Order from us for quality, customized work in due time of your choice.
Introduction
Cleveland Clinic, which is a network of more than two hundred inpatient and outpatient facilities, is a teaching healthcare organization operating in the private sector and serving millions of patients annually. Cleveland Clinics (2021) locations offer an array of medical services, including resuscitation/intensive care, cardiology, gastroenterology, urology, pediatric oncology, and neurology care, as well as maternity and imaging services.
Benefits/Difficulties with Technologies
Regarding its readiness for new technology, Cleveland Clinic (2021) has been pursuing the goal of gaining leadership in diagnostic and care technology, resulting in a series of tremendous successes, including therapeutic vaccine development and inventing novel cardiology surgery and assessment techniques and brain mapping procedures. In spite of massive progress, the organization struggles with implementing the best possible complication-detection technologies for intensive care.
Rationale for Change: Strategic Issues and the Need for New Technology
Two interconnected strategic issues permeate the organizations current situation and call for considering innovative technologies. Care quality, despite being the companys strong asset, still features opportunities for improvement. Figure 1 represents a positive difference between the organizations anticipated and actual injurious fall and septicemia-related death counts, implying the need for focused technology-based improvement. Also, Cleveland Clinic faces intense competition from the Mayo Clinic and ranks second after it in the Honor Roll hospital list and third after Mayo Clinic and the John Hopkins Center in the rating of smart hospitals, which runs counter to the organizations goal of seeking care- and technology-related excellence (Harder 2021).
Possible New Healthcare Technologies: Overview
There is a variety of technologies that Cleveland Clinic can benefit from; firstly, artificial intelligence clinical decision-making supports are being developed, including decisions that focus specifically on the early recognition of sepsis. Sepsis is the clinics prominent area of concern, so AI-based solutions could bring more value with regard to the early diagnosis of sepsis at the networks ICUs, thus increasing Cleveland Clinics chances to realize its strategic goals and improve its leadership positions in terms of smart healthcare.
Virtual reality or VR is another promising technology that Cleveland Clinic can implement in the future; VR applications offer opportunities for fall prevention training. By analogy with VR environments used for construction worker training, the technology is applied in VR headsets and training programs for frail older adults at risk of falling (Mascret et al. 2020). Considering Cleveland Clinics opportunity for improvement in injurious fall rates, incorporating VR headset use by inpatients in the existing fall prevention protocols could be a valuable chance in the future and a measure to maintain the organizations reputation as the proponent of smarter care.
Intelligent sensor-based pressure visualization systems represent another branch of technologies that could benefit Cleveland Clinic strategically by promoting the gradual elimination of hospital-acquired pressure injuries that are already rare in its inpatient facilities. Figure 2 features a bubble diagram that visualizes a systematic literature review by Silva et al. (2021) and the number of studies that mention systems targeting particular outcomes, with posture classification as the most targeted purpose. As for the most popular algorithms, systems based on neural networks seek to imitate human thinking and search for relationships and trends in the patients condition, and fuzzy logic systems apply many-valued rather than binary yes-or-no logic to visualize pressure contours and highlight the regions of interest with the highest pressure injury risks (Silva et al. 2021). For ICU rooms at Cleveland Clinics locations, intelligent systems to visualize bedsore risks might become another safety maximization strategy.
Another hypothetically feasible technology for Cleveland Clinic integrates AI-based applications with systems for the remote diagnosis of cardiovascular conditions and monitoring patients with such concerns. First implemented by the Mayo Clinic in collaboration with health IT leaders, the innovative system is anticipated to promote event-based medicine and instrumentalize inference-making AI for early heart disease detection (Anastasijevic 2021). The gradual adoption of similar solutions could be valuable for Cleveland Clinic strategically and in terms of efficiency, including enabling the organization to keep up with the competition, expanding the currently limited telehealth services, and improving quality as per the IOM frameworks effectiveness and timeliness components.
Recommended Technology: Description
Among the previously discussed technologies, the Sepsis Early Risk Assessment (SERA) AI-based algorithm for improving sepsis risk evaluation can be recommended for implementation. As shown in Figure 3, the SERA algorithm uses natural language processing to analyze clinicians written unstructured notes and combines the takeaways with patients structured EMR data to predict each patients possibility of having sepsis at the time of analysis and during the next hours or days (Goh et al. 2021). Based on the available data, the early prediction algorithm can approximate the risks of septicemia development prospectively, which enables ICU practitioners to plan care and supervision according to reliable individual risk predictions.
Recommended Technology: Impacts on Efficiency/Effectiveness
The rationale for recommending AI-based complication prediction models, such as the SERA, relates to organizational improvement and suitability for Cleveland Clinics strategic development. The SERA has been shown to significantly outperform clinicians predictions in terms of accuracy and timeliness, implying the algorithms ability to benefit Cleveland Clinics early alert system. Predictions made using the SERA algorithm tend to outperform standardized septicemia risk assessments, including sequential organ failure assessment (SOFA), its abbreviated version (qSOFA), and other tools, which can help Cleveland Clinic to improve patient outcomes thanks to detecting complications earlier (Goh et al. 2021). Based on the nursing role effectiveness model, changes to organizational structure factors, such as workload, impact nursing-sensitive client outcomes (Lukewich et al. 2019). Thus, in terms of efficiency, the technology, if transformed into a reliable clinical system, could save ICU nurses time by reducing the need for traditional risk scoring.
The AI-based sepsis detection system fits into Cleveland Clinics organizational strategy and serves it in two important ways. Firstly, the SERA tool, as it has been said, can potentially reduce the workload on intensive care nurses, which would directly promote Cleveland Clinics patient safety goals by eliminating the room for exhaustion-related care mistakes. Secondly, AI-powered tools, being considered novel and promising, can support the organizations striving for technological excellence and differentiation, thus maximizing technology leadership. Based on Porters theory of generic strategies, product differentiation, including the establishment of Cleveland Clinics own department of AI and actively investing in this area to safely implement and develop novel applications, can be a source of competitive advantage for the organization (Islami, Mustafa & Topuzovska Latkovikj 2020). It is because the organization operates in a competitive market with customers having specific needs, and Cleveland Clinic already has technology leadership and collaboration experiences enabling it to further differentiate itself from the competition.
Stakeholders Perspectives
The Discovery Accelerator Center was established as part of Cleveland Clinics collaboration with the IBM Corporation. Being involved in quantum computing systems incorporation in drug research, the group is committed to instrumentalizing AI and computer technology, which will likely cause the positive reception of the SERA as a process improvement opportunity. If implemented, the SERA algorithm will expand the centers responsibilities to include the tools incorporation and maintenance, thus assisting in reaching the objective of instrumentalizing AI (Cleveland Clinic 2021).
ICU teams, the second stakeholder group, can be anticipated to approach the new technology as a positive opportunity since preparedness for new workplace technology and informedness are fostered as the companys values. The SERAs impacts can be diverse, including the need for tool use training, instruction on patient data entry practices that will improve data recognition and inference-making, and beneficial changes to the workload, thus supporting the professional excellence objectives of the organization.
Patients represent the most educationally and professionally heterogeneous stakeholder group, which calls for making inferences from nationwide research on individuals perceptions of AI in healthcare. In 2018, in a survey of 1100 adult U.S. citizens, almost 40% of respondents positively evaluated the idea of medical advice with the use of inference-making AI, implying that a considerable number of Cleveland Clinics patients would accept the SERA with enthusiasm (Newman et al. 2020, p. 23). Based on research, the SERAs impacts are likely to include reductions in the time between sepsis onset and diagnosis and new patient data processing strategies (Goh et al. 2021). By fostering decision-making that outperforms human assessment, the technology will support patient safety maximization strategic objectives.
Conclusion
Finally, the ethical component of implementing the SERA algorithm requires discussion. Firstly, AI-based diagnostic models, including the SERA, process human-made notes, which might potentially lead to the transfer of biased, prejudiced thinking about patients to the systems decision-making processes. Cleveland Clinic can use the research-based strategy to respond to this concern and test the tool by applying data from previous clinical cases with and without sepsis to ensure the algorithms accuracy in diverse populations. Next, the informed consent principle represents an issue; given that not all patients support AI in healthcare decision-making, Cleveland Clinic will have to respond by creating a brief educational procedure for ensuring healthcare clients awareness of how their health dynamics will be tracked and how the privacy of their personal data will be guaranteed. Additionally, Cleveland Clinic will be ethically responsible for keeping the highest possible standards of safety by means of constant oversight and ensuring the systems predictability (Gerke, Minssen & Cohen 2020).
Order from us for quality, customized work in due time of your choice.