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The American Journal of Managed Care March 2017
Impact of a Pharmacy-Based Transitional Care Program on Hospital Readmissions
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Ajit A. Dhavle, PharmD, MBA; Seth Joseph, MBA; Yuze Yang, PharmD; Chris DiBlasi, MBA; and Ken Whittemore, RPh, MBA
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Effects of an Enhanced Primary Care Program on Diabetes Outcomes
Sarah L. Goff, MD; Lorna Murphy, MA, MPH; Alexander B. Knee, MS; Haley Guhn-Knight, BA; Audrey Guhn, MD; and Peter K. Lindenauer, MD, MSc
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Julie A. Schmittdiel, PhD; Jennifer C. Barrow, MSPH; Deanne Wiley, BA; Lin Ma, MS; Danny Sam, MD; Christopher V. Chau, MPH; Susan M. Shetterly, MS
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Effects of an Enhanced Primary Care Program on Diabetes Outcomes

Sarah L. Goff, MD; Lorna Murphy, MA, MPH; Alexander B. Knee, MS; Haley Guhn-Knight, BA; Audrey Guhn, MD; and Peter K. Lindenauer, MD, MSc
An insurance company—sponsored enhanced primary care program had little effect on selected outcomes for low-income patients with diabetes.
A total of 319 patients with T2D were included in the study: 72 in the Buena Salud (intervention) group and 247 in the control group. The median age was 53 years (interquartile range, 45-59 years), 63.6% were female, and Spanish was the preferred language for 57.7% (Table 1). Most (estimated 90%) of the Buena Salud patients were referred to the program by a healthcare provider, with fewer (estimated 10%) enrolled via auto-enrollment or self-referral. Baseline differences between the groups included the following: control group patients were older (aged 54 vs 50 years), more likely to be infected with HIV (11.3% vs 2.8%), and less likely to have been diagnosed with anxiety or depression (49.8% vs 73.6%) (Table 1). Baseline A1C was also lower in the control group (7.8%; SD = 2.1) compared with the Buena Salud group (8.1%; SD = 2.2), and the baseline number of ED visits per person per year for those with any visit was lower in the control group (2.1 per year; SD = 3.5) compared with Buena Salud (3.5 per year; SD = 3.7). Baseline unplanned hospitalizations differed, with 17.8% of controls and 26.4% of Buena Salud participants having had at least 1 hospitalization in the year preceding enrollment. All other variables were similar at baseline. Extractor agreement was greater than 90%.

Clinical Outcomes

A1C. There was no difference in the change in A1C values between intervention and control patients in either unadjusted or adjusted models (absolute DID = 0.38; 95% CI, –0.13 to 0.88; P = .15) (Table 2). The difference in the change in the percent of patients achieving the target A1C was –0.9% (95% CI, –10.4% to 8.6%; P = .85) in unadjusted models and –1.4% (95% CI, –10.8% to 8.1%; P = .78) in adjusted models (Table 2). 

Blood pressure and lipids. With the exception of DBP, we found no differences in the change in hemodynamic or lipid profiles between the control and Buena Salud groups in unadjusted or adjusted models (Table 2). For DBP, there was a significant difference of 2.5 mm/Hg (95% CI, 0.8-4.3; P = .004) in both the adjusted and unadjusted models. This reflected a rise in mean DBP for the Buena Salud group and a fall for controls (Table 2). 

Process Measures and Utilization

We found that the percentage of Buena Salud patients having A1C measures did not change during the study period and that although the percent of controls with the recommended number of measures dropped, the change between the 2 groups was not statistically significant (Table 3). Similarly, Buena Salud saw a 4.2% increase in patients with guideline concordant LDL-C measures while controls dropped by 5.7%, for a DID of 9.8%, but this was also not a significant change (Table 3). There was a significant difference in the percent of patients with the recommended number of microalbumin/creatinine ratio measures: the Buena Salud group increased by 25% (95% CI, 11.4%-38.8%) compared with a 2.8% increase (95% CI, –4.6% to 10.2%) among controls (P <.01). Change in the annual rate of ED visits did not differ between groups, but unplanned hospitalization rates decreased by 2.8% (95% CI, –13.7% to 8.1%) in the Buena Salud group and increased by 8.9% (95% CI, 2.9%-15.0%) among controls, resulting in a DID of 11.7% (P = .06). The difference remained the same in adjusted models, but the P value increased to .11 (Table 3). 

DISCUSSION

In this controlled before-and-after study, we found that a team-based enhanced primary care program, Buena Salud, did not appreciably improve T2D process, outcome, or utilization measures for low-income Hispanic patients during the program’s first 15 months of existence compared with patients with T2D who did not participate in the program. Positive effects included a greater increase in the percent of patients with the appropriate number of measurements of microalbumin/creatinine ratios. There was also a trend toward fewer unplanned hospitalizations for Buena Salud patients compared with controls.

Diabetes affects nearly 10% of the US population and generated $245 billion in healthcare costs in 2012, a 41% increase from 2007.24 Numerous studies have tested innovative approaches to improving care for the patients from populations with the worst T2D outcomes. For example, investigators assessed the effectiveness of a computer-based support system in the context of primary care team-based management of T2D in a controlled natural experiment. Similar to the current study, which also could be categorized as a natural experiment, the investigators found improvement in process measures, such as rates of microalbumin/creatinine and A1C testing in the intervention group (n = 435) compared with controls (n = 435) after 12 months, but a limited effect on patient outcomes or healthcare costs.25 

In another study conducted with 165 Mexican American patients in rural Texas, the investigators tested whether the addition of a nurse case manager to a diabetes education and self-management program improved patient outcomes by addressing sociocultural barriers to accessing the successful self-management program.1 The study used a pre-post controlled design, similar to the current study except that it was a prospective cohort. The outcomes included changes in A1C, fasting blood sugar, lipids, blood pressure, diabetes-related knowledge, health behaviors, and body mass index over a 6-month time period. The study found no difference in changes in outcome measures between groups. 

Conversely, in a randomized clinical trial of 299 patients in 6 health centers serving low-income patients in San Francisco, the investigators tested the impact of trained peer health coaches on A1C levels.8 Patients in the peer health coach group experienced an absolute reduction in A1C of 1.1% while controls’ A1C levels dropped by only 0.3% (P = .01, adjusted). The same research team also tested the effect of medical assistants trained as health coaches in a randomized clinical trial of 441 patients with T2D in 2 safety net primary care clinics in San Francisco.26 They found that patients in the intervention group had lower A1C and lipid levels after 6 months of exposure to the intervention, but that DBP changes did not differ between groups. The results of prior studies and the current study suggest that team-based interventions to improve diabetes care and outcomes may be successful in the controlled setting of a randomized clinical trial, but that it may be challenging to translate these interventions into practice.

What factors might be responsible for the very modest intervention effects seen in the current study? Although some randomized clinical trials have shown improvements in A1C in as little as 6 months, an enhanced primary care model implemented outside of a clinical trial may require a longer exposure to the intervention for an effect to be realized. The current study tested Buena Salud’s effectiveness in its first 15 months of existence. It is possible that it may take longer than this for the team to optimize the care it provides.27 We followed patients for a relatively brief period after enrollment, and it may take more time for team members to develop trusting relationships with care recipients. We learned through interviews with the Buena Salud team that there was no systematic process for documenting their interactions with patients during the time period studied. This meant that we could not accurately measure the intervention doses that individuals received. 

In a small study such as this, variation in expertise amongst the Buena Salud team members also could have influenced the outcomes observed. This study’s strengths included the following: comparison with a control group, use of a DID analysis that adjusted for secular trends in care and outcomes, and risk adjustment using a broad array of clinical and demographic data. The latter allowed us to address the nonrandom assignment of patients to intervention and control groups. 

Limitations

First, this was an observational study and not a randomized clinical trial. The Buena Salud program was intended to provide support for the sickest patients, as evidenced by the measured baseline differences found between Buena Salud patients and controls, but there may have been other unmeasured important differences not accounted for in our extensive risk adjustment. Second, this study evaluated patients from 1 health center. Although this allowed us to focus on the population of interest, the intervention might have different effects in other populations or in other health centers with a similar population. Third, many primary care interventions are intended to decrease costs while improving care quality and outcomes. We elected not to explore cost savings in this study with a relatively short follow-up period because additional expenditures may be needed in populations that experience significant health disparities and high burdens of chronic disease, particularly in the early phase of an intervention.28 Fourth, we had a substantial amount of missing LDL-C data, due largely to many lipid screens having only total and high-density lipoproteins documented. Finally, several diabetes guidelines have changed since the study’s inception, making some of the cut-points and screening frequencies used for analyses appropriate for the time period during which data were collected, but inconsistent with current diabetes care guidelines.

CONCLUSIONS

A team-based enhanced primary care program delivered by a multidisciplinary bilingual team that was linguistically and culturally concordant with the majority of patients enrolled in the program had a limited effect on care processes, outcomes, and utilization for low-income Hispanic patients with T2D. Care should be taken in drawing conclusions from outcomes assessed in the first year of a new program since there is likely a learning curve to engaging and partnering with patients in this context. Longitudinal effectiveness and implementation studies will contribute additional important information to our understanding of the potential benefits of enhanced primary care team interventions for vulnerable patients with T2D.

Author Affiliations: Center for Quality of Care Research (SLG, HGK, PKL), and Department of Medicine (SLG, HGK, AG, PKL), and Epidemiology and Biostatistics Core (ABK), Baystate Medical Center, Springfield, MA; Tufts University School of Medicine (SLG, AG, PKL, ABK), Boston, MA; Western New England Renal Transplant Associates (LM), Springfield, MA.

Source of Funding: This study was funded by an incubator grant from Baystate Medical Center. Dr Goff is supported by the NIH National Institute for Child Health and Human Development under award number K23HD080870.

Author Disclosures: Drs Goff, Guhn, and Lindenauer, and Mr Knee and Ms Guhn-Knight are employed by Baystate Health, which owns Health New England, a health insurance company that financially supports the Be Healthy/Buena Salud Program. Ms Murphy was employed by Baystate Health until July 2015. 

Authorship Information: Concept and design (SLG, AG); acquisition of data (SLG, LM, HGK); analysis and interpretation of data (SLG, ABK); drafting of the manuscript (SLG, LM, ABK, HGK, AG); critical revision of the manuscript for important intellectual content (SLG, LM, ABK, HGK, AG); statistical analysis (SLG, ABK); provision of patients or study materials (AG); obtaining funding (SLG); administrative, technical, or logistic support (SLG, HGK); and supervision (SLG). 

Address Correspondence to: Sarah L. Goff, MD, Baystate Medical Center, 280 Chestnut St, 3rd Fl, Rm 305, Springfield, MA 01004. E-mail: Sarah.goffmd@baystatehealth.org. 
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