Physician Behavior Impact When Revenue Shifted From Drugs to Services | Page 2

Published Online: April 21, 2014
Bruce Feinberg, DO; Scott Milligan, PhD; Tim Olson, MBA; Winston Wong, PharmD; Daniel Winn, MD; Ram Trehan, MD; and Jeffrey Scott, MD
To measure extent of treatment, therapy lines were assigned based upon grouping of chemotherapy drugs. Drugs given within 30 days of each other were grouped as a drug combination. Changes that occurred beyond 30 days triggered drug combination reassignment and incremented the line of therapy. Exceptions were made for sequential therapies, which were accounted for by distinct grouping rules. Cormorbidity scores were calculated according to Charlson using Deyo’s mapping of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD- 9-CM) codes.20 A single modification to the mapping was implemented in flagging claims likely to represent solid tumors with metastases. In addition to using claims with ICD-9 codes 196.x through 199.1, patients with solid tumors who received 2 or more lines of chemotherapy were marked as with metastatic solid tumor.

All statistical analyses were conducted using SPSS 19 statistical software (IBM, Armonk, New York) with the exception of k-nearest neighbor analyses, which were performed using SPSS Modeler 14.2. Due to the large sample sizes, χ² analyses were not used for categorical comparisons. Instead, individual measures were calculated at the practice level and group means were compared by independent t tests. All chemotherapy evaluations were based on intravenous chemotherapy claim code 96413. New and established patient visit claim codes 99201-99205, 99241- 99245, and 99211-99215 were used in these analyses.


A total of 33 physicians from 13 TRIO practices within the insurance network who participated in the first-generation pathways project chose to join the Oncology Medical Home program. Propensity score analysis identified 8 Oncology Medical Home practices and 7 firstgeneration pathways practices that were well matched for use in these analyses (Figure 2). The matching variables were chosen as a reflection of practice disease focus, treatment preferences, and overall patient volume. After matching, baseline demographics and characteristics were similar in the 2 groups (Table). The first-generation pathways control group treated 4847 patients at baseline versus 7213 for the Oncology Medical Home. The ages of patients were similarly distributed between the 2 groups. Approximately 13.5% of patients were under age 50 years, 35% were aged 50 to 64 years, approximately 25% were aged 65 to 74 years, and 25% were aged 75 years or older. Both study groups treated primarily solid tumors (85%). Other tumor types were hematologic (11%) and gynecologic (5%). Seventy percent of patients recieved first-line chemotherapy, 22% percent recieved second-line chemotherapy, and approximatley 9% recieved third-line or higher chemotherapy.

There was minimal difference in behavior change between the Oncology Medical Home and first-generation pathways providers and between study years among providers (Figure 3). The number of established patient visits in the Oncology Medical Home group remained stable from year –1 to year +1, with a mean of 3.7 and 3.8 patient visits, respectively. The same was true for the firstgeneration pathways control group, with a mean of 4.6 and 5 patient visits, respectively. New patient visits increased 2% from year –1 to year +1 for Oncology Medical Home providers compared with a decrease of 2% for the first-generation pathways control group. The percentage of chemotherapy administrations per patient remained stable among study years for both groups, with approximately 8 chemotherapy administrations per patient in each group for each year. The percentage of patients who received chemotherapy remained stable for each group among study years at approximately 15%.

There was no difference in the percentage of patients who received all-generic chemotherapy regimens between the study groups, though there was a trend for increased use of generics in year +1 for both groups; Oncology Medical Home providers increased the use of generic-only regimens by 43% in year +1 compared with first-generation pathways providers, who increased use of generics by 42%. The migration of broadly used agents gemcitabine, docetaxel, irinotecan, and oxaliplatin (transiently) from brand to generic status during the study period was largely responsible for the increase in generic-only regimen prescribing among both groups. The generic regimens lacking any of these 4 drugs accounted for 25% and 23% of control and Oncology Medical Home patients, respectively, in the program year. In the preprogram year, all-generic regimens accounted for roughly 25% of control and 28% of Oncology Medical Home patients.


We found that, surprisingly, moving financial incentives from drug administration toward cognitive services did not alter physician behavior with regard to type or frequency of chemotherapy administrations. We say surprisingly, as expectations based on prevailing wisdom suggest physicians behave in their economic best interest as long as patient outcomes are not jeopardized. This has been demonstrated in previously published reports showing that decreased reimbursement schedules correlate with increased rates of chemotherapy administration and use of more costly chemotherapeutic agents.10,21 In fact, a recent New York Times editorial cosigned by 20 leading academics cited fee-for-service reimbursement as the primary driver for the spiraling cost of cancer care in this country.11 Our results are quite inconsistent with this idea. Whether this pattern of care was pathway-influenced or is the result of National Comprehensive Cancer Network and other guidelines, brand name prescription drug detailing, cognitive dissonance, our culture of medicine, or other factors, is speculative without more information.

Additionally, the results from this study indicate that, despite a nearly 3-fold increase in E&M code reimbursement, no significant change in established or new patient visits was observed. This was contrary to expectation and could be related to external influences on physician practice behavior, including the historically lower contribution of E&M reimbursement to revenue, standardized and established practice patterns, and maximized throughput within office flow. If so, then the speculated impact of reimbursement reform may be overestimated.

The reimbursement level for generics did not differ between the control and the Oncology Medical Home participants. We acknowledge the potential impact due to the patent expiry of 4 drugs in the program year, which may have minimized pressure to increase reimbursement from E&M claims. However, this is a separate issue from the main question of whether physicians behave to maximize financial gain. The data we provided suggest that given the opportunity to maximize revenue by increasing select cognitive services, physicians remained unchanged in their behavior.

The nature of this observational study may raise questions over bias, and ultimately, conclusions. The methodology of conducting research in such circumstances is difficult; by definition, selection bias exists when programs are voluntary and financially incentivized. However, any selection bias incurred impacted both control and experimental cohorts, which were then matched by propensity scoring. To account for differences in disease focus, diagnosis mix was considered. To account for differences in heavily treated versus newly treated patients, and in earlystage versus later-stage treatment, the distribution of chemotherapy lines (“extent of treatment”) was considered. To account for overall treated patient burden, comorbidity index was considered. Taken together, we believe these measures yielded propensity scores indicative of case mix. However, we acknowledge that claims data, and nearly all secondary data sources, are not sufficient to account for more diverse disease stratifications. For example, race, socioeconomic status, biomarker status, disease stage, or disease histology are not sufficiently represented in claims data. Despite the absence these factors, we believe these cohorts are appropriately matched and the comparisons are valid.

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Issue: April 2014
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