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A Data-Driven Approach to Defining Risk-Adjusted Coding Specificity Metrics for a Large U.S. Dementia Patient Cohort
Richardson Kaylla, Penumaka Sankari, Smoot Jaleesa, Panaganti Mansi Reddy, Chinta Indu Radha, Guduri Devi Priya, Tiyyagura Sucharitha Reddy, Martin John et al.. Healthcare (Basel, Switzerland), 2024-05-24
Topic: Risk, prevention and care in population studies
Medical coding impacts patient care quality, payor reimbursement, and system reliability through the precision of patient information documentation. Inadequate coding specificity can have significant consequences at administrative and patient levels. Models to identify and/or enhance coding specificity practices are needed. Clinical records are not always available, complete, or homogeneous, and clinically driven metrics to assess medical practices are not logistically feasible at the population level, particularly in non-centralized healthcare delivery systems and/or for those who only have access to claims data. Data-driven approaches that incorporate all available information are needed to explore coding specificity practices. Using N = 487,775 hospitalization records of individuals diagnosed with dementia and discharged in 2022 from a large all-payor administrative claims dataset, we fitted logistic regression models using patient and facility characteristics to explain the coding specificity of principal and secondary diagnoses of dementia. A two-step approach was produced to allow for the flexible clustering of patient-level outcomes. Model outcomes were then used within a Poisson binomial model to identify facilities that over- or under-specify dementia diagnoses against healthcare industry standards across hospitalizations. The results indicate that multiple factors are significantly associated with dementia coding specificity, especially for principal diagnoses of dementia (AUC = 0.727). The practical use of this novel risk-adjusted metric is demonstrated for a sample of facilities and geospatially via a U.S. map. This study's findings provide healthcare facilities with a benchmark for assessing coding specificity practices and developing quality enhancements to align with healthcare industry standards, ultimately contributing to better patient care and healthcare system reliability.
Key findings
- Inadequate coding specificity can have significant consequences at administrative and patient levels.
- The results indicate that multiple factors are significantly associated with dementia coding specificity, especially for principal diagnoses of dementia (AUC = 0.727).
- The practical use of this novel risk-adjusted metric is demonstrated for a sample of facilities and geospatially via a U.S. map.
Why it matters
This study used administrative claims data to build a metric for how specifically dementia diagnoses are coded at hospitals, not to test any treatment or prevention. It does not show anything about curing or preventing Alzheimer's disease; it is about billing and documentation accuracy.
Study details
- Participants487,775
- Effect sizeAUC = 0.727
- Evidence strengthLow
How this study scored 43/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Healthcare (Basel, Switzerland)) |
| Journal's citation record | 1 / 4 | Healthcare: journal h-index 120 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 4 / 4 | 487,775 participants |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-17) |
| Citations per year | 0 / 5 | 0.0 citations a year over 4 year(s) since 2024 |
| Impact for its field and year | 0 / 6 | Cited 0.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 26 other studies in this library |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Laura H. Gunn: h-index 21 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Imperial College London: institution h-index 1,741 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38786394) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 43 / 100 | — |
What each of these 20 components measures, and what this score is not →
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APA 7th
Kaylla, R., Sankari, P., Jaleesa, S., Reddy, P. M., Radha, C. I., Priya, G. D., et al. (2024). A Data-Driven Approach to Defining Risk-Adjusted Coding Specificity Metrics for a Large U.S. Dementia Patient Cohort. Healthcare (Basel, Switzerland). https://doi.org/10.3390/healthcare12100983
Vancouver
Kaylla R, Sankari P, Jaleesa S, Reddy PM, Radha CI, Priya GD, et al. A Data-Driven Approach to Defining Risk-Adjusted Coding Specificity Metrics for a Large U.S. Dementia Patient Cohort. Healthcare (Basel, Switzerland). 2024. doi:10.3390/healthcare12100983. PMID: 38786394.
BibTeX
@article{kaylla2024-38786394,
author = {Kaylla, Richardson and Sankari, Penumaka and Jaleesa, Smoot and Reddy, Panaganti Mansi and Radha, Chinta Indu and Priya, Guduri Devi and Reddy, Tiyyagura Sucharitha and John, Martin and others},
title = {{A Data-Driven Approach to Defining Risk-Adjusted Coding Specificity Metrics for a Large U.S. Dementia Patient Cohort}},
journal = {Healthcare (Basel, Switzerland)},
year = {2024},
doi = {10.3390/healthcare12100983},
pmid = {38786394},
url = {https://doi.org/10.3390/healthcare12100983}
}
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