In recent years, nearly every major healthcare system in the United States has adopted some form of AI, such as B. Machine Learning (ML) or Natural Language Processing (NLP) to help manage healthcare operations. The unprecedented growth and development of AI has been instrumental in transforming healthcare system management, healthcare data analytics, and patient diagnosis and treatment.
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Despite this growth, healthcare systems still need to maximize the potential of AI to improve load balancing and patient throughput optimization. With staff and resource shortages at an all-time high due to the COVID-19 pandemic, it is vital for health systems to allocate their resources as efficiently as possible. Through the application of ML and predictive modeling, healthcare systems are able to create the optimal patient-provider match in any scenario, from low-intensity emergency care to specialty care. This not only optimizes its own resources, but also guarantees results that lead to high patient satisfaction and loyalty.
Use predictive models to match patient and provider
All healthcare consumers know the challenge of choosing a healthcare system and choosing a provider they call their own. These decisions are often made on the basis of random selection or arbitrary criteria, and there is no guarantee that the selected vendor will be the best fit. We all know the frustration of not knowing which provider to turn to or, even more annoying, of waiting for an inordinately long appointment.
Fortunately, there is another method for bringing patients and providers together that can take the guesswork out of these decisions. Health systems can apply machine learning algorithms that consider, among other things, patient needs, patient convenience, and provider capacity, ultimately creating the optimal match to balance the load on health systems and provide exceptional patient care.
Information entered into the predictive model may include (but is not limited to) a patient's reason for visit, symptoms, geographic location, and general demographic information. The model can then determine how the patient should best be treated (telemedicine or in-person) and which providers can meet their needs. Other factors are also considered, including patient satisfaction scores, provider scores for treatment of specific symptoms, and patient comfort, such as: B. Distance and wait times. Using these datasets, the model produces a match that optimizes these factors, ultimately leading the patient to a booking that results in a satisfying experience for both the patient and the healthcare system.
In real-world application within health systems, this method of matching patients and providers is still in its infancy – the predictive model currently takes into account patient symptoms, provider referrals, and convenience. depending on time and place. But even with its limited use so far, healthcare systems have already seen improvements in NPS scores and other key metrics. Predictive modeling will soon be able to provide even finer matches to individual patient preferences. Data-driven information companies plan to expand the model's capabilities to account for patient morbidity, medical history, and past experience with various health systems.
Adapt these tools to the needs of the health system
Patient satisfaction is the most important driver for healthcare systems, but healthcare systems cannot guarantee overall patient satisfaction and retention without optimizing burden sharing among physicians. Load balancing is especially critical in today's market where we constantly see staffing shortages and patient overload. When healthcare systems are overwhelmed, patients perform worse and are less satisfied with their care, and healthcare system NPS scores will ultimately suffer.
