A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods

Mattie, Heather; Reidy, Patrick; Bachtiger, Patrik; Lindemer, Emily; Nikolaev, Nikolay; Jouni, Mohammad; Schaefer, Joann; Sherman, Michael S.; Panch, Trishan

doi:10.1089/pop.2019.0132

Cited by 7 publications

(6 citation statements)

References 19 publications

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“… 30–32 One model was developed explicitly for ‘low-risk’ participants to assess who would be most likely to benefit from a digital health platform. 28 …”

Section: Resultsmentioning

confidence: 99%

“…In these studies, PTS regression analyses were performed using various sociodemographic factors, 26–28 30–32 health status (eg, presence of chronic conditions, prescription data, prior health resource utilisation and various health risk scores) 26–28 30–32 or previous programme engagement metrics. 28 One study found that high costs and high needs did not equate to high impactibility, as only small proportions of people with diseases that would be expected to have high burden had scores indicating high impactibility. The authors suggested that targeting a larger number of individuals with disorders associated with lower costs could improve impact substantially and that better predictors of impactibility might be medication adherence and historical healthcare resource utilisation that was unexplained by disease burden.…”

Section: Resultsmentioning

confidence: 99%

“…21 Propensity to succeed PTS modelling is an analytical approach to identify traits associated with improved engagement with or outcomes from particular preventive health intervention(s)outcomes such as cost or care quality. [26][27][28][29][30][31][32] Of the eight studies identified that used this approach, three used PTS modelling in relation to specific case management interventions. [30][31][32] One model was developed explicitly for 'low-risk' participants to assess who would be most likely to benefit from a digital health platform.…”

Section: Health Conditions Amenable To Preventive Carementioning

confidence: 99%

“…[30][31][32] One model was developed explicitly for 'low-risk' participants to assess who would be most likely to benefit from a digital health platform. 28 In these studies, PTS regression analyses were performed using various sociodemographic factors, 26-28 30-32 health status (eg, presence of chronic conditions, prescription data, prior health resource utilisation and various health risk scores) 26-28 30-32 or previous programme engagement metrics. 28 One study found that high costs and high needs did not equate to high impactibility, as only small proportions of people with diseases that would be expected to have high burden had scores indicating high impactibility.…”

Section: Health Conditions Amenable To Preventive Carementioning

confidence: 99%

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Bridging the impactibility gap in population health management: a systematic review

et al. 2021

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ObjectivesAssess whether impactibility modelling is being used to refine risk stratification for preventive health interventions.DesignSystematic review.SettingPrimary and secondary healthcare populations.PapersArticles 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’.InterventionsQualitative synthesis to identify themes for approaches to impactibility modelling.ResultsOf 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.ConclusionsThe efficiency and equity of targeted preventive care guided by risk stratification could be augmented and personalised by impactibility modelling.

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“… 30–32 One model was developed explicitly for ‘low-risk’ participants to assess who would be most likely to benefit from a digital health platform. 28 …”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Health Conditions Amenable To Preventive Carementioning

confidence: 99%

Section: Health Conditions Amenable To Preventive Carementioning

confidence: 99%

See 2 more Smart Citations

Bridging the impactibility gap in population health management: a systematic review

et al. 2021

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“…Crucially, systems should be tested on data from different patients than the ones in the training data Remote PAP, LAP and other monitoring carries significant setup and running costs, but this may also be a problem amenable to a ML solution by training an algorithm on the data from previous PAPmonitored patients to 'learn' who benefits most, thus enabling targeting the intervention to those most likely to benefit. 24,25 Use of all such devices poses significant technical challenges, particularly optimising CRT-D function, but clinical benefits could be maximised without the need for new hardware by using AI to build models capable of enhancing decision-making around implantation and optimisation. 26,27 External Sensors For All…”

Section: Supervised Machine Learningmentioning

confidence: 99%

Artificial Intelligence, Data Sensors and Interconnectivity: Future Opportunities for Heart Failure

et al. 2020

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A higher proportion of patients with heart failure have benefitted from a wide and expanding variety of sensor-enabled implantable devices than any other patient group. These patients can now also take advantage of the ever-increasing availability and affordability of consumer electronics. Wearable, on- and near-body sensor technologies, much like implantable devices, generate massive amounts of data. The connectivity of all these devices has created opportunities for pooling data from multiple sensors – so-called interconnectivity – and for artificial intelligence to provide new diagnostic, triage, risk-stratification and disease management insights for the delivery of better, more personalised and cost-effective healthcare. Artificial intelligence is also bringing important and previously inaccessible insights from our conventional cardiac investigations. The aim of this article is to review the convergence of artificial intelligence, sensor technologies and interconnectivity and the way in which this combination is set to change the care of patients with heart failure.

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A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2—Data From Nonwearables

et al. 2022

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A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods

Cited by 7 publications

References 19 publications

Bridging the impactibility gap in population health management: a systematic review

Bridging the impactibility gap in population health management: a systematic review

Artificial Intelligence, Data Sensors and Interconnectivity: Future Opportunities for Heart Failure

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2—Data From Nonwearables

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