Presentation
An Integrated Systematic Framework to Investigate Anesthesia Medication Errors: Applying Systems Engineering and Lean Principles
DescriptionMedication errors and adverse drug events (ADEs) have been a major issue in the healthcare industry in the United States. Medication errors are the most common and preventable cause of patient harm [1]. These errors usually involve administering the wrong medication or a wrong dose of medication, incorrectly administering medication, medication administration to the wrong patient, or administration at a wrong time. Reported medication errors in the hospital setting are approximately 6.5 per 100 admissions [1,2], of which 1% are fatal, 12% are life-threatening, and 28% are preventable [1]. These errors can occur during one or more than one phase of ordering/prescribing a medication, documenting a prescribed medication, dispensing, or administering that medication [2].
The annual cost associated with ADEs is approximately $30 billion in the U.S. across all healthcare settings [3]. Approximately 30% of hospitalized patients have at least one medication discrepancy at discharge [4]. This statistic is of paramount importance given there were approximately 34 million admissions in U.S. hospitals in 2024 [5]. Furthermore, medication errors and ADEs are often underreported.
We aspire to reduce the risk of medication errors by integrating a series of systems engineering methodologies and Lean principles to identify and analyze contributing causes of medication errors and provide context-specific recommendations to mitigate them. This study has been centered around anesthesia-related medication errors within an operative setting.
We obtained deidentified data concerning 116 unique anesthesia-related medication errors and near misses within operative settings of a multispecialty academic hospital. Data collected included type of procedure(s), the patient’s age and gender, as well as a summary of the incident or the near miss. Our analysis began with understanding the frequency, severity, and types of medication errors. We were also able to correlate the acquired data into different phases of anesthesia care, including pre-operative, intra-operative, and post-operative.
Through analysis of the dataset, we identified the three most prevalent types of medication errors:
1. Administration of incorrect medication dose (38.79% of total incidents)
2. Administration of incorrect medication (20.69% of total incidents)
3. Medication not administered (12.07% of total incidents)
We identified the front-line staff, e.g., attending anesthesiologist, anesthesiology resident, and nurse anesthetist, as well as the support staff involved in each medication error incident. It is noteworthy that medication errors, like many other categories of incidents in safety-sensitive industries, are not solely due to decisions and actions made by staff; rather they have contributing causes across a broader socio-technical context. In the first phase of our study, however, the role of other key players such as management and organizational factors was not discussed, as the provided database only summarized the role of front-line and support staff for each reported incident.
In the next phase, in order to expand our analysis and develop a better understanding of the medication errors and their underlying causes, we have used the Fault Tree Analysis (FTA), as a method to further break down each of the layers of our fault tree by identifying the causes of each failure mode in an upper layer. Based on our preliminary fault tree analysis of the most frequent medication errors listed above, ineffective communication among front-line staff, incorrect placement of medication, unlabeled or mislabeled medication, and not verifying medication before its administration were the major identified underlying causes of the medication errors. We will expand upon our FTA and the captured causes of the top three aforementioned medication errors by developing focus groups and interviewing our subject matter experts, e.g., anesthesiologists, residents, nurses, as well as members from the hospital’s quality improvement team, to discuss our findings about identified underlying causes and receive their inputs.
We have also been arranging a series of observations of the hospital’s operating rooms to make note of real-time procedures conducted and steps followed by healthcare providers as well as mandates and audio-visual verifications performed by them. This will enhance our understanding regarding common sources of errors. Furthermore, these observations as well as interviews with experts enable value stream mapping of the current process flow and identification of error-prone steps, delays, wait periods, and value-adding and non-value-adding activities. Our developed value stream map will be reviewed and verified by our subject matter experts. This will be an addition for systematic identification of sources of medication errors during anesthesia administration and a tool to be integrated with our fault tree analysis. All our findings will be reviewed by our subject matter experts for feedback and suggestions. We will then use our study findings to develop a series of context-specific recommendations to mitigate the risks associated with the three major frequently occurring medication errors discussed before.
References
[1] Oyebode F. Clinical errors and medical negligence. Med Princ Pract. 2013;22(4):323-33. doi: 10.1159/000346296.
[2] Tariq RA, Vashisht R, Sinha A, et al. Medication Dispensing Errors and Prevention. [Updated 2024 Feb 12]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK519065/
[3] Bates DW, Levine D, Syrowatka A, Kuznetsova M, Craig KJT, Rui A, Jackson GP, Rhee K. The potential of artificial intelligence to improve patient safety: a scoping review. NPJ Digit Med. 2021, Mar 19;4(1):54. doi: 10.1038/s41746-021-00423-6. PMID: 33742085; PMCID: PMC7979747.
[4] da Silva, B.A., Krishnamurthy, M. The alarming reality of medication error: a patient case and review of Pennsylvania and National data. J. Commun. Hosp. Intern. 2016; Med. Perspect. 6(4): 31758.
[5] American Hospital Association (AHA). Fast Facts on US Hospitals. AHA Hospital Statistics. (2024). https://www.aha.org/system/files/media/file/2024/01/fast-facts-on-us-hospitals-2024-20240112.pdf. Accessed 9 October 2024.
The annual cost associated with ADEs is approximately $30 billion in the U.S. across all healthcare settings [3]. Approximately 30% of hospitalized patients have at least one medication discrepancy at discharge [4]. This statistic is of paramount importance given there were approximately 34 million admissions in U.S. hospitals in 2024 [5]. Furthermore, medication errors and ADEs are often underreported.
We aspire to reduce the risk of medication errors by integrating a series of systems engineering methodologies and Lean principles to identify and analyze contributing causes of medication errors and provide context-specific recommendations to mitigate them. This study has been centered around anesthesia-related medication errors within an operative setting.
We obtained deidentified data concerning 116 unique anesthesia-related medication errors and near misses within operative settings of a multispecialty academic hospital. Data collected included type of procedure(s), the patient’s age and gender, as well as a summary of the incident or the near miss. Our analysis began with understanding the frequency, severity, and types of medication errors. We were also able to correlate the acquired data into different phases of anesthesia care, including pre-operative, intra-operative, and post-operative.
Through analysis of the dataset, we identified the three most prevalent types of medication errors:
1. Administration of incorrect medication dose (38.79% of total incidents)
2. Administration of incorrect medication (20.69% of total incidents)
3. Medication not administered (12.07% of total incidents)
We identified the front-line staff, e.g., attending anesthesiologist, anesthesiology resident, and nurse anesthetist, as well as the support staff involved in each medication error incident. It is noteworthy that medication errors, like many other categories of incidents in safety-sensitive industries, are not solely due to decisions and actions made by staff; rather they have contributing causes across a broader socio-technical context. In the first phase of our study, however, the role of other key players such as management and organizational factors was not discussed, as the provided database only summarized the role of front-line and support staff for each reported incident.
In the next phase, in order to expand our analysis and develop a better understanding of the medication errors and their underlying causes, we have used the Fault Tree Analysis (FTA), as a method to further break down each of the layers of our fault tree by identifying the causes of each failure mode in an upper layer. Based on our preliminary fault tree analysis of the most frequent medication errors listed above, ineffective communication among front-line staff, incorrect placement of medication, unlabeled or mislabeled medication, and not verifying medication before its administration were the major identified underlying causes of the medication errors. We will expand upon our FTA and the captured causes of the top three aforementioned medication errors by developing focus groups and interviewing our subject matter experts, e.g., anesthesiologists, residents, nurses, as well as members from the hospital’s quality improvement team, to discuss our findings about identified underlying causes and receive their inputs.
We have also been arranging a series of observations of the hospital’s operating rooms to make note of real-time procedures conducted and steps followed by healthcare providers as well as mandates and audio-visual verifications performed by them. This will enhance our understanding regarding common sources of errors. Furthermore, these observations as well as interviews with experts enable value stream mapping of the current process flow and identification of error-prone steps, delays, wait periods, and value-adding and non-value-adding activities. Our developed value stream map will be reviewed and verified by our subject matter experts. This will be an addition for systematic identification of sources of medication errors during anesthesia administration and a tool to be integrated with our fault tree analysis. All our findings will be reviewed by our subject matter experts for feedback and suggestions. We will then use our study findings to develop a series of context-specific recommendations to mitigate the risks associated with the three major frequently occurring medication errors discussed before.
References
[1] Oyebode F. Clinical errors and medical negligence. Med Princ Pract. 2013;22(4):323-33. doi: 10.1159/000346296.
[2] Tariq RA, Vashisht R, Sinha A, et al. Medication Dispensing Errors and Prevention. [Updated 2024 Feb 12]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK519065/
[3] Bates DW, Levine D, Syrowatka A, Kuznetsova M, Craig KJT, Rui A, Jackson GP, Rhee K. The potential of artificial intelligence to improve patient safety: a scoping review. NPJ Digit Med. 2021, Mar 19;4(1):54. doi: 10.1038/s41746-021-00423-6. PMID: 33742085; PMCID: PMC7979747.
[4] da Silva, B.A., Krishnamurthy, M. The alarming reality of medication error: a patient case and review of Pennsylvania and National data. J. Commun. Hosp. Intern. 2016; Med. Perspect. 6(4): 31758.
[5] American Hospital Association (AHA). Fast Facts on US Hospitals. AHA Hospital Statistics. (2024). https://www.aha.org/system/files/media/file/2024/01/fast-facts-on-us-hospitals-2024-20240112.pdf. Accessed 9 October 2024.
Authors
Associate Clinical Professor
Event Type
Oral Presentations
TimeMonday, March 3111:37am - 12:00pm EDT
LocationQueens Quay
Patient Safety and Research Initiatives (PS)