Healthcare & Life Sciences
We join what the clinical record, the claims, the people system and your approved content each know, and build AI on it with privacy, consent and audit designed in. The point is not another data layer: it is fewer staffing surprises, fewer avoidable denials, faster decisions, and AI that reaches production.
Hospitals and health systems · Care providers · Health plans and insurers · Pharma and life sciences · Digital health
None comes from a shortage of data or ambition, and none is treated by buying another platform.
Each figure is the one the client reported; the assistant has no figure, so it shows the control it demonstrated. Clients are described, not named.
A multi-site care provider
Scattered HR spreadsheets replaced by one governed analytics layer, so leaders work from live workforce data instead of weeks-old reports.
Built: Automated ingestion, modeling, row- and column-level security, semantic layer.
Read the full caseReported by the client
A regional health organization
More than 300 doctors, nurses and administrators trained by role, with progress assessed along the way. Five AI use cases were then identified and prioritized for implementation.
Built: Needs assessment by role, custom curriculum, hands-on sessions, progress tracking.
Read the full caseReported by the client
A life-sciences data and communications provider
Answers only from approved labels and inserts, tied to the account, with the rules for pharmacists, patients, clinicians and hospitals applied to every answer, by voice or text.
Built: Label-aware ingestion, account context, audience rules, speech in and out.
Read the full caseControl demonstrated
Audience rules enforced on every response; every interaction auditable.
When finance, operations and quality disagree, the dashboard is rarely the cause. The specification underneath is: what counts, from which source, at what hour, and who owns it. Pick a measure to compare its specifications and the decision that settles it.
Days counted at the midnight census, so a stay of 20 hours can count as one day or as none.
Hours from the admission order to discharge, the version patient-flow teams usually watch.
Hours spent in observation before admission, counted by some reports and left out of others.
The plain average, or the geometric mean used in payment benchmarks, which long stays pull up less.
The decision that settles itWhich clock starts the stay, do observation hours count, and which average do we report?
Sources: the clinical record, the bed board, finance, quality reporting.
Occupied beds against every bed the license allows, including beds with no staff.
Against the beds that can take a patient today, which gives a much higher figure.
A midnight snapshot, or the average across the day, which shows the afternoon peak.
Patients in observation, or boarding in the emergency department, counted or left out.
Worked example, illustrative: 230 patients in a hospital licensed for 300 beds and staffed for 250: occupancy is 77% against licensed beds and 92% against staffed beds.
The decision that settles itWhich beds are the denominator, and at what hour is the count taken?
Sources: the bed board, the clinical record, finance, state and federal reporting.
The share of claims denied, or the share of billed dollars, which can tell different stories.
Denied on first submission, or still denied after rework and appeal.
Claims stopped by the clearinghouse before the payer sees them, counted as denials or not.
Counted by the date the claim was sent, or the date the remittance came back.
Worked example, illustrative: 1,000 claims sent in a month; 90 denied on first submission and 35 still denied after rework and appeal. The same month reports a 9% initial denial rate and a 3.5% final one.
The decision that settles itInitial or final, claims or dollars, and counted from which date?
Sources: the billing system, the clearinghouse, payer remittances, finance.
Any unplanned return within 30 days for any reason, often to your own hospital only.
Specific conditions and procedures, planned readmissions excluded, and a return to any acute care hospital counted.
Transfers, patients who left against medical advice, and deaths handled differently from one report to the next.
The decision that settles itWhich stays count as the first admission, and does a return to another hospital count?
Sources: the clinical record, quality reporting, federal program reports, payer data.
Open positions counted one each, or the full-time equivalents they stand for.
Budgeted FTEs not yet filled, or only the requisitions open today.
A post covered by agency or travel staff, counted as filled or as vacant.
The decision that settles itBudgeted FTEs or open requisitions, and does agency cover count as filled?
Sources: the people system, recruiting, the budget, scheduling.
Overtime hours, or overtime pay, which the premium rate makes larger.
A share of hours worked, or of hours paid, which includes paid time off.
Charged to the nurse’s home unit, or to the unit where the shift was worked.
Worked example, illustrative: 120 overtime hours in a unit that worked 2,000 hours and was paid for 2,240, including time off: 6.0% of worked hours, or 5.4% of paid hours.
The decision that settles itHours or dollars, as a share of worked or paid hours, and charged to which unit?
Sources: time and attendance, payroll, scheduling, finance.
Receivables at full charges, or after contractual adjustments, which gives a very different number.
Divided by the average daily revenue of the last 90 days, the last three months or the year.
Credit balances netted or not, and accounts not yet final-billed counted or not.
The decision that settles itGross or net, averaged over which period, and are unbilled accounts in?
Sources: the billing system, the general ledger, finance.
Which of these do your committees argue about? Bring it to a 30-minute call, and we will tell you what it would take to agree one specification and build it once.
Talk through one of these measuresChoose yours for the two pieces of work we are asked for most. The work that runs across all three follows.
Goal Fewer staffing surprises and less agency spend.
Vacancies, overtime and credentials joined with predicted discharges and bed demand, so nurse leaders, patient flow and finance plan the same shift from the same numbers.
Case study: A people-data platform for a care provider
Goal Fewer avoidable denials and less rework.
Claims at risk of denial caught before submission, with the missing authorization, eligibility or documentation named, and actual denials fed back so the checks improve.
First step: AI & Agent Pilot
Goal Decisions inside the federal timeframes, with fewer touches per request.
Requests triaged and complete ones moved fast, reviewers seeing the clinical documentation in one place, and a specific reason recorded for every denial. The model approves or routes; clinicians decide denials.
First step: AI & Agent Pilot
Goal Fewer calls, and answers that match the plan documents.
An assistant that answers benefit and coverage questions only from the member’s own plan documents for that plan year, cites its source, and hands appeals, complaints and anything clinical to a person.
First step: AI & Agent Pilot
Goal Field and medical teams answer at call speed without leaving approved content.
Assistants that answer only from approved labels, inserts and medical content, apply each audience’s rules to every answer, and keep a record of each one.
Case study: A label-aware assistant for life sciences
Goal Safety reports that reach the safety team complete and on time.
Call-center notes, field reports and medical information requests read for possible adverse events, which go to the safety team with the key details already pulled out. People confirm every case.
First step: AI & Agent Pilot
One governed layer over the record, the roster and the claims, built on what you already run.
First step: Data Platform Accelerator
Vendor AI, departmental pilots and your own models, each with a risk tier, an owner and a review path.
First step: AI Governance Quickstart
Role-based learning and a review of what is used, so tools reach the floor and stay in use.
First step: Adoption Review
Working on something not listed here, or on all three at once? Thirty minutes is usually enough to agree where to start.
Talk through one of these prioritiesSix rules shape what we build from the first week, and the data they cover sits in systems built for different jobs.
In force
Role-based access, audit controls and safeguards for systems that handle protected health information, with minimum-necessary limits where they apply.
Compliance from Feb 16, 2026
Substance use disorder records now follow HIPAA more closely but keep their own consent and use limits, so a data platform has to know which records they are.
Decision times from 2026 · APIs from Jan 2027
Affected health plans decide prior authorization requests within 72 hours for expedited and seven calendar days for standard requests, give a specific reason for each denial, and open new data-exchange APIs from January 2027.
Since Jan 1, 2025
A notice when generative AI writes to patients about clinical matters, unless a licensed clinician reviews the message first.
Since Jan 1, 2026
Health care providers tell patients when AI is used in their care.
Revised Jan 6, 2026
Software that informs a clinical decision may be a medical device, particularly when it supports a time-critical decision the clinician cannot independently review. The question is settled with your regulatory and clinical leads before anything is built.
A summary for orientation, not legal advice. Each rule links to its source. Last checked October 2026.
FHIR, HL7 v2, C-CDA
Encounters, orders, results, notes and problem lists, from the record and its feeds.
X12 837, 835, 277
What was billed, paid and denied, and why, by payer and service line.
X12 270/271, 278, FHIR
Coverage checks, prior authorization requests, decisions and reasons.
HR, scheduling, time
Positions, shifts, overtime, agency use and credentials.
General ledger, cost, inventory
Cost per case, supply use, and the budget both are measured against.
Labels, inserts, policies
The documents an assistant may answer from, with their versions and audiences.
Pick a use case to see its review level, what it changes, what it touches, the rules that may apply and what to have in place before go-live.
Review levelFull review
What it changesWhat patients read about their care.
First step: AI Governance Quickstart
Review levelFull review
What it changesWhat enters the clinical record.
Usually bought rather than built: the work is choosing the product, governing it and measuring it.
First step: AI Governance Quickstart
Review levelStandard review
What it changesHow beds and staff are planned for the day.
First step: AI & Agent Pilot
Review levelStandard review
What it changesWhich claims are corrected before they go out.
First step: AI & Agent Pilot
Review levelFull review
What it changesWhich patients a clinician looks at first.
Often bought rather than built: the work is validating it on your patients and governing it.
First step: AI Governance Quickstart
Review levelFull review
What it changesHow fast a member gets an answer, and what the answer is.
First step: AI Governance Quickstart
Review levelStandard review
What it changesWhat members are told about their coverage.
First step: AI & Agent Pilot
Review levelFull review
What it changesWhat field and medical teams tell clinicians, pharmacists and patients.
First step: AI & Agent PilotCase study: A label-aware assistant
Review levelLight review
What it changesHow staff follow policies and procedures.
First step: AI & Agent Pilot
Orientation, not a determination. What applies depends on your organization, your role in the work and how the tool is used; it is not a legal, clinical or regulatory determination. The rules in full: Checked before anything is built.
A use case not on the list, or one already live without these safeguards? Thirty minutes is usually enough to triage it and name the first step.
Talk through your AI use caseEach step stands on its own, and none commits you to the next.
Free · about six minutes
Fifteen questions on lineage, quality, ownership, access and platform, the foundation every measure and model rests on. An instant result, and no email required.
Score your data foundationTwo weeks
Readiness scored against your own systems, a register of the gaps, a view of the cost and the next steps in order. The right start when the question is where to begin.
Ask about a two-week sprint2 to 4 weeks, once access is in place
One use case, one data foundation or one decision through governance, built in your environment once access and approvals are in place, against a number you already track, then a decision to scale, change or stop.
AI & Agent PilotData Platform AcceleratorAI Governance Quickstart
Talk through a first buildIt does not need to. We build in your cloud, on your accounts and under your access controls, and what we build is yours to run. The first diagnostic usually works from definitions, metadata and a sample rather than the full record. Where the work needs protected health information, the agreement that covers it is in place before anyone touches it.
No. We work with hospitals and health systems, other care providers, health plans and insurers, pharmaceutical and life-sciences companies, and digital health companies. The work differs: for providers it is usually workforce, patient flow and the revenue cycle; for health plans and insurers, prior authorization, member service and quality reporting; for pharmaceutical and life-sciences teams, assistants that answer only from approved content.
Use what is built in where it fits; it is often the fastest route. We help you decide which features to switch on, govern them like everything else, and build only where a question crosses systems that no single vendor sees, such as staffing against census or denials against documentation.
As data with its own rules. Records covered by 42 CFR Part 2, psychotherapy notes and other sensitive categories are tagged at the source, so access, consent and any later use can follow them. Where de-identified or limited data will do the job, that is what we use.
It can. Software that informs a clinical decision may fall under FDA oversight, depending on what it does and whether the clinician can review the basis for its output. The work on this page is operational, administrative and commercial; where a tool would inform a clinical decision, we settle the question with your regulatory and clinical leads before anything is built.
Yes. The free Data Maturity Assessment takes about six minutes. The Assessment Sprint takes two weeks. A first build takes two to four weeks once access and approvals are in place, and ends with a decision to scale, change or stop.
The census, the vacancy rate, the denial rate, or the answer you cannot yet let AI give. Thirty minutes with the person who would lead the work, on what it would take to get it right.
Healthcare data? Thirty minutes with the person who would lead the work.
