Diabetes Care Program Background

Transcription

1 Diabetes Care Program Background Document Objective The goal of our diabetes initiative was to improve the care of patients with diabetes in our Internal Medicine ambulatory practice, particularly those patients at highest risk for adverse outcomes because of difficult to control diabetes and significant social and economic stressors, by developing a system to improve glycemic control and cardiovascular risk. How did we select this objective? We chose this population based on an internal data review of health care outcomes and utilization from 1998 which showed that a sub-set of 1172 people with diabetes generated disproportionately large health care expenditures ($17 million dollars over an eight month period), and because several randomized trials had shown that better control of A1C, blood pressure, lipid levels, as well as greater use of aspirin, could reduce morbidity, mortality, and costs of care for these patients. Target Population We initially targeted vulnerable adult patients with poorly controlled Type 2 diabetes followed in our UNC Internal Medicine practice within the Ambulatory Care Center (ACC). Specifically, we identified through record review and provider referral, patients with diabetes and an A1C greater than 8.0% who received their diabetes and primary care in this practice. The benefits of the program would accrue to the patients in terms of better health and quality of life, and to the UNC Health Care system, in terms of better outcomes and lower costs. Our diabetes initiative has progressed through three phases: 1) Development of the intervention and pilot testing (1997 to 2001) 2) Formal evaluation of the program in a 217 person randomized trial (2001 to 2003) 3) Widespread dissemination of the program to 2100 patients with diabetes Table 1: Key activities by phase Development and Pilot Testing Phase Evaluation Phase (RCT) Dissemination Phase Dates 1997 to June 2001 July 2001 to April 2003 May 2003 Patients Key activities Outcomes Funding 1. Direct algorithm-based management for glycemic control by pharmacists 2. Recommendations for BP and lipid management 3. Development of patient registry A1C BP Knowledge Uncontrolled 1. UNC P&A 2. NC Medicaid / Carolina Access 3. UNC Dept Pharmacy 1. Added direct pharmacist management of blood pressure, lipid lowering, and aspirin 2. Piloted a smoking cessation intervention led A1C BP Lipids Knowledge Satisfaction 1. Clinical revenue 2. General Medicine 3. RWJ Clinical Scholars Program 4. Institutional Small Grants 1. Stepped care, intensity of intervention determined by risk 2. Automated lab surveillance 3. Statin prescribing initiative 4. Continuation of algorithmic care and telephone follow-up Uncontrolled A1c BP Lipids Clinical /Contact 1. Clinical revenue 2. General Medicine 3. The Health Care System 4. School of Pharmacy

2 Integration with our overall quality improvement program Our diabetes initiative is one of three disease management initiatives currently in place in the new UNC Center for Excellence in Chronic Illness Care. Other activities of the Center include programs for anticoagulation, chronic pain/ osteoarthritis, and heart failure. The UNC Center for Excellence in Chronic Illness Care is located administratively within UNC Hospital s Division of Performance Improvement under the leadership of the Vice President of Performance Improvement. The highest level administrative quality committee of the hospital, the CQI Council, hears reports from the medical director related to disease management initiatives on a regular basis. Resources Fiscal and staff resources UNC Health Care System has provided initial start-up and ongoing programmatic support for the diabetes initiative. During the pilot and evaluation phases, UNC Hospital, through the Department of Pharmacy, provided salary support for the two clinical pharmacist practitioners that provided the bulk of the clinical care. The NC State Division of Medical Assistance provided funding during the pilot and evaluation phases for personnel costs as well, as part of Medicaid s initiative to improve care for patients with diabetes. Our Department of Internal Medicine has also provided support for personnel. The costs of the evaluation were largely covered by grants from the Program on Health Outcomes and the Robert Wood Johnson Clinical Scholars program. In the dissemination phase (May 2003 to present), the UNC Health Care System Division of Performance Improvement has provided the bulk of the support for the project, including 0.75 FTE pharmacist salary support, 0.2 FTE support for the Medical Director, 1 FTE Program Coordinator, and support for a program assistant. The UNC School of Medicine, Division of General Internal Medicine, and UNC School of Pharmacy provide support as well. Organizational Leaders The diabetes initiative enjoys support from the highest levels of leadership within the University and the Health Care System. The Vice-President of Performance Improvement, the Chief of Staff, and the Dean of UNC School of Medicine and the CEO of UNC Health Care System all serve on the program s Advisory Board. In addition, the Vice President of Performance Improvement reviews monthly progress reports on the program and provides leadership within the UNC Health Care System administration. Initiative Leaders The initiative leaders include the Medical Director of the Center and Associate Chair of General Medicine, and the Co-director and Associate or Assistant Professor of Medicine and Pharmacy. Both have devoted substantial time and energy to the development, testing, and dissemination of the program. Staff Involvement The diabetes initiative involves a range of practice staff as part of the multidisciplinary care team. Regular meetings are held with Internal Medicine Medical Director, Business Manager, and Director of Nursing. The program administrative assistant schedules appointments and provides administrative support for the Center. An LPN is the program s nurse and is responsible for vital signs measurement, glucose measurement, and patient flow. Program assistants provide the majority of phone follow-up, maintain the registry, and teach glucose measurement. They also focus on reducing barriers, including helping with transportation and other social work issues. Other Health System resources such as nutrition, ophthalmology, and podiatry services are widely utilized. The Co-director performs direct patient care under algorithms developed by the practice s physicians; he

3 also developed the registry and has worked with the Health Care System s Informatics experts to develop automated systems for lab results. He is the daily program supervisor. The Medical Director serves as the liaison to the physicians and Hospital administration. Training The entire staff has undergone substantial training as we have changed our processes of care to implement disease management. This training includes attendance at a Diabetes Fellowship Program, weekly noon conferences and disease management meetings, and one-on-one education from program clinicians and director. The team meets weekly to problem solve and go over outcome data. In addition, several members of the team have undergone additional training in diabetes education. Both pharmacists are certified diabetes educators. Performance Measurement, Data Analysis, and Data Dissemination Performance Measurement Tool To evaluate the effectiveness of our diabetes program, we have systematically collected data on a range of important clinical outcomes, resource utilization, and costs (See Appendix 1 and 2). The principal means of collecting this information has been through the development of a patient registry and database, as well as through medical record review, and the development of an automated system to link our clinical laboratory data with our program database. These data collection mechanisms were used in our initial pilot study (an uncontrolled "before and after" trial), our subsequent randomized trial, and now for measuring performance during the full dissemination phase. Were the data collection tools able to measure what they purport to measure? We used well-validated clinical scales, or developed new ones when no previous measure existed (Rothman, SGIM. May 2002). Biometric data were collected as follows: UNC Hospital laboratories, which were unaware of the study status of the patients, obtained A1C and lipid levels. Laboratory values were calculated using a Boehringer Mannheim/ Hitachi 911 automated discrete chemistry analyzer. For lipids, we measured total cholesterol and HDL only. Clinic nurses, who were unaware of study assignment, using automated Dynamap machines, obtained blood pressure. How were measurement biases addressed? In our pilot uncontrolled trial, we used interrupted time series to attempt to account for secular trends, a Hawthorne effect, or co-interventions. However, we could not rule out the possibility of these effects. For that reason, in the second phase of our program we performed a rigorous randomized, controlled trial and showed that our program produced benefit compared with usual care. How were data collected? We collected data in three main ways. 1) Program assistants conducted face to face interviews in the UNC Internal Medicine Practice that were conducted at the time of regular clinic visits; 2) We used our institution's electronic medical record and automated laboratory database transfers to identify and quantify changes in laboratory values and admissions to our institution; 3) we reviewed hospital and practice plan-collected charge information to examine resource utilization. In the pilot study, we retrospectively identified data collected as close as possible to program entry, 6 months after entry, and 12 months after entry. For our randomized trial, we prospectively collected information at baseline, 6 and 12 months. In the dissemination phase, we collect outcome measures (blood pressure, A1C, lipid levels) based on our clinical monitoring parameters. How were the accuracy and completeness of data evaluated?

4 In the pilot study we were able to obtain complete results for 137 (87%) of 159 patients enrolled. Lab values and patient reported comorbidities were confirmed with review of our clinical information system. In the randomized controlled trial, 217 patients were enrolled and randomized, and complete follow-up was available for 193 (89%). Of the remaining patients, 4 control patients and 2 intervention patients died, and 6 control patients and 12 intervention patients were lost to follow-up. All data was maintained on a Microsoft Access Database that we created. This database included forcing functions and other data entry rules to insure that data was entered correctly. Research assistants trained in data collection and patient follow-up, followed all patients closely. Any data outliers were confirmed by patient interview or review of the clinical information system. Statistical analyses were performed as in intent to treat analysis for all patients completing the study. We conservatively carried forward baseline data for patients with complete 12 month data, but incomplete 6 month data. The program database and registry were developed and modified over the pilot phase to be userfriendly. This database used validation rules and predetermined options for data entry, thereby limiting entry error. Program assistants entered data into the database with entries then reviewed by clinical pharmacists. This system decreased data variance. Prior to data analysis, a third reviewer screened data for possible discrepancies, if discrepancies were found, these problems were solved by committee. Primary clinical outcomes included blood pressure, A1C and aspirin use at 6 and 12 months, and lipid levels at 12 months. A1C and lipid levels were obtained by the Hospital laboratories, which were unaware of the study status of the patients. Laboratory values were calculated using a Boehringer Mannheim/ Hitachi 911 automated discrete chemistry analyzer. For lipids, we measured total cholesterol and HDL only. Clinic nurses, who were unaware of study assignment, using automated Dynamap machines, obtained blood pressure. Aspirin use was obtained by self-report. Secondary outcomes (diabetes knowledge, satisfaction, use of clinical services, adverse events) were collected at 6 and 12 months and assessed through patient self-report. We measured diabetes knowledge with a scale that we developed and validated for patients with poor literacy. Satisfaction was measured using the Diabetes Treatment Satisfaction Questionnaire (DTSQ). At enrollment, the DTSQ was not administered to patients who were recently (< 3 months) diagnosed with diabetes. The use of medical services and adverse events were assessed by patient interviews and supplemented by a review of the Hospital s Clinical Information System. We used patient self-report to measure age, gender, race, insurance status, employment status, and highest level of education completed. Hypertension was defined based on a diagnosis in the medical record, or if the patient met ADA criteria at the time of the intervention (systolic blood pressure >130mm Hg or diastolic >85mm Hg). Hypercholesterolemia was also defined based on the medical record or ADA criteria at the time of the intervention, i.e., low-density lipoprotein (LDL) level >100mg/dl. Incipient nephropathy was defined as chart documentation or spot urine Microalbumin > 20mg/dl. Retinopathy was defined from chart documentation or patient report. Neuropathy was defined by patient report, chart documentation, or a monofilament test with less than 10/10 correct with no callus. The study team documented process measures. This included recording time spent in direct contact with the patients or in activities related to patient care (e.g., scheduling appointments, reviewing charts) by the diabetes management team. The minimum time recorded for each activity was 5 minutes. For control patients this primarily consisted of time spent during the initial 1-hour management session as well as return visits related to study follow-up. The study team also documented all medication changes made in response to their management (e.g., initiation of new medications, titration or alteration of current medications).

5 Data Analysis How were data organized and displayed? The data were organized, evaluated, and displayed using standard reporting techniques for studies, including tables and figures (See Appendix 1 and 2). What statistical techniques were used? In all phases we developed simple descriptive statistics that characterized our program population. In the pilot study, we performed appropriate statistical tests for single sample before and after studies, including paired t-tests and McNemar's chi squared test for proportions. In the randomized-controlled trial, all patients were analyzed based on their original assignments as an intention to treat analysis. Baseline differences between groups were assessed using t-tests for continuous outcomes and chi-squared tests for categorical outcomes. When assumptions for these parametric tests were not met, we used Wilcoxon-rank sum tests and medians were presented. For continuous outcome variables, we used repeated-measures analysis of variance (ANOVA) using the data at 3 visits: baseline, 6 and 12 months as the three levels of the within-subject factor (time), and randomized arm (intervention vs. control) as between-subject factors. When patients lacked 6- month, but had 12-month, data, we adopted the conservative analytical strategy of using their baseline outcomes as 6-month values. This occurred rarely for A1C (N=4), diastolic blood pressure (N=1), aspirin use (N=2) and our secondary outcomes (N=7). Post-hoc, pairwise comparisons (baseline vs. 6 months; baseline vs. 12 months) were examined using a contrast transformation only when we observed an overall difference. Because total cholesterol was measured only at 12 months, analysis of covariance (ANCOVA) was performed with adjustment for baseline measurement. Residual diagnosis was performed for the repeated measure ANOVA and ANCOVA models. We used log-transformations when outcomes were not normally distributed. Outliers were evaluated by examining the residuals and were excluded from the analysis when such removal was justified. For aspirin use, a dichotomous outcome, logistic regression with generalized estimating equation (GEE) was performed for overall treatment comparison using 6- and 12-month data as outcomes and baseline use as a covariate. To evaluate the use of services, and the frequency of adverse events, poisson regressions with GEE was used for evaluation of overall treatment effects over 6 and 12 months with adjustment for baseline number of events. For all models above we further adjusted for other covariates selected from baseline characteristics. Covariates were selected if p-values based on the univariate analyses comparing distributions between control and intervention arms were less than 0.2. This resulted in the inclusion of age and race in the multivariable models. To analyze process measures, we used wilcoxon rank sum tests to compare distributions of each process measure between control and experimental groups. Median values are reported. All analysis was performed using SAS 8.02 (SAS Institute, Cary, NC) and two-sided significance level of 5% was used for all statistical inference. In the dissemination phase, we have focused our analyses on benchmarking our outcomes against our trial population and national standards such as the ADA recommendations. Did you use internal or external comparisons?

6 We have compared our data against internal comparisons (after the program versus before the program in the pilot phase; intervention versus control in the randomized trial phase). In the dissemination phase, we are comparing our diabetes program participants against national benchmarks. Data Dissemination To whom have you disseminated your results? Over what time frame? We have disseminated the results of our diabetes initiative internally, locally, and nationally over the past 4 years. We have made presentations internally to the Department of Medicine (Grand Rounds), Division of General Internal Medicine, Hospital Quality Council, and to the Joint Commission Accreditation reviewers. We have presented our results locally to interested payers (State Medicaid, Blue Cross/ Blue Shield, State Health Plan, VA hospital and administration). We have presented our results nationally through the Society of General Internal Medicine, at Academy Health, a national meeting of health services research; and at CMS (formerly HCFA). We have also published journal articles on our work (See Appendix 3). Performance Improvement Activities Resulting From Performance Measures Performance Improvement Activities The performance improvement changes we have initiated as part of this project are described in Table 2, along with how they were tested and their relationship with our measurement data. Table 2 Phase Performance Improvement Change How they were tested Pilot RCT Dissemination Developed evidence-based algorithms for glucose control Pharmacist provided direct patient education about diabetes Pharmacists became certified diabetes educators Pharmacists used medication algorithms to adjust medications for glycemic control Pharmacists made recommendations to providers on blood pressure treatment Developed algorithms for lipid lowering and blood pressure control. Pharmacists able to adjust these medications and add aspirin to reduce heart disease risk. (outcomes) A1C levels before and after intervention Knowledge questionnaire Completion of certification process including exam A1C levels before and after intervention Blood pressure before and after intervention Change in cholesterol levels, aspirin use and blood pressure levels (difference between groups) Smoking cessation program developed Cessation rates at 3 months (uncontrolled) Expansion of program to full population of patients with diabetes using a stepped care approach (See Appendix 4) Automated integration of laboratory data to allow closer monitoring Expanded nutritional counseling N/A % of patients meeting testing standards; % of patients with good risk factor control % of patients referred for assessment Evidence of how the performance changes affected outcomes Mean A1C improved from 10.8 to 8.9% Knowledge improved from 50.2% to 77.5% on SKILL-D 2 pharmacists completed training and received certification Mean A1C improved from 10.8 to 8.9% No change in BP observed, suggesting that advice alone is not sufficient A1c Reduction: -2.8% vs. -1.6%, p=0.01. SBP Reduction: -8.4 mmhg vs. +1.6mmHg, p= Aspirin Use: +47% vs. +8%, p< % achieved smoking cessation 30% reduced number smoked N/A Patients with A1c in last year: 73.5% Patients with Lipids in last year: 60% Poor per A1c >9: 21% Fair per A1c 7.5-9: 21% Good per A1c < 7.5: 50% To be assessed in late 2004

7 We plan to continue regular assessment of our main outcomes in the dissemination phase, including: A1C, blood pressure, aspirin use, statin use, cholesterol levels, hospital admissions and emergency room utilization, and overall costs. Regular, ongoing data collection is made feasible by the use of automated importing of lab data and medication information from our institution s clinical information system to our diabetes program patient registry and database. We regularly (once a quarter) examine our outcomes and adjust our programs to attempt to improve the proportion of patients under good control. Results Our diabetes initiative has met and exceeded our goals for care improvement. We have successfully improved A1C by 1 percentage point, and blood pressure by almost 10 mm Hg, compared with usual care. Aspirin use among program participants is over 75%, and we are currently working to assure that all eligible patients are receiving statin drugs. These improvements exceeded our initial expectations and suggest that our model of care offers a powerful benefit to patients with chronic illness. The increased adherence with evidence-based guidelines for diabetes treatment are projected to reduce the incidence of cardiovascular disease by more than 50% and will result in overall costsavings from reduced morbidity, according to the best evidence from recent cost-effectiveness analyses conducted by the CDC. We have achieved these improvements by careful re-design of our Internal Medicine practice, judicious matching of provider skill level to tasks performed (so as to achieve greatest efficiency), and through use of information technology to move from reactive to proactive care. Moreover, we have achieved these improvements while focusing on the most vulnerable patients: those with significant barriers to care including poverty, inadequate health insurance, lack of transportation, multiple comorbid conditions, and low health literacy. In fact, sub-group analyses have shown that the more vulnerable patients with low literacy have achieved greater improvements in A1C than patients with better literacy, suggesting that our educational and supportive efforts are reaching our most needy constituency well. While successful, our program has had to overcome several obstacles to be successful. Foremost, we have had to develop a strategy to allow our programs to be economically viable. The majority of our patients have Medicare or Medicaid as their payers; we have used Medicare s incident to billing mechanism for reimbursement for evaluation and management services provided by our mid-level providers. Nevertheless, current reimbursement levels do not cover the actual costs of the care delivered. As such, we have utilized other mechanisms to insure financial viability, including: direct support from UNC Health Care System, grant support for research-related activity within the program, support from State Medicaid, and combining service provision with education, thus enabling pharmacy residents and students to participate in care while also learning about chronic disease management. In this manner, our School of Pharmacy has been an important supporter of our program. We have also developed a new type of care provider, a diabetes program assistant who is responsible for making patient phone calls, maintaining our database, training patients on selfglucose monitoring and checking their glucometer results, and generally facilitating access to care by overcoming care barriers. In the past, many of these tasks have been assigned (actively or by default) to highly paid, over-committed personnel, leading to incomplete attention to many of these issues. Our program assistants develop ongoing, personal relationships with our patients, which we believe is key to ongoing adherence.

8 Another important barrier that we have worked to overcome relates to the difficulty in managing clinical information to support prospective rather than reactive care. As with many health care systems, our institution has many different clinical and administrative databases that contain necessary information for good care. Although we are fortunate to have an electronic medical record, it is not currently designed to help measure groups of patients prospectively. To allow prospective care and tracking of key outcomes, we developed our own database in Microsoft Access (See Appendix 5). The database contains the key elements required for care and for tracking outcomes. Originally, we entered clinical information manually at the time of patient visits, which was time consuming and inefficient. In the past year, we have worked closely with our Informatics Department to develop a mechanism to allow importation of clinical data directly into our program database. This change has allowed us to monitor and manage a much larger number of patients, including patients who do not regularly come to the practice. Evidence of sustainability Our program has been in place now for 4 years. We have committed, ongoing support from our institution to maintain and expand our program, and we have documented very high provider and patient satisfaction with care. Based on these factors, we believe the long-term sustainability of our program is excellent. In fact, our providers find it hard to imagine going back to how we cared for diabetes 5 years ago. Our data suggest that patients will continue to participate in our program, and that improvements in outcomes are sustainable. Potential for program replication We believe that our diabetes program is transportable to other primary care practices within our health care system and beyond. This year, we have begun negotiations with a major payer in our area to offer our program to any patient with that insurance in this area. In order to do so, we plan to implement our program in 3 additional practices within our health care system in the next year. These practices share a common information system, and are staffed by other faculty members at our institution, making the transporting of our program somewhat easier than if we were to try to implement it in a distant, unrelated practice. If we are successful in this expansion, we will begin to offer our program on larger scale to other practices who are willing to make the changes in organization required to produce change in care.

9 A. Systolic Blood Pressure B. Diastolic Blood Pressure SBP (mmhg) * DBP (mmhg) * Time (mos) Time (mos) *Repeated ANOVA p=0.007 *Repeated ANOVA p=0.008 C. A1C D. Total Cholesterol A1C (%) * Total Cholesterol (mg/dl) * Time (mos) Time (mos) *Repeated ANOVA p=0.048 *Repeated ANCOVA p=0.35 On Aspirin (%) E. Aspirin Use * Time (mos) *GEE Analysis p < Legend Note: Mean values or percentages are reported for all figures except for A1C and Total Cholesterol, which are reported as Medians. Appendix 1: Change in Clinical Outcomes Over Time. 2A: Systolic Blood Pressure, 2B: Diastolic Blood pressure, 2C:A1C, 2D:Total Cholesterol, 2E:Aspirin Use.

10 Variable Baseline 6 Month 12 Month P P adjusted* Knowledge (%) 48.8% 59.5% 61.3% < < % 72.4% 77.5% Satisfaction (0-36) <0.05 < General Visits Urgent Care Visits ER Visits Hospitalizations Weight (Lbs) Hypoglycemic Episodes Hypotensive Episodes * Adjusted for age and race. Diabetes Treatment Satisfaction Questionnaire. Administered to patients with diabetes for 3 months. Average number per patient over the previous 6 months. Appendix 2: Secondary Outcomes 10

11 Appendix 3: Publications REFEREED JOURNALS Rothman, R, Malone, R, Bryant, B, Crigler, B, DeWalt, D, Shintani, A, Weinberger, M, Pignone, M. A Randomized Trial of Disease Management to Improve Cardiovascular Risk and A1C in Patients with Diabetes. Annals of Internal Medicine. Submitted September Rothman, R, Malone, R, Bryant, B, Wolfe, C, Padgett, P, DeWalt, D, Weinberger, M, Pignone, M. The Spoken Knowledge In Low Literacy in Diabetes (SKILLD) Scale: A Diabetes Knowledge Assessment Scale For Vulnerable Patients. Diabetes Educator. Submitted September Rothman, R, Malone, R, Bryant, B, Horlen, C, DeWalt, D, Pignone, M. The Relationship Between Literacy and Glycemic in a Diabetes Disease Management Program. Diabetes Educator. Submitted Spring Rothman, R, Malone, R, Bryant, B, Horlen, C, Pignone, M. Pharmacist-Led, Primary Care-Based Disease Management Improves Hemoglobin A1c in High-risk Patients with Diabetes. American Journal of Medical Quality 2003: 18 (2). March/April Rothman, R, Malone, R, Bryant, M, DeWalt, D, Pignone, M. Health Literacy and Diabetic (Letter). Journal of the American Medical Association 2002: 288 (21). December 4, ABSTRACTS R Malone, DA DeWalt, M Pignone, R Rothman. No Relationship Between Literacy Status and Medical Charges. SGIM Annual Meeting. May 2004 Rothman, R, Pignone, M, Malone, RM, Bryant, ME, Horlen, C, Padgett, P. The Relationship Between a New Diabetes Knowledge Test (DKT) and Glycemic for Underserved Patients with Type 2 Diabetes. SGIM Annual Meeting. May Rothman, R, Pignone, M, Malone, RM, Bryant, ME, Horlen, C, Padgett, P. The Relationship Between Health Literacy and Diabetes Related Measures for Patients with Type 2 Diabetes. SGIM Annual Meeting. May Rothman R, Pignone M, Malone RM, Bryant ME, Padgett P. A Primary Care Based, Pharmacist- Led, Comprehensive Disease Management Program for Patients with Diabetes: A Randomized led Trial. SGIM Annual Meeting. May Wolfe, C, Malone, R, Dennis, B, Bryant, B, Rothman, R, Pignone, M. Tobacco Harm Reduction in Patients with Diabetes. SERC Annual Meeting. April Meece, J, Malone, R, Bennett, J, Fields, E, Monaghan, M, Shapiro, K, Lubowski, T, Walden, S, Livengood, K. A Retrospective Analysis of Diabetes in Type 2 patients, Comparing Clinic vs. Community Based Programs. ADA Annual Meeting. January Wolfe, C, Malone, R, Dennis, B, Bryant, B, Rothman, R, Pignone, M. Tobacco Harm Reduction in Patients with Diabetes. American Society of Health-System Pharmacists Annual Meeting. December Pignone, M, Rothman, R, Malone, RM, Bryant, ME, Dennis, BH, DeWalt, D. The Impact of Literacy on the Effectiveness of a Comprehensive Diabetes Management Program. SGIM Annual Meeting Rothman, R, Pignone, M, Malone, RM, Bryant, ME, Horlen, C. A Pharmacist-Based Diabetes Management Model Can Improve Care for High Risk Diabetic Patients. SGIM Annual Meeting

12 Appendix 4: Diabetes Program Stepped Care Approach Doing poorly (A1c > 9.0%): Currently 442 Patients Primarily Focus on Proactive Follow-up: Predetermined follow-up via telephone and/or clinic a minimum of every 1 to 2 months, every 2 weeks if titrating/altering medications. Scheduled quarterly review of quality of care. Significant efforts made to address barriers to care (transportation, financial, and social). Assess need for nutrition referral. Reinforce key educational points and focus on improved diabetes related self-efficacy. Efforts documented in medical record for provider feedback. Secondarily Focus on Reactive follow-up: Follow via automated mailings containing laboratory results and recommended clinical followup. Doing OK (A1C %): Currently 435 Patients Primarily Focus on Proactive Follow-up: Predetermined followup via telephone and/or clinic a minimum of every 2 to 3 months, every 2 to 3 weeks if titrating/altering medications. Continued focus on diabetes related self-efficacy. Scheduled quarterly review of quality of care. Efforts documented in medical record for provider feedback. Secondarily Focus on Reactive follow-up: Follow via automated mailings containing laboratory results and recommended clinical follow-up. Doing well (A1C < 7.5%): Currently 1228 Patients Primarily Focus on Reactive Follow-up: Follow via automated laboratory reports for A1c and Lipids. Higher A1c triggers more intense follow-up. Secondarily Focus on Proactive Follow-up: Scheduled yearly review of quality of care and automated mailings containing laboratory results and recommended clinical follow-up. 12

13 Appendix 5: Diabetes Program Database Screen Shots 13