Bringing cohort studies to the bedside: framework for a ‘green button’ to support clinical decision-making

Abstract

When providing care, clinicians are expected to take note of clinical practice guidelines, which offer recommendations based on the available evidence. However, guidelines may not apply to individual patients with comorbidities, as they are typically excluded from clinical trials. Guidelines also tend not to provide relevant evidence on risks, secondary effects and long-term outcomes. Querying the electronic health records of similar patients may for many provide an alternate source of evidence to inform decision-making. It is important to develop methods to support these personalized observational studies at the point-of-care, to understand when these methods may provide valid results, and to validate and integrate these findings with those from clinical trials.

Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases

“…In contrast, data from health care databases (eg, commercial claims data, electronic health records, and national registries) offer large sample sizes and better representation of real world populations. Thus, they provide an opportunity to improve patient care by personalizing therapeutic decisions when there is evidence supporting heterogeneous treatment effects (HTEs) . In this study, we focus on estimation of HTEs for binary treatment options (eg, treatment and control) and outcomes.…”

“…Thus, they provide an opportunity to improve patient care by personalizing therapeutic decisions when there is evidence supporting heterogeneous treatment effects (HTEs). 6,7 In this study, we focus on estimation of HTEs for binary treatment options (eg, treatment and control) and outcomes. We conduct a head to head comparison of recently proposed methods for HTE estimation in simulation studies that reflect the characteristic challenges of health care databases.…”

Patient Similarity: Emerging Concepts in Systems and Precision Medicine

“…Various patient similarity algorithms have been deployed and have been found beneficial by improving clinical efficiency (Wang et al, 2015), enabling secure identification of similar patients and records sharing by clinicians and rare disease scientists (Buske et al, 2015a,b), predicting patients' prognosis or trajectory over time (Ebadollahi et al, 2010; Subirats et al, 2012; Wang et al, 2012; Gallego et al, 2015), providing clinical decision support (Daemen et al, 2009; Wang et al, 2011; Subirats et al, 2012; Sun et al, 2012; Gottlieb et al, 2013; Liu et al, 2013b; Gallego et al, 2015), tailoring individual treatments (Zhang et al, 2014), preventing unexpected adverse drug reactions (Hartge et al, 2006; Yang et al, 2014), flagging patients deserving more attention due to poor response to therapies (Zhang et al, 2014; Ozery-Flato et al, 2016), and pursuing comparative effectiveness studies (Wang et al, 2011), among other applications. In general, clinical guidelines often do not supply evidence on risks, secondary therapy effects, and long-term outcomes (Gallego et al, 2015).…”

“…In general, clinical guidelines often do not supply evidence on risks, secondary therapy effects, and long-term outcomes (Gallego et al, 2015). In this setting, patient similarity analytics can provide a cheaper, portable alternative or in fact adjunct to evidence-based clinical guidelines and randomized controlled trials, particularly if trial data are unavailable for conditions or patient characteristics specific to a query individual (Longhurst et al, 2014; Gallego et al, 2015). Synthesizing current patient similarity algorithms with systems medicine tools could provide actionable insights in precision medicine.…”

“…Confounder control and treatment selection bias are inherent limitations in such studies. However, groups have developed strategies to manage the potential for confounders, such as restriction, stratification, matching, inverse probability weighting, and covariate adjustment (Gallego et al, 2015). Several groups have also proposed solutions for other challenges that enable large scale patient indexing and accurate and efficient clinical data retrieval (Wang, 2015).…”