scholarly journals Outcome Measure Harmonization and Data Infrastructure for Patient-Centered Outcomes Research in Depression: Report on Registry Configuration

Author(s):  
Michelle B. Leavy ◽  
Danielle Cooke ◽  
Sarah Hajjar ◽  
Erik Bikelman ◽  
Bailey Egan ◽  
...  

Background: Major depressive disorder is a common mental disorder. Many pressing questions regarding depression treatment and outcomes exist, and new, efficient research approaches are necessary to address them. The primary objective of this project is to demonstrate the feasibility and value of capturing the harmonized depression outcome measures in the clinical workflow and submitting these data to different registries. Secondary objectives include demonstrating the feasibility of using these data for patient-centered outcomes research and developing a toolkit to support registries interested in sharing data with external researchers. Methods: The harmonized outcome measures for depression were developed through a multi-stakeholder, consensus-based process supported by AHRQ. For this implementation effort, the PRIME Registry, sponsored by the American Board of Family Medicine, and PsychPRO, sponsored by the American Psychiatric Association, each recruited 10 pilot sites from existing registry sites, added the harmonized measures to the registry platform, and submitted the project for institutional review board review Results: The process of preparing each registry to calculate the harmonized measures produced three major findings. First, some clarifications were necessary to make the harmonized definitions operational. Second, some data necessary for the measures are not routinely captured in structured form (e.g., PHQ-9 item 9, adverse events, suicide ideation and behavior, and mortality data). Finally, capture of the PHQ-9 requires operational and technical modifications. The next phase of this project will focus collection of the baseline and follow-up PHQ-9s, as well as other supporting clinical documentation. In parallel to the data collection process, the project team will examine the feasibility of using natural language processing to extract information on PHQ-9 scores, adverse events, and suicidal behaviors from unstructured data. Conclusion: This pilot project represents the first practical implementation of the harmonized outcome measures for depression. Initial results indicate that it is feasible to calculate the measures within the two patient registries, although some challenges were encountered related to the harmonized definition specifications, the availability of the necessary data, and the clinical workflow for collecting the PHQ-9. The ongoing data collection period, combined with an evaluation of the utility of natural language processing for these measures, will produce more information about the practical challenges, value, and burden of using the harmonized measures in the primary care and mental health setting. These findings will be useful to inform future implementations of the harmonized depression outcome measures.

Author(s):  
Patrik Hrkút ◽  
Štefan Toth ◽  
Michal Ďuračík ◽  
Matej Meško ◽  
Emil Kršák ◽  
...  

Author(s):  
Phillip Osial ◽  
Arnold Kim ◽  
Kalle Kauranen

Despite rapid advancements in technology, the healthcare industry is known to lag behind when it comes to adopting new changes. Most often, when a new technology such as CPOE or EHR systems presents themselves in the healthcare industry, clinicians are left struggling to keep up with their workloads while learning to adjust a new workflow. Instead of disrupting the clinician's clinical workflow, the authors propose a system for transforming clinical narratives presented in the form of discharge summaries from the i2b2 Natural Language Processing dataset into a standardized order set. The proposed system uses natural language processing techniques based on Scala, which extracts discharge summary information about a patient and is proven to be highly scalable. The goal of this system is to increase interoperability between CPOE systems by performing further transformations on the extracted data. The authors adhere to HL7's FHIR standards and use JSON as the primary medical messaging format, which is used both in the US and international healthcare industry organizations and companies.


2021 ◽  
Author(s):  
Anahita Davoudi ◽  
Hegler Tissot ◽  
Abigail Doucette ◽  
Peter E Gabriel ◽  
Ravi B. Parikh ◽  
...  

One core measure of healthcare quality set forth by the Institute of Medicine is whether care decisions match patient goals. High-quality "serious illness communication" about patient goals and prognosis is required to support patient-centered decision-making, however current methods are not sensitive enough to measure the quality of this communication or determine whether care delivered matches patient priorities. Natural language processing offers an efficient method for identification and evaluation of documented serious illness communication, which could serve as the basis for future quality metrics in oncology and other forms of serious illness. In this study, we trained NLP algorithms to identify and characterize serious illness communication with oncology patients.


2020 ◽  
Vol 26 (6) ◽  
pp. 595-612
Author(s):  
Marcos Zampieri ◽  
Preslav Nakov ◽  
Yves Scherrer

AbstractThere has been a lot of recent interest in the natural language processing (NLP) community in the computational processing of language varieties and dialects, with the aim to improve the performance of applications such as machine translation, speech recognition, and dialogue systems. Here, we attempt to survey this growing field of research, with focus on computational methods for processing similar languages, varieties, and dialects. In particular, we discuss the most important challenges when dealing with diatopic language variation, and we present some of the available datasets, the process of data collection, and the most common data collection strategies used to compile datasets for similar languages, varieties, and dialects. We further present a number of studies on computational methods developed and/or adapted for preprocessing, normalization, part-of-speech tagging, and parsing similar languages, language varieties, and dialects. Finally, we discuss relevant applications such as language and dialect identification and machine translation for closely related languages, language varieties, and dialects.


2021 ◽  
Author(s):  
Alex Luscombe ◽  
Jamie Duncan ◽  
Kevin Walby

Computational methods are increasingly popular in criminal justice research. As more criminal justice data becomes available in big data and other digital formats, new means of embracing the computational turn are needed. In this article, we propose a framework for data collection and case sampling using computational methods, allowing researchers to conduct thick qualitative research – analyses concerned with the particularities of a social context or phenomenon – starting from big data, which is typically associated with thinner quantitative methods and the pursuit of generalizable findings. The approach begins by using open-source web scraping algorithms to collect content from a target website, online database, or comparable online source. Next, researchers use computational techniques from the field of natural language processing to explore themes and patterns in the larger data set. Based on these initial explorations, researchers algorithmically generate a subset of data for in-depth qualitative analysis. In this computationally driven process of data collection and case sampling, the larger corpus and subset are never entirely divorced, a feature we argue has implications for traditional qualitative research techniques and tenets. To illustrate this approach, we collect, subset, and analyze three years of news releases from the Royal Canadian Mounted Police website (N = 13,637) using a mix of web scraping, natural language processing, and visual discourse analysis. To enhance the pedagogical value of our intervention and facilitate replication and secondary analysis, we make all data and code available online in the form of a detailed, step-by-step tutorial.


SLEEP ◽  
2017 ◽  
Vol 40 (suppl_1) ◽  
pp. A443-A444
Author(s):  
Z Harrington ◽  
JP Bakker ◽  
A Wright ◽  
S Baker-Goodwin ◽  
K Page ◽  
...  

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