stackai whitepaper
AI Agents for Home- Based, Senior, and Community Care
How home-health operators, senior-care networks, and residential and I/DD providers are deploying AI agents to survive the paperwork: authorizations, case-management packets, survey readiness, and month-end close.
A practical guide for COOs, CFOs, VPs of Operations and Compliance, and Revenue Cycle leaders in home health, hospice, senior living, and community- based care.
Home-based, senior, and community care runs on the thinnest margins in healthcare and the heaviest administrative load. The work that keeps the lights on is unglamorous and relentless: logging into state Medicaid and payer portals to check authorizations, processing case-management packets from a dozen different companies with a dozen different rulebooks, reconciling shift notes against incident reports before a state survey, and closing the books across facilities every month.
None of this is clinical, and almost none of it requires the EHR. It is exactly the kind of high-volume, rules-based, cross-system work that governed AI agents do well. This whitepaper lays out five use cases proven in production across a home-health operator, a multi-facility senior-care network, and a residential and I/DD provider: authorization lookup across state portals, case-management packet processing, residential quality and incident-reconciliation audits, finance close and reconciliation, and a cited regulatory policy assistant.
In production these are workhorses. One residential provider runs roughly 28,000 staff- level analyses and 13,000 individual-level compliance reports per 180 days. One home- health operator processes 200 to 300 case-management packets a month at about an hour each today, and faces more than 2,000 denied claims a week in a single branch. The agents that absorb this load do not replace care staff; they give a thin-margin operator back the hours and the audit trail it cannot otherwise afford.
Providers in this segment operate under a specific kind of pressure. Reimbursement is largely Medicaid and Medicare, so rates are fixed and margins are slim. Regulation is intense and enforced by periodic state survey, where a documentation gap can mean a citation, a corrective action plan, or a licensing risk. And the operational surface area is enormous: many patients, many payers, many state portals, many case-management partners, many facilities, each with its own forms and rules.
The result is that a large share of the workforce spends its day on manual, repetitive, error-prone administrative work: logging into portals one patient at a time, reading packets against rulebooks, hunting for discrepancies between systems, and rebuilding the same reports every month. It is expensive, it does not scale, and it is precisely where a small documentation error becomes a denied claim or a survey finding weeks later.
This is the highest-yield place in healthcare to deploy AI agents, because the work is high- volume, rules-based, cross-system, and almost never requires touching a clinical system of record.
PHI plus portal automation
Much of the work means logging into state Medicaid and payer portals with credentials, against patient data. That demands single-tenant deployment, encrypted bot-only credential handling, and browser automation that runs inside your environment.
Many partners, many rulebooks
Packets arrive from a dozen-plus case-management companies, each with its own forms and required fields. The platform has to classify the source and apply the right rulebook, not assume one standard.
Survey and audit readiness
Every automated action needs a defensible, exportable trail, because a state surveyor will ask you to prove it.
Thin margins demand hard ROI
These operators cannot fund science projects. The platform has to prove time and revenue recovery on your own volumes during a short, predictable-cost pilot.
Small or non-standard systems
Home-care systems like AlayaCare, eVero, Monday.com, and SharePoint, not Epic. The platform has to orchestrate across exactly this kind of mixed, smaller-vendor stack.
Generates budget-versus-actuals executive summaries per facility and reconciles AP and vendor statements.
Logs into state Medicaid and payer portals, captures authorization status and remaining units, and writes it back to the system of record.
Classifies each incoming authorization packet by sending company, applies that company's rulebook, and flags discrepancies before it is actioned.
Audits behavioral plans and tasks against regulatory criteria, and reconciles shift notes against incident reports, at site, individual, and staff level.
Cited, always-current answers on HCBS, OPWDD, ICF/IID, and state licensing rules.
The Problem
Closing the books across a multi-facility network is a multi-day manual cycle: pulling GL data, computing variance, writing executive summaries by hand, and reconciling AP and vendor statements line by line.
How An AI Agent Handles It
Once AP and AR close each period, an agent connects to the ERP, pulls the GL data, computes variance against budget and prior period, and generates an executive-summary narrative tailored to the reader (facility administrator, regional VP, CFO), routing site-specific summaries to the right recipient. Companion flows produce standalone variance reports for controllers and flag unusual GL activity. On the AP side, a reconciliation agent ingests supplier statements each month, matches them line by line against internal records, and flags missing invoices, duplicate payments, and aging discrepancies, with row-level permissioning so each analyst sees only the vendors and facilities they are entitled to.
Why It Matters
It turns a multi-day close into same-day, consistent output across facilities, and gives leadership comparable financials without waiting on manual assembly.
CFO, Controllers, Facility Administrators, AP
Days to close, reconciliation exceptions caught, reporting consistency
Multi-day close becomes same-day, consistent per-facility reporting, faster exception review
The Problem
Care coordinators spend hours a day logging into state Medicaid and payer portals, one patient at a time, to check authorization status, remaining service units, and expiration dates. It is slow, it is easy to miss a lapse, and a missed authorization means unpaid care already delivered.
How An AI Agent Handles It
A browser-automation agent logs into the portals on the operator's behalf, navigates to the authorization screens, captures current status, units remaining, expiration dates, and pending actions, and returns a structured record for each patient. It runs the loop across the full patient list on a schedule and writes the extracted data back into the system of record (for example AlayaCare), so the care-management team works from one source of truth instead of portal after portal. Because this is all PHI, the workflow runs inside the operator's single-tenant environment with encrypted, bot-only access to portal credentials.
Why It Matters
It removes hours per day of manual portal work per coordinator and catches authorization lapses before care is delivered against an expired auth, which is one of the most preventable causes of unpaid work in home care.
VP Operations, Care Management, Revenue Cycle
Coordinator hours saved, authorization-lapse rate, unpaid-care avoidance
Hours per coordinator per day recovered, fewer lapsed authorizations, single source of truth
The Problem
Authorization packets arrive from 12 to 20 different case-management companies, each with its own document standards, forms, and required fields. Staff process them by hand against the sending company's specific rules, at roughly an hour per packet, 200 to 300 packets a month.
How An AI Agent Handles It
An agent monitors the case-management inbox, ingests mixed-format inputs (PDFs, ZIPs, Word documents, images), classifies the sending company, retrieves that company's processing rulebook and prior documentation from SharePoint, looks up the client in the system of record, and runs a discrepancy check comparing the packet against the patient record. It flags anomalies (mismatched authorization dates, service-hour discrepancies, missing physician orders, wrong plan of care) and generates a recommendation of what has to change before the packet can be actioned.
Why It Matters
It compresses an hour of skilled manual review into a fast exception check, and it standardizes accuracy across partners so a packet from any of a dozen companies is checked the same way, every time.
Intake and authorization teams, Compliance, Operations
Minutes per packet, discrepancy-catch rate, rework and resubmission rate
An hour per packet becomes a fast exception review, consistent multi-partner accuracy
The Problem
Residential and I/DD providers are judged on survey readiness. They must prove that each individual's behavioral plan and recorded tasks meet regulatory criteria, and that shift notes and behavior documentation reconcile with the incident reports on file, across every site. Done by hand, this is impossible to do comprehensively, so gaps surface at the worst possible time: during the survey.
How An AI Agent Handles It
An audit agent compares each individual's behavioral plan and recorded tasks from the EHR against regulatory criteria, then reconciles shift notes and behavior documentation against incident reports. It runs on a schedule across every site and produces a site-level quality report, an individual-level report, and a staff-level analysis identifying which staff are driving compliance gaps. A sibling incident-reconciliation flow closes the long-standing gap between the shift-notes system and the incident-reporting system. In production this runs at roughly 28,000 staff-level analyses and 13,000 individual-level reports per 180 days: the workhorse the operator relies on to survive state licensing audits.
Why It Matters
It converts survey readiness from a frantic pre-survey scramble into a continuous, documented state, and it pinpoints exactly where and with whom the gaps are, so remediation is targeted.
Compliance, Quality, Residential Operations
Documentation-gap rate, reconciliation coverage, survey-finding rate
Continuous, comprehensive audit coverage, targeted remediation, defensible survey trail
The Problem
Clinical, care, and compliance staff need answers on state and federal rules governing residential and community programs (HCBS, OPWDD, ICF/IID, state licensing standards), and those rules change.
How An AI Agent Handles It
A chat-based assistant answers natural-language policy questions with verbatim citations back to the underlying regulation and internal policy. A regulation-parser subflow keeps the knowledge base in sync with the latest published state and federal updates, and companion flows crawl agency sites for changes, so answers never drift from current rule.
Why It Matters
It gives frontline staff a fast, cited, always-current answer instead of a guess, which is both a productivity tool and a compliance control in a survey-driven environment.
Compliance, Quality, Care Management, Program Directors
Time-to-answer, policy currency, answer consistency
Fast, cited, current answers, reduced compliance risk from stale guidance
Two financial statements are compared into a side-by-side balance-sheet table, with a summary of the material differences and a drafted email of the findings ready to send, the same shape that powers the multi-facility month-end close.
StackAI - template
The build: upload two statements, extract and compare the sections, then summarize and route the differences.
"We would be giving an agent our state portal logins, against patient data. Is that safe?"
"Our systems are AlayaCare, eVero, Monday.com, and SharePoint, not Epic. Does that matter?"
"Will this hold up in a state survey?"
"Margins are thin. How do we know this pays off before we commit?"
"Can it really process packets from all our different case-management partners?"
Authorization lookup and packet processing attack the two biggest sources of unpaid
work and manual hours at the front of the revenue process.
The quality and incident audit keeps you survey-ready continuously instead of
scrambling, and pinpoints where to remediate.
Finance close and reconciliation give leadership fast, comparable numbers
across facilities.
The policy assistant grounds all of it in current, cited regulation, so the same source of
truth feeds authorizations, audits, and staff answers.
Together they give a thin-margin operator back hours, revenue, and an audit trail it otherwise cannot afford.
Portal automation and packet processing run in your isolated environment, with encrypted bot- only credentials.
Reaches state Medicaid and payer portals that have no usable API.
Orchestrates AlayaCare, eVero, Monday.com, SharePoint, and your ERP, no Epic required.
Every action logged and exportable, with human- in-the-loop on consequential steps.
Compliance and operations staff add new partner rulebooks and reports without engineering.
Flat monthly subscription with ROI measured on your own volumes during the pilot.
