Skip to content
#small language model Open access

2023 年度のレセプト 2,003,387,582 件をまぜて並べると、点数の多い上位 1 % が点数の 0.4068 以上を持つが、入院・入院外・歯科・調剤の中だけで並べると上位 1 % は 0.0765〜0.2742 にとどまる——入院は件数の 0.0135 で点数の 0.3871 を持ち、入院外で 2 万点以上のレセプトは件数の 0.0081 で点数の 0.2537 を持つ [M076]

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

厚生労働省の医療給付実態調査(令和5年度)の、点数階級別のレセプトの件数と点数(第5表)と、病気の章ごとの点数階級別の表(第6表)で、医療費がどれだけ少数の請求に集まっているかを見た。 入院・入院外・歯科・調剤のレセプト 2,003,387,582 件をまぜて点数の多い順に並べると、上位 1 % が点数の少なくとも 0.4068 を持ち、上位 0.0168 で半分に届く(ジニ係数は少なくとも 0.7322・階級の中は平均で置いた下限)。ところが種類の中だけで並べると、上位 1 % が持つのは 0.0765〜0.2742 にとどまる。入院は件数の 0.0135 で点数の 0.3871 を持ち、まぜた上位 1 % の件数のうち 0.7992 が入院のレセプトである。入院外で 2 万点以上のレセプトは件数の 0.0081 で点数の 0.2537 を持ち、その点数の 0.5993 を新生物と腎尿路生殖器系の二つの章が持つ。分けるのは、種類をまぜて並べるか、種類の中で並べるかである。 本稿は公開の集計データの数の形を述べる構造的解釈であり、医学的な助言・診断・治療の推奨ではない。著者は医師ではない。個人の健康に関する判断は医師・医療機関に相談すること。査読を経ていないプレプリントである。新しい数学定理も新しい法則も主張しない。高額な診療や薬の良し悪し、医療費の高い安い、制度の評価には立ち入らない。点数の多いレセプトの患者を責めるものではない。件数はレセプトの件数で人の数ではない。集まり方は下限である。都道府県・制度・医療機関は比べない。一人の見通しではない。出典:医療給付実態調査(厚生労働省・e-Stat)を加工して作成。 作成にあたって:本稿の着想と内容は、著者自身の考察に基づくものです。文章の構成整理や英訳、数式の確認には AI(大規模言語モデル)の助力を得ました。最終的な内容の解釈や誤りがあれば、それらはすべて著者の責に帰します。お気づきの点があれば、ご教示いただければ幸いです。 ----- Using the Ministry of Health, Labour and Welfare's Survey of Medical Care Benefits (FY2023) — claim counts and points by point bracket (Table 5) and by disease chapter and point bracket (Table 6) — this paper looked at how far medical costs are concentrated in a small number of claims. Mixing the 2,003,387,582 inpatient, outpatient, dental, and pharmacy claims and ranking them by points, the top 1% holds at least 0.4068 of all points and the top 0.0168 reaches half (Gini at least 0.7322; lower bounds with bracket means). Ranked within each type alone, the top 1% holds only 0.0765–0.2742. Inpatient claims are 0.0135 of claims and 0.3871 of points, and 0.7992 of the top 1% of mixed claims are inpatient claims. Outpatient claims of 20,000 points or more are 0.0081 of claims and 0.2537 of points, and neoplasms and the genitourinary system together hold 0.5993 of those points. What separates them is whether the types are mixed into one ranking or ranked within each type. This paper is a structural interpretation of the shape of numbers in public aggregate data; it is not medical advice, not a diagnosis, and not a treatment recommendation. The author is not a physician. Decisions about individual health should be discussed with a physician or a medical institution. This is a preprint that has not been peer reviewed. No new mathematical theorem and no new law are claimed. It does not judge expensive care or drugs, whether medical costs are high or low, or the system. It does not blame patients whose claims carry many points. Counts are claims, not people. The concentration is a lower bound. Prefectures, insurance schemes, and medical institutions are not compared. It is not an outlook for any one person. Source: processed from the Survey of Medical Care Benefits (Ministry of Health, Labour and Welfare, e-Stat). On the making of this work: The ideas and content of this work stem from the author's own considerations. Assistance from an AI (a large language model) was used for structuring, English translation, and checking the algebra. Any remaining errors or misinterpretations are solely the author's. Feedback and corrections are sincerely appreciated.

View source

Similar papers

#small language model Dataset Open access Oct 2026

Socratic guiding questions in synthetic arithmetic data: matched LoRA runs (revision v2)

Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...

O'Grady, Jake, Gürhan, Asena Isik, Chee, Fong Ting et al. · 465 citations
#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.