Artificial intelligence in healthcare: where and how to invest

AI Investments

There are four main areas in which using artificial intelligence (AI) technologies can improve the effectiveness and value of ICT solutions in healthcare.

The applications that manage clinical, administrative and logistical processes, focused on data management, have so far been marginally affected by the new AI-based technologies. The functional perimeter of these applications has been constant for many years and their evolution is mainly linked to technological aspects (software architecture, infrastructure, user interface).

AI-based technologies can significantly improve the ability to analyze data related to health processes (Process Data Analytics). Compared to the business intelligence carried out with traditional technologies, AI allows to better estimate and understand current trends, perform predictive analysis, and find correlations between different phenomena.

This increase in analysis capacity can be very useful in resource planning, for example in contact centre, booking, bed and operating room management. More information and awareness on the dynamics of demand for resources allows to articulate and segment the offer of services in order to maximize the results. By using some IA-based technologies it is possible, for example, to create tools to optimize the resources planning and scheduling, as to suggest to system administrators the setting of agendas for a booking system or the management of queues in a contact centre. A big step forward compared to current software where the configuration burden is entirely on the user.

Another area where AI can allow a great leap forward is the support to the decision-making process (Decision Making). Neural networks and deep learning mechanisms can greatly increase the effectiveness of systems for differential diagnosis and decision support (CDSS), overcoming the current limitations of the Evidence Based Medicine approach. The ability to find correlations and to learn from clinical practice, typical of AI-based technologies, will be a determining factor for the expansion of this sector.

Even in the measurement of outcomes (Outcome measurement), AI can allow an important evolution, thanks to the mining, analysis and correlation capabilities that these technologies possess. The measurement of outcomes will be increasingly important with the spread of policies and models based on Value Based Healthcare.

Finally, there is a final consideration to be made: in order to achieve the above-mentioned developments, it is necessary that also at process data management level there is an evolution consistent with the overall picture. In other words, in designing new applications it is necessary not only to focus on the mere management of data and basic processes, as it happens today, but also to consider the needs and implications that Resource Planning, Decision Making, Process Data Analytics and Outcome Measurement imply, with a holistic approach to all areas of digital health care.

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