Subtopic Deep Dive

De-Implementation of Low-Value Practices
Research Guide

What is De-Implementation of Low-Value Practices?

De-Implementation of Low-Value Practices refers to systematic strategies to discontinue unnecessary medical interventions such as excessive imaging, antibiotics, or race-corrected algorithms to reduce healthcare costs and improve patient safety.

Researchers develop behavioral interventions, audit-feedback systems, and reporting standards to identify and eliminate low-value care. Frameworks like CHEERS guide economic evaluations of de-implementation efforts (Husereau et al., 2013, 1975 citations). Studies highlight variations in surgical outcomes and race-based adjustments as targets for discontinuation (Ghaferi et al., 2009, 1416 citations; Vyas et al., 2020, 1575 citations). Over 10 papers in provided lists address related burdens and guidelines.

Curated Papers

Key Challenges

Why It Matters

De-implementation targets practices like race corrections in algorithms, which direct suboptimal care, as shown by Vyas et al. (2020). Reducing hospital mortality variations from poor complication management lowers costs and saves lives (Ghaferi et al., 2009). CHEERS standards ensure transparent economic reporting for de-implementation trials, enabling scalable reductions in low-value CKD evaluations (Husereau et al., 2013; Inker et al., 2014). These efforts cut global disease burdens by reallocating resources (Vos et al., 2020).

Key Research Challenges

Identifying Low-Value Practices

Distinguishing unnecessary interventions requires analyzing outcome variations across hospitals. Ghaferi et al. (2009) show mortality differences stem from complication management failures. Accurate ICD coding errors complicate identification (O’Malley et al., 2005).

Overcoming Clinician Resistance

Behavioral barriers persist despite evidence, as in race-corrected algorithms. Vyas et al. (2020) expose hidden biases guiding decisions. Frameworks like GRADE aid evidence-based discontinuation (Alonso-Coello et al., 2016).

Measuring Economic Impact

Quantifying welfare losses from low-value care demands robust standards. Newhouse (1992) estimates medical cost inefficiencies at 1208 citations. CHEERS elaboration ensures consistent reporting (Husereau et al., 2013).

Essential Papers

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Theo Vos, Stephen S Lim, Cristiana Abbafati et al. · 2020 · The Lancet · 18.0K citations

Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

Boris Bikbov, Caroline Purcell, Andrew S. Levey et al. · 2020 · The Lancet · 6.1K citations

Consolidated Health Economic Evaluation Reporting Standards (CHEERS)—Explanation and Elaboration: A Report of the ISPOR Health Economic Evaluation Publication Guidelines Good Reporting Practices Task Force

Don Husereau, Michael Drummond, Stavros Petrou et al. · 2013 · Value in Health · 2.0K citations

KDOQI US Commentary on the 2012 KDIGO Clinical Practice Guideline for the Evaluation and Management of CKD

Lesley A. Inker, Brad C. Astor, Chester H. Fox et al. · 2014 · American Journal of Kidney Diseases · 1.8K citations

Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study

Nicole M. Kuderer, Toni K. Choueiri, Dimpy P. Shah et al. · 2020 · The Lancet · 1.8K citations

Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms

Darshali A. Vyas, Leo G. Eisenstein, David S. Jones · 2020 · New England Journal of Medicine · 1.6K citations

Hidden in Plain Sight Diagnostic algorithms and practice guidelines that adjust or “correct” their outputs on the basis of a patient’s race or ethnicity guide decisions in ways that may direct more...

Variation in Hospital Mortality Associated with Inpatient Surgery

Amir A. Ghaferi, John D. Birkmeyer, Justin B. Dimick · 2009 · New England Journal of Medicine · 1.4K citations

In addition to efforts aimed at avoiding complications in the first place, reducing mortality associated with inpatient surgery will require greater attention to the timely recognition and manageme...

Reading Guide

Foundational Papers

Start with Husereau et al. (2013) for CHEERS standards on economic reporting of de-implementation; Ghaferi et al. (2009) for surgical low-value care variations; Newhouse (1992) for cost welfare losses.

Recent Advances

Study Vyas et al. (2020) on race algorithm corrections; Vos et al. (2020) for global burden context; Alonso-Coello et al. (2016) for GRADE EtD frameworks.

Core Methods

CHEERS for economic evaluations (Husereau et al., 2013); GRADE EtD for decisions (Alonso-Coello et al., 2016); ICD accuracy checks (O’Malley et al., 2005).

How PapersFlow Helps You Research De-Implementation of Low-Value Practices

Discover & Search

Research Agent uses searchPapers and exaSearch to find de-implementation studies on low-value practices like race corrections, then citationGraph maps influences from Vyas et al. (2020) to guideline papers. findSimilarPapers expands to surgical variation analyses like Ghaferi et al. (2009).

Analyze & Verify

Analysis Agent applies readPaperContent to extract de-implementation strategies from Husereau et al. (2013), verifies claims with verifyResponse (CoVe), and uses runPythonAnalysis for GRADE grading of evidence quality in Vos et al. (2020) burden data. Statistical verification checks ICD accuracy metrics from O’Malley et al. (2005).

Synthesize & Write

Synthesis Agent detects gaps in low-value CKD guideline de-implementation via Inker et al. (2014), flags contradictions with race algorithms (Vyas et al., 2020). Writing Agent uses latexEditText, latexSyncCitations, and latexCompile to draft reports with exportMermaid diagrams of intervention flows.

Use Cases

"Analyze welfare loss from low-value surgical practices using Python."

Research Agent → searchPapers('Newhouse 1992') → Analysis Agent → runPythonAnalysis(pandas on cost data from Ghaferi et al. 2009) → matplotlib plot of mortality-cost correlations output.

"Write LaTeX review on de-implementing race-corrected algorithms."

Synthesis Agent → gap detection(Vyas et al. 2020) → Writing Agent → latexEditText(draft) → latexSyncCitations(Husereau et al. 2013) → latexCompile(PDF) output.

"Find code for audit-feedback systems in de-implementation papers."

Research Agent → searchPapers('de-implementation audit feedback') → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect(pull request stats) output.

Automated Workflows

Deep Research workflow conducts systematic reviews of 50+ papers on low-value practices: searchPapers → citationGraph(Vos et al. 2020 hub) → structured GRADE-graded report. DeepScan applies 7-step analysis with CoVe checkpoints to verify de-implementation impacts in Ghaferi et al. (2009). Theorizer generates frameworks for clinician behavior change from Alonso-Coello et al. (2016) EtD methods.

Try Doxa for De-Implementation of Low-Value Practices Research

Frequently Asked Questions

What is de-implementation of low-value practices?

De-implementation discontinues unnecessary interventions like excessive imaging or race-corrected algorithms to cut costs and harm. Targets include surgical complication mismanagement (Ghaferi et al., 2009).

What methods support de-implementation?

Audit-feedback systems, GRADE EtD frameworks, and CHEERS reporting standards guide efforts. Alonso-Coello et al. (2016) provide transparent decision-making; Husereau et al. (2013) standardize economics.

What are key papers?

Foundational: Husereau et al. (2013, 1975 citations) on CHEERS; Ghaferi et al. (2009, 1416 citations) on surgical variations. Recent: Vyas et al. (2020, 1575 citations) on race corrections.

What open problems remain?

Clinician resistance to discontinuing ingrained practices persists. Measuring precise economic welfare losses needs better ICD accuracy (O’Malley et al., 2005; Newhouse, 1992).