NURS FPX 8022 Assessment 2 SAFER Guides and Evaluating Technology Usage

SAFER Guides and Evaluating Technology Usage

Student Name

Capella University

NURS-FPX 8022

Dr. / Prof- Name

Professor Name

Introduction

Slide 01

Hello! I’m ____ and as part of today’s presentation I will share how we can use the Safety Assurance Factors for Electronic Health Record Resilience (SAFER) framework to assess patient safety technology at Massachusetts General Hospital. The main focus will be on how the framework will be used to guide the integration of an Artificial Intelligence (AI) powered Clinical Decision Support (CDS) system into our current processes in the electronic health record (EHR) system for the prevention of falls and sepsis. The SAFER Framework is a user-friendly framework that can be used as a guide to evaluate the safety, efficiency, and reliability of EHR (Sittig and Singh, 2020). The goal of the evaluation is to determine the readiness of MGH to use the predictive system to detect sepsis and risk of falling in their EHR system.

Description of Technology

Slide 02

MGH is suggesting adopting an Artificial Intelligence (AI) driven Clinical Decision Support System (CDSS) to be integrated into the Electronic Health Record (EHR) system. The system will incorporate predictive analytics and real-time (RT) information to help predict if a patient is at risk of poor clinical outcomes from a range of issues such as sepsis, medication errors, and falls (Elhaddad & Hamam, 2024). The implementation of sophisticated algorithms will help clinicians have access to real-time data on the patients’ laboratory results, best practice care standards, and individualized care plans; therefore, improving productivity and safety. Adopting this approach will help us deliver safe and timely care to our patients.

ICT addresses many of the identified gaps in the care delivered today including: 1) Enhancing efforts for early prevention of infection and early recognition of disease progression will help reduce the incidence of postoperative sepsis, which is an established performance gap; 2) Use of AI-based prompts (i.e., pop-ups) will standardise the use of best practice interventions which will ultimately eliminate the variability of care being delivered and errors in medication administration (Elhaddad & Hamam, 2024); and 3) Integrated dashboard reports will enable inter-professional communication between physicians, nurses and other providers as to the urgency and high risk of each patient; and as such care will be delivered in a timely and seamless manner.

SAFER Guides Findings for Well Performance of Technology

Slide 03

Massachusetts General Hospital has a much higher rating under the SAFER guidelines in many areas that promote the integration of AI-based clinical decision support into the current electronic health record (EHR) implementation. In terms of the system environment, MGH has a stable and interoperable EHR system that will support the amalgam of the required documents, orders, prompts, and decisions (MGH, n.d.).

The characteristics are complementary to the infrastructure required by predictive analytics, which implies that the hospital could be considered fully prepared in all technical aspects. The hospital has also been very successful in the area of clinical processes, where practice is already informed by existing infection-control bundles, fall-prevention techniques, and medication safety procedures. The hospital’s track record of integrating evidence-based practice in EHR workflow demonstrates the preparations of operations to extend the operations to AI-based alerts and predictive instruments.

Additionally, another strength area is in the organizational governance and safety culture. History There is a history of patient safety with the highest national quality ratings and continuous investment into health information technology (IT) systems (MGH, n.d.-a). The focus on leadership keeps stakeholders in line, provides adequate resources, and fosters a vigorous attitude toward technology-enabled care. Overall, SAFER outcomes highlight how MGH has a solid background in system design, clinical workflow, and governance that places the organization in the right position to proceed with predictive analytics to improve safety and outcomes.

SAFER Guides Findings Related to Risks of Technology

Slide 04

The SAFER framework provides an overview of the areas at Massachusetts General Hospital where AI-driven informatics in the EHR are not enabled optimally. The AI-based CDS will provide numerous advantages to the hospital (Cabello et al., 2024). Nevertheless, the system interfaces and interoperability are one of the areas that are regarded as “Not Implemented. Although MGH has a very strong in-house EHR infrastructure, the exchange with external providers and systems is limited by the exchange of information. This non-interoperability results in partial patient records, breakdowns in communication, and delay in treatment, and the ineffectiveness of predictive models in need of complete and timely data.

The other domain that can be categorized as Partially in Some Areas is human factors and workflow integration. Along with a successful experience MGH has had in integration of decision support in clinical workflows, issues of documentation load, fatigue of alerts, and dissatisfaction in clinicians still exist. Absent the strategic redesign, AI-based predictive alerts might only exacerbate the problems, as it means that the hospital has succeeded partway in the area (Cabello et al., 2024).

Finally, there is also a grading of contingency planning and security as rated as partially in some areas. Good IT governance and compliance practices do exist at MGH; however, due to the increasing importance of cyberattacks on EHR systems, the current protections and employee education should be tightened to ensure data integrity and security. Naturally, the interoperability and workflow integration and security scores of MGH are all Not Implemented and Partially in Some Areas, respectively.

Experiences of Using SAFER Guides

Slide 05

In retrospect on how the SAFER guides were used to evaluate technology at MGH, it was found that the process was systematised as well as insightful. Proper models, such as SAFER, have proven beneficial in enhancing the detection of patient safety issues and direct quality improvement in health IT settings (Sittig et al., 2020). The guides provided an enriching guideline in examining patient safety technology because it allowed everyone to go beyond the surface and uncover concealed dangers that could be discovered too easily. Razing assessment into categories such as system configuration, workflow integration, interoperability, and contingency planning, the SAFER framework offered a meticulous approach to the issue that highlighted both strong and weak areas.

The greatest benefit of the process was the way the framework changed the focus of the evaluation. The original focus was so much on the technical benefits of incorporating AI-based predictive analytics into the electronic health records. Organizational, cultural, and human considerations were, however, given priority due to the use of the SAFER guides. The existing literature illuminates the fact that human factors and organisational culture need to be considered with technology becoming a sustainable way of adopting health IT (Kushniruk and Kaufman, 2024). The issue of clinician burnout and alert fatigue was also critical and balanced to consider, as opposed to the algorithm performance. Likewise, contingency planning and cybersecurity were not perceived as second-order concerns but rather a part of the overall strategy to ensure the safety of patients in the face of the implementation of new technology.

The arrangement of SAFER additionally helped to optimize improvement efforts. The structure was helpful in identifying particular areas that were not applied to or partially in others instead of relying on general recommendations that were sweeping. The categorization also enhanced the improvement strategy, and the priority of actions shifted towards the top of the list by interoperability, usability, and security. By encouraging a balanced vision that combines innovation and safety, technical functionality with usability, and organizational objectives with patient outcomes, SAFER guidelines have been added to the evaluation. A systematic approach provided a clear road map to technology adoption, which is not only conforming to resilience but also improving care delivery.

Conclusion

Slide 06

When SAFER is introduced into the Massachusetts General Hospital process of integrating AI-powered predictive analytics into the EHR, it exposes its substantial grounds as well as revealing gaps. The hospital demonstrates strong performance in the areas of governance, system configuration, and evidence-based clinical processes, but such areas as interoperability, workflow integration, and cybersecurity are problematic. T

he SAFER guides proved helpful in the openness of risks and priority areas of improvement, and the change driven by technology would be safe and sustainable. By addressing the gaps, MGH will be able to leverage the impact of informatics innovation the most, enhance patient safety, and entrench the image of a relatively high-quality patient-centered care provider within the organization.

For the next and 3rd assessment of class NURS8022  visit:  NURS FPX 8022 Assessment 3