@article {1278, title = {Large care gaps in primary care management of asthma: a longitudinal practice audit.}, journal = {BMJ Open}, volume = {9}, year = {2019}, month = {2019 01 29}, pages = {e022506}, abstract = {

OBJECTIVES: Care gaps in asthma may be highly prevalent but are poorly characterised. We sought to prospectively measure adherence to key evidence-based adult asthma practices in primary care, and predictors of these behaviours.

DESIGN: One-year prospective cohort study employing an electronic chart audit.

SETTING: Three family health teams (two academic, one community-based) in Ontario, Canada.

PARTICIPANTS: 884 patients (72.1\% female; 46.0{\textpm}17.5 years old) (4199 total visits; 4.8{\textpm}4.8 visits/patient) assigned to 23 physicians (65\% female; practising for 10.0{\textpm}8.6 years).

MAIN OUTCOME MEASURES: The primary outcome was the proportion of visits during which practitioners assessed asthma control according to symptom-based criteria. Secondary outcomes included the proportion of: patients who had asthma control assessed at least once; visits during which a controller medication was initiated or escalated; and patients who received a written asthma action plan. Behavioural predictors were established a priori and tested in a multivariable model.

RESULTS: Primary outcome: Providers assessed asthma control in 4.9\% of visits and 15.4\% of patients. Factors influencing assessment included clinic site (p=0.019) and presenting symptom, with providers assessing control more often during visits for asthma symptoms (35.0\%) or any respiratory symptoms (18.8\%) relative to other visits (1.6\%) (p<0.01).

SECONDARY OUTCOMES: Providers escalated controller therapy in 3.3\% of visits and 15.4\% of patients. Factors influencing escalation included clinic site, presenting symptom and prior objective asthma diagnosis. Escalation occurred more frequently during visits for asthma symptoms (21.0\%) or any respiratory symptoms (11.9\%) relative to other visits (1.5\%) (p<0.01) and in patients without a prior objective asthma diagnosis (3.5\%) relative to those with (1.3\%) (p=0.025). No asthma action plans were delivered.

CONCLUSIONS: Major gaps in evidence-based asthma practice exist in primary care. Targeted knowledge translation interventions are required to address these gaps, and can be tailored by leveraging the identified behavioural predictors.

TRIAL REGISTRATION NUMBER: NCT01070095; Pre-results.

}, issn = {2044-6055}, doi = {10.1136/bmjopen-2018-022506}, author = {Price, Courtney and Agarwal, Gina and Chan, David and Goel, Sanjeev and Kaplan, Alan G and Boulet, Louis-Philippe and Mamdani, Muhammad M and Straus, Sharon E and Lebovic, Gerald and Gupta, Samir} } @article {1279, title = {Identifying patients with asthma in primary care electronic medical record systems Chart analysis-based electronic algorithm validation study.}, journal = {Can Fam Physician}, volume = {61}, year = {2015}, month = {2015 Oct}, pages = {e474-83}, abstract = {

OBJECTIVE: To develop and test a variety of electronic medical record (EMR) search algorithms to allow clinicians to accurately identify their patients with asthma in order to enable improved care.

DESIGN: A retrospective chart analysis identified 5 relevant unique EMR information fields (electronic disease registry, cumulative patient profile, billing diagnostic code, medications, and chart notes); asthma-related search terms were designated for each field. The accuracy of each term was tested for its ability to identify the asthma patients among all patients whose charts were reviewed. Increasingly sophisticated search algorithms were then designed and evaluated by serially combining individual searches with Boolean operators.

SETTING: Two large academic primary care clinics in Hamilton, Ont.

PARTICIPANTS: Charts for 600 randomly selected patients aged 16 years and older identified in an initial EMR search as likely having asthma (n = 150), chronic obstructive pulmonary disease (n = 150), other respiratory conditions (n = 150), or nonrespiratory conditions (n = 150) were reviewed until 100 patients per category were identified (or until all available names were exhausted). A total of 398 charts were reviewed in full and included.

MAIN OUTCOME MEASURES: Sensitivity and specificity of each search for asthma diagnosis (against the reference standard of a physician chart review-based diagnosis).

RESULTS: Two physicians reviewed the charts identified in the initial EMR search using a standardized data collection form and ascribed the following diagnoses in 398 patients: 112 (28.1\%) had asthma, 81 (20.4\%) had chronic obstructive pulmonary disease, 104 (26.1\%) had other respiratory conditions, and 101 (25.4\%) had nonrespiratory conditions. Concordance between reviewers in chart abstraction diagnosis was high (κ = 0.89, 95\% CI 0.80 to 0.97). Overall, the algorithm searching for patients who had asthma in their cumulative patient profiles or for whom an asthma billing code had been used was the most accurate (sensitivity of 90.2\%, 95\% CI 87.3\% to 93.1\%; specificity of 83.9\%, 95\% CI 80.3\% to 87.5\%).

CONCLUSION: Usable, practical search algorithms that accurately identify patients with asthma in existing EMRs are presented. Clinicians can apply 1 of these algorithms to generate asthma registries for targeted quality improvement initiatives and outcome measurements. This methodology can be emulated for other diseases.

}, keywords = {Adult, Aged, Algorithms, Asthma, Data Accuracy, electronic health records, Female, Humans, Male, Middle Aged, Ontario, Primary Health Care, Pulmonary Disease, Chronic Obstructive, Registries, Retrospective Studies, Sensitivity and Specificity}, issn = {1715-5258}, author = {Xi, Nancy and Wallace, Rebecca and Agarwal, Gina and Chan, David and Gershon, Andrea and Gupta, Samir} }