Clo­sing data gaps in mate­ri­als manage­ment in hos­pi­tals faster

Too little or too much?

Pro­cu­re­ment manage­ment in hos­pi­tals and cli­nics is often sub­ject to the ran­dom principles.

The pro­blem

Hos­pi­tals often lack an ongo­ing inven­tory. Moreo­ver, since sea­so­nal incre­a­ses in workload, e.g., due to flu out­breaks or incre­a­sed risk of acci­dents in win­ter, are sel­dom taken into account, orders and deli­ve­ries are made that sim­ply miss the actual need.

While we have a highly effi­ci­ent and reco­gni­zed health­care sys­tem, there is an epi­de­mic data emer­gency almost ever­y­where with regard to the use of effi­ci­ent data pro­ces­sing. With the PAIRS plat­form now under deve­lo­p­ment, it will be pos­si­ble to imple­ment “healthy” plan­ning and orde­ring manage­ment in the future.

The solu­tion

With the imple­men­ta­tion of a sys­tem for auto­ma­ted inven­tory con­trol and with the estab­lish­ment of an early warning sys­tem for ano­ma­lies, epi­de­mics or recur­ring waves of dise­ase, etc., sup­ply and demand plan­ning can be brought into being in line with requi­re­ments. In addi­tion, PAIRS offers more cost-effec­tive purcha­sing on a spe­cially set up data mar­ket­place, e.g. by enab­ling com­po­site orders for hig­her purcha­sing volumes.

The PAIRS plat­form enab­les early detec­tion and cri­sis pre­ven­tion from inter­nal hos­pi­tal data, i.e. pati­ent and labo­ra­tory data. From these, PAIRS can be used to gain over­ar­ching insights — espe­cially when it comes to accu­mu­la­ting abnor­ma­li­ties or ano­ma­lies. With the com­bi­na­tion of AI and human intel­li­gence, pat­terns can be iden­ti­fied from such data and early cri­sis pre­ven­tion can be initiated.

Since this is highly sen­si­tive data, the release of which is strictly pro­hi­bi­ted by the Gene­ral Data Pro­tec­tion Regu­la­tion (GDPR), it requi­res the use of a sys­tem that allows access without vio­la­ting the GDPR. The archi­tec­ture of the PAIRS plat­form, based on emer­ging stan­dards in these areas, such as IDSA and GAIA‑X, offers the gua­ran­tee of data sov­er­eig­nty and data privacy.

In addi­tion, the use of state-of-the-art tech­no­lo­gies such as Fede­r­a­ted Lear­ning or the patent-pen­ding Trus­ted Data Hub is a pri­vacy-pre­ser­ving multi-party com­pu­ting solu­tion that ensu­res data sov­er­eig­nty and secu­rity for all par­ties invol­ved. This enab­les even sen­si­tive data to be used without expo­sing the respec­tive raw data.

This solu­tion out­lined here is able to detect pat­terns in noti­ce­able ano­ma­lies, regard­less of offi­cial time­li­nes and hos­pi­tal bounda­ries, to pro­vide early detec­tion and cri­sis preparedness.

Con­tact us directly for more information

Bene­fit from active pro­ject par­ti­ci­pa­tion with many advantages

The joint pro­ject PAIRS is fun­ded as an AI light­house pro­ject wit­hin the frame­work of the “Inno­va­tion Com­pe­ti­tion Arti­fi­cial Intel­li­gence” by the Federal Minis­try of Eco­no­mics and Cli­mate Pro­tec­tion (BMWK).

Copy­right © 2022 PAIRS Projekt