Agency for Healthcare Research and Quality.

BackgroundOpioids are prescribed frequently and increasingly for the management of chronic non-cancer pain (CNCP). Current systematic reviews have a number of limitations, leaving uncertainty with regard to the benefits and harms associated with opioid therapy for CNCP. We propose to conduct a systematic review and meta-analysis to summarize the evidence for using opioids in the treatment of CNCP and the risk of associated adverse events.Methods and designEligible trials will include those that randomly allocate patients with CNCP to treatment with any opioid or any non-opioid control group. We will use the guidelines published by the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) to inform the outcomes that we collect and present. We will use the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system to evaluate confidence in the evidence on an outcome-by-outcome basis. Teams of reviewers will independently and in duplicate assess trial eligibility, abstract data, and assess risk of bias among eligible trials. To ensure interpretability of our results, we will present risk differences and measures of relative effect for all outcomes reported and these will be based on anchor-based minimally important clinical differences, when available. We will conduct a priori defined subgroup analyses consistent with current best practices.DiscussionOur review will evaluate both the effectiveness and the adverse events associated with opioid use for CNCP, evaluate confidence in the evidence using the GRADE approach, and prioritize patient-important outcomes with a focus on functional gains guided by IMMPACT recommendations. Our results will facilitate evidence-based management of patients with CNCP and identify key areas for future research.Trial registrationOur protocol is registered on PROSPERO (CRD42012003023), http://www.crd.york.ac.uk/PROSPERO.

Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic.

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention—the starting point for delivery of “All the right care, but only the right care,” an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

Preclinical in silico comparison analytical framework allows rapid and inexpensive identification of computer-based protocol care strategies that justify expensive and burdensome clinical trials.