Hospitals, hospital groups and university medicine
Not just faster staff.
Billing, bed management, funding applications, manuscripts: agents read the patient record, bed planning, the coding rulebook, funding databases and other sources, back every draft with a source reference and present it to your experts for approval.
neuland.ai HUB · Billing review
- and more systems
Case DRG-2026-11274 has been submitted for billing, length of stay nine days. Please check it against the rulebook before it goes out.
Tools chosen automatically
- Billing system · Case DRG-2026-11274read
- Rulebook · 18 rules applied, 2 hitsread
- Patient record · Progress notes on the secondary diagnosesread
- Billing system · Billing note DRG-2026-11274Draft
Two rules are triggered [1]. One secondary diagnosis lacks the progress note that supports it [2]. The code is flagged and the rule is named; nothing is deleted. Principal diagnosis and lower length-of-stay limit are checked [1]. Only the billing specialist can change the code.
- [1]Coding rules · 18 applied, 2 hits
- [2]Progress DRG-2026-11274 · no entry for the secondary diagnosis
- done: Principal diagnosis · Coding rule D003
- done: Length of stay · lower limit
- open: Secondary diagnosis without progress note
- question: Approval by billing
For CW 24, 17 requests came in by phone, fax and two mailboxes. Which ones fit the places that are opening up?
- Incoming fax · 17 requests captured in one format
- Patient administration · Diagnosis group, payer, preferred date
- Bed planning · Free places from Jun 8
- Bed planning · Admission proposal CW 24
Two places open up from Monday, June 8 [1]. Four requests match by diagnosis group and preferred date [2]. For AN-2026-0421 the payer is missing; the request stays on the list but is marked open [2]. Admissions decides together with the medical director who is admitted.
- Bed planning · CW 24 · 2 free places from Jun 8
- Patient administration · Waiting list · 4 matching requests
- Admission proposal CW 24 · PDF
- Request · Status
- AN-2026-0417 · possible from Jun 8
- AN-2026-0423 · preferred Jun 15
- AN-2026-0421 · Payer missing
- Follow-up Friday in the admissions mailbox
Biomarker study for early detection, neurology, application in Q4. Which open calls fit?
- Web search · National, EU and international, retrieved Sep 8, 2026
- Funding databases · Foundations and IIT programs
- Deadline calendar · Submission deadlines
Four open calls fit [1]. Two are a good fit, one is a partial fit because it requires a consortium with two further sites [2]. Each hit lists funder, amount, deadline and source with retrieval date. The research was done live, not from model knowledge; anything uncertain is marked as not verified.
- Web search · Funder's call page · retrieved Sep 8, 2026
- Funding databases · Program information · Version July 2026
Please send it as a list to the research office.
- Outlook · Funding list to the research office
- Deadline calendar · Next deadline Oct 15, 2026
The funding list is in the project folder, and the next deadline on October 15, 2026 is noted [1]. I do not promise eligibility: whether an application is worthwhile is decided by the clinical trials unit and third-party funding administration.
- Deadline calendar · Call 1 · Deadline Oct 15, 2026
- Funding_list_biomarker_neurology.pdf · 4 calls, traffic light with reasons
Original article, cardiology: the manuscript and the target journal's author guidelines are attached. Please run a formal check.
- Guidelines · 34 requirements, Vancouver reference style
- Word count · counted by code
- Reference check · 62 references, one by one
- Outlook · Email to the first author
Three points block submission [1]. The text is 312 words over the 4,500-word limit, the abstract 18 words over 250 [2]. The data availability statement is missing [1]. Three references are incomplete [3]. Ethics, conflicts of interest, funding and AI declaration are fulfilled. The agent does not assess argument or methodology.
- Guidelines · Author guidelines · Length and mandatory information
- Word count · 4,812 words, abstract 268
- Reference check · 62 references · 3 incomplete
- Outlook · Email to the first author
- Subject: Formal check before submission
- Attachment: Review_report_manuscript_cardiology.pdf
- Blockers: 3, each with the guideline wording
- Suggestion: per point
- Awaiting approval
- Approve
Delivered together with and at
Why now: staff is short, documentation keeps growing
Colleagues are retiring. The documentation stays. So does the research.
The bottleneck is staff, not technology. A third of the working time that is left goes into documentation instead of care, research and teaching, and university medicine carries the resulting deficit itself.
- 300,000+hospital staff will reach retirement age by 2035, at least one in four.DKG skilled staff monitoring report 2026
- 2.9 hoursof documentation per doctor per working day, 2.7 hours in nursing. Largest single item: MD reviews.DKI survey commissioned by the DKG, July 2024
- €2 billiontotal deficit facing Germany's 37 university hospitals in 2027, after more than 800 million euros in 2024.Association of German University Hospitals, 2026
What happens for as long as there is no safe offering
AI does not replace staff. Without a safe internal offering, one appears by private routes.
This is no longer an efficiency problem. It is a liability problem.
One hour less documentation per person per day would be the equivalent of roughly 20,650 doctors and 42,870 nurses gaining time at the bedside. Without an internal offering, findings, fragments of physician's letters and study documents end up in outside accounts, with no processing agreement, no log, no control. Bans have changed nothing about this.
Examples · From daily work at a tertiary care center
Medical controlling, nursing, the clinical trials unit: every process follows the same pattern. The AI pulls the documents together, prepares the draft and backs it with a source reference. The professional decision stays with people. Hundreds of agents have been built; which process your hospital starts with is your call.
Research and teaching
Research and teaching get their own agents.
In separate permission spaces.
University medicine is care, research and teaching under one roof, with separate budgets, separate purpose limitations and the same overstretched people. Research, registry and study data are opened up only with their own purpose limitation, their own data protection impact assessment and a separate permission space: what belongs to the clinical trials unit is not visible to administration, and the other way round.
Draft based on your cost rates and your separation of accounts, for academic and industry-sponsored studies alike. The clinical trials unit approves the calculation, not the system.
Open calls from the DFG, the BMBF, the EU, the federal states and foundations matched to your research idea, each with source and deadline. Research and fit assessment, not the application itself.
Formal check against the author guidelines of the target journal or against the funding guideline. Nobody judges the content but you.
Prepare curricula, learning objectives and examination formats from your own sources, plus onboarding for final-year students and specialty training. Institutes and working groups build their own agents; whatever they build inherits roles, permissions and the audit trail automatically.
Knowledge base: from rulebook to institutional knowledge
After the Hospital Future Act (KHZG), no hospital has too few systems. What is missing is the layer that makes them usable. So we do not arrive with an empty system: your in-house standards, SOPs, work instructions, coding and procedural rules, the rulebook collections you choose to include and other documents are built up, checked and opened up with source references. On top of that, the departments add a layer of their own: processing notes, templates and the documentation of the connected systems, such as HIS, DMS, coding software, study and registry systems.
The value is not in the model but in prepared, reusable knowledge. Your systems are connected once and fully opened up in the process: the neuland.ai HUB learns your terms and how they relate, for example what counts as a case in your hospital, how a coding guideline connects to a work instruction and which document superseded which. Archives, findings and imaging documentation, research and registry data in the terabyte to petabyte range deliver answers with a source reference, even when the evidence is one sentence in a document from twelve years ago. The professional tailoring happens once per process; after that, the building blocks can be reused by other departments.
Hundreds
preconfigured agents, dozens of them for hospitals and research
61
use cases in healthcare, prioritized by ROI, data protection profile and speed of implementation
4
knowledge layers: rulebook, guidelines, professional standards, institutional knowledge
Ready in the Agent Library
The Agent Library holds ready-made building blocks for specific tasks, each with its task scope, its boundary of responsibility and its operating model.
Occupancy Management Agent
35%
fewer empty-bed days
Keeps requests, waiting list and capacity together and reduces empty-bed days.
Healthcare
Bed management, facility management
Invoice Checking Agent
40 to 60%
less review effort
Automatically checks service billing for completeness and plausibility.
Healthcare
Billing, controlling
MD Review and Coding Agent
50%
less review effort
Raises first-pass coding quality: the only scalable lever before review rates tighten further.
Healthcare
Medical controlling, coding specialists
Clinical Letter Agent
60%
less writing effort
Produces structured physician's letters from treatment data.
Healthcare
Medical staff, documentation
Care Planning Agent
30%
less documentation effort
Supports nurses with structured documentation and shift planning.
Healthcare
Nursing, documentation
Clinical Study and Ethics Agent
45%
faster contract review
Guides you through contract clauses, the ethics pathway and research data up to study start.
Healthcare
Clinical trials unit, research
Figures come from the Agent Library and are not a guarantee.
Auditable for data protection, legal and employee representation
Intended purpose: support for administrative, documentary and academic work. No diagnosis, no treatment recommendation. This boundary is drawn deliberately: it makes clear where medical responsibility sits. Four things make that auditable.
Citation
Document, passage, version. If the AI finds no evidence, it says so instead of writing around it. In hospital practice, the source reference is the real benefit, not the text.
Approval
No draft takes effect without approval. The approval levels follow your responsibilities and your signing authority, not our default settings.
Log
Who asked what and when, which sources it drew on, which model answered, what was adopted and what was discarded. Exportable for any audit.
Staff council and employee representation
An employee representation body that has been bypassed will sink a project faster than any tender. A template works agreement is available, with performance and behavior monitoring as a section of its own: no analysis at individual level, no performance rating, purpose limitation set out in writing.
The offer: one process, the documentation, operations
The model is the easy part. What comes with it is the tailored process and the documentation for data protection, procurement and co-determination. The security evidence your supply chain needs under Section 30 BSIG is already in place. All of it stays operable without an AI team of your own: it is used from within everyday medical, nursing, administrative and academic work.
From the board, IT, employee representation and procurement
The questions come anyway, from the board, from IT, from employee representation and from procurement. Here they are, with the answer we would give in the room.
Getting started in four steps
A measurable result
before the investment decision.
Data protection and employee representation are involved from phase 1. Procurement clearance, co-determination and the board proposal run in parallel: the Proof of Value does not block these steps, it feeds them.
01
Your starting position, our use-case method, a realistic path. On request straight with your IT team, then as an architecture session.
02
A review of your process and system landscape. Result: a prioritized roadmap with ROI estimate, data protection classification and go-live window per use case.
03
Two or three use cases without patient data, in production. You set the success criteria. If we do not meet them, the Proof of Value ends with no follow-on commitment.
04
The platform is up in days. What matters in large hospitals is what comes afterwards: named contacts instead of a ticket system, onboarding split by medical staff, nursing, administration, IT and research, agents with prepared input fields for everyone who would rather not write prompts, and hypercare in the first weeks. Professional Services are available for connections to HIS, coding software, rostering or study systems, for example.
Name one process from your hospital.
We show you which agent prepares it.
The MD review, the physician's letter or the SOP inquiry on the ward: in the initial conversation, we show the process with matching agents, on request straight with your IT team.
