Usability problems do not heal by themselves: National survey on physicians’ experiences with EHRs in Finland
Bibliographic record
Abstract
PURPOSE: Survey studies of health information systems use tend to focus on availability of functionalities, adoption and intensity of use. Usability surveys have not been systematically conducted by any healthcare professional groups on a national scale on a repeated basis. This paper presents results from two cross-sectional surveys of physicians' experiences with the usability of currently used EHR systems in Finland. The research questions were: To what extent has the overall situation improved between 2010 and 2014? What differences are there between healthcare sectors? METHODS: In the spring of 2014, a survey was conducted in Finland using a questionnaire that measures usability and respondents' user experiences with electronic health record (EHR) systems. The survey was targeted to physicians who were actively doing clinical work. Twenty-four usability-related statements, that were identical in 2010 and 2014, were analysed from the survey. The respondents were also asked to give an overall rating of the EHR system they used. The study data comprised responses from 3081 physicians from the year 2014 and from 3223 physicians in the year 2010, who were using the nine most commonly used EHR system brands in Finland. RESULTS: Physicians' assessments of the usability of their EHR system remain as critical as they were in 2010. On a scale from 1 ('fail') to 7 ('excellent') the average of overall ratings of their principally used EHR systems varied from 3.2 to 4.4 in 2014 (and in 2010 from 2.5 to 4.3). The results show some improvements in the following EHR functionalities and characteristics: summary view of patient's health status, prevention of errors associated with medication ordering, patient's medication list as well as support for collaboration and information exchange between the physician and the nurses. Even so, support for cross-organizational collaboration between physicians and for physician-patient collaboration were still considered inadequate. Satisfaction with technical features had not improved in four years. The results show marked differences between the EHR system brands as well as between healthcare sectors (private sector, public hospitals, primary healthcare). Compared to responses from the public sector, physicians working in the private sector were more satisfied with their EHR systems with regards to statements about user interface characteristics and support for routine tasks. Overall, the study findings are similar to our previous study conducted in 2010. CONCLUSIONS: Surveys about the usability of EHR systems are needed to monitor their development at regional and national levels. To our knowledge, this study is the first national eHealth observatory questionnaire that focuses on usability and is used to monitor the long-term development of EHRs. The results do not show notable improvements in physician's ratings for their EHRs between the years 2010 and 2014 in Finland. Instead, the results indicate the existence of serious problems and deficiencies which considerably hinder the efficiency of EHR use and physician's routine work. The survey results call for considerable amount of development work in order to achieve the expected benefits of EHR systems and to avoid technology-induced errors which may endanger patient safety. The findings of repeated surveys can be used to inform healthcare providers, decision makers and politicians about the current state of EHR usability and differences between brands as well as for improvements of EHR usability. This survey will be repeated in 2017 and there is a plan to include other healthcare professional groups in future surveys.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".