Merge cases from three sources
The agent reads the treatment and service data from the connected systems and links the records belonging to each case. It merges cases that are recorded twice.
Analysis run Q2-2026, 4,180 cases, 12 wardsEveryone knows the length of stay on ward 3 is rising. No report says why.
Analyses care data to optimise treatment processes.
Public administration
Four processes that take up the most time today
Developed in Germany · Hosted in the EU or on-premises
Delivered together with
Sovereign hosting in Germany, single-tenant from the first user.
Legal, tax and compliance expertise for legally sound deployment.
Advisory and implementation for the public sector, starting with the AI readiness assessment.
Three analyses of the same ward, and none of them explains the length of stay.
Length-of-stay figures come from the hospital information system, utilisation from theatre planning, discharges from discharge management. For the monthly meeting, controlling pulls three exports, lays them side by side in a spreadsheet and checks the outliers by hand. By the time the picture is complete, the month it is meant to explain is over.
3data sourcesThe hospital information system, theatre planning and discharge management hold the same cases separately.
12wardsEach one is analysed individually for the monthly meeting.
2 dayspreparationPulling exports, merging cases, checking outliers by hand.
4weeks' lagThe anomaly reaches the table when it is already a month old.
The report for each ward is ready on the reporting date instead of four weeks later, and care management manages 35% more efficiently.
The 35% gain in process management efficiency is this agent's headline figure. The figures for sources, wards and preparation days describe a typical monthly cycle in hospital controlling; they are not measured values.
Three steps through the process.
The agent merges care data from three sources into findings with their data provenance; hospital management decides on any measures.
Name patterns and bottlenecks
The agent compares lengths of stay and utilisation across the wards. It sets out every notable deviation with the period and the cases affected.
Ward 3, length of stay 1.8 days above the hospital average, 186 casesPrepare the analysis for the steering meeting
The agent brings the finding, period and data provenance together in one report. Care management discusses it at the steering meeting.
versorgungsanalyse_Q2-2026.pdf, 6 pages, 4 findings with data provenanceWhat the agent does not do.
It does not decide on any measure.
It presents the finding and its data provenance. Care management and hospital management decide what changes on ward 3.
It does not assess any individual treatment.
It works at the level of case groups and wards. The medical assessment of a case stays with the treating clinicians.
It only works with the connected data.
Anything not documented in the hospital information system does not appear in the analysis. Controlling can see this from the stated data provenance.
Connected systems
- Hospital information system
- theatre planning system
- hospital data warehouse
Operations
- Deployment modelEU cloud, private cloud or on-premises
- Data processingGDPR-compliant, no model training on your data
- Responsible teamsManagement, quality management
The questions that come up before the first meeting.
How quickly is the Care Delivery Analysis Agent ready to use?
A pilot typically runs on your own data within 4 to 6 weeks. The agent connects to your existing systems rather than running as a separate, parallel environment.
Where is the data processed?
EU cloud, private cloud or on-premises. Processing is GDPR-compliant and models are never trained on your data. Details in the Trust Center.
Where does the figure “35% more efficient process management” come from?
It is based on experience from comparable projects and process benchmarks, not a commitment. We work out the realistic effect for your organisation during the assessment before the pilot.
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