What is de-identified clinical data?

De-identified clinical data is patient health information that has had direct identifiers removed to help protect privacy. In research, it allows organizations to analyze treatments, outcomes, and disease patterns without exposing personally identifiable patient information.

How is real-world data collected from EHR systems?

Real-world data from EHR systems is collected during routine patient care and captured in clinical fields such as diagnosis, body location, disease severity, treatment history, and outcome measures. Structured, de-identified EHR data is especially useful because it is more consistent and ready for analysis.

What is structured EHR data, and why does it matter for research?

Structured EHR data is information recorded in standardized fields, directly input from questions the specialist answers, rather than unstructured narrative text. It matters for research because it improves consistency of the data content entered by the specialist, supports faster analysis, and makes it easier to evaluate patient outcomes, treatment patterns, and disease progression at scale.

How can de-identified, tokenized patient data support real-world evidence studies?

De-identified, tokenized patient data can support real-world evidence studies by enabling researchers to analyze outcomes while helping protect privacy. Tokenization can also facilitate patient-level linkage across disparate datasets to support HIPAA compliance, creating a broader view of patient journeys, treatment use, and longitudinal outcomes.

What types of outcome measures can specialty-specific real-world data include?

Specialty-specific real-world data can include outcome measures such as disease severity scores, body location, visual acuity, intraocular pressure, lesion characteristics, symptom indexes, and progression over time. These structured measures help researchers evaluate disease burden, treatment response, and clinical outcomes.

Why do life sciences teams use specialty-specific clinical datasets?

Life sciences teams use specialty-specific clinical datasets because they provide deeper clinical context than many generalized sources. These datasets can offer disease-relevant measures, treatment insights, and outcome details that improve study design, evidence generation, and therapeutic area analysis.

What is the difference between real-world data and real-world evidence?

Real-world data refers to health information collected during routine care, sourced from EHRs, claims, or outcomes data. Real-world evidence is the clinical or scientific insight generated by analyzing real-world data to answer questions about treatment use, safety, effectiveness, or patient outcomes. The scale and granularity of ModMed Real-World Data Solutions help drive more accurate real-world evidence for life science researchers.

What should researchers look for in a real-world data provider?

Researchers should look for data quality, structured fields, specialty relevance, privacy protections, tokenization capabilities, geographic breadth, and outcomes depth. A strong real-world data provider should also deliver analysis-ready datasets that align with the intended research question and study design.

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Schweiger Dermatology Group is the largest dermatology provider in the Northeast. Before we used Clara, we were primarily a telephone based company. We had a central call center, but there was a lot of delay in communications. One of the biggest challenges we face as a practice is the, we call it like the endless circle of phone tag with our patients where, you know, a patient calls in, we send in a request to our clinical team. They have to then call the patient back and they're leaving a voicemail. Patient then calls back and that loop continues again and again. Clara does a great job of trying to prevent phone tag. It makes it easy for the patient. The interface with Clara is very straightforward. It's nice that they don't need a password to log in, that it can be done right through their text messaging, which pretty much everybody's comfortable with now. There's not a lot of extra button pushing, not a lot of extra web pages to scroll to, no app to download. It's really very easy to use. Once we implemented Clara, we put the patient in direct contact with the provider, cutting out the middleman and the message taking, and patients were able to get answers right away. We've saved hundreds and hundreds of hours of phone calls. I think one of the ways that Clara differentiates themselves from other platforms is their transparency. The ability to see all of the conversation between a patient, everything internally and externally that's happening between a staff member and a patient really allows everybody to be in the know and take any action needed. They seek to understand our problem and they come to us with really creative solutions. They've been an amazing partner from the beginning. Using Clara has definitely improved our revenue because we've been able to increase our numbers of patients. It does give the patient a little bit more control, is something you don't generally see in antiquated medical communications. Clara really does show innovation. The way we use it has worked very well with our patient support center. I would absolutely recommend Clara to other healthcare providers. I think we would still be in the dark ages of communication if we didn't use Clara.

What Your Peers Are Saying - ModMed delivers a really polished EHR product that feels like something out of the future. James McGehee, registered nurse - See Our Solutions in Action

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Clinical data without the cleanup

Breakthrough research shouldn’t stall because you have to spend months untangling free-text clinical notes before running a query. ModMed® Real-World Data (RWD) Solutions delivers de-identified, tokenized datasets ready for use. Because we capture every granular detail as structured data right in the exam room, there are no messy narratives to decipher.

Whether you are generating real-world evidence, conducting outcomes research, or running life sciences analyses, ModMed provides organized datasets so you can stop cleaning data and get straight to the insights.

The ModMed RWD Solutions data advantage

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Structured and de-identified

Bypass messy, unstructured narratives with data that is primed for clinical and operational modeling. ModMed equips your researchers with anonymized, highly structured data captured directly at the point of care.

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Power your most complex research with specialty-specific datasets captured at the granular level. With it, you get the variables needed to analyze rare and common disease populations, track outcomes, and measure real-world product use.

Longitudinal and tokenized

Build reliable real-world evidence by following patients across multiple visits. Tokenized longitudinal continuity allows researchers to see the complete context, measure true clinical progression, track operational decisions over time, and accurately assess long-term patient outcomes.

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Otolaryngology

Pain Management

Plastic Surgery

Podiatry

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ModMed RCM AI Platform

ModMed Scribe

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Structured real-world data to drive life science and outcomes research

Eliminate the delay between data and discovery

Clinical data without the cleanup

The ModMed RWD Solutions data advantage

Structured and de-identified

Specialty-specific and granular

Longitudinal and tokenized