Medical AssistantBased Care Management for High-Risk Patients in Small Primary Care Practices

Context Dissatisfaction with traditional care for patients with chronic conditions is leading to new models of care. Contribution The investigators found that protocol-based care by medical assistants for patients with diabetes mellitus, chronic obstructive pulmonary disease, or chronic heart failure did not prevent rehospitalization but did improve quality of life at a reasonable cost. Caution Relatively few practices were included in the study. Implication Medical assistants can participate in new models of care for patients with chronic conditions. Primary care faces the challenge of caring for an increasing number of patients with multiple chronic conditions with a diminishing workforce (1, 2). These patients are at high risk for potentially avoidable hospitalizations, which result in substantial system-level health care spending (3) and affect patient quality of life (4). Although most hospitalizations are deemed unavoidable (5, 6), primary care physicians report that self-management support and intensified monitoring could prevent 40% of hospitalizations in patients with multiple chronic conditions (7). Studies of nurse-led care management interventions that focus on patients at high risk for future health care use, such as Guided Care (8) or Geriatric Resources for Assessment and Care of Elders (9), have recently evaluated self-management support and intensified monitoring. Despite substantial improvements in quality of care (8) and reductions in health care use (10), these interventions did not reduce overall health care spending (8, 10), partly because savings from reduced health care use were offset by the costs of these interventions. In small primary care settings (solo practices or 2-person partnerships), resources are often limited and extensive collaborative models, such as multiprofessional care management, may be difficult to implement. The medical assistant workforce is one of the fastest growing workforces in primary care practices in the United States (11), and all primary care practices in Germany employ medical assistants. Medical assistants in the United States are trained in a 3-year part-time curriculum in practice (3920 hours) and vocational school (840 hours). Despite recent attempts to involve medical assistants in chronic care, their work focuses primarily on clerical duties (including reception) and routine tasks, such as blood sampling or electrocardiogram recording, and their median annual salary is $28860 (11). Certified medical assistants in the United States and Germany are comparable with regard to education, tasks, and remuneration (11). Involving medical assistants in chronic care management may improve access to health services (12). Early programs that expanded the medical assistant's role to include chronic care services (under the supervision of primary care physicians), such as self-management support (13) or telephone monitoring in patients with osteoarthritis (14), major depression (15), or chronic heart failure (16), showed significant results in terms of specialist consultation rates (13, 14), medication management (14), depressive symptoms (15), and self-care behavior (16). All of these programs were "scripted," meaning that medical assistants followed evidence-based protocols and algorithms with defined interview questions. We aimed to determine whether a medical assistantbased scripted care management intervention (primary carebased care management [PraCMan]) would reduce hospitalizations in primary care patients with type 2 diabetes, chronic obstructive pulmonary disease (COPD), or chronic heart failure who had a high predicted risk for future hospitalization. Methods Design We performed a cluster randomized clinical trial using primary care practices as units of randomization to minimize contamination bias. We have published details of the study design (17). Setting and Participants We conducted the study between July 2010 and June 2013 and enrolled patients from October to December 2010. Practices were eligible for the study if they employed at least 1 primary care physician (such as a general practitioner or general internist) and at least 1 medical assistant and if both were willing to participate in the study. Primary care physicians had to be enrolled in the primary carecentered care contract of a large health plan in Germany (AOK Baden-Wrttemberg) (18). Because "medical assistant" is a defined health profession in Germany (19), we did not need additional inclusion criteria for them. Practices were recruited from July to September 2010. We informed all 1177 primary care physicians within the health plan of AOK Baden-Wrttemberg about the study and invited all interested physicians to participate in the study. Eligible patients were aged 18 years or older and were receiving medical treatment for at least 1 of the following index conditions: type 2 diabetes mellitus, COPD, or chronic heart failure. Furthermore, patients had to have a high risk for future hospitalization (that is, a predicted likelihood of hospitalization within the upper quartile of the total population of health plan patients, as determined by analysis of data from the preceding 18 months [20]). We calculated the likelihood of hospitalization by assessing the deidentified insurance claims data of all health plan beneficiaries from the participating practices using the validated prediction software Case Smart Suite Germany 0.7 (Verisk Health) (21). We excluded patients who met the following criteria: active cancer (cancer diagnosis and current receipt of radiotherapy or chemotherapy), moderate to severe dementia, permanent residency in a nursing home, participation in a concurrent clinical trial (including telemonitoring studies), severe physical and mental disorders (such as dementia, psychotic disorder, or palliative care needs), or other problems that hindered active participation in the intervention (such as language barriers), as assessed by the primary care physician. Previous studies have validated this process of inclusion and exclusion (20). After providing written informed consent, participating patients provided baseline data. We concealed the allocation to intervention or control groups until each practice completed patient enrollment and baseline assessment. Randomization and Interventions Primary care practices were randomly allocated to care management or usual care in a 1:1 ratio by block randomization with variable block lengths. Because population density has a substantial effect on hospitalizations (22), we stratified randomization according to the population density of the participating practice sites (urban vs. rural), based on a map provided by the Federal Agency of Regional Development Planning. We used computer-generated randomization lists (SAS, version 9.2 [SAS Institute]). A research assistant who was not otherwise involved in the project performed the central randomization. We informed physicians about their allocation via an official letter and asked them to inform participating patients. Because of the nature of the intervention, blinding primary care physicians, medical assistants, and patients was not possible. However, we blinded the assessment of the primary and secondary end points as well as the responsible statistician to study group allocation. Training of Practice Teams We trained 71 medical assistants in a 2-day course on behavioral counseling strategies, symptom monitoring, medication assessment, and interprofessional communication by using a training manual (Appendix Table 1). Training was complemented by 20 hours of self-study. Appendix Table 1. Training Manual Intervention Using early evidence on a collaborative care concept for patients with multiple chronic conditions in an established nonprofit U.S. HMO (23), we trained medical assistants by using a paper-based assessment checklist to reveal patients' needs and resources (17). Primary care physicians and patients negotiated patient-specific goals, with a special emphasis on self-management tasks (24). Medical assistants developed specific action plans to achieve these goals together with patients and caregivers. A folder that included health information and an optional emergency plan was offered to all patients. Medical assistants monitored goal achievement and symptom deterioration either face-to-face with patients in the clinic or by telephone using paper-based checklists (25, 26). Monitoring intervals were tailored to the patient's health status but were scheduled at least once every 6 weeks. Primary care physicians met with medical assistants weekly to review patient progress. We fixed the maximum caseload for medical assistants at 20 patients (in addition to their daily duties, such as reception and phlebotomy). We financially incentivized intervention practice teams by providing $135 per enrolled patient per year to cover staff costs. Usual Care Patients in the control group received the best primary care according to evidence-based practice guidelines (18). Outcomes and Follow-up The primary outcome was the number of all-cause hospitalizations at 12 months at the patient level. We set the date of randomization as the starting date of the intervention. We did not include hospitalizations that occurred before the start of the intervention (that is, before 3 November 2010), but we did include hospitalizations that persisted after the end of the intervention (that is, after 3 November 2012). Secondary measures included the number of days in the hospital; hospitalizations related to index conditions; patient-reported quality of life, which was assessed with the 12-Item Short Form Health Survey (SF-12) (mental and physical health scales ranging from 0 [poor] to 100 [good]) (27); general health, which was assessed with the EuroQol instrument (EQ-5D) (scores ranging from 0 [poor health] to 1 [perfect health]) (28); and all-cause mortality. We estimated intervention costs using standard wages fo