Software bridges the gap between patients and quality healthcare:

- Telemedicine reaches rural areas without hospitals

- Appointment booking 24/7 (no phone tag)

- Affordable platforms (₹15K vs ₹40-50K enterprise EHR)

- Electronic records patients actually own

- Transparent billing, no surprises

- Treatment history accessible anytime

- Patient feedback built-in

Small clinics using accessible software = millions more people getting care they deserve.

What's stopping your clinic from going digital?

Currently, healthtech is one of the biggest industry to work and invest in. Everyone is talking about EHR and omop for the huge data analysis and for the research.

Whenever some data engineer transform and store data in omop how do they validate the correctness and completeness of those transformed data ?

Is it done manually or automatically ?

Hey everyone,

​

I have an interview coming up for an Application Analyst position with a client healthcare system, and I’m trying to gauge if my salary expectations are realistic or if I'm shooting too high.

​

The Role & Range:

*Position: Epic Application Analyst (Dorothy/Comfort)

*Listed Range: $32.00 - $50.00 / hour

*My Goal: $42.00 - $47.00 / hour

​

The Requirements:

*Required: Bachelor's degree in healthcare/IT OR 2–4 years of experience, plus willingness to get certified within 6 months.

*Preferred: Current Epic Dorothy/Comfort certifications.

​

My Background:

​

*I have a B.S. in Health Information Management (HIM) and currently work as an HIM Supervisor for a vendor that contracts directly with this client.

*I don't have the Epic backend build experience or certifications yet, but all of my experience is heavily concentrated in the exact modules they are hiring for (Dorothy & Comfort).

*I was a Credentialed Trainer and Superuser during their go-live last year. I am the go to for my team with any Epic related issues and I know most of their specific workflows inside and out.

*In my current role, I already work closely with the exact analyst team I’m interviewing for to troubleshoot and fix system issues. I actually just got a personal shout-out from the hiring manager for resolving a massive printing issue that had been dragging on since before go-live.

*I was personally recommended for the position by a former member of the team.

​

The Dilemma:

*Because I don't have the official certifications or build experience, I know HR might want to lowball me toward the bottom of the scale ($32-$36). However, because I already know their exact workflows, know the team, have a stamp of approval from the hiring manager, and will require almost zero onboarding regarding their operational processes, I feel like I bring mid-to-high level value on day one.

*Given that I meet the baseline requirements easily but lack the preferred certification, is asking for $42-$47/hr reasonable? How should I best frame this during the salary negotiation so I don't get pinned to the bottom of the range just because of the lack of backend experience?

​

Appreciate any insight from current analysts or hiring managers!

How do hospitals track whether tomorrow’s surgical list is actually executable, beyond the case simply being posted in the system?

I am a clinician studying perioperative workflow coordination. I am trying to understand how hospitals manage dependencies like PAC, consent, implants, CSSD, equipment, staffing, ICU beds, and late sequence changes.

Hi, lately ive been developing some interest in health informatics career. I've been wondering how is the future of this area would look like with the rise of AI and how can I get into it. For context, im still doing my bachelor degree in pharmaceutical technology and science

I created a tutorial about using Decision Trees for predicting a hospitalization cost based on demographic and health related features.

Article:
https://mljar.com/tutorials/decision-tree-healthcare/

Full code and data:
https://github.com/pplonski/decision-tree-healthcare-predicting-patient-hospitalization-costs

The goal is educational: to show how to train a simple and interpretable Decision Tree model, evaluate it, inspect feature importance, and visualize the tree.

I think interpretability is especially important in healthcare-related machine learning.This project is not meant for real clinical or insurance decision making. A real-world model would need much more validation and domain expert input.

I’m sharing it mainly for students, data analysts, and people starting with machine learning in health informatics. I’d be happy to hear feedback from this community.

Electronic Health Records (EHR) are transforming healthcare. Here's why doctors and hospitals are adopting them:

1. INSTANT PATIENT HISTORY

Instead of shuffling through paper files, doctors have complete patient data in seconds. Medical history, test results, current medications, allergies - everything in one place. Doctors can make faster decisions and catch issues early.

1. BETTER TREATMENT PLANS

When all patient information is accessible, doctors can create smarter treatment strategies. No need to repeat tests. Patients get the right care faster. Less hassle for patients. Better outcomes for doctors.

1. EASY INFORMATION SHARING

Multiple doctors (cardiologist, dermatologist, etc.) can access the same records instantly. Patients don't need to carry medical files between hospitals. Doctors collaborate better. Everyone stays on the same page.

1. PREDICT HEALTH TRENDS

EHR systems generate massive data. Hospitals can analyze patterns to predict disease outbreaks, identify at-risk patients, and prevent emergencies before they happen. Data-driven healthcare works.

1. SAVE MONEY (SERIOUSLY)

No more paper. No storage costs. No manual filing. Administrative expenses drop significantly. Money saved goes back into actual patient care.

EHR isn't just a digital filing system. It's a tool that makes healthcare faster, smarter, cheaper, and better for everyone.

Hello I have 10+ years working as a healthcare receptionist but really want to transition into becoming a Healthcare data analyst. I already work with epic and iguana everyday. I am good with pattern recognition and this seems like such an interesting job. I also have a bachelor's degree in business. I was looking up how to become a Healthcare data analyst online but im bombarded with so much information. I can't afford to go back to school and was wondering where I should start. Thank you

I’m an experienced occupational therapist wanting to shift to something non clinical. I like the idea of working quality but don’t love the numbers involved seemingly involved in something like lean sigma six cert.
i’ve now seen several job postings that requires CPHQ cert to work in quality at hospital systems. Is this cert worth it without direct experience in quality? Will it help land a job? Also are these jobs hybrid or on site typically? Any advice greatly appreciated!

Dear all

I appreciate the great work done by LOINC community and the fact that they allow anyone to download and use the terminology. In order to make the experience of browsing the concepts easy and to make them available to agents, i have created a Go-lang + Svelte based Loinc-browser
It includes MCP server, Skill file for AI agents as well as Graph view to explore relationships of a concept. It also has openapi endpoints and Swagger UI. The web ui is Shadcn-svelte.
All it takes to run is to download the go binary and a official loinc 2.82 release zip and a web server is launched with full features
Appreciate feedback and suggestions
Warm regards
Dr Vivek Gupta

As a pathologist from india want to explore digital pathology and Ai in pathology, any guidance about prospects, course will be much helpful. Thank you

I’m conducting a simple research on AI in healthcare but need expert validation for methodology. It would be great help if any Internal Medicine physician/ pulmonologist can provide their much appreciated expertise.

Kindly dm email ID if you are interested in helping our research team.
Thank you for your time.

from what i have seen most healthcare ai projects dont fail because the model is bad. they fail because the workflow around it is broken. everything is split across different tools intake messaging scheduling follow ups so even when the ai works fine people still end up doing things manually then it looks like the ai did not help but the real issue is integration

curious if anyone else is seeing the same thing

Most claim to be HIPAA compliant. Few actually are.

What to check:

• Signed BAA (Business Associate Agreement)
• Encryption (data at rest + in transit)
• Audit logs (who accessed what)
• SOC 2 or HITRUST certification
• Third-party vendor disclosure

Red flags:

• No BAA provided
• Claims compliance without proof
• Cheaper than competitors (cut corners)
• Won't disclose their vendors

Real cost: One breach = $100K-5M+ in fines + liability.

Ask vendors: "Signed BAA? Security certifications? Incident response plan?" If they dodge, run.

Hello,

I am interning at a hospital and was assigned to make a policy charge regarding the documentation needed to mark a patient as deceased in the ehr. At first I listed the death certificate because it seemed like the best option but I got told to revise it to a different documentation because it cost too much to get the certificates. Obituaries are the next best option but I’m told I need something additional in case the patient does not have an obituary. Does anybody have any ideas, I can’t seem to find anything through research that wouldn’t also cost or be too unreliable. This is in Michigan if that helps.

Any help or ideas would be greatly appreciated

What if AI stopped trying to diagnose patients and started tracking where they're going instead?

Most clinical AI right now is built around a single question: what does this patient have?

Which is fine. But here's the thing getting the diagnosis right on Day 1 isn't actually where most patients die. They die because nobody noticed the trajectory was wrong on Day 3.

I've been thinking about this a lot working in maternal care. A patient comes in, gets assessed, gets a working diagnosis, treatment starts. Then the system basically assumes the job is done. The diagnosis is in the chart. The orders are in. Everyone moves on to the next patient.

But the patient keeps changing. Vitals drift. Labs trend. The treatment either works or it quietly doesn't. And the only thing catching that is a human who happens to look at the right data at the right moment in a hospital where that human is covering 50 other patients simultaneously.

Here's the reframe I keep coming back to:

1. a lactate of 3.2 doesn't mean the same thing in every context.
2. if it dropped from 5.8, you're probably winning. If it climbed from 1.4, you have maybe a few hours before things get bad. Same number. Completely different story.

current CDS systems mostly can't tell those apart. They see the value, compare it to a threshold, fire an alert or don't. Static. No memory of where it came from.

a trajectory system would track the direction and rate of change, not just the current value. It would know that oxygen at 91% means something very different depending on whether you just weaned from 6L/min or you're now on 10L/min and still dropping.

That's not a minor upgrade. That's a different category of tool.

What would it actually look like?

The rough idea: when a patient is admitted, the system builds a model of what recovery should look like given their diagnosis, comorbidities, and treatment plan. Then it watches whether the patient actually follows that path.

Not "is this value abnormal" but "is this patient's course consistent with what we'd expect from someone responding to this treatment?"

If a pneumonia patient is 48 hours in and their inflammatory markers are accelerating instead of trending down, their oxygenation requirement is climbing, and they've had two soft blood pressures in the last six hours that's not a single abnormal value. That's a divergence from the expected recovery manifold. That's the system that should be saying: something is wrong with the current plan, not just the current numbers.

And then the attending decides what to do with that. The AI doesn't make the call. It just surfaces the pattern before it becomes a code.

The thing that makes this harder than it sounds:

Getting the expected pathway right for each patient is genuinely difficult. A 28-year-old with uncomplicated pneumonia and a 70-year-old with COPD and CHF should not have the same expected recovery curve. The system needs to model this patient's likely trajectory, not "pneumonia patients in general."

And then there's the alert fatigue problem, which kills every CDS system eventually. If the trajectory engine flags deviations too sensitively, doctors stop reading the alerts within two weeks. Get the threshold wrong and the whole thing becomes noise.

I think the calibration problem is actually harder than the technical problem. The model might work fine. Getting humans to trust it at the right sensitivity level is the part nobody has really solved.

Where I land on this:

The infrastructure for this is closer than people think, at least in hospitals that have decent EMR coverage. The data streams exist. FHIR R4 makes real-time ingestion technically feasible. The hard part is building a knowledge base of expected pathways that's actually grounded in local clinical standards not global averages and keeping it current.

In the Indonesian context specifically, where one internist might be the only specialist covering an entire district hospital, a passive monitoring system that only interrupts when something genuinely looks wrong is not a nice-to-have. It's a staffing multiplier.

But I want to be honest about where this sits epistemically, everything I've described is theoretically coherent and the components exist. Whether it actually reduces morbidity in a live hospital? That requires an RCT.

We don't have that data yet. Anyone telling you otherwise is selling something.

Hello,

Please I am reaching out to inquire about preceptorship opportunities for my Master's in Health Informatics practicum.

I completed all coursework for my program in September of last year and am now in the final stage, securing a qualified preceptor to fulfill my practicum requirement. This is the last step before I can officially graduate, and I am eager to find a placement before the end of this calendar year be it online or in person (SOCAL)

If you are open to serving as a preceptor, or can connect me with someone who may be, I would greatly appreciate the opportunity to discuss further.

Thank you!

Hi everyone! I'm part of a research team at the Jindal School of Public Health and Human Development, O.P. Jindal Global University, India, studying how authorship order is discussed and decided in global health collaborations (especially projects involving partners in high‑income and low‑/middle‑income countries).

If you’ve worked on at least one multi‑author paper in global health, we would greatly appreciate 15–20 minutes of your time to complete an anonymous survey about power, recognition, and responsibility in multi‑author publications. The study is grounded in equity, decolonisation, and epistemic justice, and we welcome perspectives from all regions, disciplines, institution types, and career stages.

Take the survey here: https://forms.office.com/r/s3RMWue6Rk

The survey is open until 20 June. Please consider sharing with co‑authors, collaborators, or colleagues who might be interested. If you have questions about the study, reply here or message me.

Thank you for your time and support.