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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; & 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
Investigating the Impact of Intervention Refusal on Hospital Readmission
Alexis Coulourides Kogan, PhD; Eileen Koons, MSW, ACSW; and Susan Enguidanos, PhD
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; & 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
Currently Reading
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.
ABSTRACT

Objectives: To examine emergency department (ED) use by individuals with disabilities in safety-net clinics that have adopted the patient-centered medical home (PCMH) model.

Study Design: This is a retrospective matched cohort study. Prior to matching, we identified 2269 nonelderly Medicaid beneficiaries with disabilities from a Los Angeles Medicaid managed care plan in PCMH clinics and 21,897 in non-PCMH clinics. 

Methods: To minimize self-selection bias from clinics and individuals, we created 3 comparison groups through a series of propensity score matching schemes that included matching clinics with similar health service utilization per patient and matching individuals with similar demographic characteristics and underlying health conditions. Rates of having at least 1 ED visit per year and excess ED use (defined as ≥2 ED visits per year) were compared across beneficiaries who received care from PCMH clinics and matched comparisons using logistic regression analyses. 

Results: After matching on clinic- and individual-level characteristics, the adjusted odds ratio (OR) of excess ED use was 25% to 33% lower (P <.05) in the PCMH group compared with the non-PCMH group. When limiting the study population to patients with at least 1 office visit, the OR of having at least 1 ED visit decreased by 21% (P <.05) for the PCMH group. Similarly, the OR of having excess ED use decreased by 38% (P <.05) for the PCMH group. 

Conclusions: Our study highlights that the adoption of the PCMH model in safety-net clinics was associated with reduced ED use in Medicaid beneficiaries with disabilities.

Am J Manag Care. 2017;23(12):e409-e415
Takeaway Points
Our study results suggest that adoption of the patient-centered medical home (PCMH) model in safety-net clinics can effectively reduce emergency department (ED) use in Medicaid beneficiaries with disabilities. Furthermore, the impact of the PCMH model on reducing ED use is greater among individuals with at least 1 office visit.
  • The adoption of the PCMH model in safety-net clinics can effectively reduce ED use by 25% to 33% in Medicaid beneficiaries with disabilities. 
  • The reduction of ED use through the PCMH model is greater among individuals with office visits than those without any office visits in a 1-year period.
  • The PCMH model can be an effective strategy to reduce excess ED use, particularly for frequent ED users among the disabled population.
Medicaid beneficiaries who qualify for services on the basis of having a disability represent a relatively small fraction of total Medicaid enrollees: in 2011, they made up 15% of all beneficiaries nationwide.1,2 Despite their small numbers, however, they account for 42% of total Medicaid spending.3 Compared with other states, California tops the nation in the discrepancy between disabled beneficiaries’ relative proportion and costs, as they make up 9% of all beneficiaries but constitute 41% of spending.1,3 Rising expenditures for beneficiaries with disabilities have been the focus for cost-cutting measures for decades.4 On the heels of the Affordable Care Act, California was one of the first states to build a reform portfolio through submission of the “Bridge to Reform” Medicaid 1115 waiver in 2010. One component of the waiver implemented a mandatory transition of seniors and persons with disabilities (SPD) from traditional fee-for-service (FFS) plans to managed care from 2011 to 2012. This waiver provision was designed to improve cost efficiency through the development of coordinated systems of care. Another major component included support for reform of safety-net facilities.5 

Medicaid beneficiaries with disabilities often have complex healthcare needs requiring a wide array of specialists and specialized facilities.6 They qualify for Medicaid based on a variety of conditions, including serious mental illness and behavioral health diagnoses, developmental disorders, severe chronic illnesses, and disabling brain or spinal cord injuries. Despite high levels of spending for these groups, they continue to frequently experience unmet needs for healthcare services, especially under FFS payment models. In a survey of working-age Medicaid beneficiaries with disabilities under FFS, more than 38% reported an unmet health need, commonly citing availability, accessibility, and language as barriers.7 To successfully transition such complex beneficiary groups to managed care requires a multifaceted approach that provides access to needed medical care and other supports and services and addresses access barriers specific to these populations, all while achieving improved cost efficiency.

Coordinated care delivery models are needed to provide effective, efficient, and patient-centered care for SPD enrolled in Medicaid. The patient-centered medical home (PCMH) is an existing model that strives to provide coordinated, accessible, high-quality care tailored to individual needs.2 This model has been adapted to various healthcare systems and populations (eg, private insurance, Medicare) with mixed results.8 Despite the somewhat variable evidence, 46 states have adopted the PCMH model to enhance their Medicaid and/or Children’s Health Insurance Program programs as of March 2015.9 Also notable is the adoption of the PCMH model by safety-net clinics with high numbers of Medicaid patients.10 Safety-net clinics provide a wide range of services to medically underserved and uninsured populations regardless of their ability to pay.11 Because Medicaid beneficiaries constitute a significant proportion of the patients who utilize safety-net clinics, building coordinated, effective, and efficient systems of care in safety-net clinics is critical.12

With such widespread adoption of the PCMH model throughout Medicaid programs, evaluation of its efficacy is critical. The existing literature on the effectiveness of the PCMH model for Medicaid beneficiaries with disabilities is limited. One of the few existing analyses showed promising results from a care management intervention aligned with PCMH principles in a North Carolina–based Medicaid program. The program yielded significant savings for its high-risk disabled population, particularly among those with chronic conditions.13  

Running concurrently with the implementation of the PCMH model, the mandatory transition of SPD from FFS to managed care in California created a special opportunity that allowed us to evaluate the impact of the PCMH model on the utilization trends of nonelderly Medicaid beneficiaries with disabilities enrolled in a Los Angeles Medicaid Managed Care Plan (MMP). 

METHODS

Study Setting

California’s transition of Medicaid SPD to managed care took place over a 12-month interval from June 2011 to May 2012. Over the same period, 12 safety-net clinics in Los Angeles County underwent practice transformation into PCMHs, receiving recognition from the National Committee for Quality Assurance (NCQA) as PCMHs in early 2012 (Figure 1).

The disability status of the study population was determined based on the Social Security definition of disability. Individuals must have an impairment, either medical, psychological, or psychiatric in nature, that keeps them from being able to perform substantial gainful activity. Medical records or functional assessments are used by the Social Security Administration to evaluate and determine applicants’ disability status.14    

Through in-person communications with leaders in these PCMH clinics, we learned that their practices included the following PCMH elements: expanded office hours, adoption of electronic health records, having at least 4 disease management programs, and following at least 2 sets of quality measures (ie, Healthcare Effectiveness Data and Information Set and Federally Qualified Health Center quality measures). Comparison clinics were 110 safety-net clinics that were contracted with the same MMP but did not acquire PCMH recognition from NCQA, the Utilization Review Accreditation Committee, or The Joint Commission prior to 2013. 

Study Design

We used a retrospective matched cohort design to compare healthcare utilization among Medicaid beneficiaries with disabilities enrolled in a single Los Angeles MMP who were assigned to either PCMH or non-PCMH safety-net clinics. Because clinic transformation and assignment to a PCMH were not completely random for both clinics and Medicaid beneficiaries, we developed a series of matching and stratification schemes in order to account for potential selection bias (Figure 2).

Matched comparison groups were constructed as follows: Group 1 included the entire cohort of Medicaid beneficiaries with disabilities. To identify comparable clinics between non-PCMH and PCMH clinics, Group 2 was created by selecting 12 of the 110 non-PCMH clinics to match 12 PCMH clinics on the basis of the average propensity score of individuals (ie, Medicaid enrollees without disabilities) enrolled prior to the implementation of the PCMH in 2011 in each clinic. This matching aimed to identify non-PCMH clinics whose patient population and performance in managing patients, in particular the healthcare utilization measures (ie, hospitalizations, ED visits, and office visits), were similar to those of PCMH clinics (Table 1). After matching on clinics, there were 2269 and 21,897 individuals with disabilities identified for the PCMH and non-PCMH groups, respectively. In addition, with the assumption that individuals with office visits had more opportunity to benefit from the PCMH model, Group 2 was stratified into individuals with at least 1 office visit (Group 2a) and without any office visit (Group 2b) during the 1-year follow-up period (Figure 2).

Approximately 80% of Medicaid beneficiaries with disabilities were assigned to the PCMH clinics based on an auto-assignment algorithm that chooses physicians solely on the member’s proximity to a clinic, age, and primary language. This assignment mechanism can be treated as a randomization process if all 3 factors in the algorithm can be controlled. Therefore, we matched Medicaid beneficiaries with disabilities who were continuously enrolled from June 1, 2012, to December 31, 2012, between non-PCMH and PCMH clinics based on an individual's demographic characteristics. This matching ensured selected individuals between non-PCMH and PCMH clinics had similar baseline characteristics in age, gender, race/ethnicity, and underlying health conditions and yielded 1283 matched dyads in Group 3 (eAppendix 1 [eAppendices available at ajmc.com]).

We excluded individuals 65 years or older in order to ensure that this study did not include anyone who received medical coverage through both Medicare and Medicaid. We also excluded individuals who switched from a PCMH clinic to a comparison clinic (0.2% of the sample) and imposed a 10-month continuous enrollment requirement, which excluded 10.2% of the sample.

Data

Data obtained from a local MMP consisted of member eligibility files, including individual demographics (age, gender, race/ethnicity, enrollment history, primary care provider [PCP] assignment, and location identification [ID] of PCP); administrative claims from January 1, 2011, to December 31, 2013, including service dates; International Classification of Diseases, Ninth Revision, Clinical Modification codes; procedure codes; and pharmacy claims. “Gender” and “Hispanic” were each coded as binary variables. Residential zip code was mapped to 2013 Census data to derive the average household income (AHI) for each study subject. Safety-net clinics were identified by matching the location ID of the assigned PCP with the location ID of a list of safety-net clinics provided by the local MMP. Hospital admissions, readmissions, and ED visits were measured using NCQA standard definitions and served as the outcomes of interest.15 Excessive ED visits were defined as 2 or more ED visits in 1 calendar year. 

Underlying health conditions were estimated using 3M Clinical Risk Groups (CRGs), a claims-based disease burden model.16 We fit the model using member eligibility files and medical and pharmacy claims from 2011 for Group 2 and from the second half of 2012 for Group 3. The CRG system assigns each individual to mutually exclusive and hierarchically ranked risk groups that identify the condition or conditions that best describe the individual’s clinical state. Such an assignment relates an individual’s historical and underlying clinical characteristics to the amount and type of healthcare resources that the individual will consume in the future. Groups were further classified into aggregated level 3 CRG groups based on estimated illness severity. The level of severity was determined based on the presence of multiple comorbidities and the interaction of conditions. We then estimated costs associated with each aggregated level 3 CRG group based on the New York State adult Medicaid program. The cost information was provided by the research team from 3M Health Information Systems. After examining the distribution of aggregated CRG weights, we categorized the population into 4 groups of similar membership count (CRGs 1-4) based on the consideration of severity of illness and resource use intensity.

Statistical Analysis

 
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