Is it being used the way it was approved

Claims tell you what was billed. Why the drug was chosen, and what happened before it was stopped, exists only in the clinician's own sentences.

Why it stays invisible

Real-world evidence needs two things: how many patients, and what happened to them. Claims data gives you the first and not the second.

Stopped for a side effect, tried another drug that did nothing, endoscopic grade was such-and-such — all of it sits in free text a clinician wrote. It does not reduce to a code, so it disappears from standardized datasets.

What we have actually found

Every number below came out of a live analysis session, and the session it came from is noted with it.

  • Fexuclue prescribed beyond the approved eight weeks: 47 instances across 21 patients. The longest ran 185 days.

    Session 87abbbbc
  • Of 89 chart patients on the same drug, 85 were absent from claims.

    Session e02c4876
  • Side-effect narratives were found at the term as written: 108 of 108 recall with prefix matching, zero false positives.

    SPEC §7.3 search accuracy measurement

What you receive

One question, one document. Nobody rewrites it afterwards.

  • The question as you asked it
  • The patient count and the record excerpts behind it
  • What was matched, on which spelling, against which denominator
  • What this number cannot say — small cells, false positives removed
  • The questions we could not answer

Where the records were issued

This is not data obtained through a partnership. Each record was issued to the patient, by the hospital, at the patient's own request.

  • 서울아산병원
  • 서울대학교병원
  • 삼성서울병원
  • 연세대학교 세브란스병원
  • 가톨릭대학교 서울성모병원
  • 분당서울대학교병원
  • 강남세브란스병원
  • 강북삼성병원
  • 아주대학교병원
  • 고려대학교 안암병원
  • 고려대학교 구로병원
  • 이화여자대학교 목동병원
  • 경희대학교병원
  • 한양대학교병원
  • 영남대학교병원
  • 한림대학교 성심병원
  • 충남대학교병원
  • 계명대학교 동산병원
  • 부산대학교병원
  • 가천대학교 길병원
  • 중앙대학교병원
  • 인하대학교병원
  • 국립암센터
  • 울산대학교병원
  • 경북대학교병원

The 25 most requested. Records have come from 127 hospitals in all.

Common questions

Can you analyze data that is not coded?
What gets lost in the coding is the part we sell. We leave the text alone and search at the level of how it was written.
Can we get patient-level data?
No. What leaves is the count, a few supporting excerpts, and how we counted.
Can we try it on our own product first?
Ask in the meeting and we run it there. That session becomes the document.
Can the result support scientific or regulatory work?
The output states the counting rule, the denominator and the limits. What you do with it is your call — we do not file or handle review on your behalf.