Sensitivity of Medicare Data to Identify Oncologists.
Ontology highlight
ABSTRACT: Background
Health services researchers have studied how care from oncologists impacts treatment and outcomes for cancer patients. These studies frequently identify physician specialty using files from the Center for Medicare and Medicaid Services (CMS) or the American Medical Association (AMA). The completeness of the CMS data resources, individually or combined, to identify oncologists is unknown. This study assessed the sensitivity of CMS data to capture oncologists included in the AMA Physician Masterfile.Methods
Oncologists were identified from three CMS data resources: physician claims, the National Plan and Provider Enumeration System Registry, and the Medicare Data on Provider Practice and Specialty file. CMS files and AMA data were linked using a unique physician identifier. Sensitivity to identify any oncologists, radiation oncologists (ROs), surgical oncologists (SOs), and medical oncologists (MOs) was calculated for individual and combined CMS files. For oncologists in the AMA data not identified as oncologists in the CMS data, their CMS specialty was assessed.Results
Individual CMS files each captured approximately 83% of the 17 934 oncologists in the AMA Masterfile; combined CMS files captured 90.4%. By specialty, combined CMS data captured 98.2% of ROs, 89.3% of MOs, and 70.1% of SOs. For ROs and SOs in the AMA data not identified as oncologists in the CMS data, their CMS specialty was usually similar to the AMA subspecialty; ROs were radiologists and SOs were surgeons.Conclusion
Using combined files from CMS identified most ROs and MOs found in the AMA, but not most SOs. Determining whether to use the AMA data or CMS files for a particular research project will depend on the specific research question and the type of oncologist included in the study.
SUBMITTER: Warren JL
PROVIDER: S-EPMC7868031 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundMedicare claims record linkage has been used to identify diagnosed dementia cases in order to estimate dementia prevalence and cost of care. Claims records in the 1990 s and early 2000 s have been found to provide 85% - ∼90% sensitivity and specificity.ObjectiveConsidering that dementia awareness has improved over time, we sought to examine sensitivity and specificity of more recent Medicare claims records against a standard criterion, clinical diagnosis of dementia.MethodsFor a sample of patients evaluated at the University of Southern California Alzheimer Disease Research Center (ADRC), we performed database linkage with Medicare claims files for a six-year period, 2007-2012. We used clinical diagnosis at the ADRC as the criterion diagnosis in order to calculate sensitivity and specificity.ResultsMedicare claims correctly identified 85% of dementia patients and 77% of individuals with normal cognition. About half of patients clinically diagnosed with mild cognitive impairment had dementia diagnoses in Medicare claims. Misclassified dementia patients (i.e., missed diagnosis by Medicare claims) had more favorable Mini-Mental State Examination and Clinical Dementia Rating scores and were less likely to present behavioral symptoms than correctly-classified dementia patients.ConclusionsDatabase linkage to Medicare claims records is an efficient and reasonably accurate tool to identify dementia cases in a population-based cohort. However, possibilities of obtaining biased results due to misclassification of dementia status need to be carefully considered to use Medicare claims diagnosis for etiologic research studies. Additional confirmation of dementia diagnosis may also be considered. A larger study is warranted to confirm our findings.
| S-EPMC7164318 | biostudies-literature
Project description:BackgroundResearchers are increasingly interested in using observational data to evaluate cancer outcomes following treatment, including cancer recurrence and disease-free survival. Because population-based cancer registries do not collect recurrence data, recurrence is often imputed from health claims, primarily by identifying later cancer treatments after initial treatment. The validity of this approach has not been established.Research designWe used the linked Surveillance, Epidemiology, and End Results-Medicare data to assess the sensitivity of Medicare claims for cancer recurrence in patients very likely to have had a recurrence. We selected newly diagnosed stage II/III colorectal (n=6910) and female breast cancer (n=3826) patients during 1994-2003 who received initial cancer surgery, had a treatment break, and then died from cancer in 1994-2008. We reviewed all claims from the treatment break until death for indicators of recurrence. We focused on additional cancer treatment (surgery, chemotherapy, radiation therapy) as the primary indicator, and used multivariate logistic regression analysis to evaluate patient factors associated with additional treatment. We also assessed metastasis diagnoses and end-of-life care as recurrence indicators.ResultsAdditional treatment was the first indicator of recurrence for 38.8% of colorectal patients and 35.2% of breast cancer patients. Patients aged 70 and older were less likely to have additional treatment (P < 0.05), in adjusted analyses. Over 20% of patients either had no recurrence indicator before death or had end-of-life care as their first indicator.ConclusionsIdentifying recurrence through additional cancer treatment in Medicare claims will miss a large percentage of patients with recurrences; particularly those who are older.
| S-EPMC4072852 | biostudies-literature
Project description:BACKGROUND:It is unclear whether Medicare data can be used to identify type and degree of collaboration between primary care providers (PCPs) [medical doctors (MDs), nurse practitioners, and physician assistants] in a team care model. METHODS:We surveyed 63 primary care practices in Texas and linked the survey results to 2015 100% Medicare data. We identified PCP dyads of 2 providers in Medicare data and compared the results to those from our survey. Sensitivity, specificity, and positive predictive value (PPV) of dyads in Medicare data at different threshold numbers of shared patients were reported. We also identified PCPs who work in the same practice by Social Network Analysis (SNA) of Medicare data and compared the results to the surveys. RESULTS:With a cutoff of sharing at least 30 patients, the sensitivity of identifying dyads was 27.8%, specificity was 91.7%, and PPV 72.2%. The PPV was higher for MD-nurse practitioner/physician assistant pairs (84.4%) than for MD-MD pairs (61.5%). At the same cutoff, 90% of PCPs identified in a practice from the survey were also identified by SNA in the corresponding practice. In 5 of 8 surveyed practices with at least 3 PCPs, about ?20% PCPs identified in the practices by SNA of Medicare data were not identified in the survey. CONCLUSIONS:Medicare data can be used to identify shared care with low sensitivity and high PPV. Community discovery from Medicare data provided good agreement in identifying members of practices. Adapting network analyses in different contexts needs more validation studies.
| S-EPMC6791761 | biostudies-literature
Project description:BackgroundIn the wake of the US opioid epidemic, there have been efforts to curb opioid prescribing. However, it is unknown whether these efforts have affected prescribing among oncologists, whose patients often require opioids for symptom management. We investigated temporal patterns in opioid prescribing for Medicare beneficiaries among oncologists and nononcologists.MethodsWe queried the Centers for Medicare and Medicaid Services Part D prescriber dataset for all physicians between January 1, 2013, and December 31, 2017. We used population-averaged multivariable negative binomial regression to estimate the association between time and per-provider opioid and gabapentinoid prescribing rate, defined as the annual number of drug claims (original prescriptions and refills) per beneficiary, among oncologists and nononcologists on a national and state level.ResultsFrom 2013 to 2017, the national opioid-prescribing rate declined by 20.7% (P < .001) among oncologists and 22.8% (P < .001) among non oncologists. During this time frame, prescribing of gabapentin increased by 5.9% (P < .001) and 23.1% (P < .001) among oncologists and nononcologists, respectively. Among palliative care providers, opioid prescribe increased by 15.3% (P < .001). During the 5-year period, 43 states experienced a decrease (P < .05) in opioid prescribing among oncologists, and in 5 states, opioid prescribing decreased more among oncologists than nononcologists (P < .05).ConclusionsBetween 2013 and 2017, the opioid-prescribing rate statistically significantly decreased nationwide among oncologists and nononcologists, respectively. Given similar declines in opioid prescribing among oncologists and nononcologists, there is concern that opioid-prescribing guidelines intended for the noncancer population are being applied inappropriately to patients with cancer and cancer survivors.
| S-EPMC7936059 | biostudies-literature
Project description:BackgroundStudying team-based primary care using 100% national outpatient Medicare data is not feasible, due to limitations in the availability of this dataset to researchers.MethodsWe assessed whether analyses using different sets of Medicare data can produce results similar to those from analyses using 100% data from an entire state, in identifying primary care teams through social network analysis. First, we used data from 100% Medicare beneficiaries, restricted to those within a primary care services area (PCSA), to identify primary care teams. Second, we used data from a 20% sample of Medicare beneficiaries and defined shared care by 2 providers using 2 different cutoffs for the minimum required number of shared patients, to identify primary care teams.ResultsThe team practices identified with social network analysis using the 20% sample and a cutoff of 6 patients shared between 2 primary care providers had good agreement with team practices identified using statewide data (F measure: 90.9%). Use of 100% data within a small area geographic boundary, such as PCSAs, had an F measure of 83.4%. The percent of practices identified from these datasets that coincided with practices identified from statewide data were 86% versus 100%, respectively.ConclusionsDepending on specific study purposes, researchers could use either 100% data from Medicare beneficiaries in randomly selected PCSAs, or data from a 20% national sample of Medicare beneficiaries to study team-based primary care in the United States.
| S-EPMC7855067 | biostudies-literature
Project description:BackgroundData collected as part of routine clinical practice could be used to detect cardiovascular outcomes in pragmatic clinical trials or clinical registry studies. The reliability of claims data for documenting outcomes is unknown.Methods and resultsWe linked records of Women's Health Initiative (WHI) participants aged ≥65 years to Medicare claims data and compared hospitalizations that had diagnosis codes for acute myocardial infarction or coronary revascularization with WHI outcomes adjudicated by study physicians. We then compared the hazard ratios for active versus placebo hormone therapy based solely on WHI-adjudicated events with corresponding hazard ratios based solely on claims data for the same hormone trial participants. Agreement between WHI-adjudicated outcomes and Medicare claims was good for the diagnosis of myocardial infarction (κ, 0.71-0.74) and excellent for coronary revascularization (κ, 0.88-0.91). The hormone:placebo hazard ratio for clinical myocardial infarction was 1.31 (95% confidence interval, 1.03-1.67) based on WHI outcomes and 1.29 (95% confidence interval, 1.00-1.68) based on Medicare data. The hazard ratio for coronary revascularization was 1.09 (95% confidence interval, 0.88-1.35) based on WHI outcomes and 1.10 (95% confidence interval, 0.89-1.35) based on Medicare data. The differences between hazard ratios derived from WHI and Medicare data were not significant in 1000 bootstrap replications.ConclusionsMedicare claims may provide useful data on coronary heart disease outcomes among patients aged ≥65 years in clinical research studies.Clinical trials registration informationURL: www.clinicaltrials.gov. Unique identifier: NCT00000611.
| S-EPMC4548886 | biostudies-literature
Project description:ObjectiveThere have been no validated Medicare claims-based algorithms available to identify epilepsy by discrete etiology of stroke (e.g., post-stroke epilepsy, PSE) in community-dwelling elderly individuals, despite the increasing availability of large datasets. Our objective was to validate algorithms that detect which patients have true PSE.MethodsWe linked electronic health records (EHR) to Medicare claims from a Medicare Pioneer Accountable Care Organization (ACO) to identify PSE. A neurologist reviewed 01/2012-12/2014 EHR data from a stratified sample of Medicare patients aged 65+ years to adjudicate a reference-standard to develop an algorithm for identifying patients with PSE. Patient sampling strata included those with: A) epilepsy-related claims diagnosis (n = 534 [all]); B) no diagnosis but neurologist visit (n = 500 [randomly sampled from 4346]); C) all others (n = 500 [randomly sampled from 16,065]). We reconstructed the full sample using inverse probability sampling weights; then used half to derive algorithms and assess performance, and the remainder to confirm performance. We evaluated predictive performance across several measures, e.g., specificity, sensitivity, negative and positive predictive values (NPV, PPV). We selected our best performing algorithms based on the greatest specificity and sensitivity.ResultsOf 20,943 patients in the reconstructed sample, 13.6% of patients with epilepsy had reference-standard PSE diagnosis, which represents a 3-year overall prevalence of 0.28% or 28/10,000, and a prevalence within the subpopulation with stroke of 3%. The best algorithm included three conditions: (a) at least one cerebrovascular claim AND one epilepsy-specific anticonvulsant OR (b) at least one cerebrovascular claim AND one electroencephalography claim (specificity 100.0% [95% CI 99.9%-100.0%], NPV 98.8% [98.6%-99.0%], sensitivity 20.6% [95% CI 14.6%-27.9%], PPV 86.5% [95% CI 71.2%-95.5%]).ConclusionMedicare claims can identify elderly Medicare beneficiaries with PSE with high accuracy. Future epidemiological surveillance of epilepsy could incorporate similar algorithms to accurately identify epilepsy by varying etiologies.
| S-EPMC6640134 | biostudies-literature
Project description:ImportanceAlthough physician sex is known to influence salary even after controlling for productivity, sex-based differences in clinical activity and reimbursement among radiation oncologists are poorly understood.ObjectivesTo evaluate differences by sex in productivity, breadth of practice, and payments and to characterize Medicare reimbursement by sex among similarly productive groups of radiation oncologists.Design, setting, and participantsA retrospective cohort study was conducted using the January 1 to December 31, 2016, Centers for Medicare & Medicaid Services Physician and Other Supplier Public Use File (POSPUF) to identify charge and payment information for individual radiation oncologists. Clinicians were part of a population-based sample of US radiation oncologists who bill Medicare in both non-facility-based (NFB) and facility-based (FB) practice settings. Analysis was conducted from June 5 to 25, 2018.Main outcomes and measuresOutcome measurements included physician productivity (measured by number of Medicare charges), physician payments (reported as total Medicare payments as well as mean payments per charge submitted and per beneficiary treated), and physician breadth of practice (measured by number of unique Medicare billing codes) in NFB and FB settings.ResultsA total of 4393 radiation oncologists (1133 women and 3260 men) were included in the POSPUF in 2016. Compared with their male counterparts, female physicians in the NFB setting submitted a mean of 1051 fewer charges (95% CI, -1458 to -644; P < .001), collected a mean of $143 610 less in revenue (95% CI, -$185 528 to -$101 692; P < .001), and used a mean of 1.32 fewer unique billing codes (95% CI, -2.23 to -0.41; P = .004). Compared with their male counterparts, female radiation oncologists in the FB setting submitted a mean of 423 fewer charges (95% CI, -506 to -341; P < .001), collected a mean of $26 735 less in revenue (95% CI, -$31 910 to -$21 560; P < .001), and submitted a mean of 1.28 fewer unique billing codes (95% CI, -1.77 to -0.78; P < .001). Women represented 46 of the 397 most highly productive radiation oncologists in the FB setting (11.6%) and collected a mean of $33 026 less (95% CI, -$52 379 to -$13 673; P = .001) than men who were similarly productive. In the NFB setting, women represented 54 of the 326 most highly productive radiation oncologists (16.6%) and collected $345 944 (95% CI, -$522 663 to -$169 225; P < .001) less than similarly highly productive men. Women collected a mean of $8.49 less per charge (95% CI, -$14.13 to -$2.86; P = .003) than men in the NFB setting.Conclusions and relevanceThis study suggests that female radiation oncologists submit fewer Medicare charges, are reimbursed less per charge they submit, and receive lower Medicare payments overall compared with male radiation oncologists. Even among similarly productive radiation oncologists, women in this study still collected less revenue than men. Further research is required to understand the sex-based barriers to economic advancement within radiation oncology.
| S-EPMC6583310 | biostudies-literature
Project description:A report on how accurately physicians used methodology in a nationwide demonstration by the Centers for Medicare & Medicaid Services to enhance quality of cancer treatment and care and promote evidence-based practices.
| S-EPMC2835481 | biostudies-literature
Project description:BackgroundDeveloping a definition of what constitutes high need among Medicare beneficiaries using administrative data is an important prerequisite to evaluating value-based payment reforms. While various definitions of high need exist, their predictive validity for different patient outcomes in the following year has not been systematically assessed for both fee-for-service (FFS) and Medicare Advantage (MA) beneficiaries.ObjectiveTo develop a definition of high need using administrative data in 2014 and to examine its predictive validity for patient outcomes in 2015 as compared to alternative definitions for both FFS and MA beneficiaries.DesignRetrospective cohort study of national Medicare claims and post-acute assessment data.ParticipantsAll Medicare beneficiaries in 2014 who survived until the end of the year (n = 54,717,039).Main measuresTwo or more complex conditions, 6 or more chronic conditions, acute or post-acute health services utilization, indicators of frailty, complete dependency in mobility or in any activities of daily living in post-acute care assessments, hospitalization, mortality, days in community, Medicare expenditures.Key resultsBased on our definition of high-need patients, 13.17% of FFS and 8.85% of MA beneficiaries were identified as high need in 2014. High-need FFS patients had mortality rates 7.1 times higher (16.23% vs. 2.27%) and hospitalization rates 3.4 times higher (40.69 vs. 12.03) in 2015 compared to other beneficiaries. Competing high-need definitions all had good specificity (≥ 0.88). Having 3 or more Hierarchical Chronic Conditions yielded a good positive predictive value for hospitalization, at 0.50, but only identified 19.71% of FFS beneficiaries hospitalized and 28.46% of FFS decedents that year as high need, as opposed to 33.92% and 51.98% for the new definition. Results were similar for MA beneficiaries.ConclusionsThe proposed high-need definition has better sensitivity and yields a sample of almost 5 million FFS and 1.5 million MA beneficiaries, facilitating outcome performance comparisons across health systems.
| S-EPMC6420563 | biostudies-literature