Effects of implementing electronic medical records on primary care billings and payments: a before-after study

Rl, Jaakkimainen; Se, Shultz; K, Tu

doi:10.9778/cmajo.20120039

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…EMRALD physicians were found to be younger, more likely to be female, to be a Canadian medical graduate and to participate in patient-enrolment models compared to the general physician population in Ontario. [ 29 ] However, this likely reflects the characteristics of physicians who have adopted EMR software and trends in the primary care workforce. Ontario has been undergoing a primary care reform for more than a decade where the new primary care models require ‘rostering’ of patients (patient-enrollment models) and the physician acts as the their most responsible physician.…”

Section: Discussionmentioning

confidence: 99%

Use of physician billing claims to identify infections in children

et al. 2018

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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.

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Section: Discussionmentioning

confidence: 99%

Use of physician billing claims to identify infections in children

et al. 2018

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“…Nonetheless, a survey of Ontario physicians in 2013 reported that 78% were using an EMR in their office 22 and patients in the EMRALD database have similar characteristics to the general population. 23 However, some patients not accessing the healthcare system may be missed. Secondly, we were unable to review all 73,003 adult charts, but instead reviewed only those meeting pre-defined nomenclature suggestive of MS.…”

Section: Discussionmentioning

confidence: 99%

Identifying individuals with multiple sclerosis in an electronic medical record

et al. 2014

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“…While we identified an optimal administrative drug algorithm in adults aged ≥20, we recognized a prescription database is limited to those aged ≥65. Although differences in population mix may impact the generalizability of our results, this study was done on a population that is similar to the Ontario population in terms of sex, age and presence of comorbid conditions [25]; therefore, we believe our findings to be reasonably generalizable, if not only in Ontario but to the rest of Canada and has the potential to inform other countries with similar administrative databases.…”

Section: Discussionmentioning

confidence: 99%

A Validation Study of Administrative Data Algorithms to Identify Patients with Parkinsonism with Prevalence and Incidence Trends

Butt

Young

et al. 2014

Neuroepidemiology

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Background: Epidemiological studies for identifying patients with Parkinson's disease (PD) or Parkinsonism (PKM) have been limited by their nonrandom sampling techniques and mainly veteran populations. This reduces their use for health services planning. The purpose of this study was to validate algorithms for the case ascertainment of PKM from administrative databases using primary care patients as the reference standard. Methods: We conducted a retrospective chart abstraction using a random sample of 73,003 adults aged ≥20 years from a primary care Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. Physician diagnosis in the EMR was used as the reference standard and population-based administrative databases were used to identify patients with PKM from the derivation of algorithms. We calculated algorithm performance using sensitivity, specificity, and predictive values and then determined the population-level prevalence and incidence trends with the most accurate algorithms. Results: We selected, ‘2 physician billing codes in 1 year' as the optimal administrative data algorithm in adults and seniors (≥65 years) due to its sensitivity (70.6-72.3%), specificity (99.9-99.8%), positive predictive value (79.5-82.8%), negative predictive value (99.9-99.7%), and prevalence (0.28-1.20%), respectively. Conclusions: Algorithms using administrative databases can reliably identify patients with PKM with a high degree of accuracy.

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Effects of implementing electronic medical records on primary care billings and payments: a before-after study

Cited by 5 publications

References 8 publications

Use of physician billing claims to identify infections in children

Use of physician billing claims to identify infections in children

Identifying individuals with multiple sclerosis in an electronic medical record

A Validation Study of Administrative Data Algorithms to Identify Patients with Parkinsonism with Prevalence and Incidence Trends

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