We evaluated the effectiveness of a diabetes life coach program designed to address the concerns of limited coordination and collaboration of care for chronically ill patients in the physician office. The program emphasized lipid, blood pressure, and glycemic control, using personal coaching, group classes, reminders, and customized feedback. The target population was all health plan members over age 18 with type 1 or 2 diabetes mellitus in 6 primary care practice sites in the Hampton Roads area of Virginia. Primary outcomes were 1 Health Plan Employer Data and Information Set measure (A1c poor control of >9% or no test), 3 American Diabetes Association (ADA) measures (A1c <7%, blood pressure of <130/80 mmHg, low-density lipoprotein cholesterol [LDL-C] of <100 mg/dL), 1 pharmacy measure (percentage of patients filling at least 1 insulin prescription), and 2 self-reported behavioral measures (percentage adherent to a meal plan and percentage adherent to an activity plan). We assessed overall program outcomes and differences between individual physician practices and evaluated outcomes separately for engaged compared with non-engaged program participants. Outcomes for 1117 participants were evaluated. Statistically significant improvement at P < 0.05 was noted in all 7 targeted measures compared with baseline. Participants who were engaged in the life coach program were 40% less likely to experience poor control of their A1c, 50% more likely to meet the ADA A1c goal of < 7%, 11% more likely to meet their blood pressure goal of <130/80 mmHg, and 7% more likely to meet their LDL-C goal of <100 mg/dL compared with those not engaged. Patients who became engaged in the program performed significantly better in the key diabetes indicators that ultimately lead to reductions in the complications of the disease over time. Our study contributes to the evidence that clinical multidisciplinary, collaborative models of care can influence and improve the management of diabetes.
Disease ManagementVol. 11, No. 2 POINT OF VIEWThe Role of Incentives in the Improvement of HealthRichard SafeerRichard SafeerSearch for more papers by this authorPublished Online:21 Apr 2008https://doi.org/10.1089/dis.2008.112726AboutSectionsPDF/EPUB ToolsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetailsCited byCost-Effectiveness of Childcare Discounts on Parent Participation in Preventive Parent Training in Low-Income Communities2 December 2011 | The Journal of Primary Prevention, Vol. 32, No. 5-6 Volume 11Issue 2Apr 2008 Information© 2008 Mary Ann Liebert, Inc.To cite this article:Richard Safeer.The Role of Incentives in the Improvement of Health.Disease Management.Apr 2008.65-67.http://doi.org/10.1089/dis.2008.112726Published in Volume: 11 Issue 2: April 21, 2008Online Ahead of Print:April 7, 2008PDF download
The role of clinical inertia in the treatment of patients with hypertension was assessed by evaluating health care providers' knowledge, attitudes, and clinical practices regarding hypertension management. A cross-sectional survey was conducted at the Forsyth Medical Group in North Carolina. Participants were physicians (N = 18, 10 sites) and support staff (N = 20, 12 sites), who were surveyed in 2006. Physician and support staff questionnaires consisted of 29 and 15 items, respectively, and were administered by trained interviewers. Though most physicians (94%) cited familiarity with the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC-7) guidelines and affirmed that hypertension management guidelines are relevant to their patients, no physicians interviewed routinely document patient hypertension management plans. Although 1 in 3 physicians cited the inability to devote enough time to patients for the discussion of hypertension management, physicians predominantly cited patient- and support-staff- related factors as most important to patients not attaining blood pressure (BP) goal. Patient lifestyle modification (89%), education (67%), and medication compliance (56%) were cited as the most important reasons for uncontrolled BP. Only one-third of physicians believe that clinical staff always obtain accurate BP measurements, and 61% believe that resistant hypertension is a reflection of inaccurate BP measurement. Many support staff claimed to be rushed when measuring patient BP, and 65% recommended BP competency training. Contradictions were evident between provider knowledge of hypertension management standards and how this knowledge is applied in clinical practice. Standardized collection of BP is critical to measuring clinical improvement in hypertension. Results are being utilized to develop clinical improvement initiatives including staff education and competency training.
As financial, social, and quality-of-life challenges associated with chronic disease in the United States continue to proliferate, disease management (DM) has been identified as a viable and positive approach that serves all areas of impact. Using an "in-house" model, Physician Health Partners, LLC, designed, developed, and implemented a DM program for the frail and elderly population. Given the special needs of this population the typical DM intervention was modified to include elements of physician involvement. The Frail and Elderly Program, as the DM program is called, produced statistically significant improvements in functional, behavioral, and clinical status and health-related quality of life. This model can help result in program success with potential benefits for individuals, practices, communities, and all whose lives are touched, directly or indirectly, by chronic disease.
Historically, health plans and disease management companies have employed "opt-out" strategies for evaluating medical management outcomes across larger populations, targeting the entire population of eligible individuals and allowing those not interested to opt out. Recent observations that the predominant effort of these programs is on high-risk patients has lead some managers to suggest that the focus be on only those individuals with an anticipated higher effectiveness and lower cost to the payers of such services. They believe such "opt-in" models, in which only higher risk participants are targeted and enrolled, will deliver higher value. The use of common opt-in models, however, is not only methodologically unsound, but experience in the field suggests there may be less overall effect as well. Calculation methods for developing impact remain extremely sensitive to methodology.
Disease management has become an important tool for improving population patient outcomes. The Louisiana State University Health Care Services Division (HCSD) has used this tool to provide care to a largely uninsured population for approximately 10 years. Eight programs currently exist within the HCSD focusing on diabetes, asthma, congestive heart failure, HIV, cancer screening, smoking cessation, chronic kidney disease, and diet, exercise, and weight control. These programs operate at hospital and clinic sites located in 8 population centers throughout southern Louisiana. The programs are structured to be managed at the system level with a clinical expert for each area guiding the scope of the program and defining new goals. Care largely adheres to evidence-based guidelines set forth by professional organizations. To monitor quality of care, indicators are defined within each area and benchmarked to achieve the most effective measures in our population. For example, hemoglobin A1c levels have shown improvements with nearly 54% of the population <7.0%. To support these management efforts, HCSD utilizes an electronic data repository that allows physicians to track patient labs and other tests as well as reminders. To ensure appropriate treatment, patients are able to enroll in the Medication Assistance program. This largely improves adherence to medications for those patients unable to afford them otherwise.
A prospective, observational study of 1289 members completing an evidence-based diabetes management program was evaluated for clinical effectiveness and cost impact. The program consisted of direct contacts by nurse educators who worked with members to complete modules in a specific order based on the individual's readiness to change and specific standards of diabetes care behaviors lacking adherence. A total of 668 members were at HbA1c target values (HbA1c 7%) at baseline. At follow-up 899 members had either reached the target level or improved their values by 1 percentage point. At baseline, 516 members recorded normal blood pressure; at follow-up 755 members either met the target level of less than 130/80 mmHg or reduced their blood pressure by at least 10/5 mmHg. Claims data indicated that 89% (n = 233) of those who had a hospitalization in the prior year did not have a hospitalization in the program year, compared to 3% (n = 32) who did not have a hospitalization in the previous year but needed a hospital visit in the program year. There were statistically significant improvements in other health behaviors and quality of life measures. Cost avoidance was estimated at $7,402,578 for the 1289 members who completed the program and reported their results. This figure includes those who were in compliance prior to the start of the intervention. The study supported the results from large multicenter trials on diabetes management when translated to an intervention.
Our objective was to test for evidence of regression to the mean in chronic obstructive pulmonary disease (COPD)-related health care utilization in a Colorado Medicaid population that met the criteria for, but were not participating in, a COPD disease management (DM) program. National Jewish Medical and Research Center had enrolled individuals who (1) had a diagnosis of COPD for at least 1 year and (2) were active participants in Colorado Medicaid's 1-year DM program called breatheWise; the present study sought a comparator group for that population. In order to test for evidence of regression to the mean (ie, high utilization from the recruitment period reducing without active intervention) in this case management model, we conducted a case-controlled analysis of total spending for a comparator population that would have met the inclusion criteria for the DM program. The present study assessed health care utilization for fiscal years 2002 and 2003 in terms of total rates of emergency room (ER) visits and hospitalizations for all causes in the comparator group of COPD patients. In addition, total costs related to both ER visits and hospitalizations were compiled. In total, 354 individuals met the inclusion criteria and were identified as the comparator group. ER visits and hospitalizations were consistent for 2002 and 2003. ER visits totaled 314 and 315 in 2002 and 2003, respectively, indicating a 0.3% increase that was not significant. Hospitalizations decreased from 0.53 admissions per patient in 2002 to 0.48 in 2003-a 9.4% reduction that was not significant. With comparable rates of ER visits and hospitalizations, total costs for health care utilization remained virtually unchanged between 2002 and 2003. There is minimal evidence of regression to the mean over 2 consecutive years in the Colorado Medicaid patients with moderate to severe COPD.
The increasing prevalence of chronic illnesses in the United States requires a fundamental redesign of the primary care delivery system's structure and processes in order to meet the changing needs and expectations of patients. Population management, systems-based practice, and planned chronic illness care are 3 potential processes that can be integrated into primary care and are compatible with the Chronic Care Model. In 2003, Harvard Vanguard Medical Associates, a multispecialty ambulatory physician group practice based in Boston, Massachusetts, began implementing all 3 processes across its primary care practices. From 2004 to 2006, the overall diabetes composite quality measures improved from 51% to 58% for screening (HgA1c x 2, low-density lipoprotein, blood pressure in 12 months) and from 13% to 17% for intermediate outcomes (HgA1c <or=7, low-density lipoprotein <or=100, systolic blood pressure <or=130). Over the same period, a secondary retrospective cohort analysis noted greater gains in composite screening and intermediate outcome measures for patients with planned visits compared to those who had no planned visits. This study illustrates how 1 delivery system integrated these disease management functions into the front lines of primary care and the positive impact of those changes on overall diabetes quality of care.
Disease ManagementVol. 11, No. 3 POINT OF VIEWFree AccessWhere We've Gone WrongRobert StoneRobert StoneSearch for more papers by this authorPublished Online:18 Jun 2008https://doi.org/10.1089/dis.2008.11301AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail "Where We've Gone Wrong." , 11(3), pp. 139–140FiguresReferencesRelatedDetails Volume 11Issue 3Jun 2008 Information© 2008 Mary Ann Liebert, Inc.To cite this article:Robert Stone.Where We've Gone Wrong.Disease Management.Jun 2008.139-140.http://doi.org/10.1089/dis.2008.11301Published in Volume: 11 Issue 3: June 18, 2008PDF download
Disease ManagementVol. 11, No. 1 LETTER TO THE EDITOREvaluating Disease Management ResultsAdam Long and Roger ReedAdam LongSearch for more papers by this author and Roger ReedSearch for more papers by this authorPublished Online:16 Feb 2008https://doi.org/10.1089/dis.2008.111732AboutSectionsPDF/EPUB ToolsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail "Evaluating Disease Management Results." , 11(1), p. 59FiguresReferencesRelatedDetails Volume 11Issue 1Feb 2008 Information© 2008 Mary Ann Liebert, Inc.To cite this article:Adam Long and Roger Reed.Evaluating Disease Management Results.Disease Management.Feb 2008.59-59.http://doi.org/10.1089/dis.2008.111732Published in Volume: 11 Issue 1: February 16, 2008PDF download
Prior to implementing a disease management (DM) strategy, a needs assessment should be conducted to determine whether sufficient opportunity exists for an intervention to be successful in the given population. A central component of this assessment is a sample size analysis to determine whether the population is of sufficient size to allow the expected program effect to achieve statistical significance. This paper discusses the parameters that comprise the generic sample size formula for independent samples and their interrelationships, followed by modifications for the DM setting. In addition, a table is provided with sample size estimates for various effect sizes. Examples are described in detail along with strategies for overcoming common barriers. Ultimately, conducting these calculations up front will help set appropriate expectations about the ability to demonstrate the success of the intervention.
The objectives of the study were to compare health care expenditures between adults with and without mental illness among individuals with obesity and chronic physical illness. We performed a cross-sectional analysis of 2440 adults (older than age 21) with obesity using a nationally representative survey of households, the Medical Expenditure Panel Survey. Chronic physical illness consisted of self-reported asthma, diabetes, heart disease, hypertension, or osteoarthritis. Mental illness included affective disorders; anxiety, somatoform, dissociative, personality disorders; and schizophrenia. Utilization and expenditures by type of service (total, inpatient, outpatient, emergency room, pharmacy, and other) were the dependent variables. Chi-square tests, logistic regression on likelihood of use, and ordinary least squares regression on logged expenditures among users were performed. All regressions controlled for gender, race/ethnicity, age, martial status, region, education, employment, poverty status, health insurance, smoking, and exercise. All analyses accounted for the complex design of the survey. We found that 25% of adults with obesity and physical illness had a mental illness. The average total expenditures for obese adults with physical illness and mental illness were $9897; average expenditures were $6584 for those with physical illness only. Mean pharmacy expenditures for obese adults with physical illness and mental illness and for those with physical illness only were $3343 and $1756, respectively. After controlling for all independent variables, among adults with obesity and physical illness, those with mental illness were more likely to use emergency services and had higher total, outpatient, and pharmaceutical expenditures than those without mental illness. Among individuals with obesity and chronic physical illness, expenditures increased when mental illness is added. Our study findings suggest cost-savings efforts should examine the reasons for high utilization and expenditures for those with obesity, chronic physical illness, and mental illness.
The purpose of this study was to examine the challenges of integrating an asthma disease management (DM) program into a primary care setting from the perspective of primary care practitioners. A second goal was to examine whether barriers differed between urban-based and nonurban-based practices. Using a qualitative design, data were gathered using focus groups in primary care pediatric practices. A purposeful sample included an equal number of urban and nonurban practices. Participants represented all levels in the practice setting. Important themes that emerged from the data were coded and categorized. A total of 151 individuals, including physicians, advanced practice clinicians, registered nurses, other medical staff, and nonmedical staff participated in 16 focus groups that included 8 urban and 8 nonurban practices. Content analyses identified 4 primary factors influencing the implementation of a DM program in a primary care setting. They were related to providers, the organization, patients, and characteristics of the DM program. This study illustrates the complexity of the primary care environment and the challenge of changing practice in these settings. The results of this study identified areas in a primary care setting that influence the adoption of a DM program. These findings can assist in identifying effective strategies to change clinical behavior in primary care practices.
Disease ManagementVol. 11, No. 1 POINT OF VIEWTherapeutic Specificity in Disease Management EvaluationScott MacStravicScott MacStravicSearch for more papers by this authorPublished Online:16 Feb 2008https://doi.org/10.1089/dis.2008.111725AboutSectionsPDF/EPUB ToolsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Volume 11Issue 1Feb 2008 Information© 2008 Mary Ann Liebert, Inc.To cite this article:Scott MacStravic.Therapeutic Specificity in Disease Management Evaluation.Disease Management.Feb 2008.7-11.http://doi.org/10.1089/dis.2008.111725Published in Volume: 11 Issue 1: February 16, 2008PDF download
Guided Care (GC) is an enhancement to primary care that incorporates the operative principles of disease management and chronic care innovations. In a 6-month quasi-experimental study, we compared the cost and utilization patterns of patients assigned to GC and Usual Care (UC). The setting was a community-based general internal medicine practice. The participants were patients of 4 general internists. They were older, chronically ill, community-dwelling patients, members of a capitated health plan, and identified as high risk. Using the Adjusted Clinical Groups Predictive Model (ACG-PM), we identified those at highest risk of future health care utilization. We selected the 75 highest-risk older patients of 2 internists at a primary care practice to receive GC and the 75 highest-risk older patients of 2 other internists in the same practice to receive UC. Insurance data were used to describe the groups' demographics, chronic conditions, insurance expenditures, and utilization. Among our results, at baseline, the GC (all targeted patients) and UC groups were similar in demographics and prevalence of chronic conditions, but the GC group had a higher mean ACG-PM risk score (0.34 vs. 0.20, p < 0.0001). During the following 6 months, the GC group had lower unadjusted mean insurance expenditures, hospital admissions, hospital days, and emergency department visits (p > 0.05). There were larger differences in insurance expenditures between the GC and UC groups at lower risk levels (at ACG-PM = 0.10, mean difference = $4340; at ACG-PM = 0.6, mean difference = $1304). Thirty-one of the 75 patients assigned to receive GC actually enrolled in the intervention. These results suggest that GC may reduce insurance expenditures for high-risk older adults. If these results are confirmed in larger, randomized studies, GC may help to increase the efficiency of health care for the aging American population.
The objective of this study was to determine if a formula diet of 520 kilocalorie (kcal, 2177 kilojoules [kJ]) compared to 850 kcal (3558 kJ) produces significantly greater weight loss and improved weight maintenance in a clinical outpatient setting. The investigation was a retrospective analysis of data from 1887 participants who underwent weight loss between December 1994 and January 2003. Participants were between the ages of 18 and 70 and completed a minimum of 12 weeks of a very-low-energy diet (VLED; 520 kcal) or a low-energy diet (LED; 850 kcal). Participants attended weekly meetings, were weighed, and received instruction in behavioral skills. Following active weight loss, participants transitioned to weight maintenance and were prescribed an individual structured meal plan aimed at maintaining body weight. Both levels of energy intake produced significant weight loss over 12 weeks (P < 0.05). Weight loss was 15.2 +/- 4.1% and 14.3 +/- 3.7% of initial body weight for participants in the VLED (n = 1231) and LED (n = 656), respectively. After controlling for baseline body weight, there was no significant difference between diets. Similarly, there was no significant difference in weight regain between VLED and LED after 12, 24, 36, and 48 weeks of weight maintenance. VLED did not produce a greater weight loss than the LED. LED provides similar weight loss with a lower incidence of adverse events and diminished need for medical monitoring. We conclude LED is an efficacious, safe, and less burdensome diet compared to VLED.
