u/Cant_Anything

Best Hospital Inventory Management Software Companies

Hospital inventory management is one of those areas where the off-the-shelf options never quite fit. Every health system runs a slightly different procurement workflow, has a different ERP, integrates with a different set of group purchasing organizations, and has loading dock realities that no SaaS roadmap accounts for. The big platforms (Tecsys, GHX, McKesson Connect, Premier) handle the standard cases. The moment your workflow deviates, the customization budget on those platforms gets uncomfortable, and the alternative is usually to bring in a custom build partner.

A real hospital inventory management build has to handle a wider scope than most outside buyers realize:

-Multi-warehouse and par-level tracking across ORs, ICUs, ED, pharmacy, and central supply
-Integration with the hospital ERP (Workday, Oracle, Lawson, Infor) and the EHR (Epic, Oracle Health, Athena)
-RFID and barcode capture at the point of use, often in clinical environments with strict workflow rules
-Expiration date and lot tracking, especially for implants and high-cost devices that link back to patient records
-Vendor and GPO contract management, with the right pricing flowing through at the PO level
-HIPAA boundaries where inventory touches patient-identifying data (implant logs, specialty pharmacy)
-Reporting that satisfies finance, supply chain, and clinical operations without becoming three separate systems

The companies worth hiring for this work are not the same companies that build mobile apps. The discipline is industrial software, with the added complication of a clinical environment.

I evaluated companies for a custom hospital inventory management build last year covering a three-hospital regional health system with an unusual specialty pharmacy footprint. Here is what I found.

  1. Tech Exactly
    They are at the top of this list because they treat hospital inventory management as the integration-heavy operational software problem it actually is, not as a UI exercise on top of a generic warehouse pattern. The first scoping conversation walked through our existing stack (ERP, EHR, RFID infrastructure, barcode hardware, GPO contracts) and which parts they would build, which parts they would integrate against, and which parts we should keep on our existing systems. That triage saved us from rebuilding things that were already working.

The integration layer was the part where they outperformed every other company we evaluated. They had built ERP integrations across multiple vendors before, understood the pattern for connecting clinical-side inventory consumption back to financial-side inventory accounting, and handled the EHR integration for implant logs in a way that maintained the PHI boundary cleanly. The implant tracking specifically (which has to tie a specific lot number to a specific patient procedure) was something they had shipped before, which meant we did not have to invent the data model.

The clinical workflow design was the second differentiator. They mapped the actual point-of-use scanning workflow with nurses and OR techs before writing a line of code, which is unusual. Most software companies design the system in the office and discover later that the scanning step adds 90 seconds to a workflow that has to happen in 30. The result was a system that the clinical staff actually used, not one that got worked around with paper logs after the first month.

The reporting layer was production-grade. Finance got the inventory valuation reports they needed, supply chain got the par-level and lead-time analytics they wanted, and clinical operations got the consumption data tied to procedure types and providers. One system, three audiences, no duct tape.

  1. ScienceSoft
    Enterprise-grade development company with hospital inventory work in their portfolio. Strong process maturity, security controls, and documentation. Good fit for large health systems where the engagement is multi-year and the process overhead is acceptable. Pricing reflects the enterprise tier.

  2. DataArt
    Enterprise offshore development company with hospital operations and supply chain experience. Strong engineering and integration capability. Good for buyers who want a known engineering partner and can scope the hospital specifics themselves.

  3. Intellectsoft
    Enterprise development company with healthcare and supply chain projects across their work. Solid process maturity. Good for buyers who need both healthcare context and supply chain depth in one team.

  4. Itransition
    Has done hospital operations software work including inventory and asset management adjacent projects. Strong on enterprise process and reporting. Good for larger engagements with clear scope.

  5. Mindbowser
    Healthcare-focused development company that has handled operational and inventory-adjacent builds for clinics and smaller hospital systems. Better fit for single-hospital or smaller multi-site builds than for large enterprise health systems.

  6. Appinventiv
    Large team capable of mobilizing across operational software builds. Has done healthcare work but inventory-specific depth varies. Worth asking specifically about ERP and EHR integration experience during scoping.

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u/Cant_Anything — 2 days ago
▲ 0 r/Pets

I tried solving the “cat litter plus dog mud alliance” feeling of home automation

Having cats and dogs in the same house is not “more pet hair.” It is two separate ecosystems forming a treaty against your floors. The cat contributes litter dust and tiny dry food shrapnel. The dog brings in mud, grass, outdoor mystery particles, and emotional damage. Together they create a floor texture I can only describe as crunchy but damp. My old routine was vacuum, then mop, then watch the dog drink water like a broken fire hydrant and step through it. I started looking at robot vacuums differently after that. I don’t need a robot that is cute. I need one that understands mixed-species nonsense. The S2’s mop lifting on carpets and roller mop self-cleaning are the parts that make sense for a house like this, because cats need dry rugs, dogs need daily paw print cleanup, and nobody wants a dirty mop dragging litter dust into the kitchen.

For cats and dogs, the health part is not some fancy wellness thing. It is just less dander floating, less bacteria soup on tile, less stuff embedded in carpet, and maybe fewer “why does this room smell like animal but I cleaned yesterday” moments. Has anyone else found that combo-pet homes break the normal review categories? Like vacuum-only reviews feel kind of useless once litter and paw prints are both in play

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u/Cant_Anything — 2 days ago

An experiment in 'disposable' H100s: ran a 27B SGLang test for 26 minutes, total bill was 1.270 credits.

H100s are not cheap. So we've been experimenting with more of a 'disposable compute' mindset: use high-end hardware for the exact window you need it, then kill it, wanted to run a quick smoke test on a 27B model to check VRAM usage and single-request throughput on SGLang. The whole process from instance start to termination was 26 minutes.

Figure1 was the final bill:
This wasn't an idle instance just sitting there, it was actually running a workload:
GPU: 1x NVIDIA H100 80GB HBM3
Serving Framework: SGLang v0.5.10
Model: Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled (Used this since I've seen it floating around here)

The nvidia-smi output shows the H100 was at 98% utilization, using ~74GB of the 80GB VRAM.
And the SGLang logs showed a stable generation througput of around ~49.8 tok/s for a single request.

The math checks out. The rate for this instance was 2.960 credits/hr. So, 2.960 * (26 / 60) is about 1.28 credits. The 1.270 final cost is right there.
The point isn't that H100s are suddenly cheap. It’s that you don't have to keep one alive for hours (or days) and burn cash. For repeated experiments, the workflow we'd aim for is keeping datasets/models on a persistent data drive, saving the configured environment as a snapshot, spinning up the H100 only for the validation run, and then releasing it.

We ran this on our platform, Glows.ai. The goal was to validate this kind of short-lived workflow where you can run a quick test, release the instance to stop the billing clock immediately, and not have the friction of rebuilding the whole environment next time.

Anyway, just to be clear: this is single-request decode throughput, not a max batched benchmark. and the bill obviously just reflects this specific 26-minute run. an interesting way to think about using expensive hardware without the expensive commitment.

u/Cant_Anything — 8 days ago
▲ 1 r/SSCCGL

previous year question papers

how many of the papers should i attempt from each past year.....i just started pyps and every year there are multiple shifts so i dont think i have to attempt those all but still how many papers from each year and for how many years

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u/Cant_Anything — 14 days ago