Dataset Information

Use of physician billing claims to identify infections in children.

ABSTRACT: 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.

SUBMITTER: Hwee J

PROVIDER: S-EPMC6231686 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Use of physician billing claims to identify infections in children.

Hwee Jeremiah J Sung Lillian L Kwong Jeffrey C JC Sutradhar Rinku R Tu Karen K Pole Jason D JD

PloS one 20181112 11

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 Administrat ...[more]

PMID: 30419045

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Project description:ImportanceWhile a gender pay gap in medicine has been well documented, relatively little research has addressed mechanisms that mediate gender differences in referral income for specialists.ObjectiveTo examine gender-based disparities in medical and surgical specialist referrals in Ontario, Canada.Design, setting, and participantsThis cross-sectional study included referrals for specialist care ascertained from Ontario Health Insurance Plan physician billings for fiscal year 2018 to 2019. Participants were specialist physicians who received new patient consultations from April 1, 2018, to March 31, 2019, and the associated referring physicians. Data were analyzed from April 2018 to March 2020, including a 12-month follow-up period.ExposuresSpecialist and referring physician gender (female or male).Main outcomes and measuresRevenue per referral was defined based on an episode-of-care approach as total billings for a 12-month period from the initial consultation. Mean total billings for female and male specialists were compared and the differential divided into the portion owing to referral volume vs referral revenue. Difference-in-differences multivariable regression analysis was used to estimate gender-based differences in revenue per referral. For each referring physician, gender-based differences in referral patterns were examined using case-control analysis, in which specialists who received a referral were compared with matched control specialists who did not receive a referral. This analysis considered the gender of the specialist and concordance between the gender of the referring physician and specialist, among other characteristics.ResultsOf 7 621 365 new referrals, 32 824 referring physicians, of whom 13 512 (41.2%) were female (mean [SD] age, 46.3 [11.6] years) and 19 312 (58.8%) were male (mean [SD] age, 52.9 [13.5] years), made referrals to 13 582 specialists, of whom 4890 (36.0%) were female (mean [SD] age, 45.6 [11.0] years) and 8692 (64.0%) were male (mean [SD] age, 51.8 [13.0] years). Male specialists received more mean (SD) referrals than did female specialists (633 [666] vs 433 [515]), and the mean (SD) revenue per referral was higher for males ($350 [$474]) compared with females ($316 [$393]). Adjusted analysis demonstrated a -4.7% (95% CI, -4.9% to -4.5%) difference in the revenue per referral between male and female specialists. Multivariable regression analysis found that physicians referred more often to specialists of the same gender (odds ratio, 1.04; 95% CI, 1.03-1.04) but had higher odds of referring to male specialists (odds ratio, 1.10; 95% CI, 1.09-1.11).Conclusions and relevanceIn this cross-sectional study of the gender pay gap in specialist referral income, the number and revenue from referrals received differed by gender, as did the odds of receiving a referral from a physician of the same gender. Future research should examine the effectiveness of different policies to address this gap, such as a centralized, gender-blinded referral system.

Project description:BackgroundClinical event committees are commonly employed for event validation in clinical studies, but little is known about the comparative performance of administrative claims data versus clinician-triggered event adjudication for ascertainment of adverse events in structural heart disease studies.Methods and resultsMedicare claims were linked to 418 patients >65 years of age who underwent transcatheter mitral valve repair (MitraClip) for severe mitral regurgitation from 2007 to 2013 as part of the EVEREST II (Endovascular Valve Edge-to-Edge Repair Study II) High-Risk Registry or the REALISM (Real World Expanded Multicenter Study of the MitraClip System) Continued-Access Registry. Each registry adjudicated mortality, heart failure hospitalization, renal failure, ventilation, and bleeding/transfusion within 1 year. Concordance of claims-based outcomes with events was assessed in 3 ways: 1-year occurrence, cumulative incidence, and synchrony of first events. For event occurrence, positive predictive value (PPV) of claims versus adjudication was the highest for mortality (PPV=97%) and heart failure hospitalization (PPV=69%) but lower for bleeding (PPV=40%) and renal failure (PPV=19%). Whereas claims-based cumulative incidence for mortality, heart failure hospitalization, and renal failure were consistent with clinician-triggered adjudication, incidence curves for bleeding events and ventilation diverged, with claims identifying a greater number of events. When events were detected by both methods, however, over 75% of event dates matched exactly. Mitral valve reinterventions were identified through claims with perfect sensitivity and specificity relative to physician adjudication.ConclusionsAscertainment of mortality, heart failure hospitalization, and renal failure was highly concordant between physician adjudication and administrative claims. Further work is necessary to determine the role of administrative claims in event ascertainment in both prospective and retrospective studies of structural heart disease.

Project description:Syndromic surveillance systems are plagued by high false-positive rates. In chronic disease monitoring, investigators have identified several factors that predict the accuracy of case definitions based on diagnoses in administrative data, and some have even incorporated these predictors into novel case detection methods, resulting in a significant improvement in case definition accuracy. Based on findings from these studies, we sought to identify physician, patient, encounter, and billing characteristics associated with the positive predictive value (PPV) of case definitions for 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory (including influenza-like illness)).The study sample comprised 4,330 syndrome-positive visits from the claims of 1,098 randomly-selected physicians working in Quebec, Canada in 2005-2007. For each visit, physician-facilitated chart review was used to assess whether the same syndrome was present in the medical chart (gold standard). We used multivariate logistic regression analyses to estimate the association between claim-chart agreement about the presence of a syndrome and physician, patient, encounter, and billing characteristics.The likelihood of the medical chart agreeing with the physician claim about the presence of a syndrome was higher when the treating physician had billed many visits for the same syndrome recently (ORper 10 visit, 1.05; 95% CI, 1.01-1.08), had a lower workload (ORper 10 claims, 0.93; 95% CI, 0.90-0.97), and when the patient was younger (ORper 5 years of age, 0.96; 95% CI, 0.94-0.97), and less socially deprived (ORmost versus least deprived, 0.76; 95% CI, 0.60-0.95).Many physician, patient, encounter, and billing characteristics associated with the PPV of surveillance case definition are accessible to public health, and could be used to reduce false-positive alerts by surveillance systems, either by focusing on the data most likely to be accurate, or by adjusting the observed data for known biases in diagnosis reporting and performing surveillance using the adjusted values.

Dataset Information

Use of physician billing claims to identify infections in children.

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Use of physician billing claims to identify infections in children.

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