Comparison of physician referral and insurance claims data-based risk prediction as approaches to identify patients for care management in primary care: An observational study

Tobias Freund, Matthias Gondan, Justine Rochon, Frank Peters-Klimm, Stephen Campbell, Michael Wensing, Joachim Szécsényi

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Background
Primary care-based care management (CM) could reduce hospital admissions in high-risk patients. Identification of patients most likely to benefit is needed as resources for CM are limited. This study aimed to compare hospitalization and mortality rates of patients identified for CM either by treating primary care physicians (PCPs) or predictive modelling software for hospitalization risk (PM).

Methods
In 2009, a cohort of 6,026 beneficiaries of a German statutory health insurance served as a sample for patient identification for CM by PCPs or commercial PM (CSSG 0.8, Verisk Health). The resulting samples were compared regarding hospitalization and mortality rates in 2010 and in the two year period before patient selection. No CM-intervention was delivered until the end of 2010 and PCPs were blinded for the assessment of hospitalization rates.

Results
In 2010, hospitalization rates of PM-identified patients were 80% higher compared to PCP-identified patients. Mortality rates were also 8% higher in PM-identified patients if compared to PCP-identified patients (10% vs. 2%). The hospitalization rate of patients independently identified by both PM and PCPs was numerically between PM- and PCP-identified patients. Time trend between 2007 and 2010 showed decreasing hospitalization rates in PM-identified patients (−15% per year) compared to increasing rates in PCP-identified patients (+34% per year).

Conclusions
PM identified patients with higher hospitalization and mortality rates compared to PCP-referred patients. But the latter showed increasing hospitalization rates over time thereby suggesting that PCPs may be able to predict future deterioration in patients with relatively good current health status. These patients may most likely benefit from preventive services like CM
Original languageEnglish
Pages (from-to)1-5
Number of pages5
JournalBMC Family Practice
Volume14
DOIs
Publication statusPublished - 2013
Externally publishedYes

Fingerprint

Patient Care Management
Insurance
Observational Studies
Primary Health Care
Referral and Consultation
Primary Care Physicians
Physicians
Hospitalization
Mortality
Health Insurance

Cite this

Freund, Tobias ; Gondan, Matthias ; Rochon, Justine ; Peters-Klimm, Frank ; Campbell, Stephen ; Wensing, Michael ; Szécsényi, Joachim. / Comparison of physician referral and insurance claims data-based risk prediction as approaches to identify patients for care management in primary care: An observational study. In: BMC Family Practice. 2013 ; Vol. 14. pp. 1-5.
@article{1be9721961c049fba75005addd33ebbe,
title = "Comparison of physician referral and insurance claims data-based risk prediction as approaches to identify patients for care management in primary care: An observational study",
abstract = "BackgroundPrimary care-based care management (CM) could reduce hospital admissions in high-risk patients. Identification of patients most likely to benefit is needed as resources for CM are limited. This study aimed to compare hospitalization and mortality rates of patients identified for CM either by treating primary care physicians (PCPs) or predictive modelling software for hospitalization risk (PM).MethodsIn 2009, a cohort of 6,026 beneficiaries of a German statutory health insurance served as a sample for patient identification for CM by PCPs or commercial PM (CSSG 0.8, Verisk Health). The resulting samples were compared regarding hospitalization and mortality rates in 2010 and in the two year period before patient selection. No CM-intervention was delivered until the end of 2010 and PCPs were blinded for the assessment of hospitalization rates.ResultsIn 2010, hospitalization rates of PM-identified patients were 80{\%} higher compared to PCP-identified patients. Mortality rates were also 8{\%} higher in PM-identified patients if compared to PCP-identified patients (10{\%} vs. 2{\%}). The hospitalization rate of patients independently identified by both PM and PCPs was numerically between PM- and PCP-identified patients. Time trend between 2007 and 2010 showed decreasing hospitalization rates in PM-identified patients (−15{\%} per year) compared to increasing rates in PCP-identified patients (+34{\%} per year).ConclusionsPM identified patients with higher hospitalization and mortality rates compared to PCP-referred patients. But the latter showed increasing hospitalization rates over time thereby suggesting that PCPs may be able to predict future deterioration in patients with relatively good current health status. These patients may most likely benefit from preventive services like CM",
author = "Tobias Freund and Matthias Gondan and Justine Rochon and Frank Peters-Klimm and Stephen Campbell and Michael Wensing and Joachim Sz{\'e}cs{\'e}nyi",
year = "2013",
doi = "10.1186/1471-2296-14-157",
language = "English",
volume = "14",
pages = "1--5",
journal = "BMC Family Practice",
issn = "1471-2296",
publisher = "BioMed Central",

}

Comparison of physician referral and insurance claims data-based risk prediction as approaches to identify patients for care management in primary care: An observational study. / Freund, Tobias; Gondan, Matthias; Rochon, Justine; Peters-Klimm, Frank; Campbell, Stephen; Wensing, Michael; Szécsényi, Joachim.

In: BMC Family Practice, Vol. 14, 2013, p. 1-5.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Comparison of physician referral and insurance claims data-based risk prediction as approaches to identify patients for care management in primary care: An observational study

AU - Freund, Tobias

AU - Gondan, Matthias

AU - Rochon, Justine

AU - Peters-Klimm, Frank

AU - Campbell, Stephen

AU - Wensing, Michael

AU - Szécsényi, Joachim

PY - 2013

Y1 - 2013

N2 - BackgroundPrimary care-based care management (CM) could reduce hospital admissions in high-risk patients. Identification of patients most likely to benefit is needed as resources for CM are limited. This study aimed to compare hospitalization and mortality rates of patients identified for CM either by treating primary care physicians (PCPs) or predictive modelling software for hospitalization risk (PM).MethodsIn 2009, a cohort of 6,026 beneficiaries of a German statutory health insurance served as a sample for patient identification for CM by PCPs or commercial PM (CSSG 0.8, Verisk Health). The resulting samples were compared regarding hospitalization and mortality rates in 2010 and in the two year period before patient selection. No CM-intervention was delivered until the end of 2010 and PCPs were blinded for the assessment of hospitalization rates.ResultsIn 2010, hospitalization rates of PM-identified patients were 80% higher compared to PCP-identified patients. Mortality rates were also 8% higher in PM-identified patients if compared to PCP-identified patients (10% vs. 2%). The hospitalization rate of patients independently identified by both PM and PCPs was numerically between PM- and PCP-identified patients. Time trend between 2007 and 2010 showed decreasing hospitalization rates in PM-identified patients (−15% per year) compared to increasing rates in PCP-identified patients (+34% per year).ConclusionsPM identified patients with higher hospitalization and mortality rates compared to PCP-referred patients. But the latter showed increasing hospitalization rates over time thereby suggesting that PCPs may be able to predict future deterioration in patients with relatively good current health status. These patients may most likely benefit from preventive services like CM

AB - BackgroundPrimary care-based care management (CM) could reduce hospital admissions in high-risk patients. Identification of patients most likely to benefit is needed as resources for CM are limited. This study aimed to compare hospitalization and mortality rates of patients identified for CM either by treating primary care physicians (PCPs) or predictive modelling software for hospitalization risk (PM).MethodsIn 2009, a cohort of 6,026 beneficiaries of a German statutory health insurance served as a sample for patient identification for CM by PCPs or commercial PM (CSSG 0.8, Verisk Health). The resulting samples were compared regarding hospitalization and mortality rates in 2010 and in the two year period before patient selection. No CM-intervention was delivered until the end of 2010 and PCPs were blinded for the assessment of hospitalization rates.ResultsIn 2010, hospitalization rates of PM-identified patients were 80% higher compared to PCP-identified patients. Mortality rates were also 8% higher in PM-identified patients if compared to PCP-identified patients (10% vs. 2%). The hospitalization rate of patients independently identified by both PM and PCPs was numerically between PM- and PCP-identified patients. Time trend between 2007 and 2010 showed decreasing hospitalization rates in PM-identified patients (−15% per year) compared to increasing rates in PCP-identified patients (+34% per year).ConclusionsPM identified patients with higher hospitalization and mortality rates compared to PCP-referred patients. But the latter showed increasing hospitalization rates over time thereby suggesting that PCPs may be able to predict future deterioration in patients with relatively good current health status. These patients may most likely benefit from preventive services like CM

U2 - 10.1186/1471-2296-14-157

DO - 10.1186/1471-2296-14-157

M3 - Article

VL - 14

SP - 1

EP - 5

JO - BMC Family Practice

JF - BMC Family Practice

SN - 1471-2296

ER -