St. Charles Health System: Looking Forward with Predictive Analytics

StCharleshealthsystemThe tradition of medicine and healthcare is, by nature, predictive. Patient assessment leads to a prescribed treatment and a prognosis for recovery. Interestingly, however, a web search on healthcare analytics will yield dashboard projects that focus on what happened yesterday or last week and very little work being done with predictive analytics. This underscores the innovative course charted by Rapid Insight customer, St. Charles Health System (SCHS).

Today, all healthcare organizations are pursuing the triple aim: a better patient experience, stronger population health and cost containment. St. Charles Health System is raising the bar with the use of data and technology in pursuit of the triple aim by establishing a goal to use predictive modeling to better manage their organization to pave new pathways in healthcare.

St. Charles is the largest provider of medical care and the largest employer in Central Oregon, with 4200 caregivers at four hospitals, 350 active and 200 visiting staff members. They have to make the most effective use of their data and actionable information driving informed decisions is vital. Especially, with four major capital projects weighing in at 80 million dollars. Optimizing their resources is crucial.

To ensure this was happening, six months ago, Dr. Michael Johnson came on board as the new Analytics Specialist for Decision Support at SCHS. In that short span of time, several predictive models are now in place and the team is seeing exciting signs of positive change. Dr. Johnson had previous experience with predictive modeling. First, during his career in the army and more recently, as Director of Institutional Research at Dickinson College. It’s at Dickinson where he first began using Rapid Insight software and saw it as a natural fit to blending data and developing predictive models for SCHS.

“The Decision Support Team is a relatively new addition to the St. Charles IT department and there is some very forward thinking in the organization,” says Johnson. He’s excited by what they have been able to accomplish in a relatively short amount of time. The flexibility of Veera™, Rapid Insight’s data blending software, allows the team to pull data from any source, including their EHR (electronic health record) and to incorporate pre-built SQL scripts provided by their data experts. The easy aggregation and transformation of this data allowed Johnson and his team to deliver predictive models very quickly and they have become essential to the hospital staff’s daily routine.

The goal of the first model, analyzing Length of Stay (LOS), is to improve patient care by optimizing LOS based on primary diagnosis. This particular model uses approximately 18 months of historical data to “score” current inpatients on their potential to exceed recommended length of stay. Johnson has used Veera’s scheduling tools to automate this process so that each night the model runs, incorporating the latest data, and creates a report that is distributed to administrators and clinical managers at all four SCHS locations early each day. These emailed reports better inform treatment and allow caregivers to modify care plans, prioritize efforts and provide care attuned to potential needs of patients with higher LOS scores.

Watch this on-demand video featuring Dr. Michael Johnson, Analytics Specialist for Decision Support at St. Charles, as he shares the path they followed that allowed them to develop, implement, and verify the accuracy of several predictive models over a very short period of time.

The second model is a project to address the potential for patient readmission. The work proved remarkably efficient. “It didn’t take much work to tweak the Length of Stay Model for the readmission project,” shared Johnson. “The same Veera job could be used to pull the historical data.”

These models were created with the intent of being able to score both pre and post discharge patients. The goal of the former is to improve patient care by minimizing the number of current inpatients who have an unscheduled readmission within 30-days of discharge. Dr. Johnson and his team also found that the Rapid Insight readmission models that incorporated data from LACE assessments came to be significantly more accurate than the LACE scores on their own. The more accurate risk rating derived through predictive modeling is a key tool for care givers in treatment and aftercare planning as they work to reduce the occurrence of readmission… a process that is costly to the organization and an experience that is unpleasant for the patient.

With this model, SCHS is able to predict whether or not a pre-discharge patient will have an unscheduled readmission. All patients are scored every day like they are in the LOS model and automated reports are sent each morning to the teams. The process for the post-discharge model is similar, but scores patients who were discharged in the last five days, and no longer at the facility. This affords caregivers and administrators deeper insights in to who could potentially walk back through the door, allowing them to better develop protocols for these sort of patients as well as tune resource planning.

A concern in healthcare is always how staff adapt to the use of analytics and caregivers at SCHS have been very receptive to have new insights that can help them improve patient care. To some, the reporting functions as a task list for patient follow-up. The reporting has also assisted with staff time management and helps to allocate limited resources to those who are most at risk. Additionally, unit staff are using the pre-discharge readmission list to assign a case manager to each patient. This ensures that high-risk readmission patients are closely monitored and aftercare gets focused attention.

For St. Charles Health System this is only the beginning and future plans for predictive analytics are evolving. Dr. Johnson shares that they will next look to begin to build models that predict which individuals are prone to expensive high utilization of services as well as a model to evaluate the potential for complication or improvement by primary diagnosis. Also in the line-up are two more readmission models. One model focuses on predicting readmit rates by physician. Specifically, identifying over and under performers based on their patient’s acuity and the case mix index. The second is a readmit model for short-stay and observation patients. The Decision Support Team would also like to create an employee turnover model for human resources.

St. Charles has clearly rejected the status quo of using retrospective, descriptive metrics, realizing that management against what has already happened is simply not good enough. With the aid of Rapid Insight Veera and Analytics, SCHS has truly adopted a forward looking analytic mindset that is helping them change the course of costly outcomes and negative patient experience. Predictive modeling is driving exciting changes in their daily operations and inspiring an innovative future with predictive analytics.

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