Presentation
Safety Event Classification for Telemedicine
DescriptionDespite over 3 decades of concerted efforts to improve patient safety, adverse patient events remain prevalent, with an estimated one out of every four patients experiencing preventable harm (Bates et al., 2023). The predominant method used by healthcare organizations to investigate patient harm is Root Cause Analysis (RCA); however, safety professionals are recognizing the limitations of using RCA’s in response to patient safety events (Peerally et al., 2017). One limitation highlighted by Peerally and colleagues is the focus on single event investigations, most often in response to severe morbidity or mortality, which is the predominant method many healthcare organizations adopt. Unfortunately, this limits their ability to dedicate resources to less severe, but more numerous events, that if addressed could have wider spread positive ramifications on overall patient safety.
We are proposing an aggregated event analysis approach, rooted in human factors system’s thinking. At the foundation of the method we are developing is the need to classify patient safety concerns in order to elevate an investigation based on frequency of events. There are many safety classification systems available with predominant systems from the World Health Organization (WHO, 2010), the Joint Commission on Accreditation of Healthcare Organizations (JCAHO, Chang et al., 2005), and HPI/Press Ganey (Throop & Stockmeier, 2011). These patient safety classification systems strive to create a standardized language around safety events, which can allow for benchmarking across organizations and supports national improvement efforts; however, these goals have remained largely unfulfilled. There has yet to be adoption of a universal system for patient safety event classification. Further, for the systems that are available there remains lack of clear definitions and some are very long or complex. These concerns are compounded when there is a lack of rigorous training on the classification systems and lack of systematic reliability analysis for the data being entered into tracking platforms. Ultimately, this results in unreliable output data. Finally, these systems were developed with an emphasis on acute patient safety events, and do not take into account the rapid proliferation of telemedicine.
We will present a newly developed Safety Event Classification for telemedicine patient safety events that was developed base don current existing systems, iterative review of patient safety data, and subject matter expert input. The Safety Classification is used to classify any potential patient safety concerns, that in aggregate can be elevated to an adverse event investigation. This allows for a more widespread, proactive approach to patient safety. We will also discuss how data integrity is assessed to ensure consistency amongst the teammates classifying the events.
We are proposing an aggregated event analysis approach, rooted in human factors system’s thinking. At the foundation of the method we are developing is the need to classify patient safety concerns in order to elevate an investigation based on frequency of events. There are many safety classification systems available with predominant systems from the World Health Organization (WHO, 2010), the Joint Commission on Accreditation of Healthcare Organizations (JCAHO, Chang et al., 2005), and HPI/Press Ganey (Throop & Stockmeier, 2011). These patient safety classification systems strive to create a standardized language around safety events, which can allow for benchmarking across organizations and supports national improvement efforts; however, these goals have remained largely unfulfilled. There has yet to be adoption of a universal system for patient safety event classification. Further, for the systems that are available there remains lack of clear definitions and some are very long or complex. These concerns are compounded when there is a lack of rigorous training on the classification systems and lack of systematic reliability analysis for the data being entered into tracking platforms. Ultimately, this results in unreliable output data. Finally, these systems were developed with an emphasis on acute patient safety events, and do not take into account the rapid proliferation of telemedicine.
We will present a newly developed Safety Event Classification for telemedicine patient safety events that was developed base don current existing systems, iterative review of patient safety data, and subject matter expert input. The Safety Classification is used to classify any potential patient safety concerns, that in aggregate can be elevated to an adverse event investigation. This allows for a more widespread, proactive approach to patient safety. We will also discuss how data integrity is assessed to ensure consistency amongst the teammates classifying the events.
Event Type
Oral Presentations
TimeTuesday, March 269:10am - 9:30am CDT
LocationSalon A-1
Hospital Environments