Comparison of Readability Scores for Written Health Information Across Formulas Using Automated vs Manual Measures.
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ABSTRACT:
SUBMITTER: Mac O
PROVIDER: S-EPMC9856555 | biostudies-literature | 2022 Dec
REPOSITORIES: biostudies-literature
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Comparison of Readability Scores for Written Health Information Across Formulas Using Automated vs Manual Measures.
Mac Olivia O Ayre Julie J Bell Katy K McCaffery Kirsten K Muscat Danielle M DM
JAMA network open 20221201 12
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2022-12-01 | GSE205417 | GEO
Project description:Various Gram staining automated systems are available to accelerate and standardize the staining process, but a systematic comparison of different systems is largely lacking. The objective of this study was to evaluate two devices in comparison to manual Gram staining. Clinical samples (n = 500; University Hospital Münster, Germany; May to June 2020) were simultaneously Gram stained manually and with two automated Gram stainers (Previ Color Gram, bioMérieux, and ColorAX2, Axonlab). The quality was assessed based on four criteria: (i) homogeneous staining of bacteria/fungi, (ii) uniform staining of the background, (iii) absence of staining artifacts, and (iv) congruency between culture and microscopy. Each criterion was rated with 0 (absence) or 1 (presence) point to calculate a quality score (0 to 4 points). The costs for each staining procedure were calculated based on consumables and hands-on time (applying the average wage of a laboratory technician in the public service for Germany and the United States). The mean (± standard deviation [SD]) quality scores were comparable for manual staining (3.06 ± 0.91) and Previ Color Gram (3.04 ± 0.90; P = 0.6), while significantly lower scores were achieved by ColorAX2 (2.57 ± 1.09; P < 0.0001). The total cost per Gram stain was €1.13/$1.34 for Previ Color Gram, €0.80/$0.83 for manual, and €0.60/$0.71 for ColorAX2, respectively. The quality and costs per slide vary significantly between instruments of different manufacturers.
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Project description:BackgroundOsteoarthritis (OA) is the most common cause of disability in people older than 65 years. Readability of online OA information has never been assessed. A 2003 study found the quality of online OA information to be poor.ObjectiveThe aim of this study was to review the readability and quality of current online information regarding OA.MethodsThe term osteoarthritis was searched across the three most popular English language search engines. The first 25 pages from each search engine were analyzed. Duplicate pages, websites featuring paid advertisements, inaccessible pages (behind a pay wall, not available for geographical reasons), and nontext pages were excluded. Readability was measured using Flesch Reading Ease Score, Flesch-Kincaid Grade Level, and Gunning-Fog Index. Website quality was scored using the Journal of the American Medical Association (JAMA) benchmark criteria and the DISCERN criteria. Presence or absence of the Health On the Net Foundation Code of Conduct (HONcode) certification, age of content, content producer, and author characteristics were noted.ResultsA total of 37 unique websites were found suitable for analysis. Readability varied by assessment tool from 8th to 12th grade level. This compares with the recommended 7th to 8th grade level. Of the 37, 1 (2.7%) website met all 4 JAMA criteria. Mean DISCERN quality of information for OA websites was "fair," compared with the "poor" grading of a 2003 study. HONcode-endorsed websites (43%, 16/37) were of a statistically significant higher quality.ConclusionsReadability of online health information for OA was either equal to or more difficult than the recommended level.
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Project description:We investigated the relationship of processing fluency of written information about exercise to participants' perceived interest, safety, self-efficacy, outcome expectation, and behavioral intention regarding the exercise. We randomly assigned 400 men and women aged 40-69 years to control or intervention conditions. Perceived self-efficacy of performing the exercise in the intervention group (i.e. easy to read) was significantly higher than that in the control group (i.e. difficult to read) (p = 0.04). Easy-to-read written health information may be important not only for making written health information comprehensible but also for increasing readers' self-efficacy for adopting health-related behaviors.
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Project description:BackgroundElectronic health records (EHRs) are a rich resource for developing applications to engage patients and foster patient activation, thus holding a strong potential to enhance patient-centered care. Studies have shown that providing patients with access to their own EHR notes may improve the understanding of their own clinical conditions and treatments, leading to improved health care outcomes. However, the highly technical language in EHR notes impedes patients' comprehension. Numerous studies have evaluated the difficulty of health-related text using readability formulas such as Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and Gunning-Fog Index (GFI). They conclude that the materials are often written at a grade level higher than common recommendations.ObjectiveThe objective of our study was to explore the relationship between the aforementioned readability formulas and the laypeople's perceived difficulty on 2 genres of text: general health information and EHR notes. We also validated the formulas' appropriateness and generalizability on predicting difficulty levels of highly complex technical documents.MethodsWe collected 140 Wikipedia articles on diabetes and 242 EHR notes with diabetes International Classification of Diseases, Ninth Revision code. We recruited 15 Amazon Mechanical Turk (AMT) users to rate difficulty levels of the documents. Correlations between laypeople's perceived difficulty levels and readability formula scores were measured, and their difference was tested. We also compared word usage and the impact of medical concepts of the 2 genres of text.ResultsThe distributions of both readability formulas' scores (P<.001) and laypeople's perceptions (P=.002) on the 2 genres were different. Correlations of readability predictions and laypeople's perceptions were weak. Furthermore, despite being graded at similar levels, documents of different genres were still perceived with different difficulty (P<.001). Word usage in the 2 related genres still differed significantly (P<.001).ConclusionsOur findings suggested that the readability formulas' predictions did not align with perceived difficulty in either text genre. The widely used readability formulas were highly correlated with each other but did not show adequate correlation with readers' perceived difficulty. Therefore, they were not appropriate to assess the readability of EHR notes.
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Project description:IntroductionData extraction from electronic health record (EHR) systems occurs through manual abstraction, automated extraction, or a combination of both. While each method has its strengths and weaknesses, both are necessary for retrospective observational research as well as sudden clinical events, like the COVID-19 pandemic. Assessing the strengths, weaknesses, and potentials of these methods is important to continue to understand optimal approaches to extracting clinical data. We set out to assess automated and manual techniques for collecting medication use data in patients with COVID-19 to inform future observational studies that extract data from the electronic health record (EHR).Materials and methodsFor 4,123 COVID-positive patients hospitalized and/or seen in the emergency department at an academic medical center between 03/03/2020 and 05/15/2020, we compared medication use data of 25 medications or drug classes collected through manual abstraction and automated extraction from the EHR. Quantitatively, we assessed concordance using Cohen's kappa to measure interrater reliability, and qualitatively, we audited observed discrepancies to determine causes of inconsistencies.ResultsFor the 16 inpatient medications, 11 (69%) demonstrated moderate or better agreement; 7 of those demonstrated strong or almost perfect agreement. For 9 outpatient medications, 3 (33%) demonstrated moderate agreement, but none achieved strong or almost perfect agreement. We audited 12% of all discrepancies (716/5,790) and, in those audited, observed three principal categories of error: human error in manual abstraction (26%), errors in the extract-transform-load (ETL) or mapping of the automated extraction (41%), and abstraction-query mismatch (33%).ConclusionOur findings suggest many inpatient medications can be collected reliably through automated extraction, especially when abstraction instructions are designed with data architecture in mind. We discuss quality issues, concerns, and improvements for institutions to consider when crafting an approach. During crises, institutions must decide how to allocate limited resources. We show that automated extraction of medications is feasible and make recommendations on how to improve future iterations.
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Project description:BackgroundManual evaluation of somatostatin receptor (SSTR) immunohistochemistry (IHC) is a time-consuming and cost-intensive procedure. Aim of the study was to compare manual evaluation of SSTR subtype IHC to an automated software-based analysis, and to in-vivo imaging by SSTR-based PET/CT.MethodsWe examined 25 gastroenteropancreatic neuroendocrine tumor (GEP-NET) patients and correlated their in-vivo SSTR-PET/CT data (determined by the standardized uptake values SUVmax,-mean) with the corresponding ex-vivo IHC data of SSTR subtype (1, 2A, 4, 5) expression. Exactly the same lesions were imaged by PET/CT, resected and analyzed by IHC in each patient. After manual evaluation, the IHC slides were digitized and automatically evaluated for SSTR expression by Definiens XD software. A virtual IHC score "BB1" was created for comparing the manual and automated analysis of SSTR expression.ResultsBB1 showed a significant correlation with the corresponding conventionally determined Her2/neu score of the SSTR-subtypes 2A (rs: 0.57), 4 (rs: 0.44) and 5 (rs: 0.43). BB1 of SSTR2A also significantly correlated with the SUVmax (rs: 0.41) and the SUVmean (rs: 0.50). Likewise, a significant correlation was seen between the conventionally evaluated SSTR2A status and the SUVmax (rs: 0.42) and SUVmean (rs: 0.62).ConclusionOur data demonstrate that the evaluation of the SSTR status by automated analysis (BB1 score), using digitized histopathology slides ("virtual microscopy"), corresponds well with the SSTR2A, 4 and 5 expression as determined by conventional manual histopathology. The BB1 score also exhibited a significant association to the SSTR-PET/CT data in accordance with the high affinity profile of the SSTR analogues used for imaging.
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Project description:Background and study aimsDuodenoscope contamination remains a persistent problem, exposing patients to infection risks. Automation in reprocessing may limit human error, reduce workload, and increase uniformity and traceability. However, its effectiveness should be evaluated before implementation. This study assessed the impact of implementing a novel water-based automated endoscope cleaning process on duodenoscope contamination.MethodsThis before-and-after intervention study compared duodenoscope cleaning methods. From January 2022 to December 2023, conventional manual cleaning was used. From January 2024 to June 2024, the AquaTYPHOON system (AT) replaced manual cleaning. Cultures from Pentax ED34-i10T2 patient-ready duodenoscopes were collected. The main outcome was the contamination rate with microorganisms of gut or oral origin (MGO). Secondary outcomes included contamination with solely gut bacteria. Non-inferiority of the AT was tested using a generalized estimating equation with a non-inferiority margin of 5%.ResultsDuring the manual cleaning period, 333 duodenoscope cultures of eight duodenoscopes were collected; during the AT period, 100 cultures were collected. Pre-introduction of the AT, the contamination rate with MGO was 21.6%, which fell to 16% post-introduction (risk difference: -5.6%, upper bound 90% confidence interval [CI] 6.8%). For gut bacteria, the contamination rate decreased from 14.4% to 9% (risk difference: -5.4%, upper bound 90% CI 3.9%), indicating non-inferiority.ConclusionsAT reduced the contamination rate with MGO, but non-inferiority was not demonstrated. For gut bacteria, AT was non-inferior to manual cleaning. These results are promising. However, future studies should confirm these findings in larger samples and explore other advantages of using the AT in duodenoscope cleaning.
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