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The American Journal of Managed Care May 2011
Genomic Testing and Therapies for Breast Cancer in Clinical Practice
Jennifer S. Haas, MD, MSPH; Kathryn A. Phillips, PhD; Su-Ying Liang, PhD; Michael J. Hassett, MD, MPH; Carol Keohane, BSN, RN; Elena B. Elkin, PhD; Joanne Armstrong, MD, MPH; and Michele Toscano, MS
Effect of the Pay-for-Performance Program for Breast Cancer Care in Taiwan
Raymond N.C. Kuo, PhD; Kuo-Piao Chung, PhD; and Mei-Shu Lai, MD, PhD
Post-treatment Surveillance in a Large Cohort of Patients With Colon Cancer
Chung-Yuan Hu, PhD; George L. Delclos, MD, PhD; Wenyaw Chan, PhD; and Xianglin L. Du, MD, PhD
Adherence to Laboratory Test Requests by Patients With Diabetes: The Diabetes Study of Northern California (DISTANCE)
Howard H. Moffet, MPH; Melissa M. Parker, MS; Urmimala Sarkar, MD, MPH; Dean Schillinger, MD; Alicia Fernandez, MD; Nancy E. Adler, PhD; Alyce S. Adams, PhD; and Andrew J. Karter, PhD
Post-treatment Surveillance in a Large Cohort of Patients With Colon Cancer
Chung-Yuan Hu, PhD; George L. Delclos, MD, PhD; Wenyaw Chan, PhD; and Xianglin L. Du, MD, PhD
Psychological Distress and Trends in Healthcare Expenditures and Outpatient Healthcare
Paul A. Pirraglia, MD, MPH; John M. Hampton, MS; Allison B. Rosen, MD, ScD; and Whitney P. Witt, PhD, MPH
Impact of Clinical Oral Chemotherapy Program on Wastage and Hospitalizations
Nikhil Khandelwal, PhD, BPharm; Ian Duncan, FSA, FIA, FCIA, MAAA; Tamim Ahmed, PhD, MBA; Elan Rubinstein, MPH, PharmD; and Cheryl Pegus, MPH, MD
Identification of Patients Likely to Benefit From Care Management Programs
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
Promoting Electronic Health Record Adoption Among Small Independent Primary Care Practices
Reena Samantaray, MPA; Victoria O. Njoku, MPH; Julian W. M. Brunner, MPH; Veena Raghavan, MPH; Mat L. Kendall, MPH; and Sarah C. Shih, MPH
Psychological Distress and Trends in Healthcare Expenditures and Outpatient Healthcare
Paul A. Pirraglia, MD, MPH; John M. Hampton, MS; Allison B. Rosen, MD, ScD; and Whitney P. Witt, PhD, MPH
Journey Forward: The New Face of Cancer Survivorship Care
Jennifer Hausman, MPH; Patricia A. Ganz, MD; Thomas P. Sellers, MPA; and Joel Rosenquist, MPA
US Insurance Program's Experience With a Multigene Assay for Early-Stage Breast Cancer
John Hornberger, MD, MS; Rebecca Chien, BA; Katie Krebs, MHA; and Louis Hochheiser, MD
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Identification of Patients Likely to Benefit From Care Management Programs
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
Racial Variation in the Cost-effectiveness of Chemotherapy for Prostate Cancer
Michael Grabner, PhD; Eberechukwu Onukwugha, PhD; Rahul Jain, PhD; and C. Daniel Mullins, PhD
Oncology Management Programs for Payers and Physicians
Dawn Holcombe, FACMPE, MBA
Impact of New Drugs and Biologics on Colorectal Cancer Treatment and Costs
Pinar Karaca-Mandic, PhD; Jeffrey S. McCullough, PhD; Mustaqeem A. Siddiqui, MBBS; Holly Van Houten, BA; and Nilay D. Shah, PhD
Promoting Electronic Health Record Adoption Among Small Independent Primary Care Practices
Reena Samantaray, MPA; Victoria O. Njoku, MPH; Julian W. M. Brunner, MPH; Veena Raghavan, MPH; Mat L. Kendall, MPH; and Sarah C. Shih, MPH
US Insurance Program's Experience With a Multigene Assay for Early-Stage Breast Cancer
John Hornberger, MD, MS; Rebecca Chien, BA; Katie Krebs, MHA; and Louis Hochheiser, MD
Journey Forward: The New Face of Cancer Survivorship Care
Jennifer Hausman, MPH; Patricia A. Ganz, MD; Thomas P. Sellers, MPA; and Joel Rosenquist, MPA
Impact of Clinical Oral Chemotherapy Program on Wastage and Hospitalizations
Nikhil Khandelwal, PhD, BPharm; Ian Duncan, FSA, FIA, FCIA, MAAA; Tamim Ahmed, PhD, MBA; Elan Rubinstein, MPH, PharmD; and Cheryl Pegus, MPH, MD
Impact of New Drugs and Biologics on Colorectal Cancer Treatment and Costs
Pinar Karaca-Mandic, PhD; Jeffrey S. McCullough, PhD; Mustaqeem A. Siddiqui, MBBS; Holly Van Houten, BA; and Nilay D. Shah, PhD
Oncology Management Programs for Payers and Physicians
Dawn Holcombe, FACMPE, MBA
Racial Variation in the Cost-effectiveness of Chemotherapy for Prostate Cancer
Michael Grabner, PhD; Eberechukwu Onukwugha, PhD; Rahul Jain, PhD; and C. Daniel Mullins, PhD
Effect of the Pay-for-Performance Program for Breast Cancer Care in Taiwan
Raymond N.C. Kuo, PhD; Kuo-Piao Chung, PhD; and Mei-Shu Lai, MD, PhD
Genomic Testing and Therapies for Breast Cancer in Clinical Practice
Jennifer S. Haas, MD, MSPH; Kathryn A. Phillips, PhD; Su-Ying Liang, PhD; Michael J. Hassett, MD, MPH; Carol Keohane, BSN, RN; Elena B. Elkin, PhD; Joanne Armstrong, MD, MPH; and Michele Toscano, MS

Identification of Patients Likely to Benefit From Care Management Programs

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
Three approaches to prospective patient identification for care management programs were compared: predictive modeling, selection by primary care physician, and a combination of both.

Objectives: To compare predictive modeling (PM), selection by primary care physician (PCP), and a combination of both as approaches to prospective patient identification for care management programs.

 

Study Design: Observational study.

 

Methods: A total of 6026 beneficiaries of a statutory health insurance program in Germany served as a sample for patient identification by PM and selection by PCP. The resulting mutually exclusive subpopulations were compared for care needs (eg, morbidity burden), healthcare utilization (previous all-cause hospitalizations and predicted costs), and prior participation in intensified care programs (as a proxy for amenability). Data sources were insurance claims data and a patient survey.

 

Results: Patients were selected for eligibility in a care management program by PM (n = 301), selection by PCP (n = 203), or a combination of both (n = 32). Compared with 5490 nonselected patients, all eligible patients had significantly higher morbidity burden and more previous hospitalizations. Compared with selection by PCP, PM identified patients at significantly higher risk for future healthcare utilization, with predicted annual healthcare costs of €8760 (95% confidence interval [CI], €8314-€9205) vs €4541 (95% CI, €4094-€4989) (P <.01). Compared with patients selected by PM, patients selected by PCP had significantly higher rates of prior participation in intensified care programs (80.8% vs 56.4%, P <.01). Patients selected independently by both approaches seemed to be at high risk for future healthcare utilization, with predicted annual healthcare costs of €8279 (95% CI, €7465-€9092), and 84.6% had prior participation in intensified care programs.

 

Conclusions: Identification of high-risk patients most likely to benefit from and participate in care management programs may be facilitated by a combination of PM and selection by PCP.

 

(Am J Manag Care. 2011;17(5):345-352)

Care management programs improve quality of care and reduce costs for patients at high risk for future healthcare utilization and actionable care needs. The best approach to identify patients most likely to benefit from care management programs remains unclear.

  • Predictive modeling identifies patients who are at high risk for future healthcare utilization and who seem less approachable for care management.
  • Selection by primary care physician identifies patients who are at lower risk for future healthcare utilization and who seem approachable for care management.
  • When combined, the 2 approaches may complement each other to identify patients who are most likely to benefit from and participate in care management programs.
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.

METHODS

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).

In Germany, population-based disease management programs (DMPs) are part of routine care for 4 of the index conditions (type 2 diabetes mellitus, chronic obstructive pulmonary disease, asthma, and congestive heart failure caused by coronary heart disease).20 German DMPs consist of regular follow-up visits up to every 3 months. They include clinical examination, laboratory tests (eg, glycosylated hemoglobin tests), and patient education.21 However, essential elements of care management interventions like individualized assessment, care planning, and frequent symptom monitoring are not routinely part of DMPs.22 Participation in German DMPs is voluntary and free of charge. German sickness funds are free to set incentives for patients to be enrolled in intensified care programs. The AOK decided to partly exempt beneficiaries from copayments (up to €40 a year) if they are willing to participate in DMPs. We considered prior voluntary participation in at least 1 of these programs during 2007-2008 as a proxy for “approachability,” indicating patients’ willingness to actively manage their disease and to participate in a care management program.23

Data Analysis

Quantitative data are presented as means, 95% confidence intervals (CIs) of means, absolute numbers, and proportions. Two-sided c2 test was used to compare distributions of categorical variables. Means of continuous variables were compared by univariate analysis of variance, with performance of Games-Howell test as a statistical post hoc test.24 This test accounts for unequal variance and largely heterogeneous sample sizes. P <.05 (2-sided) was considered statistically significant. All statistical analyses were performed using SPSS version 18.0 (SPSS Inc, Chicago, IL).

RESULTS

The mean number of AOK beneficiaries per primary care practice was 603 (95% CI, 409-797). All beneficiaries were screened independently for case finding by PM and selection by PCP. Primary care physicians screened a mean of 464 (95% CI, 294-631) AOK beneficiaries per practice.

Predictive modeling identified 301 patients for eligibility in a care management program, while selection by PCP identified 203 patients who would seem to benefit from a care management intervention on the basis of clinical judgment. Another group of 32 patients was concordantly identified using both PM and selection by PCP. All groups were mutually exclusive.

 
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