Associations Between Lean IT Management and Financial Performance in US Hospitals
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To understand the relationship between Lean implementation in information technology (IT) departments and hospital performance, particularly with respect to operational and financial outcomes. METHODS: Primary data were sourced from 1222 hospitals that responded to the National Survey of Lean (NSL)/Transformational Performance Improvement, which was fielded to 4500 general medical-surgical hospitals across the United States. Secondary sources included hospital performance data from the Agency for Healthcare Research and Quality (AHRQ) and the Centers for Medicare & Medicaid Services (CMS). We performed 2 sets of multivariable regressions using data gathered from US hospitals, linked to AHRQ and CMS performance outcomes. We examined 10 different outcomes measuring financial performance, quality of care, and patient experience, and their associations with Lean adoption within hospital IT departments. We then focused only on those hospitals that adopted Lean in IT to identify specific practices associated with performance. RESULTS: Controlling for other factors, adoption of Lean IT management was associated with lower length of stay ( b = -0.098, P = .018) and inpatient expense per discharge ( b = -0.112, P = .090). Specifically, use of visual management tools (eg, A3 storyboards, status sheets) was associated with lower adjusted inpatient expense per discharge ( b = -0.176, P = .034) and higher earnings before interest, taxes, depreciation, and amortization margin ( b = 0.124, P = .042). Such tools were also associated with hospital participation in bundled payment programs (odds ratio = 2.326; P = .046; 95% confidence interval, 0.979-5.527) and percentage of net revenue paid on a shared risk basis ( b = 0.188, P = .031). CONCLUSIONS: Lean IT management was associated with positive financial performance, particularly with hospital participation in value-based payment. More detailed study is needed to understand other influential factors and types of work processes, activities, or mechanisms by which high-functioning IT can contribute to financial outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".