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

Abstract

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.

Keywords: public health; risk management; therapeutics.

Figure 1

PRISMA diagram. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Figure 2

Use of impactibility modelling enhances identification of individuals most likely respond to preventive care and allows weighted resourcing. (A) Population with a given condition at risk of an outcome over a specific period of time, stratified by risk. (B) After impactibility analysis, different options can be targeted to the most amenable people. The numbers and positions of dots per intervention highlight that the likelihood of treatment success can be found throughout the stratified population and is not necessarily determined by risk level.

Figure 3

Use of impactibility modelling (step 03) to enhance identification of patients amenable to benefit and likelihood of achieving the triple aim. ACSC, ambulatory care sensitive condition.