Hospital Analytics

A few years ago, the health sector started to adopt artificial intelligence (AI) to improve its capacity to make diagnoses, improve the quality of the care provided to patients and also be more efficient in its processes. Last year, Accenture already indicated that the numbers should only grow: the AI ​​market in healthcare was forecast to reach $ 6.6 billion by 2021. 

To give you an idea, a World Bank study showed that the use of artificial intelligence applied to the analysis of electronic medical records could generate savings of R $ 22 billion by avoiding unnecessary repetitions of tests and treatments. With the increase in the availability of data and the progress of analysis techniques, albeit in a more incipient way than in the international scenario, what we followed was hospitals across the country seeking to incorporate technology and make it an important ally to bring innovations and efficiency to the sector. 

With Covid-19, this whole process has been accelerated. For the post-pandemic, technology must assume a new position: hospital managers who wish to incorporate AI into their administrative and care routines will need to focus on the evolution of hospital processes and create means for data collection and treatment. In this context, in addition to the hospital management systems, which allow monitoring all hospital sectors in an integrated manner, considering indicators and reports that offer a detailed analysis for decision making, there are analytical solutions supported by Artificial Intelligence (AI) that can bring even more assertiveness to the sector. They are already part of the routine of some health institutions and are an important driving force behind the great transformation that is taking place in the health sector. 

Here, are the 3 benefits of using analytical solutions supported by artificial intelligence (AI) in the health area.

Better supply chain management 

Supply chain management, in general, is a complex process within healthcare institutions. Even more the pharmacy, sector in which care is needed in the control and release for the nursing team of the medications that will be administered to patients, as well as attention to the need to manage the items from an economic point of view. 

An analytical solution supported by artificial intelligence considers several factors – such as seasonal diseases, specialties of health institutions, history, etc. – so that managers can anticipate the acquisition of medicines. This contributes to the lack of inputs and unnecessary supplies. As medicines have an expiration date, they may not be able to withstand the time foreseen for the next seasonality and, therefore, waste. Predictive analysis allows you to create tools that manage purchasing needs, control batch and validity, and even the fractionation of medications.

Disease prediction 

The trend is that technology is increasingly focused on maintaining health and preventing diseases. And hospitals should be aware of this because the aging of the population will cause an increase in cases of chronic diseases, cancer, mental illness, and neurological problems in the coming decades. The analytical solutions allow tracing analyzes linked to both regional and seasonal elements as well as external elements. In other words, it is possible to assess climatic conditions, rainfall, the history of the incidence of the disease, the Human Development Index (HDI), the level of education, and even if the region is resistant (or not) to preventive measures. 

 If a disease is associated with certain times of the year, with more rain, for example, hospitals can prepare to face it. This means considering historical variables from the last few years about medicines needed for treatment, type of care provided length of stay of patients in beds and professionals involved in care, among many others.

Facilitate the scale of shifts and assistance

 The absence of professionals at the front line of assistance – and even at the administrative level – can impact the entire hospital routine and jeopardize adequate care to save lives. One of the great challenges facing the institutions is, therefore, the scheduling to mitigate eventual absences. And there are several factors to be considered: contractual work hours, vacations and leave, sectors in which each of the professionals works productivity and individual performance, among many others. The restrictions also increased with the new coronavirus pandemic, which made the escalation even more complex. 

By distributing all of these variables in an analytical model, it is possible to have a much more just duty scale, which avoids the tiredness of professionals and provides labor support so that there are no future problems with justice. The model can also consider the expectations and agenda of the professionals involved, including doctors, which contributes to reducing absenteeism.

Conclusion

For hospital managers who intend to consider the analytical solution for their institution. The analytical data will support more strategic and assertive decision-making, but they remain the responsibility of managers and the clinical staff. Thus, a culture focused on data is a priority. The professionals need to have, then, a mindset focused on the data to analyze whether that orientation makes (or not) sense and what is the best way to apply it.

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