Widespread health information exchange (HIE) is a national objective motivated by the promise of improved care and a reduction in costs. Previous reviews have found little rigorous evidence that HIE ...positively affects these anticipated benefits. However, early studies of HIE were methodologically limited. The purpose of the current study is to review the recent literature on the impact of HIE.
We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct our systematic review. PubMed and Scopus databases were used to identify empirical articles that evaluated HIE in the context of a health care outcome.
Our search strategy identified 24 articles that included 63 individual analyses. The majority of the studies were from the United States representing 9 states; and about 40% of the included analyses occurred in a handful of HIEs from the state of New York. Seven of the 24 studies used designs suitable for causal inference and all reported some beneficial effect from HIE; none reported adverse effects.
The current systematic review found that studies with more rigorous designs all reported benefits from HIE. Such benefits include fewer duplicated procedures, reduced imaging, lower costs, and improved patient safety. We also found that studies evaluating community HIEs were more likely to find benefits than studies that evaluated enterprise HIEs or vendor-mediated exchanges. Overall, these finding bode well for the HIEs ability to deliver on anticipated improvements in care delivery and reduction in costs.
There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained ...language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model-GatorTron-using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on five clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve five clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og .
Quality of health-related online search results Kitchens, Brent; Harle, Christopher A.; Li, Shengli
Decision Support Systems,
January 2014, 2014-01-00, 20140101, Letnik:
57
Journal Article
Recenzirano
Consumers are increasingly searching for health information online and using that information to inform their decisions and behavior. Because the negative consequences of basing decisions on ...inaccurate or untrustworthy health information may be particularly serious, it is important to understand the quality of online health information. This study empirically investigates the quality of health information that is returned by popular search engines when queried using a large, comprehensive set of health-related search terms. Findings indicate that a majority of such information returned by popular search engines is of a high quality but quality levels vary across different health topic areas. In particular, searches for terms related to preventive health and social health issues tend to produce lower quality results than terms related to diagnosis and treatment of physical disease or injury. While the overall prevalence of high quality information is greater than that of low quality, the observed variance across health-related terms has important implications for consumers, policy makers, health information providers, and search engines.
► Health-related search engine queries tend to return high quality information. ► The likelihood of obtaining high quality information does not vary by results rank. ► Searches on preventive and social health return lower quality information. ► Variations in search results have important managerial and policy implications.
This study assesses the accuracy of electronic health record–based screening questionnaires about social risk factors using external single-domain questionnaires as a comparator.
Purpose
The number of patients tapered from long‐term opioid therapy (LTOT) has increased in recent years in the United States. Some patients tapered from LTOT report improved quality of life, while ...others face increased risks of opioid‐related hospital use. Research has not yet established how the risk of opioid‐related hospital use changes across LTOT dose and subsequent tapering. Our objective was to examine associations between recent tapering from LTOT with odds of opioid‐related hospital use.
Methods
Case‐crossover design using 2014–2018 health information exchange data from Indiana. We defined opioid‐related hospital use as hospitalizations, and emergency department (ED) visits for a drug overdose, opioid abuse, and dependence. We defined tapering as a 15% or greater dose reduction following at least 3 months of continuous opioid therapy of 50 morphine milligram equivalents (MME)/day or more. We used conditional logistic regression to estimate odds ratios (OR) with 95% confidence intervals (CI).
Results
Recent tapering from LTOT was associated with increased odds of opioid‐related hospital use (OR: 1.50, 95%CI: 1.34–1.63), ED visit (OR: 1.52; 95%CI: 1.35–1.72), and inpatient hospitalization (OR: 1.40; 95%CI: 1.20–1.65). We found no evidence of heterogeneity of the effect of tapering on opioid‐related hospital use by gender, age, and race. Recent tapering among patients on a high baseline dose (>300 MME) was associated with increased odds of opioid‐related hospital use (OR: 2.95, 95% CI: 2.12–4.11, p < 0.001) compared to patients on a lower baseline doses.
Conclusions
Recent tapering from LTOT is associated with increased odds of opioid‐related hospital use.
Background
Despite the popularity of maternal and infant health mobile apps, ongoing consumer engagement and sustained app use remain barriers. Few studies have examined user experiences or perceived ...benefits of maternal and infant health app use from consumer perspectives.
Objective
This study aims to assess users’ self-reported experiences with maternal and infant health apps, perceived benefits, and general feedback by analyzing publicly available user reviews on two popular app stores—Apple App Store and Google Play Store.
Methods
We conducted a qualitative assessment of publicly available user reviews (N=2422) sampled from 75 maternal and infant health apps designed to provide health education or decision-making support to pregnant women or parents and caregivers of infants. The reviews were coded and analyzed using a general inductive qualitative content analysis approach.
Results
The three major themes included the following: app functionality, where users discussed app features and functions; technical aspects, where users talked about technology-based aspects of an app; and app content, where users specifically focused on the app content and the information it provides. The six minor themes included the following: patterns of use, where users highlighted the frequency and type of use; social support, where users talked about receiving social support from friends, family and community of other users; app cost, where users talked about the cost of an app within the context of being cost-effective or a potential waste of money; app comparisons, where users compared one app with others available in app stores; assistance in health care, where users specifically highlighted the role of an app in offering clinical assistance; and customer care support, where users specifically talked about their interaction with the app customer care support team.
Conclusions
Users generally tend to value apps that are of low cost and preferably free, with high-quality content, superior features, enhanced technical aspects, and user-friendly interfaces. Users also find app developer responsiveness to be integral, as it offers them an opportunity to engage in the app development and delivery process. These findings may be beneficial for app developers in designing better apps, as no best practice guidelines currently exist for the app environment.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The 2016 Centers for Disease Control and Prevention guideline for prescribing opioids for chronic pain (Guideline hereafter) emphasized tapering patients from long-term opioid therapy (LTOT) when the ...harms outweigh the benefits.
To examine tapering from LTOT before and after the Guideline release, we conducted a retrospective cohort study of adults with high-dose LTOT (mean of >50 Morphine Milligram Equivalents MME/day) from 2014 to 2018 from one Midwest state's Health Information Exchange. We identified tapering (dose reductions in mean MME/day greater than 15%, 30%, 50%) and rapid discontinuation episodes (reduction to zero MME/day) over a 6-month follow-up period relative to a 3-month baseline period. We used segmented regressions to estimate outcomes adjusted for time trends and relevant state laws limiting opioid prescribing.
The Guideline release was associated with statistically significant immediate increase in the patient likelihood of experiencing tapering (15%: 1.8% point 95% confidence interval (CI): 1.2–2.6; 30%: 1.4% point, 95% CI: 0.7–2.2; 50%: 0.8% point, 95% CI: 0.2–1.4) and rapid discontinuation episodes (0.006% point, 95% CI: 0.001–0.01). After the Guideline release, the patient likelihood of tapering increased over time (15%: 0.4% point/month, 95% CI: 0.3–0.5; 30%: 0.3% point/month, 95% CI:0.2–0.4; 50%: 0.3% point/month, 95% CI: 0.2–0.3; rapid discontinuation: 0.01% point/month, 95% CI: 0.007–0.01). Tapering and rapid discontinuation trends was similar among gender and race categories.
The Guideline may be a useful tool in altering opioid prescribing practices, particularly for patients on shorter durations of LTOT.
•Guideline release increases a patient likelihood of being tapered from LTOT.•Tapering may be more pronounced in patients on shorter durations of LTOT•A patient likelihood of being tapered from LTOT was similar among genders and race.•Guideline may be a useful tool in altering opioid prescribing for patients on LTOT.
This study aims to develop a generalizable architecture for enhancing an enterprise data warehouse for research (EDW4R) with results from a natural language processing (NLP) model, which allows ...discrete data derived from clinical notes to be made broadly available for research use without need for NLP expertise. The study also quantifies the additional value that information extracted from clinical narratives brings to EDW4R.
Clinical notes written during one month at an academic health center were used to evaluate the performance of an existing NLP model and to quantify its value added to the structured data. Manual review was utilized for performance analysis. The architecture for enhancing the EDW4R is described in detail to enable reproducibility.
Two weeks were needed to enhance EDW4R with data from 250 million clinical notes. NLP generated 16 and 39% increase in data availability for two variables.
Our architecture is highly generalizable to a new NLP model. The positive predictive value obtained by an independent team showed only slightly lower NLP performance than the values reported by the NLP developers. The NLP showed significant value added to data already available in structured format.
Given the value added by data extracted using NLP, it is important to enhance EDW4R with these data to enable research teams without NLP expertise to benefit from value added by NLP models.
Objective Prediabetes is a high-risk state for developing diabetes and associated complications. The purpose of this paper was to report trends in prevalence of prediabetes for individuals aged 16 ...and older in England without previously diagnosed diabetes. Setting Data collected by the Health Survey for England (HSE) in England in the years 2003, 2006, 2009 and 2011. Participants Individuals aged 16 and older who participated in the HSE and provided a blood sample. Primary outcome variable Individuals were classified as having prediabetes if glycated haemoglobin was between 5.7% and 6.4% and were not previously diagnosed with diabetes. Results The prevalence rate of prediabetes increased from 11.6% to 35.3% from 2003 to 2011. By 2011, 50.6% of the population who were overweight (body mass index (BMI)>25) and ≥40 years of age had prediabetes. In bivariate relationships, individuals with greater socioeconomic deprivation were more likely to have prediabetes in 2003 (p=0.0008) and 2006 (p=0.0246), but the relationship was not significant in 2009 (p=0.213) and 2011 (p=0.3153). In logistic regressions controlling for age, sex, race/ethnicity, BMI and high blood pressure, the second most socioeconomically deprived had a significantly elevated risk of having prediabetes (2011, OR=1.45; 95% CI 1.26 to 1.88). Conclusions There has been a marked increase in the proportion of adults in England with prediabetes. The socioeconomically deprived are at substantial risk. In the absence of concerted and effective efforts to reduce risk, the number of people with diabetes is likely to increase steeply in coming years.
To synthesize data quality (DQ) dimensions and assessment methods of real-world data, especially electronic health records, through a systematic scoping review and to assess the practice of DQ ...assessment in the national Patient-centered Clinical Research Network (PCORnet).
We started with 3 widely cited DQ literature-2 reviews from Chan et al (2010) and Weiskopf et al (2013a) and 1 DQ framework from Kahn et al (2016)-and expanded our review systematically to cover relevant articles published up to February 2020. We extracted DQ dimensions and assessment methods from these studies, mapped their relationships, and organized a synthesized summarization of existing DQ dimensions and assessment methods. We reviewed the data checks employed by the PCORnet and mapped them to the synthesized DQ dimensions and methods.
We analyzed a total of 3 reviews, 20 DQ frameworks, and 226 DQ studies and extracted 14 DQ dimensions and 10 assessment methods. We found that completeness, concordance, and correctness/accuracy were commonly assessed. Element presence, validity check, and conformance were commonly used DQ assessment methods and were the main focuses of the PCORnet data checks.
Definitions of DQ dimensions and methods were not consistent in the literature, and the DQ assessment practice was not evenly distributed (eg, usability and ease-of-use were rarely discussed). Challenges in DQ assessments, given the complex and heterogeneous nature of real-world data, exist.
The practice of DQ assessment is still limited in scope. Future work is warranted to generate understandable, executable, and reusable DQ measures.
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