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 build confidence amongst staff and ensure that they are able to deal with AI-generated information.

Data Reconciliation, or part-risk area, is one of the partial-risk areas. Non-standardized interactions with external systems and/or the lack of full integration with providers is one of the factors that could mean that key patient information might not be captured when patients move between providers. A misaligned external data with the MGH predictive tools is more likely to occur if the information isn’t available for the decision maker. This can be reduced in risks at the rate of implementing the popular procedures of data sharing and more interoperability frameworks.

Reflection on Using the SAFER Guides for Risk Assessment

Slide 6:

The SAFER guides provide an organized and detailed outline of the technology strengths and risks that can be assessed of the MGH organization. It was also an opportunity for me to have a broader view of clinical workflows and the preparedness of infrastructure and problems at the end user level as a whole. The guidelines, in contrast, are aimed at safety assurance and technical best practices, to encourage a subtler understanding of human factors important to successful technology assimilation, such as clinician workload and alert fatigue. In addition, the guides highlighted the lack of information literacy, interoperability and training of patients. The balanced perspective enabled the ability to have a twofold approach that highlighted all the strengths, such as the current successes of real-time CDS, and the well-developed CPOE technology. Guidelines also produced more real world thinking which would anticipate and remove any barriers to an implementation in the complexities of the MGH organisation.

Conclusion

Slide 7:

As AI-powered predictive analytics becomes entrenched in MGH’s EHR, it’s poised to be a pivotal moment towards safer, innovative, and proactive healthcare provision. Use of technologies is one of the new ways to improve Leapfrog and Medicare compare ratings that is data-driven. The risks identified by SAFER will also be mitigated which will improve the resilience of the system and the maximum uptake of the system by the users. If an executive is on board, MGH will be a national model in the world of health informatics innovations. Training, monitoring and system improvement efforts will continue to result in long-term success with high quality, patient centered outcomes.