Fachkräfte für medizinische Dokumentation
GesundheitswesenKI-Exposition
- Datenquelle: BLSVeröffentlicht: '26.08
Sehr hoch· relativ
NiedrigVier relative BänderSehr hochWert auf Berufsgruppenebene
Skala, Bezugsmenge und Quelle
Vier relative Bänder (Niedrig / Mittel / Hoch / Sehr hoch)
831 detaillierte Berufe in der BLS-Beschäftigungsprojektionstabelle. Der Wert wird je Code der National Employment Matrix (NEM) vergeben, daher erhalten Berufe mit demselben NEM-Code dasselbe Band
- Datenquelle: AnthropicVeröffentlicht: '26.03
0.667
0.000Spannweite der hier geführten Werte0.745Skala, Bezugsmenge und Quelle
Index der beobachteten Exposition, 0–1 wie veröffentlicht
Auf O*NET-Aufgaben abgebildet
- Datenquelle: ILOVeröffentlicht: '25
0.52
0.09Spannweite der hier geführten Werte0.70Wert auf Berufsgruppenebene
Skala, Bezugsmenge und Quelle
Index der Exposition gegenüber generativer KI, 0–1 wie veröffentlicht
ISCO-08-Berufsgattung — alle Berufe mit demselben Code erhalten diesen Wert
Von dieser Website berechnet, nicht von der IAO veröffentlicht: Von den 1.012 Berufen, die diese Website mit dem IAO-Datensatz verknüpft, erreichen oder übertreffen 15% diesen Wert.
Welche Art von Wert diese Quelle veröffentlicht
Die Kategorie von BLS ist ein relativer Rang und kein absolutes Niveau, und sie ist keine eigene Messung: Sie fasst die Perzentilränge des Berufs aus mehreren veröffentlichten Studien in vier Bänder zusammen. Sie ist weder eine Beschäftigungs- oder Lohnprognose noch eine Einführungswahrscheinlichkeit und unterscheidet nicht zwischen Automatisierung und Augmentierung.
Task-level exposure
Nur exponierte Aufgaben| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Compile and maintain patients' medical records to document condition and treatment and to provide data for research or cost control and care improvement efforts.29-2071 | 0.012224.8 | 0.011510.3 |
Review records for completeness, accuracy, and compliance with regulations.29-2071 | 0.007615.5 | 0.018616.7 |
Transcribe medical reports.29-2071 | 0.007415.1 | 0.00918.1 |
Process and prepare business or government forms.29-2071 | 0.006513.2 | 0.00655.8 |
Identify, compile, abstract, and code patient data, using standard classification systems.29-2071 | 0.006012.2 | 0.043939.3 |
Retrieve patient medical records for physicians, technicians, or other medical personnel.29-2071 | 0.005411.0 | 0.012311.0 |
Prepare statistical reports, narrative reports, or graphic presentations of information, such as tumor registry data for use by hospital staff, researchers, or other users.29-2071 | 0.00224.5 | 0.00252.2 |
Plan, develop, maintain, or operate a variety of health record indexes or storage and retrieval systems to collect, classify, store, or analyze information.29-2071 | 0.00183.7 | 0.00433.8 |
Enter data, such as demographic characteristics, history and extent of disease, diagnostic procedures, or treatment into computer. | —0 | 0.00302.7 |
| Not observed on any surface — 11 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Protect the security of medical records to ensure that confidentiality is maintained. | —0 | —0 |
Process patient admission or discharge documents. | —0 | —0 |
Release information to persons or agencies according to regulations. | —0 | —0 |
Manage the department or supervise clerical workers, directing or controlling activities of personnel in the medical records department. | —0 | —0 |
Resolve or clarify codes or diagnoses with conflicting, missing, or unclear information by consulting with doctors or others or by participating in the coding team's regular meetings. | —0 | —0 |
Train medical records staff. | —0 | —0 |
Assign the patient to diagnosis-related groups (DRGs), using appropriate computer software. | —0 | —0 |
Post medical insurance billings. | —0 | —0 |
Consult classification manuals to locate information about disease processes. | —0 | —0 |
Compile medical care and census data for statistical reports on diseases treated, surgery performed, or use of hospital beds. | —0 | —0 |
Develop in-service educational materials. | —0 | —0 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Assign the patient to diagnosis-related groups (DRGs), using appropriate computer software.O*NET Task ID 22877 | 1.0 |
Enter data, such as demographic characteristics, history and extent of disease, diagnostic procedures, or treatment into computer.O*NET Task ID 22880 | 1.0 |
Process and prepare business or government forms.O*NET Task ID 22884 | 1.0 |
Transcribe medical reports.O*NET Task ID 22893 | 1.0 |
Compile and maintain patients' medical records to document condition and treatment and to provide data for research or cost control and care improvement efforts.O*NET Task ID 22878 | 0.5 |
Consult classification manuals to locate information about disease processes.O*NET Task ID 22879 | 0.5 |
Identify, compile, abstract, and code patient data, using standard classification systems.O*NET Task ID 22881 | 0.5 |
Maintain or operate a variety of health record indexes or storage and retrieval systems to collect, classify, store, or analyze information.O*NET Task ID 22882 | 0.5 |
Post medical insurance billings.O*NET Task ID 22883 | 0.5 |
Process patient admission or discharge documents.O*NET Task ID 22885 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page
Occupation information
Aktuelle Veränderungen mit Bezug zu diesem Beruf
Apr. 2026: ATE 0.36 by 2026 in SF Bay Tier 1 — the earliest crossover within healthcare support category. By 2030, 57.9% of healthcare support occupations in Tier 1 cross moderate-risk threshold.
[Quelle: arXiv 2604.00186 (Gupta & Kumar, 2026)]März 2026: Published blog post: highest-risk healthcare support role. Automation risk 62%, medical coding 70% automated.
[Quelle: AI Changing Work Blog]