complications. These were compared across race, ethnicity, Degauss scored socioeconomic status, and insurance type. Utilizing these metrics, LPCH and related hospitals can efficiently note disparities in cardiac care and develop targeted solutions to minimize them.
In 2018, Lucile Packard Children’s Hospital Stanford (LPCH) opened the Bonnie Uytengsu and Family Surgery and Interventional Center, which houses six surgical suites and six interventional treatment rooms including interventional radiology (IR) and cardiac catheterization (CATH)/electrophysiology (EP) labs used for cutting-edge treatment. Accurately modeling the volume incurred by LPCH service lines is important as future projections will not only inform decisions on capital expansions, surgical service growth, and patient volume allocation within the LPCH system, but also improve patient care. To date, a capacity planning model for clinic capacity, operating room (OR) utilization, and bed demand exists, yet it does not incorporate IR (comprised of IR Pain, IR Neuro, and General IR), CATH, and EP (the core service lines). The LPCH Capacity Planning project aims to close the existing gap in the model and increase hospital leaders’ understanding of the trends and relationships between IR, CATH, and EP and other hospital service lines. To this end, a descriptive analysis of the core service lines was completed, the conversion rates between all LPCH service lines and IR, CATH, and EP were calculated, and a user-friendly volume and use time projection simulation was developed.
Since the COVID-19 pandemic hit the US, healthcare providers have turned to telemedicine visits (by video and telephone) as a valuable alternative to standard in-person visits. Our SURF project aimed to develop a methodology for estimating differences in patient outcomes and patterns of care for patients who had their initial visit via telemedicine versus in person at Stanford Healthcare Cardiology clinics. The result was an instrumental variables approach that exploits the facts that patients are randomly assigned to providers for their initial visit and providers vary in their availability of telemedicine versus in person appointment slots. Applying two-stage least-squares regression and instrumental casual forest methods, we find that patients who have their initial visit via telemedicine may have slightly less follow-up visits on average.
A recent analysis of discharge antimicrobial prescriptions at Lucile Packard Children’s Hospital (LPCH) Stanford found that 1 in 5 were suboptimal based on the antimicrobial choice, dose, frequency, formulation, route, and/or duration. Prescriptions are tagged as ‘suboptimal’ based on discrepancies between the order and guideline recommendations. Prescription discrepancies may have a significant impact on patient outcomes including treatment failure and readmission. This project focused on understanding the drivers of discrepancies by analyzing historical prescription data and engaging with stakeholders at the hospitals. Upon identifying drivers that can be eliminated through process automation, the team developed a solution to provide real time clinical decision support to prescribers to improve prescription efficiency and accuracy.
Figure: Augmentin and Cephalexin were found to drive discrepancies by volume and rate.
Continuous glucose monitoring provides patients and clinicians with more information to better manage diabetes. However, this high resolution data can be overwhelming, with nearly 100 glucose readings per patient every single day. The TIDE platform guides clinicians to the most important data, and to the patients who will most benefit from outreach.
A lab error is a failure to obtain an accurate result from a lab sample, which can occur during ordering, testing, analyzing, and/or displaying the final result of processing the lab sample. Lab errors often require additional access to central lines, which potentially increase central-line infections. Lab errors also may require additional peripheral draws, which are sometimes traumatic for pediatric patients. The project aims to reduce the number of lab draw errors conducted by nurses at Lucile Packard Children’s Hospital thereby reducing patient trauma, patient transfusions, and additional valuable RN time required to redraw.
Within Stanford Healthcare’s Supply Chain, shortages can be costly and detrimental to hospital operations. Currently, there is no proactive strategy to identify potential future shortages, nor is there a systematic process to address shortages when they occur. This has been brought to the forefront of attention during the Covid-19 pandemic, which has caused significant disruption to both supply and demand. One solution to help reduce and prevent shortages is through Demand Planning: an emerging function in healthcare that helps hospitals predict, align, and maintain critical supply levels to effectively meet variable demand. This project therefore aims to evaluate and recommend ways Stanford Healthcare can leverage Demand Planning tools and processes to improve supply chain efficiency and resiliency.
Since the COVID-19 pandemic hit the U.S., healthcare providers have turned to telemedicine visits (by video and telephone) as a valuable alternative to standard in-person visits. Over a year later, many medical practices are unsure of the degree to which they should continue utilizing telemedicine as well as the significance of the patient benefit. Our project studied this question for Stanford Healthcare’s Cardiovascular Medicine by interviewing stakeholders involved in the patient scheduling process and analyzing the variation in telemedicine use by provider, patient, and visit characteristics in order to highlight insights about telemedicine usage and processes over the last year.
Improving Flow, Quality of Care, and Decision Making: A Data Driven Analysis of LPCH Surgical Heart Center
The Betty Irene Moore Children’s Heart Center at Lucile Packard Children’s Hospital (LPCH) Stanford takes on the most complex cardiac cases, while still surpassing outcomes of peer institutions. As a world-leading pediatric heart center, patients often need to wait up to nine months before they can be seen for surgery, which means that last minute cancellations of these crucial surgical procedures can cause undue emotional and physical distress on both the patient, and the patient’s family, which leads not only to patient dissatisfaction, but possibly even negative safety outcomes for these patients. The most common reason surgeries are cancelled last minute are due to an unforeseen lack of bed capacity in the intensive care or acute care units at the hospital. As such, the goal of this project was to better understand “patient flow” through the heart center to improve decision-making for senior leaders, with the end goal of identifying ways to reduce cancellations of patient surgical procedures.
Improving Nurse Staffing Planning: A Data Driven Discovery of LPCH Pediatric ICU Nurse-to-Patient Assignment Rules
In the LPCH PICU, current staff level planning is based primarily on the annual average midnight patient census, which does not account for variations due to seasonality, day-of-the-week peaks, and midday churn. Consequently the shift level sees last-minute adjustments that are in check with nurse-to-patient ratios (above and over California’s minimum 1:2) based on staffing guidelines, patient procedures, and acuity. We identified patient characteristics in the electronic health record to validate historical safe nurse staffing and simulate staffing levels with current guidelines. By comparing the expected nurse staffing to actual staffing, we identified and quantified patient churn and patient data related features as driving the errors between simulated and historical nurse staffing. By including these factors along with variations across shift types, we produced new estimates of historical staffing to align nurse staffing levels with unit demand.
Peripheral intravenous (PIV) therapy is used widely in hospitalized patients, but injuries associated with fluids leaking into the tissue surrounding the IV are fairly common, especially in pediatric populations. These injuries can range in severity from minor swelling to serious tissue damage. Our project focuses on understanding the prevalence of these injuries and current practice for monitoring PIVs, reporting injuries, and treating with antidotes. We aim to provide recommendations regarding injury reporting and current practice to reduce the prevalence and severity of pediatric PIV injuries at the Lucile Packard Children’s Hospital.
At Lucile Packard Children’s Hospital, interpretation is critical to patient experience. Interpretation has the power not only to transform patient equity but also to improve health outcomes. While interpretation is a valued resource within the hospital, that resource is limited. The Interpreter Services department must work to appropriately staff, schedule, and coordinate in-person interpreters within the hospital while simultaneously maintaining and providing virtual interpretation through iPad and telephone media. The importance of interpretation to LPCH’s patients and families amplifies the need for heightened attention to the allocation of these resources. The goal of this project was to combine qualitative and quantitative analysis to better understand the current state of interpreter services within LPCH while additionally identifying unmet patient needs.
Hospital-acquired venous thromboembolisms (HA-VTEs) have been identified as an increasing problem in pediatric patients. HA-VTEs are associated with significant increases in morbidity and mortality. To prevent HA-VTEs in pediatric patients, Lucile Packard Children’s Hospital (LPCH) employs the prevention guidelines developed by Solutions for Patient Safety, a national network of over 140 children’s hospitals in the United States. These guidelines are used to prevent HA-VTEs in patients over 12 and HA-VTEs not related to central venous catheters. We find, though, that the majority of LPCH patients are not covered by the current prevention guidelines. Our project seeks to identify risk factors specific to LPCH patients to inform the development of a predictive model and risk stratification tool to improve the quality of care at LPCH.
Following a surgical intervention, pediatric patients with heart disease have increased risk of forming damaging and potentially fatal blot clots. Anticoagulation therapy is a common strategy used to prevent potential organ dysfunction caused by these clots. Warfarin, the current principal medication used for oral anticoagulation, has a narrow therapeutic window and long half-life, making it a difficult and time-intensive drug to manage. Patient responses to the drug vary significantly and are particularly understudied in pediatrics. As a result, there is no standardized approach to warfarin dosing, which necessitates prescription tweaks and extended hospital stays. The goal of our project is to leverage data analysis to provide initial warfarin dosing recommendations based on given patient parameters.
Pediatric Endocrinology – Improve Care for T1D Patients Through Automation and Capacity Optimization
LPCH Pediatric Endocrinology Clinic is using Continuous Glucose Monitoring (CGM) to assess the health of T1D patients, data collected by CGM process is used to algorithmically rank and prioritize patient recommendations. CGM data and the prioritized patient recommendations enable clinical diabetes educator (CDE) to review the data and contact the patient, if necessary. TIDE is an interactive tool that facilitates reviewing the CGM data, number of flags and CGM metrics, highlighting the high-risk patients.
The goal of our project is to test if the Pediatric Endocrinology clinic can increase its capacity without increasing resources through technological and operational innovations. We are looking at options that facilitate population-level glucose management by enabling the clinic to provide care for more patients while maintaining or improving glucose metrics. We are working on a 3-pronged strategy to achieve our goal:
- A model that will allow the clinic management to have an estimate of CDE capacity and the number of patients that can be reviewed and contacted
- Technology improvements to optimize provider capacity & process efficacy
- Payment Model that allows for proper reimbursement for the services provided
Lucile Packard Children’s hospital has been pioneering the introduction of target based care for their surgical patients. The overarching goal is to assign each patient that walks through the door to their respective cohort. This would allow the hospital to provide the target length of stay for the patient as well as a standardized clinical pathway. The problem right now, however, is that the process of creating cohorts is manual and time consuming. The goal of this project was to automate the creation of cohorts by gathering a centralized dataset which allows for the filtering of outlier patients from their cohorts to ensure that the set targets are accurate.
Hospital construction of building 500P and renovation of building 300P at Stanford Health Care will lead to shifting capacities in different hospital units over the next five years. The current workflow does not support University Medicine to operationalize six teams across the two future buildings. Given the patient care constraints for each team, it is important to consider the spatial distribution of patients located in different hospital units assigned to any given team. Maximizing the co-location of patients in the same units would allow for more time spent with patients for each team and less time walking in between them.
The objective of this project is to understand the current state of patient co-location in the hospital and to predict future trends given the changing unit capacities. We aim to develop a simulation model that determines future patient volume and unit occupancy and their impact on the co-location of Medicine patients.
Lucile Packard Children’s Hospital (LPCH) handles some of the highest acuity cases in the nation and is revered as one of the country’s top hospitals. LPCH currently conducts their nurse staffing assignment manually, incorporating a multitude of factors such as shift types, nurse skills, policies and regulations, and variability in OR utilization and procedure lengths. The effective allocation of nurses to procedures can yield significant improvements in terms of the reduction of idle time and overtime.
Multiple factors and constraints impact how surgeons are scheduled in multiple OR blocks and clinics. These include subspecialty coverage across clinics and fixed clinic days in a specific location. Current methods of scheduling are not quantitative and rarely result in optimal scheduling. The goal of this project is to use a mathematical program to optimize certain conditions, such as maximizing clinic coverage and minimizing driving time, while respecting relevant constraints, such as fixed sessions at a certain clinic.
Planning to provide the highest quality patient care requires three complex decisions: how to estimate patient demand, how to allocate physical capacity, and how to allocate staff. The first decision, the level of patient demand, depends on forecasting based on historical data and strategic initiatives. The second decision, how to allocate physical capacity a given level of demand, depends on a careful analysis of hospital priorities and bottlenecks. The third decision, how to staff, depends on patient needs and staff competencies. The goal of this project is to understand, from the point of view of these decisions, how to best re-allocate and staff APU and SSU capacity to accommodate higher summertime surgical volume.
Scheduled operating room cases are sometimes ‘bumped’ in order to accommodate urgent surgical cases. Numerous factors weigh on the selection of which service should be bumped and how to reschedule the bumped case. The urgency of the situation may make it difficult to decide quickly and fairly. The current method to select the case to be bumped is based on a manual process and a paper log. The log is used to tracks which service was bumped last and which service is due to be bumped next. For this project, we created and implemented a tool that will provide a more efficient, equitable, and automated method to help determine which case to bump when an emergency case arrives.
Accurate preparation of surgical supplies, instrumentation, and equipment is crucial to ensure that each surgery begins and ends on time and with minimal waste. Currently, the process is driven by preference cards that are not representative of what is used in the OR. This results in three significant sources of avoidable costs: 1) delays caused by nurses struggling to find missing equipment immediately before or during a case 2) wasted, unused supplies 3) supplies that are used but not billed for. The goal of our project is to automate the maintenance of preference cards by using historical usage data; to recommend updates to the existing preference cards; and thus, minimize delays, wasted supplies, and improper billing.
Hospital quality of care, nurse retention, and nurse satisfaction depend on the quality of the hospital’s decision-making processes. These decisions include: how to assign nurses to patients, which patients are at risk of decline, when to transfer patients between units, and how to bill for services provided. To assist with these decisions, hospitals currently rely on several closely related, but independent tools. These tools share several shortcomings: the data input is manual; the output requires manual manipulation; and only a small fraction of all relevant data are considered. We propose a single system to replace these tools. The central component of the system is a tool to estimate patient acuity and nurse workload from EMR data. In the proposed system the data input is automated; the output does not require further calculation; and all relevant data in the EMR are considered. Such a system can support the quality of care, nurse retention, and nurse satisfaction.
Planning a Major Hospital Expansion
Lucile Packard Children’s Hospital Stanford is opening a major expansion in December, 2018. To plan the use of the new space effectively, one must forecast demand, allocate capacity to match this demand, and understand the operational implications of this match. The goal of this project is to design tools to meet to plan for surgical services after the expansion. These include a forecasting tool to estimate demand; a simulation tool to test how much capacity will be necessary to accommodate the forecast demand; and an optimization tool to find the best reassignment of surgeons to operating rooms and clinics in the new operational model.
The patients of the Endocrinology medical service use numerous remote care monitoring devices (e.g., insulin pumps and glucometers). The goal of this project is to design a comprehensive system for patients to learn to use these devices; to deliver the data to healthcare providers; and to put a system in place to analyze the data and track the outcomes of the work.
Intensive glucose control in patients with type 1 diabetes (T1D) is essential to prevent the development of vascular complications. However, in the United States only ~25% of children and adolescents with T1D meet long-term glucose control goals. Despite the high incidence and significant morbidity and mortality associated with T1D, few quantitative tools to personalize care models are available and deteriorating glucose control is often detected only retrospectively. A typical patient may check their blood glucose 4-10 times/day and receive feedback from their care team once every 3 months based on a single lab value (HbA1c) that represents glucose control over the past 3 months. Continuous glucose monitors (CGMs), which record blood glucose levels once every 5 minutes, offer an opportunity to use high-frequency data to identify and predict deteriorating glucose control.
The Imaging services Department at Lucile Packard Children’s Hospital provides services to a patient population with highly variable needs. The variability at each step of the process can impact patient experience and the operations of the department. The process can potentially be improved through redesigned work flows and changes in operating processes.
Our objective is to maximize the Imaging Services’ machine utilization while also improving patient experience by reducing wait time through the minimization of scan delays. Opportunities include emphasis on proactive thinking in patient preparation, optimizing operating hours for increased efficiency, and improving data capture for monitoring performance metrics.
The operating room is a major cost and revenue center. More effective operating room management and scheduling can provide significant benefits. The post-anesthesia care unit (PACU), where patients go to recover after their surgical procedures, is at times a bottleneck. If the PACU reaches capacity, operating room procedures are delayed or canceled. We present a data-driven approach to determine the order of procedures in the operating rooms that minimizes the likelihood of PACU holds. Specifically, we use a machine learning approach to estimate the required PACU time for each type of surgical procedure; we develop and solve integer programming models to schedule procedures in the operating rooms so as to minimize maximum PACU occupancy; and we use discrete event simulation to compare our optimized schedule to the existing schedule.
High-frequency collection of patients’ physiological waveforms at the bedside provides a dense information stream we may use to identify and predict clinical events. Due to data size and complexity, new techniques are required to efficiently suppress noise and extract relevant information. We are constructing an adaptable machine learning toolset to process these waveforms to support applications including crisis event prediction, readmission risk identification, and long-term patient trend analysis. The purpose of these tools is to support sharing methodology between the wide range of relevant problems.
The Stanford School of Medicine Department of Medicine is a national leader in high quality care and cutting edge research. As a national thought leader, we strive to develop practices that will reduce the growing cost of healthcare and excel in a value-based compensation structure. This includes ensuring that our team of over 550 physicians has competitive compensation structured to incentivize the highest possible quality care and research. Towards this end, we are exploring moving from a compensation structure centered on bonuses to one based on salaries. We seek to map out what such a compensation structure would look like, how it would incentivize care and research, and what its impact would be if the Stanford payor-mix were to include more Value-Based and Capitated pay.
The 2020 presidential elections are inching closer, and more than a few candidates are proposing or considering a universal health insurance system. Schulman and Milstein provide a “back of the envelope” estimation for the financial impact of a “Medicare for All” approach to health insurance on US hospitals. This is a tremendous estimation of the impact of such a policy on hospital revenue on an aggregate level. However, we aim to develop a model to inform policy makers and voters about the potential impact of such a policy on deeper levels. We will populate this model using available data on Medicare and private insurer reimbursement. Currently, our model measures the aggregate impact on hospitals in the United States. Future iterations will allow users to explore more granular analysis, such as individual hospitals. This model will be made available to the public to do their own analysis using a user-friendly UI powered by RShiny. The UI will display creative visualizations of the impact of “Medicare for All.”
In December of 2018, Lucile Packard Children’s Hospital (LPCH) went live with a new sepsis algorithm to align its standard of care with national recommendations. Clinical evidence is clear that once a patient is diagnosed as septic (the body’s maladaptive response to a severe infection), early delivery of care within the “golden hour” improves patient outcomes. What is more difficult to measure, however, is adherence to these guidelines, which prescribe simple interventions of antibiotics and aggressive fluid resuscitation within the golden hour.
The Stanford Supply Chain Department is responsible for non-labor spend, contract management, and item and implant procurement for the Stanford hospital system including Stanford Health Care (Adult), ValleyCare, and Lucile Packard Children’s Hospital. The organization participates in a number of cost savings initiatives with physicians in order to reduce spend, ranging from vendor consolidation, contract negotiation, and value-based selection of new cost-effective medical technologies, such as for Spine surgeries. To successfully drive these initiatives, Supply Chain depends on reliable and accessible data to perform the relevant analytics with its available tools. However, these systems have not been thoroughly investigated for the construction of a standardized playbook for cost savings: one that generalizes to all of the enterprise’s departments and units. Our team was tasked with generating a comprehensive data mapping of all the databases, analytical tools, and external benchmarking data services that Stanford uses. This data mapping will help with different goals such as establishing a source of truth for item price, increasing PO accuracy, easily finding and tracking spend per item/department, and being able to present surgeons with an accurate cost-per-case after every surgery. By interviewing over 50 internal and external employees and stakeholders we were able to generate a detailed data mapping and a comprehensive documentation of the many analytical tools and databases used regularly by Supply Chain.
Early identification of non-accidental trauma (NAT) is critical as up to 30% of victims, by some estimates, will be re-injured without adequate protection conferred by early identification. Unfortunately, the identification of NAT is often inconsistent and difficult owing to the sensitive nature of the screening process. This had led to the development of screening protocols by some children’s hospitals to decrease bias and improve NAT detection. Students will work with the clinician and administrative leaders to design a system to improve our screening and reporting of NAT at LPCH.
Perioperative services at Lucile Packard Children’s Hospital provides surgical services to a patient population with highly variable needs. Many of the departments in perioperative services are interconnected and interdependent. In order for perioperative services to perform as efficiently as possible, they must be equipped with the right analytical tools. The objective of this project is to facilitate patient access and efficient resource use through data-driven decisionmaking in perioperative services.
Effective management of operating room resources relies on accurate predictions of surgical case durations. This prediction problem is known to be particularly difficult in pediatric hospitals due to the extreme variation in pediatric patient populations. We pursue two supervised learning approaches: (1) We directly predict the surgical case durations using features derived from electronic medical records and from hospital operational information. For this regression problem, we propose a novel metric for measuring accuracy of predictions which captures key issues relevant to hospital operations. We evaluate several prediction models; some are automated (they do not require input from surgeons) while others are semi-automated (they do require input from surgeons). We see that many of our automated methods generally outperform currently used algorithms and our semi-automated methods can outperform surgeons by a substantial margin. (2) We consider a classification problem in which each prediction provided by a surgeon is predicted to be correct, an overestimate, or an underestimate. This classification mechanism builds on the metric mentioned above and could potentially be useful for detecting human errors. Both supervised learning approaches give insights into the feature engineering process while creating the basis for decision support tools.
Acute Kidney Injury (AKI)
At LPCH about 25% of Intensive Care Unit patients and 5% of Acute Care Unit patients experience an acute kidney injury (AKI) during their stay, a condition associated with chronic kidney disease (CKD) in the months following discharge. The goals of this project are: (1) to better understand what risk factors are associated with the development of CKD after discharge for AKI survivors through retrospective data analysis, and (2) to develop and implement a process for identifying high-risk AKI survivor patients and enrolling them into a AKI survivor clinic post-discharge.
Crisis Event Prediction
Hospitalized patients often exhibit predictive indicators 24-48hr prior to experiencing a life-threatening event. Detecting and filtering these predictors can enable early identification of patient decline, allowing preventative measures to be taken. Current algorithms utilize select clinical variables for predictive modeling, but have generally excluded congenital heart disease due to the heterogeneity of both patient and disease. The goal of this project is to improve on previous methods by incorporating the physiological waveforms collected by bedside monitors. We accomplish this by leveraging deep learning techniques to extract information from these dense data.
Predictive Model of Patient Flow
Patients who receive major cardiovascular surgery, e.g., unifocalization, require a long recovery stay in the hospital. In order to accommodate more such patients without disrupting patient flow, it is necessary to map their path through LPCH, the resources they need, the other patients who also need those resources, and to create a model to support decisions on how to most efficiently allocate these resources. We will use historical data, expert interviews, and mathematical optimization to build such a model.
The LPCH Cardiovascular (CV) Surgery Unit is one of a select few facilities where families can turn to have complex pediatric cardiac surgeries. However, surgical scheduling has not yet been optimized to allow for more families to take advantage of our world class care. Our project to improve CV access–by optimizing the proxy of block utilization–led us to develop three resources: (1) Detailed process flows of the Pulmonary Artery Reconstruction (PAR) and general CV surgery scheduling processes, capturing relevant decision and outcome variables, (2) Identification of strategies to increase utilization of current OR blocks and (3) A prototype, Excel-based automated scheduling tool, featuring beta versions of ideal functionalities along with the TAP Lists. We analyzed short run-time procedures and suggest that schedulers use our analyses to increase access and OR block utilization. Scheduling more of these types of procedures on days with remaining OR block time would improve access and avoid bumps from delays. With the expansion of the department, continued data analyses and iterations of the tool are critical for further optimization.
Central line associated bloodstream infections (CLABSIs) are hospital-acquired complications that contribute to increased patient morbidity and longer lengths of stay. Lucille Packard Children’s Hospital (LPCH) has previously reported 2.2 CLABSIs per 1000 line-days, which is higher than the national average of 1.5 CLABSIs per 1000 line-days. One of the leading strategies for reducing CLABSI rates is improving compliance with central line bundle elements – a standard of care for dressing changes, antiseptic washes, sterile line accesses etc. Our objective was to use data to reduce CLABSIs by changing institutional practices and individual behaviors.
Lucile Packard Children’s Hospital (LPCH) is home to the premier pediatric heart center on the west coast, working with some of the most challenging cases. LPCH is working to design a new operational model for the Heart Center Clinic. The goals are: 1) to increase capacity by 15% with existing MD/APP staff; and 2) to provide high quality care while actively managing clinic bandwidth, including temporary closure of specific providers to new patients, and proactive addition of new clinics when they lack sufficient capacity.