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Tag: accuracy

Designing an electronic medical record alert to identify hospitalised patients with HIV: successes and challenges

El-Nahal W, Grader-Beck T, Gebo K, Holmes E, Herne K, Moore R, Thompson D, Berry S. Designing an electronic medical record alert to identify hospitalised patients with HIV: successes and challenges. BMJ Health Care Inform 2022;29:e100521. doi:10.1136/bmjhci-2021-100521

An electronic medical record (EMR) alert system was developed to use readily available data elements to accurately identify hospitalized people with HIV. Authors described the design and implementation of the EMR alert and methods to evaluate its accuracy for identifying people with HIV. Over 24 months, the EMR alert was used to notify an intervention team and data abstraction team in real time about admissions of people with HIV. Sensitivity was assessed by comparing the machine-learning alert system to manual chart reviews. Positive predictive value (probability that a patient with a positive test result actually has the disease), was assessed by false positives identified in chart review (not having HIV despite alert triggering). Results demonstrated high sensitivity (sensitivity=100%, 95% CI 82-100%) and good predictive value (84%, 95% CI 82-86%). A combination of data (diagnosis, prescriptions, and lab orders) in the EMR alert system achieved high sensitivity and positive predictive value in identifying people with HIV. ICD Code diagnoses were the strongest contributors to predictive value, compared to the other criteria. Use of data-driven alerts in electronic health record systems can facilitate the deployment of multidisciplinary teams for medication review, education, case management, and outpatient linkage to follow-up.


Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder

Megerian JT, Dey S, Melmed RD, Coury DL, Lerner M, Nicholls CJ, Sohl K, Rouhbakhsh R, Narasimhan A, Romain J, Golla S, Shareef S, Ostrovsky A, Shannon J, Kraft C, Liu-Mayo S, Abbas H, Gal-Szabo DE, Wall DP, & Taraman S (2022). Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder. NPJ Digital Medicine, 5(1), 57–57.

Researchers conducted a double-blinded, multi-site, active comparator cohort study to test the accuracy of artificial intelligence software for diagnosing autism spectrum disorder (ASD). The software device collects data about child behavioral features from 3 sources (caregiver questionnaire, analysis of two short 1 minute home videos recorded and uploaded by the child’s caregiver, provider questionnaire). Data are processed using a machine learning algorithm to indicate whether a person is ASD positive, ASD negative, or inconclusive (i.e., inputted data are not sufficient for a predictive output). Researchers evaluated the software in a study with 425 children aged 18-72 months for whom a caregiver or provider had a concern about developmental delay. Researchers compared the software outputs to the clinical standard (diagnosis made by a provider based on DSM-5 criteria). Results demonstrated that data collection with the software device took less time to administer and require less specialty training relative to clinical standard process. For about 33% of the sample, the algorithm output supported accurate diagnoses compared with clinical evaluation. Of the children for whom the software algorithm made a definite evaluation, 98.4% with clinically diagnosed ASD received an ASD positive result and 78.9% without a clinical diagnosis of ASD received an ASD negative result. All children who received a false-positive result (n=15) had a non-ASD developmental condition. Only one child received a false negative result in this study. Overall, this machine learning tool demonstrated high sensitivity and good specificity for diagnosing ASD. The tool can potentially expand the ability to effectively diagnose children with ASD in primary care to facilitate early intervention and more efficient use of specialist resources.


Study: Wearables Can Empower Patients, but Barriers Prevent Greater Adoption

Article Excerpt: Using wearables to track health data could empower patients, but there are several barriers to effective use, including the need for provider support. The review, published in JMIR, analyzed 20 studies published in Europe and the U.S. that collectively included more than 7,000 participants. Researchers found three main overarching themes: the role of providers and potential benefits to care, driving behavior change and barriers to use.

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Article Source: MobiHealthNews


Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma

Lekkas D, Jacobson N. (2021). Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma. Scientific Reports. 11(1): 10303. doi: 10.1038/s41598-021-89768-2

Researchers examined the efficacy of using time-anchored Global Positioning System (GPS) location data to detect post-traumatic stress disorder (PTSD) diagnostic status among women (ages 18–65 years) who had experienced child abuse (n = 185). Read More


Scientists Seek Ways to Finally Take a Real Measure of Pain

Article Excerpt: The National Institutes of Health is pushing for development of what its director, Dr. Francis Collins, has called a “pain-o-meter.” Spurred by the opioid crisis, the goal isn’t just to signal how much pain someone’s in. It’s also to determine what kind it is and what drug might be the most effective.

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Article Source: AP News


Fitness trackers are terrible at counting calories, says Stanford study

Article Excerpt: The researchers conducted a study to assess the quality of wearable trackers after finding a lack of data in peer-reviewed journals. “Anytime we get data from a patient via a device, we have questions about the accuracy,” said Euan Ashley, an associate professor at Stanford, who focuses on cardiovascular medicine.

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Article Source: CNBC


Study shows medical apps for chronic disease management have significant quality issues

Article Excerpt: We have come to a place in mobile health (mHealth) where the problem is no longer a lack of available apps. Patients and healthcare providers are using health related apps on their smartphones. Research studies have shown promising evidence that certain disease outcomes can be improved with implementation of a mobile app. The issue is now ensuring the quality and safety for patient-facing apps. It is unclear if apps can cause harm for certain users.

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Article Source: iMedicalApps


European Commission forms group to create guidelines for health app data quality

Article Excerpt: The European Commission has formed a working group that will create guidelines to evaluate the accuracy and reliability of health app data. The group’s first meeting will be in March.

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Article Source: MobiHealthNews


MIT offshoot set to launch health app reviews

Article Excerpt: A nonprofit institute, spun off from the healthcare entrepreneurship program at Massachusetts Institute of Technology, will soon start producing consumer reviews of mobile apps and other digital health tools that have been vetted by Harvard University clinicians, the nonprofit’s co-founder said.

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Article Source: Medical Marketing & Media