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Brunette CA, Yi T, Danowski ME, Cardellino M, Harrison A, Assimes TL, Knowles JW, Christensen KD, Sturm AC, Sun YV, Hui Q, Pyarajan S, Shi Y, Whitbourne SB, Gaziano JM, Muralidhar S, Vassy JL. Development and utility of a clinical research informatics application for participant recruitment and workflow management for a return of results pilot trial in familial hypercholesterolemia in the Million Veteran Program. JAMIA Open 2024; 7:ooae020. [PMID: 38464744 PMCID: PMC10923213 DOI: 10.1093/jamiaopen/ooae020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 06/26/2023] [Accepted: 02/14/2024] [Indexed: 03/12/2024] Open
Abstract
Objective The development of clinical research informatics tools and workflow processes associated with re-engaging biobank participants has become necessary as genomic repositories increasingly consider the return of actionable research results. Materials and Methods Here we describe the development and utility of an informatics application for participant recruitment and enrollment management for the Veterans Affairs Million Veteran Program Return Of Actionable Results Study, a randomized controlled pilot trial returning individual genetic results associated with familial hypercholesterolemia. Results The application is developed in Python-Flask and was placed into production in November 2021. The application includes modules for chart review, medication reconciliation, participant contact and biospecimen logging, survey recording, randomization, and documentation of genetic counseling and result disclosure. Three primary users, a genetic counselor and two research coordinators, and 326 Veteran participants have been integrated into the system as of February 23, 2023. The application has successfully handled 3367 task requests involving greater than 95 000 structured data points. Specifically, application users have recorded 326 chart reviews, 867 recruitment telephone calls, 158 telephone-based surveys, and 61 return of results genetic counseling sessions, among other available study tasks. Conclusion The development of usable, customizable, and secure informatics tools will become increasingly important as large genomic repositories begin to return research results at scale. Our work provides a proof-of-concept for developing and using such tools to aid in managing the return of results process within a national biobank.
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Affiliation(s)
- Charles A Brunette
- Veterans Affairs Boston Healthcare System, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Thomas Yi
- Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - Morgan E Danowski
- Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - Mark Cardellino
- Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - Alicia Harrison
- Genetic Counseling Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Themistocles L Assimes
- VA Palo Alto Health Care System, Palo Alto, CA, United States
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
- Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, United States
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Joshua W Knowles
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
- Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, United States
- Family Heart Foundation, Pasadena, CA, United States
| | - Kurt D Christensen
- PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, United States
- Department of Population Medicine, Harvard Medical School, Boston, MA, United States
| | | | - Yan V Sun
- Atlanta VA Health Care System, Decatur, GA, United States
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, United States
| | - Qin Hui
- Atlanta VA Health Care System, Decatur, GA, United States
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, United States
| | - Saiju Pyarajan
- Veterans Affairs Boston Healthcare System, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Yunling Shi
- Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - Stacey B Whitbourne
- Veterans Affairs Boston Healthcare System, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
- Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - J Michael Gaziano
- Veterans Affairs Boston Healthcare System, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
- Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Sumitra Muralidhar
- Office of Research and Development, Veterans Health Administration, Washington, DC, United States
| | - Jason L Vassy
- Veterans Affairs Boston Healthcare System, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
- Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, United States
- Population Precision Health, Ariadne Labs, Boston, MA, United States
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2
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Bichel-Findlay J, Koch S, Mantas J, Abdul SS, Al-Shorbaji N, Ammenwerth E, Baum A, Borycki EM, Demiris G, Hasman A, Hersh W, Hovenga E, Huebner UH, Huesing ES, Kushniruk A, Hwa Lee K, Lehmann CU, Lillehaug SI, Marin HF, Marschollek M, Martin-Sanchez F, Merolli M, Nishimwe A, Saranto K, Sent D, Shachak A, Udayasankaran JG, Were MC, Wright G. Recommendations of the International Medical Informatics Association (IMIA) on Education in Biomedical and Health Informatics: Second Revision. Int J Med Inform 2023; 170:104908. [PMID: 36502741 DOI: 10.1016/j.ijmedinf.2022.104908] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 10/23/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND The purpose of educational recommendations is to assist in establishing courses and programs in a discipline, to further develop existing educational activities in the various nations, and to support international initiatives for collaboration and sharing of courseware. The International Medical Informatics Association (IMIA) has published two versions of its international recommendations in biomedical and health informatics (BMHI) education, initially in 2000 and revised in 2010. Given the recent changes to the science, technology, the needs of the healthcare systems, and the workforce of BMHI, a revision of the recommendations is necessary. OBJECTIVE The aim of these updated recommendations is to support educators in developing BMHI curricula at different education levels, to identify essential skills and competencies for certification of healthcare professionals and those working in the field of BMHI, to provide a tool for evaluators of academic BMHI programs to compare and accredit the quality of delivered programs, and to motivate universities, organizations, and health authorities to recognize the need for establishing and further developing BMHI educational programs. METHOD An IMIA taskforce, established in 2017, updated the recommendations. The taskforce included representatives from all IMIA regions, with several having been involved in the development of the previous version. Workshops were held at different IMIA conferences, and an international Delphi study was performed to collect expert input on new and revised competencies. RESULTS Recommendations are provided for courses/course tracks in BMHI as part of educational programs in biomedical and health sciences, health information management, and informatics/computer science, as well as for dedicated programs in BMHI (leading to bachelor's, master's, or doctoral degree). The educational needs are described for the roles of BMHI user, BMHI generalist, and BMHI specialist across six domain areas - BMHI core principles; health sciences and services; computer, data and information sciences; social and behavioral sciences; management science; and BMHI specialization. Furthermore, recommendations are provided for dedicated educational programs in BMHI at the level of bachelor's, master's, and doctoral degrees. These are the mainstream academic programs in BMHI. In addition, recommendations for continuing education, certification, and accreditation procedures are provided. CONCLUSION The IMIA recommendations reflect societal changes related to globalization, digitalization, and digital transformation in general and in healthcare specifically, and center on educational needs for the healthcare workforce, computer scientists, and decision makers to acquire BMHI knowledge and skills at various levels. To support education in BMHI, IMIA offers accreditation of quality BMHI education programs. It supports information exchange on programs and courses in BMHI through its Working Group on Health and Medical Informatics Education.
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Affiliation(s)
| | - Sabine Koch
- Health Informatics Centre, Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Sweden
| | - John Mantas
- Health Informatics Lab, School of Health Sciences, National and Kapodistrian University of Athens, Greece
| | - Shabbir S Abdul
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taiwan
| | | | - Elske Ammenwerth
- UMIT - Private University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria
| | - Analia Baum
- Hospital Italiano de Buenos Aires, Health Informatics Department, Argentina
| | | | - George Demiris
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Arie Hasman
- Department of Medical Informatics Amsterdam UMC, location AMC, The Netherlands
| | - William Hersh
- Department of Medical Informatics & Clinical Epidemiology, School of Medicine, Oregon Health & Science University, United States
| | - Evelyn Hovenga
- Digital Health, Australian Catholic University, Australia
| | - Ursula H Huebner
- Hochschule Osnabrueck - University AS Osnabrueck, Department of Business Management and Social Sciences, Germany
| | | | - Andre Kushniruk
- School of Health Information Science, University of Victoria, Canada
| | - Kye Hwa Lee
- Department of Information Medicine, Asan Medical Center and University of Ulsan College of Medicine, South Korea
| | - Christoph U Lehmann
- Clinical Informatics Center, University of Texas Southwestern Medical Center, United States
| | | | | | - Michael Marschollek
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Germany
| | | | - Mark Merolli
- Department of Physiotherapy, School of Health Sciences, Centre for Health, Exercise and Sports Medicine, Centre for Digital Transformation of Health, The University of Melbourne, Australia
| | - Aurore Nishimwe
- Health Informatics Program, College of Medicine and Health Sciences, University of Rwanda, Rwanda
| | - Kaija Saranto
- Health and Human Services Informatics, University of Eastern Finland, Finland
| | - Danielle Sent
- Department of Medical Informatics Amsterdam UMC, location AMC, The Netherlands
| | - Aviv Shachak
- Institute of Health Policy, Management and Evaluation (Dalla Lana School of Public Health), University of Toronto, Canada
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Williams TB, Schmidtke C, Roessger K, Dieffenderfer V, Garza M, Zozus M. Informing training needs for the revised certified clinical data manager (CCDMTM) exam: analysis of results from the previous exam. JAMIA Open 2022; 5:ooac010. [PMID: 35274085 PMCID: PMC8903177 DOI: 10.1093/jamiaopen/ooac010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 11/29/2021] [Accepted: 02/08/2022] [Indexed: 11/12/2022] Open
Abstract
Objective To inform training needs for the revised Certified Clinical Data Manager (CCDMTM) Exam. Introduction Clinical data managers hold the responsibility for processing the data on which research conclusions and regulatory decisions are based, highlighting the importance of applying effective data management practices. The use of practice standards such as the Good Clinical Data Management Practices increases confidence in data, emphasizing that the study conclusions likely hold much more weight when utilizing standard practices. Methods A quantitative, descriptive study, and application of classic test theory was undertaken to analyze past data from the CCDMTM Exam to identify potential training needs. Data across 952 sequential exam attempts were pooled for analysis. Results Competency domain-level analysis identified training needs in 4 areas: design tasks; data processing tasks; programming tasks; and coordination and management tasks. Conclusions Analysis of past CCDMTM Exam results using classic test theory identified training needs reflective of exam takers. Training in the identified areas could benefit CCDMTM Exam takers and improve their ability to apply effective data management practices. While this may not be reflective of individual or organizational needs, recommendations for assessing individual and organizational training needs are provided. This study analyzed past data from the Certified Clinical Data ManagerTM Exam (CCDMTM) to identify potential training needs. Data across 952 sequential exam attempts were pooled for analysis. The study identified training needs in 4 areas of clinical data management: design tasks; data processing tasks; programming tasks; and coordination and management tasks. Training in the 4 identified areas could benefit CCDMTM Exam takers and improve their ability to apply effective data management practices.
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Affiliation(s)
- Tremaine Brueon Williams
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Carsten Schmidtke
- Department of Human Resource & Workforce Development, University of Arkansas, Fayetteville, Arkansas, USA
| | - Kevin Roessger
- Department of Adult and Lifelong Learning, University of Arkansas, Fayetteville, Arkansas, USA
| | - Vicki Dieffenderfer
- Department of Human Resource & Workforce Development, University of Arkansas, Fayetteville, Arkansas, USA
| | - Maryam Garza
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Meredith Zozus
- Department of Population Health Sciences, Joe R. and Teresa Lozano Long School of Medicine, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA
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Zhao Y, Fu S, Bielinski SJ, Decker PA, Chamberlain AM, Roger VL, Liu H, Larson NB. Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation. J Med Internet Res 2021; 23:e22951. [PMID: 33683212 PMCID: PMC7985804 DOI: 10.2196/22951] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 08/25/2020] [Accepted: 01/20/2021] [Indexed: 11/29/2022] Open
Abstract
Background Stroke is an important clinical outcome in cardiovascular research. However, the ascertainment of incident stroke is typically accomplished via time-consuming manual chart abstraction. Current phenotyping efforts using electronic health records for stroke focus on case ascertainment rather than incident disease, which requires knowledge of the temporal sequence of events. Objective The aim of this study was to develop a machine learning–based phenotyping algorithm for incident stroke ascertainment based on diagnosis codes, procedure codes, and clinical concepts extracted from clinical notes using natural language processing. Methods The algorithm was trained and validated using an existing epidemiology cohort consisting of 4914 patients with atrial fibrillation (AF) with manually curated incident stroke events. Various combinations of feature sets and machine learning classifiers were compared. Using a heuristic rule based on the composition of concepts and codes, we further detected the stroke subtype (ischemic stroke/transient ischemic attack or hemorrhagic stroke) of each identified stroke. The algorithm was further validated using a cohort (n=150) stratified sampled from a population in Olmsted County, Minnesota (N=74,314). Results Among the 4914 patients with AF, 740 had validated incident stroke events. The best-performing stroke phenotyping algorithm used clinical concepts, diagnosis codes, and procedure codes as features in a random forest classifier. Among patients with stroke codes in the general population sample, the best-performing model achieved a positive predictive value of 86% (43/50; 95% CI 0.74-0.93) and a negative predictive value of 96% (96/100). For subtype identification, we achieved an accuracy of 83% in the AF cohort and 80% in the general population sample. Conclusions We developed and validated a machine learning–based algorithm that performed well for identifying incident stroke and for determining type of stroke. The algorithm also performed well on a sample from a general population, further demonstrating its generalizability and potential for adoption by other institutions.
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Affiliation(s)
- Yiqing Zhao
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Sunyang Fu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Suzette J Bielinski
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Paul A Decker
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Alanna M Chamberlain
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Veronique L Roger
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Hongfang Liu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Nicholas B Larson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
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5
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Bakken S. The maturation of clinical research informatics as a subdomain of biomedical informatics. J Am Med Inform Assoc 2021; 28:1-2. [PMID: 33450764 DOI: 10.1093/jamia/ocaa312] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 11/23/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Suzanne Bakken
- School of Nursing, Columbia University, New York, New York, USA.,Department of Biomedical Informatics, Columbia University, New York, New York, USA.,Data Science Institute, Columbia University, New York, New York, USA
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6
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Zakaria N, Wahabi H, Qahtani MA. Development and usability testing of Riyadh Mother and Baby Multi-center cohort study registry. J Infect Public Health 2020; 13:1473-1480. [PMID: 32402648 DOI: 10.1016/j.jiph.2020.02.035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 02/19/2020] [Accepted: 02/24/2020] [Indexed: 11/27/2022] Open
Abstract
RATIONAL AND OBJECTIVES A medical registry is a software application that gather and keep clinical and non-clinical data to serve as recording tool for a specific disease longitudinally. In this study, Riyadh Mother and Baby Multicenter was designed as longitudinal study to understand the effect of non-communicable disease on mothers and their babies. A registry was built for the study; to improve data collection process thus enhance the data analysis and to enhance quality of healthcare provided by timely improvement of the services. The objective of this study is to test the usability of the cohort registry developed for clinical research and service improvement. METHODS Think aloud method, a qualitative approach was employed to elicit behaviors of participants while interacting with the registry interface prototype while the focus group session was conducted in order to understand the participants' insights and how participants reach consensus on the functionality and user interface design. Both deductive and inductive thematic analysis were performed on the qualitative data. After two iterative design cycles, improvements were made to the registry prototype. RESULTS The registry was found to be efficient, easy to learn, satisfactory, and easy to remember, and resulted in fewer errors. Major design features such as font size and colors were improved based on participants' feedback. In addition to the tested attributes, additional themes of design and benefits were found inductively. CONCLUSION Usability testing of the cohort registry showed that the system was easy to use due to its simple and custom- made design. Improvements of the registry based on the participants' feedback helped in enhancing its usability attributes.
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Affiliation(s)
- Nasriah Zakaria
- Medical Informatics and e-learning Unit, Medical Education Department, College of Medicine, King Saud University, Riyadh 11461, Saudi Arabia; The Research Chair for Health Informatics and Health Promotion, King Saud University, Riyadh 11461, Saudi Arabia.
| | - Hayfaa Wahabi
- Research Chair of Evidence-Based Healthcare and Knowledge Translation, Deanship of Research, King Saud University, Riyadh 11461, Saudi Arabia; Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh 11461, Saudi Arabia
| | - Mohammed Al Qahtani
- Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh 11461, Saudi Arabia
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The REDCap consortium: Building an international community of software platform partners. J Biomed Inform 2019. [PMID: 31078660 DOI: 10.1016/j.jbi.2019.103208.] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.
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Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, McLeod L, Delacqua G, Delacqua F, Kirby J, Duda SN. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform 2019; 95:103208. [PMID: 31078660 DOI: 10.1016/j.jbi.2019.103208] [Citation(s) in RCA: 11482] [Impact Index Per Article: 2296.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 04/10/2019] [Accepted: 05/07/2019] [Indexed: 02/06/2023]
Abstract
The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.
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Affiliation(s)
- Paul A Harris
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Robert Taylor
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Brenda L Minor
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Veida Elliott
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michelle Fernandez
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lindsay O'Neal
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Laura McLeod
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Giovanni Delacqua
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Francesco Delacqua
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jacqueline Kirby
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephany N Duda
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
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Complexities, variations, and errors of numbering within clinical notes: the potential impact on information extraction and cohort-identification. BMC Med Inform Decis Mak 2019; 19:75. [PMID: 30944012 PMCID: PMC6448181 DOI: 10.1186/s12911-019-0784-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Numbers and numerical concepts appear frequently in free text clinical notes from electronic health records. Knowledge of the frequent lexical variations of these numerical concepts, and their accurate identification, is important for many information extraction tasks. This paper describes an analysis of the variation in how numbers and numerical concepts are represented in clinical notes. METHODS We used an inverted index of approximately 100 million notes to obtain the frequency of various permutations of numbers and numerical concepts, including the use of Roman numerals, numbers spelled as English words, and invalid dates, among others. Overall, twelve types of lexical variants were analyzed. RESULTS We found substantial variation in how these concepts were represented in the notes, including multiple data quality issues. We also demonstrate that not considering these variations could have substantial real-world implications for cohort identification tasks, with one case missing > 80% of potential patients. CONCLUSIONS Numbering within clinical notes can be variable, and not taking these variations into account could result in missing or inaccurate information for natural language processing and information retrieval tasks.
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Lin FPY, Pokorny A, Teng C, Epstein RJ. TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records. Sci Rep 2017; 7:6918. [PMID: 28761061 PMCID: PMC5537364 DOI: 10.1038/s41598-017-07111-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Accepted: 06/21/2017] [Indexed: 12/13/2022] Open
Abstract
Vast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR). To exploit this untapped resource, and thus facilitate the discovery of informative covariates from unstructured clinical narratives, we have built a novel computational pipeline termed Text-based Exploratory Pattern Analyser for Prognosticator and Associator discovery (TEPAPA). This pipeline combines semantic-free natural language processing (NLP), regular expression induction, and statistical association testing to identify conserved text patterns associated with outcome variables of clinical interest. When we applied TEPAPA to a cohort of head and neck squamous cell carcinoma patients, plausible concepts known to be correlated with human papilloma virus (HPV) status were identified from the EMR text, including site of primary disease, tumour stage, pathologic characteristics, and treatment modalities. Similarly, correlates of other variables (including gender, nodal status, recurrent disease, smoking and alcohol status) were also reliably recovered. Using highly-associated patterns as covariates, a patient's HPV status was classifiable using a bootstrap analysis with a mean area under the ROC curve of 0.861, suggesting its predictive utility in supporting EMR-based phenotyping tasks. These data support using this integrative approach to efficiently identify disease-associated factors from unstructured EMR narratives, and thus to efficiently generate testable hypotheses.
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Affiliation(s)
- Frank Po-Yen Lin
- Department of Oncology, St Vincent's Hospital & The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia.
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.
| | - Adrian Pokorny
- Department of Oncology, St Vincent's Hospital & The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
| | - Christina Teng
- Department of Medical Oncology, Liverpool Hospital, Liverpool, Sydney, NSW, Australia
| | - Richard J Epstein
- Department of Oncology, St Vincent's Hospital & The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
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Weng C, Kahn MG. Clinical Research Informatics for Big Data and Precision Medicine. Yearb Med Inform 2016:211-218. [PMID: 27830253 DOI: 10.15265/iy-2016-019] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVES To reflect on the notable events and significant developments in Clinical Research Informatics (CRI) in the year of 2015 and discuss near-term trends impacting CRI. METHODS We selected key publications that highlight not only important recent advances in CRI but also notable events likely to have significant impact on CRI activities over the next few years or longer, and consulted the discussions in relevant scientific communities and an online living textbook for modern clinical trials. We also related the new concepts with old problems to improve the continuity of CRI research. RESULTS The highlights in CRI in 2015 include the growing adoption of electronic health records (EHR), the rapid development of regional, national, and global clinical data research networks for using EHR data to integrate scalable clinical research with clinical care and generate robust medical evidence. Data quality, integration, and fusion, data access by researchers, study transparency, results reproducibility, and infrastructure sustainability are persistent challenges. CONCLUSION The advances in Big Data Analytics and Internet technologies together with the engagement of citizens in sciences are shaping the global clinical research enterprise, which is getting more open and increasingly stakeholder-centered, where stakeholders include patients, clinicians, researchers, and sponsors.
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Affiliation(s)
- C Weng
- Chunhua Weng, PhD, FACMI, Department of Biomedical Informatics, Columbia University, 622 W 168 Street, PH-20, New York, NY 10032, USA, E-mail:
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Randell R, Backhouse MR, Nelson EA. Videoconferencing for site initiations in clinical studies: Mixed methods evaluation of usability, acceptability, and impact on recruitment. Inform Health Soc Care 2015; 41:362-72. [PMID: 26694583 DOI: 10.3109/17538157.2015.1064424] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND A critical issue for multicentre clinical studies is conducting site initiations, ensuring sites are trained in study procedures and comply with relevant governance requirements before they begin recruiting patients. How technology can support site initiations has not previously been explored. OBJECTIVE This study sought to evaluate use of off-the-shelf web-based videoconferencing to deliver site initiations for a large national multicentre study. METHODS Participants in the initiations, including podiatrists, diabetologists, trial coordinators, and research nurses, completed an online questionnaire based on the System Usability Scale (SUS) (N = 15). This was followed by semi-structured interviews, with a consultant diabetologist, a trial coordinator, and three research nurses, exploring perceived benefits and limitations of videoconferencing. RESULTS The mean SUS score for the videoconferencing platform was 87.2 (SD = 13.7), suggesting a good level of usability. Interview participants perceived initiations delivered by videoconferencing as being more interactive and easier to follow than those delivered by teleconference. In comparison to face-to-face initiations, videoconferencing takes less time, easily fitting in with the work of staff at the local sites. Perceptions of impact on communication varied according to the hardware used. CONCLUSION Off-the-shelf videoconferencing is a viable alternative to face-to-face site initiations and confers advantages over teleconferencing.
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Affiliation(s)
- Rebecca Randell
- a School of Healthcare, Baines Wing , University of Leeds , Leeds , UK
| | - Michael R Backhouse
- b Leeds Institute of Rheumatic and Musculoskeletal Medicine , University of Leeds , Chapel Allerton Hospital, Chapeltown Road, Leeds , UK
| | - E Andrea Nelson
- a School of Healthcare, Baines Wing , University of Leeds , Leeds , UK
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13
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Garcí;a-de-León-Chocano R, Sáez C, Muñoz-Soler V, Garcí;a-de-León-González R, García-Gómez JM. Construction of quality-assured infant feeding process of care data repositories: definition and design (Part 1). Comput Biol Med 2015; 67:95-103. [DOI: 10.1016/j.compbiomed.2015.09.024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Revised: 09/24/2015] [Accepted: 09/25/2015] [Indexed: 10/22/2022]
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14
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Kurc T, Qi X, Wang D, Wang F, Teodoro G, Cooper L, Nalisnik M, Yang L, Saltz J, Foran DJ. Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies. BMC Bioinformatics 2015; 16:399. [PMID: 26627175 PMCID: PMC4667532 DOI: 10.1186/s12859-015-0831-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Accepted: 11/16/2015] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND We describe a suite of tools and methods that form a core set of capabilities for researchers and clinical investigators to evaluate multiple analytical pipelines and quantify sensitivity and variability of the results while conducting large-scale studies in investigative pathology and oncology. The overarching objective of the current investigation is to address the challenges of large data sizes and high computational demands. RESULTS The proposed tools and methods take advantage of state-of-the-art parallel machines and efficient content-based image searching strategies. The content based image retrieval (CBIR) algorithms can quickly detect and retrieve image patches similar to a query patch using a hierarchical analysis approach. The analysis component based on high performance computing can carry out consensus clustering on 500,000 data points using a large shared memory system. CONCLUSIONS Our work demonstrates efficient CBIR algorithms and high performance computing can be leveraged for efficient analysis of large microscopy images to meet the challenges of clinically salient applications in pathology. These technologies enable researchers and clinical investigators to make more effective use of the rich informational content contained within digitized microscopy specimens.
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Affiliation(s)
- Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, USA.
| | - Xin Qi
- Department of Pathology & Laboratory Medicine, Rutgers -- Robert Wood Johnson Medical School, New Brunswick, USA.
- Rutgers Cancer Institute of New Jersey, New Brunswick, USA.
| | - Daihou Wang
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, USA.
| | - Fusheng Wang
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, USA.
- Department of Computer Science, Stony Brook University, Stony Brook, USA.
| | - George Teodoro
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, USA.
- Department of Computer Science, University of Brasilia, Brasília, Brazil.
| | - Lee Cooper
- Department of Biomedical Informatics, Emory University, Atlanta, USA.
| | - Michael Nalisnik
- Department of Biomedical Informatics, Emory University, Atlanta, USA.
| | - Lin Yang
- Department of Biomedical Engineering, University of Florida, Gainesville, USA.
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, USA.
| | - David J Foran
- Department of Pathology & Laboratory Medicine, Rutgers -- Robert Wood Johnson Medical School, New Brunswick, USA.
- Rutgers Cancer Institute of New Jersey, New Brunswick, USA.
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15
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Daniel C, Choquet R. Information Technology for Clinical, Translational and Comparative Effectiveness Research. Findings from the Yearbook 2015 Section on Clinical Research Informatics. Yearb Med Inform 2015; 10:178-82. [PMID: 26293866 DOI: 10.15265/iy-2015-030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVES To summarize excellent current research in the field of Bioinformatics and Translational Informatics with application in the health domain and clinical care. METHOD We provide a synopsis of the articles selected for the IMIA Yearbook 2015, from which we attempt to derive a synthetic overview of current and future activities in the field. As last year, a first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section. Each section editor has evaluated separately the set of 1,594 articles and the evaluation results were merged for retaining 15 articles for peer-review. RESULTS The selection and evaluation process of this Yearbook's section on Bioinformatics and Translational Informatics yielded four excellent articles regarding data management and genome medicine that are mainly tool-based papers. In the first article, the authors present PPISURV a tool for uncovering the role of specific genes in cancer survival outcome. The second article describes the classifier PredictSNP which combines six performing tools for predicting disease-related mutations. In the third article, by presenting a high-coverage map of the human proteome using high resolution mass spectrometry, the authors highlight the need for using mass spectrometry to complement genome annotation. The fourth article is also related to patient survival and decision support. The authors present datamining methods of large-scale datasets of past transplants. The objective is to identify chances of survival. CONCLUSIONS The current research activities still attest the continuous convergence of Bioinformatics and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care. Indeed, there is a need for powerful tools for managing and interpreting complex, large-scale genomic and biological datasets, but also a need for user-friendly tools developed for the clinicians in their daily practice. All the recent research and development efforts contribute to the challenge of impacting clinically the obtained results towards a personalized medicine.
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Affiliation(s)
- C Daniel
- Christel Daniel, MD, PhD, INSERM UMRS 1142, CCS Patient - Assistance Publique - Hôpitaux de Paris, 05 rue Santerre - 75 012 PARIS, France, Tel: +33 1 48 04 20 29, E-mail:
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16
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He S, Botkin JR, Hurdle JF. An Analysis of Information Technology Adoption by IRBs of Large Academic Medical Centers in the United States. J Empir Res Hum Res Ethics 2014; 10:31-6. [DOI: 10.1177/1556264614560146] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The clinical research landscape has changed dramatically in recent years in terms of both volume and complexity. This poses new challenges for Institutional Review Boards’ (IRBs) review efficiency and quality, especially at large academic medical centers. This article discusses the technical facets of IRB modernization. We analyzed the information technology used by IRBs in large academic institutions across the United States. We found that large academic medical centers have a high electronic IRB adoption rate; however, the capabilities of electronic IRB systems vary greatly. We discuss potential use-cases of a fully exploited electronic IRB system that promise to streamline the clinical research work flow. The key to that approach utilizes a structured and standardized information model for the IRB application.
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Affiliation(s)
- Shan He
- Intermountain Healthcare, Salt Lake City, UT, USA
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17
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He S, Narus SP, Facelli JC, Lau LM, Botkin JR, Hurdle JF. A domain analysis model for eIRB systems: addressing the weak link in clinical research informatics. J Biomed Inform 2014; 52:121-9. [PMID: 24929181 PMCID: PMC4384433 DOI: 10.1016/j.jbi.2014.05.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2013] [Revised: 02/20/2014] [Accepted: 05/06/2014] [Indexed: 10/25/2022]
Abstract
Institutional Review Boards (IRBs) are a critical component of clinical research and can become a significant bottleneck due to the dramatic increase, in both volume and complexity of clinical research. Despite the interest in developing clinical research informatics (CRI) systems and supporting data standards to increase clinical research efficiency and interoperability, informatics research in the IRB domain has not attracted much attention in the scientific community. The lack of standardized and structured application forms across different IRBs causes inefficient and inconsistent proposal reviews and cumbersome workflows. These issues are even more prominent in multi-institutional clinical research that is rapidly becoming the norm. This paper proposes and evaluates a domain analysis model for electronic IRB (eIRB) systems, paving the way for streamlined clinical research workflow via integration with other CRI systems and improved IRB application throughput via computer-assisted decision support.
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Affiliation(s)
- Shan He
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
| | - Scott P Narus
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Intermountain Medical Center, Intermountain Healthcare, Murray, UT, USA
| | - Julio C Facelli
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Lee Min Lau
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; 3M Health Information Systems, Murray, UT, USA
| | - Jefferey R Botkin
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - John F Hurdle
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
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18
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Luo J, Apperson-Hansen C, Pelfrey CM, Zhang GQ. RMS: a platform for managing cross-disciplinary and multi-institutional research project collaboration. BMC Med Inform Decis Mak 2014; 14:106. [PMID: 25433526 PMCID: PMC4264263 DOI: 10.1186/s12911-014-0106-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2014] [Accepted: 11/03/2014] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Cross-institutional cross-disciplinary collaboration has become a trend as researchers move toward building more productive and innovative teams for scientific research. Research collaboration is significantly changing the organizational structure and strategies used in the clinical and translational science domain. However, due to the obstacles of diverse administrative structures, differences in area of expertise, and communication barriers, establishing and managing a cross-institutional research project is still a challenging task. We address these challenges by creating an integrated informatics platform to reduce the barriers to biomedical research collaboration. RESULTS The Request Management System (RMS) is an informatics infrastructure designed to transform a patchwork of expertise and resources into an integrated support network. The RMS facilitates investigators' initiation of new collaborative projects and supports the management of the collaboration process. In RMS, experts and their knowledge areas are categorized and managed structurally to provide consistent service. A role-based collaborative workflow is tightly integrated with domain experts and services to streamline and monitor the life-cycle of a research project. The RMS has so far tracked over 1,500 investigators with over 4,800 tasks. The research network based on the data collected in RMS illustrated that the investigators' collaborative projects increased close to 3 times from 2009 to 2012. Our experience with RMS indicates that the platform reduces barriers for cross-institutional collaboration of biomedical research projects. CONCLUSION Building a new generation of infrastructure to enhance cross-disciplinary and multi-institutional collaboration has become an important yet challenging task. In this paper, we share the experience of developing and utilizing a collaborative project management system. The results of this study demonstrate that a web-based integrated informatics platform can facilitate and increase research interactions among investigators.
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Affiliation(s)
- Jake Luo
- />Center of Biomedical Data and Language Processing, University of Wisconsin Milwaukee, 2025 E Newport Avenue, Northwestern Quadrant-B, Room 6469, Milwaukee, Wisconsin 53211 USA
- />School of Medicine, Case Western Reserve University, Cleveland, Ohio USA
| | - Carolyn Apperson-Hansen
- />Center for Clinical Investigation, BRB 109, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106 USA
- />School of Medicine, Case Western Reserve University, Cleveland, Ohio USA
| | - Clara M Pelfrey
- />Center for Clinical Investigation, BRB 109, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106 USA
- />School of Medicine, Case Western Reserve University, Cleveland, Ohio USA
| | - Guo-Qiang Zhang
- />Division of Medical Informatics, 2103 Cornell Road, Wolstein Research Building, Room 6128, Ohio, 44106 USA
- />School of Medicine, Case Western Reserve University, Cleveland, Ohio USA
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19
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Ohno-Machado L. Disseminating informatics knowledge and training the next generation of leaders. J Am Med Inform Assoc 2014; 21:954-6. [DOI: 10.1136/amiajnl-2014-noveditorial] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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20
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Wachs JP, Frenkel B, Dori D. Operation room tool handling and miscommunication scenarios: an object-process methodology conceptual model. Artif Intell Med 2014; 62:153-63. [PMID: 25466935 DOI: 10.1016/j.artmed.2014.10.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2013] [Revised: 10/15/2014] [Accepted: 10/23/2014] [Indexed: 11/19/2022]
Abstract
OBJECTIVE Errors in the delivery of medical care are the principal cause of inpatient mortality and morbidity, accounting for around 98,000 deaths in the United States of America (USA) annually. Ineffective team communication, especially in the operation room (OR), is a major root of these errors. This miscommunication can be reduced by analyzing and constructing a conceptual model of communication and miscommunication in the OR. We introduce the principles underlying Object-Process Methodology (OPM)-based modeling of the intricate interactions between the surgeon and the surgical technician while handling surgical instruments in the OR. This model is a software- and hardware-independent description of the agents engaged in communication events, their physical activities, and their interactions. The model enables assessing whether the task-related objectives of the surgical procedure were achieved and completed successfully and what errors can occur during the communication. METHODS AND MATERIAL The facts used to construct the model were gathered from observations of various types of operations miscommunications in the operating room and its outcomes. The model takes advantage of the compact ontology of OPM, which is comprised of stateful objects - things that exist physically or informatically, and processes - things that transform objects by creating them, consuming them or changing their state. The modeled communication modalities are verbal and non-verbal, and errors are modeled as processes that deviate from the "sunny day" scenario. Using OPM refinement mechanism of in-zooming, key processes are drilled into and elaborated, along with the objects that are required as agents or instruments, or objects that these processes transform. The model was developed through an iterative process of observation, modeling, group discussions, and simplification. RESULTS The model faithfully represents the processes related to tool handling that take place in an OR during an operation. The specification is at various levels of detail, each level is depicted in a separate diagram, and all the diagrams are "aware" of each other as part of the whole model. Providing ontology of verbal and non-verbal modalities of communication in the OR, the resulting conceptual model is a solid basis for analyzing and understanding the source of the large variety of errors occurring in the course of an operation, providing an opportunity to decrease the quantity and severity of mistakes related to the use and misuse of surgical instrumentations. Since the model is event driven, rather than person driven, the focus is on the factors causing the errors, rather than the specific person. This approach advocates searching for technological solutions to alleviate tool-related errors rather than finger-pointing. Concretely, the model was validated through a structured questionnaire and it was found that surgeons agreed that the conceptual model was flexible (3.8 of 5, std=0.69), accurate, and it generalizable (3.7 of 5, std=0.37 and 3.7 of 5, std=0.85, respectively). CONCLUSION The detailed conceptual model of the tools handling subsystem of the operation performed in an OR focuses on the details of the communication and the interactions taking place between the surgeon and the surgical technician during an operation, with the objective of pinpointing the exact circumstances in which errors can happen. Exact and concise specification of the communication events in general and the surgical instrument requests in particular is a prerequisite for a methodical analysis of the various modes of errors and the circumstances under which they occur. This has significant potential value in both reduction in tool-handling-related errors during an operation and providing a solid formal basis for designing a cybernetic agent which can replace a surgical technician in routine tool handling activities during an operation, freeing the technician to focus on quality assurance, monitoring and control of the cybernetic agent activities. This is a critical step in designing the next generation of cybernetic OR assistants.
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Affiliation(s)
- Juan P Wachs
- School of Industrial Engineering, Purdue University, West Lafayette 47906, IN, USA.
| | | | - Dov Dori
- Massachusetts Institute of Technology, Cambridge, MA, USA; Technion, Israel Institute of Technology, Haifa, Israel
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21
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Abstract
We review traits of reusable clinical data and offer a typology of clinical repositories with a range of known examples. Sources of clinical data suitable for research can be classified into types reflecting the data's institutional origin, original purpose, level of integration and governance. Primary data nearly always come from research studies and electronic medical records. Registries collect data on focused populations primarily to track outcomes, often using observational research methods. Warehouses are institutional information utilities repackaging clinical care data. Collections organize data from more organizations than a data warehouse, and more original data sources than a registry. Therefore even if they are heavily curated, their level of internal integration, and thus ease of use, can be less than other types. Federations are like collections except that physical control over data is distributed among donor organizations. Federations sometimes federate, giving a second level of organization. While the size, in number of patients, varies widely within each type of data source, populations over 10 K are relatively numerous, and much larger populations can be seen in warehouses and federations. One imagined ideal structure for research progress has been called an "Information Commons". It would have longitudinal, multi-leveled (environmental through molecular) data on a large population of identified, consenting individuals. These are qualities whose achievement would require long term commitment on the part of many data donors, including a willingness to make their data public.
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Affiliation(s)
- Ted D Wade
- Division of Biostatistics and Bioinformatics, National Jewish Health, Denver, CO 80206-2761 USA
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22
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Bhandari N, Shi Y, Jung K. Seeking health information online: does limited healthcare access matter? J Am Med Inform Assoc 2014; 21:1113-7. [PMID: 24948558 DOI: 10.1136/amiajnl-2013-002350] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Consumers facing barriers to healthcare access may use online health information seeking and online communication with physicians, but the empirical relationship has not been sufficiently analyzed. Our study examines the association of barriers to healthcare access with consumers' health-related information searching on the internet, use of health chat groups, and email communication with physicians, using data from 27,210 adults from the 2009 National Health Interview Survey. Individuals with financial barriers to healthcare access, difficulty getting timely appointments with doctors, and conflicts in scheduling during clinic hours are more likely to search for general health information online than those without these access barriers. Those unable to get timely appointments with physicians are more likely to participate in health chat groups and email physicians. The internet may offer a low-cost source of health information and could help meet the heightened demand for health-related information among those facing access barriers to care.
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Affiliation(s)
- Neeraj Bhandari
- Department of Health Policy and Administration, College of Health and Human Development, The Pennsylvania State University, State College, Pennsylvania, USA
| | - Yunfeng Shi
- Center for Health Care and Policy Research, College of Health and Human Development, The Pennsylvania State University, State College, Pennsylvania, USA
| | - Kyoungrae Jung
- Department of Health Policy and Administration, College of Health and Human Development, The Pennsylvania State University, State College, Pennsylvania, USA
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23
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Shin SY, Kim WS, Lee JH. Characteristics desired in clinical data warehouse for biomedical research. Healthc Inform Res 2014; 20:109-16. [PMID: 24872909 PMCID: PMC4030054 DOI: 10.4258/hir.2014.20.2.109] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Revised: 03/22/2014] [Accepted: 04/06/2014] [Indexed: 11/25/2022] Open
Abstract
Objectives Due to the unique characteristics of clinical data, clinical data warehouses (CDWs) have not been successful so far. Specifically, the use of CDWs for biomedical research has been relatively unsuccessful thus far. The characteristics necessary for the successful implementation and operation of a CDW for biomedical research have not clearly defined yet. Methods Three examples of CDWs were reviewed: a multipurpose CDW in a hospital, a CDW for independent multi-institutional research, and a CDW for research use in an institution. After reviewing the three CDW examples, we propose some key characteristics needed in a CDW for biomedical research. Results A CDW for research should include an honest broker system and an Institutional Review Board approval interface to comply with governmental regulations. It should also include a simple query interface, an anonymized data review tool, and a data extraction tool. Also, it should be a biomedical research platform for data repository use as well as data analysis. Conclusions The proposed characteristics desired in a CDW may have limited transfer value to organizations in other countries. However, these analysis results are still valid in Korea, and we have developed clinical research data warehouse based on these desiderata.
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Affiliation(s)
- Soo-Yong Shin
- Department of Biomedical Informatics, Asan Medical Center, Seoul, Korea
| | - Woo Sung Kim
- Department of Biomedical Informatics, Asan Medical Center, Seoul, Korea. ; Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jae-Ho Lee
- Department of Biomedical Informatics, Asan Medical Center, Seoul, Korea. ; Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. ; Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
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24
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Vawdrey DK, Weng C, Herion D, Cimino JJ. Enhancing electronic health records to support clinical research. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2014; 2014:102-8. [PMID: 25954585 PMCID: PMC4419762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The "Learning Health System" has been described as an environment that drives research and innovation as a natural outgrowth of patient care. Electronic health records (EHRs) are necessary to enable the Learning Health System; however, a source of frustration is that current systems fail to adequately support research needs. We propose a model for enhancing EHRs to collect structured and standards-based clinical research data during clinical encounters that promotes efficiency and computational reuse of quality data for both care and research. The model integrates Common Data Elements (CDEs) for clinical research into existing clinical documentation workflows, leveraging executable documentation guidance within the EHR to support coordinated, standardized data collection for both patient care and clinical research.
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Affiliation(s)
- David K Vawdrey
- Columbia University Department of Biomedical Informatics, New York, NY
| | - Chunhua Weng
- Columbia University Department of Biomedical Informatics, New York, NY
| | - David Herion
- Department of Clinical Research Informatics, NIH Clinical Center, Bethesda, MD
| | - James J Cimino
- Laboratory for Informatics Development, NIH Clinical Center, Bethesda, MD ; Columbia University Department of Biomedical Informatics, New York, NY
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25
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Kahn MG, Bailey LC, Forrest CB, Padula MA, Hirschfeld S. Building a common pediatric research terminology for accelerating child health research. Pediatrics 2014; 133:516-25. [PMID: 24534404 PMCID: PMC3934328 DOI: 10.1542/peds.2013-1504] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/01/2013] [Indexed: 11/24/2022] Open
Abstract
Longitudinal observational clinical data on pediatric patients in electronic format is becoming widely available. A new era of multi-institutional data networks that study pediatric diseases and outcomes across disparate health delivery models and care settings are also enabling an innovative collaborative rapid improvement paradigm called the Learning Health System. However, the potential alignment of routine clinical care, observational clinical research, pragmatic clinical trials, and health systems improvement requires a data infrastructure capable of combining information from systems and workflows that historically have been isolated from each other. Removing barriers to integrating and reusing data collected in different settings will permit new opportunities to develop a more complete picture of a patient's care and to leverage data from related research studies. One key barrier is the lack of a common terminology that provides uniform definitions and descriptions of clinical observations and data. A well-characterized terminology ensures a common meaning and supports data reuse and integration. A common terminology allows studies to build upon previous findings and to reuse data collection tools and data management processes. We present the current state of terminology harmonization and describe a governance structure and mechanism for coordinating the development of a common pediatric research terminology that links to clinical terminologies and can be used to align existing terminologies. By reducing the barriers between clinical care and clinical research, a Learning Health System can leverage and reuse not only its own data resources but also broader extant data resources.
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Affiliation(s)
- Michael G. Kahn
- Department of Pediatrics, University of Colorado, Aurora, Colorado
| | - L. Charles Bailey
- Children’s Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Christopher B. Forrest
- Children’s Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Michael A. Padula
- Children’s Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Steven Hirschfeld
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland
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26
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Pathak J, Kho AN, Denny JC. Electronic health records-driven phenotyping: challenges, recent advances, and perspectives. J Am Med Inform Assoc 2014; 20:e206-11. [PMID: 24302669 DOI: 10.1136/amiajnl-2013-002428] [Citation(s) in RCA: 167] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Affiliation(s)
- Jyotishman Pathak
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
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27
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Kukafka R, Allegrante JP, Khan S, Bigger JT, Johnson SB. Understanding facilitators and barriers to reengineering the clinical research enterprise in community-based practice settings. Contemp Clin Trials 2013; 36:166-74. [DOI: 10.1016/j.cct.2013.06.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2013] [Revised: 06/12/2013] [Accepted: 06/16/2013] [Indexed: 11/16/2022]
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28
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Abstract
Alzheimer's disease (AD) is an urgent public health challenge that is rapidly approaching epidemic proportions. New therapies that defer or prevent the onset, delay the decline, or improve the symptoms are urgently needed. All phase 3 drug development programs for disease-modifying agents have failed thus far. New approaches to drug development are needed. Translational neuroscience focuses on the linkages between basic neuroscience and the development of new diagnostic and therapeutic products that will improve the lives of patients or prevent the occurrence of brain disorders. Translational neuroscience includes new preclinical models that may better predict human efficacy and safety, improved clinical trial designs and outcomes that will accelerate drug development, and the use of biomarkers to more rapidly provide information regarding the effects of drugs on the underlying disease biology. Early translational research is complemented by later stage translational approaches regarding how best to use evidence to impact clinical practice and to assess the influence of new treatments on the public health. Funding of translational research is evolving with an increased emphasis on academic and NIH involvement in drug development. Translational neuroscience provides a framework for advancing development of new therapies for AD patients.
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Nelson EK, Piehler B, Rauch A, Ramsay S, Holman D, Asare S, Asare A, Igra M. Ancillary study management systems: a review of needs. BMC Med Inform Decis Mak 2013; 13:5. [PMID: 23294514 PMCID: PMC3564696 DOI: 10.1186/1472-6947-13-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2012] [Accepted: 12/28/2012] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The valuable clinical data, specimens, and assay results collected during a primary clinical trial or observational study can enable researchers to answer additional, pressing questions with relatively small investments in new measurements. However, management of such follow-on, "ancillary" studies is complex. It requires coordinating across institutions, sites, repositories, and approval boards, as well as distributing, integrating, and analyzing diverse data types. General-purpose software systems that simplify the management of ancillary studies have not yet been explored in the research literature. METHODS We have identified requirements for ancillary study management primarily as part of our ongoing work with a number of large research consortia. These organizations include the Center for HIV/AIDS Vaccine Immunology (CHAVI), the Immune Tolerance Network (ITN), the HIV Vaccine Trials Network (HVTN), the U.S. Military HIV Research Program (MHRP), and the Network for Pancreatic Organ Donors with Diabetes (nPOD). We also consulted with researchers at a range of other disease research organizations regarding their workflows and data management strategies. Lastly, to enhance breadth, we reviewed process documents for ancillary study management from other organizations. RESULTS By exploring characteristics of ancillary studies, we identify differentiating requirements and scenarios for ancillary study management systems (ASMSs). Distinguishing characteristics of ancillary studies may include the collection of additional measurements (particularly new analyses of existing specimens); the initiation of studies by investigators unaffiliated with the original study; cross-protocol data pooling and analysis; pre-existing participant consent; and pre-existing data context and provenance. For an ASMS to address these characteristics, it would need to address both operational requirements (e.g., allocating existing specimens) and data management requirements (e.g., securely distributing and integrating primary and ancillary data). CONCLUSIONS The scenarios and requirements we describe can help guide the development of systems that make conducting ancillary studies easier, less expensive, and less error-prone. Given the relatively consistent characteristics and challenges of ancillary study management, general-purpose ASMSs are likely to be useful to a wide range of organizations. Using the requirements identified in this paper, we are currently developing an open-source, general-purpose ASMS based on LabKey Server (http://www.labkey.org) in collaboration with CHAVI, the ITN and nPOD.
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Affiliation(s)
| | | | | | - Sarah Ramsay
- Statistical Center for HIV/AIDS Research & Prevention (SCHARP), Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Drienna Holman
- Statistical Center for HIV/AIDS Research & Prevention (SCHARP), Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Smita Asare
- University of California, San Francisco, CA, USA
- Immune Tolerance Network, Bethesda, MD, USA
| | - Adam Asare
- University of California, San Francisco, CA, USA
- Immune Tolerance Network, Bethesda, MD, USA
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