A prediction model to estimate completeness of electronic physician claims databases

Abstract

ObjectivesElectronic physician claims databases are widely used for chronic disease research and surveillance, but quality of the data may vary with a number of physician characteristics, including payment method. The objectives were to develop a prediction model for the number of prevalent diabetes cases in fee-for-service (FFS) electronic physician claims databases and apply it to estimate cases among non-FFS (NFFS) physicians, for whom claims data are often incomplete.DesignA retrospective observational cohort design was adopted.SettingData from the Canadian province of Newfoundland and Labrador were used to construct the prediction model and data from the province of Manitoba were used to externally validate the model.ParticipantsA cohort of diagnosed diabetes cases was ascertained from physician claims, insured resident registry and hospitalisation records. A cohort of FFS physicians who were responsible for the diagnosis was ascertained from physician claims and registry data.Primary and secondary outcome measuresA generalised linear model with a γ distribution was used to model the number of diabetes cases per FFS physician as a function of physician characteristics. The expected number of diabetes cases per NFFS physician was estimated.ResultsThe diabetes case cohort consisted of 31 714 individuals; the mean cases per FFS physician was 75.5 (median=49.0). Sex and years since specialty licensure were significantly associated (p<0.05) with the number of cases per physician. Applying the prediction model to NFFS physician registry data resulted in an estimate of 18 546 cases; only 411 were observed in claims data. The model demonstrated face validity in an independent data set.ConclusionsComparing observed and predicted disease cases is a useful and generalisable approach to assess the quality of electronic databases for population-based research and surveillance.

Estimating the completeness of physician billing claims for diabetes case ascertainment using population-based prescription drug data

“…be included in predictive models to estimate disease prevalence. In a recent study, 20 we included physician characteristics in predictive models to adjust for underestimation of diabetes prevalence due to a loss of physician claims.…”

Trends in chronic disease incidence rates from the Canadian Chronic Disease Surveillance System

“…For example, an increase in the number of healthcare practitioners paid by salary who submit administrative data via shadow-billing rather than the traditional fee-for-service method may increase the number of missing diagnoses codes. 21 Changes in clinical practice and screening and diagnoses criteria also likely influence trends in chronic disease incidence captured by the CCDSS over time. 3 (continued) Fit of the negative binomial regression models with year as restricted cubic spline containing three, four, and five knots and tests for departures of year from a linear trend, stratified by chronic disease and region FIGURE 1 Age-standardized chronic disease incidence rate estimates for Canada Abbreviations: COPD, chronic obstructive pulmonary disease; IHD, ischemic heart disease.…”

“…In Canada for example, Newfoundland and Labrador physician service records do not consistently capture patient information from physicians who do not bill on a feefor-service basis, which disproportionately affects the availability of diagnostic information for rural populations. 21 One of the routine uses of administrative health data in Canada is for chronic disease surveillance through the Canadian Chronic Disease Surveillance System (CCDSS). 22 The CCDSS 23 was created in 2009 to facilitate the collection and reporting of standardized, national estimates of diagnosed chronic disease prevalence, incidence, and health outcomes.…”

A validated case definition for chronic rhinosinusitis in administrative data: a Canadian perspective

“…Last, the cohort in this study was identified using physician claims data as opposed to inpatient hospital encounters. The limitation of using physician claims is the risk of incomplete data within health care systems where physicians are not required to submit encounter claims for remuneration, such as systems with salary or capitation . In Alberta, the majority of remuneration is fee‐for‐service and when physicians receive an alternative payment plan, they are still required to submit shadow billing claims.…”