Identification of Patients Likely to Benefit From Care Management Programs
Published Online: May 23, 2011
Tobias Freund, MD; Cornelia Mahler, MA, RN, PhD; Antje Erler, MD, MPH; Jochen Gensichen, MD, MA, MPH; Dominik Ose, DrPH, MPH; Joachim Szecsenyi, MD, MSc; and Frank Peters-Klimm, MD
Care management programs are seen as an effective approach to meet the challenge of increasing numbers of patients with complex care needs.1,2 Targeting these programs to patients with multiple chronic conditions and at high risk for cost-intensive care offers the greatest opportunities for improving quality of care and reducing healthcare costs.3 Because care management programs require allocation of restricted human and financial resources, the targeted population should be not only cost-intensive but also “care sensitive.” Care sensitivity implies 2 dimensions. First, patients have to be approachable (ie, willing and able to participate in intensified care programs). Second, their clinical needs have to be actionable (ie, care programs should be able to mitigate their needs). Therefore, practicable tools are needed to screen patient populations for “high-risk” and care-sensitive individuals.4
Since the 1980s, several statistical models commonly known as predictive models have been developed to predict future healthcare utilization and costs.5,6 These models are based on morbidity, prior healthcare utilization, and cost data, which can easily be obtained from health insurance claims data. Predictive models can be used to identify high-risk individuals who may benefit from care management programs.7 Further characterization of these individuals has revealed actionable healthcare needs.8 When used to identify patients likely to benefit from care management programs, predictive modeling (PM)–based selection is hypothesized as superior to selection by primary care physician (PCP).9 However, to our knowledge, the approaches have not been directly compared. This article extends the existing literature on patient selection for care management programs with results from a direct comparison of case selection by PM, selection by PCP, and a combination of both. A long-term patient–provider relationship enables PCPs to assess a patient’s clinical risk and care sensitivity. The results of this comparison may be useful in developing an approach to identify care-sensitive patients who are at high risk for future healthcare utilization and who are likely to benefit from care management programs.
In 2009, insurance claims data among a systematic sample of all beneficiaries of the General Regional Health Fund (Allgemeine Ortskrankenkasse [AOK]) from 10 primary care practices in southwestern Germany were obtained for PM and further analysis. The 10 primary care practices (6 single practices and 4 group practices with 2 PCPs) were recruited from rural areas (5 practices) and from urban areas (5 practices). In Germany, 85% of the population are insured by sickness funds. These statutory health insurance programs based on a social security–based healthcare system are mainly funded by earmarked premiums. In 2009, the AOK insured 34% of all patients insured by a social security–based healthcare system in Germany.10 The AOK covers all areas of healthcare delivery, and its contributions are jointly paid by beneficiaries and by employers. There are no age restrictions for AOK beneficiaries. Beneficiaries with their own income have copayments of up to 2% of their annual income.
The data set included medical and pharmacy claims from January 2007 to December 2008. Deidentified data were obtained from AOK beneficiaries of all ages. This explorative study was part of a series of studies to develop a care management program for high-risk patients in primary care. Details of the study have been described elsewhere.11 The University Hospital Heidelberg Institutional Review Board, Heidelberg, Germany, approved the study. Informed consent was obtained from all participants in the survey.
Case Finding by PM
For PM, we used the software package Case Smart Suite Germany (Verisk Health, Munich, Germany). This PM program is an extension of diagnostic cost group PM, which has previously been applied in comparative case-finding studies.12 Information from the past 2 years (2007-2008) served as inputs for PM, including all International Statistical Classification of Diseases, 10th Revision (German modification) (ICD-10-GM) diagnosis codes assigned in outpatient and inpatient settings, prior costs, and hospital admissions, as well as demographic data. Clinically similar ICD-10-GM codes are classified into diagnostic groups. These groups are then further collapsed into condition categories, which reflect similar levels of resource use and are organized by body system or disease (eg, congestive heart failure). Individuals may have multiple diagnostic groups or condition categories. In the next step, grouping into hierarchical clinical categories is applied to every individual. Therefore, each individual is labeled exclusively, with the highest hierarchical clinical category within 1 condition category (eg, acute congestive heart failure exacerbation). However, different coexisting hierarchical clinical categories within 1 individual will increase predicted future healthcare utilization. Based on logistic regression analysis, the software package computes a likelihood of hospitalization for each individual, which indicates the likelihood of at least 1 hospital admission within the next 12 months (2009-2010 for the present study).13 Despite the fact that hospitalizations are a source of major healthcare spending, predicted costs and predicted hospitalizations may not be used interchangeably to identify patients at high risk who may potentially benefit from care management interventions. Hospitalizations may reflect potentially actionable costs, as a significant proportion of these are owing to ambulatory care–sensitive conditions,14 whereas distinct cost-intensive procedures like fertility treatment, dialysis, biological agents, or chemotherapy seem to represent less actionable costs. For this study, patients with a likelihood of hospitalization score above the 90th percentile were defined as high-risk patients. We included patients with at least 1 of the following ICD-10-GM index conditions: type 2 diabetes mellitus (codes E11-E14), chronic obstructive pulmonary disease
(codes J43-J44), asthma (code J45), chronic heart failure (codes I11.0, I13.0, I13.2, I25.5, and I50), and late-life depression (codes F32-F33 [>60 years]). Excluded were patients with dementia (codes F00-F03), dialysis (codes Z49 and Z99.2), or active cancer disease (codes C00-C97).
Case Finding Using Selection by PCP
Fourteen PCPs from 10 participating primary care practices were asked to screen a list of all AOK beneficiaries in their practice to select up to 30 patients for future participation in a care management program aimed at reducing avoidable hospitalizations. Case selection was restricted to the same inclusion and exclusion criteria as aforedescribed. Primary care physicians were informed about the aims and intervention elements of the planned care management intervention. However, avoidable hospitalizations were not further specified before case selection. Primary care physicians were blinded to results of PM until they submitted their final list of selected patients.
Characterization of Selected Patients
We analyzed insurance claims data for all 6026 beneficiaries to determine morbidity burden and prior healthcare uti-lization. An adapted version of the Charlson Comorbidity Index was used to determine weighted comorbidity counts based on inpatient and outpatient ICD-10-GM diagnoses.15 This index has been shown to be predictive of 1-year mortality and healthcare costs.16 Single index condition prevalence, prior hospital admissions, and demographic variables were obtained from insurance claims data (2007-2008). We invited all selected patients (by PM, selection PCP, or a combination of both) who were enrolled in a PCP-centered care contract to fill out a paper-based questionnaire containing the following instruments: European Quality of Life 5D index17 to measure health-related quality of life, Medication Adherence Report Scale18 to measure self-reported medication adherence, and additional items to measure sociodemographic variables. Because of data security regulations, patient participation in the survey was restricted to enrollees in a PCP-centered care contract.19 Participation in the patient survey was incentivized by a lottery of 5 shopping vouchers (€100).
PDF is available on the last page.