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AI helps avoid under or overspending of medicines – Healthcare

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AI helps avoid under or overspending of medicines – Healthcare

Avoid shortages or excess purchases of medicines thanks to a web app capable, through an algorithm, of supporting purchasing centers for calculating pharmaceutical requirements in public tenders. It was developed by the Steinbocc Projectcreated by the Liuc-Cattaneo University, in collaboration with Equaliaassociation of manufacturers of generics and biosimilars.

The study examines data for the tenders announced from 2016 to 2021 and referred to an antibiotic, an anesthetic and an anticancer: 3 active ingredients analyzed at regional level, all with expired patents, whose consumption optimization would be an important saving for the National Health System. “The analysis highlighted large gaps between actual consumption and the procurement requests by the contracting authorities – explains Emanuela Foglia, a researcher at Liuc -. In some cases the need is overestimated, creating serious inconvenience for pharmaceutical companies, unable to plan production efficiently; in other contexts needs are underestimated, forcing healthcare facilities to purchase at higher costs out of tender, or by sourcing abroad”. Two crucial elements emerge from the study to make purchasing processes efficient: the aggregation and centralization of tenders at a regional level to reduce the fragmentation of requests from local health authorities and hospitals and the identification of the optimal price below which the probability of deserted races.
“The study – says Massimiliano Rocchi, Egualia vice president – confirms what the pharmaceutical companies denounce: the regional fragmentation and the economic unsustainability of public procurement models in tenders for off-patent drugs, based on a single contractor at the lowest price. It is also essential to introduce for off-patent chemical synthesis drugs, as it already is for biosimilars, the rule of framework agreements with multiple suppliers, with predetermined quotas”.

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