Health Technology and Innovation Discussion Paper
Health Technology and Innovation Discussion Paper
The technology used in the health system
It is common knowledge that the need for new technology innovation in numerous healthcare and nursing training areas has never been greater. I have noticed in my nursing career the critical need for innovation and creativity in healthcare in general, particularly in cancer care in the oncology unit. Incorporating technology is essential because technology has the significant potential to shape care models in hospitals; thus, I recommend the buy of artificial intelligence (Reddy et al., 2020).
Impact of the technology
First, health facilities that have implemented “VoIP” in their facilities have demonstrated that, in many instances, the technologies can enhance work and skillful approach while growing efficiencies and patient care. VoIP sends information via the internet and enables users/staff members/caregivers to communicate instantly, improving efficiencies and increasing patients in the hospital. How does it accomplish this? (Reddy et al., 2020). VoIP enables employees to instant message each other rather than spending time attempting to locate employees via phone or pager. Improved staff communication can improve hospital flow. Alarms or alerts can be programmed into patient rooms and automatically transmitted to caregivers, resulting in faster reaction times. Even if they are not in the office/hospital, VoIP enables employees and caregivers to stay connected and informed. By utilizing VoIP, we can reduce “overflow in emergency rooms due to a lack of staff, insufficient exam rooms, and insufficient inpatient beds,” improve patient care and avoid bottlenecks in patient flow (Reddy et al., 2020). Health Technology and Innovation Discussion Paper
How this technology supports healthcare decisions
However, AI is still in its early stages, but it holds a great deal of promise for many patients who want to schedule doctor’s appointments and be assertive in their healthcare. For example, the AI can schedule appointments based on the severity of the patient’s symptoms, reducing patient cluster analysis. The hospital’s staffing challenges will be reduced by removing excess emergency room visits. I also suggest AI because it can supervise the patient’s overall health and instantly inform a sentient nurse when variables cannot be controlled, such as patient monitoring and cardiac rhythms. It can be supervised 24 hours a day, seven days a week (Wang & Preininger, 2019).
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This will help homecare assistants stay informed about their cancer patients’ progress, thereby assisting staff by preparing them for the patient, resulting in a smoother patient flow in the oncology unit. Cerebro-AI for nurse staffing is a great current example of AI for staffing, as it allows a direct and responsive healthcare workforce marketplace. Using a mobile application, nurses can quickly seek employment at all area hospitals with the tap of a finger (Wang & Preininger, 2019). The ability of AI to data to be able into real-world items that require action, which can make life better and overall health, is the essential characteristic that I believe AI can be used for and would be a good investment. Following the oncology unit might improve flow and staffing (Wang & Preininger, 2019).
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How does the use of technology affect decision-making?
However, because AI collects so much data about patients, a policy for the moral use of patient data systems is required. To summarize, various principles, such as the principle of privacy, as well as the patient’s decision-making and right to autonomy, are critical for patients in planning their health data. However, for the hospital, morality can inform them of various tactics and decisions to be implemented in areas such as equity and fairness in care access. Patients should sign a consent form agreeing to participate in Jvion as part of my policy design (Wang & Preininger, 2019).
How has the technology evolved, and where is it heading
Jvion is one technology system worth investigating because it can be used in various ways to help the oncology and cardiology units and any unit needing improvement. On the other hand, the Internet of Medical Things is a better innovation that can aid in patient education and thus moderate readmission rates in the cardiology unit (Panesar, 2019). Considering the numerous benefits it may bring to both the hospital and the patients, it may be a good purchase for the hospital. Actual reporting is critical in the cardiology unit, and this technology provides that through objective reporting because its gadgets can record and notify actual activity. Because the technology allows for remote monitoring, the patient can be informed and advised to reduce readmissions (Panesar, 2019).
How has this evolution improved healthcare decision making
Staff will be required to use username and password, generally, pro software, data backups, and cryptography to assist with PHI protection because PHI is stored on a storage or computer device. Staff will only be permitted to take PHI in the office, and they will be permitted to cut and paste previous notations to save time. When a computer screen is not used for more than 2 minutes, it will lock automatically. All information will be used solely for patient assessment, recording, prioritization, and improving patient outcomes. I recommend training on The Ethical Framework for Ethical Making Decisions and requiring staff to reacquaint themselves with it and use this moral system in day-to-day practice (Panesar, 2019).
References
Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 27(3), 491-497 academic.oup.com/jamia/article-abstract/27/3/491/5612169
Wang, F., & Preininger, A. (2019). AI in health: state of the art, challenges, and future directions. Yearbook of medical informatics, 28(01), 016-026. www.thieme-connect.com/products/ejournals/html/10.1055/s-0039-1677908
Panesar, A. (2019). Machine learning and AI for healthcare (pp. 1-73). Coventry, UK: Apress. link.springer.com/book/10.1007/978-1-4842-6537-6
