Study cost calculation
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.
Hospitals, hospital groups and university medicine
Not just faster staff. A faster organization.
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
Note DRG-2026-11274Draft
- 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
Six processes from everyday hospital work: what the agent prepares and what stays with you.
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.
DKR · OPS/ICD · In-house rules and past MD decisions · HIS, DMS, coding software
Preparing MD reviews
The most time-consuming documentation item according to the DKI. Five steps, three systems, several people involved before the first line of the answer is written. What stays with you: the professional assessment and approval by medical controlling or the treating physician. Nothing leaves the hospital without approval.
What the AI handles
- Pull together the case, findings and relevant coding guidelines
- Draft the justification for the length of stay
- Back every sentence with document, passage and version
Your templates · HIS, findings systems, medication list
Draft physician's letter
Discharge letters are written under time pressure from scattered sources. Follow-up queries from the next treating physician are the result. What stays with you: medical review and sign-off in line with your signing authority. No diagnosis, no treatment recommendation by the system.
What the AI handles
- Draft based on your letter template from progress notes, findings and medication
- Every statement with its origin in the record
- If evidence is missing, the agent says so instead of writing around it
DKR · OPS/ICD · In-house rules · Coding software, HIS
Coding review and case completeness
Secondary diagnoses and procedures get lost, billing objections pile up, revenue is left on the table. What stays with you: the coding decision with the coding specialist or medical controlling. We set the target range in the potential analysis, using your figures.
What the AI handles
- Match documentation and coding against the rulebook and in-house rules
- Before billing, not in the objection
- Notes with source references, no automatic changes
Separation of accounts · Cost rate catalog · Study management, ERP, funding databases
Study costs and third-party funding
Calculations live in inherited spreadsheets. Calls for proposals from federal, state, EU, DFG and foundation sources are searched one at a time. What stays with you: approval of the calculation and the decision to apply, with the clinical trials unit and third-party funding administration.
What the AI handles
- Draft calculation based on your separation of accounts and your cost rates
- The Förderungsnavigator finds matching calls across five levels, with source and deadline
Nursing standards · Expert standards · HIS nursing module
Nursing progress documentation
Documentation happens at the end of the shift, from memory, under time pressure. 2.7 hours a day are the lever. What stays with you: the registered nurse responsible for the shift reviews and approves. No rating of people, no analysis at individual level.
What the AI handles
- Structured progress documentation from shift keywords, following your nursing model
- Matching against nursing standards
- Notes on missing mandatory information
SOPs, hygiene and in-house standards, work instructions · Intranet, DMS, network drives
SOP and in-house standard inquiries
Anyone looking for the work instruction from 2019 asks someone who has been here long enough. When staff change, that knowledge goes. What stays with you: the professional interpretation. Information only, no approval needed: what is not backed by evidence is not claimed.
What the AI handles
- Answer with document, passage and valid version
- Superseded versions are marked as such
- Only documents the person asking is allowed to see
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.
Third-party funding search
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.
Manuscript and application checks
Formal check against the author guidelines of the target journal or against the funding guideline. Nobody judges the content but you.
Teaching and your own agents
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
Your institutional knowledge is opened up before you start.
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.
- Hundredspreconfigured agents, dozens of them for hospitals and research
- 61use cases in healthcare, prioritized by ROI, data protection profile and speed of implementation
- 4knowledge layers: rulebook, guidelines, professional standards, institutional knowledge
Ready in the Agent Library
An agent for every recurring task. Adopt it and adapt it to your in-house standards.
The Agent Library holds ready-made building blocks for specific tasks, each with its task scope, its boundary of responsibility and its operating model.
Auditable for data protection, legal and employee representation
The agent never decides. It prepares.
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.
CitationFor every answerDocument, 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.
ApprovalMedical and nursing sign-offNo draft takes effect without approval. The approval levels follow your responsibilities and your signing authority, not our default settings.
LogComplete, per inquiryWho 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 representationFrom day oneAn 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
Process, documentation, operations: an offer for years, not for a quarter.
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.
You name one process from your own hospital, for example the MD review, the physician's letter or the SOP inquiry on the ward. We do not deliver a tool but a tailored process: with a defined task boundary, approval levels following your signing authority and connections to your systems, such as HIS, DMS, coding software, study and research systems. Before we start, we record how long a case takes and how many cases arise. After the Proof of Value, we measure again. If the measurement shows relief, the next area follows. If it does not, it ends there.
From the board, IT, employee representation and procurement
Cost, staff council, existing platform, procurement, data location: eight answers for your committees.
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.
The concern is justified: a single account can generate a multiple of the expected volume in a short time. Included, with no add-on module: budgets per user group (clinics, administration, research, individual departments), hard cost caps per user or group, alerts before the allowance is reached and consumption reporting down to user, group, application and model level. On top of that, model routing sends clearly defined tasks to small, specialized models and reserves expensive models for cases where the complexity demands it. Consumption-based items are billed against the reporting, not against an estimate and not as a flat rate.
Relief for people, not control over them. Ruled out are analyses at individual level, performance or behavior ratings and any extension of purpose without a written agreement. A template works agreement is available, as is a dedicated session with employee representatives and no sales agenda. They are involved from phase 1, not only at sign-off. Deliberately not counted in: no job cuts, no reduction in posts. The business case holds without that assumption, and it has to, because a university hospital is critical infrastructure.
No. The neuland.ai HUB connects to existing portals and specialist applications instead of replacing them. Headless: your front end stays, the neuland.ai HUB delivers through the API. Embedded: neuland.ai HUB components run inside your own portal. Federated: both platforms work together. For your users, the interface does not change. And a hospital that introduces another platform tomorrow can keep the neuland.ai HUB above it as a sovereign control and knowledge layer.
University hospitals are public contracting authorities. We are set up for that. The Proof of Value stays below the procurement thresholds and carries no follow-on commitment. For production operation, the specification, criteria catalog, model contracts with DPA, SLA and exit clause, plus the evidence for data protection, IT security and co-determination, are available as one package. Procurement clearance, co-determination and the board proposal run in parallel with the Proof of Value. It does not block those steps, it feeds them.
The protection requirement determines where it runs, not the feature set. Your own or the university data center for everything involving patient data, staff data and Section 203 relevance. Air-gapped for research, registry and psychotherapy data. Sovereign German cloud for scaling without patient data and without US hyperscalers. Hybrid is the recommended target state: the protection requirement per data set decides, governance stays central. Every function is identical in every operating model. The neuland.ai HUB is model-agnostic; changing models is configuration, not migration. As a supplier, we bring what Section 30 BSIG requires of your supply chain: ISO 27001 certification, documented subprocessors and reporting channels for security incidents.
The Agent Library shows dozens of agents for this industry, for example for billing, bed management, funding applications or manuscripts. Each agent states its task and an efficiency figure from the library; these are library figures, not a promise. You adopt one or more agents and adapt instructions and knowledge to your hospital.
You choose from a list of powerful language models and can switch and compare them in the chat, even mid-conversation. In Auto mode, the neuland.ai HUB chooses based on complexity, only among the models approved for your group. Models can be swapped without rebuilding the platform; if you have your own contracts with model providers, you use them via BYOK (Bring Your Own Key). The Models page shows the comparison.
The neuland.ai HUB is built for large knowledge bases: ingestion pipelines at petabyte scale, scalable on a Kubernetes GPU cluster; knowledge in the petabyte range can be processed. Which role may see what remains governed by permissions per role and permission space.
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
60 minutes: initial conversation
Your starting position, our use-case method, a realistic path. On request straight with your IT team, then as an architecture session.
02
1 to 2 workshops: potential analysis
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
4 to 8 weeks: Proof of Value
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
After that: support in operation
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.