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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.

Lean Healthcare Project Teams

Six Modest Proposals for Health Care Measurement

 

1. The Streetlight Effect and Measuring What Matters

It was dark and a man lost his keys. He searches for them under a streetlight, and a friend comes over to help. Eventually, the friend asks, “Are you sure you lost your keys here?” The man says, “No. I lost them in the park.” So the friend asks, “Why are we looking here?” The man answers, “Because this is where the light is.”

This story describes a form of observational bias called the streetlight effect, in which we look for things where it’s easy, not where it’s important.

“I would argue that we do this all the time in health care measurement,” says Ari Robicsek, Chief Medical Analytics Officer for Providence St. Joseph Health.

Every hospital measures length of stay, for example, and many use this measure as a surrogate catch-all for quality and efficiency. “But let me ask, who really cares about length of stay?” says Robicsek. “Is it patients? Is that the first metric that comes to mind when a patient is thinking about hospital quality? Is it doctors? Probably not. Is it administrators? Even from an administrative point of view, you’re not going to realize the financial benefit of reduced length of stay, unless at the same time you reduce labor, or you find ways to fill those empty beds with paying customers, which is a much more complex measure than simply looking at length of stay.”

Length of stay may not be a great measure, but if we have to start somewhere with health care measurement, what’s the harm in tracking it? “If we assign resources to working on the wrong problem, those resources aren’t working on the right problem,” warns Robicsek.

Additionally, with length of stay specifically, a big push to get patients out the door risks sending them home before they’re ready — and when that happens, those patients may end up with complications and get readmitted. “We see one streetlight metric, length of stay, giving birth to another streetlight metric, 30-day readmissions, and so on,” he says.

“My modest proposal: We should measure the things that matter,” says Robicsek. “Yes, sometimes that’s going to mean that we need to collect data differently than the way we do today, or, said another way, sometimes we’re going to have to put up some lights in the park.”

2. Balancing Risk Adjustment

Map Showing Distribution of Glycemic Control in Diabetic Patients on Chicago North Shore

Map showing distribution of glycemic control in diabetic patients on Chicago’s North Shore. Click To Enlarge.

Robicsek shares a map showing distribution of glycemic control in diabetic patients on Chicago’s North Shore, where green is good and red is bad. The map overlays closely with an income map.

If we set up a bonus program for primary care doctors where they receive more money if their patients have better glycemic control, it’s easy to guess where most physicians will want to practice. This is why we need risk adjustment.

“Absent good risk adjustment, physicians working in disadvantage geographies are going to have the worst-looking outcomes,” Robicsek explains. “They’re going to get paid less. The poor get poorer, etc. Absent good risk adjustment, physicians are going to have an incentive to cherry-pick, that is, focus on the patients who are going to make them look good.”

“But with good risk adjustment, we have the opportunity to identify those providers who are outperforming expectations, who are doing a great job with the difficult-to-manage patients, and we can learn from them.”

There are disadvantages to risk adjustment, however, when done poorly. The most common problem is doing little more than creating the illusion that risk adjustment has occurred. “A lot of the risk adjustment models in use are lousy, including some of the ones used by CMS (Centers for Medicare and Medicaid Services),” Robicsek says. “I would argue that those do very little other than creating the patina of fairness, and I would argue when that happens, we’ve probably done more harm than good with risk adjustment.”

Another concern is that sometimes risk adjustment can justify outcome disparities that are amenable to management. A blood-pressure management metric risk-adjusted on race, for example, could remove the incentive for physicians to determine how to manage blood pressure in African-American patients, perversely promoting or entrenching existing inequalities.

“My proposal here: For every new measure that we build, we need to have a conversation about what amount of risk adjustment is enough,” says Robicsek.

3. Measuring to Learn

How much is enough? When we can learn from the measure, he explains. “So much of the health care measurement that we do is for the purpose of rank-ordering or some form of reward or punishment. I would argue that most of the measurement that we do should be taking into account the fact that, as humans, we’re curious and we’re altruistic — most of it should be to learn.”

Providence St. Joseph Health Total Knee Replacement Direct Variable Cost per Case - Ratio of Cost Doctor to System

Providence St. Joseph Health total knee replacement direct variable cost per case and ratio of cost, doctor to system. Click To Enlarge.

In a graph of total knee replacement at Providence St. Joseph Health, each circle represents one high-volume orthopedic surgeon. Each of these surgeons performs high volumes of elective primary unilateral total knee replacement, and they all have great outcomes. But the difference between them is cost.

Every circle above the line represents a surgeon whose cost per case is high. Every circle below represents a surgeon who cost per case is low relative to their colleagues.

Are the doctors low on implant costs consistently low across other elements of care? Not necessarily. “In medicine, variation is the state of nature,” says Robicsek. “There are almost no clinicians who are consistently high cost, or consistently low cost across these elements of care. My takeaway from this is that we all have an opportunity to learn from each other.”

“My modest proposal: Most of the health care measurement that we do should not be for reward or punishment. It should be to learn.”

4. Whose Patient Is It?

Providence recently evaluated OPPE (Ongoing Professional Practice Evaluation), which the health system uses, and found that it was assigning 40% of hospital patients to the wrong doctor. “Who can blame them?” Robicsek asks. “It’s easy in a hospitalization for a patient to have three different, or five different, attendings of record. How do you know who to assign that patient to?”

“In a world where we’re measuring for reward and punishment, we feel obligated to assign one outcome or one hospitalization to a single clinician, but imagine if we were able to move away from that and we were measuring to learn,” he says. “Then we would have the ability to do things like ignore who the provider was and ask ourselves what specific elements of care, what specific combinations of behaviors, lead to the best outcomes.”

“Or we could recognize that medicine is a team sport,” he adds. “Let’s ask the question, can we tie outcomes to teams rather than to individuals? My modest proposal here: We practice in teams. Let’s recognize that in the way we measure.”

5. Metrics Aren’t Free

“To anyone who has ever said, ‘Let’s just add one more thing to this dashboard’: Metrics are not free.”

“Every time we build the metric, if it is done correctly, somebody needs to build business specs, technical specs. Someone needs to do data governance, coding. Somebody needs to do validation, automation, documentation, visualization, and then somebody needs to maintain the thing moving forward. Easily that’s a cost of $10,000,” says Robicsek.

6. The “Give a Darn” Test for Health Care Measurement

When measuring what matters, how do we know what that is? Robicsek describes a thought experiment where he sits with a small group of physicians considering a metric. “Imagine I told you that you’re doing better than your colleague on this measure,” he says to them. “Would you feel good about yourself? Imagine I told you you’re doing worse than your colleagues on this measure. Would you feel motivated to change your practice?”

“If the answer to both of those questions is not yes, let’s not build this measure. It’s not worth our time. We’ll go focus on something else.”

Providence St. Joseph Health Give a Darn Test for Health Care Measurement - Reviewing One Measure in a Small Group

  Click To Enlarge.

“Sitting at the front of this room is my partner in crime, Dr. Caleb Stowell, looking like the cat who ate the canary. He’s showing [the surgeons] the results of the process that I’ve described. They’re measuring to learn. He’s identified a measure that passes the ‘give a darn’ test for them, and some of those surgeons are literally leaning in. I work for 51-hospital system, but where this change happens, where you win hearts and minds, is in rooms like this.”

Robicsek’s final proposal: Try the “give a darn” test for health care measurement. And note that in many “give-a-darn” conversations, one metric that comes up as incredibly important to physicians is patient-reported outcomes.

From the NEJM Catalyst event Provider-Driven Data Analytics to Improve Outcomes, held at Cedars-Sinai Medical Center, January 31, 2019.

 

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.

How Hospitals Can Raise Patient Satisfaction, CAHPS Scores

Sara Heath

Editor
sheath@xtelligentmedia.com

Improving patient satisfaction scores, such as CAHPS, is key for driving practice reputation and reimbursements.

Healthcare organizations with high patient satisfaction and CAHPS scores see a multitude of benefits. High patient satisfaction scores usually result in higher reimbursement payments from CMS, better patient retention rates, and the assurance for hospital staff that they fostered a positive experience for patients.

A May 2016 report from Vocera showed that patient satisfaction is the top-ranked priority at healthcare organizations. Due to the importance of ensuring favorable feedback from patients, the demand for patient experience officers and patient advocate executives is increasing, with these professionals pulling equal rank with other C-suite executives, the report said.

The primary measure for patient satisfaction is the Consumer Assessment of Healthcare Providers and Systems (CAHPS). The CAHPS survey is developed and funded by the Agency for Healthcare Research and Quality (AHRQ) in partnership with CMS, and forms a component of some value-based reimbursement programs.

CMS also uses CAHPS scores to inform its star ratings, which are publicly available ratings about the quality of healthcare facilities.

Several types of CAHPS surveys are utilized throughout the care continuum, ranging from hospitals to nursing homes to health plans. However, the Hospital CAHPS (HCAHPS) and Clinician and Group CAHPS (CGCAHPS) are the most prominent and commonly used surveys.

Both surveys measure many of the same factors, including nurse care, doctor care, and facility environment.

The HCAHPS survey also includes questions about experiences within the hospital, including pain management, and continuity of care experiences.

CGCAHPS surveys target their questions to the general practitioner, asking questions about ease of healthcare access and how often the patient has been visiting the office.

Because HCAHPS and CGCAHPS are used for both reimbursement and patient rating purposes, it is important for healthcare organizations to improve their scores. Healthcare organizations can improve their CAHPS scores by understanding what is important to patients, what the surveys measure, and how to meet patient needs.

Improving Patient-Provider Communication

Provider Picture

The first two sets of HCAHPS questions pertain to nurse and physician communications with patients. These questions ask whether nurses and physicians communicated clearly with patients, and whether patients understood their diagnoses, prognoses, and treatment options.

Clear communication about healthcare information is integral to a positive healthcare experience, experts say. Hospitalization is often a stressful and worrying time for patients, and made even worse when clinicians do not adequately communicate what is going on and how they will treat a patient’s ailments.

In addition to allaying patient worry, providing meaningful explanations of conditions and treatments will help the patient taken ownership of her own health.

“Patients have a need for information,” explained Deirdre Mylod, PhD, Executive Director of the Institute for Innovation and Senior Vice President of Research and Analytics at Press Ganey.

“It’s not just making consumers happy to meet that need, but it’s also providing the right care. When you give people the right information, they can engage in care, they can be active participants, they’re better prepared to care for themselves at home, they’re less likely to be readmitted.”

Clear communication will require collaboration between the different members of the care team, added Mylod.

“As a patient, when one team member tells me one thing and somebody else tells me another, now I’m afraid and I’m thinking you’re not working together. Now I’m more scared than I need to be in a hospital,” she pointed out.

HCAHPS also asks patients whether nurses and physicians treated them with respect and empathy. Clinicians must tap into their interpersonal skills to provide compassionate care to their patients, while being mindful of cultural norms and barriers.

The healthcare industry might be falling short in this respect. A January 2017 survey conducted by Oliver Wyman and the Altarum Institute found that 40 percent of low-income patients have walked away from appointments feeling disrespected.

The survey, funded by the Robert Wood Johnson Foundation, showed that in addition to reducing patient satisfaction, lacking compassion also lowered quality of care. Patients who felt disrespected were three times less likely to trust their clinicians, and two times less likely to adhere to treatments.

Healthcare organizations should support their clinicians in pursuit of being more empathic. Organizations can host cultural sensitivity seminars, work with patients to continue to develop their interpersonal skills, and educate clinicians on evidence-based best practices for enhancing patient-provider communication.

Improving the Physical Hospital Environment

Hospital Setting

Two HCAHPS questions pertain to the hospital environment: hospital cleanliness and hospital noise levels.

In order to maintain an appropriately clean and sanitary facility, organizations must support their custodial staff and reinforce the importance of a healthcare facility being clean.

The American Hospital Association has long advocated for improving the hospital setting for patient satisfaction. In a 2016 guide, AHA listed the ways in which organizations can create environments more suitable for patient rest and recovery.

To create a quiet and peaceful environment, AHA says hospitals should implement and enforce rules about quiet hours and lights-out times.

“It makes sense that patients rate hospitals poorly when they cannot get good sleep or rest and have the additional stress of noise added to the already stressful situation of being unwell,” AHA wrote. “Data shows that noise in hospitals is the factor that scores lowest on HCAHPS scores nationwide.”

Healthcare organizations can take it a step further than HCAHPS mandates. Many hospitals are turning to their patients to inform room design that will facilitate a more comfortable experience.

When designing its new facilities in Delaware and Orlando, leaders at Nemours Children’s Health consulted with its patient and family advisory board to decide which features would best suit pediatric patient rooms.

“The parents came in and tested all of the furniture that they might be sleeping on in the rooms. They provided input into what we actually purchased,” recalled Nemours Chief Information Officer Bernie Rice.

“The children came in as well and helped pick colors and room layouts as far as if the counter was too high,” he continued. “They were very valuable and heavily influenced our construction and design to make sure it was a very family- and patient-friendly environment.”

Being Attentive and Reducing Unnecessary Discomfort

Improving Patient Discomfort

One highly-debated part of patient experience surveys is pain management. Amidst a raging opioid abuse epidemic, many experts question whether pain management should be a part of patient satisfaction scores that result in provider reimbursements. By tying payments to pain management, some clinicians may feel compelled to prescribe opioids when there could be other potentially less-risky forms of pain management.

In November 2016, CMS removed the pain management questions from the HCAHPS survey. However, the agency maintained that pain management is an important part of patient care and experience.

“CMS continues to believe that pain control is an appropriate part of routine patient care that hospitals should manage, and is an important concern for patients, their families, and their caregivers,” CMS said in a public statement. “CMS is continuing the development and field testing of alternative questions related to provider communications and pain, and will solicit comment on these alternatives in future rulemaking.”

While the pain management portions of the HCAHPS survey are currently under construction, clinicians should still work to reduce unnecessary patient discomfort.

Press Ganey is adopting this approach when consulting on patient experience, Mylod said.

“The way that we approach improvement for patient experience measures is to reframe it,” she explained. “The exercise is not to make consumers happy. The exercise is to reduce patient suffering.”

To boost scores in this realm, Mylod suggests clinicians – especially nurses – become even more attentive. This means not only answering call buttons, but also making regular rounds to hospital beds to ensure they meet all patient needs.

During these rounds, nurses can ask if the patient needs assistance using the restroom or if they need an object, such as a television remote, handed to them. Paying attention to these seemingly inconsequential needs could reduce adverse safety events, Mylod explained. If a patient gets up to retrieve a book, for example, he could fall and hurt himself, affecting the patient experience, increasing length of stay, or requiring additional expenses related to an injury.

Streamlining discharge processStreamlining the Discharge and Follow-up Process

HCAHPS asks patients about how doctors and nurses managed continuous care and the discharge process. The survey asks whether clinicians checked in on post-discharge care plans, made it clear which provider will follow-up with ongoing needs, and whether that care will be adequate for the patient’s condition.

At patient advocacy group Planetree, leaders have developed a hospital discharge plan to ensure clinicians meet patient needs.

The plan includes identifying a family care partner that will help take care of the patient following hospital discharge, said Planetree’s Director of Research Jill Harrison, PhD.

From there, clinicians check in with the patient and appointed caregiver to determine which functions they will need to learn for optimal at-home care.

“Planetree has a program that allows people to say that they want help with wound changes, or help ambulate their loved one, or help check a tracheotomy if the patient has one,” Harrison said. “Caregivers go through a training program with the nursing staff and learn how to provide that care so that when patients get out of the hospital setting their family members are ready to take that all on.”

Other key healthcare players are advocating for a similar strategy. AARP has been sponsoring a law in state legislatures across the country to support family caregiver engagement. The organization says caregiver engagement will help support continuity of care.

Research confirms that family caregiver engagement can reduce hospital readmissions by up to 25 percent.

Hospitals that implement family caregiver engagement and discharge plans may see not only increases in HCAHPS scores, but in quality of healthcare, as well.

The importance of improving patient satisfaction and CAHPS scores is well-founded. These scores help inform CMS value-based reimbursements and hospital ratings published on the CMS website. Many healthcare organizations also use these scores to inform their own internal practice improvement processes.

However, when it comes to improving patient satisfaction, it is also important for practice leaders to look beyond the survey. Improving patient satisfaction means understanding the facility’s unique patient population and its needs. What will please one group of patients may not satisfy another, and hospital leaders must bear that in mind.

While supporting initiatives specifically geared toward improving CAHPS scores, healthcare organizations should also consider projects that will serve their unique population.

Issuing practice-specific patient input surveys or consulting with a patient advisory council will help healthcare organizations move beyond surface-level satisfaction and find solutions that will be truly meaningful for patients.

I trust this article has provided you with insight and approaches that can help you pinpoint those drivers that most strongly influence a patient’s willingness to recommend a hospital. If you are interested in learning more about using these methods, contact us at:  TPMG Global® – Improving HCAHPS Scores and The Patient Experience

The Heart of the Matter: Hospital’s Improved Diagnostic Process Saves Lives and Money

You expect to find many lifesaving techniques in hospitals—expensive medical research, groundbreaking procedures—but when it comes to treating patients with cardiovascular disease, the approach one Taiwanese hospital used might surprise you: data analysis.

Heart disease is one of the leading causes of death in Taiwan, so it’s no wonder the country’s healthcare professionals are looking for ways to improve treatment options.

That’s why a Lean Six Sigma project team at Cathay General Hospital in the city of Taipei examined the emergent angioplasty process for treating patients suffering from acute ST-elevation myocardial infarction (STEMI), a heart attack caused by coronary heart disease. Improving aspects such as the wait time between diagnosis and treatment could help to save many lives.

Doctors and quality managers from the hospital’s Quality Management Center used Minitab Statistical Software to assess the hospital’s process and confidently re-engineer both the diagnosis and treatment processes while increasing savings in medical resources.

The Challenge

A project team at Cathay General Hospital used Minitab Statistical Software to analyze data that would improve treatment for patients suffering from heart attacks. Above, the hospital is shown in its Taipei City, Taiwan, location.

Patients with STEMI are diagnosed through electrocardiogram findings and cardiac markers, and the recommended course of treatment for these patients is angioplasty completed within 90 minutes of arrival.

Medical professionals refer to this period as the door-to-balloon (D2B) time, because angioplasty involves inserting a small balloon inside the blocked blood vessel with a catheter. When inflated at the site of the blockage, the balloon enables blood flow to resume.

To maximize the patients’ chances for survival, the team needed to evaluate each step of the process. They needed to identify which variables were responsible for a D2B time that exceeded the recommended treatment time, and, more importantly, what adjustments could be made to minimize it.

How Minitab Helped

The team analyzed D2B time—which includes an electrocardiogram, the wait time before the operation, and the time for balloon inflation—using Minitab Statistical Software.

However, you can only trust the results of an analysis if you trust the data you’re analyzing. To ensure the data were trustworthy, the project team used Minitab to conduct a Gage R&R Study of their measurement system. This method evaluates a system’s precision, including its repeatability and reproducibility to ensure that measurements are consistent and reliable.

Minitab’s Assistant menu makes it easy to choose and use the right tool, even if you’re not a statistician. The dialog box above helps users create a data collection worksheet for a Gage R&R study.

Once they verified the precision of their measurements, the team analyzed D2B data from 40 STEMI cases that occurred over a nine-month period.

First, they tested the data to see if it followed a normal distribution, which is a key assumption in many types of analysis. The data were not normally distributed, but using Minitab the team easily applied a Box-Cox transformation to normalize it. The team then used the transformed data to create an I-MR control chart to evaluate if their process was stable over time. This type of control chart plots both individual observations (I) and the moving ranges (MR) to show how the mean and variation in the observations change over time.

The I-MR control chart above displays the normalized data from the Box-Cox transformation and identifies unusual sources of variation in the data.

The project team also used Minitab to conduct a process capability analysis to determine whether their process met performance specifications and provide insight into how they might improve their process. In this case, the upper specification limit for D2B time was 90 minutes. The results of the capability analysis confirmed that the hospital’s handling of STEMI cases had significant room for process improvement.

The team examined each step in handling a STEMI patient and identified several areas in which efficiency could be significantly enhanced, including confirming the diagnosis, medicating the patient, preparing for the operation, transferring the patient to the catheterization laboratory, and inflating the balloon.

Results

After assessing the STEMI process, the team implemented improvements such as sending patients who arrive with chest pain directly to an electrocardiogram test, printing treatment sheets automatically as opposed to writing them by hand, making a STEMI medication pack available in the emergency department, contacting the catheterization staff upon diagnosis confirmation, prepackaging all STEMI operation equipment in one box, and discontinuing the use of operation time as a forum to teach staff members who are not familiar with the procedure.

The team then collected additional data and reevaluated the process. Using Minitab to analyze the new data, the team demonstrated that the average D2B time dropped from 139.2 to 57.9 minutes—a 58.4% improvement. Furthermore, capability analysis showed that this new process could meet specifications.

A more efficient process means patients receive angioplasty more quickly, which saves lives. Moreover, the average hospital stay for STEMI patients has decreased by three days since the new process was implemented, and the hospital has saved $4.4 million in medical resources. The project was recognized by the Taiwan Joint Commission of Hospital Accreditation, and was awarded the Symbol of National Quality by the Institute for Biotechnology and Medicine Industry.

Applying data analysis and Lean Six Sigma methods to the health care system doesn’t grab headlines like an experimental surgery might. But as more hospitals use data analysis to make procedures better, faster, and safer, its benefits will be seen every day in the faces of patients whose lives are saved.

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

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

Mapping the Healthcare Value Stream

Using Six Sigma to Reduce Pressure Ulcers at a Hospital

Since 2001, Thibodaux Regional Medical Center (TRMC) in Louisiana has applied Six Sigma and change management methods to a range of clinical and operational issues. One project that clearly aligned with the hospital’s strategic plan was an initiative to reduce nosocomial or hospital-acquired pressure ulcers, because this is one of the key performance metrics indicating quality of care.

Although the pressure ulcer rate at the medical center was much better than the industry average, the continuous quality improvement data detected an increase between the last quarter of 2003 and the second quarter of 2004.

In October 2004, a Six Sigma project to address this issue was approved by the hospital’s senior executives. A team began to clarify the problem statement. Their vision was to be the “Skin Savers” by resolving issues leading to the development of nosocomial pressure ulcers. The project team included a Black Belt, enterostomal therapy registered nurse (ETRN), medical surgical RN, ICU RN, rehab RN and RN educator.

Scoping the Project

Through the scoping process, the team determined that inpatients with a length of stay longer than 72 hours would be included, while pediatric patients would be excluded. The project Y was defined as the nosocomial rate of Stage 2, 3 and 4 pressure ulcers calculated per 1,000 patient days. Targets were established to eliminate nosocomial Stage 3 and Stage 4 pressure ulcers and reduce Stage 2 pressure ulcers from 4.0 to less than 1.6 skin breaks per 1,000 patient days by the end of the second quarter of 2005.

The team developed a threats and opportunities matrix to help validate the need for change (Table 1). They encountered some initial resistance from staff, but were able to build acceptance as the project began to unfold.

Table 1: Threats and Opportunities Matrix
Threat Opportunity
Short Term Increase length of stay Improve quality of care
Increase costs Decrease medical complications to patient
Increase medical complications to patient
Long Term Decrease patient satisfaction Improve preventative care measures
Increase morbidity rate Improve hospital status/image
Decrease physician satisfaction Increase profitability
Increase number of lawsuits Improve customer satisfaction
Decrease reimbursement
Loss of accreditation

Measurement and Analysis

During the Measure phase, the team detailed the current process, including inputs and outputs. Using cause and effect tools, process steps having the greatest impact on the customer were identified as opportunities for improvement. The team also reviewed historical data and determined that overall process capability was acceptable, but that the sub-processes had a great deal of room for improvement. Improving these sub-processes would positively affect the overall process and further improve quality of care.

Measurement system analysis on the interpretation of the Braden Scale was performed to verify that results obtained by staff RNs were consistent with the results obtained by the enterostomal therapy RN, because this is the tool used to identify patients at risk of developing a pressure ulcer. This analysis indicated that the current process of individual interpretation was unreliable and would need to be standardized and re-evaluated during the course of the project.

A cause and effect matrix was constructed to rate the outputs of the process based on customer priorities and to rate the effect of the inputs on each output (Figure 1). The matrix identified areas in the process that have the most effect on the overall outcome, and consequently the areas that need to be focused on for improvement (Table 2).

The team identified several critical Xs affecting the process:

  • Frequency of the Braden Scale – The Braden Scale is an assessment tool used to identify patients at risk of developing pressure ulcers. Policy dictates how frequently this assessment is performed.
  • Heel protectors in use – Heel protectors are one of the basic preventative treatment measures taken to prevent pressure ulcers.
  • Incontinence protocol followed – Protocol must be followed to prevent against constant moisture on the patient’s skin that can lead to a pressure ulcer.
  • Proper bed – Special beds to relieve pressure on various parts of the body are used for high-risk patients as a preventative measure.
  • Q2H (every two hours) turning – Rotating the patient’s body position every two hours is done to prevent development of pressure ulcers.

Figure 1: Cause-and-Effect Matrix

Table 2: Data Analysis

Process

Defects

Opportunities

% Defective

Z Score

Overall Process

64

16,311

0.39

2.66

Braden Scale Frequency

10

76

13.16

1.12

Proper Bed

24

76

31.58

0.48

Q2H Turning

49

76

64.47

-0.37

Data analysis revealed that the bed type was not a critical factor in the process, but the use of heel protectors, incontinence protocol compliance, and Q2H turning were critical to the process of preventing nosocomial pressure ulcers. The impact of the Braden Scale frequency of performance was not identified until further analysis was performed (Figure 2).

Figure 2: One-Way Analysis of Means for Sub-Process Defects

Evaluating data specific to at-risk patients, the team separated populations who developed nosocomial pressure ulcers from those who did not have skin breakdowns. The Braden Scale result at the time of inpatient admission from each population was analyzed to see the effect on development of a nosocomial pressure ulcer. One unexpected finding was that the admit Braden Scale result was higher for patients who develop nosocomial pressure ulcers than for those who do not develop them, showing that patients at risk are not being identified in a timely manner, thus delaying the initiation of necessary preventative measures.

The team then looked at defects for Braden Scale frequency of performance for each population of patients using a chi square test. They found the frequency of Braden Scale performance did have an effect on the development of nosocomial pressure ulcers. This was confirmed with binary logistic regression analysis (Table 3).

Table 3: Binary Logistic Regression Analysis
Process

Coefficient

Odds

Probability

Odds Ratio

No Defects

–0.5222

0.59

0.37

N/A

Braden Scale Defects

2.54322

7.55

0.88

12.72

Bed Defects

1.56220

2.83

0.74

4.77

Q2 Turn Defects

–2.16870

0.07

0.07

0.11

The most significant X is the Braden Scale frequency of performance. This analysis confirmed the need to increase the frequency of Braden Scale performance to identify at-risk patients.

Recommendations for Improvement

During the Improve phase, recommended changes were identified for each cause of failure on the FMEA with a risk priority number of greater than 200. Some of the recommendations include:

  • Frequency of Braden Scale performance to be increased to every five days
  • Braden Scale assessment in hospital information system (HIS) to include descriptions for each response
  • Global competency test on interpretation of Braden Scale to be repeated annually
  • Prompts to be added in HIS to initiate prevention/treatment protocols
  • ET Accountability Tracking Tool to be issued for non-compliance with prevention and treatment protocols as needed

The Braden Scale R&R was repeated after improvements were made on the interpretation of results. The data revealed an exact match between RNs and the ETRN 40 percent of the time, and RNs were within the acceptable limits (+/– 2) 80 percent of the time. Standard deviation was 1.9, placing the results within the specification limits. The data indicated that the RNs tend to interpret results slightly lower than the ETRN, which is a better side to err on because lower Braden Scale results identify patients at risk of developing pressure ulcers.

The Control Phase

Another round of data collection began during the Control phase to demonstrate the impact of the improvements that had been implemented. A formal control plan was developed to ensure that improvements would be sustained over time, and the project was turned over to the process owner with follow-up issues documented in the Project Transition Action Plan.

The team implemented multiple improvements, including compilation of a document concerning expectations for skin assessment with input from nursing and staff. They also gave a global competency test on interpretation of the Braden Scale, which will be repeated annually. The Braden Scale frequency was increased to five days, and they corrected the HIS calculation to trigger clinical alerts for repeat of the Braden Scale. Prompts were added for initiating the Braden Scale, and monthly chart audits were developed for documentation of Q2H turning. A turning schedule was posted in patient rooms to identify need and document results of Q2H turning of patient. Additional solutions included the following:

  • ETRN to attend RN orientation to discuss skin issues
  • Revise treatment protocol to be more detailed
  • Wound care products to be reorganized on units
  • Unit educators to address skin issues during annual competency testing
  • CNA and RN to report at shift change to identify patients with skin issues
  • Task list to be created for CNAs
  • ET accountability tracking tool to be issued for non-compliance with prevention and treatment protocols as needed

Results and Recognition

Since this was a quality-focused project, the benefits are measured in cost avoidance and an overall improved quality of care. A 60 percent reduction in the overall nosocomial pressure ulcer rate resulted in an annual cost avoidance of approximately $300,000.

To make sure their initiatives are producing a positive impact on the patient care environment, the hospital continuously measures patient and employee satisfaction through Press Ganey. Inpatient satisfaction is consistently ranked in the 99th percentile and employee satisfaction in the 97th percentile. TRMC also has received recognition in the industry for their achievements, including the Louisiana Performance Excellence Award for Quality Leadership (Baldrige criteria), Studer Firestarter Award and Press Ganey Excellence Award.

“This project is a perfect example of the need to verify underlying causes using valid data, rather than trusting your instincts alone,” said Sheri Eschete, Black Belt and leader of the pressure ulcer project at TRMC. “Six Sigma provided us with the tools to get to the real problem so that we could make the right improvements. There had been a perception that not turning the patients often enough was the issue, but the data revealed that it was really the frequency of the Braden Scale. Leveraging the data helped us to convince others and implement appropriate changes.”

The nosocomial pressure ulcer rate is monitored monthly as one of the patient-focused outcome indicators of quality care. The results are maintained on the performance improvement dashboard (Figures 3 and 4).

Figure 3: Stage 3 and 4 Nosocomial Ulcers

Figure 4: Stage 2 Nosocomial Ulcers

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

Lean Six Sigma in Healthcare: Improving Patient Satisfaction

Riverview Hospital Association
For providers like Riverview Hospital Association, serving Wisconsin Rapids, Wis. and surrounding areas, recent changes in the U.S. healthcare system have placed more emphasis on improving the quality of care and increasing patient satisfaction. “In this era of healthcare reform, it is even more essential for providers to have a systematic method to improve the way care is delivered,” says Christopher Spranger, director of Lean Six Sigma and Quality Improvement at Riverview Hospital Association. “We have had a Lean Six Sigma program in place for four years, and we are continuously working on ways to make our hospital safer and more efficient.

The Challenge

Under what is known as Hospital Inpatient Value-Based Purchasing (VBP), a portion of the Medicare payments hospitals receive are tied directly to patient satisfaction metrics and the quality of care, rather than entirely on the volume of Medicare patients treated. A new rule from the Centers for Medicare and Medicaid Services (CMS) grants Medicare incentive payments to hospitals that are meeting high quality of care standards, or have shown sufficient improvements. Hospitals that do not meet these standards are subject to a reduction in Medicare payments. “Nearly all hospitals treat a sufficiently large percentage of Medicare patients,” says Spranger, “so this rule presents a significant challenge with substantial financial implications for us—and for many other hospitals.”

Incentive payments are determined by how well hospitals score on two point-based domains. One domain takes into account the clinical process of care, where the hospital is judged on its performance in meeting twelve predefined clinical measures. The second domain is based upon the overall patient experience and measured through a survey called the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). HCAHPS scores depend on the percentage of positive responses received for each question within each specific ”dimension,“ or category. Dimensions include communication with doctors and nurses, clarity of discharge information, overall hospital rating and more.

When Spranger and the Lean Six Sigma team at Riverview discovered that the hospital’s overall HCAHPS survey scores were lower than desired for the discharge information dimension, they set out to improve scores with Lean Six Sigma techniques and data analysis.

How Data Analysis Helped

The Lean Six Sigma team’s goal was to increase the percentage of “yes” responses for the following yes/no HCAHPS survey question: “During this hospital stay, did doctors, nurses, or other hospital staff talk to you about whether you would have the help you needed when you left the hospital?” To start, the team assessed their baseline performance by plotting outcomes from the previous 24 months with a Minitab control chart. The chart revealed a stable process and a current average positive monthly response rate of 85 percent. The goal was to improve positive monthly responses and exceed the preset benchmark of 91 percent.

Minitab Control Chart

To determine underlying factors that could be causing low scores, Spranger and the team chose to analyze data the hospital already had on-hand about the patient, which was collected as part of the HCAHPS survey. They had access to the responder’s age, gender, length of stay, primary language, education level, hospital unit visited and more. With Minitab charts, histograms and plots, they were able to explore the variables and responses graphically.

The team analyzed the variables further, and used the Minitab Assistant to guide their statistical analysis. They followed interactive decision trees to determine which analysis to use, and selected Analysis of Variance (ANOVA) to compare sample means and to look for significant differences within variables that could be impacting the HCAHPS survey question.

Minitab Assistant

The Riverview team began this project with a preset assumption that differences in the education level or the primary language of the respondent might offer insight into the low scores, but their analyses of these variables revealed no statistical significance. However, analysis of other variables uncovered statistically significant differences within respondent age groups and the hospital unit visited.

Surpassing the Benchmark

The Riverview Lean Six Sigma team was able to narrow the project scope and use the insights they gathered to improve patient satisfaction for groups identified as scoring the lowest. “Historically, health organizations try to increase patient satisfaction through staff training and other large-scale solutions,” says Spranger. “Now, with a data-driven approach, we are able to better target improvements.” The improvements for this project targeted low-scoring patient groups from specific age ranges, as well as patients who stayed in specific hospital units.

The team also used the improvement effort to solve key problems identified in the current process for handling patient discharges, which included timing of education, ensuring the involvement of a family caregiver, and clarifying outcomes with the patient. They redesigned the discharge education process into three phases to address previous issues with timing, collaborated with primary care physician clinics to ensure consistency, created a process to ensure that a primary family caregiver was identified to engage in care management after discharge, and clarified terminology in discharge documentation that was considered vague.

After implementing these improvement strategies, the team compared the current proportion of “yes” responses to responses before the project began. They achieved an impressive gain in the proportion of “yes” responses and met their goal to surpass the 91 percent benchmark for average positive monthly responses.

I trust this article has provided you with insight and approaches that can help you pinpoint those drivers that most strongly influence a patient’s willingness to recommend a hospital. If you are interested in learning more about using these methods, contact us at:  TPMG Global® – Improving HCAHPS Scores and The Patient Experience

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

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