An overarching challenge in healthcare today is doing more with less — improving quality, building partnerships and managing population health while lowering costs. One of healthcare organizations' greatest tools in meeting this challenge is data. Gathering and analyzing information on hospital processes enables leaders to identify trends and develop solutions to problems.
St. Luke's Episcopal Hospital in Houston saw the power of data firsthand when it partnered with GE Healthcare Performance Solutions in its Care Optimization Project to tackle capacity issues. David King, MSHA, assistant vice president at St. Luke's Episcopal Health System and project manager of the Care Optimization Project, and Karen Myers, MSN, RN, vice president and CNO of St. Luke's Episcopal Hospital, discuss how technology and process improvement strategies helped the hospital improve efficiency and lower costs.
Technology and people — you need both to succeed
In 2011, St. Luke's began its Care Optimization Project, which involves implementation of a comprehensive suite of capacity management technologies and process improvement strategies. Through this approach, the hospital has reduced its average length of stay from 6.57 days to 6.13 days so far. In addition to increasing patients' access to hospital beds, this length of stay reduction represents huge cost savings. According to Mr. King, each incremental day a patient stays in the hospital costs St. Luke's an estimated $22 million in variable costs over one year.
The hospital was able to reduce length of stay and its related costs by using data to drive performance improvement strategies such as Lean. "It's a blend of the utilization of technology with process improvement," Ms. Myers says. "It's getting data collection with technology to help guide and drive process improvement to enhance performance."
For example, it initially took St. Luke's approximately 78 minutes to transfer patients to a ready bed. Although the hospital had a centralized bed system, it didn't have the technology to capture the time it took for each phase of patient flow in order to identify opportunities to enhance processes, according to Ms. Myers. After the hospital implemented new technology and improved communication between staff members, the transfer time dropped to roughly 60 minutes. The technology was the tool that enabled hospital staff to improve their processes; neither one alone was responsible for the improvement. "Technology is the backbone, but Karen's team and our support service departments are doing the real performance improvement work," Mr. King says. "Technology never solves the problem [alone]. It's an aid and a guide, but not a solution. The solution is the people."
Getting the word out: Transparency of data
Analyzing data alone does not support people's ability to improve processes, however. Hospital leaders need to communicate this data consistently over time so people can understand how their actions affect outcomes. "The key is to give weekly feedback," Ms. Myers says. If you're making a dramatic change in the way you go about work, you have to have a feedback loop and be consistent." St. Luke's has dashboards to make data transparent to all leaders, staff and physicians.
This transparency not only helps staff members and physicians track improvement, but it can also lead to greater buy-in and engagement, according to Ms. Myers and Mr. King. "Physicians are very data-driven. They respond to graphs, charts — real data. [Giving] them numbers adds to the richness of the dialogue you're able to have," Ms. Myers says.
Moving from "gut" decisions to data-driven decisions
Using data to guide and support process changes indicates a shift in the way decisions are made at healthcare organizations. "As leaders we need to lead by evidence, not based on gut instincts," Ms. Myers says. She says people can say a bed transfer took "forever," but how long is forever? Putting a number to that observation quantifies the problem and enables the hospital to track progress in improving efficiency. "It gives you real data to be able to speak to [the problem] credibly vs. emotionally," she says.
For example, St. Luke's used data from its radio-frequency identification system to measure the utilization of infusion pumps on different units. Based on this information, the hospital changed the way pumps were provided to nurses: instead of giving all units the same number of pumps, each unit had a certain number of pumps based on its use of the pumps. In addition, the hospital was able to order 20 percent fewer infusion pumps than the manufacturer recommended because it had data to show real utilization on different units, which encouraged significant cost savings, according to Mr. King.
Predictive capability
While St. Luke's is using real-time data to improve efficiency and capacity issues, it is also using predictive modeling to estimate and plan for future needs. Similar to estimated wait times for hospital emergency departments, the goal of the predictive model is to provide estimated bed placement times for different bed types across the hospital, such as cardiology, the intensive care unit and neurology, according to Mr. King. The data St. Luke's is currently tracking will provide historical data the model will use to predict needs in different scenarios.
Furthermore, St. Luke's plans to use the sophisticated data modeling to guide the design of a future new hospital. Data indicating challenges in the existing hospital, such as in staffing and operations, can be used to help St. Luke's design and operate the new hospital in a way that will eliminate the challenges.
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St. Luke's Episcopal Hospital in Houston saw the power of data firsthand when it partnered with GE Healthcare Performance Solutions in its Care Optimization Project to tackle capacity issues. David King, MSHA, assistant vice president at St. Luke's Episcopal Health System and project manager of the Care Optimization Project, and Karen Myers, MSN, RN, vice president and CNO of St. Luke's Episcopal Hospital, discuss how technology and process improvement strategies helped the hospital improve efficiency and lower costs.
Technology and people — you need both to succeedIn 2011, St. Luke's began its Care Optimization Project, which involves implementation of a comprehensive suite of capacity management technologies and process improvement strategies. Through this approach, the hospital has reduced its average length of stay from 6.57 days to 6.13 days so far. In addition to increasing patients' access to hospital beds, this length of stay reduction represents huge cost savings. According to Mr. King, each incremental day a patient stays in the hospital costs St. Luke's an estimated $22 million in variable costs over one year.
The hospital was able to reduce length of stay and its related costs by using data to drive performance improvement strategies such as Lean. "It's a blend of the utilization of technology with process improvement," Ms. Myers says. "It's getting data collection with technology to help guide and drive process improvement to enhance performance."
For example, it initially took St. Luke's approximately 78 minutes to transfer patients to a ready bed. Although the hospital had a centralized bed system, it didn't have the technology to capture the time it took for each phase of patient flow in order to identify opportunities to enhance processes, according to Ms. Myers. After the hospital implemented new technology and improved communication between staff members, the transfer time dropped to roughly 60 minutes. The technology was the tool that enabled hospital staff to improve their processes; neither one alone was responsible for the improvement. "Technology is the backbone, but Karen's team and our support service departments are doing the real performance improvement work," Mr. King says. "Technology never solves the problem [alone]. It's an aid and a guide, but not a solution. The solution is the people."
Getting the word out: Transparency of data
Analyzing data alone does not support people's ability to improve processes, however. Hospital leaders need to communicate this data consistently over time so people can understand how their actions affect outcomes. "The key is to give weekly feedback," Ms. Myers says. If you're making a dramatic change in the way you go about work, you have to have a feedback loop and be consistent." St. Luke's has dashboards to make data transparent to all leaders, staff and physicians.
This transparency not only helps staff members and physicians track improvement, but it can also lead to greater buy-in and engagement, according to Ms. Myers and Mr. King. "Physicians are very data-driven. They respond to graphs, charts — real data. [Giving] them numbers adds to the richness of the dialogue you're able to have," Ms. Myers says.
Moving from "gut" decisions to data-driven decisionsUsing data to guide and support process changes indicates a shift in the way decisions are made at healthcare organizations. "As leaders we need to lead by evidence, not based on gut instincts," Ms. Myers says. She says people can say a bed transfer took "forever," but how long is forever? Putting a number to that observation quantifies the problem and enables the hospital to track progress in improving efficiency. "It gives you real data to be able to speak to [the problem] credibly vs. emotionally," she says.
For example, St. Luke's used data from its radio-frequency identification system to measure the utilization of infusion pumps on different units. Based on this information, the hospital changed the way pumps were provided to nurses: instead of giving all units the same number of pumps, each unit had a certain number of pumps based on its use of the pumps. In addition, the hospital was able to order 20 percent fewer infusion pumps than the manufacturer recommended because it had data to show real utilization on different units, which encouraged significant cost savings, according to Mr. King.
Predictive capability
While St. Luke's is using real-time data to improve efficiency and capacity issues, it is also using predictive modeling to estimate and plan for future needs. Similar to estimated wait times for hospital emergency departments, the goal of the predictive model is to provide estimated bed placement times for different bed types across the hospital, such as cardiology, the intensive care unit and neurology, according to Mr. King. The data St. Luke's is currently tracking will provide historical data the model will use to predict needs in different scenarios.
Furthermore, St. Luke's plans to use the sophisticated data modeling to guide the design of a future new hospital. Data indicating challenges in the existing hospital, such as in staffing and operations, can be used to help St. Luke's design and operate the new hospital in a way that will eliminate the challenges.
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