L’intégration de l’IA dans le secteur de la santé pourrait-elle contribuer à réduire les problèmes de personnel infirmier ?

Across the United States, nurse staffing shortages are putting a strain on hospital workers and jeopardizing patient safety. In April, 10,000 nurses threatened to walk off the job at New York’s three largest hospital systems to push for rules setting minimum staff ratios, according to the New York Times. The union representing the nurses was able to reach a deal with hospital administrators that led to the hiring of 1,450 new nurses and establish minimum ratios, but the potential strike symbolized rising tensions in the medical field.

Nurse staffing shortages must be addressed to prevent worker burnout, which compromises both employee and patient safety. There is a proven correlation between staffing and patient outcomes across quality, safety, and experience, according to the National Institutes of Health. However, the ideal nurse-to-patient ratio is unclear, given that nurses in each hospital will come from a different background as far as education, experience and training. It’s equally difficult to craft a one-size-fits all approach when each hospital or healthcare facility meets unique patient needs. For this reason, addressing nurse staffing issues requires an individualized approach. 

There are many ways that hospitals and nurses can collaborate to fix staffing concerns.  Leveraging technology can play a big role in crafting a comprehensive safety plan that both parties agree upon. Among the many technological developments in healthcare, artificial intelligence is likely to play an increasingly large role in hospital safety operations.

For the last decade, staffing shortages have impacted hospital and healthcare facility business operations, especially during emergency events. A creative approach is overdue, and artificial intelligence might have a have a part in ensuring the continuity of patient care and security.

What Are AI Integrations In Healthcare?

AI integrations are being developed in the healthcare field largely to find fast and accurate diagnoses of preventable conditions; however, it’s not the only benefit of the technology. There are many more ways in which deep learning is being integrated throughout the industry, with benefits potentially being realized in surgical procedures, nursing, dosage error reduction, and the accelerated development of new treatments. Furthermore, rather than computerize the healthcare industry, AI in healthcare should give healthcare providers more time to interact with patients.

Beyond care, AI in healthcare is anticipated to reduce insurance fraud, provide administrative workflow assistance, improve supply chain management, and enhance the efficiency of finance, IT, and HR. It’s not difficult to see why healthcare organizations are keen to press ahead with AI-powered services. It has been forecasted that within ten years, AI has the potential to improve healthcare outcomes by 30 to 40 percent while simultaneously cutting treatment and operational costs in half.

Machine learning is a subset of artificial intelligence based mathematical algorithms, automating the building of analytical models that use algorithms to learn from data, as per HealthcareIT Outcomes. The machine learns from its own mistakes, and ultimately produces reliable, repeatable decisions. This technology is being successfully implemented in healthcare to analyze incoming patient vitals and referencing it against patient data to predict negative health events, such as hospital re-admissions or emergency room visits.

One of the largest obstacles to incorporating AI into healthcare will be skepticism on behalf of the patients and workers. However, applications for predictive analytics and other similar technology do not necessarily have to involve hands-on patient care, and instead can shape management strategy. This way, with nurse and physician staffing shortages throughout the industry, safety managers can ensure resources available are utilized in the best way possible to prevent burn-out and maintain quality of care.

How Can AI Integrations Improve Nurse Staffing Shortages?

AI Integrations can help address nurse staffing concerns in several key ways. AI has the potential to help nurses with documentation, cutting down on the amount of time completing bureaucratic tasks and allowing nurses to focus on patients. This will prove especially useful when it comes to compiling patient data for health files. Smart algorithms and AI could reduce the need for information to be entered manually and could link content, so that further workflows and tasks could be automatically initiated at the right time.

The AI software can also shape a nurse workflow ecosystem, reducing the need for content to be entered manually and tasks can be initiated automatically, according to HealthcareIT. The software will recognize patterns automatically, evaluate the nurse’s planned goals, and make necessary adaptations. This will cut down on time wasted for employees, and make sure that patients are receiving the most appropriate treatments.

There is also evidence that artificial intelligence reduces hospital admissions. Research suggests that machine learning can help hospitals and healthcare facilities improve intake predictions and prevent overdiagnosis. Implementing these systems could help care workers prevent more than 10% of hospital readmissions.

Not only would this be a positive for the patients, who could spend less unnecessary time in hospitals, it can also potentially help nurses prioritize their time based on the severity of the patient’s illness or injury. Reliable data would ultimately reduce staffing burdens, as nurses would have a better understanding of how to utilize their time while on call without jeopardizing anyone’s safety. 

Leveraging Technology To Address Staffing Concerns

Artificial Intelligence is not the only technology that can  reduce staffing burden by streamlining operations. Healthcare personnel can leverage technology to better communicate internally, employee absences can be more easily managed and on-call nurses can be better prepared for the shift. A mass notification system is an essential tool for healthcare facilities that allows nurses and other staff to communicate through SMS text, email, and voice calls.

A polling module feature within a mass notification system can also help address nurse staffing issues.  If a nurse manager is spending valuable time scrambling to fill a last-minute callout or resolve ongoing understaffing, it can potentially disrupt daily operations and take away from patient care. The automated poll empowers nursing supervisors and other management personnel to solicit information from a select group of nurses and other staff.  

Healthcare personnel can use the polling module to fill a staffing shortage quickly by sending out a quota poll to nursing personnel that automatically concludes after a certain amount of required responses are reached. An automated message follows informing respondents what steps to take next, as well as that the poll closed. The automated poll collects basic text-based responses, which can be organized into reports that allow healthcare organizations to make informed staffing decisions.