565021

research-article2015

HPPXXX10.1177/1524839914565021Health Promotion PracticePage et al. / Care Management Medical Home Center Model

Care Management Medical Home Center Model: Preliminary Results of a Patient-Centered Approach to Improving Care Quality for Diabetic Patients Timothy F. Page, PhD1 St. Anthony Amofah, MD, MBA, FACP2 Shelia McCann, MEd, SM, GPC2 Julie Rivo, BA3 Asha Varghese, BS4 Terisa James, MSW2 Marc Rivo, MD, MPH2 Mark L. Williams, PhD1 This article presents preliminary findings of the impact of an innovative care management model for diabetic patients. The model was implemented by seven Federally Qualified Health Centers serving 10,000 diabetic patients in Miami-Dade County. A primary intervention of this model is a centralized care management team that makes previsit phone calls to diabetic patients who have scheduled appointments. These previsit phone calls optimize patient knowledge and self-management goals, and provide patient care coordinators with relevant clinical information to optimize the office visit and help to ensure completion of recommended diabetic preventive and chronic care services. Data suggest that following the implementation of this care management model, more diabetic patients are receiving regular care, and compliance with recommended tests and screenings has improved. Keywords: diabetes; chronic disease; community intervention; health promotion; outcome evaluation; program planning and evaluation

Health Promotion Practice Month XXXX Vol. XX , No. (X) 1­–8 DOI: 10.1177/1524839914565021 © 2015 Society for Public Health Education

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Florida International University, Miami, FL, USA Health Choice Network of Florida, Miami, FL, USA 3 Duke University, Durham, NC, USA 4 General Electric Foundation, Fairfield, CT, USA 2

Authors’ Note: The authors would like to acknowledge the important contributions of the following to the development, implementation, and ongoing support of the Care Management Medical Home Center: Kevin Kearns, Chief Executive Officer and President of Health Choice Network of Florida; Rogelio Fernandez, RN, Health Choice Network of Florida; Alex Romillo, Chief Operating Officer, Health Choice Network of Florida; Ricardo Gomez, Health Choice Network; Natalia Valera, Health Choice Network of Florida; Marlen Bazan-DeLeon, Health Choice Network; Hirut Kassaye, Health Choice Network; Robert Linder, Borinquen Community Health System; Deborah Gracia, DO, Borinquen Community Health System; Ray Kayal, Camillus Health; Fernandia Mercade, MD, Camillus Health; Brodes Hartley, Community Health of South Florida; Mario Jardon, Citrus Health Network; Noel Fernandez, DO, Citrus Health Network; Annie Neasman, Jessie Trice Community Health Center; Edwin Bosa Osorio, Jessie Trice Federally Qualified Health Center; and Mark Rabinowitz, MD, Miami Beach Community Health Center. In addition, the authors acknowledge the generous financial support and leadership training provided by the GE Foundation. The authors also acknowledge the generous financial support provided by Health Foundation of South Florida (HFSF), and specifically HFSF President Steve Marcus and Vice President Peter Wood. HFSF funding was essential in assisting 7 South Florida FQHCs and their 22 primary care practice to attain Level 3 National Committee for Quality Assurance (NCQA) Patient-Centered Medical Home recognition, NCQA’s highest level of distinction and the first primary care offices in the state of Florida to achieve this level on the 2011 NCQA standards. Address correspondence to Timothy F. Page, Florida International University, 11200 SW 8th Street, AHC 2 Room 554, Miami, FL 33130, USA; e-mail: [email protected].

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Background >> Diabetes is a pandemic chronic disease with a rising prevalence in many countries around the world (Centers for Disease Control and Prevention [CDC], 2011; International Diabetes Federation [IDF], 2012; Sicree & Shaw, 2007). An estimated 371 million people are living with diabetes worldwide, half of whom are undiagnosed (IDF, 2012). In 2011, a total of 11% of the total health care expenditures in the world, approximately $465 billion dollars, were related to diabetes (IDF, 2012). It is projected that by 2030, a total of 439 million adults (aged 20-79 years) will have diabetes. This represents a 69% increase in prevalence in developing countries and a 20% increase in developed countries, driven by urbanization, population aging, and dietary changes (Shaw, Sicree, & Zimmet, 2010). In the United States, 25.8 million people have diabetes, representing 8.3% of the population (CDC, 2011). The cost of diabetes in the United States in 2007 was $217 billion. This included direct medical costs ($116 billion), indirect costs ($58 billion), cost of undiagnosed diabetes ($18 billion), and cost of prediabetes ($25 billion; American Diabetes Association, 2007; Dall et al., 2010). By 2012, the total estimated cost had risen to $245 billion, consisting of $176 billion in direct medical costs and $69 billion in reduced productivity. In 2012, indirect costs were identified to be increased absenteeism ($5 billion), reduced productivity while at work ($20.8 billion), reduced productivity for those not in the labor force ($2.7 billion), inability to work due to a disease-related disability ($21.6 billion), and lost capacity for productivity due to early mortality ($18.5 billion; American Diabetes Association, 2013). The direct and indirect costs of diabetes disproportionately affect low-income and minority populations who are more likely to have diabetes at a younger age and in a more severe form (Dieren, Beulens, Schouw, Grobbee, & Neal, 2010; Harris, 2001). Other barriers to chronic disease control include patient self-care barriers to medication and treatment adherence, such as depression, low self-efficacy, low health literacy, and poor patient–provider communication (Duru et al., 2009). These factors are more prevalent in minority populations and may be partially implicated in the disparity in diabetes outcomes that may lead to poor health outcomes, higher costs, and a reduced quality of life. Planned care visits and patient-centered medical homes (PCMHs) have shown effectiveness in improving care for people living with diabetes (Bray, Thompson, Wynn, Cummings, & Whetstone, 2006; Kimura, DaSilva, & Marshall, 2008; Wolber & Ward, 2010). A recent systematic review by Bojadzievski and

Gabbay (2011) analyzed how the elements of the PCMH model can be applied to improve diabetes care and reported demonstration pilots that include diabetes as a target disease. The eight medical home pilot initiatives reviewed in the study showed encouraging results for the PCMH model as a viable mechanism to improve the quality and reduce costs of diabetes care. PCMH models among the different projects included payment reforms, care planning, patient coaching, and learning collaboratives for care providers. In addition, the Mayo Health System Diabetes Translation Project conducted from 1996 to 1999 found that care planning for diabetic patients improved both compliance and clinical outcomes (Montori et al., 2002). The purpose of this study was to measure the preliminary impact of an innovative care management model, the Care Management Medical Home Center (CMMHC), on improving access to and quality of care for 10,000 patients with diabetes served by a network of federally qualified health centers (FQHCs) in MiamiDade County. Previsit planning is one of the core features of CMMHC. The positive impact of contacting the patient via a telephone call on improving attendance to an upcoming provider visit has been verified in the literature (Hashim, Franks, & Fiscella, 2001; Haynes & Sweeney, 2006; Johnson, Mold, & Pontious, 2007; Mitchell & Selmes, 2007). Telephone calls have been shown to be more effective than text message reminders, although this may change for the new generation of cell phone users (Downer, Meara, & Da Costa, 2005; Liew et al., 2009). Specifically, the effectiveness of an automated call system has been compared with a clinical or administrative staff member call system. Automated calls have been shown to be less effective than human-contacted calls in decreasing the no-show rate (Parikh et al., 2010). Shared medical appointments and group visits will be added to the care management plan in the future. An innovative aspect of CMMHC relative to other care management models is that coordination is done by a centralized team rather than by participating FQHCs. The purpose of this study was to describe the CMMHC model and to provide preliminary results of the model’s effect on the number of diabetic patients receiving regular care and on compliance with recommended tests and screenings.

Method >> Setting

Health Choice Network (HCN) is a national model of successful collaboration among community health centers and behavioral health centers that provide primary

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 Intervention Description

Figure 1 The Care Management Medical Home Center Model

and behavioral health care to more than 830,000 patients across 11 states. Health Choice Network of Florida (HCNFL) is a member of HCN with 24 member community health centers and behavioral health centers serving more than 500,000 patients. In Miami-Dade County, HCNFL’s membership includes seven FQHCs serving more than 200,000 patients. HCN and its member FQHCs have been at the forefront of implementing health information technologies critical to improving health care quality, such as electronic health records (EHRs), EHR-integrated evidence-based care pathways, and clinical performance indicator reporting. In 2012 with funding from The Health Foundation of South Florida, HCNFL assisted six of its Miami-Dade County FQHCs to successfully receive Level 3 PCMH designation on the National Committee for Quality Assurance’s (NCQA) 2011 PCMH standards. These 22 primary care practices and 99 primary care clinicians were the first primary care practices in Florida to receive NCQA’s highest level of distinction on the 2011 standards. In 2012, with a 3-year funding commitment from the GE Foundation Developing Health, HCNFL established a CMMHC to better serve its 10,000 diabetic patients in South Florida. Rather than having individual centers employ staff to coordinate patient care, HCNFL’s approach leverages the economies of scale of a centralized team of nurses and patient navigators in order to reach out to patients with scheduled appointments and complete motivational appointment reminder conversations. The centralized team’s focus on patient encouragement, identification of barriers to keeping appointments, and communication of patient feedback to care teams creates an important element in enabling FQHCs to improve health outcomes. Figure 1 illustrates the communication flow between the centralized care team, FQHC care coordinators, patient care teams, and patients.

The objectives of CMMHC are to increase patient compliance with scheduled appointments, follow through on referrals, and complete tests/exams in a timely manner. CMMHC is a health center–controlled, network-based model that uses a centralized team of nurses and health technicians who call patients with diabetes 7 days prior to any scheduled appointment in order to conduct motivational reminder conversations. Centralized, network-based care team coordinators review status of referrals and actions taken in support of self-management goals. They acknowledge patient progress in managing the daily challenges of living with diabetes and encourage them to continue the effort. They listen to patients and hear their questions and motivate them to talk to their primary care provider and patient care team about concerns or issues. Before the end of the call, each patient has an expectation for his or her upcoming appointment and a list of questions to ask during the visit. The centralized team informs patient care team coordinators positioned in community health centers about questions and concerns, appointment needs, and any specific tests and exams that are due or outstanding for each patient. The patient care team coordinators work with the patient and patient support services departments to further facilitate patient completion of the appointment. When the patient arrives for the scheduled visit, the patient care team is prepared to answer questions, address issues/concerns, and provide additional support. The patient knows what to expect during the visit and has the confidence to ask questions and participate in making the visit successful. The CMMHC model leverages a centralized health information infrastructure to establish and maintain diabetes patient registries. One registry, CMMHC Daily Call Roster, enables the centralized care coordinator team to identify patients with scheduled appointments for previsit preparation, as described earlier. A second registry, the Diabetic Patient Registry and Report Card, supports performance analysis by center and across the full program. FQHCs use data from the Diabetic Patient Registry and Report Card to identify populations of patients for follow-up activities. Additional supplemental reporting tools identify specific patients whose care has lapsed and/or who have outstanding care needs. Data Collection HCNFL and the FQHCs use aggregated patient data to track and report population specific chronic disease outcomes and trends. Data for this evaluation were obtained from the Diabetic Patient Registry and Report

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Card for the seven centers participating in CMMHC. During the first year, one of the original community health centers ceased operation, reducing the number of participating centers to six. This registry presents demographic and clinical care measures in 30 categories for 179 items. Produced monthly, the registry enables analysis of changes over time and comparisons within patient populations. Patient experience describes the number of primary and specialist patient visits to identify opportunities to engage patients about their care. Demographics identify sex, race/ethnicity, special populations, and insurance carried by patients. Clinical measures include self-management goals and recommended tests/screenings. Measures This study measured the impact of the CMMHC on the number of patients in the registry receiving care. A diabetic patient was defined as being in the registry and receiving care if he or she had at least two primary care visits to the FQHC in the previous 12 months. The number of patients that remained part of the model from baseline to 12 months post-implementation was computed for the six centers. Diabetic care compliance measures included diabetic foot, eye, dental, and urine protein exams; creatinine screening; receipt of flu and Pneumovax vaccines; and HbA1c (glycated hemoglobin Alc) and cholesterol (LDL [low-density lipoprotein]) tests. Screenings for colorectal cancer and cervical cancer were also included as part of routine health measures in an effort to address additional population health concerns in Miami-Dade County. Patients were considered in compliance with care recommendations if they had completed each of the exams or screenings at least once in the past 12 months. Compliance with HbA1c testing was defined as having had at least 2 HbA1c tests as least 91 days apart in the past 12 months. Compliance rates at baseline and 6 months post-implementation included data from the seven centers participating during that timeframe. Compliance rates at 12 months post-implementation contained data only from the six remaining centers participating in model delivery. Analyses The effect of CMMHC on the number of diabetic patients in the registry receiving regular care was measured as the change in the number of patients from baseline to 12 months post-implementation. Changes were assessed using data from the patient registry. At baseline, 6 months post-implementation, and 12 months

post-implementation, the number of diabetic patients in the registry was measured. The number of patients in the registry was measured for each FQHC and then aggregated. The change in the number of patients in the registry was measured by comparing the 12 month and baseline patient counts. The percentage increase in the number of patients in the registry was computed by dividing the change in the number of patients from baseline to 12 months post-implementation by the baseline number of patients. The effect of the CMMHC model on patient compliance with primary care provider orders was also assessed using data from the patient registry. To show the changes in compliance in the clinics over time, the percentage of diabetic patients in the registry compliant with each measure during the prior 12 months was computed at baseline, 6 months, and 12 months post-implementation. These measures were computed for each FQHC and then aggregated. For each test, the proportion in compliance was measured as the number who had successfully completed the test or screening within the specified time period divided by the number of patients in the registry at that time. The percentage change in the compliance measures was calculated as the change in compliance rates from baseline to 12 months post-implementation divided by the baseline compliance rates. Z tests of differences in the proportions at baseline and 12 months post-implementation were performed to determine whether the differences were statistically significant.

Results >> Patient demographics, calculated at baseline, are reported in Table 1 for each participating FQHC. Most patients in the registry were older than 50 years, uninsured or covered by Medicaid, and from a racial or ethnic minority group. From baseline to 12 months post-implementation, the total number of patients in the registry increased by 12% (925 patients), from 7,664 at baseline to 8,589 at 12 months. An additional 519 patients were added during the first 6 months of program implementation, and an additional 406 patients were added between Months 6 and 12. During the first year in the six FQHCs that remained in the project, 5,539 of the 8,589 patients (65%) in the registry were successfully contacted by a member of the centralized care team. Of the patients in the registry, 8% did not have a working telephone number and therefore were unable to receive the previsit phone call. Data on the effect of CMMHC on compliance measures are reported in Table 2. Ten of the 11 measures

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Table 1 Patient Demographics

Gender  Male  Female Age (years)  18-29  30-39  40-49  50-59  60-69  70+ Race  Asian   African American  White  Other Ethnicity   Hispanic or Latino  Non-Hispanic  Unreported/refused Insurance  Uninsured  Private  Medicare  Medicaid Number of patients

FQHC 1

FQHC 2

FQHC 3

FQHC 4

FQHC 5

FQHC 6

FQHC 7

66.8 33.2

61.2 38.8

40.4 59.6

48.0 52.1

36.4 63.6

29.7 70.3

39.1 61.0

1.1 3.7 15.1 38.1 30.7 11.4

1.4 2.3 20.8 44.3 25.8 5.5

2.4 6.1 17.7 36.0 27.3 10.5

2.5 4.8 17.8 40.0 27.4 7.5

2.2 3.3 14.6 37.3 33.6 9.2

2.2 4.3 13.0 37.1 31.0 12.5

3.2 7.2 14.2 34.6 29.4 11.4

0.1 50.3 45.7 3.9

0.2 45.7 49.5 4.1

1.8 36.9 59.8 1.1

0.2 6.6 89.0 4.11

0.4 20.7 78.7 0

0.4 65.5 27.2 6.9

1.6 25.2 72.4 0.7

46.3 53.1 0.6

53.4 45.4 1.1

49.1 50.5 0.4

91.1 8.5 0.5

78.9 21.1 0

31.2 68.2 0.6

66.3 31.3 2.4

75.0 2.3 10.4 12.3 1255

91.3 0.2 0.9 7.5 438

70.9 2.8 8.8 17.5 2609

57.5 0.5 8.0 34.0 438

77.1 0.2 9.4 13.3 459

74.5 2.2 13.6 9.8 1718

67.4 3.6 10.5 18.5 1206

NOTE: FQHC = federally qualified health centers. Demographics reflect the patients in the registry at each center during the first month of Care Management Medical Home Center implementation. A patient is defined as being in the registry if he or she had two or more primary care visits to the FQHC in the previous 12 months.

showed improvement from baseline to 12 months post-implementation. Compliance with eye exams increased by 5.6 percentage points overall, from 5.3% at baseline to 10.9% at 12 months (p < .001). The 5.6 percentage point change represents a 106.7% increase in compliance with eye exams. Compliance with diabetic foot exams increased by 10.9 percentage points, from 46.4% at baseline to 57.3% at 12 months (p < .0001). The 10.9 percentage point increase represents a 23.6% increase over the baseline measure. Compliance with dental exams increased slightly from 9.9% to 11.3%, but the difference was not statistically significant (p = .14). Compliance with urine protein exams increased by 4.4 percentage points, or 7% (p < .001). Compliance with creatinine screenings increased by

1.9 percentage points, or 2.5% (p = .016). Compliance with colorectal cancer screenings increased by 5.1 percentage points, or 44.6% (p < .001). Compliance with cervical cancer screenings increased slightly by 0.3 percentage points, but this difference was not statistically significant. Compliance with flu vaccines increased from 25% to 44.3% (p < .001). The 19.3 percentage point increase represents an increase of 77.4%. Compliance with Pneumovax vaccine increased from 24.3% at baseline to 32.9% at 12 months, representing changes of 8.6 percentage points and 35.1% (p < .001). Compliance with HbA1c tests increased by 2.4 percentage points from 34.1% at baseline to 36.6% at 12 months, an increase of 7.1% (p < .001). There was no change in compliance with LDL tests.

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Table 2 Effect of CMMHC on Compliance Measures Among Patients in the Registry Measure Eye exam Foot exam Dental exam Urine protein exam Creatinine screening Colorectal cancer screening Cervical cancer screening Flu vaccine Pneumovax vaccine HbA1c test LDL test

Baseline

6 Months

12 Months

Change

p

5.3 46.4 9.9 62.7 76.0 11.4 25.4 25.0 24.3 34.1 71.7

8.2 50.1 10.6 65.5 78.3 13.4 26.2 30.2 25.6 34.2 72.0

10.9 57.3 11.3 67.1 77.6 16.4 25.8 44.3 32.9 36.6 71.7

5.6*** 10.9*** 1.5 4.4*** 1.9** 5.1*** 0.3 19.3*** 8.6*** 2.4*** −0.1

Care Management Medical Home Center Model: Preliminary Results of a Patient-Centered Approach to Improving Care Quality for Diabetic Patients.

This article presents preliminary findings of the impact of an innovative care management model for diabetic patients. The model was implemented by se...
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