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

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Abstract

Background

With 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.

Methods

We 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.

Results

Of 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.

Conclusions

All 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.

Related collections

Most cited references 26

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Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.

Amit X Garg, Neill K. J. Adhikari, Heather McDonald … (2005)

Developers of health care software have attributed improvements in patient care to these applications. As with any health care intervention, such claims require confirmation in clinical trials. To review controlled trials assessing the effects of computerized clinical decision support systems (CDSSs) and to identify study characteristics predicting benefit. We updated our earlier reviews by searching the MEDLINE, EMBASE, Cochrane Library, Inspec, and ISI databases and consulting reference lists through September 2004. Authors of 64 primary studies confirmed data or provided additional information. We included randomized and nonrandomized controlled trials that evaluated the effect of a CDSS compared with care provided without a CDSS on practitioner performance or patient outcomes. Teams of 2 reviewers independently abstracted data on methods, setting, CDSS and patient characteristics, and outcomes. One hundred studies met our inclusion criteria. The number and methodologic quality of studies improved over time. The CDSS improved practitioner performance in 62 (64%) of the 97 studies assessing this outcome, including 4 (40%) of 10 diagnostic systems, 16 (76%) of 21 reminder systems, 23 (62%) of 37 disease management systems, and 19 (66%) of 29 drug-dosing or prescribing systems. Fifty-two trials assessed 1 or more patient outcomes, of which 7 trials (13%) reported improvements. Improved practitioner performance was associated with CDSSs that automatically prompted users compared with requiring users to activate the system (success in 73% of trials vs 47%; P = .02) and studies in which the authors also developed the CDSS software compared with studies in which the authors were not the developers (74% success vs 28%; respectively, P = .001). Many CDSSs improve practitioner performance. To date, the effects on patient outcomes remain understudied and, when studied, inconsistent.

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Accuracy of administrative databases in identifying patients with hypertension

Karen Tu, Norman RC Campbell, Zhong-Liang Chen … (2007)

Background Traditionally, the determination of the occurrence of hypertension in patients has relied on costly and time-consuming survey methods that do not allow patients to be followed over time. Objectives To determine the accuracy of using administrative claims data to identify rates of hypertension in a large population living in a single-payer health care system. Methods Various definitions for hypertension using administrative claims databases were compared with 2 other reference standards: (1) data obtained from a random sample of primary care physician offices throughout the province, and (2) self-reported survey data from a national census. Results A case-definition algorithm employing 2 outpatient physician billing claims for hypertension over a 3-year period had a sensitivity of 73% (95% confidence interval [CI] 69%–77%), a specificity of 95% (CI 93%–96%), a positive predictive value of 87% (CI 84%–90%), and a negative predictive value of 88% (CI 86%–90%) for detecting hypertensive adults compared with physician-assigned diagnoses. Compared with self-reported survey data, the algorithm had a sensitivity of 64% (CI 63%–66%), a specificity of 94%(CI 93%–94%), a positive predictive value of 77% (76%–78%), and negative predictive value of 89% (CI 88%–89%). When this algorithm was applied to the entire province of Ontario, the age- and sex-standardized prevalence of hypertension in adults older than 35 years increased from 20% in 1994 to 29% in 2002. Conclusions It is possible to use administrative data to accurately identify from a population sample those patients who have been diagnosed with hypertension. Given that administrative data are already routinely collected, their use is likely to be substantially less expensive compared with serial cross-sectional or cohort studies for surveillance of hypertension occurrence and outcomes over time in a large population.

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Review: electronic health records and the reliability and validity of quality measures: a review of the literature.

Kitty Chan, Jinnet B Fowles, Jonathan P Weiner (2010)

Previous reviews of research on electronic health record (EHR) data quality have not focused on the needs of quality measurement. The authors reviewed empirical studies of EHR data quality, published from January 2004, with an emphasis on data attributes relevant to quality measurement. Many of the 35 studies reviewed examined multiple aspects of data quality. Sixty-six percent evaluated data accuracy, 57% data completeness, and 23% data comparability. The diversity in data element, study setting, population, health condition, and EHR system studied within this body of literature made drawing specific conclusions regarding EHR data quality challenging. Future research should focus on the quality of data from specific EHR components and important data attributes for quality measurement such as granularity, timeliness, and comparability. Finally, factors associated with poor or variability in data quality need to be better understood and effective interventions developed.

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Author and article information

Contributors

Karen Tu: (416) 480-4055 , karen.tu@ices.on.ca

Jessica Widdifield: jessica.widdifield@alum.utoronto.ca

Jacqueline Young: jacqueline.young@ices.on.ca

William Oud: William.Oud@MackenzieHealth.ca

Noah M. Ivers: noah.ivers@utoronto.ca

Debra A. Butt: debra.butt@sympatico.ca

Chad A. Leaver: cleaver@infoway-inforoute.ca

Liisa Jaakkimainen: liisa.jaakkimainen@ices.on.ca

Journal

Journal ID (nlm-ta): BMC Med Inform Decis Mak

Journal ID (iso-abbrev): BMC Med Inform Decis Mak

Title: BMC Medical Informatics and Decision Making

Publisher: BioMed Central (London )

ISSN (Electronic): 1472-6947

Publication date (Electronic): 13 August 2015

Publication date PMC-release: 13 August 2015

Publication date Collection: 2015

Volume: 15

Electronic Location Identifier: 67

Affiliations

[ ]Institute for Clinical Evaluative Sciences, G1 06, 2075 Bayview Avenue, Toronto, ON M4N 3M5 Canada

[ ]Department of Family and Community Medicine, University of Toronto, Toronto, Canada

[ ]University Health Network-Toronto Western Family Health Team, Toronto, Canada

[ ]Department of Family and Community Medicine-The Scarborough Hospital, Toronto, Canada

[ ]Women’s College Research Institute and Family Practice Health Centre, Women’s College Hospital, Toronto, Canada

[ ]Canada Health Infoway, Toronto, Canada

[ ]Department of Family and Community Medicine-Sunnybrook Health Sciences Centre, Toronto, Canada

Article

Publisher ID: 195

DOI: 10.1186/s12911-015-0195-x

PMC ID: 4535372

PubMed ID: 26268511

SO-VID: d2509f90-a59b-4283-a55a-9125a9907248

License:

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.