Improving patient outcomes and reducing healthcare costs are key goals of any healthcare organization. Critical condition monitoring (CCM) and clinical intelligence (CI) are two essential tools that can contribute to an organization’s quality improvement efforts. CCM involves the continuous monitoring of patients’ vital signs and other physiological parameters to assess the risk level of patients. CI refers to the use of advanced analytics and machine learning to identify patterns and alert clinical teams as soon as a patient is at risk. By combining CCM and CI, clinicians are better able to care for a larger number of patients, focus their attention on at-risk patients and make evidence-based decisions in real time. To embrace the true potential of this technology, hospital systems should take five critical steps.

1. Implement Real-Time Monitoring:
   
   CCM technology uses advanced algorithms to analyze real-time data from the EHR and other medical devices, and alert teams when subtle changes are detected. CCM assists the clinical team (including the Rapid Response Teams[1] [2] ) with access to real-time, actionable information in multiple clinical settings including emergency departments, intensive care units (ICU), and general hospital floors, etc.
   
   The use of real-time monitoring can help reduce the risk of complications, thereby reducing the need for more complex interventions and decreasing the cost incurred by extended hospital stays. When the rate of complications decreases, overall patient health outcomes improve. For example, patients receiving mechanical ventilation support are at risk for ventilator-associated pneumonia and acute respiratory distress syndrome (ARDS). CCM systems can calculate and monitor the appropriate oxygenation level and other care parameters on all ventilated patients. Alerts are sent to the clinical team when the parameters are outside of recommended levels. By using CCM, a higher quality of care and management is possible.
   
   
2. Incorporate Early Warning Scoring Systems:
   
   The burden of monitoring at-risk patients can be quite high. Early warning scores (EWS)[3] [4]  calculate a patient’s risk of deterioration by assigning a weighted value to various physiologic parameters and vital signs. The composite score is used to assign a risk level of high or low to a wider patient population, which helps the clinical teams to identify potential at-risk patients sooner and intervene to prevent adverse events.
   
   A study has shown that 10% of floor patients experience unexpected decompensation, and half of this group is transferred to ICU; these patients often suffer poor clinical outcomes and are at an increased risk of death.¹ Thus, the use of EWS systems can reduce the monitoring burden, by allowing a team to triage patients and ensure those at the highest risk levels receive early, life-saving interventions. In addition, EWS can also be used to monitor a patient’s progress over time and identify trends and patterns in a patient’s condition.
   
   
3. Integrate Clinical Intelligence:
   
   Real-time data and artificial intelligence (AI) can help teams track patient status and reduce the time to diagnosis. A recent report shows an AI algorithm’s ability to detect early signs of sepsis by up to 32% and reduce false positives by up to 17%.² Since sepsis is a leading cause of death in the US, accounting for half of all hospital deaths, early detection is critical to improve outcomes.
   
   Many CI systems integrate AI with clinical algorithms and guidance to identify patterns in large datasets, which can then be used to assess risk levels and predict future outcomes or events. By using CI, providers can prioritize their care and allocate resources accordingly to optimized intervention.
   
   In addition, CI can help healthcare providers comply with ever-changing clinical guidance. Penn Medicine implemented a CI system to improve compliance with sedation and ventilation protocols.³ The application advised respiratory therapists when a patient’s vital signs met certain criteria, allowing therapists to determine whether the patient is ready to breathe on their own. The use of this CI reduced the time patients spent on a mechanical ventilator by more than 24 hours.³
   
   
4. Establish clear protocols for responding to alert:
   
   The key consideration when implementing CCM and CI is the need to establish clear protocols for responding to alerts generated by the system. Optimal and effective workflows should outline who is responsible for responding to alerts, how alerts are communicated, and what actions should be taken. Effective processes must coordinate multiple care team members and departments to ensure appropriate and timely responses to alerts to ensure favorable outcomes.
   
   In the Texas Medical Center, a large urban hospital used the Decisio InsightIQ software and a coordinated, multidisciplinary workflow in the hospital (ED and rapid response teams). After the implementation, the in-hospital mortality rate of patient treated by the rapid response team was reduced by 47%, and the hospital length of stay decreased 4.7 days.⁴ In the ED, mortality was reduced by 12% and length of stay decreased by 6.5%.⁵
   
   
5. Address the Nursing Shortage:
   
   The nursing shortage is daunting, and hospitals are facing an extremely high turnover rate.⁶ How then do hospitals ensure patients are being properly monitored and taken care of? Many hospital systems are turning to clinical surveillance and virtual nursing programs to augment and streamline the daily tasks of bedside nursing. The utilization of CCM can reduce some of the monitoring burden that is shouldered by bedside nurses; however, this requires training and staff retention for ideal results.
   
   According to a study, reducing turnover also reduced the patients’ length of stay, complications, and medication errors, while also increasing the patient satisfaction scores.⁷ While some may argue the costs of CCM and CI could be prohibitive, one need only to look at the ‘costs’ of employee turnover, or the costs of retention to see opportunities.
   
   Systems that choose to implement these five steps, are investing in their future growth and sustainability. Technology will continue to advance and provide opportunities, and health care needs will continue to grow. Thus, we must address the challenges head on and embrace the advantages that technology can provide. Use of CCM and CI can improve the outcomes and experience of both the patient and the provider.

DECISIO is a Texas-based company that has developed InsightIQ, an FDA-cleared, web-native clinical intelligence software that uses continuous, smart bedside monitoring that empowers clinical teams to efficiently identify at-risk patients remotely while complying with established clinical guidelines. InsightIQ displays clinically relevant information alongside relevant clinical protocols that are configurable to the unit workflows, enabling care providers to accelerate interventions. The InsightIQ software can meet the needs of a wide range of hospital systems and departments to help your team provide quick intervention, experience and increase the overall quality of care.

References

1. Jones D, Holmes J, Currey J, et al. Proceedings of the 12th International Conference on Rapid Response Systems and Medical Emergency Teams. Anaesth Intensive Care. 2017;45(4):511-517. doi:10.1177/0310057X1704500416
2. Goh KH, Wang L, Yeow AYK, et al. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nat Commun. 2021;12(1):711. doi:10.1038/s41467-021-20910-4
3. ECRI Institute. Penn Medicine app promotes more rapid weaning from mechanical ventilation and sedation—an award-winning initiative. Health Devices. 2019 Jun 26. Available at: https://www.ecri.org/components/HDJournal/Pages/13th_HD_Achievement_Award_Winner_Penn.aspx?PF=1&source=print
4. Morgan CK, Amspoker AB, Howard C. Continuous Cloud-Based Early Warning Score Surveillance to Improve the Safety of Acutely Ill Hospitalized Patients. J Healthcare Qual. 2021;43(1):59-66. doi: 10.1097/JHQ.0000000000000272
5. Howard C, Amspoker AB, Morgan CK, et al. Implementation of automated early warning decision support to detect acute decompensation in the emergency department improves hospital mortality. BMJ Open Qual. 2022;11(2):e001653. doi: 10.1136/bmjoq-2021-001653
6. Lagasse J. RN turnover in healthcare on the rise. Healthcare Finance. Published January 4, 2023. Available at: https://www.healthcarefinancenews.com/news/rn-turnover-healthcare-rise
7. Zhou G, E H, Kuang Z, et al. Clinical decision support system for hypertension medication based on knowledge graph [published correction appears in Comput Methods Programs Biomed. 2023 May;233:107371]. Comput Methods Programs Biomed. 2022;227:107220. doi:10.1016/j.cmpb.2022.107220