Evidence map›Paper›PMID 26268511›Full record

ArticleBMC medical informatics and decision making2015

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

Karen Tu, Jessica Widdifield, Jacqueline Young, William Oud, Noah M Ivers, Debra A Butt, Chad A Leaver, Liisa Jaakkimainen

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  6. Implications of five different risk models in primary prevention guidelines.European heart journal. Quality of care & clinical outcomes · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Karen TuInstitute for Clinical Evaluative Sciences, G1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada. karen.tu@ices.on.ca.
Jessica WiddifieldInstitute for Clinical Evaluative Sciences, G1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Jacqueline YoungInstitute for Clinical Evaluative Sciences, G1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
William OudInstitute for Clinical Evaluative Sciences, G1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Noah M IversInstitute for Clinical Evaluative Sciences, G1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Debra A ButtDepartment of Family and Community Medicine, University of Toronto, Toronto, Canada.
Chad A LeaverCanada Health Infoway, Toronto, Canada.
Liisa JaakkimainenInstitute for Clinical Evaluative Sciences, G1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Medical Record LinkageAdultElectronic Health RecordsHumansOntarioPhysicians, Family

Identifiers

PMID26268511
PMCPMC4535372

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.