Making The Case For Quality At A Glance, December 2015 ✓ Solved
Making the Case For Quality At a Glance . . . December 2015
This teaching case study features characters, hospitals, and healthcare data that are all fictional. Upon use of the case study in classrooms or organizations, readers should be able to create a control chart and interpret its results, and identify situations that would be appropriate for control chart analysis. The case is best suited for MBA operations courses and modules, particularly those focused on operations/process improvement.
Using Control Charts in a Healthcare Setting by Jack Boepple. After spending 10 years on the road as a healthcare operations improvement consultant, Isabella “Izzy” Cvengros decided it was time to settle down. Although Cvengros loved what she was doing, she had recently become engaged and wanted to spend more time with her future husband.
However, with family members spread throughout the country, there was really no home to go back to. As a consultant, Cvengros had been assigned to a wide variety of healthcare projects over the years, learning a great deal. She also enjoyed seeing so many different parts of the United States, with a particular fondness for New Hampshire, so she and her fiancé focused their job search there.
On October 10, 2014, Cvengros found herself with a good problem. She had just completed a series of interviews with two Nashua, NH, hospitals: Farrell Memorial Hospital and Penner Mobley Health Services, and both had gone very well. She interviewed for the same job at both facilities—director of operations improvement—and leadership from both facilities indicated she was proceeding to the final round of interviews, which entailed meeting each hospital’s executive team.
As a certified Lean Six Sigma Black Belt, Cvengros was thrilled to hear both hospitals’ progressive views on continuous improvement. While she saw examples of many quality tools and analyses being performed at each hospital, she did not notice any control charts being used. Although control charts are typically associated with manufacturing processes, Cvengros knew they could be applied to any industry’s processes, including hospitals.
Because she had employed control charts with great success in several of her assignments, she incorporated this experience as part of her interview responses. Both hospitals were intrigued and asked if she could provide an example during her next round of interviews. Cvengros agreed, but to make the analysis more meaningful, she asked each hospital to provide her with data so the example control chart analysis would be more meaningful and relevant to them.
Since one of the key discussion points during her interviews at both facilities revolved around reducing the patient’s length of stay, Cvengros asked for data on their estimated date of discharge (EDD) by week from January through September 2014.
Understanding Control Charts
Control charts are a crucial 'safety net' in process management, which involves understanding and managing variations in any process, particularly in healthcare where ensuring high-quality patient care is paramount. They allow organizations to monitor their performance and make informed decisions based on data rather than guesswork.
In the context of the case study, two hospitals exemplify differing results regarding patient outcomes as indicated by the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) score. Control charts would serve to illustrate the variability in their workflow and patient care processes, helping to pinpoint periods of improvement or decline.
The main types of variations that control charts help to identify include common cause variation, which arises from inherent fluctuations within the process, and special cause variation, indicated by sudden changes in process performance. By interpreting these charts accurately, healthcare facilities can devise targeted strategies to tackle inefficiencies and enhance patient experience.
Application of Control Charts in Healthcare
To properly deploy control charts in healthcare settings, professionals must first gather relevant data. In Cvengros’s case, this involved EDD data, which is significant for assessing the operational effectiveness of patient management. The accurate prediction of discharge dates indicates overall efficiency in patient care processes, allowing hospitals to optimize bed management and enhance care transitions.
In looking at EDD accuracy metrics, data can be structured into control charts to visualize the number of estimated discharges actually realized versus those not achieved. This illustration serves multiple functions: it fosters communication among care teams, facilitates adjustments in patient management, and ultimately fosters a culture of continuous improvement.
Challenges in Implementation
While control charts are powerful tools, their implementation is not without challenges. Hospital staff may initially resist shifting from traditional methods to data-based approaches. Engaging them through training and evidence-based results can mitigate these hurdles. Additionally, ensuring that staff members understand how to interpret control charts is essential for them to employ insights that drive process enhancement.
Moreover, integrating control charting into existing workflow processes can require substantial organizational change. It is crucial to cultivate an organizational culture that values data-driven decision-making to embed these practices effectively and garner consistent results over time.
Within healthcare management, gaining buy-in at all levels enhances the likelihood of sustained improvements. Leadership's active participation in emphasizing quality improvement through control charts can inspire frontline staff to embrace these practices as part of their daily routines.
Conclusion
In summary, control charts are invaluable for operational excellence in healthcare settings. As demonstrated through the story of Isabella Cvengros, implementing these tools can provide insights into patient management and broader organizational performance. By understanding and acting upon the data derived from control charts, healthcare organizations can work towards their quality improvement goals more effectively.
This proactive management of patient outcomes and process variations brings hospitals closer to their targets of operational efficiency, patient satisfaction, and overall health improvement.
References
- HCAHPS, [Hospital Consumer Assessment of Healthcare Providers and Systems].
- Centers for Medicare and Medicaid Services. [Medicare Hospital Compare].
- Institute for Healthcare Improvement. "How-to Guide: Multidisciplinary Rounds."
- Curaspan. "Estimating the Date of Discharge: Five Reasons to Do It."
- Health Affairs. "Improving Care Transitions."
- iSixSigma. "Common Cause Variation." [isixsigma.com/dictionary/common-cause-variation].
- Wikipedia. "Statistical process control." [en.wikipedia.org/wiki/Statistical_process_control].
- Wikipedia. "Run Chart." [en.wikipedia.org/wiki/Run_chart].
- Nancy R. Tague. "The Quality Toolbox, Second Edition." ASQ Quality Press, 2005.
- Boepple, Jack. "Using Control Charts in a Healthcare Setting."