Bridging the impactibility gap in population health management: a systematic review
Objectives Assess whether impactibility modelling is being used to refine risk stratification for preventive health interventions.Design Systematic review.Setting Primary and secondary healthcare populations.Papers Articles published from 2010 to 2020 on the use or implementation of impactibility mo...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2021-12-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/11/12/e052455.full |
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| author | Alex Bottle Andi Orlowski Rachel Ashton Wayne Smith Sally Snow Heather Humphreys Rebecca Siân Jones |
| author_facet | Alex Bottle Andi Orlowski Rachel Ashton Wayne Smith Sally Snow Heather Humphreys Rebecca Siân Jones |
| author_sort | Alex Bottle |
| collection | DOAJ |
| description | Objectives Assess whether impactibility modelling is being used to refine risk stratification for preventive health interventions.Design Systematic review.Setting Primary and secondary healthcare populations.Papers Articles published from 2010 to 2020 on the use or implementation of impactibility modelling in population health management, reported with the terms ‘intervenability’, ‘amenability’, and ‘propensity to succeed’ (PTS) and associated with the themes ‘care sensitivity’, ‘characteristic responders’, ‘needs gap’, ‘case finding’, ‘patient selection’ and ‘risk stratification’.Interventions Qualitative synthesis to identify themes for approaches to impactibility modelling.Results Of 1244 records identified, 20 were eligible for inclusion. Identified themes were ‘health conditions amenable to care’ (n=6), ‘PTS modelling’ (n=8) and ‘comparison or combination with clinical judgement’ (n=6). For the theme ‘health conditions amenable to care’, changes in practice did not reduce admissions, particularly for ambulatory care sensitive conditions, and sometimes increased them, with implementation noted as a possible issue. For ‘PTS modelling’, high costs and needs did not necessarily equate to high impactibility and targeting a larger number of individuals with disorders associated with lower costs had more potential. PTS modelling seemed to improve accuracy in care planning, estimation of cost savings, engagement and/or care quality. The ‘comparison or combination with clinical judgement’ theme suggested that models can reach reasonable to good discriminatory power to detect impactable patients. For instance, a model used to identify patients appropriate for proactive multimorbid care management showed good concordance with physicians (c-statistic 0.75). Another model employing electronic health record scores reached 65% concordance with nurse and physician decisions when referring elderly hospitalised patients to a readmission prevention programme. However, healthcare professionals consider much wider information that might improve or impede the likelihood of treatment impact, suggesting that complementary use of models might be optimum.Conclusions The efficiency and equity of targeted preventive care guided by risk stratification could be augmented and personalised by impactibility modelling. |
| format | Article |
| id | doaj-art-9235ff0bc31e4ae7a59e9a8123c56bda |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-9235ff0bc31e4ae7a59e9a8123c56bda2024-12-10T07:35:12ZengBMJ Publishing GroupBMJ Open2044-60552021-12-01111210.1136/bmjopen-2021-052455Bridging the impactibility gap in population health management: a systematic reviewAlex Bottle0Andi Orlowski1Rachel Ashton2Wayne Smith3Sally Snow4Heather Humphreys5Rebecca Siân Jones6Department of Primary Care and Public Health, Imperial College London, London, UKDepartment of Primary Care and Public Health, Imperial College London, London, UKHealth Economics Unit, Stoke on Trent, UK1 Health Economics Unit, Stoke on Trent, UKHealth Economics Unit, London, UKHealth Economics Unit, London, UKImperial College Libraries, Imperial College London, London, UKObjectives Assess whether impactibility modelling is being used to refine risk stratification for preventive health interventions.Design Systematic review.Setting Primary and secondary healthcare populations.Papers Articles published from 2010 to 2020 on the use or implementation of impactibility modelling in population health management, reported with the terms ‘intervenability’, ‘amenability’, and ‘propensity to succeed’ (PTS) and associated with the themes ‘care sensitivity’, ‘characteristic responders’, ‘needs gap’, ‘case finding’, ‘patient selection’ and ‘risk stratification’.Interventions Qualitative synthesis to identify themes for approaches to impactibility modelling.Results Of 1244 records identified, 20 were eligible for inclusion. Identified themes were ‘health conditions amenable to care’ (n=6), ‘PTS modelling’ (n=8) and ‘comparison or combination with clinical judgement’ (n=6). For the theme ‘health conditions amenable to care’, changes in practice did not reduce admissions, particularly for ambulatory care sensitive conditions, and sometimes increased them, with implementation noted as a possible issue. For ‘PTS modelling’, high costs and needs did not necessarily equate to high impactibility and targeting a larger number of individuals with disorders associated with lower costs had more potential. PTS modelling seemed to improve accuracy in care planning, estimation of cost savings, engagement and/or care quality. The ‘comparison or combination with clinical judgement’ theme suggested that models can reach reasonable to good discriminatory power to detect impactable patients. For instance, a model used to identify patients appropriate for proactive multimorbid care management showed good concordance with physicians (c-statistic 0.75). Another model employing electronic health record scores reached 65% concordance with nurse and physician decisions when referring elderly hospitalised patients to a readmission prevention programme. However, healthcare professionals consider much wider information that might improve or impede the likelihood of treatment impact, suggesting that complementary use of models might be optimum.Conclusions The efficiency and equity of targeted preventive care guided by risk stratification could be augmented and personalised by impactibility modelling.https://bmjopen.bmj.com/content/11/12/e052455.full |
| spellingShingle | Alex Bottle Andi Orlowski Rachel Ashton Wayne Smith Sally Snow Heather Humphreys Rebecca Siân Jones Bridging the impactibility gap in population health management: a systematic review BMJ Open |
| title | Bridging the impactibility gap in population health management: a systematic review |
| title_full | Bridging the impactibility gap in population health management: a systematic review |
| title_fullStr | Bridging the impactibility gap in population health management: a systematic review |
| title_full_unstemmed | Bridging the impactibility gap in population health management: a systematic review |
| title_short | Bridging the impactibility gap in population health management: a systematic review |
| title_sort | bridging the impactibility gap in population health management a systematic review |
| url | https://bmjopen.bmj.com/content/11/12/e052455.full |
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