Abstract
Every recognized hospital’s patient management unit (PMU) has focused its efforts on improving clinical patient care, with a process approach, analyzing it from adult emergency overcrowding to prolonged stays in clinical services. It is generating many patients waiting for beds in the emergency service. The PMU does not have a business intelligence (BI) platform that provides information in real-time, generating a blind browsing problem. The purpose is to demonstrate the need for a BI platform using Artificial Intelligence (AI) to analyze in real-time the relevant information for decision making. The methodology consists of analyzing qualitative and quantitatively the statistics of the last three years, both from the emergency service and from the clinical services. This study shows that the saturation of the emergency service responds to the number of patients waiting for beds, which interferes with outpatient care. The projections for 2020 underestimated the demand, and the efforts to open hospital beds and home hospitalization quotas allowed to shovel said excess demand. The average stay numbers continue to increase, as does the number of hospitalized patients for emergencies, generating a progressive growth in demand. It is necessary to have a BI system adapted with AI to perform real-time analysis of the GRD, to be able to act during hospitalization and not afterward.
Highlights
Innovation is not an element that characterizes the public health sector—being able to incorporate cutting-edge technological tools in the business intelligence (BI) area, such as Artificial Intelligence (AI), which allows data processing in real-time and not ex-post of the big data provided by the group related diagnostics (GRD) [1], will generate a radical change in the way of doing management clinic, much more proactive and focused on where the problem really lies
The head of the patient management unit (PMU) and the respective heads of service make joint visits, emphasizing those patients who have a stay equal to or greater than ten days, believing that they are those who prolong the average stay of the this biassed view of the process prevents us from being able to visualize whether the stays of patients with less than ten days conform to the standard of the norm, and these may be far outside the standard and it is necessary to have a BI system adapted with AI to perform real-time analysis of the GRD, to be able to act during hospitalization and not afterward
Power is obtained by knowing the data, to know the data, tools are needed to help you understand them, which is why after this sequential analysis of the care process or this production line of clinical services, we can recommend that the incorporation of technological tools of the artificial intelligence type, to achieve a finished data processing, necessary for decision making
Summary
Innovation is not an element that characterizes the public health sector—being able to incorporate cutting-edge technological tools in the BI area, such as AI, which allows data processing in real-time and not ex-post of the big data provided by the group related diagnostics (GRD) [1], will generate a radical change in the way of doing management clinic, much more proactive and focused on where the problem really lies. The initial objective of these units was the care of patients suffering from an acute or chronic decompensated pathology with a potential risk of life compromise. They have evolved to the increase of banal consultations that can be resolved at the primary health care level 1 [2].
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