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Improved methods to identify nonmedical opioid use can help direct health care resources to individuals who need them. Automated algorithms that use large databases of electronic health care claims or records for surveillance are a potential means to achieve this goal. In this systematic review, we reviewed the utility, attempts at validation, and application of such algorithms to detect nonmedical opioid use.

To systematically review the literature pertaining to asynchronous automated electronic notifications of laboratory results to clinicians.

To determine the effect of health information exchange (HIE) on medication prescribing for hospital inpatients in a cluster-randomized controlled trial, and to examine the prescribing effect of availability of information from a large pharmacy insurance plan in a natural experiment.

Given the strong push to empower patients and make them partners in their health care, we evaluated the current capability of hospitals to offer health information technology that facilitates patient engagement (PE).

Predictive analytics create opportunities to incorporate personalized risk estimates into clinical decision support. Models must be well calibrated to support decision-making, yet calibration deteriorates over time. This study explored the influence of modeling methods on performance drift and connected observed drift with data shifts in the patient population.

To compare the efficiency and safety of using speech recognition (SR) assisted clinical documentation within an electronic health record (EHR) system with use of keyboard and mouse (KBM).

Therapeutic intent, the reason behind the choice of a therapy and the context in which a given approach should be used, is an important aspect of medical practice. There are unmet needs with respect to current electronic mapping of drug indications. For example, the active ingredient sildenafil has 2 distinct indications, which differ solely on dosage strength. In progressing toward a practice of precision medicine, there is a need to [...]

Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. We aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML.

All default electronic health record and drug reference database vendor drug-dose alerting recommendations (single dose, daily dose, dose frequency, and dose duration) were silently turned on in inpatient, outpatient, and emergency department areas for pediatric-only and nonpediatric-only populations. Drug-dose alerts were evaluated during a 3-month period. Drug-dose alerts fired on 12% of orders (104 098/834 911). System-level and drug-specific strategies to decrease drug-dose alerts were analyzed. System-level strategies included: (1) [...]

To demonstrate a data-driven method for personalizing lung cancer risk prediction using a large clinical dataset.