Join the disruptors of health science

Insel, Thomas R.

doi:10.1038/551023a

Cited by 13 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Technology companies are expected to increase their investment in the health care sector through the development of artificial intelligence and machine learning to support clinical practice. 41 Drawn by the prospect of large profits, the arrival of several CDSS vendors on the market may be associated with exaggerated reports on the effectiveness of these products for improving clinical practice, which fail to consider the quality of the guidance messages or rules and the way in which they have been developed. Preliminary experience with CDSSs precipitated concerns regarding their accuracy because the technology, for example, suggested wrong treatments that exposed patients to potential harms in some instances.…”

Section: Policy Implicationsmentioning

confidence: 99%

Effectiveness of a Hospital-Based Computerized Decision Support System on Clinician Recommendations and Patient Outcomes

et al. 2019

View full text Add to dashboard Cite

IMPORTANCE Sophisticated evidence-based information resources can filter medical evidence from the literature, integrate it into electronic health records, and generate recommendations tailored to individual patients. OBJECTIVE To assess the effectiveness of a computerized clinical decision support system (CDSS) that preappraises evidence and provides health professionals with actionable, patient-specific recommendations at the point of care. DESIGN, SETTING, AND PARTICIPANTS Open-label, parallel-group, randomized clinical trial among internal medicine wards of a large Italian general hospital. All analyses in this randomized clinical trial followed the intent-to-treat principle. Between November 1, 2015, and December 31, 2016, patients were randomly assigned to the intervention group, in which CDSS-generated reminders were displayed to physicians, or to the control group, in which reminders were generated but not shown. Data were analyzed between February 1 and July 31, 2018. INTERVENTIONS Evidence-Based Medicine Electronic Decision Support (EBMEDS), a commercial CDSS covering a wide array of health conditions across specialties, was integrated into the hospital electronic health records to generate patient-specific recommendations. MAIN OUTCOMES AND MEASURES The primary outcome was the resolution rate, the rate at which medical problems identified and alerted by the CDSS were addressed by a change in practice. Secondary outcomes included the length of hospital stay and in-hospital all-cause mortality. RESULTS In this randomized clinical trial, 20 563 patients were admitted to the hospital. Of these, 6480 (31.5%) were admitted to the internal medicine wards (study population) and randomized (3242 to CDSS and 3238 to control). The mean (SD) age of patients was 70.5 (17.3) years, and 54.5% were men. In total, 28 394 reminders were generated throughout the course of the trial (median, 3 reminders per patient per hospital stay; interquartile range [IQR], 1-6). These messages led to a change in practice in approximately 4 of 100 patients. The resolution rate was 38.0% (95% CI, 37.2%-38.8%) in the intervention group and 33.7% (95% CI, 32.9%-34.4%) in the control group, corresponding to an odds ratio of 1.21 (95% CI, 1.11-1.32; P < .001). The length of hospital stay did not differ between the groups, with a median time of 8 days (IQR, 5-13 days) for the intervention group and a median time of 8 days (IQR, 5-14 days) for the control group (P = .36). In-hospital all-cause mortality also did not differ between groups (odds ratio, 0.95; 95% CI, 0.77-1.17; P = .59). Alert fatigue did not differ between early and late study periods. (continued) Key Points Question Can a multispecialty computerized clinical decision support system (CDSS) reduce inappropriate prescribing in a general hospital? Findings In this randomized clinical trial of 6480 patients, the CDSS alerted to a median of 3 reminders for prescription problems per patient per hospital stay. The CDSS led to a change in practice in approximately 4 of 100 patients, an...

show abstract

Section: Policy Implicationsmentioning

confidence: 99%

Effectiveness of a Hospital-Based Computerized Decision Support System on Clinician Recommendations and Patient Outcomes

et al. 2019

View full text Add to dashboard Cite

show abstract

“…As for the World Health Organization (WHO) ( Barratt et al , 2017 ) this could be actioned by Expert Synthesis Groups led by a key expert. As we propose a strategic plan, overall we must be cognisant of initiatives in other areas such as growth of big data ( Frégnac, 2017 ; Insel, 2017 ), whole cell maps ( Horwitz and Johnson, 2017 ) and their biology ( Kosik et al , 2016 ). Funding for this initial approach would be required and may come from, for example, The Wellcome Trust.…”

Section: Five Timely Reasons For Immediate Actionmentioning

confidence: 99%

‘Man Up’: the importance and strategy for placing male reproductive health centre stage in the political and research agenda

2018

View full text Add to dashboard Cite

Approximately 1 in 20 young men today have sperm counts low enough to impair fertility, whereas this may not have been the case historically. The cause(s) of such a decline in male reproductive health is unknown, despite it being a global health issue. Concomitantly, little progress has been made in answering fundamental questions in andrology or in developing new diagnostic tools or alternative management strategies to ICSI in infertile men. We advocate formulation of a detailed roadmap for male reproductive health to facilitate development of a research agenda that highlights the present unmet needs and key unanswered questions, and seeks to deliver effective funding and investment to address them. This vision we term ‘a Male Reproductive Health Ecosystem’.

show abstract

“…The flexibility of ML approaches allows researchers to analyze very heterogeneous samples and large amounts of various data types. In an age in which biology is an increasingly complex information science (Insel, ), the presented computational approaches provide a framework with which to define and validate clinical phenotypes based on basic behavioral and biological dimensions.…”

Section: What Has ML Achieved In the Field Of Stress Research?mentioning

confidence: 99%

Machine Learning for Prediction of Posttraumatic Stress and Resilience Following Trauma: An Overview of Basic Concepts and Recent Advances

Schultebraucks

Galatzer‐Levy

2019

Journal of Traumatic Stress

View full text Add to dashboard Cite

Posttraumatic stress responses are characterized by a heterogeneity in clinical appearance and etiology. This heterogeneity impacts the field's ability to characterize, predict, and remediate maladaptive responses to trauma. Machine learning (ML) approaches are increasingly utilized to overcome this foundational problem in characterization, prediction, and treatment selection across branches of medicine that have struggled with similar clinical realities of heterogeneity in etiology and outcome, such as oncology. In this article, we review and evaluate ML approaches and applications utilized in the areas of posttraumatic stress, stress pathology, and resilience research, and present didactic information and examples to aid researchers interested in the relevance of ML to their own research. The examined studies exemplify the high potential of ML approaches to build accurate predictive and diagnostic models of posttraumatic stress and stress pathology risk based on diverse sources of available information. The use of ML approaches to integrate high-dimensional data demonstrates substantial gains in risk prediction even when the sources of data are the same as those used in traditional predictive models. This area of research will greatly benefit from collaboration and data sharing among researchers of posttraumatic stress disorder, stress pathology, and resilience.

show abstract

Join the disruptors of health science

Cited by 13 publications

References 8 publications

Effectiveness of a Hospital-Based Computerized Decision Support System on Clinician Recommendations and Patient Outcomes

Effectiveness of a Hospital-Based Computerized Decision Support System on Clinician Recommendations and Patient Outcomes

‘Man Up’: the importance and strategy for placing male reproductive health centre stage in the political and research agenda

Machine Learning for Prediction of Posttraumatic Stress and Resilience Following Trauma: An Overview of Basic Concepts and Recent Advances

Contact Info

Product

Resources

About