A framework for the estimation and reduction of hospital readmission penalties using predictive analytics

Background Recent US legislation imposes financial penalties on hospitals with excessive patient readmissions. Predictive analytics for hospital readmissions have seen an increase in research due to the passage of this legislation. However, many current systems ignore the formulas used by the Center...
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Autor*in:	Baechle, Christopher [verfasserIn] Agarwal, Ankur

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Scientific algorithms of big data Big data applications Big data tools Natural language processing Naïve Bayes classification

Anmerkung:	© The Author(s) 2017

Übergeordnetes Werk:	Enthalten in: Journal of Big Data - Berlin : SpringerOpen, 2014, 4(2017), 1 vom: 02. Nov.
Übergeordnetes Werk:	volume:4 ; year:2017 ; number:1 ; day:02 ; month:11

Links:	Volltext

DOI / URN:	10.1186/s40537-017-0098-z

Katalog-ID:	SPR036631094

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520			\|a Background Recent US legislation imposes financial penalties on hospitals with excessive patient readmissions. Predictive analytics for hospital readmissions have seen an increase in research due to the passage of this legislation. However, many current systems ignore the formulas used by the Centers for Medicare and Medicaid Services for imposing penalties. This research expands upon current methodologies and directly incorporates federal penalization formulas when selecting patients for which to dedicate resources. Methods Hospital discharge summaries are structured using clinical natural language processing techniques. Naïve Bayes classifiers are then used to assign a probability of readmission to each patient. Hospital Readmission Reductions Program formulas and probability of readmission are applied using four readmission scenarios to estimate the cost of readmission. The highest cost patients are identified and readmission mitigation efforts are attempted. Results The results show that the average penalty savings over currently employed binary classification to be 51.93%. Binary classification is also shown to select more patients than necessary for readmission intervention. Additionally, intervening in only high-risk patients saved an average of 90.07% compared to providing all patients with costly aftercare. Conclusion Focusing resources toward the potentially most expensive patients offers considerably better results than unfocused efforts. Utilizing direct calculation to estimate readmission costs has shown to be a more efficient use of resources than current readmission reduction methods.
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spelling	10.1186/s40537-017-0098-z doi (DE-627)SPR036631094 (SPR)s40537-017-0098-z-e DE-627 ger DE-627 rakwb eng Baechle, Christopher verfasserin (orcid)0000-0002-6018-066X aut A framework for the estimation and reduction of hospital readmission penalties using predictive analytics 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Background Recent US legislation imposes financial penalties on hospitals with excessive patient readmissions. Predictive analytics for hospital readmissions have seen an increase in research due to the passage of this legislation. However, many current systems ignore the formulas used by the Centers for Medicare and Medicaid Services for imposing penalties. This research expands upon current methodologies and directly incorporates federal penalization formulas when selecting patients for which to dedicate resources. Methods Hospital discharge summaries are structured using clinical natural language processing techniques. Naïve Bayes classifiers are then used to assign a probability of readmission to each patient. Hospital Readmission Reductions Program formulas and probability of readmission are applied using four readmission scenarios to estimate the cost of readmission. The highest cost patients are identified and readmission mitigation efforts are attempted. Results The results show that the average penalty savings over currently employed binary classification to be 51.93%. Binary classification is also shown to select more patients than necessary for readmission intervention. Additionally, intervening in only high-risk patients saved an average of 90.07% compared to providing all patients with costly aftercare. Conclusion Focusing resources toward the potentially most expensive patients offers considerably better results than unfocused efforts. Utilizing direct calculation to estimate readmission costs has shown to be a more efficient use of resources than current readmission reduction methods. Scientific algorithms of big data (dpeaa)DE-He213 Big data applications (dpeaa)DE-He213 Big data tools (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213 Naïve Bayes classification (dpeaa)DE-He213 Agarwal, Ankur aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 4(2017), 1 vom: 02. Nov. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:4 year:2017 number:1 day:02 month:11 https://dx.doi.org/10.1186/s40537-017-0098-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2017 1 02 11
allfields_unstemmed	10.1186/s40537-017-0098-z doi (DE-627)SPR036631094 (SPR)s40537-017-0098-z-e DE-627 ger DE-627 rakwb eng Baechle, Christopher verfasserin (orcid)0000-0002-6018-066X aut A framework for the estimation and reduction of hospital readmission penalties using predictive analytics 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Background Recent US legislation imposes financial penalties on hospitals with excessive patient readmissions. Predictive analytics for hospital readmissions have seen an increase in research due to the passage of this legislation. However, many current systems ignore the formulas used by the Centers for Medicare and Medicaid Services for imposing penalties. This research expands upon current methodologies and directly incorporates federal penalization formulas when selecting patients for which to dedicate resources. Methods Hospital discharge summaries are structured using clinical natural language processing techniques. Naïve Bayes classifiers are then used to assign a probability of readmission to each patient. Hospital Readmission Reductions Program formulas and probability of readmission are applied using four readmission scenarios to estimate the cost of readmission. The highest cost patients are identified and readmission mitigation efforts are attempted. Results The results show that the average penalty savings over currently employed binary classification to be 51.93%. Binary classification is also shown to select more patients than necessary for readmission intervention. Additionally, intervening in only high-risk patients saved an average of 90.07% compared to providing all patients with costly aftercare. Conclusion Focusing resources toward the potentially most expensive patients offers considerably better results than unfocused efforts. Utilizing direct calculation to estimate readmission costs has shown to be a more efficient use of resources than current readmission reduction methods. Scientific algorithms of big data (dpeaa)DE-He213 Big data applications (dpeaa)DE-He213 Big data tools (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213 Naïve Bayes classification (dpeaa)DE-He213 Agarwal, Ankur aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 4(2017), 1 vom: 02. Nov. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:4 year:2017 number:1 day:02 month:11 https://dx.doi.org/10.1186/s40537-017-0098-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2017 1 02 11
allfieldsGer	10.1186/s40537-017-0098-z doi (DE-627)SPR036631094 (SPR)s40537-017-0098-z-e DE-627 ger DE-627 rakwb eng Baechle, Christopher verfasserin (orcid)0000-0002-6018-066X aut A framework for the estimation and reduction of hospital readmission penalties using predictive analytics 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Background Recent US legislation imposes financial penalties on hospitals with excessive patient readmissions. Predictive analytics for hospital readmissions have seen an increase in research due to the passage of this legislation. However, many current systems ignore the formulas used by the Centers for Medicare and Medicaid Services for imposing penalties. This research expands upon current methodologies and directly incorporates federal penalization formulas when selecting patients for which to dedicate resources. Methods Hospital discharge summaries are structured using clinical natural language processing techniques. Naïve Bayes classifiers are then used to assign a probability of readmission to each patient. Hospital Readmission Reductions Program formulas and probability of readmission are applied using four readmission scenarios to estimate the cost of readmission. The highest cost patients are identified and readmission mitigation efforts are attempted. Results The results show that the average penalty savings over currently employed binary classification to be 51.93%. Binary classification is also shown to select more patients than necessary for readmission intervention. Additionally, intervening in only high-risk patients saved an average of 90.07% compared to providing all patients with costly aftercare. Conclusion Focusing resources toward the potentially most expensive patients offers considerably better results than unfocused efforts. Utilizing direct calculation to estimate readmission costs has shown to be a more efficient use of resources than current readmission reduction methods. Scientific algorithms of big data (dpeaa)DE-He213 Big data applications (dpeaa)DE-He213 Big data tools (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213 Naïve Bayes classification (dpeaa)DE-He213 Agarwal, Ankur aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 4(2017), 1 vom: 02. Nov. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:4 year:2017 number:1 day:02 month:11 https://dx.doi.org/10.1186/s40537-017-0098-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2017 1 02 11
allfieldsSound	10.1186/s40537-017-0098-z doi (DE-627)SPR036631094 (SPR)s40537-017-0098-z-e DE-627 ger DE-627 rakwb eng Baechle, Christopher verfasserin (orcid)0000-0002-6018-066X aut A framework for the estimation and reduction of hospital readmission penalties using predictive analytics 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Background Recent US legislation imposes financial penalties on hospitals with excessive patient readmissions. Predictive analytics for hospital readmissions have seen an increase in research due to the passage of this legislation. However, many current systems ignore the formulas used by the Centers for Medicare and Medicaid Services for imposing penalties. This research expands upon current methodologies and directly incorporates federal penalization formulas when selecting patients for which to dedicate resources. Methods Hospital discharge summaries are structured using clinical natural language processing techniques. Naïve Bayes classifiers are then used to assign a probability of readmission to each patient. Hospital Readmission Reductions Program formulas and probability of readmission are applied using four readmission scenarios to estimate the cost of readmission. The highest cost patients are identified and readmission mitigation efforts are attempted. Results The results show that the average penalty savings over currently employed binary classification to be 51.93%. Binary classification is also shown to select more patients than necessary for readmission intervention. Additionally, intervening in only high-risk patients saved an average of 90.07% compared to providing all patients with costly aftercare. Conclusion Focusing resources toward the potentially most expensive patients offers considerably better results than unfocused efforts. Utilizing direct calculation to estimate readmission costs has shown to be a more efficient use of resources than current readmission reduction methods. Scientific algorithms of big data (dpeaa)DE-He213 Big data applications (dpeaa)DE-He213 Big data tools (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213 Naïve Bayes classification (dpeaa)DE-He213 Agarwal, Ankur aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 4(2017), 1 vom: 02. Nov. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:4 year:2017 number:1 day:02 month:11 https://dx.doi.org/10.1186/s40537-017-0098-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2017 1 02 11
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Predictive analytics for hospital readmissions have seen an increase in research due to the passage of this legislation. However, many current systems ignore the formulas used by the Centers for Medicare and Medicaid Services for imposing penalties. This research expands upon current methodologies and directly incorporates federal penalization formulas when selecting patients for which to dedicate resources. Methods Hospital discharge summaries are structured using clinical natural language processing techniques. Naïve Bayes classifiers are then used to assign a probability of readmission to each patient. Hospital Readmission Reductions Program formulas and probability of readmission are applied using four readmission scenarios to estimate the cost of readmission. The highest cost patients are identified and readmission mitigation efforts are attempted. Results The results show that the average penalty savings over currently employed binary classification to be 51.93%. Binary classification is also shown to select more patients than necessary for readmission intervention. Additionally, intervening in only high-risk patients saved an average of 90.07% compared to providing all patients with costly aftercare. Conclusion Focusing resources toward the potentially most expensive patients offers considerably better results than unfocused efforts. 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author	Baechle, Christopher
spellingShingle	Baechle, Christopher misc Scientific algorithms of big data misc Big data applications misc Big data tools misc Natural language processing misc Naïve Bayes classification A framework for the estimation and reduction of hospital readmission penalties using predictive analytics
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topic_title	A framework for the estimation and reduction of hospital readmission penalties using predictive analytics Scientific algorithms of big data (dpeaa)DE-He213 Big data applications (dpeaa)DE-He213 Big data tools (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213 Naïve Bayes classification (dpeaa)DE-He213
topic	misc Scientific algorithms of big data misc Big data applications misc Big data tools misc Natural language processing misc Naïve Bayes classification
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title	A framework for the estimation and reduction of hospital readmission penalties using predictive analytics
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title_full	A framework for the estimation and reduction of hospital readmission penalties using predictive analytics
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title_sort	framework for the estimation and reduction of hospital readmission penalties using predictive analytics
title_auth	A framework for the estimation and reduction of hospital readmission penalties using predictive analytics
abstract	Background Recent US legislation imposes financial penalties on hospitals with excessive patient readmissions. Predictive analytics for hospital readmissions have seen an increase in research due to the passage of this legislation. However, many current systems ignore the formulas used by the Centers for Medicare and Medicaid Services for imposing penalties. This research expands upon current methodologies and directly incorporates federal penalization formulas when selecting patients for which to dedicate resources. Methods Hospital discharge summaries are structured using clinical natural language processing techniques. Naïve Bayes classifiers are then used to assign a probability of readmission to each patient. Hospital Readmission Reductions Program formulas and probability of readmission are applied using four readmission scenarios to estimate the cost of readmission. The highest cost patients are identified and readmission mitigation efforts are attempted. Results The results show that the average penalty savings over currently employed binary classification to be 51.93%. Binary classification is also shown to select more patients than necessary for readmission intervention. Additionally, intervening in only high-risk patients saved an average of 90.07% compared to providing all patients with costly aftercare. Conclusion Focusing resources toward the potentially most expensive patients offers considerably better results than unfocused efforts. Utilizing direct calculation to estimate readmission costs has shown to be a more efficient use of resources than current readmission reduction methods. © The Author(s) 2017
abstractGer	Background Recent US legislation imposes financial penalties on hospitals with excessive patient readmissions. Predictive analytics for hospital readmissions have seen an increase in research due to the passage of this legislation. However, many current systems ignore the formulas used by the Centers for Medicare and Medicaid Services for imposing penalties. This research expands upon current methodologies and directly incorporates federal penalization formulas when selecting patients for which to dedicate resources. Methods Hospital discharge summaries are structured using clinical natural language processing techniques. Naïve Bayes classifiers are then used to assign a probability of readmission to each patient. Hospital Readmission Reductions Program formulas and probability of readmission are applied using four readmission scenarios to estimate the cost of readmission. The highest cost patients are identified and readmission mitigation efforts are attempted. Results The results show that the average penalty savings over currently employed binary classification to be 51.93%. Binary classification is also shown to select more patients than necessary for readmission intervention. Additionally, intervening in only high-risk patients saved an average of 90.07% compared to providing all patients with costly aftercare. Conclusion Focusing resources toward the potentially most expensive patients offers considerably better results than unfocused efforts. Utilizing direct calculation to estimate readmission costs has shown to be a more efficient use of resources than current readmission reduction methods. © The Author(s) 2017
abstract_unstemmed	Background Recent US legislation imposes financial penalties on hospitals with excessive patient readmissions. Predictive analytics for hospital readmissions have seen an increase in research due to the passage of this legislation. However, many current systems ignore the formulas used by the Centers for Medicare and Medicaid Services for imposing penalties. This research expands upon current methodologies and directly incorporates federal penalization formulas when selecting patients for which to dedicate resources. Methods Hospital discharge summaries are structured using clinical natural language processing techniques. Naïve Bayes classifiers are then used to assign a probability of readmission to each patient. Hospital Readmission Reductions Program formulas and probability of readmission are applied using four readmission scenarios to estimate the cost of readmission. The highest cost patients are identified and readmission mitigation efforts are attempted. Results The results show that the average penalty savings over currently employed binary classification to be 51.93%. Binary classification is also shown to select more patients than necessary for readmission intervention. Additionally, intervening in only high-risk patients saved an average of 90.07% compared to providing all patients with costly aftercare. Conclusion Focusing resources toward the potentially most expensive patients offers considerably better results than unfocused efforts. Utilizing direct calculation to estimate readmission costs has shown to be a more efficient use of resources than current readmission reduction methods. © The Author(s) 2017
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title_short	A framework for the estimation and reduction of hospital readmission penalties using predictive analytics
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A framework for the estimation and reduction of hospital readmission penalties using predictive analytics

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