Reducing Patient Falls Case

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  1. What other potential root causes might influence patient falls?
  2. Equipped with the data, what would you do about the hypotheses that proved to be unsupported?
  3. Based on the correctly identified hypothesis in the case scenario, what would be your course of action if you were the CEO/president of St. Xavier Memorial Hospital?
  4. What do you think of the CNO’s (Sara Mullins) position of “waiting and seeing what the data tells us” instead of immediately jumping to conclusions?

Case 13: Reducing Patient Falls: The Sleuth Resident

OBJECTIVES

  1. Describe how generating hypotheses can support the quality management process. 2. Examine collaboration among caregivers and quality management leaders. 3. Analyze patient safety data using quality management problem analysis tools. 4. Identify causes of variation in patient safety outcomes and relevant safety-enhancing technologies. 5. Evaluate efforts to overcome complacency with current patient safety performance.

INTRODUCTION

Also read: Preventing Falls in Hospitals

Patient falls are a leading Sentinel Event that are required to be reported by hospitals to The Joint Commission, the accreditation organization (The Joint Commission, 2019). Thousands of patients of acute care and rehabilitation hospitals are injured in falls that cause at least some sort of injury (Bouldin et al., 2013; Oliver, Healey, & Haines; 2010). The Centers for Medicare and Medicaid no longer pays hospitals for costs related to patient falls, which they consider preventable (Centers for Medicare and Medicaid Services; 2018). For these reasons, healthcare organizations seek to identify the root causes for patient falls and develop best practices for preventing them.

Red Valley Clinic is a large academic medical center that has served the Greater Red Valley metro area for 80 years. It has three large hospitals (300–500 beds each) and 28 medical group practices ranging from primary care to specialists, such as ophthalmologists, endocrinologists, orthopedic surgeons, and physical rehabilitation. Out of the three large hospitals, St. Xavier Memorial Hospital is the one that has been in operation the longest, and it is the flagship of Red Valley Clinic. Five years ago, Red Valley Clinic invested close to US$200 million to expand and update St. Xavier Memorial to modern standards. After the renovation, St. Xavier Memorial became a 340-bed hospital.

DATA FILE FOR CASE 13

Data files for students are available by accessing the following url: https://www.springerpub.com/hqm The data file for Case 13 provides summary data for 24 months of patient falls for one hospital by unit. Also includes total falls and averages for

five hypothesized root causes (patient age, bed age, acuity, RN years of experience, and average census) for each of the 15 patient units. Ideal data for creating scatterplots.

CASE SCENARIO

“Another month, another great review!” said Dr. Sanjeep Metha, a third-year resident at Red Valley Clinic as he came out of their monthly operations review. He high-fived his friend and colleague, Dr. Carson Stanley, another third-year resident at Red Valley who begrudgingly joined his friend’s hand in the air. Dr. Stanley was visibly troubled.

“What’s wrong?” inquired Dr. Metha. “Did you not like that glowing review of our units? In the past year, we almost eliminated infections, reduced length-of-stay, and increased revenue for the organization. Seven more months and we will be writing our own ticket, man! Cheer up!”

“Yes, I agree,” replied Dr. Stanley, “that our teams have done some great work on those fronts, but, does it not bother you how poorly we are doing on patient falls?”

“Oh, man, here we go! Why do you always insist on fixating on the negative?” asked Dr. Metha. “Well, for one, because that is a huge patient safety issue. Plus, I know we can do better,” Dr. Stanley answered. “Man, I can’t hang out with you when you are being Mr. Negative! I’ll catch up with you later, OK? We’ll celebrate!” said Dr. Metha as he bid farewell

to his friend and went down a different hallway. Dr. Stanley continued walking down the wide, well-lit, pristine clinic hallway, deep in thought. He did not notice the nurses coming and going, the

family who came out of one of the rooms full of joy, getting ready to get Dad home today, or the environmental services tech cleaning the spill into which he almost stepped. After a few minutes walking, he realized he had no idea how he got there. He then turned around and went back to his office to review the charts of the patients on whom he had to round.

The reason troubling Dr. Stanley was that over the 3 years he has been a resident, patient falls throughout St. Xavier Memorial have remained constant at between 30 and 40 every month. Not only is this number significantly higher than the national benchmark of 3.56 falls per 1,000 patient days (Bouldin et al., 2013), but for Dr. Stanley, one fall is too many. He needed to find a way to reduce falls. He set an ambitious goal to reduce patient falls by half over the next 18 months.

Dr. Stanley knew he first needed data to try to find some opportunities to reduce patient falls. He enlisted the help of Dan Stroman, a young, wide-eyed data analyst in the Process Excellence department to get him some data. “Sure thing, Dr. Stanley,” was Dan’s enthusiastic response. “Just let me know what you need.”

Dr. Stanley asked Dan to give him the falls data for the last 2 years by nursing unit for St. Xavier Memorial Hospital. He was delighted when, upon returning to his office that evening, he had an email from Dan Stroman with the subject, “Patient Falls data you requested.” Dr. Stanley thought, “Man this guy works fast,” as he smiled and opened the email (see Case 13 Data file provided in the Instructor’s and Student ancillary materials).

Upon studying the data, it was immediately obvious to Dr. Stanley that some nursing units were more prone to patient falls than others. He made the decision right then to find out the differences between nursing units and determine which, if any, of those differences could explain the higher numbers of falls for some units. Like a good quality improvement sleuth, Dr. Stanley knew that there is no substitute for “going and seeing for yourself.” So, the following day, he visited not only those units on which he normally rounded, but other units as well. He introduced himself and engaged with the staff there (nurses, transporters, techs). As part of his conversations with them, he always asked one of two questions, depending on the number of falls in that unit:

  1. What do you think contributes to your low number of patient falls? 2. What do you think contributes to your high number of patient falls? He listened to their many theories and set out to test them with data. Dr. Stanley generated hypotheses that could be proved or disproved using the data

file. He was determined to get answers for his questions.

Hypothesis A: Older Patients Fall More Often

One day, while rounding on 4-West, Dr. Stanley asked the charge nurse, “What do you think contributes to your high number of patient falls?” “Oh, I know exactly why we have such a high number of falls every month,” she answered. “It’s because we have the oldest population of patients in the

whole hospital! Older patients are more prone to falls. It’s that simple.” Dr. Stanley went back to his office and called Dan Stroman. “Hello Dan! It’s Dr. Stanley!” “Hello, Dr. Stanley! How can I help you today?” Dan said with his usual excitement. “Could you pull the average age of the patients by unit for the last 2 years?” Dr. Stanley asked. He paused for a moment, and when Dan did not

immediately answer, he added, “It does not have to be by month, just an average for the whole year, by unit, for the last 2 years.” “Okay, let me see what I can do,” replied Dan as he started helping Dr. Stanley in his quest. The next morning, Dr. Stanley walked in to an email from Dan. He opened and studied the data. He wanted to find out if there was positive correlation

between patient age and number of falls by unit. He knew that an easy way to do that is by using a scatterplot, so he plotted the data and studied the result.

Hypothesis B: Older Beds Cause More Patient Falls

While rounding the Surgery floor another day, Dr. Stanley asked one of the staff nurses in 2-East, “What do you think contributes to your low number of patient falls?”

“Well, we have some really well-trained staff and everyone is on their toes and when there is a bed alarm, we hustle!” replied the nurse. “I think that the new beds have definitively had an impact.”

“New beds? What do you mean by that?” asked Dr. Stanley. “Yes, a few years ago, about 4 or 5 years ago, we got these new beds,” she started walking toward an empty room so she could show Dr. Stanley. “They

have a lot of nice features, and all the alarms work great! Bed alarms alert personnel when a patient at risk for a fall attempts to leave the bed without assistance. When I was upstairs in 4-North, half of those beds did not have alarms.”

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Last Updated on November 7, 2020 by Essay Pro