All medical institutions, whether large or small, for-profit or non-profit, ultimately have two goals: 1) to improve clinical outcomes and 2) to improve administrative outcomes. As it turns out, these are usually two sides of the same coin, because in most cases, improving one improves the other. There is no better example of this than in the management of hospital beds.
Patient beds are a hospital's most expensive resource, and it is therefore essential that beds are well managed and used efficiently. Because of their centrality to a hospital's ability to treat patients, the management of bed availability affects other hospital departments. In fact, bed management is usually seen a subset of a larger challenge: patient flow from intake to discharge. Properly managing this requires innovative technologies, capacity and process management, and advanced optimization capabilities.
Predicting patient demand is a critical, ongoing challenge for any hospital. Unexpected spikes in ER activity can create bottlenecks up front and a lack of beds down the road. This might prompt patient transfers to hospitals hit more capacity. Or, while there might be enough beds, the units are understaffed and require expensive overtime. Or perhaps a spike occurs in the number of senior citizens who, while now well enough for discharge, are unable to look after themselves alone. While senior advocates search for a safe senior facility in which to place that person, they cannot be discharged and so continue to take up a bed. As you can see, the number of factors that must be considered is wide-ranging and difficult to model beyond the simple question of whether they are healthy or not.
SmarTek21 can apply artificial intelligence and machine learning to patient flow and other data systems to help hospitals and health systems understand and address their capacity issues. By accurately predicting issues as varied as emergency room visits, staffing needs, patient demographics, elective and non-elective surgical admissions, and historical bottlenecks, it is possible for ER, surgical, and inpatient leadership to make better, more informed decisions and implement department-specific action plans. Doing so increases patient throughput and improved outcomes across the entire health system.
One of the biggest challenges of improving clinical outcomes is to ensure proper post-discharge patient care. In far too many cases, discharged patients are provided with some handouts but too little opportunity to interact with medical personnel. Worse yet, language barriers can prevent patients and their families from properly following up. Conversational AI systems based on SmartBotHub can solve these problems. Patients can ask questions using normal, everyday language and the system can provide them with highly accurate information based on that patient’s specific context and history. And SmartBotHub’s multilingual support allows you to provide the same level of patient support regardless of language. Instead, SmartBotHub intelligently translates each customer’s intent, as expressed through their own language, to determine the best way to get them what they need. This goes beyond trying to mimic human-like interactions or tricking the user into thinking they are conversing with a live agent. It’s about understanding their intent and addressing it quickly and effectively.