Improving Flow, Quality of Care, and Decision Making: A Data Driven Analysis of LPCH Surgical Heart Center

Surgical demand at Lucile Packard Children’s Hospital heart center has been steadily increasing over the past several years, in large part due to its cutting-edge pediatric cardiothoracic surgery program that is known for performing complex unifocalization and pulmonary artery reconstruction procedures for patients. To keep up with demand, the heart center is operating at nearly full capacity at all times, creating a systematic bottleneck in the cardiovascular intensive care unit (CVICU). More than other intensive care units, utilization of beds in the CVICU can fluctuate wildly from day-to-day, due to the high variability in the recovery time for a pediatric cardiovascular surgical patient, which can range from spending one night in the CVICU, to spending hundreds of nights. Compounding this issue, the surgical schedule is currently not optimized to account for patient complexity, so the case-mix of low, medium, and high complexity patients in the CVICU could have high variability from week-to-week. Our project aims to address some of these issues, identify ways to improve the quality of care for patients, and provide insights for better decision making regarding scheduling. With these guiding objectives, our team explored several subprojects to address these project goals. First, we examined which patient population is most often occupying beds in the CVICU. Sorting patients in the CVICU by emergent/urgent, elective, and non-surgical, we found that over time, the percent of beds in the CVICU occupied by emergent/urgent patients has been steadily increasing, while the percent of beds in the CVICU occupied by elective patients has been decreasing.

To further explore this, we conducted a historical analysis of the frequency and volume of various patient populations flowing into the CVICU according to the current way leadership at the heart center tracks them: transfers, orbiters, cardiac surgical cases, and medical inpatients/transfers. Many of these inflow sources are highly variable making them hard to predict. Last minute or “urgent” surgical cases are the most difficult to accommodate, so for our historical analysis, we filtered our data to examine cases that were entered in the system as needing surgery within the next 24 hours. Using this filtered data we discovered that on average, the heart center can expect ~2 “urgent transfer” surgical cases per week, ~2 “urgent orbiter” surgical cases per week, ~1 heart transplant case every other week, and ~6 cardiovascular surgical cases (excluding CATH/EP cases) per week. Assuming Poisson arrival times, we created a reference graphic for hospital leadership to use, that depicts the frequency and volume the heart center can expect per week, on average, for patient populations from each of these different admission sources. 

Next, our team began to analyze the relationship between occasional “upticks” in emergent cases that happen throughout the year, and an increase in the number of cancellations in the heart center. Specifically, we noted that in early February of 2021, the heart center had 10 cardiovascular OR / CATH cancellations in one week, nearly 12 percent of the total number of cancellations they had in all of fiscal year 2020 (86). Interestingly, we noticed that the spike in cancellations between February 1 and February 7 coincided with the CVICU receiving 7 “urgent transfer” surgical cases that same week, an event that according to our earlier analysis (assuming Poisson arrivals), should only occur about once a year.

Conducting a deeper dive into the association between the number of “urgent transfer” surgical cases every week, and the number of cancellations, we found that according to the data available from 2019-09-01 to 2021-04-05, many (greater than 4) “urgent transfer” surgical cases per week is associated with more cardiovascular OR / CATH surgical cancellations.

Finally, following multiple interviews with project sponsors and healthcare providers, our team started to notice a common idea manifest: that individual patient complexity, along with the overall unit complexity of the heart center might be associated with an increase or decrease in quality of care for patients. With this, we formulated a hypothesis that serves to study whether the quality of care for low and/or intermediate-risk patients is affected by the number of high complexity patients in the unit. We hypothesized that the quality of care will decrease when there is a high number of complex patients in the unit. To test this theory, our team analyzed various proxies for quality of care and patient complexity, ultimately choosing to use STAT category scores as a proxy for patient and unit complexity, and hospital acquired conditions (HACs) and patient discharge status as proxies for quality of care. We used HAC and patient discharge data to formulate a risk score for each patient, and after thorough investigation and data analysis, our hypothesis proved to be true. Our team found that in weeks where the average complexity of the entire unit is high (average unit STAT score above 2.89 – the median average STAT score) there is an increased risk for all patients compared to weeks where the overall unit status is of lower complexity (average unit STAT score less than 2.89). Although we originally hypothesized that only the low and medium complexity patients’ quality of care would be hindered, our analysis shows that for low, medium, and high complexity patients the risk score increases when the complexity of the unit increases. 

We would like to thank Dr. David Scheinker, Dr. Andy Shin, Blake Gentile, Yuan Shi, Saied Mehdian, Jonaelle Lucas, Andrew Nowak, the MS&E 463 teaching team, Heart Center “Flow” Team, and the leadership, administration, clinicians, and staff of the Lucile Packard Children’s Hospital who provided us with data and extensive feedback through interviews.

Luke Carani 

Management Science and Engineering 

Allie Jones

Human Biology 

David Scheinker

Founder & Director, SURF Stanford Medicine