Cargando…

Modeling Coding Intensity of Procedures in a U.S. Population-Based Hip/Knee Arthroplasty Inpatient Cohort Adjusting for Patient- and Facility-Level Characteristics

Variations in procedure coding intensity, defined as excess coding of procedures versus industry (instead of clinical) standards, can result in differentials in quality of care for patients and have additional implications for facilities and payors. The literature regarding coding intensity of proce...

Descripción completa

Detalles Bibliográficos
Autores principales: Rios, Nancy G., Oldiges, Paige E., Lizano, Marcela S., Doucet Wadford, Danielle S., Quick, David L., Martin, John, Korvink, Michael, Gunn, Laura H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332158/
https://www.ncbi.nlm.nih.gov/pubmed/35893190
http://dx.doi.org/10.3390/healthcare10081368
_version_ 1784758577809850368
author Rios, Nancy G.
Oldiges, Paige E.
Lizano, Marcela S.
Doucet Wadford, Danielle S.
Quick, David L.
Martin, John
Korvink, Michael
Gunn, Laura H.
author_facet Rios, Nancy G.
Oldiges, Paige E.
Lizano, Marcela S.
Doucet Wadford, Danielle S.
Quick, David L.
Martin, John
Korvink, Michael
Gunn, Laura H.
author_sort Rios, Nancy G.
collection PubMed
description Variations in procedure coding intensity, defined as excess coding of procedures versus industry (instead of clinical) standards, can result in differentials in quality of care for patients and have additional implications for facilities and payors. The literature regarding coding intensity of procedures is limited, with a need for risk-adjusted methods that help identify over- and under-coding using commonly available data, such as administrative claims. Risk-adjusted metrics are needed for quality control and enhancement. We propose a two-step approach to risk adjustment, using a zero-inflated Poisson model, applied to a hip-knee arthroplasty cohort discharged during 2019 (n = 313,477) for patient-level risk adjustment, and a potential additional layer for adjustment based on facility-level characteristics, when desired. A 21.41% reduction in root-mean-square error was achieved upon risk adjustment for patient-level factors alone. Furthermore, we identified facilities that over- and under-code versus industry coding expectations, adjusting for both patient-level and facility-level factors. Excess coding intensity was found to vary across multiple levels: (1) geographically across U.S. Census regional divisions; (2) temporally with marked seasonal components; (3) by facility, with some facilities largely departing from industry standards, even after adjusting for both patient- and facility-level characteristics. Our proposed method is simple to implement, generalizable, it can be used across cohorts with different sets of information available, and it is not limited by the accessibility and sparsity of electronic health records. By identifying potential over- and under-coding of procedures, quality control personnel can explore and assess internal needs for enhancements in their health delivery services and monitor subsequent quality improvements.
format Online
Article
Text
id pubmed-9332158
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-93321582022-07-29 Modeling Coding Intensity of Procedures in a U.S. Population-Based Hip/Knee Arthroplasty Inpatient Cohort Adjusting for Patient- and Facility-Level Characteristics Rios, Nancy G. Oldiges, Paige E. Lizano, Marcela S. Doucet Wadford, Danielle S. Quick, David L. Martin, John Korvink, Michael Gunn, Laura H. Healthcare (Basel) Article Variations in procedure coding intensity, defined as excess coding of procedures versus industry (instead of clinical) standards, can result in differentials in quality of care for patients and have additional implications for facilities and payors. The literature regarding coding intensity of procedures is limited, with a need for risk-adjusted methods that help identify over- and under-coding using commonly available data, such as administrative claims. Risk-adjusted metrics are needed for quality control and enhancement. We propose a two-step approach to risk adjustment, using a zero-inflated Poisson model, applied to a hip-knee arthroplasty cohort discharged during 2019 (n = 313,477) for patient-level risk adjustment, and a potential additional layer for adjustment based on facility-level characteristics, when desired. A 21.41% reduction in root-mean-square error was achieved upon risk adjustment for patient-level factors alone. Furthermore, we identified facilities that over- and under-code versus industry coding expectations, adjusting for both patient-level and facility-level factors. Excess coding intensity was found to vary across multiple levels: (1) geographically across U.S. Census regional divisions; (2) temporally with marked seasonal components; (3) by facility, with some facilities largely departing from industry standards, even after adjusting for both patient- and facility-level characteristics. Our proposed method is simple to implement, generalizable, it can be used across cohorts with different sets of information available, and it is not limited by the accessibility and sparsity of electronic health records. By identifying potential over- and under-coding of procedures, quality control personnel can explore and assess internal needs for enhancements in their health delivery services and monitor subsequent quality improvements. MDPI 2022-07-23 /pmc/articles/PMC9332158/ /pubmed/35893190 http://dx.doi.org/10.3390/healthcare10081368 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rios, Nancy G.
Oldiges, Paige E.
Lizano, Marcela S.
Doucet Wadford, Danielle S.
Quick, David L.
Martin, John
Korvink, Michael
Gunn, Laura H.
Modeling Coding Intensity of Procedures in a U.S. Population-Based Hip/Knee Arthroplasty Inpatient Cohort Adjusting for Patient- and Facility-Level Characteristics
title Modeling Coding Intensity of Procedures in a U.S. Population-Based Hip/Knee Arthroplasty Inpatient Cohort Adjusting for Patient- and Facility-Level Characteristics
title_full Modeling Coding Intensity of Procedures in a U.S. Population-Based Hip/Knee Arthroplasty Inpatient Cohort Adjusting for Patient- and Facility-Level Characteristics
title_fullStr Modeling Coding Intensity of Procedures in a U.S. Population-Based Hip/Knee Arthroplasty Inpatient Cohort Adjusting for Patient- and Facility-Level Characteristics
title_full_unstemmed Modeling Coding Intensity of Procedures in a U.S. Population-Based Hip/Knee Arthroplasty Inpatient Cohort Adjusting for Patient- and Facility-Level Characteristics
title_short Modeling Coding Intensity of Procedures in a U.S. Population-Based Hip/Knee Arthroplasty Inpatient Cohort Adjusting for Patient- and Facility-Level Characteristics
title_sort modeling coding intensity of procedures in a u.s. population-based hip/knee arthroplasty inpatient cohort adjusting for patient- and facility-level characteristics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332158/
https://www.ncbi.nlm.nih.gov/pubmed/35893190
http://dx.doi.org/10.3390/healthcare10081368
work_keys_str_mv AT riosnancyg modelingcodingintensityofproceduresinauspopulationbasedhipkneearthroplastyinpatientcohortadjustingforpatientandfacilitylevelcharacteristics
AT oldigespaigee modelingcodingintensityofproceduresinauspopulationbasedhipkneearthroplastyinpatientcohortadjustingforpatientandfacilitylevelcharacteristics
AT lizanomarcelas modelingcodingintensityofproceduresinauspopulationbasedhipkneearthroplastyinpatientcohortadjustingforpatientandfacilitylevelcharacteristics
AT doucetwadforddanielles modelingcodingintensityofproceduresinauspopulationbasedhipkneearthroplastyinpatientcohortadjustingforpatientandfacilitylevelcharacteristics
AT quickdavidl modelingcodingintensityofproceduresinauspopulationbasedhipkneearthroplastyinpatientcohortadjustingforpatientandfacilitylevelcharacteristics
AT martinjohn modelingcodingintensityofproceduresinauspopulationbasedhipkneearthroplastyinpatientcohortadjustingforpatientandfacilitylevelcharacteristics
AT korvinkmichael modelingcodingintensityofproceduresinauspopulationbasedhipkneearthroplastyinpatientcohortadjustingforpatientandfacilitylevelcharacteristics
AT gunnlaurah modelingcodingintensityofproceduresinauspopulationbasedhipkneearthroplastyinpatientcohortadjustingforpatientandfacilitylevelcharacteristics