ArticleNPJ digital medicine2021
Early prediction of diagnostic-related groups and estimation of hospital cost by processing clinical notes.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed.
- Artificial intelligence and medical futility.BMC medical informatics and decision making · 2026Review
- The Impact of DRG Review Teams on Obstetric Disease Management and Cost Control: A Variance Analysis Approach.The journal of obstetrics and gynaecology research · 2026Article
- A data and knowledge cross-level fusion-driven learning framework for detecting missing diagnosis.NPJ digital medicine · 2026Article
- Artificial intelligence-assisted medical coding and DRG management: current applications, challenges, and future perspectives.Frontiers in medicine · 2026Review
- Financial Heterogeneity During Early Diagnosis-Related Group Payment Reform: A Two-Year Hospital-Based Study.Risk management and healthcare policy · 2026Article
- Using generative artificial intelligence in clinical practice: a narrative review and proposed agenda for implementation.The Medical journal of Australia · 2025Review
- A tree-structured multiobjective optimization framework for constructing diagnosis-related groups.NPJ digital medicine · 2025Article
- Can LLMs effectively assist medical coding? Evaluating GPT performance on DRG and targeted clinical tasks.BMC medical informatics and decision making · 2025Article
- Impact of a new case-based payment scheme on volume distribution across public hospitals in Zhejiang, China: does 'Same disease, same price' matter.International journal for equity in health · 2025Article
- Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models.Frontiers in artificial intelligence · 2025Review
- Comparative models on low multiplier DRG classification for advanced lung cancer.Frontiers in public health · 2025Article
- Optimising the paradigms of human AI collaborative clinical coding.NPJ digital medicine · 2024Article
- ARDSFlag: an NLP/machine learning algorithm to visualize and detect high-probability ARDS admissions independent of provider recognition and billing codes.BMC medical informatics and decision making · 2024Article
- Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting.BMC medical informatics and decision making · 2024Article
- DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients.NPJ digital medicine · 2024Article
- Impact of Medical-Pharmaceutical Separation Reform on Hospitalization Expenditure in Tertiary Public Hospitals: Difference-in-Difference Analysis Based on Panel Data from Beijing.Risk management and healthcare policy · 2024Article
- Evaluation of inpatient services of tertiary comprehensive hospitals based on DRG payment.Frontiers in public health · 2024Article
- Classification of neurologic outcomes from medical notes using natural language processing.Expert systems with applications · 2023Article
- Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study.Journal of medical Internet research · 2023Article
- Challenges of implementing diagnostic-related groups and healthcare promotion in Iran: A strategic applied research.Health science reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
As healthcare providers receive fixed amounts of reimbursement for given services under DRG (Diagnosis-Related Groups) payment, DRG codes are valuable for cost monitoring and resource allocation. However, coding is typically performed retrospectively post-discharge. We seek to predict DRGs and DRG-based case mix index (CMI) at early inpatient admission using routine clinical text to estimate hospital cost in an acute setting. We examined a deep learning-based natural language processing (NLP) model to automatically predict per-episode DRGs and corresponding cost-reflecting weights on two cohorts (paid under Medicare Severity (MS) DRG or All Patient Refined (APR) DRG), without human coding efforts. It achieved macro-averaged area under the receiver operating characteristic curve (AUC) scores of 0·871 (SD 0·011) on MS-DRG and 0·884 (0·003) on APR-DRG in fivefold cross-validation experiments on the first day of ICU admission. When extended to simulated patient populations to estimate average cost-reflecting weights, the model increased its accuracy over time and obtained absolute CMI error of 2·40 (1·07%) and 12·79% (2·31%), respectively on the first day. As the model could adapt to variations in admission time, cohort size, and requires no extra manual coding efforts, it shows potential to help estimating costs for active patients to support better operational decision-making in hospitals.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.