EMR Analysis

Preliminary research of the current prevalence and management patterns of speech and language disorders is necessary before more in-depth epidemiologic analyses may be undertaken. One source of data for this type of study is electronic medical records (EMR) extracted from primary care clinics themselves. . EMRs provide patient histories in the form of diagnoses, prescriptions, billing information, referrals laboratory testing and other information recorded by the attending physician. Thus, EMRs present a rich and comprehensive source of data, through which an accurate and precise understanding of primary care medicine may be gained. According to Griever et al. (2013), 49% of family medicine practices in Canada used EMR software to record
In their 2010 retrospective cohort study, Harris et al. determined that a lack of “consult letters [and] visit notes” limited the utility of EMRs for creating a diabetes case definition (351). Birtwhistle et al. (2009) also identified the absence of often critical sources of EMR-derived data, in addition to several other noteworthy challenges such as “dirty data…[including] misspelled words, extra words, inconsistent word strings…; inconsistent data…[as in] diagnoses stored in several different places…[and an absence of] standardization” (418). Tu et al. (2014), who performed a retrospective comparison of several administrative databases in Ontario, supported Birtwhistle et al. (2009)’s assertion that “lack of standardization” (418) creates significant problems for researchers using EMR-derived data: “the quality of the data captured and the ability to identify discrete data elements in the EMR may not be the same across EMR software packages from different vendors” (Tu et al., 2014,
CPCSSN is a network functioning across Canada to survey chronic diseases and improve quality of data for research purposes as well as care for patients (CPCSSN, 2013). Primary care clinics participating in CPCSSN provide consent to have health information on patients served by their practices collected and used for academic research (CPCSSN, 2013). CPCSSN currently extracts data from 600 primary care physicians across Canada for 750,000 patients. EMR software products included in the CPCSSN database include Accuro, Bell, DaVinci, Healthscreen, Jonoke, Med Access, Nightingale, Oscar, Practice Solutions, Purkinje, Telin Mediplan and Wolff. In the Southern Alberta Primary Care Research Network, a regional network within CPCSSN specifically, extractions are made from Med Access, Wolff and Telin Mediplan. The CPCSSN database currently tracks the prevalence of eight major conditions: diabetes, osteoarthritis, hypertension, chronic obstructive pulmonary disease (COPD), Parkinson’s disease (PD), epilepsy, depression and dementia (CPCSSN, 2013). A major issue with utilizing EMR-extracted data for research is that the data is used for a purpose other than that for which it was originally recorded, however CPCSSN has made significant efforts to extract EMR records and convert them