Home health ops · AI documentation for home health
Apricot
Apricot is AI documentation software for home health agencies that drafts OASIS visit notes, suggests ICD-10 codes, and flags chart problems before QA.
Strong fit
- Home health agencies whose nurses finish Start of Care charts late at night
- Agencies that want OASIS drafting, ICD-10 coding help, and chart QA in one tool
- Operators that want to keep their current home health EMR
Weak fit
- Agencies shopping for a full EMR with scheduling and billing
- Private-duty home care agencies that do not complete OASIS
- Buyers who need a published per-clinician price
Bottom line
Apricot is AI documentation built for home health, starting with the Start of Care visit. It drafts the OASIS chart, suggests codes, and flags gaps before QA. It fits agencies whose clinicians chart late into the night and want cleaner OASIS work without replacing their EMR.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Trent Smith ran his own home health and hospice agency for seven years before he founded Apricot in Oklahoma. In 2022 his agency got early access to AI and tested it on nursing notes. The company is named for apricot, the academic color of nursing.
Apricot is AI documentation for home health. It started with the Start of Care visit, the longest OASIS visit, and drafts the note from a pre-visit summary, in-home data capture, and a guided debrief after the visit. Clinicians review and finish the chart inside Apricot before it goes to the EMR.
Coding tools suggest ICD-10-CM codes with the reasoning shown and check face-to-face encounters against certification rules. A QA module flags missing or conflicting details before a chart reaches the office reviewer. Each generated answer links back to the file, field, or recording it came from.
Apricot says clinicians cut documentation time by up to 85% and QA teams review 40% more charts in the same time. It also says fewer than 5% of questions need a clinician edit. Insight Partners led its Series A in 2025, Insight's first investment in an Oklahoma company.
With that round, Apricot said it would add the remaining OASIS visits, therapy visits, and routine visits. Ask which visit types are live for your disciplines before you sign.
Axxess, KanTime, or Alora fit when the agency needs the EMR itself, with scheduling, billing, and OASIS built in. Apricot works on top of an EMR, not in place of one.
Careficient fits an agency that wants a home health EMR from a single vendor. Mosai fits when referral intake, document flow, and clinical analytics are the bigger gap.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Axxess | 7.9 | 7.5 |
| Apricot | 7.8 | 7.8 |
| KanTime | 7.6 | 7.3 |
| Mosai | 7.5 | 7.4 |
| Alora | 7.2 | 7.0 |
| Careficient | 6.8 | 6.6 |
Pricing
| Item | Detail |
|---|---|
| Model | A subscription quoted by Apricot for each agency. |
| What usually drives cost | For Apricot, cost depends on the number of clinicians, which visit types you turn on, whether you add coding and QA, and the work to connect your EMR. |
| What to ask before you buy | Ask Apricot for the cost per clinician or per visit, what coding and QA add, and which visit types are live today. |
| Published pricing | Apricot does not publish a price list. |
Leadership
Methodology
| Weight | Factor | What it measures |
|---|---|---|
| 35% | Customer outcomes | Whether buyers get measurable operational or clinical-workflow results after go-live |
| 30% | Product | Capability depth, reliability, and fit for the job the category buys |
| 20% | Implementation | How hard it is to stand up, integrate, train, and stabilize |
| 15% | Pricing clarity | Whether a buyer can model total cost without a mystery quote |
| Label | Meaning |
|---|---|
| Highly recommend | Strong outcomes and product with manageable caveats |
| Recommend | Solid fit for the right buyer; know the tradeoffs |
| Conditional | Only with a specific use case or heavy caveats |
| Not recommended | Avoid for most buyers in this category |
Read our full methodology for how we weight scores and assign recommend labels.