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The American Journal of Managed Care December 2017
Chronic Disease Outcomes From Primary Care Population Health Program Implementation
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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; & Jeremiah D. Schuur, MD, MHS
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
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Alexis Coulourides Kogan, PhD; Eileen Koons, MSW, ACSW; and Susan Enguidanos, PhD
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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; & Stuart L. Goldberg, MD
Trends in Bisphosphonate Initiation Within an Integrated Healthcare Delivery System
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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

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
This study examined emergency department use by Medicaid beneficiaries with disabilities in safety-net clinics that have adopted the patient-centered medical home model compared with matched comparison beneficiaries.
To identify clinics with performances similar to PCMH clinics prior to the implementation of the PCMH model, individual-level propensity scores were calculated using pre-PCMH baseline data from nondisabled Medicaid beneficiaries who were enrolled throughout 2011. The matching was done by using a logistic regression model with the dependent variable of PCMH status and the independent variables of age, gender, race/ethnicity, AHI, CRG category, inpatient admission (yes/no), number of ED visits, and number of office visits.17 The resulting propensity score was the conditional probability of being assigned to a PCMH clinic. Group 2 clinic-level propensity scores consisted of averaged individual-level scores. The matching was conducted by pair matching without replacement within a caliper distance equaling 0.2 of the standard deviation of the logit of the propensity score. The matching results are presented in Table 1.

Propensity score matching was used to identify comparable individuals with disabilities between PCMH and non-PCMH clinics in Group 3. The model setup was to use PCMH status as a dependent variable and age, gender, race/ethnicity, AHI, and CRG category as independent variables. After a 1-to-1 individual match using the caliper limit described earlier, the PCMH and comparison groups included 1283 individuals assigned to 1 of 7 and 5 clinics, respectively. The matching result is presented in eAppendix 1. As a sensitivity test for the matching result of Group 3, we also adopted propensity score weighting to adjust for any potential selection bias. The same set of variables from eAppendix 1 was used to estimate an inverse probability of being in the PCMH group. The synthetic PCMH and non-PCMH groups were then generated by weighting the propensity score to achieve distributional equivalence.18

We used simple and multiple logistic regression models to describe the association between PCMH status and utilization outcomes in 2013. Independent variables in the adjusted model included gender, race/ethnicity, AHI (based on zip code), and CRG category. A 2-tailed P <.05 was treated as significant in all statistical tests. All data management and analyses were conducted using SAS version 9.3 (SAS Institute; Cary, North Carolina).  

RESULTS

Group 1 was composed of 2269 individuals with disabilities assigned to PCMH clinics and 21,897 to non-PCMH clinics. In unadjusted analyses, the rate of excess ED visits (≥2 ED visits per person per year) was 12.9% lower in the PCMH group compared with the non-PCMH group (P <.05). The adjusted odds ratio (OR) of having excess ED visits was 25% lower in the PCMH group compared with the non-PCMH group (P <.05). The rate of excess ED visits was 17% lower in Group 2 when comparing the PCMH group with the non-PCMH group (P <.05) and the adjusted OR was 33% lower (P <.05). 

After limiting individuals to those with at least 1 office visit (Group 2a), in unadjusted analyses, the rates of at least 1 ED visit and excess ED visits were 14% lower and 28% lower, respectively, in the PCMH group compared with the non-PCMH group (P <.05 for both). The adjusted ORs of having at least 1 ED visit and excess ED visits were 21% lower and 38% lower, respectively, in the PCMH group compared with the non-PCMH group (P <.05 for both). However, there were no significant reductions in ED use found among individuals without any office visits in the PCMH group.

Group 3, a subset of Group 2 after pair-matching based on demographic characteristics and underlying health conditions, had similar results to Group 2. In unadjusted analyses, the rates of at least 1 ED visit and excess ED visits were 3% lower and 16% lower, respectively, in the PCMH group compared with the non-PCMH group (P >.05). The adjusted OR of having excess ED visits was 28% lower in the PCMH group (P <.05) (Table 2). The sensitivity test of Group 3 using propensity score weighting had similar results to Group 3, where the adjusted OR of at least 1 ED visit was 7% lower (95% CI, 0.79-1.10) and the adjusted OR of at least 2 ED visits was 28% lower (95% CI, 0.58-0.88).

Rates of readmissions or acute hospitalizations were not significantly different between PCMH and non-PCMH groups, aside from Group 2a having a 21% lower rate of acute hospitalization and a 28% lower OR of acute hospitalization in the PCMH group. Readmission rates were consistently lower in the PCMH group across all the matching sets (Table 2).

The stratification analyses of Group 2 to examine the association between patients’ characteristics and ED use among those with and without office visits are presented in Table 3 and Table 4. Baseline characteristics including PCMH versus non-PCMH group (OR, 0.79), age (OR, 1.01), female versus male (OR, 1.34), CRG category 4 versus category 1 (OR, 2.08), and household income ranging from $30,000-$40,000 versus less than $30,000 (OR, 1.33) were significantly associated with having at least 1 ED visit among patients with at least 1 office visit. However, among individuals without any office visits, only CRG category was significantly associated with having at least 1 ED visit (Table 3). Similar patterns were found when assessing the association between patients’ characteristics and excess ED visits (Table 4).

DISCUSSION

Research findings have shown that Medicaid beneficiaries in California were associated with frequent hospital admissions and heavy reliance on the ED compared with the commercially insured population.19 The ED use rate in California Medicaid grew between 2005 and 2010, increasing from 572 to 651 visits per 1000 enrollees.19 This rate was 4 times higher than that among privately insured enrollees and 2.5 times higher than that among the uninsured. In addition, Medicaid patients consistently had the highest rates of visits for potentially preventable conditions.20 Medicaid beneficiaries with disabilities can be expected to have even higher rates of ED use. 

Our study, through propensity score matching and stratification analysis, demonstrates a consistent finding that safety-net clinics operating under a PCMH model can decrease excess ED use by approximately 30%. The scale of reduction grows when subdividing the study population to those with at least 1 office visit per year. These results provide encouraging evidence that the PCMH model can potentially be an effective strategy to reduce excessive ED use, particularly for frequent users among the disabled population.

The PCMH model has shown promising results in managing healthcare utilization for a wide range of populations (eg, members of commercial insurance plans, elderly people, patients with chronic conditions, and children).21-27 People with disabilities, however, who tend to have a higher need for healthcare services, have not often been the subject of research on the impact of practice transformation on healthcare utilization. Further, examples from other states13,28 may not reflect the experience of states like California, where most insurers delegate responsibilities, including utilization and chronic disease management, to a contracted independent physician association. 

Qualitative evidence on the success of the PCMH model in California’s Medicaid program was reported by Chu et al, based on semi-structured interviews.29 The major areas covered in the interviews included the adoption of information technology, involving patients in decision making, number of disease management programs, and measurement of quality and access to care (eAppendix 2). The findings reported in the study included that PCMH clinics often operate during expanded office hours and offer broader disease management programs that cover more chronic conditions than do non-PCMH clinics. An executive at one PCMH clinic also stated that health information technology was useful in informing and improving decision making. There were no definitive expert opinions on attributes related to quality of care and patient engagement. The interview results pointed to the importance of access to care in the success of PCMHs and resonated with our findings based on group 2a that the scale of PCMH impact in reducing ED visits was greater among individuals with office visits.

Limitations

There were several limitations in this study. As we lacked the baseline data of individuals with disabilities, we used nondisabled Medicaid beneficiaries’ health service utilization as a proxy for clinics’ overall performance. This choice was based on the assumptions that the performance of clinics should stay relatively consistent for nondisabled individuals and individuals with disabilities and that the implementation of the PCMH was the only difference between PCMH and comparison groups. The small number of PCMH clinics and lack of information on staffing and quality performance scores prevented us from conducting analyses with clinics’ information directly. In addition, we did not consider fixed effects from clinics in the analysis. However, because we examined the variation of the propensity score of PCMH clinics when adjusting for beneficiaries’ demographic characteristics and healthcare resource utilization, the small standard deviation of 0.01, with a mean propensity score of 0.14, indicated that the fixed effects from clinics probably have limited impact on the estimation of standard error. Understanding that selection bias could happen at the clinic level (ie, willingness to apply for the PCMH recognition) and at the individual level (ie, preference of PCP), we conducted a series of propensity score matchings to identify clinics as well as individuals with disabilities who had similar baseline characteristics for the comparison. Although our findings may have limited generalizability due to the multiple matching steps, the potential endogeneity problems should be largely resolved with comparable clinics and study groups after matching.

CONCLUSIONS

People with disabilities are an understudied population, with higher need for healthcare services than many other populations. With a focus on nonelderly Medicaid beneficiaries with disabilities, our study’s findings highlight that the adoption of the PCMH model in safety-net clinics was associated with a reduction of excess ED use.

Author Affiliations: Department of Healthcare Economics, Landmark Health (LC), Huntington Beach, CA; Department of Pharmaceutical Economics and Policy, University of Southern California (NS), Los Angeles, CA; Department of Healthcare Outcome and Analysis, L.A. Care Health Plan (MT, KM), Los Angeles, CA; San Mateo County Health System (LR), San Mateo, CA; Inland Empire Health Plan (JNS), Rancho Cucamonga, CA.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. 

Authorship Information: Concept and design (LC, NS, MT, LR, JNS); acquisition of data (LC, LR, JNS); analysis and interpretation of data (LC, NS, MT, KM, LR); drafting of the manuscript (LC, KM, LR); critical revision of the manuscript for important intellectual content (LC, NS, KM, LR, JNS); statistical analysis (LC, LR); provision of patients or study materials (LC, LR); administrative, technical, or logistic support (MT, KM); and supervision (NS, MT, KM). 

Address Correspondence to: Lihao Chu, PhD, Landmark Health, 7755 Center Ave, Ste 630, Huntington Beach, CA 92647. E-mail: lchu@landmarkhealth.org.
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