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Record W2353307170 · doi:10.1093/pch/19.3.138

Top 10 tips for effective use of electronic health records

2014· article· en· W2353307170 on OpenAlexaff
Rey Wuerth, Catherine Campbell, W. James King

Bibliographic record

VenuePaediatrics & Child Health · 2014
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of OttawaChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMeaningful useUsabilityIncentiveElectronic health recordQuality (philosophy)MedicineNursingHealth recordsHealth careMedical emergencyFamily medicineComputer science

Abstract

fetched live from OpenAlex

The use of electronic health record (EHR) systems has become common in both outpatient and hospital-based care, with 57% of primary care physicians using EHRs as of 2013 (doubling since 2006) (1). Successful adoption of an EHR system is dependent on many factors including the type of EHR, the practice setting, interface design, usability and incentives (2). While there are many potential benefits and risks of EHR use, one critical characteristic is its ability to influence the patient-provider interaction. The following 10 tips aim to help maintain quality of care while transitioning from a paper-based charting system to EHRs. While some of these may appear to be intuitive, providers struggle with the transition and may benefit from such focused guidance. Whether you currently use an EHR system or plan to use one in the near future, we encourage you to implement these tips into daily practice to enable you to continue to improve the quality of care you provide and ensure that care remains patient centred. Be aware of how much time you focus on the patient and compare it with how much time you focus on the computer. Both computer use and communicating with the patient require focused attention (3–7). Also, be cognizant of screen gazing – we have a habit of glancing at the computer screen while receiving nonrelevant clinical information. This is especially true at the onset of the encounter, when first introducing yourself to a new patient and when sensitive issues are discussed. During these key moments, give patients your full, undivided attention. While listening, make sure that you face and make eye contact with your patient and their family; just as importantly, push your monitor away and take your hands off the keyboard and mouse (3). Continue communicating with your patient while you enter data into their record. When discussing data, point to the screen and show the patient the results such as specific laboratory values and where they fall within their normal range. Tell your patient what you are doing as you are doing it and affirm a shift back to the computer (3,4). Before writing in the patient chart, acknowledge or ask for permission. While talking about previous tests, say “allow me show you a trend of your results”. Such strategies improve the patient-physician relationship and also give the patient more confidence in the care they receive (3,4). Do not allow the computer to dictate your interaction with the patient. In addition to ergonomic benefits, mobile computer screens can be used effectively to hide – or, more importantly, display – test results and notes to the patient or their family. When discussing results or notes, turn the monitor toward the patient and highlight what you are discussing. This facilitates patient engagement and increases their satisfaction with the encounter (3,5). Review the list of concerns, problems and previous notes before entering the examination room to improve efficiency and optimize patient satisfaction. Separation of routine data entry will increase the time available for interacting with your patient and their family. When appropriate, use transition times to access the electronic system (3,5). Adjust your typing style and timing around your patient's needs. Begin with your patient's concerns and allow them to drive the flow of information while you direct content to clinically relevant topics. Do not start and stop your patient from expressing themselves to write something down or look something up (3). Many EHR systems include documentation templates or allow users to create their own. Once you feel comfortable using templates, they can significantly reduce the time required to chart for patient visits, findings, referrals, etc (3). As you become more familiar with the EHR system, you will be enhancing your screen-scanning speed, browsing speed and accuracy, and learning to use the decision support effectively. Even computer-savvy users have room to enhance their clinical effectiveness by learning short cuts (3). It can take up to a year before a health care professional becomes competent and confident in their use of EHRs (5).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.061
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0060.005
Open science0.0020.004
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0610.037

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.385
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2014
Admission routes1
Has abstractyes

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