Did changing primary care delivery models change performance? A population based study using health administrative data

<p>Abstract</p> <p>Background</p> <p>Primary care reform in Ontario, Canada started with the introduction of new enrollment models, the two largest of which are Family Health Networks (FHNs), a capitation-based model, and Family Health Groups (FHGs), a blended fee-for-service model. The purpose of this study was to evaluate differences in performance between FHNs and FHGs and to compare performance before and after physicians joined these new primary care groups.</p> <p>Methods</p> <p>This study used Ontario administrative claims data to compare performance measures in FHGs and FHNs. The study population included physicians who belonged to a FHN or FHG for at least two years. Patients were included in the analyses if they enrolled with a physician in the two years after the physician joined a FHN or FHG, and also if they saw the physician in a two year period prior to the physician joining a FHN or FHG. Performance was derived from the administrative data, and included measures of preventive screening for cancer (breast, cervical, colorectal) and chronic disease management (diabetes, heart failure, asthma).</p> <p>Results</p> <p>Performance measures did not vary consistently between models. In some cases, performance approached current benchmarks (Pap smears, mammograms). In other cases it was improving in relation to previous measures (colorectal cancer screening). There were no changes in screening for cervical cancer or breast cancer after joining either a FHN or FHG. Colorectal cancer screening increased in both FHNs and FHGs. After enrolling in either a FHG or a FHN, prescribing performance measures for diabetes care improved. However, annual eye examinations decreased for younger people with diabetes after joining a FHG or FHN. There were no changes in performance measures for heart failure management or asthma care after enrolling in either a FHG or FHN.</p> <p>Conclusions</p> <p>Some improvements in preventive screening and diabetes management which were seen amongst people after they enrolled may be attributed to incentive payments offered to physicians within FHGs and FHNs. However, these primary care delivery models need to be compared with other delivery models and fee for service practices in order to describe more specifically what aspects of model delivery and incentives affect care.</p

De-identification of primary care electronic medical records free-text data in Ontario, Canada

<p>Abstract</p> <p>Background</p> <p>Electronic medical records (EMRs) represent a potentially rich source of health information for research but the free-text in EMRs often contains identifying information. While de-identification tools have been developed for free-text, none have been developed or tested for the full range of primary care EMR data</p> <p>Methods</p> <p>We used <it>deid </it>open source de-identification software and modified it for an Ontario context for use on primary care EMR data. We developed the modified program on a training set of 1000 free-text records from one group practice and then tested it on two validation sets from a random sample of 700 free-text EMR records from 17 different physicians from 7 different practices in 5 different cities and 500 free-text records from a group practice that was in a different city than the group practice that was used for the training set. We measured the sensitivity/recall, precision, specificity, accuracy and F-measure of the modified tool against manually tagged free-text records to remove patient and physician names, locations, addresses, medical record, health card and telephone numbers.</p> <p>Results</p> <p>We found that the modified training program performed with a sensitivity of 88.3%, specificity of 91.4%, precision of 91.3%, accuracy of 89.9% and F-measure of 0.90. The validations sets had sensitivities of 86.7% and 80.2%, specificities of 91.4% and 87.7%, precisions of 91.1% and 87.4%, accuracies of 89.0% and 83.8% and F-measures of 0.89 and 0.84 for the first and second validation sets respectively.</p> <p>Conclusion</p> <p>The <it>deid </it>program can be modified to reasonably accurately de-identify free-text primary care EMR records while preserving clinical content.</p

Linking population-based survey and cancer registry data to examine the association between behaviours consistent with cancer prevention recommendations and cancer risk in Ontario

Introduction Certain subject behaviours and characteristics increase the risk of some cancer types (e.g., obesity, alcohol intake) while others reduce cancer risk (e.g., physical activity). In 2007, the World Cancer Research Fund (WCRF) and American Institute for Cancer Research (AICR) published recommendations to reduce cancer risk related to these behaviours. Objectives and Approach The objective is to examine the association between self-reported behaviour consistent with WCRF/AICR recommendations for body fatness, physical activity, vegetable/fruit consumption, and alcohol intake and the risk of all cancers combined and specific cancer types. The study cohort, comprised of the Canadian Community Health Survey (CCHS) Ontario sample, will be linked with health administrative databases, including the Ontario Cancer Registry to determine cancer outcomes. Individuals will be assessed for behaviours consistent with WCRF/AICR recommendations based on their responses to CCHS questions and the association of these behaviours with cancer risk will be explored using multivariable Cox proportional hazard regression models. Results To detect a log hazard ratio of 1.10 (where a=0.05, power=0.80, proportion of the sample assigned to the exposure group=0.25 and R2=0.20), a sample size of 4,538 is required. Based on the number of records in the CCHS data frame (159,474) and an assumption that the CCHS sample experiences cancer incidence at a similar rate to the rest of the Ontario population, we expect to have 5,000 cancer cases for these analyses. Upon completion of the analysis, we will report hazard ratios that estimate the difference in cancer risk between individuals reporting behaviour consistent with the WCRF/AICR recommendations and those reporting behaviour not consistent with the recommendations. Conclusion/Implications WCRF/AICR recommendations were developed as the basis for primary cancer prevention, both for individuals and population-wide policies and programs. The current study will quantify the difference in overall cancer risk between individuals who do and do not adhere to selected WCRF/AICR recommendations for the first time in a Canadian population

Age- and sex-adjusted all-cause and cardiovascular-related¹ one-year mortality (from incident diagnosis) by cardiovascular disease, per 100 persons, in the Métis and general Ontario population, April 1 2006 to March 31 2012.

<p>CI: Confidence Interval</p><p>¹ ICD 9/10 diagnostic codes to define cardiovascular-related mortality were obtained from: Statistics Canada. Comparability of ICD-10 and ICD-9 for Mortality Statistics in Canada. Ottawa ON, 2005. ICD-9 codes: 390–448. ICD-10 codes: I00-I78.</p><p>Age- and sex-adjusted all-cause and cardiovascular-related^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121779#t004fn002" target="_blank">1</a>} one-year mortality (from incident diagnosis) by cardiovascular disease, per 100 persons, in the Métis and general Ontario population, April 1 2006 to March 31 2012.</p

Cardiovascular disease types and corresponding definitions in province-wide health administrative databases.

<p>ICD: International Classification of Diseases, version 9 or 10; OHIP: Ontario Health Insurance Plan; DAD: Discharge Abstract Database; NACRS: National Ambulatory Care Reporting System; PPV: Positive predictive value; MRD: most responsible diagnosis; N/A: Not available</p><p>¹ A similar validated algorithm published (using [OHIP + DAD claim] in place of [NACRS + 2^nd NACRS (2 claims in any study year)]: Schultz SE, Rothwell DM, Chen Z, Tu K. Identifying cases of congestive heart failure from administrative data: a validation study using primary care patient records. Chronic Dis Inj Can 2013; 33(3): 160–6.</p><p>² Single NACRS I480 main diagnosis: Atzema CL, Austin PC, Miller E, Chong AC, Yun L, Dorian P. A population-based description of atrial fibrillation in the emergency department, 2002–2010. Ann Emerg Med 2013;62(6):570–7</p><p>³ Validated algorithm published at: Tu K, Campbell NR, Chen Z, Cauch-Dudek K, McAlister FA. Accuracy of administrative databases in identifying patients with hypertension. Open Med 2007; 1(1): 18–26.</p><p>Cardiovascular disease types and corresponding definitions in province-wide health administrative databases.</p

Demographic characteristics of the Métis and the general Ontario population as of April 1, 2006.

<p>IQR: Interquartile Range</p><p>¹Income quintile was determined from postal codes obtained from the Registered Persons Database and neighbourhood-level median household income from Statistics Canada census data. Quintiles range from poorest (Q1) to wealthiest (Q5).</p><p>Demographic characteristics of the Métis and the general Ontario population as of April 1, 2006.</p