The American Journal of Managed Care June 2009
Improving the Outcomes of Disease Management by Tailoring Care to the Patient's Level of Activation
Tailoring coaching to each patient’s activation level may improve clinical indicators and reduce utilization more than the usual disease management coaching.
Objective: To determine whether an approach that assesses patient capabilities for self-management and then tailors coaching support based on this assessment would be more effective in improving outcomes than the usual disease management approach.
Study Design: A quasi-experimental pre-post design was used, with an intervention group coached with a tailored approach and a control group coached in the usual way.
Methods: Data derived from telephonic coaching and from archival utilization data were used in the analysis. Differences in activation scores, clinical indicators, and utilization rates between intervention and control group members were assessed. Propensity scores were used to weigh the data and equalize baseline differences between the intervention and control groups. Analysis of variance repeated measures were used to examine changes over time. This analytic approach assessed whether individual changes over time in the intervention group were significantly different from individual changes over time in the control group.
Results: Overall, the findings showed a consistent picture that indicated a positive impact of the tailored intervention. Activation scores increased, clinical indicators improved, and utilization rates declined to a greater extent in the intervention group than in the control group.
Conclusion: The findings suggest that tailoring coaching to patients' activation level and using the same metric to track progress improves the outcomes of disease management.
(Am J Manag Care. 2009;15(6):353-360)
The findings suggest that tailoring coaching to patients’ activation level and using the same metric to track progress would improve both the outcomes and the efficiency of disease management programs.
- Patients who received coaching tailored to their individual level of activation showed greater improvement in their biometrics and in their adherence to recommended regimens, and showed greater reductions in hospitalizations and in emergency department use than did patients coached in the usual way.
- Coaches who systematically assess patients’ knowledge, skill, and confidence for self-management can be more targeted and efficient in allocating their time and effort.
Most patient education programs assume that giving information to patients translates into patient gains in knowledge and skill. This assumption is rarely tested. Thus, a key factor assumed to influence outcomes, gains in patient capabilities, remains unexamined and unmeasured in DM assessments. Further, because patient capabilities for self-management go unmeasured, systematically tailoring education and support to patients’ level of knowledge and skill is not possible. What results is a “one size fits all” approach that often fits no one.
The purpose of this study was to determine whether the outcomes of DM could be improved by customizing education and support to the individual patient’s level of activation (knowledge, skill, and confidence regarding the management of one’s own health and healthcare). Would an approach that tailors support for self-management be more effective in improving outcomes than the generalized approach typically used in DM? The research addresses one of the central elements of DM, support for patient self-management, and assesses whether systematic measurement and tailoring can improve patient self-management capabilities and in turn the outcomes of DM. Does tailoring patient care plans based on the Patient Activation Measure (PAM) result in gains in activation, improvements in clinical indicators, and lower healthcare utilization, compared with patients receiving usual DM support?
BACKGROUND ON ACTIVATION
The PAM was designed to assess an individual’s knowledge, skill, and confidence with respect to managing his or her health.5 Based on responses to the 13-item scale, each person is assigned an “activation score.”6 The PAM has been shown to be a valid measure that predicts a range of health behaviors. For example, individuals identified as highly activated according to the measure are more likely to obtain preventive care (eg, health screenings, immunizations) and to exhibit other behaviors known to be beneficial to health. These include maintaining good diet and exercise practices; self-management behaviors (eg, monitoring, adherence to treatment); and health information seeking.5-10 More importantly, recent studies show that activation is changeable, and that increases in activation are followed by improvements in several health-related behaviors and health outcomes.8,9 A prospective study of diabetes patients showed that PAM scores predicted hospitalizations and glycemic control 2 years into the future.9
The research suggests that activation is developmental and that people pass through 4 different levels of activation on their way to becoming effective self-managers:
• Level 1: Patients tend to be overwhelmed and unprepared to play an active role in their own health.
• Level 2: Patients lack knowledge and confidence for self-management.
• Level 3: Patients are beginning to take action, but lack confidence and skill to support behaviors.
• Level 4: People have adopted many of the behaviors to support their health, but may not be able to maintain them in the face of life stressors.
Tailoring Individual Care Plans to Activation Levels
The intervention was conducted within the LifeMasters DM program (LifeMasters, Irvine, CA). Intervention coaches used baseline PAM scores to segment patients into the 4 levels of activation. Coaches were trained and provided guidelines to customize telephone coaching based on the activation level. The behaviors encouraged for each activation level were based on empirical data indicating what is realistic at a particular level of activation.11,12 The goal was to ask patients to do things that they could succeed at, thereby allowing them to begin to build confidence in their ability to manage their health.13
Coaches working with patients at level 1 were trained to build patient self-awareness and understanding of behavior patterns, which are important foundations for tackling further competencies in later steps. At level 2, coaches worked with patients to make small changes in their existing behaviors, such as reducing portion sizes at meals, taking the stairs at work, and reading food labels at the grocery store. At level 3, coaches focused on the adoption of new behaviors (eg, 30 minutes of exercise 3 times a week) and the development of problem-solving skills. At level 4, coaches worked with patients on relapse prevention and handling new or challenging situations as they arise.
Coaches serving the control group did not have access to their patients’ PAM scores and were not trained in interpreting and using the PAM score for coaching.
A quasi-experimental pre–post design was used, with an intervention group consisting of the coaches and their patients based in 1 call center and the control group consisting of the coaches and their patients based in a different geographically separate call center of the LifeMasters program. LifeMasters selected the 2 call centers for the study out of 4 potential sites, based on the similarity of their nurse coaches’ tenure and years of experience.
The preintervention period was for 1 year before implementation, and the postintervention period was for the 6 months after implementation.
The intervention was tested against “usual DM coach support,” which focuses on providing education to program participants and identifying and intervening in potentially acute situations to avoid hospitalizations and emergency department (ED) visits. Thus, we tested whether the intervention group achieved health and utilization outcomes significantly better than those in the group that received the usual coaching approach.
Unexpected issues arose with regard to data availability. A large client of LifeMasters decided to bring their DM services in-house, affecting a large number of the patients: 25% of the participants from the intervention group (1047 of 4254) and 93% of the control group participants (1548 of 1668). Although these members participated in the LifeMasters program during the study period, their utilization data were not available for inclusion in the analysis. To compensate for the loss in the control group, 906 new control group members were selected from the same control group call center for a conditotal control group of 2574. The new control group members were chosen by matching individuals pairwise with intervention group members on age, sex, number of months coached, and primary diagnosis. The PAM data on these newly added control participants were not available.
Over the course of the study other availability issues emerged regarding the utilization data. Three other vendors supplied utilization data for their patients, but only for the preintervention period or the postintervention period. This lack of data was for all their members, so selection bias should be less of an issue. Of the 1026 control group members for whom we should have had utilization data (120 from the original control group and the added 906 new control members), we obtained preintervention and postintervention utilization data for 62% (n = 635). Similarly, in the intervention group, among those who were expected to have data available, we only received utilization data in the preintervention and postintervention periods for 79% (n = 2529). The missing cases were treated as missing data in the utilization analysis only. Those who did not have utilization data were not dropped from the other analyses as long as they remained in the LifeMasters program.
Because utilization data were not available for all the study participants, we conducted an analysis to determine whether the participants for whom we had utilization data were different from those for whom we did not have data. Demographic characteristics and baseline clinical indicators were similar for both the control and intervention group participants with and without utilization data.
Table 1 shows the characteristics of the participants. Characteristic distributions are shown both with and without propensity score weights. There are no significant differences between the groups in terms of age or whether they screened positive for depression. There are differences, however, in the distributions of primary diagnoses. Those in the control group had 2 more months of coaching, more participants whose conditions had a high severity rating, and on average had slightly more comorbidities. No significant differences remained after the propensity weights were added.
Patients self-reported results of self-monitoring and clinical indicators such as blood pressure, low-density lipoprotein (LDL) cholesterol levels, and their glycosylated hemoglobin (A1C) levels. All patients are screened for possible depression through the use of the Patient Health Questionnaire-2.
The PAM data were collected via the phone from both intervention and control group participants. There was an attempt to repeat the PAM every 2 to 3 months; however, only a subset of participants completed 3 PAM surveys over the study period (n = 245 intervention; n = 112 control).
Utilization variables included office visits, ED visits, and hospital admissions. Each variable represented a count of the events per month. Data were included only when participants had data in both the preintervention and postintervention periods. A severity rating, based on the claims data, was used in the analysis as a control variable.
Clinical indicators include both biometric variables and variables reflecting adherence to medical recommendations.
Biometric variables were A1C levels for those with a diagnosis of diabetes; LDL cholesterol levels for those with diabetes, coronary artery disease (CAD), or congestive heart failure (CHF); and blood pressure for those with a diagnosis of CAD, CHF, diabetes, or hypertension.
Medication and immunization adherence was measured by patient reports of:
• Aspirin antiplatelet therapy (diabetes and CAD).
• Antilipidemic therapy (CAD).
• Beta-blocker therapy (CAD, CHF).
• Angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker therapy (CHF).
• Influenza immunization (all).
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