Evidence map›Paper›PMID 33736636›Full record

ArticleBMC medical informatics and decision making2021

Primary care practices' ability to predict future risk of expenditures and hospitalization using risk stratification and segmentation.

David A Dorr, Rachel L Ross, Deborah Cohen, Devan Kansagara, Katrina Ramsey, Bhavaya Sachdeva, Jonathan P Weiner

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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

  1. Pooled it
  2. Pooled it
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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

7 authors.

David A DorrDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3030 SW Moody Ave, Portland, OR, 97201, USA. dorrd@ohsu.edu.ORCID 0000-0003-2318-7261
Rachel L RossDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3030 SW Moody Ave, Portland, OR, 97201, USA.
Deborah CohenDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3030 SW Moody Ave, Portland, OR, 97201, USA.
Devan KansagaraDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3030 SW Moody Ave, Portland, OR, 97201, USA.
Katrina RamseyDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3030 SW Moody Ave, Portland, OR, 97201, USA.
Bhavaya SachdevaDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3030 SW Moody Ave, Portland, OR, 97201, USA.
Jonathan P WeinerJohns Hopkins University, Baltimore, MD, USA.

Funding

AHRQ HHS T32 HS022241
6 · The paper itself

Abstract

backgroundPatients with complex health care needs may suffer adverse outcomes from fragmented and delayed care, reducing well-being and increasing health care costs. Health reform efforts, especially those in primary care, attempt to mitigate risk of adverse outcomes by better targeting resources to those most in need. However, predicting who is susceptible to adverse outcomes, such as unplanned hospitalizations, ED visits, or other potentially avoidable expenditures, can be difficult, and providing intensive levels of resources to all patients is neither wanted nor efficient. Our objective was to understand if primary care teams can predict patient risk better than standard risk scores.

methodsSix primary care practices risk stratified their entire patient population over a 2-year period, and worked to mitigate risk for those at high risk through care management and coordination. Individual patient risk scores created by the practices were collected and compared to a common risk score (Hierarchical Condition Categories) in their ability to predict future expenditures, ED visits, and hospitalizations. Accuracy of predictions, sensitivity, positive predictive values (PPV), and c-statistics were calculated for each risk scoring type. Analyses were stratified by whether the practice used intuition alone, an algorithm alone, or adjudicated an algorithmic risk score.

resultsIn all, 40,342 patients were risk stratified. Practice scores had 38.6% agreement with HCC scores on identification of high-risk patients. For the 3,381 patients with reliable outcomes data, accuracy was high (0.71-0.88) but sensitivity and PPV were low (0.16-0.40). Practice-created scores had 0.02-0.14 lower sensitivity, specificity and PPV compared to HCC in prediction of outcomes. Practices using adjudication had, on average, .16 higher sensitivity.

conclusionsPractices using simple risk stratification techniques had slightly worse accuracy in predicting common outcomes than HCC, but adjudication improved prediction.

Indexed as

Health Care ReformHealth ExpendituresHospitalizationHumansPrimary Health CareRisk AssessmentCare managementChronic diseasePrimary careRisk assessment

Identifiers

PMID33736636
PMCPMC7977271

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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.