Evidence map›Paper›PMID 26048275›Full record

ArticleJournal of general internal medicine2015

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network.

Clemens S Hong, Steven J Atlas, Jeffrey M Ashburner, Yuchiao Chang, Wei He, Timothy G Ferris, Richard W Grant

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of general internal medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 2 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

34 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Pilot study on the use of a multimorbidity index in patients receiving home parenteral nutrition.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Observational
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Clemens S HongGeneral Medicine Division, Massachusetts General Hospital, 50 Staniford Street, 9th Floor, Boston, MA, 02114, USA. cshong@partners.org.
Steven J AtlasGeneral Medicine Division, Massachusetts General Hospital, 50 Staniford Street, 9th Floor, Boston, MA, 02114, USA.
Jeffrey M AshburnerGeneral Medicine Division, Massachusetts General Hospital, 50 Staniford Street, 9th Floor, Boston, MA, 02114, USA.
Yuchiao ChangGeneral Medicine Division, Massachusetts General Hospital, 50 Staniford Street, 9th Floor, Boston, MA, 02114, USA.
Wei HeGeneral Medicine Division, Massachusetts General Hospital, 50 Staniford Street, 9th Floor, Boston, MA, 02114, USA.
Timothy G FerrisGeneral Medicine Division, Massachusetts General Hospital, 50 Staniford Street, 9th Floor, Boston, MA, 02114, USA.
Richard W GrantKaiser Permanente Division of Research, Oakland, CA, USA.

Funding

Harvard Clinical and Translational Science CenterKL2TR001100 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2013 to 2017
$8.5M
NCATS NIH HHS KL2 TR001100
6 · The paper itself

Abstract

backgroundImproving the ability to risk-stratify patients is critical for efficiently allocating resources within healthcare systems.

objectiveThe purpose of this study was to evaluate a physician-defined complexity prediction model against outpatient Charlson score (OCS) and a commercial risk predictor (CRP).

designUsing a cohort in which primary care physicians reviewed 4302 of their adult patients, we developed a predictive model for estimated physician-defined complexity (ePDC) and categorized our population using ePDC, OCS and CRP.

participants143,372 primary care patients in a practice-based research network participated in the study. MAIN MEASURES: For all patients categorized as complex in 2007 by one or more risk-stratification method, we calculated the percentage of total person time from 2008-2011 for which eligible cancer screening was incomplete, HbA1c was ≥ 9 %, and LDL was ≥ 130 mg/dl (in patients with cardiovascular disease). We also calculated the number of emergency department (ED) visits and hospital admissions per person year (ppy). KEY

resultsThere was modest agreement among individuals classified as complex using ePDC compared with OCS (36.7 %) and CRP (39.6 %). Over 4 follow-up years, eligible ePDC-complex patients had higher proportions (p < 0.001) of time with: incomplete cervical (17.8 % vs. 13.3 % for OCS; 19.4 % vs. 11.2 % for CRP), breast (21.4 % vs. 14.9 % for OCS; 22.7 % vs. 15.0 % for CRP), and colon (25.9 % vs. 18.7 % for OCS; 27.0 % vs. 18.2 % for CRP) cancer screening; HbA1c ≥ 9 % (15.6 % vs. 8.1 % for OCS; 15.9 % vs. 6.9 % for CRP); and LDL ≥ 130 mg/dl (12.4 % vs. 7.9 % for OCS; 11.8 % vs 9.0 % for CRP). ePDC-complex patients had higher rates (p < 0.003) of: ED visits (0.21 vs. 0.11 ppy for OCS; 0.17 vs. 0.15 ppy for CRP), and admissions in patients 45-64 and ≥ 65 years old (0.11 vs. 0.10 ppy AND 0.24 vs. 0.21 ppy for OCS).

conclusionOur measure for estimated physician-defined complexity compared favorably to commonly used risk-prediction approaches in identifying future suboptimal quality and utilization outcomes.

Indexed as

Clinical CompetenceAcademic Medical CentersAdultAgedAlgorithmsCohort StudiesEarly Detection of CancerFemaleHospitalizationHumansMaleMassachusettsMiddle AgedModels, TheoreticalPatient Care ManagementPhysicians, Primary Care

Identifiers

PMID26048275
PMCPMC4636571

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.