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Austin RR, Lu SC, Jantraporn R, Park S, Geiger-Simpson E, Koithan M, Kreitzer M, Delaney CW. Documentation of Complementary and Integrative Health Therapies in the Electronic Health Record: A Scoping Review. JOURNAL OF INTEGRATIVE AND COMPLEMENTARY MEDICINE 2023; 29:483-491. [PMID: 36897742 DOI: 10.1089/jicm.2022.0748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
Introduction: Complementary and integrative health (CIH) therapies refers to massage therapy, acupuncture, aromatherapy, and guided imagery. These therapies have gained increased attention in recent years, particularly for their potential to help manage chronic pain and other conditions. National organizations not only recommend the use of CIH therapies but also the documentation of these therapies within electronic health records (EHRs). Yet, how CIH therapies are documented in the EHR is not well understood. The purpose of this scoping review of the literature was to examine and describe research that focused on CIH therapy clinical documentation in the EHR. Methods: The authors conducted a literature search using six electronic databases: Cumulative Index to Nursing and Allied Health Literature (CINAHL), Ovid MEDLINE, Scopus, Google Scholar, Embase, and PubMed. Predefined search terms included "informatics," "documentation," "complementary and integrative health therapies," "non-pharmacological approaches," and "electronic health records" using AND/OR statements. No restrictions were placed on publication date. The inclusion criteria were as follows: (1) Original peer-reviewed full article in English, (2) focus on CIH therapies, and (3) CIH therapy documentation practice used in the research. Results: The authors identified 1684 articles, of which 33 met the criteria for a full review. A majority of the studies were conducted in the United States (20) and hospitals (19). The most common study design was retrospective (9), and 26 studies used EHR data as a data source for analysis. Documentation practices varied widely across all studies, ranging from the feasibility of documenting integrative therapies (i.e., homeopathy) to create changes in the EHR to support documentation (i.e., flowsheet). Discussion: This scoping review identified varying EHR clinical documentation trends for CIH therapies. Pain was the most frequent reason for use of CIH therapies across all included studies and a broad range of CIH therapies were used. Data standards and templates were suggested as informatics methods to support CIH documentation. A systems approach is needed to enhance and support the current technology infrastructure that will enable consistent CIH therapy documentation in EHRs.
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Affiliation(s)
- Robin R Austin
- School of Nursing, University of Minnesota, Minneapolis, Minnesota, USA
- Earl E. Bakken Center for Spirituality and Healing, University of Minnesota, Minneapolis, Minnesota, USA
| | - Sheng-Chieh Lu
- Department of Symptom Research, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Suhyun Park
- School of Nursing, University of Minnesota, Minneapolis, Minnesota, USA
| | | | - Mary Koithan
- College of Nursing, Washington State University, Spokane, Washington, USA
| | - MaryJo Kreitzer
- School of Nursing, University of Minnesota, Minneapolis, Minnesota, USA
- Earl E. Bakken Center for Spirituality and Healing, University of Minnesota, Minneapolis, Minnesota, USA
| | - Connie W Delaney
- School of Nursing, University of Minnesota, Minneapolis, Minnesota, USA
- Earl E. Bakken Center for Spirituality and Healing, University of Minnesota, Minneapolis, Minnesota, USA
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Petch J, Kempainnen J, Pettengell C, Aviv S, Butler B, Pond G, Saha A, Bogach J, Allard-Coutu A, Sztur P, Ranisau J, Levine M. Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center. JCO Clin Cancer Inform 2023; 7:e2200182. [PMID: 37001040 PMCID: PMC10281330 DOI: 10.1200/cci.22.00182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 02/10/2023] [Indexed: 04/03/2023] Open
Abstract
PURPOSE This study documents the creation of automated, longitudinal, and prospective data and analytics platform for breast cancer at a regional cancer center. This platform combines principles of data warehousing with natural language processing (NLP) to provide the integrated, timely, meaningful, high-quality, and actionable data required to establish a learning health system. METHODS Data from six hospital information systems and one external data source were integrated on a nightly basis by automated extract/transform/load jobs. Free-text clinical documentation was processed using a commercial NLP engine. RESULTS The platform contains 141 data elements of 7,019 patients with newly diagnosed breast cancer who received care at our regional cancer center from January 1, 2014, to June 3, 2022. Daily updating of the database takes an average of 56 minutes. Evaluation of the tuning of NLP jobs found overall high performance, with an F1 of 1.0 for 19 variables, with a further 16 variables with an F1 of > 0.95. CONCLUSION This study describes how data warehousing combined with NLP can be used to create a prospective data and analytics platform to enable a learning health system. Although upfront time investment required to create the platform was considerable, now that it has been developed, daily data processing is completed automatically in less than an hour.
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Affiliation(s)
- Jeremy Petch
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, Canada
- Institute for Health Policy Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Division of Cardiology, Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
- Population Health Research Institute, Hamilton Health Sciences, Hamilton, Canada
| | - Joel Kempainnen
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, Canada
| | | | | | | | - Greg Pond
- Escarpment Cancer Research Institute, Hamilton Health Sciences, Hamilton, Canada
| | - Ashirbani Saha
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, Canada
- Escarpment Cancer Research Institute, Hamilton Health Sciences, Hamilton, Canada
- Department of Oncology, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | - Jessica Bogach
- Department of Surgery, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | | | - Peter Sztur
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, Canada
| | - Jonathan Ranisau
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, Canada
| | - Mark Levine
- Hamilton Health Sciences, Hamilton, Canada
- Escarpment Cancer Research Institute, Hamilton Health Sciences, Hamilton, Canada
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Pavlenko E, Strech D, Langhof H. Implementation of data access and use procedures in clinical data warehouses. A systematic review of literature and publicly available policies. BMC Med Inform Decis Mak 2020; 20:157. [PMID: 32652989 PMCID: PMC7353743 DOI: 10.1186/s12911-020-01177-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 07/02/2020] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND The promises of improved health care and health research through data-intensive applications rely on a growing amount of health data. At the core of large-scale data integration efforts, clinical data warehouses (CDW) are also responsible for data governance, managing data access and (re)use. As the complexity of the data flow increases, greater transparency and standardization of criteria and procedures are required in order to maintain objective oversight and control. Therefore, the development of practice oriented and evidence-based policies is crucial. This study assessed the spectrum of data access and use criteria and procedures in clinical data warehouses governance internationally. METHODS We performed a systematic review of (a) the published scientific literature on CDW and (b) publicly available information on CDW data access, e.g., data access policies. A qualitative thematic analysis was applied to all included literature and policies. RESULTS Twenty-three scientific publications and one policy document were included in the final analysis. The qualitative analysis led to a final set of three main thematic categories: (1) requirements, including recipient requirements, reuse requirements, and formal requirements; (2) structures and processes, including review bodies and review values; and (3) access, including access limitations. CONCLUSIONS The description of data access and use governance in the scientific literature is characterized by a high level of heterogeneity and ambiguity. In practice, this might limit the effective data sharing needed to fulfil the high expectations of data-intensive approaches in medical research and health care. The lack of publicly available information on access policies conflicts with ethical requirements linked to principles of transparency and accountability. CDW should publicly disclose by whom and under which conditions data can be accessed, and provide designated governance structures and policies to increase transparency on data access. The results of this review may contribute to the development of practice-oriented minimal standards for the governance of data access, which could also result in a stronger harmonization, efficiency, and effectiveness of CDW.
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Affiliation(s)
- Elena Pavlenko
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- QUEST - Center for Transforming Biomedical Research, Charité - University Medicine, Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany
- Institute for History, Ethics and Philosophy of Medicine, Hannover Medical School (MHH), Hannover, Germany
| | - Daniel Strech
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- QUEST - Center for Transforming Biomedical Research, Charité - University Medicine, Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany
- Institute for History, Ethics and Philosophy of Medicine, Hannover Medical School (MHH), Hannover, Germany
| | - Holger Langhof
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
- QUEST - Center for Transforming Biomedical Research, Charité - University Medicine, Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany.
- Institute for History, Ethics and Philosophy of Medicine, Hannover Medical School (MHH), Hannover, Germany.
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Gómez-López G, Dopazo J, Cigudosa JC, Valencia A, Al-Shahrour F. Precision medicine needs pioneering clinical bioinformaticians. Brief Bioinform 2020; 20:752-766. [PMID: 29077790 DOI: 10.1093/bib/bbx144] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 09/14/2017] [Indexed: 01/18/2023] Open
Abstract
Success in precision medicine depends on accessing high-quality genetic and molecular data from large, well-annotated patient cohorts that couple biological samples to comprehensive clinical data, which in conjunction can lead to effective therapies. From such a scenario emerges the need for a new professional profile, an expert bioinformatician with training in clinical areas who can make sense of multi-omics data to improve therapeutic interventions in patients, and the design of optimized basket trials. In this review, we first describe the main policies and international initiatives that focus on precision medicine. Secondly, we review the currently ongoing clinical trials in precision medicine, introducing the concept of 'precision bioinformatics', and we describe current pioneering bioinformatics efforts aimed at implementing tools and computational infrastructures for precision medicine in health institutions around the world. Thirdly, we discuss the challenges related to the clinical training of bioinformaticians, and the urgent need for computational specialists capable of assimilating medical terminologies and protocols to address real clinical questions. We also propose some skills required to carry out common tasks in clinical bioinformatics and some tips for emergent groups. Finally, we explore the future perspectives and the challenges faced by precision medicine bioinformatics.
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Affiliation(s)
| | - Joaquín Dopazo
- Clinical Bioinformatics Area of the Fundacio´n Progreso y Salud (Seville)
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Hazlehurst B, Green CA, Perrin NA, Brandes J, Carrell DS, Baer A, DeVeaugh-Geiss A, Coplan PM. Using natural language processing of clinical text to enhance identification of opioid-related overdoses in electronic health records data. Pharmacoepidemiol Drug Saf 2019; 28:1143-1151. [PMID: 31218780 PMCID: PMC6772185 DOI: 10.1002/pds.4810] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 04/24/2019] [Accepted: 05/08/2019] [Indexed: 01/04/2023]
Abstract
Purpose To enhance automated methods for accurately identifying opioid‐related overdoses and classifying types of overdose using electronic health record (EHR) databases. Methods We developed a natural language processing (NLP) software application to code clinical text documentation of overdose, including identification of intention for self‐harm, substances involved, substance abuse, and error in medication usage. Using datasets balanced with cases of suspected overdose and records of individuals at elevated risk for overdose, we developed and validated the application using Kaiser Permanente Northwest data, then tested portability of the application using Kaiser Permanente Washington data. Datasets were chart‐reviewed to provide a gold standard for comparison and evaluation of the automated method. Results The method performed well in identifying overdose (sensitivity = 0.80, specificity = 0.93), intentional overdose (sensitivity = 0.81, specificity = 0.98), and involvement of opioids (excluding heroin, sensitivity = 0.72, specificity = 0.96) and heroin (sensitivity = 0.84, specificity = 1.0). The method performed poorly at identifying adverse drug reactions and overdose due to patient error and fairly at identifying substance abuse in opioid‐related unintentional overdose (sensitivity = 0.67, specificity = 0.96). Evaluation using validation datasets yielded significant reductions, in specificity and negative predictive values only, for many classifications mentioned above. However, these measures remained above 0.80, thus, performance observed during development was largely maintained during validation. Similar results were obtained when evaluating portability, although there was a significant reduction in sensitivity for unintentional overdose that was attributed to missing text clinical notes in the database. Conclusions Methods that process text clinical notes show promise for improving accuracy and fidelity at identifying and classifying overdoses according to type using EHR data.
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Affiliation(s)
- Brian Hazlehurst
- Center for Health Research, Kaiser Permanente Northwest, Portland, OR
| | - Carla A Green
- Center for Health Research, Kaiser Permanente Northwest, Portland, OR
| | - Nancy A Perrin
- Center for Health Research, Kaiser Permanente Northwest, Portland, OR
| | - John Brandes
- Center for Health Research, Kaiser Permanente Northwest, Portland, OR
| | - David S Carrell
- Health Research Institute, Kaiser Permanente Washington, Seattle, WA
| | - Andrew Baer
- Group Health Research Institute, Group Health Cooperative, Seattle, WA
| | | | - Paul M Coplan
- Epidemiology, Medical Affairs, Purdue Pharma, LP, Stamford, CT.,Adjunct, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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Abstract
Introduction: In aggregate, existing data quality (DQ) checks are currently represented in heterogeneous formats, making it difficult to compare, categorize, and index checks. This study contributes a data element-function conceptual model to facilitate the categorization and indexing of DQ checks and explores the feasibility of leveraging natural language processing (NLP) for scalable acquisition of knowledge of common data elements and functions from DQ checks narratives. Methods: The model defines a “data element”, the primary focus of the check, and a “function”, the qualitative or quantitative measure over a data element. We applied NLP techniques to extract both from 172 checks for Observational Health Data Sciences and Informatics (OHDSI) and 3,434 checks for Kaiser Permanente’s Center for Effectiveness and Safety Research (CESR). Results: The model was able to classify all checks. A total of 751 unique data elements and 24 unique functions were extracted. The top five frequent data element-function pairings for OHDSI were Person-Count (55 checks), Insurance-Distribution (17), Medication-Count (16), Condition-Count (14), and Observations-Count (13); for CESR, they were Medication-Variable Type (175), Medication-Missing (172), Medication-Existence (152), Medication-Count (127), and Socioeconomic Factors-Variable Type (114). Conclusions: This study shows the efficacy of the data element-function conceptual model for classifying DQ checks, demonstrates early promise of NLP-assisted knowledge acquisition, and reveals the great heterogeneity in the focus in DQ checks, confirming variation in intrinsic checks and use-case specific “fitness-for-use” checks.
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Raman SR, Curtis LH, Temple R, Andersson T, Ezekowitz J, Ford I, James S, Marsolo K, Mirhaji P, Rocca M, Rothman RL, Sethuraman B, Stockbridge N, Terry S, Wasserman SM, Peterson ED, Hernandez AF. Leveraging electronic health records for clinical research. Am Heart J 2018; 202:13-19. [PMID: 29802975 DOI: 10.1016/j.ahj.2018.04.015] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 04/23/2018] [Indexed: 12/11/2022]
Abstract
Electronic health records (EHRs) can be a major tool in the quest to decrease costs and timelines of clinical trial research, generate better evidence for clinical decision making, and advance health care. Over the past decade, EHRs have increasingly offered opportunities to speed up, streamline, and enhance clinical research. EHRs offer a wide range of possible uses in clinical trials, including assisting with prestudy feasibility assessment, patient recruitment, and data capture in care delivery. To fully appreciate these opportunities, health care stakeholders must come together to face critical challenges in leveraging EHR data, including data quality/completeness, information security, stakeholder engagement, and increasing the scale of research infrastructure and related governance. Leaders from academia, government, industry, and professional societies representing patient, provider, researcher, industry, and regulator perspectives convened the Leveraging EHR for Clinical Research Now! Think Tank in Washington, DC (February 18-19, 2016), to identify barriers to using EHRs in clinical research and to generate potential solutions. Think tank members identified a broad range of issues surrounding the use of EHRs in research and proposed a variety of solutions. Recognizing the challenges, the participants identified the urgent need to look more deeply at previous efforts to use these data, share lessons learned, and develop a multidisciplinary agenda for best practices for using EHRs in clinical research. We report the proceedings from this think tank meeting in the following paper.
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Affiliation(s)
| | | | | | | | - Justin Ezekowitz
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, United Kingdom
| | - Stefan James
- Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Keith Marsolo
- Cinncinatti Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cinncinatti, OH
| | | | - Mitra Rocca
- Food and Drug Administration, Silver Spring, MD
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Bailey SR, Stevens VJ, Fortmann SP, Kurtz SE, McBurnie MA, Priest E, Puro J, Solberg LI, Schweitzer R, Masica AL, Hazlehurst B. Long-Term Outcomes From Repeated Smoking Cessation Assistance in Routine Primary Care. Am J Health Promot 2018. [PMID: 29534598 DOI: 10.1177/0890117118761886] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
PURPOSE To test the association between repeated clinical smoking cessation support and long-term cessation. DESIGN Retrospective, observational cohort study using structured and free-text data from electronic health records. SETTING Six diverse health systems in the United States. PARTICIPANTS Patients aged ≥18 years who were smokers in 2007 and had ≥1 primary care visit in each of the following 4 years (N = 33 691). MEASURES Primary exposure was a composite categorical variable (comprised of documentation of smoking cessation medication, counseling, or referral) classifying the proportions of visits for which patients received any cessation assistance (<25% (reference), 25%-49%, 50%-74%, and ≥75% of visits). The dependent variable was long-term quit (LTQ; yes/no), defined as no indication of being a current smoker for ≥365 days following a visit where nonsmoker or former smoker was indicated. ANALYSIS Mixed effects logistic regression analysis adjusted for age, sex, race, and comorbidities, with robust standard error estimation to account for within site correlation. RESULTS Overall, 20% of the cohort achieved LTQ status. Patients with ≥75% of visits with any assistance had almost 3 times the odds of achieving LTQ status compared to those with <25% visits with assistance (odds ratio = 2.84; 95% confidence interval: 1.50-5.37). Results were similar for specific assistance types. CONCLUSIONS These findings provide support for the importance of repeated assistance at primary care visits to increase long-term smoking cessation.
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Affiliation(s)
- Steffani R Bailey
- 1 Department of Family Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Victor J Stevens
- 2 Kaiser Permanente Center for Health Research, Portland, OR, USA
| | | | - Stephen E Kurtz
- 2 Kaiser Permanente Center for Health Research, Portland, OR, USA
| | | | | | | | | | - Rebecca Schweitzer
- 6 Department is Office of Public Health Studies, University of Hawai'i at Manoa, Honolulu, HI, USA
| | | | - Brian Hazlehurst
- 2 Kaiser Permanente Center for Health Research, Portland, OR, USA
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Avilés-Santa ML, Heintzman J, Lindberg NM, Guerrero-Preston R, Ramos K, Abraído-Lanza AL, Bull J, Falcón A, McBurnie MA, Moy E, Papanicolaou G, Piña IL, Popovic J, Suglia SF, Vázquez MA. Personalized medicine and Hispanic health: improving health outcomes and reducing health disparities - a National Heart, Lung, and Blood Institute workshop report. BMC Proc 2017; 11:11. [PMID: 29149222 PMCID: PMC5667592 DOI: 10.1186/s12919-017-0079-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Persons of Hispanic/Latino descent may represent different ancestries, ethnic and cultural groups and countries of birth. In the U.S., the Hispanic/Latino population is projected to constitute 29% of the population by 2060. A personalized approach focusing on individual variability in genetics, environment, lifestyle and socioeconomic determinants of health may advance the understanding of some of the major factors contributing to the health disparities experienced by Hispanics/Latinos and other groups in the U.S., thus leading to new strategies that improve health care outcomes. However, there are major gaps in our current knowledge about how personalized medicine can shape health outcomes among Hispanics/Latinos and address the potential factors that may explain the observed differences within this heterogeneous group, and between this group and other U.S. demographic groups. For that purpose, the National Heart, Lung, and Blood Institute (NHLBI), in collaboration with the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), and the Food and Drug Administration (FDA), held a workshop in which experts discussed (1) potential approaches to study medical treatments and health outcomes among Hispanics/Latinos and garner the necessary evidence to fill gaps of efficacy, effectiveness and safety of therapies for heart, lung, blood and sleep (HLBS) disorders and conditions--and their risk factors; (2) research opportunities related to personalized medicine to improve knowledge and develop effective interventions to reduce health disparities among Hispanics/Latinos in the U.S.; and (3) the incorporation of expanded sociocultural and socioeconomic data collection and genetic/genomic/epigenetic information of Hispanic/Latino patients into their clinical assessments, to account for individual variability in ancestry; physiology or disease risk; culture; environment; lifestyle; and socioeconomic determinants of health. The experts also provided recommendations on: sources of Hispanic/Latino health data and strategies to enhance its collection; policy; genetics, genomics and epigenetics research; and integrating Hispanic/Latino health research within clinical settings.
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Affiliation(s)
- M Larissa Avilés-Santa
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, 6701 Rockledge Drive, Room 10188, Bethesda, MD 20892-7936 USA
| | - John Heintzman
- Department of Family Medicine, Oregon Health and Science University, 318 SW Sam Jackson Park Rd, Portland, OR 97239 USA
| | - Nangel M Lindberg
- Kaiser Permanente Northwest Center for Health Research, 3800 N. Interstate Ave, Portland, OR 97227 USA
| | - Rafael Guerrero-Preston
- Johns Hopkins University School of Medicine, 1550 Orleans Street, CRB2 Room 5M, Baltimore, MD 21231 USA
| | - Kenneth Ramos
- University of Arizona Health Sciences, 1295 North Martin Avenue, PO Box 210202, Tucson, AZ 86721 USA
| | - Ana L Abraído-Lanza
- Columbia University, Mailman School of Public Health, 722 West 168th Street, New York, NY 10032 USA
| | - Jonca Bull
- Office of Minority Health, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993 USA
| | - Adolph Falcón
- National Alliance for Hispanic Health, 1600 P St NW, Washington, DC 20009 USA
| | - Mary Ann McBurnie
- Kaiser Permanente Northwest Center for Health Research, 3800 N. Interstate Ave, Portland, OR 97227 USA
| | - Ernest Moy
- National Center for Health Statistics, 3311 Toledo Road, Hyattsville, MD 20782 USA
| | - George Papanicolaou
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, 6701 Rockledge Drive, Room 10188, Bethesda, MD 20892-7936 USA
| | - Ileana L Piña
- Albert Einstein College of Medicine, Montefiore Heart and Vascular Center, 111 East 210th Street, Bronx, NY 10467-2401 USA
| | - Jennifer Popovic
- Program for Health Data and Standardized Methods, Center for Health Data Analytics
- eHealth, Quality & Analytics Division, RTI International
- 307 Waverley Oaks Road, Suite 101, Waltham, MA 02452 USA
| | - Shakira F Suglia
- Rollins School of Public Health, Emory University, 1518 Clifton Rd Rm 4005, Atlanta, GA 30322 USA
| | - Miguel A Vázquez
- Department of Internal Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390-8856 USA
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Baldwin JL, Singh H, Sittig DF, Giardina TD. Patient portals and health apps: Pitfalls, promises, and what one might learn from the other. HEALTHCARE (AMSTERDAM, NETHERLANDS) 2017; 5:81-85. [PMID: 27720139 PMCID: PMC8386919 DOI: 10.1016/j.hjdsi.2016.08.004] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Revised: 08/11/2016] [Accepted: 08/29/2016] [Indexed: 02/05/2023]
Abstract
Widespread use of health information technology (IT) could potentially increase patients' access to their health information and facilitate future goals of advancing patient-centered care. Despite having increased access to their health data, patients do not always understand this information or its implications, and digital health data can be difficult to navigate when displayed in a small-format, complex interface. In this paper, we discuss two forms of patient-facing health IT tools-patient portals and applications (apps)-and highlight how, despite several limitations of each, combining high-yield features of mobile health (mHealth) apps with portals could increase patient engagement and self-management and be more effective than either of them alone. Patient portal adoption is variable, and due to design and interface limitations and health literacy issues, many people find the portal difficult to use. Conversely, apps have experienced rapid adoption and traditionally have more consumer-friendly features with easy log-in access, real-time tracking, and simplified data display. These features make the applications more intuitive and easy-to-use than patient portals. While apps have their own limitations and might serve different purposes, patient portals could adopt some high-yield features and functions of apps that lead to engagement success with patients. We thus suggest that to improve user experience with future portals, developers could look towards mHealth apps in design, function, and user interface. Adding new features to portals may improve their use and empower patients to track their overall health and disease states. Nevertheless, both these health IT tools should be subjected to rigorous evaluation to ensure they meet their potential in improving patient outcomes.
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Affiliation(s)
- Jessica L Baldwin
- Houston Veterans Affairs Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, USA; Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
| | - Hardeep Singh
- Houston Veterans Affairs Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, USA; Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Dean F Sittig
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA
| | - Traber Davis Giardina
- Houston Veterans Affairs Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, USA; Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
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11
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Johnson SB. Clinical Research Informatics: Supporting the Research Study Lifecycle. Yearb Med Inform 2017; 26:193-200. [PMID: 29063565 PMCID: PMC6239240 DOI: 10.15265/iy-2017-022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Indexed: 12/27/2022] Open
Abstract
Objectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years 2015-2016. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.
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Affiliation(s)
- S. B. Johnson
- Healthcare Policy and Research, Weill Cornell Medicine, New York, USA
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Mata C, Oliver A, Lalande A, Walker P, Martí J. On the Use of XML in Medical Imaging Web-Based Applications. Ing Rech Biomed 2017. [DOI: 10.1016/j.irbm.2016.10.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Sittig DF, Wright A, Ash J, Singh H. New Unintended Adverse Consequences of Electronic Health Records. Yearb Med Inform 2016:7-12. [PMID: 27830226 DOI: 10.15265/iy-2016-023] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Although the health information technology industry has made considerable progress in the design, development, implementation, and use of electronic health records (EHRs), the lofty expectations of the early pioneers have not been met. In 2006, the Provider Order Entry Team at Oregon Health & Science University described a set of unintended adverse consequences (UACs), or unpredictable, emergent problems associated with computer-based provider order entry implementation, use, and maintenance. Many of these originally identified UACs have not been completely addressed or alleviated, some have evolved over time, and some new ones have emerged as EHRs became more widely available. The rapid increase in the adoption of EHRs, coupled with the changes in the types and attitudes of clinical users, has led to several new UACs, specifically: complete clinical information unavailable at the point of care; lack of innovations to improve system usability leading to frustrating user experiences; inadvertent disclosure of large amounts of patient-specific information; increased focus on computer-based quality measurement negatively affecting clinical workflows and patient-provider interactions; information overload from marginally useful computer-generated data; and a decline in the development and use of internally-developed EHRs. While each of these new UACs poses significant challenges to EHR developers and users alike, they also offer many opportunities. The challenge for clinical informatics researchers is to continue to refine our current systems while exploring new methods of overcoming these challenges and developing innovations to improve EHR interoperability, usability, security, functionality, clinical quality measurement, and information summarization and display.
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Affiliation(s)
- D F Sittig
- Dean F. Sittig, University of Texas Health Science Center at Houston, School of Biomedical Informatics, and UT-Memorial Hermann Center for Health Care Quality, and Safety, Houston, Texas, USA, E-mail:
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Murphy DR, Meyer AN, Bhise V, Russo E, Sittig DF, Wei L, Wu L, Singh H. Computerized Triggers of Big Data to Detect Delays in Follow-up of Chest Imaging Results. Chest 2016; 150:613-20. [PMID: 27178786 DOI: 10.1016/j.chest.2016.05.001] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Revised: 04/14/2016] [Accepted: 05/02/2016] [Indexed: 02/08/2023] Open
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