Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks?

Background General practice based registration networks (GPRNs) provide information on population health derived from electronic health records (EHR). Morbidity estimates from different GPRNs reveal considerable, unexplained differences. Previous research showed that population characteristics could...
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Autor*in:	van den Dungen, C [verfasserIn] Hoeymans, N van den Akker, M Biermans, MCJ van Boven, K Joosten, JHK Verheij, RA de Waal, MWM Schellevis, FG van Oers, JAM

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Family practice Incidence Electronic medical records Practice characteristics Population health Prevalence

Anmerkung:	© van den Dungen et al.; licensee BioMed Central Ltd. 2014

Übergeordnetes Werk:	Enthalten in: BMC family practice - London : BioMed Central, 2000, 15(2014), 1 vom: 30. Okt.
Übergeordnetes Werk:	volume:15 ; year:2014 ; number:1 ; day:30 ; month:10

Links:	Volltext

DOI / URN:	10.1186/s12875-014-0176-7

Katalog-ID:	SPR027391108

Internformat


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245	1	0	\|a Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks?
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520			\|a Background General practice based registration networks (GPRNs) provide information on population health derived from electronic health records (EHR). Morbidity estimates from different GPRNs reveal considerable, unexplained differences. Previous research showed that population characteristics could not explain this variation. In this study we investigate the influence of practice characteristics on the variation in incidence and prevalence figures between general practices and between GPRNs. Methods We analyzed the influence of eight practice characteristics, such as type of practice, percentage female general practitioners, and employment of a practice nurse, on the variation in morbidity estimates of twelve diseases between six Dutch GPRNs. We used multilevel logistic regression analysis and expressed the variation between practices and GPRNs in median odds ratios (MOR). Furthermore, we analyzed the influence of type of EHR software package and province within one large national GPRN. Results Hardly any practice characteristic showed an effect on morbidity estimates. Adjusting for the practice characteristics did also not alter the variation between practices or between GPRNs, as MORs remained stable. The EHR software package `Medicom' and the province `Groningen' showed significant effects on the prevalence figures of several diseases, but this hardly diminished the variation between practices. Conclusion Practice characteristics do not explain the differences in morbidity estimates between GPRNs.
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912			\|a GBV_ILN_60
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912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
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912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
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912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
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912			\|a GBV_ILN_4249
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912			\|a GBV_ILN_4307
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912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
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Indexfelder

author_variant	d d c v ddc ddcv n h nh d a m v dam damv m b mb b k v bk bkv j j jj r v rv w m d wm wmd f s fs o j v oj ojv
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publishDate	2014
allfields	10.1186/s12875-014-0176-7 doi (DE-627)SPR027391108 (SPR)s12875-014-0176-7-e DE-627 ger DE-627 rakwb eng van den Dungen, C verfasserin aut Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van den Dungen et al.; licensee BioMed Central Ltd. 2014 Background General practice based registration networks (GPRNs) provide information on population health derived from electronic health records (EHR). Morbidity estimates from different GPRNs reveal considerable, unexplained differences. Previous research showed that population characteristics could not explain this variation. In this study we investigate the influence of practice characteristics on the variation in incidence and prevalence figures between general practices and between GPRNs. Methods We analyzed the influence of eight practice characteristics, such as type of practice, percentage female general practitioners, and employment of a practice nurse, on the variation in morbidity estimates of twelve diseases between six Dutch GPRNs. We used multilevel logistic regression analysis and expressed the variation between practices and GPRNs in median odds ratios (MOR). Furthermore, we analyzed the influence of type of EHR software package and province within one large national GPRN. Results Hardly any practice characteristic showed an effect on morbidity estimates. Adjusting for the practice characteristics did also not alter the variation between practices or between GPRNs, as MORs remained stable. The EHR software package `Medicom' and the province `Groningen' showed significant effects on the prevalence figures of several diseases, but this hardly diminished the variation between practices. Conclusion Practice characteristics do not explain the differences in morbidity estimates between GPRNs. Family practice (dpeaa)DE-He213 Incidence (dpeaa)DE-He213 Electronic medical records (dpeaa)DE-He213 Practice characteristics (dpeaa)DE-He213 Population health (dpeaa)DE-He213 Prevalence (dpeaa)DE-He213 Hoeymans, N aut van den Akker, M aut Biermans, MCJ aut van Boven, K aut Joosten, JHK aut Verheij, RA aut de Waal, MWM aut Schellevis, FG aut van Oers, JAM aut Enthalten in BMC family practice London : BioMed Central, 2000 15(2014), 1 vom: 30. Okt. (DE-627)326644911 (DE-600)2041495-X 1471-2296 nnns volume:15 year:2014 number:1 day:30 month:10 https://dx.doi.org/10.1186/s12875-014-0176-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 1 30 10
spelling	10.1186/s12875-014-0176-7 doi (DE-627)SPR027391108 (SPR)s12875-014-0176-7-e DE-627 ger DE-627 rakwb eng van den Dungen, C verfasserin aut Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van den Dungen et al.; licensee BioMed Central Ltd. 2014 Background General practice based registration networks (GPRNs) provide information on population health derived from electronic health records (EHR). Morbidity estimates from different GPRNs reveal considerable, unexplained differences. Previous research showed that population characteristics could not explain this variation. In this study we investigate the influence of practice characteristics on the variation in incidence and prevalence figures between general practices and between GPRNs. Methods We analyzed the influence of eight practice characteristics, such as type of practice, percentage female general practitioners, and employment of a practice nurse, on the variation in morbidity estimates of twelve diseases between six Dutch GPRNs. We used multilevel logistic regression analysis and expressed the variation between practices and GPRNs in median odds ratios (MOR). Furthermore, we analyzed the influence of type of EHR software package and province within one large national GPRN. Results Hardly any practice characteristic showed an effect on morbidity estimates. Adjusting for the practice characteristics did also not alter the variation between practices or between GPRNs, as MORs remained stable. The EHR software package `Medicom' and the province `Groningen' showed significant effects on the prevalence figures of several diseases, but this hardly diminished the variation between practices. Conclusion Practice characteristics do not explain the differences in morbidity estimates between GPRNs. Family practice (dpeaa)DE-He213 Incidence (dpeaa)DE-He213 Electronic medical records (dpeaa)DE-He213 Practice characteristics (dpeaa)DE-He213 Population health (dpeaa)DE-He213 Prevalence (dpeaa)DE-He213 Hoeymans, N aut van den Akker, M aut Biermans, MCJ aut van Boven, K aut Joosten, JHK aut Verheij, RA aut de Waal, MWM aut Schellevis, FG aut van Oers, JAM aut Enthalten in BMC family practice London : BioMed Central, 2000 15(2014), 1 vom: 30. Okt. (DE-627)326644911 (DE-600)2041495-X 1471-2296 nnns volume:15 year:2014 number:1 day:30 month:10 https://dx.doi.org/10.1186/s12875-014-0176-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 1 30 10
allfields_unstemmed	10.1186/s12875-014-0176-7 doi (DE-627)SPR027391108 (SPR)s12875-014-0176-7-e DE-627 ger DE-627 rakwb eng van den Dungen, C verfasserin aut Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van den Dungen et al.; licensee BioMed Central Ltd. 2014 Background General practice based registration networks (GPRNs) provide information on population health derived from electronic health records (EHR). Morbidity estimates from different GPRNs reveal considerable, unexplained differences. Previous research showed that population characteristics could not explain this variation. In this study we investigate the influence of practice characteristics on the variation in incidence and prevalence figures between general practices and between GPRNs. Methods We analyzed the influence of eight practice characteristics, such as type of practice, percentage female general practitioners, and employment of a practice nurse, on the variation in morbidity estimates of twelve diseases between six Dutch GPRNs. We used multilevel logistic regression analysis and expressed the variation between practices and GPRNs in median odds ratios (MOR). Furthermore, we analyzed the influence of type of EHR software package and province within one large national GPRN. Results Hardly any practice characteristic showed an effect on morbidity estimates. Adjusting for the practice characteristics did also not alter the variation between practices or between GPRNs, as MORs remained stable. The EHR software package `Medicom' and the province `Groningen' showed significant effects on the prevalence figures of several diseases, but this hardly diminished the variation between practices. Conclusion Practice characteristics do not explain the differences in morbidity estimates between GPRNs. Family practice (dpeaa)DE-He213 Incidence (dpeaa)DE-He213 Electronic medical records (dpeaa)DE-He213 Practice characteristics (dpeaa)DE-He213 Population health (dpeaa)DE-He213 Prevalence (dpeaa)DE-He213 Hoeymans, N aut van den Akker, M aut Biermans, MCJ aut van Boven, K aut Joosten, JHK aut Verheij, RA aut de Waal, MWM aut Schellevis, FG aut van Oers, JAM aut Enthalten in BMC family practice London : BioMed Central, 2000 15(2014), 1 vom: 30. Okt. (DE-627)326644911 (DE-600)2041495-X 1471-2296 nnns volume:15 year:2014 number:1 day:30 month:10 https://dx.doi.org/10.1186/s12875-014-0176-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 1 30 10
allfieldsGer	10.1186/s12875-014-0176-7 doi (DE-627)SPR027391108 (SPR)s12875-014-0176-7-e DE-627 ger DE-627 rakwb eng van den Dungen, C verfasserin aut Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van den Dungen et al.; licensee BioMed Central Ltd. 2014 Background General practice based registration networks (GPRNs) provide information on population health derived from electronic health records (EHR). Morbidity estimates from different GPRNs reveal considerable, unexplained differences. Previous research showed that population characteristics could not explain this variation. In this study we investigate the influence of practice characteristics on the variation in incidence and prevalence figures between general practices and between GPRNs. Methods We analyzed the influence of eight practice characteristics, such as type of practice, percentage female general practitioners, and employment of a practice nurse, on the variation in morbidity estimates of twelve diseases between six Dutch GPRNs. We used multilevel logistic regression analysis and expressed the variation between practices and GPRNs in median odds ratios (MOR). Furthermore, we analyzed the influence of type of EHR software package and province within one large national GPRN. Results Hardly any practice characteristic showed an effect on morbidity estimates. Adjusting for the practice characteristics did also not alter the variation between practices or between GPRNs, as MORs remained stable. The EHR software package `Medicom' and the province `Groningen' showed significant effects on the prevalence figures of several diseases, but this hardly diminished the variation between practices. Conclusion Practice characteristics do not explain the differences in morbidity estimates between GPRNs. Family practice (dpeaa)DE-He213 Incidence (dpeaa)DE-He213 Electronic medical records (dpeaa)DE-He213 Practice characteristics (dpeaa)DE-He213 Population health (dpeaa)DE-He213 Prevalence (dpeaa)DE-He213 Hoeymans, N aut van den Akker, M aut Biermans, MCJ aut van Boven, K aut Joosten, JHK aut Verheij, RA aut de Waal, MWM aut Schellevis, FG aut van Oers, JAM aut Enthalten in BMC family practice London : BioMed Central, 2000 15(2014), 1 vom: 30. Okt. (DE-627)326644911 (DE-600)2041495-X 1471-2296 nnns volume:15 year:2014 number:1 day:30 month:10 https://dx.doi.org/10.1186/s12875-014-0176-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 1 30 10
allfieldsSound	10.1186/s12875-014-0176-7 doi (DE-627)SPR027391108 (SPR)s12875-014-0176-7-e DE-627 ger DE-627 rakwb eng van den Dungen, C verfasserin aut Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van den Dungen et al.; licensee BioMed Central Ltd. 2014 Background General practice based registration networks (GPRNs) provide information on population health derived from electronic health records (EHR). Morbidity estimates from different GPRNs reveal considerable, unexplained differences. Previous research showed that population characteristics could not explain this variation. In this study we investigate the influence of practice characteristics on the variation in incidence and prevalence figures between general practices and between GPRNs. Methods We analyzed the influence of eight practice characteristics, such as type of practice, percentage female general practitioners, and employment of a practice nurse, on the variation in morbidity estimates of twelve diseases between six Dutch GPRNs. We used multilevel logistic regression analysis and expressed the variation between practices and GPRNs in median odds ratios (MOR). Furthermore, we analyzed the influence of type of EHR software package and province within one large national GPRN. Results Hardly any practice characteristic showed an effect on morbidity estimates. Adjusting for the practice characteristics did also not alter the variation between practices or between GPRNs, as MORs remained stable. The EHR software package `Medicom' and the province `Groningen' showed significant effects on the prevalence figures of several diseases, but this hardly diminished the variation between practices. Conclusion Practice characteristics do not explain the differences in morbidity estimates between GPRNs. Family practice (dpeaa)DE-He213 Incidence (dpeaa)DE-He213 Electronic medical records (dpeaa)DE-He213 Practice characteristics (dpeaa)DE-He213 Population health (dpeaa)DE-He213 Prevalence (dpeaa)DE-He213 Hoeymans, N aut van den Akker, M aut Biermans, MCJ aut van Boven, K aut Joosten, JHK aut Verheij, RA aut de Waal, MWM aut Schellevis, FG aut van Oers, JAM aut Enthalten in BMC family practice London : BioMed Central, 2000 15(2014), 1 vom: 30. Okt. (DE-627)326644911 (DE-600)2041495-X 1471-2296 nnns volume:15 year:2014 number:1 day:30 month:10 https://dx.doi.org/10.1186/s12875-014-0176-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 1 30 10
language	English
source	Enthalten in BMC family practice 15(2014), 1 vom: 30. Okt. volume:15 year:2014 number:1 day:30 month:10
sourceStr	Enthalten in BMC family practice 15(2014), 1 vom: 30. Okt. volume:15 year:2014 number:1 day:30 month:10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Family practice Incidence Electronic medical records Practice characteristics Population health Prevalence
isfreeaccess_bool	true
container_title	BMC family practice
authorswithroles_txt_mv	van den Dungen, C @@aut@@ Hoeymans, N @@aut@@ van den Akker, M @@aut@@ Biermans, MCJ @@aut@@ van Boven, K @@aut@@ Joosten, JHK @@aut@@ Verheij, RA @@aut@@ de Waal, MWM @@aut@@ Schellevis, FG @@aut@@ van Oers, JAM @@aut@@
publishDateDaySort_date	2014-10-30T00:00:00Z
hierarchy_top_id	326644911
id	SPR027391108
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Morbidity estimates from different GPRNs reveal considerable, unexplained differences. Previous research showed that population characteristics could not explain this variation. In this study we investigate the influence of practice characteristics on the variation in incidence and prevalence figures between general practices and between GPRNs. Methods We analyzed the influence of eight practice characteristics, such as type of practice, percentage female general practitioners, and employment of a practice nurse, on the variation in morbidity estimates of twelve diseases between six Dutch GPRNs. We used multilevel logistic regression analysis and expressed the variation between practices and GPRNs in median odds ratios (MOR). Furthermore, we analyzed the influence of type of EHR software package and province within one large national GPRN. Results Hardly any practice characteristic showed an effect on morbidity estimates. Adjusting for the practice characteristics did also not alter the variation between practices or between GPRNs, as MORs remained stable. The EHR software package `Medicom' and the province `Groningen' showed significant effects on the prevalence figures of several diseases, but this hardly diminished the variation between practices. 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author	van den Dungen, C
spellingShingle	van den Dungen, C misc Family practice misc Incidence misc Electronic medical records misc Practice characteristics misc Population health misc Prevalence Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks?
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topic_title	Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks? Family practice (dpeaa)DE-He213 Incidence (dpeaa)DE-He213 Electronic medical records (dpeaa)DE-He213 Practice characteristics (dpeaa)DE-He213 Population health (dpeaa)DE-He213 Prevalence (dpeaa)DE-He213
topic	misc Family practice misc Incidence misc Electronic medical records misc Practice characteristics misc Population health misc Prevalence
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title	Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks?
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title_full	Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks?
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author_browse	van den Dungen, C Hoeymans, N van den Akker, M Biermans, MCJ van Boven, K Joosten, JHK Verheij, RA de Waal, MWM Schellevis, FG van Oers, JAM
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title_sort	do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks?
title_auth	Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks?
abstract	Background General practice based registration networks (GPRNs) provide information on population health derived from electronic health records (EHR). Morbidity estimates from different GPRNs reveal considerable, unexplained differences. Previous research showed that population characteristics could not explain this variation. In this study we investigate the influence of practice characteristics on the variation in incidence and prevalence figures between general practices and between GPRNs. Methods We analyzed the influence of eight practice characteristics, such as type of practice, percentage female general practitioners, and employment of a practice nurse, on the variation in morbidity estimates of twelve diseases between six Dutch GPRNs. We used multilevel logistic regression analysis and expressed the variation between practices and GPRNs in median odds ratios (MOR). Furthermore, we analyzed the influence of type of EHR software package and province within one large national GPRN. Results Hardly any practice characteristic showed an effect on morbidity estimates. Adjusting for the practice characteristics did also not alter the variation between practices or between GPRNs, as MORs remained stable. The EHR software package `Medicom' and the province `Groningen' showed significant effects on the prevalence figures of several diseases, but this hardly diminished the variation between practices. Conclusion Practice characteristics do not explain the differences in morbidity estimates between GPRNs. © van den Dungen et al.; licensee BioMed Central Ltd. 2014
abstractGer	Background General practice based registration networks (GPRNs) provide information on population health derived from electronic health records (EHR). Morbidity estimates from different GPRNs reveal considerable, unexplained differences. Previous research showed that population characteristics could not explain this variation. In this study we investigate the influence of practice characteristics on the variation in incidence and prevalence figures between general practices and between GPRNs. Methods We analyzed the influence of eight practice characteristics, such as type of practice, percentage female general practitioners, and employment of a practice nurse, on the variation in morbidity estimates of twelve diseases between six Dutch GPRNs. We used multilevel logistic regression analysis and expressed the variation between practices and GPRNs in median odds ratios (MOR). Furthermore, we analyzed the influence of type of EHR software package and province within one large national GPRN. Results Hardly any practice characteristic showed an effect on morbidity estimates. Adjusting for the practice characteristics did also not alter the variation between practices or between GPRNs, as MORs remained stable. The EHR software package `Medicom' and the province `Groningen' showed significant effects on the prevalence figures of several diseases, but this hardly diminished the variation between practices. Conclusion Practice characteristics do not explain the differences in morbidity estimates between GPRNs. © van den Dungen et al.; licensee BioMed Central Ltd. 2014
abstract_unstemmed	Background General practice based registration networks (GPRNs) provide information on population health derived from electronic health records (EHR). Morbidity estimates from different GPRNs reveal considerable, unexplained differences. Previous research showed that population characteristics could not explain this variation. In this study we investigate the influence of practice characteristics on the variation in incidence and prevalence figures between general practices and between GPRNs. Methods We analyzed the influence of eight practice characteristics, such as type of practice, percentage female general practitioners, and employment of a practice nurse, on the variation in morbidity estimates of twelve diseases between six Dutch GPRNs. We used multilevel logistic regression analysis and expressed the variation between practices and GPRNs in median odds ratios (MOR). Furthermore, we analyzed the influence of type of EHR software package and province within one large national GPRN. Results Hardly any practice characteristic showed an effect on morbidity estimates. Adjusting for the practice characteristics did also not alter the variation between practices or between GPRNs, as MORs remained stable. The EHR software package `Medicom' and the province `Groningen' showed significant effects on the prevalence figures of several diseases, but this hardly diminished the variation between practices. Conclusion Practice characteristics do not explain the differences in morbidity estimates between GPRNs. © van den Dungen et al.; licensee BioMed Central Ltd. 2014
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title_short	Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks?
url	https://dx.doi.org/10.1186/s12875-014-0176-7
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author2	Hoeymans, N van den Akker, M Biermans, MCJ van Boven, K Joosten, JHK Verheij, RA de Waal, MWM Schellevis, FG van Oers, JAM
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Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks?

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