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Case Study: Surmounting Staff Scheduling at Valley Baptist Health System

By Carolyn Pexton and Blake Hubbard

Case Study: Surmounting Staff Scheduling at Valley Baptist Health System

Located in Harlingen, Texas, Valley Baptist Health System is a full-service, not-for-profit community health network ably serving the population of south Texas and beyond. The system is comprised of multiple organizations including Valley Baptist Medical Center, a 611-bed acute care hospital providing the number one rated orthopedics service in Texas, a state of the art children’s center and a lead level III trauma facility. The organization also serves as a teaching facility for The University of Texas Health Science Center.

In 2002, Valley Baptist Health System began to implement GE’s Six Sigma approach as a rigorous methodology for process improvement and a philosophy for organizational transformation. The adoption of Six Sigma at Valley Baptist fostered a revitalized culture that embraces the voice of the customer, breaks down barriers to change and raises the bar on performance expectations. Through this initiative, the team at Valley Baptist began to examine the most critical opportunities for improvement and select projects that would align with strategic objectives and produce measurable results.

As with most healthcare providers today, maintaining appropriate staffing levels and improving productivity are among the top concerns at Valley Baptist. During the initial wave of Six Sigma training projects, the team at Valley Baptist launched an effort to review and improve the staff scheduling process for one nursing unit in orthopedics. Within this particular unit, there had been a history of overtime and use of agency hours that did not seem to correlate with changes in patient volume. Patient census would fluctuate while staffing levels remained the same, and the higher hourly wage for overtime and agencies had begun to strain the overall labor budget.

The primary focus for this project was to improve the unit’s ability to responsibly meet staffing targets while protecting the quality of patient care. It is a challenge to reach that optimal level – avoiding overstaffing yet appropriately meeting daily needs. Paramount in this effort was the notion that targets would be met without adversely impacting customers. Patient satisfaction scores had to remain constant or increase, and this mandate was built into the project and measured through the use of upper and lower specification limits.

A cross functional project team was assembled including the chief nursing officer as sponsor, the assistant vice president from human resources, the nursing house supervisor, the nurse manager from the cardiac care unit, a representative from IT and a charge nurse. The introduction of any new change initiative can elicit skepticism, but since Six Sigma concentrates on fixing the process rather than assigning blame, once the approach was understood much of the skepticism subsided. Stakeholder analysis and other CAP (change acceleration process) tools helped to surface concerns and improve communication.

Also supporting this project were metrics to measure productivity for nurses and managers that had been introduced through the adoption of Six Sigma. The dual emphasis on productivity and quality provides a framework for offering cost effective care and aligns with the customer-centered mission at Valley Baptist.

Defining the Goal

During the Define phase of the project, the team concentrated on clearly identifying the problem and establishing goals. The nursing units in general had struggled to meet their staffing targets and were over budget on labor costs. For this project, the team decided to focus on one orthopedics nursing unit based on three criteria: the unit was not extremely specialized or unique so it offered the best representation of nursing as a whole; the manager was very supportive of the initiative; and this unit offered clear opportunity for improvement and results.

To understand the current scheduling process, the project team used the SIPOC tool to develop a high-level process map. SIPOC stands for suppliers, inputs, process, output and customers. Inputs are obtained from suppliers, value is added through your process, and an output is provided that meets or exceeds your customer’s requirements. SIPOC is extremely useful during process mapping.

Measuring and Analyzing the Issues

As they moved through the Measure and Analyze phases, the project team focused on data collection and the identification of the critical “Xs” that were impacting staff scheduling. Historical data was gathered from the payroll system to analyze regular time, overtime, agency use, sick time, vacation, jury, funeral leave and FMLA. They examined 24 pay periods for each data point. Fortunately, the team was able to extract the data they needed from existing systems and avoid manual data collection, which is more labor intensive and can increase the project timeline.

Given the availability of continuous data for the “Y” or effect and the potential Xs or causes, regression analysis was the tool chosen to help the team understand the relationship between variation from the staffing goals and vacation, FMLA, sick leave, overtime, agency nurse usage, and so on. Through regression analysis, they were able to determine that three critical Xs could explain 95 percent of the variation: agency use, overtime and census. The next step would be to understand underlying factors – data would point the team to interesting findings that disputed their original theories.

The Improve Phase

During the Improve phase, the team used many of the CAP and Work-out tools. Such acceptance-building techniques are key to success, since improvements introduce changes in process and human behavior. The team conducted a Work-out session to develop new standard operating procedures for better management of overtime and agency usage – critical drivers in staffing.

The chief nursing officer attended the sessions to underscore the importance of this initiative from a leadership perspective. The project team used the process map to indicate where they might have opportunities for improvement, and then conducted separate Work-outs on each area. They brought in nursing staff, house supervisors and other stakeholders to participate in the search for solutions.

This project translates to $460,000 in potential savings for one unit. Conservatively, if it were spread across the health system the savings could exceed $5 million.

Never Assume

This project furnished a classic example as to how Six Sigma can be used to either corroborate or dispel original theories. Management at Valley Baptist had initially assumed they were over budget on labor costs due to sick leave, FMLA, vacation and people not showing up, which would have naturally necessitated the additional overtime and agency hours. The data and analysis proved those assumptions to be incorrect.

It turns out there were several factors contributing to the staff scheduling challenges. One illuminating aspect to come from the Work-outs was the realization that nurses didn’t like floating in and out of units – this came up in every session. There were also issues with the staffing matrix which attempted to set parameters based on volume. Compliance was not ideal, and the matrix itself was based on data that was not completely current. Another complication was that maintaining information in the matrix involved labor intensive, manual processes that were difficult to control.

The team discovered the use of overtime was not always need-based. Units would regularly schedule 48 hours for each nurse, with the extra eight hours of overtime built-in as “traditional” usage. This became an accepted practice and although in theory, adjustments are supposed to be made when the patient flow is lighter, this was not happening. On the form used to submit data the nurses would have to guess what hours they might actually work. The matrix might indicate compliance, but the payroll data actually showed them clocked in for 14-15 hours instead of 12.

Another critical issue is that the nursing unit lacked appropriate mechanisms for shift coordination and handoff. There were two fully independent teams between the day and night shifts, and there was not a smooth transition between them. Part of the problem stemmed from a lack of written guidelines governing the overtime between shifts. Nurses would finish their regular 12-hour shift and stay on overtime to complete tasks rather than pass them on to the next shift.

The central metric of this Six Sigma initiative was worked hours divided by equivalent patient days. Valley Baptist Health System defines worked hours as those hours during which an employee was actually working – including regular time and overtime, and excluding non-productive hours such as sick and vacation time. Equivalent patient days is the volume statistic utilized within the Orthopedics Unit. It is the typical patient days number adjusted to reflect short-term observation (STO) patient volume.

Results and the Control Phase

The development of new standard operating procedures has clearly had a positive impact on the organization. This gave staff a plan they can follow and established accountability. The unit began a process for transition meetings between shifts. The outgoing nurse now takes the incoming nurse to the patient’s room, introduces them and provides a report on the current status and whether there are outstanding orders. In addition to improving operations for the hospital, this change has also been well received by patients, as reflected in rising satisfaction scores during the pilot.

The project on staff scheduling has led to an overall reduction in the higher hourly cost of overtime and agency use, and has translated to $460 thousand in potential savings for this one unit. Conservatively, if this project were spread across the health system the savings could exceed $5 million. It is also important to note that this project started at the 0 sigma level and increased to Six Sigma for nine consecutive pay periods.

“At Valley Baptist, we continually seek opportunities to improve productivity,” said Jim Springfield, President and CEO. “This focus is critical for our future success and ability to meet patient needs.”

To ensure results are maintained, managers use control charts and trend reports with data from HR, time and attendance and payroll systems. This provides real time information on productivity, tracking worked hours versus patient days to show alignment with targets on an ongoing basis.

Organizational and Customer Impact

The bottom line is that nurses, management and patients are all happier as a result of this project. With the pilot in the Control phase, Valley Baptist has held Work-outs to determine how they might broaden the SOPs and implement this approach across the system in all nursing units.

“Staff has become much more flexible. We initially encountered some resistance, but using the CAP tools and working through the process helped to create a shared need and vision.”

Leadership involvement and support turned out to be a significant factor in the overall success of the project. This initiative represented a major culture change from previous CQI and TQM approaches to quality improvement. All previous efforts had involved hard work and good intentions, but prior to Six Sigma, they lacked the framework and rigor to institute statistically valid long-term results.

The health system is moving toward autonomy through additional Green Belt and Black Belt training with projects, and through participation in a Master Black Belt course at GE’s Healthcare Institute in Waukesha, Wisconsin. This experience provides instruction and interaction that prepares the MBB to come back and teach within the organization.

“Coming from the HR side, it’s important for organizations to know it’s possible to change the way you’ve always done things, and that employees will adapt to a new approach. If you can overcome the stress surrounding change you can realize increased efficiency. This is a positive way to control staffing without employing slash and burn techniques.”

Irma Pye, senior vice president at Valley Baptist, attended a conference in Utah with other healthcare executives. When the issue of performance improvement and staffing came up, someone mentioned they’d attempted to do a project on this and it had failed because they couldn’t afford to alienate and potentially lose good employees. Irma spoke up and let them know that based on her own recent experience, you can indeed address this issue and it can work if it is approached in the right way using the right techniques.

“Usually, when you ask the department manager to trim labor costs they think it can’t be done because it will antagonize employees . . . they’ll either take a job somewhere else, or stay there with negative feelings which impacts morale. This approach was able to affect change, while avoiding issues of layoffs or pay cuts.”

Using Predictive Analytics to Help Seniors Maintain Their Independence

Evan McLaughlin 09 September, 2019

action-adult-care-339620-1

We might not be able to observe the progressive loss of cognitive and intellectual abilities someone with dementia is experiencing from the outside, but healthcare clinicians can detect it when they observe their ability to bathe, groom or dress themselves deteriorate. Minitab consultant  and Insights 2019 speaker David Patrishkoff is researching how to help with the aid of Minitab software.

Activities of Daily Living (ADLs)

The healthcare industry calls basic self-care tasks like one’s ability to properly feed themselves, move around or go to the bathroom “Activities of Daily Living,” or ADLs. Since the 1950s, healthcare professionals have scored ADLs with pre-set criteria (see this worksheet from the National Palliative Care Research Center for example).

After populating a worksheet like this, a healthcare professional can flag the functional capabilities of older adults and use the results to assess their ability to live independently.

What if symptoms could be caught earlier?
Enter Machine Learning and Predictive Analytics

David Patrishkoff

 

David Patrishkoff

There is evidence that deterioration of ADL scores are preventable. Screening can greatly help as the first step in the process too. For example, preventing elderly patients from falling has been shown to reduce the use of home healthcare, and the associated costs.

Building off of related research, Minitab consultant David Patrishkoff set out to use Machine Learning to help detect ADL deterioration earlier in the process and address it accordingly.

In healthcare, interventions are activities or strategies (such as screenings or vaccinations) to assess, improve, maintain, promote or modify health of individuals or groups. David uses Minitab Statistical Software and Salford Predictive Modeler (SPM) to examine 1,200+ interventions and therapies that nurses and home care workers provide to people across the country, and select the best ones to maintain or improve their independence and their ADLs.

“I start in Minitab with data visualizations and clean up the data, then jump into SPM for the really heavy lifting of very complex data sets,” David said. “I have columns of data where I have one of any 83,000 prescriptions that are prescribed to people. There are 43,000 diagnosis codes too. The algorithms in SPM can deal with highly dimensional data.”

A Master Black Belt who began his career in the automotive industry, David has consulted in about 60 different industries worldwide and trained nearly 30,000 professionals in Lean Six Sigma and patient safety.

Applying Predictive Analytics to Problem-solving

David first began using TreeNet in SPM to enhance his research into causes of traffic accident injuries and deaths, and he is applying some of the same methods now to ADLs and home care.

There is a belief that you have to be a data scientist coding in Python and R to handle these kinds of problems, he notes, but that’s not necessarily true. David recommends learning to use predictive analytics software like SPM to see how you can do better root cause analysis.

David also credits the 64-bit version of Minitab 19 with helping him with larger data sets he was unable to work with in previous versions.

“It helped me tremendously,” he said. “I had old files that were too big and then with the 64-bit Minitab 19 it further helped my analysis.”

What’s Next?

David has been speaking at conferences about his research and how classic Six Sigma and operational excellence practitioners can build on their knowledge of statistical methods to take the next step into the data science revolution. He plans to present and publish further findings next year on how to provide home healthcare clinicians a stable methodology to improve patient outcomes.

Fewer X-Ray Errors Reduce Cancer Risk, Wait Time and Costs Evan McLaughlin 27

Evan McLaughlin 27 November, 2019

Clinicians examining a radiograph

In hospital and clinic settings, making the right decisions doesn’t just reduce costs from duplicative work and process inefficiencies — it results in better outcomes for patients. Think about needing to take an extra X-ray because the first captured the wrong foot. Even if it’s the right limb, what if they captured it from the wrong angle?

Over the 14 years he worked in healthcare quality improvement, Art Wheeler saw this and many other process improvement scenarios. Most recently, as decision support manager for quality improvement services at one of the country’s largest not-for-profit freestanding pediatric healthcare networks, he was the primary statistician, as well as a mentor and coach for Six Sigma Black Belts and Green Belts, program managers and project leaders for 8 1/2 years.

An expert in statistical quality control, one of his key responsibilities was ensuring data was collected in a way that was sound and ensured the best chances for detecting statistical significance of any reported improvements. He also developed the charts and writeups for the analysis sections of corresponding published articles and responded to reviewer questions or comments to help ensure acceptance.

Remember that extra X-ray scenario we mentioned earlier? Art served as a consultant on a duplicate X-ray study, which found each unnecessary scan cost facilities an extra $150 to $300 and overall patients were waiting longer. One study of 18 US pediatric emergency departments showed radiology errors are the third most common event in pediatric emergency research networks and human errors rather than equipment issues caused 87% of them.

Besides reducing errors, the team were also motivated to achieve their goal of zero errors at two clinics so they could also reduce lifetime radiation exposure for individuals, which in turn diminishes their risk of developing cancer. Efforts like this were part of the hospital’s “Zero Hero” program – they would measure the time period and the number of cases involved, aim to reduce incidents to zero and record how long they maintained zero incidents.

It wasn’t all black and white though. They needed to understand the context behind the duplicate X-rays to truly make improvements. With a retrospective review of a 14-month period at two facilities, they knew there were good and bad reasons behind the 170+ duplicate X-rays that were recorded, for a duplicate radiograph. Each duplicate radiograph was classified as …

  1. No error, where they intentionally studied from multiple views;
  2. Incorrect location, when the patient’s initial complaint did not match the initial radiograph (e.g. the aforementioned wrong foot);
  3. Incorrect laterality, when it’s the wrong side; or
  4. Unnecessary radiograph, a known issue when a clinical athletic trainer preordered multiple radiographs without physician evaluation and assessment.

The Pareto chart below shows the most common error during the 14-month period was incorrect location.

pareto-chart-radiograph-error-classification-resize

The quality improvement team took steps to meet their zero percent goal in both clinics, which included issuing surveys to patients and families during registration to help document where they needed to be X-rayed and if they had been X-rayed in the past.

The unnecessary radiograph was also a known issue when a clinical athletic trainer preordered multiple radiographs without physician evaluation and assessment. An intervention was made to fix this, making physicians responsible for putting their own radiograph orders in the Electronic Medical Record.

Overall these steps improved communication between physicians, clinical athletic trainers, radiology technologists, patients and families, and greatly contributed to better outcomes for everyone involved.

The Performance Management Group (TPMG) announces its 3rd Quarter Lean Six Sigma Excellence in Healthcare Delivery Certification Graduates.

8/30/2018 – Phoenix, Arizona  USA

The Performance Management Group (TPMG) announces its 3rd Quarter Lean Six Sigma Excellence in Healthcare Delivery Certification Graduates.

TPMG Education Services would like to congratulate its 3rd Quarter 2018 Lean Six Sigma Certification Graduates.

This accomplishment acknowledges they have fulfilled the requirements for the program of study and, from this day forward, they are certified as Lean Six Sigma Green Belts and Black Belts.  This designation is conferred upon them as of Friday August 24, 2018.  They are now authorized to place their respective “LSSBB” or “LSSGB” designation, which acknowledges this credential, following their name.

Congratulations Lean Six Sigma Black Belt Certification Graduates:

  1. Joyce Taylor, Director – Telligen
  2. Janice L. Stanton ,  Manager of Pre-Design Services – Gresham, Smith and Partners
  3. Ann Hastings, Business Intelligence Data Analyst – St. Luke’s Health System
  4. Ismael Groves – Director of Program Operations, Consumerism – Banner Healthcare

Congratulations Lean Six Sigma Green Belt Certification Graduates:

  1. Landin Shan, Project Manager of Shanghai Market – Shanghai United Healthcare
  2. Solomon Fatima, A/R Management Supervisor – Shanghai United Healthcare
  3. May XU, Outpatient Cashier Supervisor – Shanghai United Healthcare
  4. Clement Qi, IT Manager of Shanghai Market – Shanghai United Healthcare
  5. Susan Fang, Clinical Manager – Shanghai United Healthcare
  6. Sabeen Irfan, Clinical Operations Manager – Shanghai United Healthcare
  7. Zhang Ying, Lab Associate Manager – Shanghai United Healthcare
  8. XiaoMeng Sun, Medical Staff Office Supervisor – Shanghai United Healthcare

For more information regarding lean six sigma training, certification and consulting – contact TPMG llc at 623.643.9837 or logon to http://www.helpingmakeithappen.com.

Patient Safety: Akron Children’s Hospital Uses Lean Six Sigma and Minitab in the NICU

Serious about Patient Safety: Akron Children’s Hospital Uses Lean Six Sigma and Minitab in the NICU

 Akron Children’s Hospital is serious about enhancing the patient experience, along with delivering quality healthcare in a timely, efficient manner. While the hospital formally established the Mark A. Watson Center for Operations Excellence in 2008, it has been performing quality improvement since its early beginnings 125 years ago. It’s no wonder the healthcare provider has consistently earned Best Children’s Hospitals rankings in 7 of the 10 specialties evaluated annually by U.S. News & World Report—including cancer, diabetes and endocrinology, pulmonology, neonatology, neurology and neurosurgery, and orthopedics.

The hospital encourages employees across all skill levels and departments to become involved in quality improvement, offering several levels of Lean Six Sigma training. As part of its green belt training and certification, employees learn to use Lean Six Sigma by leading and completing long-term projects with the guidance of experienced black belts.

One such green belt project, which began at the hospital’s Mahoning Valley, Ohio campus, had a goal to decrease one particular safety event—unplanned extubations in the hospital’s neonatal intensive care unit (NICU). To complete this project, the hospital improvement team relied on Lean Six Sigma tactics and the data analysis tools in Minitab Statistical Software.

The Challenge

Akron Children’s Hospital relies on Minitab Statistical Software to analyze their Lean Six Sigma project data. The hospital used Minitab to verify improvements made to the intubation process in the NICU.

An intubation is a medical procedure in which a breathing tube is placed into a patient’s trachea. This tube connects the patient to a machine called a ventilator, which helps the patient breathe. The procedure is common for both pediatric patients and adults in intensive care, but is most common for premature newborn babies residing in a hospital’s NICU. Babies born prematurely often have undeveloped lungs, which cause breathing problems and the need for the assistance of a ventilator.

Although this medical procedure is commonly performed, it is not without risk, and can cause trauma to or introduce an infection into the patient’s airway. Unplanned removal of the breathing tube, which is also known as an unplanned extubation, is a likely occurrence that can cause harm. Unplanned extubations are the fourth most common adverse event in NICUs across the U.S.

Akron Children’s Hospital’s Department of Respiratory Care had been collecting data on the rate of unplanned extubations in the Mahoning Valley NICU for well over a year, but had not had the capacity to investigate the occurrences further. Bonnie Powell, a Registered Respiratory Therapist and manager of respiratory services at Akron Children’s Hospital, was a green belt candidate during the time unplanned extubation data were collected. As part of her Lean Six Sigma training and certification, she set out to lead a project that would decrease the rate of unplanned extubations in the Mahoning Valley NICU.

“I knew this project was the perfect fit for me because as a respiratory therapist, I’ve been part of the frontline staff primarily responsible for intubating,” Powell says. “When you’re the one actually putting the tube into the patient, it just affects you more because you know the trauma that you could be causing to them.”

How Minitab Helped

While there’s not a true benchmark rate that NICUs should strive to stay below regarding unplanned extubations, the Vermont Oxford Network—a research collaboration of nearly 1,000 global NICUs including Akron Children’s—considers 2 in 100 intubated patient days to be the upper limit of acceptable. Previous data collected on the rate of unplanned extubations at the Mahoning Valley NICU revealed a rate of 3 in 100 intubated days.

“Any unplanned extubation has the potential to cause harm to the patient and negatively impact overall patient satisfaction,” says Powell. “We wanted to improve our performance on this metric.”

Powell’s Lean Six Sigma project team included a multidisciplinary group of nurses, respiratory therapists, a neonatal nurse practitioner, and a neonatologist.

The team began by using Lean Six Sigma tools to brainstorm reasons why unplanned extubations were occurring, as well as solutions for stopping them. “The fishbone diagram and cause maps were among the most helpful tools we used,” Powell says. “We looked at the highest impact solutions, as well as how easy they would be to implement, and prioritized solutions from there.

“This step helped us to organize and roll out our seven improvements into two phases,” she says.

Along with more frequent communication between nurses and respiratory therapists before, during, and after an intubation, as well as educational information distributed in meetings and via email, one improvement implemented was the “two to turn” rule. “Anytime an intubated patient is repositioned, one caregiver is turning the patient and another is holding the tube at the patient’s mouth,” Powell explains.

The team applied the improvements for several months, as collecting enough data to meet the required 100 intubated days for pre- and post-improvement comparison proved difficult for many reasons.

“There is a continuing trend in neonatal care to use devices such as masks and nasal prongs to connect the patient to the ventilator to help with breathing. When these devices are used, there is no need for a breathing tube, which reduces the number of intubated days and lengthened our post-improvement data collection period,” Powell says. “That, coupled with greater attention to our weaning protocol, which focused on shortening the time babies need ventilator support of their breathing, contributed to why we saw a reduced amount of intubated days.

“Of course, fewer intubated days was a good thing in this case, and supported the idea that our improvements were working,” adds Powell.

To compare unplanned extubations, pre- and post-improvement, the team visualized their data using control charts in Minitab Statistical Software.

Minitab graphs clearly reveal the impact of improvement efforts. This control chart displays the reduction in unplanned extubations after Lean Six Sigma improvements were implemented.

To verify their results statistically, the team ran a 2 proportions test in Minitab to see if their unplanned extubation rates decreased after improvements were put into place.

Hypothesis testing in Minitab makes it easy to determine if there is enough evidence in a sample of data to infer that a certain condition is true for an entire population.

The analysis showed the team that after improvements were implemented, the unplanned extubation rate had indeed decreased.

The team also used Minitab to perform process capability analysis both pre- and post-improvement. This tool provided another before-and-after comparison of unplanned extubation rates, and aided the project team in assessing whether the new process was capable and in statistical control.

“I have never taken a statistics course and have no background in this type of work,” Powell notes, “but Minitab, coupled with the instruction I received from the Center for Operations Excellence, made it easy for me to analyze and understand my data.”

Trauda Gilbert, deployment leader for the Center for Operations Excellence at Akron Children’s, echoes Powell. “To be able to use Minitab to visually demonstrate the before and after effect with a control chart, which you can then share with your team and champion is really valuable. Minitab also makes it easy for front-line staff to document that they have made a statistically significant difference. To be able to do that without having to interact with a biostatistician or one of the other very rarely found statistical resources in our organization, is very beneficial,” she notes.

“Healthcare quality is a little different than manufacturing because we can’t just run a DOE and tweak a process line,” says Gilbert. “Even though we’re different, Minitab still helps us out.”

Results

The data revealed a dramatic reduction in intubated days after the improvements were made, as well as a considerable reduction in the rate of unplanned extubations at the Mahoning Valley campus. The reductions brought their rates in line with the Vermont Oxford Network’s suggestion of 2 unplanned extubations in 100 intubated patient days.

“This project showed us that simple improvements can create real change,” says Powell. “The cultural change this project instilled in our team was exciting—the recognition that even they could make a difference is huge.”

Cost savings resulting from the reduction in supplies and staff time needed to care for unplanned extubations can be calculated, but the overall financial impacts are hard to quantify. “The larger costs of unplanned extubations—such as a longer NICU length of stay, ventilator-associated pneumonia, and other setbacks that the patient can experience from the event—can be difficult to tease out,” Powell says.

“Neonatal patients are some of our key customers here,” she continues. “Due to the fact that they were born early, they come back to our institution for care frequently, especially initially. Making sure they have a safe experience early is critical, because the results of good care at this stage can have exponential benefits for patients in the future.”

In addition to improving the patient experience, the project helped Powell obtain her Lean Six Sigma belt certification. “I did get my green belt as a result, and we’ve also rolled out selected improvements to the NICU at our Akron campus,” she says. “We’re in the process of collecting data there as well, so this project didn’t just stop in Mahoning Valley.”

Powell’s project is just one example of an estimated 300 documented projects that have been completed throughout the Akron Children’s organization. The total financial savings of the hospital’s operations excellence program is estimated to be more than $25 million since its official beginnings in 2008.

Learn more about lean six sigma in healthcare :  Six Sigma Master Class – Improving Healthcare Processes

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