Presentation
Identifying Performance Shaping Factors That Affect Nurses’ and Providers’ Situational Workload in The NICU
DescriptionBACKGROUND: High workload is a threat to care quality, patient safety, and clinicians’ well-being and job satisfaction. Workload, which lacks a universally accepted definition, is a complex multi-dimensional construct that is affected by external task demands as well as environmental, organizational, and psychological factors. The importance of managing high workload is nowhere more evident than in neonatal intensive care units (NICUs). Critically ill neonates are highly vulnerable to iatrogenic events due to their immaturity and fragility, and high workload has been directly associated with increased incidence of adverse neonatal safety outcomes.2 Conventional workload management tools predominantly measure and predict workload using unit-level (e.g., staffing ratios) or patient-level (e.g., acuity) data rather than data collected across the four levels of workload recommended by human factors engineers (HFEs) - unit, job, patient, and situation. As a result, current tools are not designed to identify mutable microsystem factors and are under-measuring workload experienced by clinicians.3-8 Patient safety researchers have been slow to develop scalable workload measurement systems or other health information technology interventions to improve workload management and patient safety. A promising development in workload research is the increasing emphasis on measuring situational workload (SITWL), which best explains the workload experienced by clinicians due to healthcare microsystem design. SITWL is most affected by performance obstacles (i.e., delays, interruptions, etc.) in the local work environment and can be applied at the unit, job, or patient-levels. Most importantly, it is diagnostic of underlying contributory factors and therefore actionable for improvement. Our ongoing research into SITWL is significant because it will improve neonatal patient safety by changing the way clinician workload in the NICU is measured and used for workload management, organizational learning, and improvement.
OBJECTIVES: In an effort to design, develop, and validate new real-time multivariable situational workload (SITWL) models, our objective was to identify performance shaping factors, such as performance obstacles and facilitators, in the local work environment (NICU) that contribute to the SITWL of Registered Nurses (RNs) and Neonatal Nurse Practitioners (NNPs).
STRATEGIES: Guided by the Systems Engineering Initiative for Patient Safety (SEIPS) framework1, we have conducted an in-depth human factors-based analysis of clinicians’ interactions with performance shaping (i.e., performance barriers and facilitators) that contribute or alleviate SITWL in the NICU microsystem. We conducted 120-to-180-minute direct observations and 30-minute post-observation interviews with 30 RNs and 17 NNPs from two academic NICUs (119-bed Level IV NICU and 25-bed Level III NICU). During randomly selected 2-3 hour observations of routine work shifts, the number of interactions a clinician had with specific performance shaping factors (PSFs) during each observation were tallied and categorized within an observational checklist developed by our team, based on the SEIPS framework. Tasks were categorized by the following elements of the SEIPS model: Environment, Technologies/Tools, Organization, and Tasks. Data collected from these observations are from randomly selected RNs & NNPs with varying levels of experience (ranging from 4 months to 27 years), type of shifts (e.g. day/night shifts and a variety of 8-, 12-, and 24-hour shifts), and times of day. Within 48 hours of the observed shift, research assistants conducted semi-structured interviews with the RNs/NNPs to further explore previously observed PSFs and general contributory factors to workload in the unit. Observation field notes and interview transcripts were then coded and analyzed to identify SITWL themes and the specific PSFs that RNs/NNPs attributed to increased or decreased SITWL. One goal of these analyses is to identify performance barriers (e.g., hassles, frustrations, etc.), which are experienced in the unit, that increase clinician workload and diminish job satisfaction and/or interfere with the ability to deliver high-quality, safe, and timely care to patients. The second goal is to use these data to identify facilitators (i.e., unit resources, such as available resource nurses, etc.) or any strategies (i.e., methods of organization, teamwork, etc.) that reduce stress and workload in the NICU. The identified PSFs are being mapped to readily accessible electronic health record (EHR) data to develop and validate real-time models of RN and NNP workload. These models will be used to identify overburdened clinicians, guide dynamic workload management, and to provide short-term (i.e., next shift) predictions of staffing needs. The models will also be used to guide quality improvement activities to address mutable system factors that contribute to high workload and job dissatisfaction.
RESULTS: Based on our analysis of the RN observational data, the following PSFs have emerged as consistently increasing nursing workload in the NICU: Patient rooms not stocked with needed supplies (21 incidents observed); Spending time seeking for supplies/equipment (18 incidents observed); Interruptions from other clinicians (18 incidents observed); and Distractions/interruptions from patient family members (15 incidents observed). Additionally, the post-observation interviews revealed further clarification of the top barriers experienced by RNs in the NICU as: Supply issues (e.g. Patient rooms not stocked with needed supplies, spending time looking for supplies, etc.) mentioned 26 times, Poor Patient assignment/Staffing issues mentioned 25 times, and Communication issues (e.g., conflicting orders within the EHR, poor communication between the NICU Team and OR Team regarding post-op process, etc.) were mentioned 19 times as being the top contributors to workload for RNs in the NICU.
Our analyses of the NNP observational data revealed the following PSFs as top contributors to NNP workload within the NICU: Need for clarification of orders/plan of care (18 incidents observed); Distractions from frequent pages/notifications (17 incidents observed); and Spending time dealing with family needs (16 incidents observed). The NNP interview data also revealed the following 3 factors as the top barriers to workload: Patient acuity and Communication issues (e.g. high volume of nonurgent communication, facilitating tasks between various sub-specialty teams, the increase of necessary communication required by the NNP during any given shift due to unexpected events, etc.) were mentioned 8 times; Alert Fatigue was mentioned 7 times; and both Teaching/Orienting (e.g. orienting a new NNP, teaching opportunities for inexperienced RN staff at bedside, etc.) and an Inexperienced RN Staff were mentioned 6 times as the top contributors to NNP workload.
RNs and NNPs were also asked to list their top facilitators to workload during each interview. For RNs, “Podmates” (co-workers) was mentioned as the top facilitator that alleviates RN workload. For NNP interview data, “Team Members” was the top facilitator mentioned that alleviates NNP workload within the NICU.
CONCLUSION:
Our preliminary results have identified the specific microsystem factors that contribute the most to RN and NNP workload. Although there are obvious differences between the workload-influencing PSFs collected for these distinct clinical roles (e.g. supply issues listed as top contributor to RN workload but have little to no effect on NNP workload), there are also important similarities. For example, both RNs and NNPs listed their colleagues as the top facilitators that help alleviate workload during a shift in the NICU. We anticipate additional analyses on these rich data will provide more insights into contributors of workload (direct or indirect), potential workarounds, and any significant effect(s) one position (RN or NNP PSFs) has on another within the NICU as a whole.
Acknowledgements: This work was supported by grants R01HS028430-2 from the Agency for Healthcare Research and Quality (AHRQ) and 1R01HD109303-01A1 from the National Institute of Child Health and Human Development (NICHD) to D. J. France (Vanderbilt University Medical Center) and A. Gurses (Johns Hopkins Health System). The views of this abstract represent those of the authors and not AHRQ nor the NICHD.
OBJECTIVES: In an effort to design, develop, and validate new real-time multivariable situational workload (SITWL) models, our objective was to identify performance shaping factors, such as performance obstacles and facilitators, in the local work environment (NICU) that contribute to the SITWL of Registered Nurses (RNs) and Neonatal Nurse Practitioners (NNPs).
STRATEGIES: Guided by the Systems Engineering Initiative for Patient Safety (SEIPS) framework1, we have conducted an in-depth human factors-based analysis of clinicians’ interactions with performance shaping (i.e., performance barriers and facilitators) that contribute or alleviate SITWL in the NICU microsystem. We conducted 120-to-180-minute direct observations and 30-minute post-observation interviews with 30 RNs and 17 NNPs from two academic NICUs (119-bed Level IV NICU and 25-bed Level III NICU). During randomly selected 2-3 hour observations of routine work shifts, the number of interactions a clinician had with specific performance shaping factors (PSFs) during each observation were tallied and categorized within an observational checklist developed by our team, based on the SEIPS framework. Tasks were categorized by the following elements of the SEIPS model: Environment, Technologies/Tools, Organization, and Tasks. Data collected from these observations are from randomly selected RNs & NNPs with varying levels of experience (ranging from 4 months to 27 years), type of shifts (e.g. day/night shifts and a variety of 8-, 12-, and 24-hour shifts), and times of day. Within 48 hours of the observed shift, research assistants conducted semi-structured interviews with the RNs/NNPs to further explore previously observed PSFs and general contributory factors to workload in the unit. Observation field notes and interview transcripts were then coded and analyzed to identify SITWL themes and the specific PSFs that RNs/NNPs attributed to increased or decreased SITWL. One goal of these analyses is to identify performance barriers (e.g., hassles, frustrations, etc.), which are experienced in the unit, that increase clinician workload and diminish job satisfaction and/or interfere with the ability to deliver high-quality, safe, and timely care to patients. The second goal is to use these data to identify facilitators (i.e., unit resources, such as available resource nurses, etc.) or any strategies (i.e., methods of organization, teamwork, etc.) that reduce stress and workload in the NICU. The identified PSFs are being mapped to readily accessible electronic health record (EHR) data to develop and validate real-time models of RN and NNP workload. These models will be used to identify overburdened clinicians, guide dynamic workload management, and to provide short-term (i.e., next shift) predictions of staffing needs. The models will also be used to guide quality improvement activities to address mutable system factors that contribute to high workload and job dissatisfaction.
RESULTS: Based on our analysis of the RN observational data, the following PSFs have emerged as consistently increasing nursing workload in the NICU: Patient rooms not stocked with needed supplies (21 incidents observed); Spending time seeking for supplies/equipment (18 incidents observed); Interruptions from other clinicians (18 incidents observed); and Distractions/interruptions from patient family members (15 incidents observed). Additionally, the post-observation interviews revealed further clarification of the top barriers experienced by RNs in the NICU as: Supply issues (e.g. Patient rooms not stocked with needed supplies, spending time looking for supplies, etc.) mentioned 26 times, Poor Patient assignment/Staffing issues mentioned 25 times, and Communication issues (e.g., conflicting orders within the EHR, poor communication between the NICU Team and OR Team regarding post-op process, etc.) were mentioned 19 times as being the top contributors to workload for RNs in the NICU.
Our analyses of the NNP observational data revealed the following PSFs as top contributors to NNP workload within the NICU: Need for clarification of orders/plan of care (18 incidents observed); Distractions from frequent pages/notifications (17 incidents observed); and Spending time dealing with family needs (16 incidents observed). The NNP interview data also revealed the following 3 factors as the top barriers to workload: Patient acuity and Communication issues (e.g. high volume of nonurgent communication, facilitating tasks between various sub-specialty teams, the increase of necessary communication required by the NNP during any given shift due to unexpected events, etc.) were mentioned 8 times; Alert Fatigue was mentioned 7 times; and both Teaching/Orienting (e.g. orienting a new NNP, teaching opportunities for inexperienced RN staff at bedside, etc.) and an Inexperienced RN Staff were mentioned 6 times as the top contributors to NNP workload.
RNs and NNPs were also asked to list their top facilitators to workload during each interview. For RNs, “Podmates” (co-workers) was mentioned as the top facilitator that alleviates RN workload. For NNP interview data, “Team Members” was the top facilitator mentioned that alleviates NNP workload within the NICU.
CONCLUSION:
Our preliminary results have identified the specific microsystem factors that contribute the most to RN and NNP workload. Although there are obvious differences between the workload-influencing PSFs collected for these distinct clinical roles (e.g. supply issues listed as top contributor to RN workload but have little to no effect on NNP workload), there are also important similarities. For example, both RNs and NNPs listed their colleagues as the top facilitators that help alleviate workload during a shift in the NICU. We anticipate additional analyses on these rich data will provide more insights into contributors of workload (direct or indirect), potential workarounds, and any significant effect(s) one position (RN or NNP PSFs) has on another within the NICU as a whole.
Acknowledgements: This work was supported by grants R01HS028430-2 from the Agency for Healthcare Research and Quality (AHRQ) and 1R01HD109303-01A1 from the National Institute of Child Health and Human Development (NICHD) to D. J. France (Vanderbilt University Medical Center) and A. Gurses (Johns Hopkins Health System). The views of this abstract represent those of the authors and not AHRQ nor the NICHD.
Event Type
Oral Presentations
TimeTuesday, April 11:30pm - 1:52pm EDT
LocationQueens Quay
Patient Safety and Research Initiatives (PS)