Currently Viewing:
The American Journal of Managed Care December 2017
Chronic Disease Outcomes From Primary Care Population Health Program Implementation
Jeffrey M. Ashburner, PhD, MPH; Daniel M. Horn, MD; Sandra M. O’Keefe, MPH; Adrian H. Zai, MD, PhD; Yuchiao Chang, PhD; Neil W. Wagle, MD, MBA; and Steven J. Atlas, MD, MPH
Expanding the "Safe Harbor" in High-Deductible Health Plans: Better Coverage and Lower Healthcare Costs
A. Mark Fendrick, MD, and Rashna Soonavala
Impact of Consumer-Directed Health Plans on Low-Value Healthcare
Rachel O. Reid, MD, MS; Brendan Rabideau, BA; and Neeraj Sood, PhD
Insurance Switching and Mismatch Between the Costs and Benefits of New Technologies
David Cutler, PhD; Michael Ciarametaro, MBA; Genia Long, MPP; Noam Kirson, PhD; and Robert Dubois, MD, PhD
ED-Based Care Coordination Reduces Costs for Frequent ED Users
Michelle P. Lin, MD, MPH; Bonnie B. Blanchfield, ScD, CPA; Rose M. Kakoza, MD, MPH; Vineeta Vaidya, MS; Christin Price, MD; Joshua S. Goldner, MD; Michelle Higgins, PA-C; Elisabeth Lessenich, MD, MPH; Karl Laskowski, MD, MBA; and Jeremiah D. Schuur, MD, MHS
Currently Reading
Evaluation of the Quality Blue Primary Care Program on Health Outcomes
Qian Shi, PhD, MPH; Thomas J. Yan, MS; Peter Lee, BS; Paul Murphree, MD, MHA; Xiaojing Yuan, MPH; Hui Shao, PhD, MHA; William H. Bestermann, MD; Selina Loupe, BS; Dawn Cantrell, BA; David Carmouche, MD; John Strapp, BA; and Lizheng Shi, PhD, MSPharm
Real-World Economic Value of a 21-Gene Assay in Early-Stage Breast Cancer
Stanley E. Waintraub, MD; Donna McNamara, MD; Deena Mary Atieh Graham, MD; Andrew L. Pecora, MD; John Min, BS; Tommy Wu, BA; Hyun Gi Noh, MSC; Jacqueline Connors, RN, OCN; Ruth Pe Benito, MPH, BS; Kelly Choi, MD; Eric Schultz, BS; and Stuart L. Goldberg, MD
Trends in Bisphosphonate Initiation Within an Integrated Healthcare Delivery System
Rami J. Hosein, MD, MPH; Joan C. Lo, MD; Bruce Ettinger, MD; Bonnie H. Li, MS; Fang Niu, MS; Rita L. Hui, PharmD, MS; and Annette L. Adams, PhD, MPH
Reduction of Emergency Department Use in People With Disabilities
Lihao Chu, PhD; Neeraj Sood, PhD; Michael Tu, MS; Katrina Miller, MD; Lhasa Ray, MD; and Jennifer N. Sayles, MD
Impact of Statin Guidelines on Statin Utilization and Costs in an Employer-Based Primary Care Clinic
Holly E. Gurgle, PharmD, BCACP, CDE; Marisa B. Schauerhamer, PharmD; Simón A. Rodriguez, PharmD; and Carrie McAdam-Marx, MSCI, PhD, RPh

Evaluation of the Quality Blue Primary Care Program on Health Outcomes

Qian Shi, PhD, MPH; Thomas J. Yan, MS; Peter Lee, BS; Paul Murphree, MD, MHA; Xiaojing Yuan, MPH; Hui Shao, PhD, MHA; William H. Bestermann, MD; Selina Loupe, BS; Dawn Cantrell, BA; David Carmouche, MD; John Strapp, BA; and Lizheng Shi, PhD, MSPharm
Implementation of the Quality Blue Primary Care program in Louisiana was associated with a shift in primary care delivery and reductions in overall cost.

Objectives: This study aimed to investigate the role of the Quality Blue Primary Care (QBPC) program on healthcare utilization and overall cost among the beneficiaries of Blue Cross and Blue Shield of Louisiana (BCBSLA).

Study Design: Retrospective observational cohort study using claims data from adults residing in QBPC-implemented regions continuously enrolled through BCBSLA from June 2012 to December 2014 (N = 89,034). 

Methods: Controlling for age, gender, and risk score by propensity score weighting, inpatient, outpatient, and corresponding medical expenditures were each compared between the QBPC group and the control group using a difference-in-differences regression model.

Results: Average total cost increased in both the QBPC and control groups in 2014, but the increase was significantly less in the intervention group—a difference of $27.09 per member per month (PMPM) (P ≤.001). Savings in total cost were derived largely from a decrease in hospitalizations—a difference of $18.85 PMPM (P = .0023). Furthermore, savings were associated with shifts in healthcare utilization by the intervention group toward proactive management, including increased primary care physician visits (P = .0106) and higher screening rates for diabetes (P = .0019). Inpatient admissions also decreased in the QBPC group, most significantly among those with chronic conditions (P <.05). Conversely, an unexpected increase was observed in emergency department visits. 

Conclusions: The QBPC program was associated with a shift in primary care delivery and reductions in overall cost. Savings were achieved largely through reductions in hospitalization costs.

Am J Manag Care. 2017;23(12):e402-e408
Takeaway Points
  • Quality Blue Primary Care key integrations included health information exchange tools, standardized chronic condition management plans, and continuing medical education programs. 
  • Primary care was delivered at lower total cost—approximately $27 lower per member per month.
  • Savings were derived largely from a shift to more primary care and decreases in hospitalization costs. 
  • Further intervention is necessary to manage emergency department visits.
The United States spent in excess of $3.0 trillion, or $9523 per person, on healthcare in 2014, reflecting an increase of 5.3% from 2013.1,2 Louisiana ranks among the lowest states in healthcare quality and among the highest in healthcare expenditures per capita.3-5 The national trend is further exacerbated by the rising number of aging individuals with chronic conditions, estimated to account for more than 75% of total healthcare costs.6 Patients with more than 1 chronic condition are estimated to account for 95% of all Medicare spending.7 The concentration of healthcare expenditures involved with chronic conditions is a major concern for individuals, insurance companies, and policy makers alike, and understanding how to effectively care for individuals with multiple chronic conditions is one of the most important challenges our healthcare system is facing.8,9

Various initiatives have targeted health management and quality improvement for patients with chronic conditions. Among the components in the chronic care model,10 delivery system design and clinical information systems were proven effective in improving the management of chronic conditions.11 Beyond those, innovations toward enhanced primary care performance12 and payment systems also play important roles. In 2009, Blue Cross Blue Shield of Massachusetts launched a global payment system, the Alternative Quality Contract, to improve healthcare quality by offering substantial financial incentives to primary care providers based on performance against quality measures. Improvement in quality and reduced spending growth were observed 1 year after implementation.13 Based on the success observed in Massachusetts, we recognize a significant potential in working with primary care networks to improve healthcare quality and expenditures.

The cornerstone of effective management of chronic conditions is collaborative team-based care.14 In recognition of these solutions, Blue Cross and Blue Shield of Louisiana (BCBSLA) is taking a lead role in engaging and supporting primary care physicians (PCPs) to redesign healthcare. In 2012, BCBSLA piloted a population health and quality improvement program called Quality Blue Primary Care (QBPC) (see eAppendix: Design and Implementation of Quality Blue Primary Care Program [eAppendix available at]) to improve patient care delivery for PCPs in any of 3 specialties: family medicine, internal medicine, and general practice. In the QBPC program, BCBSLA contracts with PCPs and provides a free Web-based patient-centric health information exchange tool to support the existing standard of care. This tool was designed to identify and manage chronic conditions that are prevalent and burdensome, while providing practices with data and resources that enable proactive, efficient, and high-quality care. QBPC leverages the current framework of the chronic care model and aims to create a minimally disruptive, efficient, and active care management process, whereby a BCBSLA-employed case manager acts as the care coordinator. Integration of the Web-based tool across multiple medical providers, including physicians, nurses, and care coordinators, enables all team members to act on timely key patient data. The program also equips primary care providers with an outcomes-based reimbursement structure that supports increased value and drives cost reduction through care coordination.15 

QBPC was designed to improve the current quality of care. In this study, we identify changes in healthcare utilization and expenditures associated with implementation of the QBPC

program in Louisiana.


Study Design

This study was a retrospective observational cohort study of BCBSLA enrollees. Pre- and postintervention data were collected and balanced by propensity score weighting for both the QBPC and control groups, and a difference-in-differences (DID) multivariate regression analysis was used to identify changes in healthcare utilization and expenditures associated with the implementation of the QBPC program.

Data Source

Our study used medical and pharmacy claims data from BCBSLA. The study population included adults who were continuously enrolled in BCBSLA medical and pharmacy insurance from July 2012 through December 2014. All QBPC providers were enrolled in QBPC from July 2013 to December 2013 (enrollment period) and remained in QBPC during all of 2014 (outcome period). The members who visited the QBPC providers in 2014 were defined as the intervention group, and the members who visited non-QBPC providers in 2014 were defined as the control group. The baseline period was defined as 1 year before the QBPC start date (July 2012-June 2013).

Sample Selection

The selected members were 18 years and older and were required to be residents in regions with QBPC providers (Baton Rouge, Lake Charles, Monroe, New Orleans, and Shreveport). BCBSLA needed to be the primary payer for the selected members. Members with supplementary plans only (BlueChoice 65, Variable Income Plan, Cancer and Serious Disease plan, dental, vision, or part D) were excluded from our sample. We also excluded members who crossed over between the comparison groups in the outcome period and members who had extremely high annual expenditures on inpatient care (≥$100,000 per year) (Figure 1 shows the flow chart of sample selection).

Outcomes and Key Covariates

Inpatient admissions, office-based visits, and emergency department (ED) visits per 1000 members were estimated as utilization outcomes. Inpatient care included total admissions and admissions with any one, or more, of the following diagnoses: cardiovascular disease (CVD), hypertension (HTN), diabetes, and chronic kidney disease (CKD). Diagnoses were determined by International Classification of Diseases, Ninth Revision, Clinical Modification codes, independent of the individual order of diagnosis. Total office-based visits were estimated and further specified as visits to PCPs/nurse practitioners (NPs) and visits to specialists. ED visits were defined as total ED visits, ambulatory ED visits, and admitted ED visits (ie, ED visits followed by inpatient care).

Health expenditures in this study were defined as the allowed amount paid by BCBSLA, presented as dollars per member per month (PMPM). Total costs were summed by total medical costs and total prescription costs, estimated and shown in result table separately. The costs linked to utilization were captured and categorized by ED (ambulatory ED and admitted ED), inpatient admissions and admissions with chronic conditions, and office-based visits (eg, PCPs/NPs, specialists).

Diabetes management outcomes were measured by screening test rates of glycated hemoglobin (A1C), low-density lipoprotein cholesterol, and microalbuminuria. 

Explanatory Variables

Age was defined as the age at the end of the baseline period. The DxCG risk score was classified into 5 levels: healthy, stable, at risk, struggling, and in crisis. Insurance types were defined as the specific products members enrolled in through BCBSLA, listed as preferred provider organization (PPO), health maintenance organization (HMO), and Community Blue/Blue Connect (designed for lower monthly premiums).

Statistical Analysis

The demographic characteristics for the QBPC and control groups were described at the baseline period by means and percentages. The statistical differences between the 2 groups were compared by t test for continuous variables and χ2 test for categorical variables.

To mitigate differences in members’ baseline characteristics across QBPC and control groups, propensity score weights (PSWs) were estimated by age, gender, risk score, residential region, and insurance type in a logistic regression. The propensity score (PS) was predicted for both the QBPC and control groups, and the inverse and normalized PSs were used as PSWs in the outcomes analysis.16

Multivariate regression analysis of a DID model with PSW was used to estimate the impact of QBPC on healthcare utilization and expenditures and the quality of diabetes management, controlling for age, gender, product type, and categorized risk score. Generalized linear model (GLM) was used with Poisson distribution and log link function for outcome of utilization. Gamma distribution and log link function for outcome of expenditure, and binomial distribution and logit link function for lab test rate, were assigned in GLM. 

Rate ratios (RRs) and 95% CIs from multivariate regression models were presented, and a 2-tailed alpha level of 0.05 was used to determine statistical significance. SAS software version 9.4 was used to conduct statistical analyses (SAS; Cary, North Carolina).


Demographic Characteristics 

A total of 89,034 BCBSLA members were included in the study sample, with 13,914 enrollees in the QBPC group and 75,120 enrollees in the control group (Table 1). An average age of 46.9 years was observed in the QBPC group compared with 45.2 years in the control group. A total of 54.1% of enrollees in the QBPC group were female compared with 52.5% in the control group. The general health of enrollees measured by risk score was worse in the QBPC group compared with the control group. Higher proportions of enrollees in the QBPC group were defined as at risk, struggling, and in crisis compared with the control group, which contained higher proportions of enrollees defined as healthy and stable. Enrollees in the QBPC group were primarily from Baton Rouge (58.6%), New Orleans (19.6%), and Shreveport (15.2%), whereas enrollees in the control group were primarily from New Orleans (34.7%), Baton Rouge (27.9%), and Lake Charles (16.0%). A total of 67.4% of enrollees in the QBPC group held PPO plans and 32.3% were HMO plan members, whereas 78.4% of enrollees in the control group held PPO plans and 21.51% were HMO plan members. There were no significant differences in gender, risk categories, and residential regions between the intervention and control groups after propensity score weighting (P >.05).  

Healthcare Utilization 

Total office-based visits increased in both the QBPC and control groups (Table 2). The increase in the intervention group was significantly less than the increase in the control group (RR, 0.99; P = .0066) due to increased visits to specialists by members of the control group (RR, 0.97; P ≤.0001). However, visits to PCPs/NPs increased in both the QBPC and control groups by 60.26 and 7.59 per 1000 members, respectively. The increase in the QBPC group was significantly greater than the increase in the control group (RR, 1.02; P = .0106). 

Copyright AJMC 2006-2020 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up