Machine Learning Approaches for Early DRG Classification and Resource Allocation
Abstract
Recent research has highlighted the need for upstream planning in healthcare service delivery systems, patient scheduling, and resource allocation in the hospital inpatient setting. This study examines the value of upstream planning within hospital-wide resource allocation decisions based on machine learning (ML) and mixed-integer programming (MIP), focusing on prediction of diagnosis-related groups (DRGs) and the use of these predictions for allocating scarce hospital resources. DRGs are a payment scheme employed at patients’ discharge, where the DRG and length of stay determine the revenue that the hospital obtains. We show that early and accurate DRG classification using ML methods, incorporated into an MIP-based resource allocation model, can increase the hospital’s contribution margin, the number of admitted patients, and the utilization of resources such as operating rooms and beds. We test these methods on hospital data containing more than 16,000 inpatient records and demonstrate improved DRG classification accuracy as compared to the hospital’s current approach. The largest improvements were observed at and before admission, when information such as procedures and diagnoses is typically incomplete, but performance was improved even after a substantial portion of the patient’s length of stay, and under multiple scenarios making different assumptions about the available information. Using the improved DRG predictions within our resource allocation model improves contribution margin by 2.9% and the utilization of scarce resources such as operating rooms and beds from 66.3% to 67.3% and from 70.7% to 71.7%, respectively. This enables 9.0% more nonurgent elective patients to be admitted as compared to the baseline.
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Previous studies have showcased advancements in DRGs classification accuracy through various machine-learning algorithms and deep neural networks.
“Previous studies have showcased advancements in DRGs classification accuracy through various machine-learning algorithms10 and deep neural networks11.”
From DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients (2024) · cites this paper as [10]
These studies focused on using structured data as input variables instead of clinical text.
“These studies focused on using structured data as input variables instead of clinical text.”
From DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients (2024) · cites this paper as [10]
Previous studies have underscored the effectiveness of employing diverse machine learning algorithms and deep neural networks for DRG prediction within healthcare systems outside the United States.
“Previous studies have underscored the effectiveness of employing diverse machine learning algorithms and deep neural networks for DRG prediction within healthcare systems outside the United States10,11.”
From DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients (2024) · cites this paper as [10]
The prior study utilized derived patient-related data, including coded diagnosis and approximated DRG results, and evaluated the model with hospital-specific information on resources.
“However, the study utilized derived patient-related data, including coded diagnosis and approximated DRG results, and evaluated the model with hospital-specific information on resources.”
From Early prediction of diagnostic-related groups and estimation of hospital cost by processing clinical notes (2021) · cites this paper as [7]
A prior study applied statistical machine learning for early DRG prediction and resource allocation at a 350-bed hospital in Germany, examined a range of feature selection and classification techniques in different prediction settings, and showed their DRG predictions were relevant for optimizing scarce hospital resources allocation and the improvement of contribution margin for the hospital.
“A prior study7 applied statistical machine learning for early DRG prediction and resource allocation at a 350-bed hospital in Germany, examined a range of feature selection and classification techniques in different prediction settings, and showed their DRG predictions were relevant for optimizing scarce hospital resources allocation and the improvement of contribution margin for the hospital.”
From Early prediction of diagnostic-related groups and estimation of hospital cost by processing clinical notes (2021) · cites this paper as [7]
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- Authors
- Gartner, D., Kolisch, R., Neill, D. B., & Padman, R.
- Affiliations
- The H. John Heinz III College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213; and TUM School of Management, Technische Universität München, 80333 Munich, Germany
- North Carolina State University (US)
- Carnegie Mellon University (US)
- Venue
- INFORMS Journal on Computing
- Published
- 2015-11
- Volume
- 27
- Issue
- 4
- Pages
- 718-734
- ISSN
- 0899-1499 (ISSN-L)
- 1091-9856
- 1526-5528
- OpenAlex
- W2178355895
- Cited by
- 58
- References
- 43
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 4 · 2023: 2 · 2022: 6 · 2021: 9 · 2020: 11 · 2019: 5 · 2018: 2 · 2017: 3 · 2016: 1
- FWCI
- 3.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- INFORMS journal on computing (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Topics
- Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- diagnosis-related groups, hospital resource allocation, mixed integer programming, machine learning prediction, hospitalization, operating room utilization, hospital bed utilization, contribution margin, length of hospital stay, patient scheduling
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