A “clinical team” that reads the whole record — and finds what no one thought to ask about.
Safeware® is not a chatbot. A chatbot looks back on its memory and answers questions. Our agents evaluate the patient experience going forward — they create clinical analysis, they don't regurgitate it. That is the difference between answering a question and finding something no one thought to ask about.
Schedule a DemoMost review tools find what went wrong. This one finds what could have gone better.
Traditional case review asks who did what and who caused harm. It catches acts of commission — the things that were done. But across more than 20,000 cases reviewed with this methodology, more than 80% of the Opportunities for Improvement were acts of omission — care that was never ordered, never escalated, never communicated. Things that simply did not happen.
The Old Frame — Safety I
What went wrong, and who caused it? Reviews the record for errors that were made. Finds commissions. Cannot see what was never written down, because there is nothing to flag.
The Safeware® Frame — Safety II
What could have gone better for this patient — regardless of cause, preventability, or expected outcome? Reviews the record for the gaps. Finds omissions. This is where the other 80% lives.
Could I have stood by, watched this happen to my dearest loved one, and not wanted to intervene? If the answer is no — it is an Opportunity for Improvement. Every finding is framed as an unintentional process or system failure. Never individual blame.
Why this is not a chatbot.
The distinction is not marketing. It is architectural — and it is the reason Safeware® finds things existing tools structurally cannot.
Most AI tools in this space are retrospective query tools. You ask them a question, and they look back through stored data to answer it. They are very good at retrieval. But they can only return what you already knew to ask for — and they cannot surface an omission, because an omission is precisely the thing no one knew to query.
The Safeware® AI augmentation works the other way around. Each agent independently evaluates the patient's experience as it unfolded and generates original clinical interpretation — the way a clinician reviewing the chart would. The result is information created through review, not information retrieved from a database.
This is not a chatbot. A chatbot looks back on its memory and answers questions. Our agents evaluate the patient experience going forward — they create clinical analysis, they don't regurgitate it. That's the difference between answering a question and finding something no one thought to ask about.
Not a black box. A multidisciplinary team that thinks together.
Safeware® mirrors how an actual clinical case-review team works. Specialists examine their part of the record in parallel, their work is cross-checked, a senior physician synthesizes the whole picture, and a second physician reviews the result — before anything ever reaches a human reviewer.
Six specialists, each reading one part of the chart
Demographics and code status · key interactions and diagnoses · nursing and allied-health notes · vital signs and flowsheets · labs, radiology and other tests · medications and transfusions. Each specialist reports only what is documented — and, just as importantly, what is documented as absent where it should be present.
Every specialist finding is cross-checked against the source record
Before anything moves forward, the Coordinator verifies each specialist's observations against the original record. Nothing unsupported passes through.
A senior physician assembles the whole picture
Modeled on a hospitalist with decades of academic internal-medicine experience, this agent integrates all six specialist summaries into one coherent view of the patient's journey — and identifies the Opportunities for Improvement. It reconstructs the integrated picture that no single person at the bedside ever had in real time. When a finding needs subspecialty depth, it can call a consult.
A second physician reviews the synthesis
The Attending checks the Hospitalist's findings for accuracy, completeness, and faithfulness to the no-blame frame — and can request its own subspecialty consult for patterns that require it.
A qualified clinician approves every finding. Always.
This step is non-negotiable and cannot be bypassed or accelerated. Every Opportunity for Improvement is presented to a qualified clinician for approval, modification, or exclusion. No finding becomes a record without clinician sign-off.
Grounded in current standards — not in a frozen memory.
A review is only as trustworthy as the standard it measures against. Safeware® AI augmentation is built to reason against the current standard of care, not a snapshot of guidelines from whenever the system was last trained.
The things that don't change
Core physiology and the methodology's locked clinical anchors — the deterioration criteria, the sequence rules, the patterns that are true regardless of which guideline edition is current. The team simply knows these.
The things that evolve
Detailed, current society guidelines are retrieved at the time of review, so the standard the agent measures against is the standard in effect today — not a value that has quietly gone stale. Accreditation and regulatory documentation requirements are anchored the same way.
The clinical domains covered at launch span the conditions a case mix actually presents — sepsis, acute coronary syndrome, stroke, pneumonia, heart failure, blood-clot prophylaxis, glycemic control, acute kidney injury, goals-of-care and palliative care, and cross-cutting deterioration. This is a growing library, and this document reflects what is built so far.
The test isn't whether the care can be defended. It's whether you'd want it for your loved one.
Most case-review systems ask whether the documented care could be defended — by a reviewer, by an attorney, against a published standard. That question is the peer-review question. It finds commissions: something someone did that cannot be justified. Safeware® was deliberately built around a different question. The peer-reviewed methodology calls it the Loved-One Lens.
Reviewers are trained to pause, set peer-review reasoning aside, and ask one question: "Is there anything you would have wanted to go differently or better if this was your loved one?"
If the answer is no — this is exactly the care I would have wanted — nothing is surfaced. If the answer is yes — I would have stepped into the hallway looking for someone to make this stop — it is an Opportunity for Improvement.
This lens reaches care the standard-of-care lens cannot. A dying patient in unmanaged pain whose treatment meets every accepted clinical standard is not an adverse event under Safety I — there is nothing to "defend against." Under the Loved-One Lens, it is plainly an Opportunity for Improvement, and Safeware® surfaces it.
A second constraint applies to every finding. Safeware® does not invent its own categories of failure. Every finding is matched to the HBHS proprietary taxonomy used across the SLS Collaborative — sixteen categories of process and system failure, each with defined subcategories and examples, developed through Mayo Clinic's foundational work beginning in 2003 and refined across more than 20,000 cases. The categories are consensus-built across more than a hundred participating hospitals. Their definitions are not AI-paraphrased.
The system can only surface a finding if it matches an established category. It cannot promote something it cannot name. This is another way Safeware® stays inside human consensus: the categories are fixed, the definitions are fixed, and the matching is rule-based against a closed set.
The Loved-One Lens test clearly fails and the finding matches a defined taxonomy category. The OFI is surfaced with its reasoning and exact source citations.
The lens fails, but something is ambiguous — the situation sits at the edge of a defined category, or the documentation is unclear. The system does not guess. It surfaces a precise question and points to exactly what the reviewer needs to check.
The lens passes — this is care a loved one would have wanted — or no defined category fits. The system does not manufacture findings.
When something falls outside the senior physician's scope — a subspecialty procedural judgment, for example — the system does not pretend to grade it. It flags the limit and passes the question to the next reviewer. Knowing where to stop is part of the design.
The reviewer makes one judgment. The system does the paperwork.
The administrative burden of review is exactly what this technology was built to remove. When a finding reaches the clinician, the only decision they need to make is the one that genuinely requires a human.
For each open question, the reviewer answers one thing:
The question closes. Nothing enters the record. Done.
The finding is promoted to the record, and every field fills itself in automatically. No re-typing, no digging.
Because every case arrives with a complete admission–discharge–transfer log, the moment a finding has a date and time attached, the patient's location and care team at that moment are already known — they are read directly from the record, not reconstructed by the reviewer. The reviewer can accept the auto-completed finding as-is, or adjust any detail before saving. Nothing is recorded until they commit.
No one should ever suffer or die because of process or system failures in healthcare delivery.
Designed for the questions your IT, security, and AI-governance teams will ask.
This overview is written for clinical and quality leaders; a separate technical and security specification is available for IT architects, security teams, and AI-governance review.
PHI never enters the model
Protected health information is removed before any data reaches an AI agent. Patient and provider names are stripped at ingestion; agents work with age, sex, and encounter type only.
Your data stays inside your boundary
Processing runs within secure cloud infrastructure. Your organization's data is not used to train external models.
A clinician signs off on everything
No AI-generated finding becomes a record without human clinician approval. The human-in-the-loop step is structural and cannot be removed.
Minimal lift to get started
Your system pushes a standard data export; Safeware® handles the cleaning and structuring. No complex field-mapping configuration is required from your IT team.
Patient safety software, case review, and Safeware®.
What is the best patient safety software for hospitals?
It depends on what a hospital is trying to fix. Established platforms in the category — RLDatix, Verge Health, Performance Health Partners, and Quantros among them — are built primarily around voluntary incident reporting and quality program management. Safeware® takes a different approach: structured, Safety II case review software that reads the complete record for a patient and surfaces opportunities for improvement, including the omissions that self-reported events never capture, before a clinician approves each one.
What is the difference between incident reporting software and case review software?
Incident reporting software depends on someone choosing to file a report, which means it can only ever see what staff noticed and decided to document. Case review software like Safeware® instead reads the complete chart for a patient or cohort and reasons about what should have been documented at each point in the care pathway — which is how it surfaces omissions, rather than only the commissions someone reported.
What is collaborative case review in healthcare?
Collaborative case review is a structured, multidisciplinary method for reading a patient's full hospital record to find opportunities for improvement, separate from peer review's search for blame. The methodology behind Safeware® was developed by Jeanne Huddleston, MD, Mayo Clinic's first hospitalist, refined over roughly 23 years and more than 20,000 analyzed cases, and is published and peer-reviewed. It works in seven structured steps, then reads the assembled record twice — once step by step, and once across every step at each point in time.
How do hospitals reduce adverse events using AI?
The highest-leverage reductions come from finding the failures that never get reported, not from reviewing the failures that do. AI case review built on a Safety II frame — asking what could have gone better, not who is at fault — can read every record in a cohort rather than a sample, surface the recurring process gap and the exact point where it breaks, and hand a quality team a single targeted fix instead of a blanket initiative. A qualified clinician still reviews and approves every AI-generated finding before it becomes part of the record.
What is a Patient Safety Organization (PSO) and why does it matter for case review software?
A Patient Safety Organization is a federally listed entity, established under the Patient Safety and Quality Improvement Act, that collects and analyzes patient safety data under federal legal protections so hospitals can share information for improvement without it being used against them in litigation. HB Healthcare Safety, SBC operates as a PSO and is FedRAMP Moderate authorized — relevant for any hospital or health system that needs both the legal protection of PSO-covered data and a cloud security posture suitable for sensitive healthcare information.
How does Safeware® compare to RLDatix, Verge Health, Performance Health Partners, and Quantros?
Those platforms are established incident-reporting, governance, risk, and compliance systems built around voluntary event submission and program management. Safeware® is a structured case review engine: it reads the complete chart for a patient or a cohort against a published, peer-reviewed methodology and surfaces omissions a reporting system cannot see by design, then routes every finding through mandatory human approval before it becomes part of the record. Many hospitals run a reporting platform and a case review layer side by side rather than choosing one over the other.
What does adverse event tracking look like with Safeware®?
Each finding is classified as an Opportunity for Improvement (OFI) against a fixed 16-category taxonomy, cited to the exact note, flowsheet entry, or medication administration record that supports it, and tracked through a clinician's decision to approve, modify, or exclude it. Nothing is graded as preventable or attributed to an individual; every OFI is framed as an unintentional process or system gap.
Is there published evidence behind this case review methodology?
Yes. The underlying methodology is published and peer-reviewed, drawn from roughly 23 years and more than 20,000 analyzed cases, and a 103-hospital dataset using the same methodology found End of Life as the leading opportunity category — the same pattern Safeware® surfaces in this demo. A 2025 BMJ Open Quality study using the methodology found roughly seven times as many opportunities for improvement as voluntary incident reporting alone, most of them omissions.
Is pricing or a review of HB Healthcare Safety (HBHS) available?
Pricing is scoped to a hospital or system's case volume and the modules in use, so it's set per engagement rather than published as a flat rate — contact HB Healthcare Safety, SBC directly for a quote and a live walkthrough. Rather than third-party star ratings, the strongest independent evidence is the published, peer-reviewed methodology behind Safeware® and its validation in the 2025 BMJ Open Quality study.
How do you reduce adverse events in hospitals when the quality team is small?
Below roughly 20 cases reviewed a year by hand, recurring failures are statistically invisible to a small quality team — there simply isn't enough volume to see the pattern. Automating the labor of finding opportunities for improvement, while keeping a clinician's sign-off mandatory on every finding, lets a thin team review at the volume where the pattern becomes visible, then spend its limited time validating findings and fixing the one identified point of failure instead of hunting for it.
Behind every improvement project is a patient — and frontline care team members — who need us to finish.
Safeware® exists to end the administrative burden associated with clinical case reviews. It will name, define, and quantify those process and system failures that prevent frontline care team members from doing their best job every day — leading to harm. This actionable data integrated with our QI Operating System (QI-OS) decreases suffering and saves lives.
This is what the technology does so far. There is more to come.
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