Evidence map›Paper›PMID 28000151›Full record

ArticleDrug safety2017

The Impact of Provider Networks on the Co-Prescriptions of Interacting Drugs: A Claims-Based Analysis.

Mei-Sing Ong, Karen L Olson, Laura Chadwick, Chunfu Liu, Kenneth D Mandl

Abstract read
In one paragraph

Article in Drug safety, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  17. Evaluating Breast Cancer Care Coordination at a Rural National Cancer Institute Comprehensive Cancer Center Using Network Analysis and Geospatial Methods.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2019
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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

5 authors.

Mei-Sing OngComputational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA. Mei-Sing.Ong@childrens.harvard.edu.
Karen L OlsonComputational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA.
Laura ChadwickComputational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA.
Chunfu LiuHealthCore Inc., Alexandria, VA, USA.
Kenneth D MandlComputational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA.

Funding

RESEARCH TRAINING-MEDICAL INFORMATICS 90T15LM007092 · NLM · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Nils Gehlenborg · 1992 to 2026
$32.8M
Instrumenting i2b2 for Improved Medication Research: Adding the Patient VoiceR01GM104303 · NIGMS · BOSTON CHILDREN'S HOSPITAL · PI MANDL, KENNETH D. · 2013 to 2016
$1.6M
Epidemiology of Care Teams: Network Analysis of Providers and Shared PatientsR21GM107645 · NIGMS · BOSTON CHILDREN'S HOSPITAL · PI MANDL, KENNETH D. · 2013 to 2014
$496k
NIGMS NIH HHS R01 GM104303NIGMS NIH HHS R21 GM107645NLM NIH HHS T15 LM007092
6 · The paper itself

Abstract

introductionMultiple provider prescribing of interacting drugs is a preventable cause of morbidity and mortality, and fragmented care is a major contributing factor. We applied social network analysis to examine the impact of provider patient-sharing networks on the risk of multiple provider prescribing of interacting drugs.

methodsWe performed a retrospective analysis of commercial healthcare claims (years 2008-2011), including all non-elderly adult beneficiaries (n = 88,494) and their constellation of care providers. Patient-sharing networks were derived based on shared patients, and care constellation cohesion was quantified using care density, defined as the ratio between the total number of patients shared by provider pairs and the total number of provider pairs within the care constellation around each patient.

resultsIn our study, 2% (n = 1796) of patients were co-prescribed interacting drugs by multiple providers. Multiple provider prescribing of interacting drugs was associated with care density (odds ratio per unit increase in the natural logarithm of the value for care density 0.78; 95% confidence interval 0.74-0.83; p < 0.0001). The effect of care density was more pronounced with increasing constellation size: when constellation size exceeded ten providers, the risk of multiple provider prescribing of interacting drugs decreased by nearly 37% with each unit increase in the natural logarithm of care density (p < 0.0001). Other predictors included increasing age of patients, increasing number of providers, and greater morbidity.

conclusionImproved care cohesion may mitigate unsafe prescribing practices, especially in larger care constellations. There is further potential to leverage network analytics to implement large-scale surveillance applications for monitoring prescribing safety.

Indexed as

Drug InteractionsAge FactorsCooperative BehaviorDatabases, FactualFemaleHumansInappropriate PrescribingInterprofessional RelationsMaleMiddle AgedPractice Patterns, Physicians'Retrospective Studies

Identifiers

PMID28000151
PMCPMC5385257

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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.