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Gathering and learning from relevant clinical data: a new framework.

ABSTRACT: Given the rising costs of health care in today's economic environment, the need for effective, value-driven care has never been more pressing. While the U.S. health care system strives continually to improve patient outcomes, it struggles with the inadequacies due to variation in care and the inefficiencies of unnecessary resource utilization. The tools traditionally used to study care, from retrospective studies to randomized controlled trials, may be inadequate to address the complicated, interdependent questions related to defining effective care. To overcome the deficiencies of these traditional tools and better optimize our health care system, a new kind of methodology is required--one that integrates the functionality of previously existing tools in a novel way. Standardized Clinical Assessment and Management Plans (SCAMPs) were designed to accomplish this goal. A SCAMP is a care pathway, designed by clinicians, to guide medical decision making around a particular disorder. SCAMPs are unique in that they invite knowledge-based diversions from their recommendations and are accompanied by data collection and continuous improvement processes. Through these mechanisms, SCAMPs successfully reduce practice variation, optimize resource use, and create an integrated medical learning system which overcomes many of the inadequacies of traditional research tools. As such, the SCAMP paradigm may represent an important breakthrough in the effort to define and implement effective health care.

SUBMITTER: Farias M

PROVIDER: S-EPMC4310765 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Gathering and learning from relevant clinical data: a new framework.

Farias Michael M Friedman Kevin G KG Lock James E JE Rathod Rahul H RH

Academic medicine : journal of the Association of American Medical Colleges 20150201 2

Given the rising costs of health care in today's economic environment, the need for effective, value-driven care has never been more pressing. While the U.S. health care system strives continually to improve patient outcomes, it struggles with the inadequacies due to variation in care and the inefficiencies of unnecessary resource utilization. The tools traditionally used to study care, from retrospective studies to randomized controlled trials, may be inadequate to address the complicated, inte ...[more]

PMID: 25295963

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Project description:Pharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implications. Accordingly, the announcements of clinical trial results tend to determine the future course of events, hence being closely monitored by the public. Most works focus on retrospective analysis of announcement impact on company stock prices, bypassing the consideration of the problem in the predictive paradigm. In this work, we aim to close this gap by proposing a framework that allows predicting the numerical values of announcement-induced changes in stock prices. In fact, it is a problem of the impact prediction of the specific event on the corresponding time series. Our framework includes a BERT model for extracting the sentiment polarity of announcements, a Temporal Fusion Transformer for forecasting the expected return, a graph convolution network for capturing event relationships, and gradient boosting for predicting the price change. We operate with one of the biggest FDA (the Food and Drug Administration) datasets, consisting of 5436 clinical trial announcements from 681 companies for the years 2018-2022. During the study, we get several significant outcomes and domain-specific insights. Firstly, we obtain statistical evidence for the clinical result promulgation influence on the public pharma market value. Secondly, we witness inherently different patterns of responses to positive and negative announcements, reflected in a stronger and more pronounced reaction to negative clinical news. Thirdly, we discover two factors that play a crucial role in a predictive framework: (1) the drug portfolio size of the company, indicating the greater susceptibility to an announcement in the case of low diversification among drug products and (2) the announcement network effect, manifesting through an increase in predictive power when exploiting interdependencies of events belonging to the same company or nosology. Finally, we prove the viability of the forecast setting by getting ROC AUC scores predominantly greater than 0.7 for the classification of price change on historical data. We emphasize the transferability and generalizability of the developed framework on other datasets and domains but on the condition of the presence of two key entities: events and the associated time series.

Dataset Information

Gathering and learning from relevant clinical data: a new framework.

Publications

Gathering and learning from relevant clinical data: a new framework.

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