Electronic Medical Record Administrative Data Linked Database Research Articles

Patient- and Physician-Level Factors Associated With Adherence to C-CHANGE Recommendations in Primary Care Settings in Ontario

BackgroundWe previously found large variation among family physicians in adherence to the Canadian Cardiovascular Harmonization of National Guidelines Endeavour (C-CHANGE). We assessed the role of patient- and physician-level factors in the variation in adherence to recommendations for managing cardiovascular disease risk factors. MethodsWe conducted a retrospective study using multilevel logistic regression analyses with the Electronic Medical Record Administrative data Linked Database (EMRALD) housed at ICES in Ontario. Five quality indicators based on C-CHANGE guidelines were modelled. Effects of clustering and between-group variation, patient-level (sociodemographics, comorbidities) and physician-level characteristics (demographic and practice information) were assessed to determine odds ratios of receiving C-CHANGE recommended care. ResultsIn all, 324 Ontario physicians practicing in 41 clinics who provided care to 227,999 adult patients were studied. We found significant variation in quality indicators, with 15% to 39% of the total variation attributable to nonpatient factors. The largest variation was in performing 2-hour plasma glucose testing in prediabetic patients. Patient-level factors most frequently associated with recommendation adherence included sex, age, and multi-comorbidities. Women were more likely than men to have their body mass index measured, and their blood pressure controlled, but less likely to receive antiplatelet medications and liver-enzyme testing if overweight or obese. ConclusionsThe majority of variations in adherence were attributable to patient attributes, but a substantial proportion of unexplained variation was due to differences among physicians and clinics. This finding may signal suboptimal processes or structures and warrant further investigation to improve the quality of primary care management of cardiovascular disease in Ontario.

SUN-154 Identifying Type 1 Diabetes in Electronic Medical Records and Administrative Health Data in Ontario: A Validation Study

Background There has been growing interest in using administrative and electronic medical record (EMR) data to identify type 1 diabetes (T1D) for clinical and health services research. Our aim was to create an algorithm identifying T1D using EMR data alone and/or in combination with administrative data in Ontario, Canada. Methods The Electronic Medical Record Administrative data Linked Database (EMRALD) was used to create a reference standard for diabetes type as determined by chart abstraction by an endocrinologist. EMRALD contains data from over 350 family physicians using Practice Solutions® EMR. We included patients receiving care from participating physicians who were diagnosed with diabetes prior to Sept 30, 2015. A random 25% sample of patients with diabetes (identified using a validated algorithm) were selected for the reference standard. Patients who were not prescribed insulin or who were prescribed insulin and an oral hypoglycemic agent other than metformin were automatically classified as not having T1D without chart abstraction. Remaining patients underwent chart abstraction to determine diabetes type. 50 charts were randomly re-abstracted for inter- and intra-rater reliability. Characteristics of patients by diabetes type were reviewed to select variables for algorithm inclusion. Preliminary Results There were 23,310 patients with diabetes as of September 30, 2015. The reference standard consisted of 5828 patients: 5305 were classified as not T1D without chart abstraction and 523 underwent chart abstraction. Inter- and intra-rater reliability were high (Cohen’s kappa 0.82 and 0.96, respectively). After chart abstraction,14 patients were classified as not having diabetes, 233 patients were classified as T1D (45.8%), 261 as not T1D (51.3%), and 15 as possible T1D (2.9%). The prevalence of T1D was 4.0%. Compared to 5581 patients without definite T1D, patients with T1D were younger (median 40 years [IQR 30-53] v. 66[57-75]), had a younger age of onset (15 years [10-25] v. 47[40-55]), and a lower body mass index (BMI) (26±5 v. 32±8). Age at diagnosis and BMI were documented in 349(71.8%) of abstracted charts and 1386(23.8%), respectively. Renal insufficiency (eGFR <30ml/min, dialysis, or renal transplant) was present in 13(5.6%) and 136(2.4%) patients with and without T1D, respectively. Metformin use was uncommon in patients with T1D compared to those without T1D [15(6.4%) v. 4223(75.7%)]. Bolus insulin was recorded by the primary care physician for 115(49.4%) patients with T1D and 490 (8.8%) patients without T1D. For patients with T1D, 65(27.9%) used an insulin pump and 41(21.4%) had a history of diabetic ketoacidosis. Conclusions Age, age at diagnosis, BMI, medications, and insulin pump use differed between subjects with and without T1D. Future work includes development of algorithms incorporating these variables and variables from other data sources to identify T1D.

Comparing prescribing and dispensing databases to study antibiotic use: a validation study of the Electronic Medical Record Administrative data Linked Database (EMRALD).

Monitoring and studying community antibiotic use is a critical component in combating rising antimicrobial resistance. To validate an electronic medical record dataset containing antibiotic prescriptions and to quantify some important differences between prescribing and dispensing databases. We evaluated antibiotics prescribed and dispensed to patients ≥65 years of age during 2011-15. We compared the EMRALD prescribing database with the validated Ontario Drug Benefit (ODB) dispensing database. Using ODB as the gold standard and limiting to EMRALD physicians, we calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) with 95% CIs. We also compared the relative change in antibiotic use prescribed by all physicians to this population over time between the databases using Poisson regression models. In this population, 74% of all antibiotics dispensed were from non-EMRALD physicians. Trends in use were discordant over time. When we limited ODB to EMRALD prescribers only to assess the validity of EMRALD data, we observed good sensitivity and excellent specificity for correctly identifying antibiotics at 85% (95% CI 84%-85%) and 98% (95% CI 98%-98%), respectively. The PPV was 78% (95% CI 78%-78%) and the NPV was 99% (95% CI 99%-99%). All performance measures were higher among the highest prescribing physicians. We demonstrated EMRALD is well suited for studying antibiotic prescribing by EMRALD physicians. However, due to the frequency with which patients receive antibiotic prescriptions from their non-primary care physicians, we caution against the use of non-population-based prescribing databases to infer antibiotic use rates or trends over time.

Temporal trends in severe obesity prevalence in children and youth from primary care electronic medical records in Ontario: a repeated cross-sectional study.

There are no current estimates of severe obesity in Canadian children. The objectives of this study were to determine the prevalence of severe obesity in children aged 18 years or less in Ontario and to determine temporal trends from 2004 to 2015. This was a repeated cross-sectional study using height/length and weight of children aged 18 years or less from the Electronic Medical Record Administrative data Linked Database (EMRALD), a database of primary care electronic medical records in Ontario. We calculated body mass index (for age and sex) z-scores (zBMI). Two years of data (2014 and 2015) were used to determine the period prevalence of severe obesity. We used multivariable linear regression generalized estimating equations to estimate the association of calendar year and mean zBMI. In total, 55 233 children were included. The prevalence of severe obesity (zBMI > 3) increased with increasing age: it was 0.9% (95% confidence interval [CI] 0.7% to 1.0%) among children less than 5 years of age, 2.7% (95% CI 2.3% to 3.1%) among 5- to 9-year-olds, 2.9% (95% CI 2.4% to 3.3%) among 10- to 14-year-olds and 3.7% (95% CI 3.1% to 4.3%) among those aged 15-18. Boys aged 5-9 years had a significantly higher prevalence of severe obesity than their female counterparts (3.5% [95% CI 2.9% to 4.2%] v. 1.7% [95% CI 1.3% to 2.2%]). From 2004 to 2015, the mean zBMI decreased by 0.015 (95% CI -0.018 to -0.012) units per year, with the overall prevalence of severe obesity in all ages highest in 2005 (3%) and a decrease to 2% in 2015. The prevalence of severe obesity among children and adolescents in Ontario is consistent with that in other developed countries with the exception of the United States. There is evidence of plateauing of estimates and a small decrease in zBMI over time. Further understanding of the impact of prevention efforts on these estimates is an important next step.

Measuring Cardiovascular Quality in Primary Care Using Canadian Cardiovascular Harmonization of National Guidelines Endeavour and Electronic Medical Record Data in Ontario

BackgroundThis project uses electronic medical record (EMR) data to assess performance by family physicians (FPs) in the screening for, diagnosis, and management of cardiovascular disease (CVD) and risk factors against national harmonized guidelines by the Canadian Cardiovascular Harmonization of National Guidelines Endeavour (C-CHANGE). MethodsA retrospective cohort study using the Electronic Medical Record Administrative Data Linked Database (EMRALD) was conducted. A set of quality indicators (QIs) were developed on the basis of the 2014 C-CHANGE guidelines. Twenty-three readily measurable QIs were used to measure performance in the screening for and management of CVD, and to identify gaps in performance. ResultsOur study population consisted of 324 Ontario FPs and 284,959 patients. We assessed 23 of the 74 recommendations. There was variance in rates of adherence to QIs related to screening rates for CVD. Highest adherence to C-CHANGE guidelines was related to laboratory testing for patients with hypertension and prescription of antihypertensive therapies (≥ 91.4%). Lowest adherence to the guidelines was seen in administration of oral glucose tolerance tests for assessing prediabetic patients (4.4%). ConclusionsFP EMR data can be used to measure adherence to one-third of the C-CHANGE recommendations. There are varying levels of adherence among the measurable C-CHANGE recommendations, and there is room for improvement in quality of primary care management of CVD in Ontario. There is potential to use EMR data to assess changes to CVD management in FP practice using guidelines if recommendations are quantifiable and measurable.

Use of physician billing claims to identify infections in children.

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.

Identify Patients with Congestive Heart Failure through Analyzing Free-Text Clinical Notes

IntroductionA number of challenges exist in analyzing unstructured free text data in electronic medical records (EMRs). EMR text are difficult to represent and model due to their high dimensionality, heterogeneity, sparsity, incompleteness, random errors and the presence of noise.&#x0D; Objectives and ApproachStandard Natural Language Processing (NLP) tools make errors when applied to clinical notes due to physician use of unconventional language, involving polysemy, abbreviations, ambiguity, misspelling, variations, and negation.&#x0D; This paper presents a novel NLP framework, “Clinical Learning On Natural Expression” (CLONE), to automatically learn from a large primary care EMR database, analyzing free text clinical notes from primary care practices. CLONE’s predictive clinical models using text mining and neural network approach to extract features to identify patterns. To demonstrate effectiveness, we evaluate CLONE’s ability in a case study to identify patients with a specific chronic condition: congestive heart failure (CHF).&#x0D; ResultsA random selected sample of 7500 patients from Electronic Medical Record Administrative data Linked Database (EMRALD) is used. In this dataset, each patient’s medical chart includes a reference standard, manually reviewed by medical practitioners. Prevalence of CHF is approximately 2%. The low prevalence leads to another challenging problem in machine learning: imbalanced datasets. After pre-processing, we build deep learning models to represent and extract important medical information from free text to identify CHF patients through analyzing patient charts. We evaluated the effectiveness of CLONE by comparing the predicted labels with the standard references on a holdout test dataset. Comparing it with a number of alternative algorithms, we improve the overall accuracy to over 90% on a test dataset.&#x0D; Conclusion/ImplicationsAs the role of NLP in EMR data expands, the CLONE natural language processing framework can lead to substantial reduction in manual processing, while improving predictive accuracy.

Use of linked electronic health records to evaluate cardiovascular risk prediction models in Ontario, Canada

IntroductionElectronic health records (EHR) contain individual-level clinical information not found in traditional administrative databases. As part of the CANHEART-Strategy for Patient Oriented Research (SPOR) initiative, we created a linked EHR-administrative data cohort that enables us to measure the Framingham and ACC/AHA Pooled Cohort cardiovascular risk prediction scores in Ontario, Canada.&#x0D; Objectives and ApproachAn EHR primary care cohort was created using the Electronic Medical Record Administrative data Linked Database (EMRALD) database, which contains the blood pressure and lipid values, weight and height measures, prescriptions and smoking status of up to 350,000 patients in Ontario, Canada. We enriched the lipid information through linkage to the Ontario Laboratory Information System, which is a repository of 90%+ of all lipid tests in Ontario. Individual-level information on co-morbidities, hospitalizations and mortality attributed to cardiovascular causes (e.g. myocardial infarction, stroke, cardiovascular mortality) were obtained through linkage to provincial health administrative and vital statistics databases using CANHEART methodology (www.canheart.ca).&#x0D; ResultsPatients were entered into the cohort between 2008 and 2014 if they had measurements for blood pressure and lipids (total cholesterol and high-density lipoprotein) taken within a year of each other during this accrual window. The earliest such group of values was chosen and determined the individual’s index date. Age, sex, smoking, diabetes and anti-hypertensive treatment status were extracted from EHR or administrative data to calculate the two scores. Patients were excluded if not aged 40-75 on the index date or if they had a history of cardiovascular disease. A cohort of 84,628 Ontario residents (mean age 55.0 years) had the elements required to calculate both scores. Follow-up for outcome events were done through record linkage to the end of 2014, with a mean follow-up of 3.62 years.&#x0D; Conclusion/ImplicationsThe creation of this cohort will allow for the validation of the Framingham and AHA/ACC Pooled Cohort equations in the diverse Ontario population. It would also enable the possible development of a new ‘made-in-Canada’ cardiovascular risk prediction model.

Using family physician Electronic Medical Record data to measure the pathways of cancer care

&#x0D; IntroductionGaps in care have been identified along the disease pathway for specific cancers. However, no real-world data exists to identify wait times along these cancer pathways. Secondary use of family physician (FP) electronic medical record data (EMR) can augment existing health administrative data in measuring steps in the care pathways.&#x0D; Objectives and ApproachWe used FP EMR data to identify care pathways for lung cancer and breast cancer patients from the description of symptoms, to the initiation of investigations, referrals to specialty care and the receipt of specific treatments (surgery, chemotherapy, radiation treatment). Data from the Electronic Medical Record Administrative data Linked Database (EMRALD) held at the Institute for Clinical Evaluative Sciences (ICES) was used to identify a cohort of lung cancer and breast cancer patients. Data abstractors examined the FP EMR notes to identify pre-diagnostic symptoms, pre-diagnostic radiological test, biopsy results, oncology and surgical specialist referrals and post-diagnostic surgical and oncological consultations.&#x0D; ResultsTo date, abstractors have reviewed the FP EMR notes for 300 lung cancer patient and 1200 breast cancer patients. Abstractors identified an index date where there was documentation of the first abnormal test result and/or a FP progress note documenting a “suspicious” or “concerning” sign or symptom. For both lung cancer and breast cancer patients, a pre-diagnostic index date was identified in 88.5% of FP EMR notes. For lung cancer patients 66.7% based were based on abnormal chest x-rays and for breast cancer patients 81.1% were based on abnormal mammograms. Pre-diagnostic symptoms were identified in 62.1% of FP EMR notes and 81.6% had post-diagnostic consultation notes. Wait times from the index date to seeing an oncological specialist were less than four weeks for all patients.&#x0D; Conclusion/ImplicationsWe are able to use information from FP EMRs linked to health administrative data to identify pre-diagnostic care received by patients prior to their cancer diagnosis. This information can be used to identify care gaps and measure wait times in receiving cancer care from a patient’s perspective.

Using Biomedical Text as Data and Representation Learning for Identifying Patients with an Osteoarthritis Phenotype in the Electronic Medical Record

IntroductionElectronic medical records (EMRs) are increasingly used in health services research. Accurate/efficient identification of a target population with a specific disease phenotype is a necessary precursor to studying the health of these individuals.&#x0D; Objectives and ApproachWe explored the use of biomedical text as inputs to supervised phenotype identification algorithms. We employed a two-stage classification approach to map the discrete, sparse high-dimensional biomedical text data to a dense low dimensional vector space using methods from unsupervised machine learning. Next we used these learned vectors as inputs to supervised machine learning algorithms for phenotype identification.&#x0D; We were able to demonstrate the applicability of the approach to identifying patients with an osteoarthritis (OA) phenotype using primary care data from the Electronic Medical Record Administrative data Linked Database (EMRALD) held at ICES.&#x0D; ResultsEMRALD contains approximately 20Gb of biomedical text data on approximately 500,000 patients. The unit of analysis for this study is the patient. We were interested in identifying OA patients using solely text data as features.&#x0D; Labelled outcome information wass available from a random sample of 7,500 patients. We divided patients into training (N=6000), validation (N=750) and test (N=750) cohorts. We learned low dimensional representations of the input text data on the entire EMRALD corpus (N=500,000). We used learned numeric vectors as inputs to supervised machine learning models for OA classification (N=6,000 training set patients).&#x0D; We compared models in terms of accuracy, sensitivity, specificity, PPV and NPV. The best learned models achieved approximately 90\% sensitivity and 80\% specificity. Classification accuracy varied as a function of learned inputs.&#x0D; Conclusion/ImplicationsWe developed an approach to phenotype identification using solely biomedical text as an input. Preliminary results suggest our two-stage ML approach has improved operating characteristics compared to existing clinically derived decision rules for OA classification. Future work will explore the generalizability of this methodology to other disease phenotypes.

Rotavirus vaccine coverage and factors associated with uptake using linked data: Ontario, Canada.

BackgroundIn August 2011, Ontario, Canada introduced a rotavirus immunization program using Rotarix™ vaccine. No assessments of rotavirus vaccine coverage have been previously conducted in Ontario.MethodsWe assessed vaccine coverage (series initiation and completion) and factors associated with uptake using the Electronic Medical Record Administrative data Linked Database (EMRALD), a collection of family physician electronic medical records (EMR) linked to health administrative data. Series initiation (1 dose) and series completion (2 doses) before and after the program’s introduction were calculated. To identify factors associated with series initiation and completion, adjusted odds ratios (aOR) and 95% confidence intervals (95%CI) were calculated using logistic regression.ResultsA total of 12,525 children were included. Series completion increased each year of the program (73%, 79% and 84%, respectively). Factors associated with series initiation included high continuity of care (aOR = 2.15; 95%CI, 1.61–2.87), maternal influenza vaccination (aOR = 1.55; 95%CI,1.24–1.93), maternal immmigration to Canada in the last five years (aOR = 1.47; 95% CI, 1.05–2.04), and having no siblings (aOR = 1.62; 95%CI,1.30–2.03). Relative to the first program year, infants were more likely to initiate the series in the second year (aOR = 1.71; 95% CI 1.39–2.10) and third year (aOR = 2.02; 95% CI 1.56–2.61) of the program. Infants receiving care from physicians with large practices were less likely to initiate the series (aOR 0.91; 95%CI, 0.88–0.94, per 100 patients rostered) and less likely to complete the series (aOR 0.94; 95%CI, 0.91–0.97, per 100 patients rostered). Additional associations were identified for series completion.ConclusionsFamily physician delivery achieved moderately high coverage in the program’s first three years. This assessment demonstrates the usefulness of EMR data for evaluating vaccine coverage. Important insights into factors associated with initiation or completion (i.e. high continuity of care, smaller roster sizes, rural practice location) suggest areas for research and potential program supports.

Quality indicators for the detection and management of chronic kidney disease in primary care in Canada derived from a modified Delphi panel approach.

The detection and management of chronic kidney disease lies within primary care; however, performance measures applicable in the Canadian context are lacking. We sought to develop a set of primary care quality indicators for chronic kidney disease in the Canadian setting and to assess the current state of the disease's detection and management in primary care. We used a modified Delphi panel approach, involving 20 panel members from across Canada (10 family physicians, 7 nephrologists, 1 patient, 1 primary care nurse and 1 pharmacist). Indicators identified from peer-reviewed and grey literature sources were subjected to 3 rounds of voting to develop a set of quality indicators for the detection and management of chronic kidney disease in the primary care setting. The final indicators were applied to primary care electronic medical records in the Electronic Medical Record Administrative data Linked Database (EMRALD) to assess the current state of primary care detection and management of chronic kidney disease in Ontario. Seventeen indicators made up the final list, with 1 under the category Prevalence, Incidence and Mortality; 4 under Screening, Diagnosis and Risk Factors; 11 under Management; and 1 under Referral to a Specialist. In a sample of 139 993 adult patients not on dialysis, 6848 (4.9%) had stage 3 or higher chronic kidney disease, with the average age of patients being 76.1 years (standard deviation [SD] 11.0); 62.9% of patients were female. Diagnosis and screening for chronic kidney disease were poorly performed. Only 27.1% of patients with stage 3 or higher disease had their diagnosis documented in their cumulative patient profile. Albumin-creatinine ratio testing was only performed for 16.3% of patients with a low estimated glomerular filtration rate (eGFR) and for 28.5% of patients with risk factors for chronic kidney disease. Family physicians performed relatively better with the management of chronic kidney disease, with 90.4% of patients with stage 3 or higher disease having an eGFR performed in the previous 18 months and 83.1% having a blood pressure recorded in the previous 9 months. We propose a set of measurable indicators to evaluate the quality of the management of chronic kidney disease in primary care. These indicators may be used to identify opportunities to improve current practice in Canada.

Patterns of Care Among Patients Referred to Rheumatologists in Ontario, Canada.

Our aim was to characterize referrals to rheumatologists, the early care management of patients with rheumatic diseases, and timeliness of care and treatment. We conducted a retrospective observational study involving patients with first-time rheumatology referrals between 2000 and 2013 in the primary care Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. Referrals were characterized in terms of diagnoses, patient demographics, diagnostic tests, treatment initiated by family physicians and rheumatologists, and other specialists seen prior to rheumatology consultation. Timeliness of referrals, rheumatologist consultations, and treatment were determined overall and for each diagnostic category. Among 2,430 patients referred to a rheumatologist, 69% were female, with an average age of 53 years. The principal diagnosis associated with the referral included osteoarthritis (32%), systemic inflammatory rheumatic diseases (31%), regional musculoskeletal conditions (16%), chronic pain conditions (14%), osteoporosis (2%), and other/miscellaneous (5%). Family physicians most frequently prescribed nonsteroidal antiinflammatory drugs/cyclooxygenase 2 inhibitors (38%), and their pre-referral diagnostic testing practice varied considerably. The duration of time from symptom onset to rheumatology consultation varied by diagnoses, with the shortest being for patients with systemic rheumatic diseases; for rheumatoid arthritis (RA), the median time to consultation was 327 days. Most of the delay occurred prior to referral; 36% of RA patients initiated a disease-modifying antirheumatic drug within 6 months of symptom onset. Approximately 1 in 3 referrals to rheumatologists were for a systemic inflammatory rheumatic disease. We observed substantial delays to rheumatology consultations and variations in patterns of care that could be amenable to quality improvement interventions.

Identifying Patients With Atrial Fibrillation in Administrative Data

BackgroundIdentifying patients with atrial fibrillation (AF) using administrative data is important for epidemiologic and outcomes research. Although administrative data cover large populations, it is necessary to assess their validity in identifying AF patients. MethodsWe used Ontario family physician electronic medical records from the Electronic Medical Record Administrative data Linked Database (EMRALD) as a reference standard to assess the accuracy of administrative data algorithms in identifying patients with AF. From a random sample of 7500 adult patients, patients with AF as recorded in family physician records were identified. ResultsThe optimal algorithm consisted of any of: hospitalization or an emergency room code for AF or prescription for an AF-specific antiarrhythmic agent or billing code for cardioversion, or prescription for an anticoagulant that was accompanied by a physician billing code. for arrhythmia. The algorithm sensitivity was 80.7% (95% confidence interval [CI], 75.1-86.3), specificity 99.1% (95% CI, 98.9-99.3), positive predictive value 71.1% (95% CI, 65.1-77.1), and negative predictive value 99.5% (95% CI, 99.3-99.7). This algorithm, applied to the Ontario population, resulted in a calculated increase in AF prevalence from 1.68% to 2.36% over the years 2000-2014. Anticoagulation rates for AF patients increased from 53% in 2011 to 60% in 2014. Among AF patients receiving anticoagulants, novel oral anticoagulant utilization increased from < 5% in 2011 to > 50% in 2014. ConclusionsIdentifying patients with AF can be done using administrative data, and the algorithm can be used to assess trends in disease burden over time and patterns of care in large populations.

Are family physicians comprehensively using electronic medical records such that the data can be used for secondary purposes? A Canadian perspective.

BackgroundWith the introduction and implementation of a variety of government programs and policies to encourage adoption of electronic medical records (EMRs), EMRs are being increasingly adopted in North America. We sought to evaluate the completeness of a variety of EMR fields to determine if family physicians were comprehensively using their EMRs and the suitability of use of the data for secondary purposes in Ontario, Canada.MethodsWe examined EMR data from a convenience sample of family physicians distributed throughout Ontario within the Electronic Medical Record Administrative data Linked Database (EMRALD) as extracted in the summer of 2012. We identified all physicians with at least one year of EMR use. Measures were developed and rates of physician documentation of clinical encounters, electronic prescriptions, laboratory tests, blood pressure and weight, referrals, consultation letters, and all fields in the cumulative patient profile were calculated as a function of physician and patient time since starting on the EMR.ResultsOf the 167 physicians with at least one year of EMR use, we identified 186,237 patients. Overall, the fields with the highest level of completeness were for visit documentations and prescriptions (>70 %). Improvements were observed with increasing trends of completeness overtime for almost all EMR fields according to increasing physician time on EMR. Assessment of the influence of patient time on EMR demonstrated an increasing likelihood of the population of EMR fields overtime, with the largest improvements occurring between the first and second years.ConclusionsAll of the data fields examined appear to be reasonably complete within the first year of adoption with the biggest increase occurring the first to second year. Using all of the basic functions of the EMR appears to be occurring in the current environment of EMR adoption in Ontario. Thus the data appears to be suitable for secondary use.

Validation of infant immunization billing codes in administrative data

Ontario has a single payer provincial health insurance program. Administrative data may provide a potentially robust source of information for post-marketing vaccine studies. Vaccine-specific immunization billing codes were introduced in 2011. Our objective was to validate Ontario's universal health care administrative datasets to assess infant immunization status. Electronic medical record data from the Electronic Medical Record Administrative data Linked Database (EMRALD) was used as the reference standard to calculate performance characteristics of the Ontario Health Insurance Plan (OHIP) database vaccine-specific and general immunization codes for 4 primary infant immunizations: diphtheria, tetanus, acellular pertussis, inactivated polio, Haemophilus influenzae type B (DTaP-IPV-Hib) combination vaccine, pneumococcal conjugate vaccine, measles, mumps, rubella (MMR) vaccine, and meningococcal conjugate serogroup C vaccine. OHIP billing claims had specificity ranging from 81% to 92%, sensitivity 70% to 83%, positive predictive value (PPV) 97% to 99%, and negative predictive value (NPV) 13% to 46% for identifying the various specific vaccines in administrative data. For cohorts vaccinated in the new code introduction phase, using both the vaccine-specific and general codes had higher sensitivity than the vaccine-specific codes alone. In conclusion, immunization billing claims from administrative data in Ontario had high specificity and PPV, moderate sensitivity, and low NPV. This study identifies some of the applications of utilizing administrative data for post-marketing vaccine studies. However, limitations of these data decrease their utility for measuring vaccine coverage and effectiveness. Therefore, the establishment of a comprehensive and linkable immunization registry should be a provincial priority.

Canadian Administrative Health Data Can Identify Patients with Myasthenia Gravis

Introduction: Incidence and prevalence estimates for myasthenia gravis (MG) have varied widely, and the ability of administrative health data (AHD) records to accurately identify cases of MG is yet to be ascertained. The goal of the current study was to validate an algorithm to identify patients with MG in Ontario, Canada using AHD - thereby enabling future disease surveillance. Methods: A reference standard population was established using automated key word searching within EMRALD (Electronic Medical Record Administrative data Linked Database) and chart review of potential cases. AHD algorithms were generated and tested against the reference standard. The data was used to calculate MG prevalence rates. Results: There were 123,997 eligible adult patients, and 49 patients had definite MG (forming the reference standard). An algorithm requiring: (1 hospital discharge abstract with MG listed as a reason for hospitalization or a comorbid condition), or (5 outpatient MG visits and 1 relevant diagnostic test, within 1 year), or (3 pyridostigmine prescriptions, within 1 year) identified MG with sensitivity = 81.6%, specificity = 100%, positive predictive value = 80.0% and negative predictive value = 100%. The population prevalence within our cohort was 0.04%. Conclusions: This novel validation method demonstrates the feasibility of using administrative health data to identify patients with myasthenia gravis among the Ontario population.

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

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.

An administrative data validation study of the accuracy of algorithms for identifying rheumatoid arthritis: the influence of the reference standard on algorithm performance.

BackgroundWe have previously validated administrative data algorithms to identify patients with rheumatoid arthritis (RA) using rheumatology clinic records as the reference standard. Here we reassessed the accuracy of the algorithms using primary care records as the reference standard.MethodsWe performed a retrospective chart abstraction study using a random sample of 7500 adult patients under the care of 83 family physicians contributing to the Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. Using physician-reported diagnoses as the reference standard, we computed and compared the sensitivity, specificity, and predictive values for over 100 administrative data algorithms for RA case ascertainment.ResultsWe identified 69 patients with RA for a lifetime RA prevalence of 0.9%. All algorithms had excellent specificity (>97%). However, sensitivity varied (75-90%) among physician billing algorithms. Despite the low prevalence of RA, most algorithms had adequate positive predictive value (PPV; 51-83%). The algorithm of “[1 hospitalization RA diagnosis code] or [3 physician RA diagnosis codes with ≥1 by a specialist over 2 years]” had a sensitivity of 78% (95% CI 69–88), specificity of 100% (95% CI 100–100), PPV of 78% (95% CI 69–88) and NPV of 100% (95% CI 100–100).ConclusionsAdministrative data algorithms for detecting RA patients achieved a high degree of accuracy amongst the general population. However, results varied slightly from our previous report, which can be attributed to differences in the reference standards with respect to disease prevalence, spectrum of disease, and type of comparator group.

Identifying individuals with multiple sclerosis in an electronic medical record

Background: The increasing use of electronic medical records (EMRs) presents an opportunity to efficiently evaluate and improve quality of care for individuals with MS. Objectives: We aimed to establish an algorithm to identify individuals with MS within EMRs. Methods: We used a sample of 73,003 adult patients from 83 primary care physicians in Ontario using the Electronic Medical Record Administrative data Linked Database (EMRALD). A reference standard of 247 individuals with MS was identified through chart abstraction. The accuracy of identifying individuals with MS in an EMR was assessed using information in the cumulative patient profile (CPP), prescriptions and physician billing codes. Results: An algorithm identifying MS in the CPP performed well with 91.5% sensitivity, 100% specificity, 98.7% PPV and 100% NPV. The addition of prescriptions for MS-specific medications and physician billing code 340 used four times within any 12-month timeframe slightly improved the sensitivity to 92.3% with a PPV of 97.9%. Conclusions: Data within an EMR can be used to accurately identify patients with MS. This study has positive implications for clinicians, researchers and policy makers as it provides the potential to identify cohorts of MS patients in the primary care setting to examine quality of care.