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Can Claims Data Algorithms Identify the Physician of Record?

ABSTRACT: BACKGROUND:Claims-based algorithms based on administrative claims data are frequently used to identify an individual's primary care physician (PCP). The validity of these algorithms in the US Medicare population has not been assessed. OBJECTIVE:To determine the agreement of the PCP identified by claims algorithms with the PCP of record in electronic health record data. DATA:Electronic health record and Medicare claims data from older adults with diabetes. SUBJECTS:Medicare fee-for-service beneficiaries with diabetes (N=3658) ages 65 years and older as of January 1, 2008, and medically housed at a large academic health system. MEASURES:Assignment algorithms based on the plurality and majority of visits and tie breakers determined by either last visit, cost, or time from first to last visit. RESULTS:The study sample included 15,624 patient-years from 3658 older adults with diabetes. Agreement was higher for algorithms based on primary care visits (range, 78.0% for majority match without a tie breaker to 85.9% for majority match with the longest time from first to last visit) than for claims to all visits (range, 25.4% for majority match without a tie breaker to 63.3% for majority match with the amount billed tie breaker). Percent agreement was lower for nonwhite individuals, those enrolled in Medicaid, individuals experiencing a PCP change, and those with >10 physician visits. CONCLUSIONS:Researchers may be more likely to identify a patient's PCP when focusing on primary care visits only; however, these algorithms perform less well among vulnerable populations and those experiencing fragmented care.

SUBMITTER: DuGoff EH

PROVIDER: S-EPMC5601011 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Can Claims Data Algorithms Identify the Physician of Record?

DuGoff Eva H EH Walden Emily E Ronk Katie K Palta Mari M Smith Maureen M

Medical care 20180301 3

<h4>Background</h4>Claims-based algorithms based on administrative claims data are frequently used to identify an individual's primary care physician (PCP). The validity of these algorithms in the US Medicare population has not been assessed.<h4>Objective</h4>To determine the agreement of the PCP identified by claims algorithms with the PCP of record in electronic health record data.<h4>Data</h4>Electronic health record and Medicare claims data from older adults with diabetes.<h4>Subjects</h4>Me ...[more]

PMID: 28319581

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Project description:While medical records have detailed information, they are limited in reach to the availability and accessibility of those records. On the other hand, administrative data while limited in scope, have a much further reach in coverage of an entire population. However, few studies have validated the use of administrative data for identifying infections in pediatric populations. Pediatric patients from Ontario, Canada aged <18 years were randomly sampled from the Electronic Medical Record Administrative data Linked Database (EMRALD). Using physician diagnoses from the electronic medical record (EMR) as the reference standard, we determined the criterion validity of physician billing claims in administrative data for identifying infectious disease syndromes from 2012 to 2014. Diagnosis codes were assessed by infection category (respiratory, skin and soft tissue, gastrointestinal, urinary tract and otitis externa) and for all infections combined. Sensitivity analyses assessed the performance if patients had more than one reason to visit the physician. We analysed 2,139 patients and found 33.3% of all visits were for an infection, and respiratory infections accounted for 67.6% of the infections. When we combined all infection categories, sensitivity was 0.74 (95% CI 0.70-0.77), specificity was 0.95 (95% CI 0.93-0.96), positive predictive value (PPV) was 0.87 (95% CI 0.84-0.90), and negative predictive value (NPV) was 0.88 (95% CI 0.86-0.89). For respiratory infections, sensitivity was 0.77 (95% CI 0.73-0.81), specificity was 0.96 (95% CI 0.95-0.97), PPV was 0.85 (95% CI 0.81-0.88), and NPV was 0.94 (95% CI 0.92-0.95). Similar performance was observed for skin and soft tissue, gastrointestinal, urinary tract, and otitis externa infections, but with lower sensitivity. Performance measures were highest when the patient visited the physician with only one health complaint. We found when using linked EMR data as the reference standard, administrative billing codes are reasonably accurate in identifying infections in a pediatric population.

Project description:BackgroundLack of using a validated algorithm to select patients is a source of selection bias in oncology studies using administrative claims. The objective of this study to evaluate published algorithms to identify patients with soft tissue sarcoma (STS) in administrative claims and to evaluate new algorithms to improved performance.MethodsTwo cancer populations including STS cases and non-STS controls were selected from the MarketScan Explorys Linked Claims-Electronic Medical Record (EMR) Database between January 1, 2000 and July 31, 2018. Eligible cases had a diagnosis on a clinical record for STS in the EMR while controls had no evidence of STS on any EMR records. Both cases and controls were enrolled in administrative claims during a period of observation and were aged???18 years. A split sample was used to test and validate algorithms using data from administrative claims. Values for sensitivity, specificity, and positive predictive value (PPV) were calculated for 14 algorithms. Prior literature validating algorithms in administrative claims across other cancer types report both sensitivity and specificity ranging from as low as 73% to as high as 95%. This was used as a benchmark for defining algorithm success.ResultsThere were 784 STS cases and 249,062 non-STS cancer controls eligible for analysis. Requiring at least two claims with an ICD-CM diagnosis code for STS achieved a sensitivity of 67% but had a specificity of 72%. Algorithms that required NCCN-recommended systemic treatment for STS improved the specificity to over 90% but dropped the sensitivity to below 20%. Other combinations of diagnostic tests, symptoms, and procedures did not improve performance.ConclusionsThe algorithms tested in this study sample did not achieve sufficient performance and suggest the ability to accurately identify the STS population in administrative data is problematic. Difficulties are likely due to the origin of STS in a variety of locations, the non-specific symptoms of STS, and the common diagnostic tests recommended to diagnose the disease. Future research applying machine learning to examine timing and patterns of variables that comprise the diagnostic process may further investigate the ability to accurately identify STS cases in claims databases.

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Can Claims Data Algorithms Identify the Physician of Record?

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Can Claims Data Algorithms Identify the Physician of Record?

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