Dataset Information

Tables or bar graphs? Presenting test results in electronic medical records.

ABSTRACT: BACKGROUND:Electronic personal health records offer a promising way to communicate medical test results to patients. We compared the usability of tables and horizontal bar graphs for presenting medical test results electronically. METHODS:We conducted experiments with a convenience sample of 106 community-dwelling adults. In the first experiment, participants viewed either table or bar graph formats (between subjects) that presented medical test results with normal and abnormal findings. In a second experiment, participants viewed table and bar graph formats (within subjects) that presented test results with normal, borderline, and abnormal findings. RESULTS:Participants required less viewing time when using bar graphs rather than tables. This overall difference was due to superior performance of bar graphs in vignettes with many test results. Bar graphs and tables performed equally well with regard to recall accuracy and understanding. In terms of ease of use, participants did not prefer bar graphs to tables when they viewed only one format. When participants viewed both formats, those with experience with bar graphs preferred bar graphs, and those with experience with tables found bar graphs equally easy to use. Preference for bar graphs was strongest when viewing tests with borderline results. CONCLUSIONS:Compared to horizontal bar graphs, tables required more time and experience to achieve the same results, suggesting that tables can be a more burdensome format to use. The current practice of presenting medical test results in a tabular format merits reconsideration.

SUBMITTER: Brewer NT

PROVIDER: S-EPMC3770735 | biostudies-literature | 2012 Jul-Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Tables or bar graphs? Presenting test results in electronic medical records.

Brewer Noel T NT Gilkey Melissa B MB Lillie Sarah E SE Hesse Bradford W BW Sheridan Stacey L SL

Medical decision making : an international journal of the Society for Medical Decision Making 20120403 4

<h4>Background</h4>Electronic personal health records offer a promising way to communicate medical test results to patients. We compared the usability of tables and horizontal bar graphs for presenting medical test results electronically.<h4>Methods</h4>We conducted experiments with a convenience sample of 106 community-dwelling adults. In the first experiment, participants viewed either table or bar graph formats (between subjects) that presented medical test results with normal and abnormal fi ...[more]

PMID: 22472914

Similar Datasets

Project description:BackgroundElectronic medical records (EMRs) of patients with lung cancer (LC) capture a variety of health factors. Understanding the distribution of these factors will help identify key factors for risk prediction in preventive screening for LC.ObjectiveWe aimed to generate an integrated biomedical graph from EMR data and Unified Medical Language System (UMLS) ontology for LC, and to generate an LC health factor distribution from a hospital EMR of approximately 1 million patients.MethodsThe data were collected from 2 sets of 1397 patients with and those without LC. A patient-centered health factor graph was plotted with 108,000 standardized data, and a graph database was generated to integrate the graphs of patient health factors and the UMLS ontology. With the patient graph, we calculated the connection delta ratio (CDR) for each of the health factors to measure the relative strength of the factor's relationship to LC.ResultsThe patient graph had 93,000 relations between the 2794 patient nodes and 650 factor nodes. An LC graph with 187 related biomedical concepts and 188 horizontal biomedical relations was plotted and linked to the patient graph. Searching the integrated biomedical graph with any number or category of health factors resulted in graphical representations of relationships between patients and factors, while searches using any patient presented the patient's health factors from the EMR and the LC knowledge graph (KG) from the UMLS in the same graph. Sorting the health factors by CDR in descending order generated a distribution of health factors for LC. The top 70 CDR-ranked factors of disease, symptom, medical history, observation, and laboratory test categories were verified to be concordant with those found in the literature.ConclusionsBy collecting standardized data of thousands of patients with and those without LC from the EMR, it was possible to generate a hospital-wide patient-centered health factor graph for graph search and presentation. The patient graph could be integrated with the UMLS KG for LC and thus enable hospitals to bring continuously updated international standard biomedical KGs from the UMLS for clinical use in hospitals. CDR analysis of the graph of patients with LC generated a CDR-sorted distribution of health factors, in which the top CDR-ranked health factors were concordant with the literature. The resulting distribution of LC health factors can be used to help personalize risk evaluation and preventive screening recommendations.

Project description:Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements-one of the most common implantable devices-as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8-53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t?=?5.14; p?<?0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.

Project description:BackgroundModified Rankin Scale (mRS) scores are used to measure functional outcomes after stroke. Researchers create horizontal stacked bar graphs (nicknamed "Grotta bars") to illustrate distributional differences in scores between groups. In well-conducted randomized controlled trials, Grotta bars have a causal interpretation. However, the common practice of exclusively presenting unadjusted Grotta bars in observational studies can be misleading in the presence of confounding. We demonstrated this problem and a possible solution using an empirical comparison of 3-month mRS scores among stroke/TIA patients discharged home versus elsewhere after hospitalization.Patients and methodsUsing data from the Berlin-based B-SPATIAL registry, we estimated the probability of being discharged home conditional on prespecified measured confounding factors and generated stabilized inverse probability of treatment (IPT) weights for each patient. We visualized mRS distributions by group with Grotta bars for the IPT-weighted population in which measured confounding was removed. We then used ordinal logistic regression to quantify unadjusted and adjusted associations between being discharged home and the 3-month mRS score.ResultsOf 3184 eligible patients, 2537 (79.7%) were discharged home. In the unadjusted analyses, those discharged home had considerably lower mRS compared with patients discharged elsewhere (common odds ratio, cOR = 0.13, 95% CI: 0.11-0.15). After removing measured confounding, we obtained substantially different mRS distributions, visually apparent in the adjusted Grotta bars. No statistically significant association was found after confounding adjustment (cOR = 0.82, 95% CI: 0.60-1.12).Discussion and conclusionThe practice of presenting only unadjusted stacked bar graphs for mRS scores together with adjusted effect estimates in observational studies can be misleading. IPT weighting can be implemented to create Grotta bars that account for measured confounding, which are more consistent with the presentation of adjusted results in observational studies.

Project description:BACKGROUND:Administrative health records (AHRs) and electronic medical records (EMRs) are two key sources of population-based data for disease surveillance, but misclassification errors in the data can bias disease estimates. Methods that combine information from error-prone data sources can build on the strengths of AHRs and EMRs. We compared bias and error for four data-combining methods and applied them to estimate hypertension prevalence. METHODS:Our study included rule-based OR and AND methods that identify disease cases from either or both data sources, respectively, rule-based sensitivity-specificity adjusted (RSSA) method that corrects for inaccuracies using a deterministic rule, and probabilistic-based sensitivity-specificity adjusted (PSSA) method that corrects for error using a statistical model. Computer simulation was used to estimate relative bias (RB) and mean square error (MSE) under varying conditions of population disease prevalence, correlation amongst data sources, and amount of misclassification error. AHRs and EMRs for Manitoba, Canada were used to estimate hypertension prevalence using validated case definitions and multiple disease markers. RESULTS:The OR method had the lowest RB and MSE when population disease prevalence was 10%, and the RSSA method had the lowest RB and MSE when population prevalence increased to 20%. As the correlation between data sources increased, the OR method resulted in the lowest RB and MSE. Estimates of hypertension prevalence for AHRs and EMRs alone were 30.9% (95% CI: 30.6-31.2) and 24.9% (95% CI: 24.6-25.2), respectively. The estimates were 21.4% (95% CI: 21.1-21.7), for the AND method, 34.4% (95% CI: 34.1-34.8) for the OR method, 32.2% (95% CI: 31.8-32.6) for the RSSA method, and ranged from 34.3% (95% CI: 34.1-34.5) to 35.9% (95% CI, 35.7-36.1) for the PSSA method, depending on the statistical model. CONCLUSIONS:The OR and AND methods are influenced by correlation amongst the data sources, while the RSSA method is dependent on the accuracy of prior sensitivity and specificity estimates. The PSSA method performed well when population prevalence was high and average correlations amongst disease markers was low. This study will guide researchers to select a data-combining method that best suits their data characteristics.

Dataset Information

Tables or bar graphs? Presenting test results in electronic medical records.

Publications

Tables or bar graphs? Presenting test results in electronic medical records.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets