Machine-learning-based prediction models for high-need high-cost patients using nationwide clinical and claims data
Open accessAbstract
High-need, high-cost (HNHC) patients—usually defined as those who account for the top 5% of annual healthcare costs—use as much as half of the total healthcare costs. Accurately predicting future HNHC patients and designing targeted interventions for them has the potential to effectively control rapidly growing healthcare expenditures. To achieve this goal, we used a nationally representative random sample of the working-age population who underwent a screening program in Japan in 2013–2016, and developed five machine-learning-based prediction models for HNHC patients in the subsequent year. Predictors include demographics, blood pressure, laboratory tests (e.g., HbA1c, LDL-C, and AST), survey responses (e.g., smoking status, medications, and past medical history), and annual healthcare cost in the prior year. Our prediction models for HNHC patients combining clinical data from the national screening program with claims data showed a c-statistics of 0.84 (95%CI, 0.83–0.86), and overperformed traditional prediction models relying only on claims data.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Studies have shown the promise of machine learning in predicting events like unexpected 30-day readmission and identifying high-need high-cost patients that could preempt avoidable health expenses.
“Also, studies have shown the promise of machine learning in predicting events like unexpected 30-day readmission8,23 and identifying high-need high-cost patients24 that could preempt avoidable health expenses.”
From Early prediction of diagnostic-related groups and estimation of hospital cost by processing clinical notes (2021) · cites this paper as [24]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 39 · Cited by 66
Research works printed but not linked: 1
- [2]Mitchell, E. M. Concentration of Health Expenditures in the U.S. Civilian Noninstitutionalized Population, 2014. https://meps.ahrq.gov/data_files/publications/st497/stat497.shtml (2016). 28422468
- Authors
- Osawa, I., Goto, T., Yamamoto, Y., & Tsugawa, Y.
- Affiliations
- University of Tokyo Hospital (JP)
- The University of Tokyo (JP)
- University of California, Los Angeles (US)
- Corresponding author
- Goto, Tadahiro
- Venue
- npj Digital Medicine
- Published
- 2020-11-11
- Volume
- 3
- Issue
- 1
- Pages
- 148-148
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC7658979
- OpenAlex
- W3100369282
- Cited by
- 66
- References
- 39
- Citations by year
- 2026: 13 · 2025: 16 · 2024: 7 · 2023: 5 · 2022: 17 · 2021: 8
- FWCI
- 2.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Epidemiology › Chronic Disease Management Strategies
- Keywords
- high-need high-cost patients, healthcare cost prediction, machine learning prediction models, health care expenditure, claims data, clinical data integration, targeted interventions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
