Evidence map›Paper›PMID 33014699›Full record

ArticleOperations research for health care2020

Optimizing Interventions Across the HIV Care Continuum: A Case Study Using Process Improvement Analysis.

Geoffrey J Barrow, Michael Fairley, Margaret L Brandeau

Abstract read
In one paragraph

Article in Operations research for health care, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Geoffrey J BarrowFaculty of Medical Sciences, University of the West Indies, Mona, Jamaica.
Michael FairleyDepartment of Management Science and Engineering, Stanford University, Stanford, CA, USA.
Margaret L BrandeauDepartment of Management Science and Engineering, Stanford University, Stanford, CA, USA.

Funding

Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR37DA015612 · NIDA · STANFORD UNIVERSITY · PI DOUGLAS K OWENS · 2019 to 2026
$6.3M
NIDA NIH HHS R37 DA015612
6 · The paper itself

Abstract

UNAIDS' 90-90-90 goal for 2020 is for 90% of HIV-infected people to know their status, 90% of infected individuals to receive antiretroviral therapy (ART), and 90% of those on ART to achieve viral suppression. To achieve these ambitious goals, effective care delivery programs are needed. In this paper we present a case study showing how HIV care can be improved by viewing the patient care process as a production process and applying methods of process improvement analysis. We examine the continuum of HIV care at a hospital-based HIV clinic in Kingston, Jamaica. We perform qualitative analysis to identify key programmatic, personnel, and clinical areas for process improvement. We then perform quantitative analysis. We develop a stochastic model of the care process which we use to evaluate the effects of potential process improvements on the number of patients who receive ART and the number who achieve viral suppression. We also develop a model for optimal investment of a fixed budget among interventions aimed at improving the care cascade and we use the model to determine the optimal investment among three interventions that the clinic could invest in. By viewing the patient care process as a production process and applying qualitative and quantitative process improvement analysis, our case study illustrates how clinics can identify the best ways to maximize clinical outcomes. Our methods are generalizable to other HIV care clinics as well as to clinics that provide care for other chronic conditions (e.g., diabetes, hepatitis B, or opioid use disorder).

Indexed as

HIVHIV care cascadeMarkov modeloptimizationprocess improvementqualitative analysis

Identifiers

PMID33014699
PMCPMC7528976

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.