r/healthcareIT

Kaiser doctor. His laptop was open with patient records, no privacy screen, and he was actively taking phone appointments for at least an hour. “Let’s see here I’m looking at your xray now, it shows you have [medical condition] and at your weight of [number], you could stand to lose a few pounds. Diet can only go so far. Also I can see here that you’re on [prescription medications] at [dosages]”.

I called and reported him to Kaiser’s Ethics & Compliance board. The representative was gobsmacked and said based on all of the details I provided, they’d be able to find him easily.

I happen to run IT at a small mental health nonprofit, we’re a few years away from an EHR deployment at least, and I feel like I’m busting my ass to try and create a relatively safe system to protect our participants data (which is absolutely HIPAA protected). Our environment is a disaster after decades of neglect, it’s laughable. Goes to show you can do everything you can to up your cybersecurity stature, drop millions on an EHR, buy and configure the right licensing, do endless trainings with your staff and lo and behold some asshole decides it’s “no big deal” and pulls some shit like this.

I'm running things half on paper half digital for a while now, intake is still mostly paper forms we scan in after, scheduling is a shared google calendar, billing is separate, etc. But I saw that most of the clinics stopped doing paper stuff and just moved to everything digital

We're still small, maybe 15-20 patients a week, so I'm not sure if going fully digital at our size would even pay for itself right now or if we should wait till were bigger??

I'm thinking of just switching intake first since that does feel like it would make things tangibly easier, and leaving scheduling and billing alone for now unless that ends up being a mistake too, but idk if that's good approach or should I just switch everything all at once and be done with it

En casi todas las demos de software médico que he visto en Venezuela aparece la palabra “inteligencia artificial”. Pero cuando preguntas qué datos necesitan esos modelos, cómo se validarían en tu hospital o quién responde clínicamente por un falso negativo, la conversación se queda muy corta.

Revisando la evidencia publicada y lo que he visto en proyectos reales, llegué a una conclusión simple: en salud, el cuello de botella no es el algoritmo, son los datos y la institución que lo va a usar.

Algunos datos concretos:

• ~76% de los dispositivos médicos con IA autorizados por la FDA son de radiología. La “IA clínica” aprobada es, en la práctica, IA de imágenes (nódulos, triaje, reconstrucción), no sistemas que “gestionan” el hospital.
• El Epic Sepsis Model, implementado en cientos de hospitales de EEUU, tuvo una AUROC de 0,63 y un valor predictivo positivo de 12% en la validación externa de 2021 (muchos falsos positivos y sepsis que no detectaba).
• En la versión nueva (ESM v2), un estudio prospectivo multicéntrico con 227k encuentros reporta AUROC 0,82–0,92 pero VPP sólo 0,13–0,26, con rendimiento distinto en cada hospital y necesidad explícita de validación local.

Con esa foto en mente, me puse a escribir una guía aterrizada para clínicas venezolanas:

• Qué tipo de IA médica está realmente aprobada hoy.
• Por qué el mismo modelo funciona en un hospital y falla en otro.
• Las seis capas que una institución tiene que resolver antes de pensar en IA clínica (datos estructurados, histórico, interoperabilidad, infraestructura, gobernanza, validación local).
• Qué sí funciona hoy en una clínica venezolana: automatización determinista e IA administrativa con supervisión humana.
• Cinco preguntas que separan una propuesta de IA seria de una presentación comercial.

Si trabajas en salud, TI hospitalaria o estás montando producto HealthTech para la región, te puede interesar leerla y discutir:

Inteligencia artificial en hospitales de Venezuela: qué funciona hoy y qué todavía no.

Me interesa especialmente feedback de gente que esté en hospitales (clínicos, TI, proveedores) y de quienes hayan intentado meter IA en entornos con historias clínicas en papel o sistemas que no se hablan entre sí.

I am thinking about trying an AI tool for SOAP notes because I spend a lot of time finishing them after every patient visit and I want to see if it will speed things up, I still want to check everything before it goes into the chart, I just want something that will put the first draft together from the visit so I can edit it and move on, I usually end up spending extra time after my last appointment just wrapping up notes and I would rather use that time for the next patient or other work, I am planning to try one soon if it fits into my workflow and keeps the notes accurate, I see a few tools come up when I search but they all look pretty similar and I think i would go with prompted ai it is hard to tell which one actually fits into a normal workflow, for anyone using one right now, which AI are you using and how much time does it save each day? EDIT: Thanks for the replies, this is really helpful. 

Baseline the numbers 90 days before go-live, or you'll have nothing to compare against.

Then track a handful of things that actually move:

• Denial rate and days in AR (this is where most of the money is)
• Documentation minutes per encounter, pulled from audit logs
• Duplicate tests ordered within 30 days
• Time to produce audit evidence when someone asks

Two honest caveats. Ignore the first 90 days after go-live, throughput always dips. And full ROI usually shows up at 24-36 months, not year one.

The trap is attributing everything to the EHR when you also changed staffing and workflow in the same window.

(I work in healthcare compliance software, so the audit angle is my bias.)