NURS FPX 8022 Assessment 2 SAFER Guides and Evaluating Technology Usage

NURS FPX 8022 Assessment 2 SAFER Guides and Evaluating Technology Usage Student Name NURS-FPX8022 Nursing Technology and Advanced Healthcare Information Systems Capella University Professor Name Submission Date   SAFER Guides and Evaluating Technology Usage Slide 1: Hi, my name is_______, and I will talk about the proposed system for predictive analytics, built into electronic health record system at Massachusetts general hospital to improve patient safety and outcomes. Slide 2: Postoperative sepsis, adverse events and patient falls are the key issues that need to be addressed. Enhancing early detection and intervention and ensuring the EHR resiliency guidelines will also be incorporated into the problem. To create and deploy a real time, predictive analytics system to forecast clinical deterioration (falls and/or sepsis) prior to the onset of adverse outcomes using information from the EHR. Furthermore, technology can help to reduce delays in care, ensure coordination of care and reduce unnecessary complications. Finally to assess, implement and continually improve the security and effectiveness of the use of EHRs and related technologies and technologies with SAFER Guides. I will explore and critically discuss in the presentation how the SAFER guides can be used to improve the resilience of EHR integration and to make hospitals safer. Implementation of Predictive Analytics-Enhanced EHR Slide 3: The proposed technology of predictive analytics is integrated with the existing EHR, the information of patients are reviewed in real-time with an artificial intelligence model, the model may give alerts of any potential risk, such as sepsis or fall. The risk detection and alerts are available in real time and integrated into existing clinical processes, decision support systems and monitoring processes. The clinical parameters and vital signs are continuously analyzed and physicians and nurses are alerted accordingly. This guarantees timely clinical decisions and interventions for prevention. Consistently, the technology proves beneficial when it comes to care planning, allocation of resources, and individual guidance. The system enables saving time, energy and resources in clinical work, operational productivity and safety. The deficiency is directly mitigated by the ability of the predictive model to provide early warnings of adverse events and prevent them. High rates of postoperative sepsis (4.69), adverse event scores (1.02) and fall rates (0.199) were identified as areas of concern by the leapfrog data. Also, predictive models can reduce the death rate from sepsis by as much as 30 percent, and the rate of patient falls by 25 percent. Better surveillance and intervention through information and communication technology systems, including a CDS using AI. In addition, patient portals also enhance engagement, access and adherence to the discharge plan. The seamless integration is easy to get into EHR, and aids safety regulations and performance improvement objectives at MGH (Massachusetts General Hospital, n.d.). Not only does it enhance Leapfrog and Medicare Compare scores, but it also makes for a patient-centered, resilient and proactive care environment. SAFER Guides Findings: Areas of Strong Performance Slide 4: The MGH has excellent technical infrastructure and patient safety culture indicated by the SAFER Guide ratings of “Fully in all areas” and clinical process integration is considered good. Its benefits are that the data is easily accessible in real time, clinical decision support is well developed and it uses Computerized Physician Order entry (CPOE) to enhance the accuracy of medications and decrease medical errors. Instant results from the tests are shared with the clinicians and alert is set for abnormal results enabling swift clinical response. Use of EHR integrated tools such as CDS, CPOE, etc. at the facility highlights a system in place that has been developed to encourage evidence-based decision making. Data access – both for documentation and analysis – can be relied upon for consistent patient care delivery and efficient operations. The services provided in MGH’s contingency planning, too, are rated highly. The organization have back-ups and data recovery procedures in place that ensure that the organisation is able to keep delivering services during outages. When providing care, the resilience guarantees that serviced do not go down giving uninterrupted services, thus keeping patient safety and working procedure stable. Functional safety of the interfaces with the system is also well-developed. Today, there’s interoperability between the various departments within the facility and common EHR modules to enable data sharing with no duplication of documentation. Smooth interface seamlessly delivers clinical experience with no communications delay; particularly important when shifting from one point to another in the clinical interaction. The strengths work synergistically to decrease preventable adverse events, delays in treatment and clinician workload. High levels of existing automation, employee training and security measures also contribute to safe usage of EHR systems. Overall, the results of the SAFER Guide indicate that MGH’s existing system is well-positioned to create a safe, responsive and well-integrated health IT system. The existing EHR platform’s predictive analytics will only add to these benefits and continue to improve patient outcomes and hospital performance. SAFER Guides Findings: Identified Risks Slide 5: One of the key risks identified by the SAFER guide in the context of integration of AI-based predictive analytics into the existing EHR system is the potential for creating a “Trojan horse. Regarding integration of AI based predictive analytics into the existing EHR system, the SAFER guide findings point to the risk of creating a “Trojan horse. The risks are classified as “Not Implemented” or “Partially in some areas,” and system level changes and focused attention are needed on them that pose risk to patient safety or data integrity. One of the challenges is clinician’s burn out and disruptions of work flow. The existing system doesn’t have the necessary workload balancing workflows and processes for predictive alert fatigue. Without prioritization and/or filtering capabilities of the alerts, there is a risk of alert fatigue which can cause the staff to miss out on important alerts. At the present moment machine learning tools would be of no clinical value as nurses don’t know how to interpret or act upon complex predictive risk-scores. The risk associated with this will require a lot of education, hands-on training and clinical simulation to