Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app

Background Despite the growth in mobile technologies (mHealth) to support Community Health Worker (CHW) supervision, the nature of mHealth-facilitated supervision remains underexplored. One strategy to support supervision at scale could be artificial intelligence (AI) modalities, including machine learning. We developed an open access, machine learning web application (CHWsupervisor) to predictively code instant messages exchanged between CHWs based on supervisory interaction codes. We document the development and validation of the web app and report its predictive accuracy. Methods CHWsupervisor was developed using 2187 instant messages exchanged between CHWs and their supervisors in Uganda. The app was then validated on 1242 instant messages from a separate digital CHW supervisory network in Kenya. All messages from the training and validation data sets were manually coded by two independent human coders. The predictive performance of CHWsupervisor was determined by comparing the primary supervisory codes assigned by the web app, against those assigned by the human coders and calculating observed percentage agreement and Cohen’s kappa coefficients. Results Human inter-coder reliability for the primary supervisory category of messages across the training and validation datasets was ‘substantial’ to ‘almost perfect’, as suggested by observed percentage agreements of 88–95% and Cohen’s kappa values of 0.7–0.91. In comparison to the human coders, the predictive accuracy of the CHWsupervisor web app was ‘moderate’, suggested by observed percentage agreements of 73–78% and Cohen’s kappa values of 0.51–0.56. Conclusions Augmenting human coding is challenging because of the complexity of supervisory exchanges, which often require nuanced interpretation. A realistic understanding of the potential of machine learning approaches should be kept in mind by practitioners, as although they hold promise, supportive supervision still requires a level of human expertise. Scaling-up digital CHW supervision may therefore prove challenging. Trial registration: This was not a clinical trial and was therefore not registered as such. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	O’Donovan, James [verfasserIn] Kahn, Ken MacRae, MacKenzie Namanda, Allan Saul Hamala, Rebecca Kabali, Ken Geniets, Anne Lakati, Alice Mbae, Simon M. Winters, Niall

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Machine learning Artificial intelligence Supervision Community Health Worker Digital Health Training

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: Human resources for health - London : Biomed Central, 2003, 20(2022), 1 vom: 16. März
Übergeordnetes Werk:	volume:20 ; year:2022 ; number:1 ; day:16 ; month:03

Links:	Volltext

DOI / URN:	10.1186/s12960-021-00699-5

Katalog-ID:	SPR05055784X

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allfields	10.1186/s12960-021-00699-5 doi (DE-627)SPR05055784X (SPR)s12960-021-00699-5-e DE-627 ger DE-627 rakwb eng O’Donovan, James verfasserin (orcid)0000-0002-7248-5436 aut Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Despite the growth in mobile technologies (mHealth) to support Community Health Worker (CHW) supervision, the nature of mHealth-facilitated supervision remains underexplored. One strategy to support supervision at scale could be artificial intelligence (AI) modalities, including machine learning. We developed an open access, machine learning web application (CHWsupervisor) to predictively code instant messages exchanged between CHWs based on supervisory interaction codes. We document the development and validation of the web app and report its predictive accuracy. Methods CHWsupervisor was developed using 2187 instant messages exchanged between CHWs and their supervisors in Uganda. The app was then validated on 1242 instant messages from a separate digital CHW supervisory network in Kenya. All messages from the training and validation data sets were manually coded by two independent human coders. The predictive performance of CHWsupervisor was determined by comparing the primary supervisory codes assigned by the web app, against those assigned by the human coders and calculating observed percentage agreement and Cohen’s kappa coefficients. Results Human inter-coder reliability for the primary supervisory category of messages across the training and validation datasets was ‘substantial’ to ‘almost perfect’, as suggested by observed percentage agreements of 88–95% and Cohen’s kappa values of 0.7–0.91. In comparison to the human coders, the predictive accuracy of the CHWsupervisor web app was ‘moderate’, suggested by observed percentage agreements of 73–78% and Cohen’s kappa values of 0.51–0.56. Conclusions Augmenting human coding is challenging because of the complexity of supervisory exchanges, which often require nuanced interpretation. A realistic understanding of the potential of machine learning approaches should be kept in mind by practitioners, as although they hold promise, supportive supervision still requires a level of human expertise. Scaling-up digital CHW supervision may therefore prove challenging. Trial registration: This was not a clinical trial and was therefore not registered as such. Machine learning (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Supervision (dpeaa)DE-He213 Community Health Worker (dpeaa)DE-He213 Digital Health (dpeaa)DE-He213 Training (dpeaa)DE-He213 Kahn, Ken aut MacRae, MacKenzie aut Namanda, Allan Saul aut Hamala, Rebecca aut Kabali, Ken aut Geniets, Anne aut Lakati, Alice aut Mbae, Simon M. aut Winters, Niall aut Enthalten in Human resources for health London : Biomed Central, 2003 20(2022), 1 vom: 16. März (DE-627)373756585 (DE-600)2126923-3 1478-4491 nnns volume:20 year:2022 number:1 day:16 month:03 https://dx.doi.org/10.1186/s12960-021-00699-5 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_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_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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 20 2022 1 16 03
spelling	10.1186/s12960-021-00699-5 doi (DE-627)SPR05055784X (SPR)s12960-021-00699-5-e DE-627 ger DE-627 rakwb eng O’Donovan, James verfasserin (orcid)0000-0002-7248-5436 aut Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Despite the growth in mobile technologies (mHealth) to support Community Health Worker (CHW) supervision, the nature of mHealth-facilitated supervision remains underexplored. One strategy to support supervision at scale could be artificial intelligence (AI) modalities, including machine learning. We developed an open access, machine learning web application (CHWsupervisor) to predictively code instant messages exchanged between CHWs based on supervisory interaction codes. We document the development and validation of the web app and report its predictive accuracy. Methods CHWsupervisor was developed using 2187 instant messages exchanged between CHWs and their supervisors in Uganda. The app was then validated on 1242 instant messages from a separate digital CHW supervisory network in Kenya. All messages from the training and validation data sets were manually coded by two independent human coders. The predictive performance of CHWsupervisor was determined by comparing the primary supervisory codes assigned by the web app, against those assigned by the human coders and calculating observed percentage agreement and Cohen’s kappa coefficients. Results Human inter-coder reliability for the primary supervisory category of messages across the training and validation datasets was ‘substantial’ to ‘almost perfect’, as suggested by observed percentage agreements of 88–95% and Cohen’s kappa values of 0.7–0.91. In comparison to the human coders, the predictive accuracy of the CHWsupervisor web app was ‘moderate’, suggested by observed percentage agreements of 73–78% and Cohen’s kappa values of 0.51–0.56. Conclusions Augmenting human coding is challenging because of the complexity of supervisory exchanges, which often require nuanced interpretation. A realistic understanding of the potential of machine learning approaches should be kept in mind by practitioners, as although they hold promise, supportive supervision still requires a level of human expertise. Scaling-up digital CHW supervision may therefore prove challenging. Trial registration: This was not a clinical trial and was therefore not registered as such. Machine learning (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Supervision (dpeaa)DE-He213 Community Health Worker (dpeaa)DE-He213 Digital Health (dpeaa)DE-He213 Training (dpeaa)DE-He213 Kahn, Ken aut MacRae, MacKenzie aut Namanda, Allan Saul aut Hamala, Rebecca aut Kabali, Ken aut Geniets, Anne aut Lakati, Alice aut Mbae, Simon M. aut Winters, Niall aut Enthalten in Human resources for health London : Biomed Central, 2003 20(2022), 1 vom: 16. März (DE-627)373756585 (DE-600)2126923-3 1478-4491 nnns volume:20 year:2022 number:1 day:16 month:03 https://dx.doi.org/10.1186/s12960-021-00699-5 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_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_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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 20 2022 1 16 03
allfields_unstemmed	10.1186/s12960-021-00699-5 doi (DE-627)SPR05055784X (SPR)s12960-021-00699-5-e DE-627 ger DE-627 rakwb eng O’Donovan, James verfasserin (orcid)0000-0002-7248-5436 aut Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Despite the growth in mobile technologies (mHealth) to support Community Health Worker (CHW) supervision, the nature of mHealth-facilitated supervision remains underexplored. One strategy to support supervision at scale could be artificial intelligence (AI) modalities, including machine learning. We developed an open access, machine learning web application (CHWsupervisor) to predictively code instant messages exchanged between CHWs based on supervisory interaction codes. We document the development and validation of the web app and report its predictive accuracy. Methods CHWsupervisor was developed using 2187 instant messages exchanged between CHWs and their supervisors in Uganda. The app was then validated on 1242 instant messages from a separate digital CHW supervisory network in Kenya. All messages from the training and validation data sets were manually coded by two independent human coders. The predictive performance of CHWsupervisor was determined by comparing the primary supervisory codes assigned by the web app, against those assigned by the human coders and calculating observed percentage agreement and Cohen’s kappa coefficients. Results Human inter-coder reliability for the primary supervisory category of messages across the training and validation datasets was ‘substantial’ to ‘almost perfect’, as suggested by observed percentage agreements of 88–95% and Cohen’s kappa values of 0.7–0.91. In comparison to the human coders, the predictive accuracy of the CHWsupervisor web app was ‘moderate’, suggested by observed percentage agreements of 73–78% and Cohen’s kappa values of 0.51–0.56. Conclusions Augmenting human coding is challenging because of the complexity of supervisory exchanges, which often require nuanced interpretation. A realistic understanding of the potential of machine learning approaches should be kept in mind by practitioners, as although they hold promise, supportive supervision still requires a level of human expertise. Scaling-up digital CHW supervision may therefore prove challenging. Trial registration: This was not a clinical trial and was therefore not registered as such. Machine learning (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Supervision (dpeaa)DE-He213 Community Health Worker (dpeaa)DE-He213 Digital Health (dpeaa)DE-He213 Training (dpeaa)DE-He213 Kahn, Ken aut MacRae, MacKenzie aut Namanda, Allan Saul aut Hamala, Rebecca aut Kabali, Ken aut Geniets, Anne aut Lakati, Alice aut Mbae, Simon M. aut Winters, Niall aut Enthalten in Human resources for health London : Biomed Central, 2003 20(2022), 1 vom: 16. März (DE-627)373756585 (DE-600)2126923-3 1478-4491 nnns volume:20 year:2022 number:1 day:16 month:03 https://dx.doi.org/10.1186/s12960-021-00699-5 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_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_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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 20 2022 1 16 03
allfieldsGer	10.1186/s12960-021-00699-5 doi (DE-627)SPR05055784X (SPR)s12960-021-00699-5-e DE-627 ger DE-627 rakwb eng O’Donovan, James verfasserin (orcid)0000-0002-7248-5436 aut Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Despite the growth in mobile technologies (mHealth) to support Community Health Worker (CHW) supervision, the nature of mHealth-facilitated supervision remains underexplored. One strategy to support supervision at scale could be artificial intelligence (AI) modalities, including machine learning. We developed an open access, machine learning web application (CHWsupervisor) to predictively code instant messages exchanged between CHWs based on supervisory interaction codes. We document the development and validation of the web app and report its predictive accuracy. Methods CHWsupervisor was developed using 2187 instant messages exchanged between CHWs and their supervisors in Uganda. The app was then validated on 1242 instant messages from a separate digital CHW supervisory network in Kenya. All messages from the training and validation data sets were manually coded by two independent human coders. The predictive performance of CHWsupervisor was determined by comparing the primary supervisory codes assigned by the web app, against those assigned by the human coders and calculating observed percentage agreement and Cohen’s kappa coefficients. Results Human inter-coder reliability for the primary supervisory category of messages across the training and validation datasets was ‘substantial’ to ‘almost perfect’, as suggested by observed percentage agreements of 88–95% and Cohen’s kappa values of 0.7–0.91. In comparison to the human coders, the predictive accuracy of the CHWsupervisor web app was ‘moderate’, suggested by observed percentage agreements of 73–78% and Cohen’s kappa values of 0.51–0.56. Conclusions Augmenting human coding is challenging because of the complexity of supervisory exchanges, which often require nuanced interpretation. A realistic understanding of the potential of machine learning approaches should be kept in mind by practitioners, as although they hold promise, supportive supervision still requires a level of human expertise. Scaling-up digital CHW supervision may therefore prove challenging. Trial registration: This was not a clinical trial and was therefore not registered as such. Machine learning (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Supervision (dpeaa)DE-He213 Community Health Worker (dpeaa)DE-He213 Digital Health (dpeaa)DE-He213 Training (dpeaa)DE-He213 Kahn, Ken aut MacRae, MacKenzie aut Namanda, Allan Saul aut Hamala, Rebecca aut Kabali, Ken aut Geniets, Anne aut Lakati, Alice aut Mbae, Simon M. aut Winters, Niall aut Enthalten in Human resources for health London : Biomed Central, 2003 20(2022), 1 vom: 16. März (DE-627)373756585 (DE-600)2126923-3 1478-4491 nnns volume:20 year:2022 number:1 day:16 month:03 https://dx.doi.org/10.1186/s12960-021-00699-5 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_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_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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 20 2022 1 16 03
allfieldsSound	10.1186/s12960-021-00699-5 doi (DE-627)SPR05055784X (SPR)s12960-021-00699-5-e DE-627 ger DE-627 rakwb eng O’Donovan, James verfasserin (orcid)0000-0002-7248-5436 aut Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Despite the growth in mobile technologies (mHealth) to support Community Health Worker (CHW) supervision, the nature of mHealth-facilitated supervision remains underexplored. One strategy to support supervision at scale could be artificial intelligence (AI) modalities, including machine learning. We developed an open access, machine learning web application (CHWsupervisor) to predictively code instant messages exchanged between CHWs based on supervisory interaction codes. We document the development and validation of the web app and report its predictive accuracy. Methods CHWsupervisor was developed using 2187 instant messages exchanged between CHWs and their supervisors in Uganda. The app was then validated on 1242 instant messages from a separate digital CHW supervisory network in Kenya. All messages from the training and validation data sets were manually coded by two independent human coders. The predictive performance of CHWsupervisor was determined by comparing the primary supervisory codes assigned by the web app, against those assigned by the human coders and calculating observed percentage agreement and Cohen’s kappa coefficients. Results Human inter-coder reliability for the primary supervisory category of messages across the training and validation datasets was ‘substantial’ to ‘almost perfect’, as suggested by observed percentage agreements of 88–95% and Cohen’s kappa values of 0.7–0.91. In comparison to the human coders, the predictive accuracy of the CHWsupervisor web app was ‘moderate’, suggested by observed percentage agreements of 73–78% and Cohen’s kappa values of 0.51–0.56. Conclusions Augmenting human coding is challenging because of the complexity of supervisory exchanges, which often require nuanced interpretation. A realistic understanding of the potential of machine learning approaches should be kept in mind by practitioners, as although they hold promise, supportive supervision still requires a level of human expertise. Scaling-up digital CHW supervision may therefore prove challenging. Trial registration: This was not a clinical trial and was therefore not registered as such. Machine learning (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Supervision (dpeaa)DE-He213 Community Health Worker (dpeaa)DE-He213 Digital Health (dpeaa)DE-He213 Training (dpeaa)DE-He213 Kahn, Ken aut MacRae, MacKenzie aut Namanda, Allan Saul aut Hamala, Rebecca aut Kabali, Ken aut Geniets, Anne aut Lakati, Alice aut Mbae, Simon M. aut Winters, Niall aut Enthalten in Human resources for health London : Biomed Central, 2003 20(2022), 1 vom: 16. März (DE-627)373756585 (DE-600)2126923-3 1478-4491 nnns volume:20 year:2022 number:1 day:16 month:03 https://dx.doi.org/10.1186/s12960-021-00699-5 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_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_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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 20 2022 1 16 03
language	English
source	Enthalten in Human resources for health 20(2022), 1 vom: 16. März volume:20 year:2022 number:1 day:16 month:03
sourceStr	Enthalten in Human resources for health 20(2022), 1 vom: 16. März volume:20 year:2022 number:1 day:16 month:03
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institution	findex.gbv.de
topic_facet	Machine learning Artificial intelligence Supervision Community Health Worker Digital Health Training
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authorswithroles_txt_mv	O’Donovan, James @@aut@@ Kahn, Ken @@aut@@ MacRae, MacKenzie @@aut@@ Namanda, Allan Saul @@aut@@ Hamala, Rebecca @@aut@@ Kabali, Ken @@aut@@ Geniets, Anne @@aut@@ Lakati, Alice @@aut@@ Mbae, Simon M. @@aut@@ Winters, Niall @@aut@@
publishDateDaySort_date	2022-03-16T00:00:00Z
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author	O’Donovan, James
spellingShingle	O’Donovan, James misc Machine learning misc Artificial intelligence misc Supervision misc Community Health Worker misc Digital Health misc Training Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app
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topic_title	Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app Machine learning (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Supervision (dpeaa)DE-He213 Community Health Worker (dpeaa)DE-He213 Digital Health (dpeaa)DE-He213 Training (dpeaa)DE-He213
topic	misc Machine learning misc Artificial intelligence misc Supervision misc Community Health Worker misc Digital Health misc Training
topic_unstemmed	misc Machine learning misc Artificial intelligence misc Supervision misc Community Health Worker misc Digital Health misc Training
topic_browse	misc Machine learning misc Artificial intelligence misc Supervision misc Community Health Worker misc Digital Health misc Training
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app
ctrlnum	(DE-627)SPR05055784X (SPR)s12960-021-00699-5-e
title_full	Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app
author_sort	O’Donovan, James
journal	Human resources for health
journalStr	Human resources for health
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author_browse	O’Donovan, James Kahn, Ken MacRae, MacKenzie Namanda, Allan Saul Hamala, Rebecca Kabali, Ken Geniets, Anne Lakati, Alice Mbae, Simon M. Winters, Niall
container_volume	20
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author-letter	O’Donovan, James
doi_str_mv	10.1186/s12960-021-00699-5
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title_sort	analysing 3429 digital supervisory interactions between community health workers in uganda and kenya: the development, testing and validation of an open access predictive machine learning web app
title_auth	Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app
abstract	Background Despite the growth in mobile technologies (mHealth) to support Community Health Worker (CHW) supervision, the nature of mHealth-facilitated supervision remains underexplored. One strategy to support supervision at scale could be artificial intelligence (AI) modalities, including machine learning. We developed an open access, machine learning web application (CHWsupervisor) to predictively code instant messages exchanged between CHWs based on supervisory interaction codes. We document the development and validation of the web app and report its predictive accuracy. Methods CHWsupervisor was developed using 2187 instant messages exchanged between CHWs and their supervisors in Uganda. The app was then validated on 1242 instant messages from a separate digital CHW supervisory network in Kenya. All messages from the training and validation data sets were manually coded by two independent human coders. The predictive performance of CHWsupervisor was determined by comparing the primary supervisory codes assigned by the web app, against those assigned by the human coders and calculating observed percentage agreement and Cohen’s kappa coefficients. Results Human inter-coder reliability for the primary supervisory category of messages across the training and validation datasets was ‘substantial’ to ‘almost perfect’, as suggested by observed percentage agreements of 88–95% and Cohen’s kappa values of 0.7–0.91. In comparison to the human coders, the predictive accuracy of the CHWsupervisor web app was ‘moderate’, suggested by observed percentage agreements of 73–78% and Cohen’s kappa values of 0.51–0.56. Conclusions Augmenting human coding is challenging because of the complexity of supervisory exchanges, which often require nuanced interpretation. A realistic understanding of the potential of machine learning approaches should be kept in mind by practitioners, as although they hold promise, supportive supervision still requires a level of human expertise. Scaling-up digital CHW supervision may therefore prove challenging. Trial registration: This was not a clinical trial and was therefore not registered as such. © The Author(s) 2022
abstractGer	Background Despite the growth in mobile technologies (mHealth) to support Community Health Worker (CHW) supervision, the nature of mHealth-facilitated supervision remains underexplored. One strategy to support supervision at scale could be artificial intelligence (AI) modalities, including machine learning. We developed an open access, machine learning web application (CHWsupervisor) to predictively code instant messages exchanged between CHWs based on supervisory interaction codes. We document the development and validation of the web app and report its predictive accuracy. Methods CHWsupervisor was developed using 2187 instant messages exchanged between CHWs and their supervisors in Uganda. The app was then validated on 1242 instant messages from a separate digital CHW supervisory network in Kenya. All messages from the training and validation data sets were manually coded by two independent human coders. The predictive performance of CHWsupervisor was determined by comparing the primary supervisory codes assigned by the web app, against those assigned by the human coders and calculating observed percentage agreement and Cohen’s kappa coefficients. Results Human inter-coder reliability for the primary supervisory category of messages across the training and validation datasets was ‘substantial’ to ‘almost perfect’, as suggested by observed percentage agreements of 88–95% and Cohen’s kappa values of 0.7–0.91. In comparison to the human coders, the predictive accuracy of the CHWsupervisor web app was ‘moderate’, suggested by observed percentage agreements of 73–78% and Cohen’s kappa values of 0.51–0.56. Conclusions Augmenting human coding is challenging because of the complexity of supervisory exchanges, which often require nuanced interpretation. A realistic understanding of the potential of machine learning approaches should be kept in mind by practitioners, as although they hold promise, supportive supervision still requires a level of human expertise. Scaling-up digital CHW supervision may therefore prove challenging. Trial registration: This was not a clinical trial and was therefore not registered as such. © The Author(s) 2022
abstract_unstemmed	Background Despite the growth in mobile technologies (mHealth) to support Community Health Worker (CHW) supervision, the nature of mHealth-facilitated supervision remains underexplored. One strategy to support supervision at scale could be artificial intelligence (AI) modalities, including machine learning. We developed an open access, machine learning web application (CHWsupervisor) to predictively code instant messages exchanged between CHWs based on supervisory interaction codes. We document the development and validation of the web app and report its predictive accuracy. Methods CHWsupervisor was developed using 2187 instant messages exchanged between CHWs and their supervisors in Uganda. The app was then validated on 1242 instant messages from a separate digital CHW supervisory network in Kenya. All messages from the training and validation data sets were manually coded by two independent human coders. The predictive performance of CHWsupervisor was determined by comparing the primary supervisory codes assigned by the web app, against those assigned by the human coders and calculating observed percentage agreement and Cohen’s kappa coefficients. Results Human inter-coder reliability for the primary supervisory category of messages across the training and validation datasets was ‘substantial’ to ‘almost perfect’, as suggested by observed percentage agreements of 88–95% and Cohen’s kappa values of 0.7–0.91. In comparison to the human coders, the predictive accuracy of the CHWsupervisor web app was ‘moderate’, suggested by observed percentage agreements of 73–78% and Cohen’s kappa values of 0.51–0.56. Conclusions Augmenting human coding is challenging because of the complexity of supervisory exchanges, which often require nuanced interpretation. A realistic understanding of the potential of machine learning approaches should be kept in mind by practitioners, as although they hold promise, supportive supervision still requires a level of human expertise. Scaling-up digital CHW supervision may therefore prove challenging. Trial registration: This was not a clinical trial and was therefore not registered as such. © The Author(s) 2022
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container_issue	1
title_short	Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app
url	https://dx.doi.org/10.1186/s12960-021-00699-5
remote_bool	true
author2	Kahn, Ken MacRae, MacKenzie Namanda, Allan Saul Hamala, Rebecca Kabali, Ken Geniets, Anne Lakati, Alice Mbae, Simon M. Winters, Niall
author2Str	Kahn, Ken MacRae, MacKenzie Namanda, Allan Saul Hamala, Rebecca Kabali, Ken Geniets, Anne Lakati, Alice Mbae, Simon M. Winters, Niall
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doi_str	10.1186/s12960-021-00699-5
up_date	2024-07-03T16:18:12.298Z
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