Traditionally, Lean and Six Sigma methodologies were developed for the world of manufacturing, to help companies reduce waste and errors. Today, many hospitals and health systems embrace Lean and Six Sigma methodologies to improve healthcare delivery, clinical workflow and other patient care processes.
A growing number of hospitals are training their staff in Lean Six Sigma to achieve Green and Black Belt certification. However, the current practice of Lean Six Sigma requires a tremendous amount of manual data entry, analysis and discussion to identify the best ways to improve workflow and care processes. Hospitals are limited when they have to manually sort through massive amounts of unstructured data to identify root causes of problems. Or, the application of DMAIC (define, measure, analyze, inspect, control) can become time-consuming when the measurement and analysis of data must be performed manually or by third parties after de-identifying patient information to avoid breaches in patient privacy and information security.
Hospitals that are progressive and forward-thinking are already evaluating ways to apply big data analytics to their patient data to make improvements driven by Lean Six Sigma methodologies. Natural language processing solutions can sort through the wealth of unstructured data generated by progress notes and consultation reports. These sources of information can be invaluable when determining the root causes of waste and efficiency in the system. A routine hospital process that normally takes eight hours may be reduced to three hours by applying Lean methodologies of identifying the sources of waste, eliminating unnecessary steps, organizing and sorting, and replacing manual processes with automation.
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In the case of reducing errors, some hospitals may have a culture of trying to identify the physicians and nurses who are making the highest number of medical errors and replacing them. A better approach would be to implement processes that will mistake-proof the system so that certain medical errors are not even possible.
Applying Six Sigma methodologies across key sources of medical errors may identify unique opportunities for improvement if hospitals examine all the unstructured data that gets generated when physicians enter orders and when nurses administer medications. Root cause analyses of common medical errors that occur in hospitals often reveal that the human error could have been avoided. For example, a nurse may administer a medication to the wrong patient. This type of error can be avoided if the following steps are built in to the medication dispensing workflow:
- The patient wristband has a barcode and a location-based tracker.
- The medication administration cart uses location-based tracking and will only dispense the medication after the nurse scans the patient's barcode.
- The medication administration cart displays a photo of the pill and a photo of the patient to serve as a reminder for the nurse.
- The cup that holds the dispensed medication uses location-based tracking to ensure that it is in the correct patient's room.
- The patient's bedside computer has a camera that images the pills and displays the drug name and dosage on the screen so that both the nurse and patient can perform a final check and confirm that the patient is about to receive the proper medication.
Most hospitals have incorporated some of these error-proofing steps, but I doubt that any have incorporated all of these steps. Some of these technologies are not even available to hospitals. Some of these steps are so time-consuming that they would significantly hinder workflow and cause significant delays in patient care unless hospitals hire additional nursing staff. Having too many error-proofing steps may create too much waste and inefficiency in the system.
At some point, hospital leaders and administrators must make decisions that balance workflow efficiency and reduce medical errors. This is where big data analytics may be able to provide hospital executives with the kind of information that will help them make information-driven decisions to optimize workflow and minimize errors.
About the author:
Joseph Kim is a physician technologist who has a passion for leveraging health IT to improve public health. Dr. Kim is the founder of NonClinicalJobs.com and is an active social media specialist. Let us know what you think about the story; email email@example.com or contact @SearchHealthIT on Twitter.
This was first published in February 2014