Precision Health-Enabled Machine Learning to Identify Need for Wraparound Social Services Using Patient- and Population-Level Data Sets: Algorithm Development and Validation.

Background: Emerging interest in precision health and the increasing availability of patient- and population-level data sets present considerable potential to enable analytical approaches to identify and mitigate the negative effects of social factors on health. These issues are not satisfactorily addressed in typical medical care encounters, and thus, opportunities to improve health outcomes, reduce costs, and improve coordination of care are not realized. Furthermore, methodological expertise on the use of varied patient- and population-level data sets and machine learning to predict need for supplemental services is limited.

Multi-center colonoscopy quality measurement utilizing natural language processing.

Background: An accurate system for tracking of colonoscopy quality and surveillance intervals could improve the effectiveness and cost-effectiveness of colorectal cancer (CRC) screening and surveillance. The purpose of this study was to create and test such a system across multiple institutions utilizing natural language processing (NLP).

Methods: From 42,569 colonoscopies with pathology records from 13 centers, we randomly sampled 750 paired reports.

Regenstrief Institute's Medical Gopher: a next-generation homegrown electronic medical record system.

Objective: Regenstrief Institute developed one of the seminal computerized order entry systems, the Medical Gopher, for implementation at Wishard Hospital nearly three decades ago. Wishard Hospital and Regenstrief remain committed to homegrown software development, and over the past 4 years we have fully rebuilt Gopher with an emphasis on usability, safety, leveraging open source technologies, and the advancement of biomedical informatics research. Our objective in this paper is to summarize the functionality of this new system and highlight its novel features.

Clinical decision support with natural language processing facilitates determination of colonoscopy surveillance intervals.

Background & Aims: With an increased emphasis on improving quality and decreasing costs, new tools are needed to improve adherence to evidence-based practices and guidelines in endoscopy. We investigated the ability of an automated system that uses natural language processing (NLP) and clinical decision support (CDS) to facilitate determination of colonoscopy surveillance intervals.

Methods: We performed a retrospective study at a single Veterans Administration medical center of patients age 40 years and older who had an index outpatient colonoscopy from 2002 through 2009 for any indication except surveillance of a previous colorectal neoplasia.

Design of a medication reconciliation application: facilitating clinician-focused decision making with data from multiple sources.

Background: Medication reconciliation is an essential, but resource-intensive process without a "gold standard" to measure medication adherence. Medication reconciliation applications that focus on facilitating clinicians' decision-making are needed. Since no single available source of medication information is adequate, combining data sources may improve usefulness and outcomes.

Natural language processing accurately categorizes findings from colonoscopy and pathology reports.

Background & Aims: Little is known about the ability of natural language processing (NLP) to extract meaningful information from free-text gastroenterology reports for secondary use.

Methods: We randomly selected 500 linked colonoscopy and pathology reports from 10,798 nonsurveillance colonoscopies to train and test the NLP system. By using annotation by gastroenterologists as the reference standard, we assessed the accuracy of an open-source NLP engine that processed and extracted clinically relevant concepts.

Fibromyalgia syndrome module at OMERACT 9: domain construct.

The objective of the module was to (1) establish a core domain set for fibromyalgia (FM) assessment in clinical trials and practice, (2) review outcome measure performance characteristics, (3) discuss development of a responder index for assessment of FM in clinical trials, (4) review objective markers, (5) review the domain of cognitive dysfunction, and (6) establish a research agenda for outcomes research. Presentations at the module included: (1) Results of univariate and multivariate analysis of 10 FM clinical trials of 4 drugs, mapping key domains identified in previous patient focus group: Delphi exercises and a clinician/researcher Delphi exercise, and breakout discussions to vote on possible essential domains and reliable measures; (2) Updates regarding outcome measure status; (3) Update on objective markers to measure FM disease state; and (4) Review of the issue of cognitive dysfunction (dyscognition) in FM. Consensus was reached as follows: (1) Greater than 70% of OMERACT participants agreed that pain, tenderness, fatigue, patient global, multidimensional function and sleep disturbance domains should be measured in all FM clinical trials; dyscognition and depression should be measured in some trials; and stiffness, anxiety, functional imaging, and cerebrospinal fluid biomarkers were identified as domains of research interest.

Involvement in decision-making and breast cancer survivor quality of life.

Objective: This study examined the long-term effects on women's health related quality of life (HRQOL) of involvement in decision-making about their treatment for breast cancer and about follow-up care after treatment.

Methods: Using a cross-sectional survey design, a sample of breast cancer survivors from Western Washington who were 2, 5, and 10 years postdiagnosis were recruited via a cancer registry and interviewed about their HRQOL and their involvement in decision-making about their cancer treatment and follow-up care.

Main Outcome Measures: HRQOL was assessed using the SF-36.

Frequent search for sense by long-term breast cancer survivors associated with reduced HRQOL.

This study examined breast cancer survivors' reports of continuing efforts to make sense of their breast cancer experience and associations of such efforts with post-traumatic stress symptomology (PTSS) and long-term health related quality of life (HRQOL). A cross-sectional survey was conducted with 636 women in 2002-2003 two, five and ten years after their diagnosis of breast cancer. Only a minority of women with breast cancer reported frequently searching for sense in their cancer experience, or wondering "why did this happen to me?" two or more years after diagnosis.

Conceptualizing and measuring illness self-concept: a comparison with self-esteem and optimism in predicting fibromyalgia adjustment.

Illness self-concept (ISC), or the extent to which individuals are consumed by their illness, was theoretically described and evaluated with the Illness Self-Concept Scale (ISCS), a new 23-item scale, to predict adjustment in fibromyalgia. To establish convergent and discriminant validity, illness self-concept was compared to self-esteem and optimism in predicting health status, illness intrusiveness, depression, and life satisfaction. The ISCS demonstrated good reliability (alpha = .