2,038 research outputs found

    Quality Assessment of Healthcare Databases

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    The assessment of data quality and suitability plays an important role in improving the validity and generalisability of the results of studies based on secondary use of health databases. The availability of more and more updated and valid information on data quality and suitability provides data users and researchers an useful tool to optimize their activities. In this paper, we have summarized and synthesized the main aspects of Data Quality Assessment (DQA) applied in the field of secondary use of healthcare databases, with the aim of drawing attention to the critical aspects having to be considered and developed for improving the correct and effective use of secondary sources. Four developing features are identified: standardizing DQA methods, reporting DQA methods and results, synergy between data managers and data users, role of Institutions. Interdisciplinarity, multi-professionality and connection between government institutions, regulatory bodies, universities and the scientific community will provide the "toolbox" for i) developing standardized and shared DQA methods for health databases, ii) defining the best strategies for disseminating DQA information and results

    Systematic review on the prevalence, frequency and comparative value of adverse events data in social media

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    Aim: The aim of this review was to summarize the prevalence, frequency and comparative value of information on the adverse events of healthcare interventions from user comments and videos in social media. Methods: A systematic review of assessments of the prevalence or type of information on adverse events in social media was undertaken. Sixteen databases and two internet search engines were searched in addition to handsearching, reference checking and contacting experts. The results were sifted independently by two researchers. Data extraction and quality assessment were carried out by one researcher and checked by a second. The quality assessment tool was devised in-house and a narrative synthesis of the results followed. Results: From 3064 records, 51 studies met the inclusion criteria. The studies assessed over 174 social media sites with discussion forums (71%) being the most popular. The overall prevalence of adverse events reports in social media varied from 0.2% to 8% of posts. Twenty-nine studies compared the results from searching social media with using other data sources to identify adverse events. There was general agreement that a higher frequency of adverse events was found in social media and that this was particularly true for ‘symptom’ related and ‘mild’ adverse events. Those adverse events that were under-represented in social media were laboratory-based and serious adverse events. Conclusions: Reports of adverse events are identifiable within social media. However, there is considerable heterogeneity in the frequency and type of events reported, and the reliability or validity of the data has not been thoroughly evaluated

    Optimizing Electronic Medication Prior Authorization: Reducing Prescription Delays

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    Background: Within the United States, chronic disease in children has doubled over the last 20 years. Many diseases defined as chronic (attention deficit, epilepsy, and diabetes) require daily medication regimens for optimal management. To be covered by insurance, many of these medications require prior authorization (PA) from the patients’ pharmacy benefits policy. Delays in processing and receiving PA orders can lead to worsening disease and inadequate disease management. In 2014, a pediatric academic medical center in the Midwest found that processing medications from prescription order to PA approval took nurses an average of over 90 hours. In August 2020, the organization implemented an electronic prior authorization (ePA) system that interfaced with the organization’s electronic health record (EHR). The primary goals of this implementation were to reduce medication PA turnaround times and to increase employee engagement with the ePA system. The goals of this quality improvement (QI) project are to optimize the existing ePA system with the medication PA process to reduce average medication PA turnaround times and to increase the approval rates for medication PAs by five percent. Project Design: Three interventions support the outputs of this QI project. Increase the availability of the ePA system by changing the patient and pharmacy benefits insurance matching interface logic. Reduce the number of medications falsely requiring PA by removing them from the ePA system. Increase PA processing efficiency by improving the workflow for attaching documents required for PA approval. To accomplish and measure these interventions, data reports and surveys were developed to establish baselines and to measure ePA turnaround times, PA approval rates, and user satisfaction both pre- and post-intervention. User satisfaction was measured utilizing a secure online survey emailed to ambulatory division nurses. Results: The median medication ePA turnaround pre- and post-interventions was unchanged at 36 hours. The ePA approval percentage dropped from 55.7% in June 2021 to 46.9% in August 2021. The primary QI project outcomes of reducing turnaround time and increasing the approval rate by 5% were not met. A user involvement survey was sent to 194 nurses with a response rate of 29% pre intervention and 8% post intervention. Overall user satisfaction was measured using a net promotor score which registered scores of –70 pre- and –82 post-intervention, revealing overall dissatisfaction with the ePA system. The use of an alternative ePA system outside the organization’s EHR was discovered after the QI project data was reviewed and showed that roughly 45% of ePAs were completed using this alternative system during the QI project timeframe. Recommendations: User involvement surveys measure user engagement with electronic systems and measuring user satisfaction is beneficial to providing direction for interventions as well as predicting future utilization of healthcare informatics projects. Conclusion: Though most of the goals for this QI project outcome were not met, use of the alternative ePA system confirmed the Technology Acceptance Model that users prefer the electronic system that they perceive as being the most useful. Nurses using ePA will use the system that best addresses their own user experiences regarding content, accuracy, format, timeliness, ease of use, and overall satisfaction

    Addressing data accuracy and information integrity in mHealth using ML

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    The aim of the study was finding a way in which Machine Learning can be applied in mHealth Solutions to detect inaccurate data that can potentially harm patients. The result was an algorithm that classified accurate and inaccurate data

    Assessment and Comparison of Behavior Risk Factor Surveillance Systems for the U.S., Canada, and Italy.

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    Behavior risk factors include health risk factors that increase a person\u27s chances of developing a disease, such as having a high blood pressure, high blood cholesterol, tobacco smoke, physical inactivity, obesity or overweight, diabetes, poor nutrition, lack of sex education and car safety. They can be classified as: Background risk factors, such as age, sex, level of education and genetic compositions; Behavioral risk factors, such as smoking, unhealthy diet and physical inactivity; and Intermediate risk factors, such a serum cholesterol levels, diabetes, hypertension and obesity/overweight. This study describes a comparison and assessment of Behavior Risk Factor Surveillance Systems for the U.S., Canada, and Italy. The aim of this project is to assess and analyze the behavior surveillance systems of U.S., Canada and Italy, compare their strengths and weaknesses and provide recommendations that can be used as a guide for the design of new BRFS systems or the assessment of existing systems. The purpose of the assessment is to identify ways of improving the respective systems, and also to compare public health BRFS systems in the three different countries. The attributes used in the evaluation of the systems include simplicity, flexibility, data quality, acceptability, sensitivity, predictive value positive, representativeness, timeliness, and stability. The criteria and standards are based on the CDC Guidelines for Evaluating Surveillance Systems published on 1988 and updated on 2001
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