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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 to 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. Controlling for key demographic and disease variables, greater involvement in search two, five and ten years post-diagnosis was associated with higher levels of PTSS (R2 = 0.26; p < 0.001) and with reduced HRQOL as measured using the SF-36 scale (p < 0.01 for all scales). It appears that continued efforts to search for sense in the cancer experience two or more years after cancer diagnosis are associated with PTSS and lower levels of HRQOL in long-term survivors of breast cancer. [ABSTRACT FROM AUTHOR]

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., Objective: The objective of this study was to leverage a comprehensive range of clinical, behavioral, social risk, and social determinants of health factors in order to develop decision models capable of identifying patients in need of various wraparound social services., Methods: We used comprehensive patient- and population-level data sets to build decision models capable of predicting need for behavioral health, dietitian, social work, or other social service referrals within a safety-net health system using area under the receiver operating characteristic curve (AUROC), sensitivity, precision, F1 score, and specificity. We also evaluated the value of population-level social determinants of health data sets in improving machine learning performance of the models., Results: Decision models for each wraparound service demonstrated performance measures ranging between 59.2%% and 99.3%. These results were statistically superior to the performance measures demonstrated by our previous models which used a limited data set and whose performance measures ranged from 38.2% to 88.3% (behavioural health: F1 score P<.001, AUROC P=.01; social work: F1 score P<.001, AUROC P=.03; dietitian: F1 score P=.001, AUROC P=.001; other: F1 score P=.01, AUROC P=.02); however, inclusion of additional population-level social determinants of health did not contribute to any performance improvements (behavioural health: F1 score P=.08, AUROC P=.09; social work: F1 score P=.16, AUROC P=.09; dietitian: F1 score P=.08, AUROC P=.14; other: F1 score P=.33, AUROC P=.21) in predicting the need for referral in our population of vulnerable patients seeking care at a safety-net provider., Conclusions: Precision health-enabled decision models that leverage a wide range of patient- and population-level data sets and advanced machine learning methods are capable of predicting need for various wraparound social services with good performance., (©Suranga N Kasthurirathne, Shaun Grannis, Paul K Halverson, Justin Morea, Nir Menachemi, Joshua R Vest. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 09.07.2020.)

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. We trained (n=250) and tested (n=500) an NLP-based program with 19 measurements that encompass colonoscopy quality measures and surveillance interval determination, using blinded, paired, annotated expert manual review as the reference standard. The remaining 41,819 nonannotated documents were processed through the NLP system without manual review to assess performance consistency. The primary outcome was system accuracy across the 19 measures., Results: A total of 176 (23.5%) documents with 252 (1.8%) discrepant content points resulted from paired annotation. Error rate within the 500 test documents was 31.2% for NLP and 25.4% for the paired annotators (P=0.001). At the content point level within the test set, the error rate was 3.5% for NLP and 1.9% for the paired annotators (P=0.04). When eight vaguely worded documents were removed, 125 of 492 (25.4%) were incorrect by NLP and 104 of 492 (21.1%) by the initial annotator (P=0.07). Rates of pathologic findings calculated from NLP were similar to those calculated by annotation for the majority of measurements. Test set accuracy was 99.6% for CRC, 95% for advanced adenoma, 94.6% for nonadvanced adenoma, 99.8% for advanced sessile serrated polyps, 99.2% for nonadvanced sessile serrated polyps, 96.8% for large hyperplastic polyps, and 96.0% for small hyperplastic polyps. Lesion location showed high accuracy (87.0-99.8%). Accuracy for number of adenomas was 92%., Conclusions: NLP can accurately report adenoma detection rate and the components for determining guideline-adherent colonoscopy surveillance intervals across multiple sites that utilize different methods for reporting colonoscopy findings.

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. We analyzed data from 10,798 reports, with 6379 linked to pathology results and 300 randomly selected reports. NLP-based CDS surveillance intervals were compared with those determined by paired, blinded, manual review. The primary outcome was adjusted agreement between manual review and the fully automated system., Results: κ statistical analysis produced a value of 0.74 (P < .001) for agreement between the full text annotation and the NLP-based CDS system. Fifty-five reports (18.3%; 95% confidence interval, 14.1%-23.2%) differed between manual review and CDS recommendations. Of these, NLP error accounted for 30 (54.5%), incomplete resection of adenomatous tissue accounted for 14 (25.5%), and masses observed without biopsy findings of cancer accounted for 4 (7.2%). NLP-based CDS surveillance intervals had higher levels of agreement with the standard (81.7%) than the level agreement between experts (72% agreement between paired reviewers)., Conclusions: A fully automated system that uses NLP and a guideline-based CSD system can accurately facilitate guideline-recommended adherence surveillance for colonoscopy., (Copyright © 2014 AGA Institute. Published by Elsevier Inc. All rights reserved.)

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., Materials and Methods: Applying a user-centered design process, the new Gopher was built upon a rich-internet application framework using an agile development process. The system incorporates order entry, clinical documentation, result viewing, decision support, and clinical workflow. We have customized its use for the outpatient, inpatient, and emergency department settings., Results: The new Gopher is now in use by over 1100 users a day, including an average of 433 physicians caring for over 3600 patients daily. The system includes a wizard-like clinical workflow, dynamic multimedia alerts, and a familiar 'e-commerce'-based interface for order entry. Clinical documentation is enhanced by real-time natural language processing and data review is supported by a rapid chart search feature., Discussion: As one of the few remaining academically developed order entry systems, the Gopher has been designed both to improve patient care and to support next-generation informatics research. It has achieved rapid adoption within our health system and suggests continued viability for homegrown systems in settings of close collaboration between developers and providers., (Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.)

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. The primary outcome was the highest level of pathology. Secondary outcomes were location of the most advanced lesion, largest size of an adenoma removed, and number of adenomas removed., Results: The NLP system identified the highest level of pathology with 98% accuracy, compared with triplicate annotation by gastroenterologists (the standard). Accuracy values for location, size, and number were 97%, 96%, and 84%, respectively., Conclusions: The NLP can extract specific meaningful concepts with 98% accuracy. It might be developed as a method to further quantify specific quality metrics., (Copyright © 2013 AGA Institute. Published by Elsevier Inc. All rights reserved.)

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., Objectives: We aimed to design a medication reconciliation application that could incorporate multiple data sources and convey information about patients' adherence to prescribed medications. We discuss design decisions integral to developing medication reconciliation applications for the electronic health record. The discussion is relevant for health IT developers, clinical providers, administrators, policy makers, and patients. Three hypotheses drove our design of this application: 1) Medication information comes from a variety of sources, each having benefits and limitations; 2) improvements in patient safety can result from reducing the cognitive burden and time required to identify medication changes; 3) a well-designed user interface can facilitate clinicians' understanding and clinical decision making., Methods: Relying on evidence about interface design and medication reconciliation, an application for the electronic health record at an academic medical center in the U.S. was designed. Multiple decisions that considered the availability, value, and display of the medication data are explored: Information from different sources; interval changes in medications; the sorting of information; and the user interface., Results: THE PROTOTYPE MEDICATION RECONCILIATION APPLICATION DESIGN REFLECTS THE VISUAL ORGANIZATION, CATEGORIZATION, MODALITY OF INTERACTIONS, AND PRESENTATION OF MEDICATION INFORMATION FROM THREE DATA SOURCES: patient, electronic health record, and pharmacy., Conclusions: A new medication reconciliation user interface displays information from multiple sources, indicates discrepancies among sources, displays information about adherence, and sorts the medication list in a useful display for clinical decision making. Gathering, verifying, and updating medication data are resource-intensive processes. The outcomes of integrating, interpreting, and presenting medication information from multiple sources remain to be studied.

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. (2) FM domain outcome measures have generally proven to be reliable, discriminative, and feasible. More sophisticated and comprehensive measures are in development, as is a responder index for FM. (3) Increasing numbers of objective markers are being developed for FM assessment. (4) Cognitive dysfunction assessment by self-assessed and applied outcome measures is being developed. In conclusion, a multidimensional symptom core set is proposed for evaluation of FM in clinical trials. Research on improved measures of single domains and composite measures is ongoing.

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., Results: Multiple regression analyses examining demographic and disease characteristics revealed age, and education, but not stage of cancer at diagnosis, to be significant predictors of perceived involvement in decision-making about cancer treatment and follow-up. Controlling for demographic and disease characteristics, perceived involvement in decision-making about treatment overall, surgery, chemotherapeutic treatment, and follow-up care were each associated with improved HRQOL, including the general health and vitality subscales of the SF-36 (p < .05). Involvement in decision-making about surgery was also associated with better mental health among survivors of breast cancer. Congruence of involvement in decision-making with a patient's preferred level of involvement was also associated with improved survivor HRQOL on several subscales., Conclusions: Perceived involvement in decision-making about breast cancer treatment, and about follow-up care is associated with better HRQOL for survivors 2, 5, and 10 years postdiagnosis. Prospective studies may be warranted to determine the possible mechanisms by which perceived involvement in decision-making about aspects of treatment other than surgery type might influence survivor HRQOL., ((c) 2009 APA, all rights reserved.)