The paper contains 2298 words and is expected to last 4 minutes
As Louis Pasteur famously said, “Chance favors the prepared mind.” But some highly specialized Settings, such as hospital infusion centers, are so fraught with uncertainty that it seems impossible to prepare in advance. Staffing is complex, patients have to wait for long periods of time, and nurses are often overwhelmed.
Exhausted medical staff are naturally dissatisfied with their work, and as a result, their patient care is often unsatisfactory. However, emerging technologies promise to reduce uncertainty and optimize the patient experience as well as the health care staff experience while keeping the center running efficiently and efficiently.
Start your day
A director of nursing at a cancer center that is one of the nation’s leading medical institutions reminded me of Pasteur’s famous quote. At one meeting, she described progress on a major project she was working on with a colleague to overhaul the center’s infusion department. She drew an analogy: when she first took over, the infusion area was like a golf tournament course.
People go to work in the morning, make small talk or share something funny, and then head off to the next day filled with the unknown. Some nurses may have 15 patients, others just three, but they don’t know each other and can’t make adjustments.
Her colleague, a specialist clinical nurse who oversees surgery, offers a more apt analogy. She thinks the infusion area looks more like a war zone. Before the modification, patients sometimes waited as long as 525 minutes. When the nurse arrived, the patient was already waiting in the hall. Which nurse will be assigned to which patient is not arranged at all, and then the nurses will have conflicts over assignments, which will delay the process. Caregivers typically have a full schedule, and now, with an emergency, at the end of the day. You’re going to burn out.
After a lot of hard work, the two leaders and the team they assembled refreshed the process of the infusion area — assigning nurses to patients in advance, signing forms, preparing treatment rooms, dispensing drugs, preparing rooms and chairs. Everything is ready.
However, no nurse was willing to accept the assignment. Even if five nurses arrive an hour early (while 11 patients are waiting), they will argue over the day. By 10 or 11 a.m., the medical center was a mess, and by 2 or 2.30 p.m., it was empty. This is common in infusion centers across the country.
A long day
Another problem plaguing the team, and hundreds of teams across the country, is that disregard for schedules means there is no set end time, and employees often end up working late into the night. At many infusion centers, nurses can’t remember the last time they left work on time. They can’t leave the office while they’re treating patients. As a result, many infusion centers pay large overtime fees each month, leaving nurses increasingly tired and dissatisfied.
A 2018 survey of 22,000 nurses found that half of all caregivers work overtime, averaging nine hours a week. Not only does this cause trouble in calculating staffing budgets, it also leads to a high turnover of nursing staff — and then a huge waste of time filling vacancies, which is even more burdensome for existing staff.
Contribute intelligent solutions to personnel arrangements
A huge wave of innovation is sweeping through the nation’s health system. In addition to medical advances, the infrastructure on which health centres operate is also undergoing change. In particular, with the introduction of artificial intelligence (AI), the center’s work arrangements — from scheduling to facility utilization to staffing — are increasingly satisfying to patients and medical staff.
Ai-based platforms have made significant progress in scheduling appointments, taking into account such factors as duration, what is involved in the appointment, whether the patient has other relevant appointments and the total number of patients on the day. The system can predict possible appointments or cancellations based on the work done in the treatment room, so that nurses are not swamped every day.
Ai-based platforms can also handle patient placement in emergency situations. Although no one-day arrangement can be perfect, the system will continue to learn according to the daily situation, and the algorithm will also continue to learn and improve on the basis of actual data, and then adjust the subsequent work arrangement.
What is truly amazing is the application of AI to nurse assignment. The new system “reads” a patient’s attendance, checks which doctors and nurses are assigned, and then interprets acuity. The patient needs a nurse’s constant attention while he or she is on an infusion, just as an airplane needs a pilot’s attention during the flight – 100 percent at the beginning and end of the trip. Intelligently assigning patients to nurses ensures that each appointment starts and ends with only one patient, and that the nurse is properly assigned to several other patients in close proximity “halfway” through the patient’s infusion.
So applying AI to infusion centers is a bit like air traffic control at a busy airport. It can organize its work in an orderly way, even if the unexpected happens at any time. Powerful tools can help you make critical decisions based on data that ultimately affects people’s lives — and they can process HL7 data in real time to help you make those decisions.
impact
Applying AI solutions to infusion centers is no longer a fantasy, as some of the world’s leading large care centers have successfully put them to work in the past few years. After the introduction of AI in the center, overtime hours of employees are significantly reduced, nurses’ job satisfaction is improved, daily workload and personal workload of nursing staff are more reasonable, and staff turnover rate is also reduced.
To be more precise, the two leaders set up an AI-based system about two years ago. Since then, they have developed a sophisticated model for determining sensitivity that can be used in scheduling software to better determine how long appointments take and how needed caregivers are.
Nurses were more satisfied with the work assigned by the software, and the software set rules for “bargaining” at work to ensure progress and patient experience. In addition, considering the workload of staff, the center also set a maximum of eight patients per nurse per day. In four years, the two leaders completed a seemingly “impossible” task, and patients’ waiting time has been reduced from 525 minutes to less than 30 minutes.
Since the infusion center’s situation changes every day, AI-based technology cannot perfect every challenge the infusion center faces, but the system is constantly learning and improving its predictive analysis capabilities. As a result, work arrangements will become more reasonable, always prepared for unexpected situations.
Leave a comment like follow
We share the dry goods of AI learning and development. Welcome to pay attention to the “core reading technology” of AI vertical we-media on the whole platform.
(Add wechat: DXSXBB, join readers’ circle and discuss the freshest artificial intelligence technology.)