Learn how predictive analytics and intelligent algorithms can help you improve care

Join Spacelabs at AACN’s National Teaching Institute and Critical Care Exposition in Orlando, Florida, May 21-23, 2019. Come see the newest technology from Spacelabs in action, earn CERP credits while continuing your education on meeting today’s critical healthcare challenges, and don’t miss your opportunity to get a souvenir photo with our Spacelabs astronaut.

ExpoEd Session Schedule

	Tuesday, May 21
	11:30 am Add to schedule	Sepsis Risk Identification Using Near-Real-Time Algorithms and Predictive Analytics Carolyn C. Scott, RN, M.Ed., MHA Executive Vice President, PeraHealth	CERP A: 0.50 EXED275
	1:00 pm Add to schedule	Optimize ICU Bed Management and Reduce Unplanned Transfers with Predictive Analytics Carolyn C. Scott, RN, M.Ed., MHA Executive Vice President, PeraHealth	CERP B: 0.50 EXED274
	2:30 pm Add to schedule	Identifying Patients for Palliative Care Through the Use of Predictive Analytics Carolyn C. Scott, RN, M.Ed., MHA Executive Vice President, PeraHealth	CERP B: 0.50 EXED276
	Wednesday, May 22
	11:30 am Add to schedule	Identifying Patients for Palliative Care Through the Use of Predictive Analytics Carolyn C. Scott, RN, M.Ed., MHA Executive Vice President, PeraHealth	CERP B: 0.50 EXED276
	1:00 pm Add to schedule	Sepsis Risk Identification Using Near-Real-Time Algorithms and Predictive Analytics Carolyn C. Scott, RN, M.Ed., MHA Executive Vice President, PeraHealth	CERP A: 0.50 EXED275
	2:30 pm Add to schedule	Optimize ICU Bed Management and Reduce Unplanned Transfers with Predictive Analytics Carolyn C. Scott, RN, M.Ed., MHA Executive Vice President, PeraHealth	CERP B: 050 EXED274
	Thursday, May 23
	9:30 am Add to schedule	Optimize ICU Bed Management and Reduce Unplanned Transfers with Predictive Analytics Carolyn C. Scott, RN, M.Ed., MHA Executive Vice President, PeraHealth	CERP B: 050 EXED274
	10:15 am Add to schedule	Identifying Patients for Palliative Care Through the Use of Predictive Analytics Carolyn C. Scott, RN, M.Ed., MHA Executive Vice President, PeraHealth	CERP B: 0.50 EXED276
	11:00 am Add to schedule	Sepsis Risk Identification Using Near-Real-Time Algorithms and Predictive Analytics Carolyn C. Scott, RN, M.Ed., MHA Executive Vice President, PeraHealth	CERP A: 0.50 EXED275

Course Descriptions

Sepsis Risk Identification Using Near-Real-Time Algorithms and Predictive Analytics

Many tools for sepsis identification rely on vital signs and lab values. Although important, these data are often lagging indicators of change in patient condition. In this session, information will be provided which will detail how additional clinically salient variables can be included in an algorithm which more quickly identifies patient deterioration with fewer false positives than existing tools. Further the results from this algorithm can be trended and reflected in real time, allowing for more timely intervention by clinical staff. This information has resulted in improved sepsis-related clinical and financial outcomes, and is a gold standard for tracking patient condition over time.

Objectives
• Explain why inclusion of nursing assessments in a sepsis-related algorithm provides a more robust way to identify patient condition change.
• Identify sepsis-related outcomes achieved by organizations using a real-time algorithm that includes nursing assessments, labs and vitals.

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Optimize ICU Bed Management and Reduce Unplanned Transfers with Predictive Analytics

The impact of unplanned transfers to the ICU may increase patient morbidity and mortality, length of stay and resource utilization, while the evidence supports a decrease in patient and caregiver satisfaction. Recent evidence reveals that as many as 80% of unplanned transfers are preventable. Although less common, bounce-backs to the ICU may result in similar costs and negative outcomes for providers and the patients they serve. This session demonstrates the use of real-time predictive analytics in patients at risk for unplanned transfers and helps participants to identify these patients so that appropriate clinical intervention can occur. Results from organizations using this technology is provided, which reflects how mortality results and associated costs of care have improved. Details are given to participants about the technology can be used to identify patients who are optimal candidates for safe discharge from the ICU to a lower level of care.

Objectives
• List four negative impacts of unplanned transfers to the ICU.
• Explain how predictive analytics-based technology is used to mitigate unplanned transfers to and optimize safe discharge from ICU units.

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Identifying Patients for Palliative Care Through the Use of Predictive Analytics

Despite the increased number of palliative care teams in the United States, access to palliative care services in the hospital continues to be inadequate. The availability of a simple method to identify appropriate patients for this type of care has, historically, been difficult. Effective palliative care services can improve patient and family satisfaction, enhance clinical outcomes and decrease cost of care. This session presents information on how organizations are using a clinically salient algorithm to automatically identify patients who meet criteria for palliative care consults. Results relative to clinical and financial outcomes and visualization of how the algorithm is utilized in the acute care setting are provided.

Objectives
• List three benefits of using a clinically-salient algorithm and trigger to identify patients who meet criteria for palliative care consults.
• Define outcomes achieved using predictive analytics and patient condition visualization to identify patients for palliative care consults.

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