IU Indianapolis ScholarWorks :: Browsing by Subject "Medical informatics"

Browsing by Subject "Medical informatics"

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Acceptance of use of personal health record: factors affecting physicians' perspective
(2011-10-19) Agrawal, Ekta; Jones, Josette F.; Weiner, Michael; Simmermaker, Jennifer
Acceptance of PHR by physicians is fundamental as they play important role towards the promotion of PHR adoption by providing the access to the data to be maintained in PHR and also, using the information within the PHR for decision making. Therefore it is important to measure physicians' perspective on usefulness of PHR, and also the value and trust they have in PHR usage. Review of previous researches identifies the lack of availability of a valid survey instrument that can be used to measure physicians' perception on all different aspects of PHR use and acceptance. Using the integrated literature review methodology and Unified Theory of Acceptance and Use of Technology (UTAUT) as a guiding framework, this study was aimed to identify the factors that can be used in the development of comprehensive evaluation instrument to understand physicians' acceptance of PHR. Total 15 articles were selected for literature review and using the content analysis method, 189 undifferentiated data units were extracted from those articles. These data units were then categorized into the four core constructs of UTAUT. ―Other categorization system was also created for the data units that could not be classified into one of the UTAUT core constructs. Among four core UTAUT constructs, Performance Expectancy is found to be the most influential factor in physicians' acceptance of PHR, followed by ―Other factors, Facilitating Condition and Social Influence. Effort expectancy was found to be the least influential. The identified specific factors within each domain can be used to develop a valid survey instrument to measure physicians' perception on PHR.
Advancing Toxicology-Based Cancer Risk Assessment with Informatics
(2010-05-03T19:38:33Z) Bercu, Joel P.; Mahoui, Malika; Romero, Pedro R.; Stevens, James L.; Jones, Josette F.; Palakal, Mathew J.
Since exposure to carcinogens can occur in the environment from various point sources, cancer risk assessment attempts to define and limit potential exposure such that the risk of developing cancer is negligible. While cancer risk assessment is widely used with certain methodologies well accepted in the scientific literature and regulatory guidances, there are still gaps which increase uncertainties when assessing risk including: (1) mixtures of genotoxins, (2) genotoxic metabolites, and (3) nongenotoxic carcinogens. An in silico model was developed to predict the cancer risk of a genotoxin which improved methodology for a single compound and mixtures. Monte Carlo simulations performed with a carcinogenicity potency database to estimate the overall carcinogenic risk of a mixture of genotoxic compounds showed that structural similarity would not likely increase the overall cancer risk. A cancer risk model was developed for genotoxic metabolites using excretion material in both animals and humans to determine the probability not exceeding a 1 in 100,000 excess cancer risk. Two model nongenotoxic compounds (fenofibrate and methapyraline) were tested in short-term microarray studies to develop a framework for cancer risk assessment. It was determined that a threshold for potential key events could be derived using benchmark dose analysis in combination with well developed ontologies (Kegg/GO), which were at or below measured tumorigenic and precursor events. In conclusion, informatics was effective in advancing toxicology-based cancer risk assessment using databases and predictive techniques which fill critical gaps in its methodology.
An Automated System for Generating Situation-Specific Decision Support in Clinical Order Entry from Local Empirical Data
(2011-10-19) Klann, Jeffrey G.; Schadow, Gunther; Downs, Stephen M.; Finnell, John T.; Palakal, Mathew J.; Szolovits, Peter
Clinical Decision Support is one of the only aspects of health information technology that has demonstrated decreased costs and increased quality in healthcare delivery, yet it is extremely expensive and time-consuming to create, maintain, and localize. Consequently, a majority of health care systems do not utilize it, and even when it is available it is frequently incorrect. Therefore it is important to look beyond traditional guideline-based decision support to more readily available resources in order to bring this technology into widespread use. This study proposes that the wisdom of physicians within a practice is a rich, untapped knowledge source that can be harnessed for this purpose. I hypothesize and demonstrate that this wisdom is reflected by order entry data well enough to partially reconstruct the knowledge behind treatment decisions. Automated reconstruction of such knowledge is used to produce dynamic, situation-specific treatment suggestions, in a similar vein to Amazon.com shopping recommendations. This approach is appealing because: it is local (so it reflects local standards); it fits into workflow more readily than the traditional local-wisdom approach (viz. the curbside consult); and, it is free (the data are already being captured). This work develops several new machine-learning algorithms and novel applications of existing algorithms, focusing on an approach called Bayesian network structure learning. I develop: an approach to produce dynamic, rank-ordered situation-specific treatment menus from treatment data; statistical machinery to evaluate their accuracy using retrospective simulation; a novel algorithm which is an order of magnitude faster than existing algorithms; a principled approach to choosing smaller, more optimal, domain-specific subsystems; and a new method to discover temporal relationships in the data. The result is a comprehensive approach for extracting knowledge from order-entry data to produce situation-specific treatment menus, which is applied to order-entry data at Wishard Hospital in Indianapolis. Retrospective simulations find that, in a large variety of clinical situations, a short menu will contain the clinicians' desired next actions. A prospective survey additionally finds that such menus aid physicians in writing order sets (in completeness and speed). This study demonstrates that clinical knowledge can be successfully extracted from treatment data for decision support.
Best Practices for Health Informatician Involvement in Interprofessional Health Care Teams
(Thieme, 2018-01) Holden, Richard J.; Binkheder, Samar; Patel, Jay; Viernes, Sara Helene P.; BioHealth Informatics, School of Informatics and Computing
Academic and nonacademic health informatics (HI) professionals (informaticians) serve on interprofessional health care teams with other professionals, such as physicians, nurses, pharmacists, dentists, and nutritionists. Presently, we argue for investing greater attention to the role health informaticians play on interprofessional teams and the best practices to support this role.
Clinical, technical, and implementation characteristics of real-world health applications using FHIR
(Oxford University Press, 2022-10-12) Griffin, Ashley C.; He, Lu; Sunjaya, Anthony P.; King, Andrew J.; Khan, Zubin; Nwadiugwu, Martin; Douthit, Brian; Subbian, Vignesh; Nguyen, Viet; Braunstein, Mark; Jaffe, Charles; Schleyer, Titus; Medicine, School of Medicine
Objective: Understanding the current state of real-world Fast Healthcare Interoperability Resources (FHIR) applications (apps) will benefit biomedical research and clinical care and facilitate advancement of the standard. This study aimed to provide a preliminary assessment of these apps' clinical, technical, and implementation characteristics. Materials and methods: We searched public repositories for potentially eligible FHIR apps and surveyed app implementers and other stakeholders. Results: Of the 112 apps surveyed, most focused on clinical care (74) or research (45); were implemented across multiple sites (56); and used SMART-on-FHIR (55) and FHIR version R4 (69). Apps were primarily stand-alone web-based (67) or electronic health record (EHR)-embedded (51), although 49 were not listed in an EHR app gallery. Discussion: Though limited in scope, our results show FHIR apps encompass various domains and characteristics. Conclusion: As FHIR use expands, this study-one of the first to characterize FHIR apps at large-highlights the need for systematic, comprehensive methods to assess their characteristics.
Clinicians' use of Health Information Exchange technologies for medication reconciliation in the U.S. Department of Veterans Affairs: a qualitative analysis
(Springer Nature, 2024-10-08) Snyder, Margie E.; Nguyen, Khoa A.; Patel, Himalaya; Sanchez, Steven L.; Traylor, Morgan; Robinson, Michelle J.; Damush, Teresa M.; Taber, Peter; Mixon, Amanda S.; Fan, Vincent S.; Savoy, April; Dismore, Rachel A.; Porter, Brian W.; Boockvar, Kenneth S.; Haggstrom, David A.; Locke, Emily R.; Gibson, Bryan S.; Byerly, Susan H.; Weiner, Michael; Russ-Jara, Alissa L.; Medicine, School of Medicine
Background: Medication reconciliation is essential for optimizing medication use. In part to promote effective medication reconciliation, the Department of Veterans Affairs (VA) invested substantial resources in health information exchange (HIE) technologies. The objectives of this qualitative study were to characterize VA clinicians' use of HIE tools for medication reconciliation in their clinical practice and to identify facilitators and barriers. Methods: We recruited inpatient and outpatient prescribers (physicians, nurse practitioners, physician assistants) and pharmacists at four geographically distinct VA medical centers for observations and interviews. Participants were observed as they interacted with HIE or medication reconciliation tools during routine work. Participants were interviewed about clinical decision-making pertaining to medication reconciliation and use of HIE tools, and about barriers and facilitators to use of the tools. Qualitative data were analyzed via inductive and deductive approaches using a priori codes. Results: A total of 63 clinicians participated. Over half (58%) were female, and the mean duration of VA clinical experience was 7 (range 0-32) years. Underlying motivators for clinicians seeking data external to their VA medical center were having new patients, current patients receiving care from an external institution, and clinicians' concerns about possible medication discrepancies among institutions. Facilitators for using HIE software were clinicians' familiarity with the HIE software, clinicians' belief that medication information would be available within HIE, and their confidence in the ability to find HIE medication-related data of interest quickly. Six overarching barriers to HIE software use for medication coordination included visual clutter and information overload within the HIE display; challenges with HIE interface navigation; lack of integration between HIE and other electronic health record interfaces, necessitating multiple logins and application switching; concerns with the dependability of HIE medication information; unfamiliarity with HIE tools; and a lack of HIE data from non-VA facilities. Conclusions: This study is believed to be the first to qualitatively characterize clinicians' HIE use with respect to medication reconciliation. Results inform recommendations to optimize HIE use for medication management activities. We expect that healthcare organizations and software vendors will be able to apply the findings to develop more effective and usable HIE information displays.
A Consensus Action Agenda for Achieving the National Health Information Infrastructure
(Oxford University Press, 2004) Yasnoff, William A.; Humphreys, Betsy L.; Overhage, J. Marc; Detmer, Don E.; Brennan, Patricia Flatley; Morris, Richard W.; Middleton, Blackford; Bates, David W.; Fanning, John P.; Medicine, School of Medicine
BACKGROUND: Improving the safety, quality, and efficiency of health care will require immediate and ubiquitous access to complete patient information and decision support provided through a National Health Information Infrastructure (NHII). METHODS: To help define the action steps needed to achieve an NHII, the U.S. Department of Health and Human Services sponsored a national consensus conference in July 2003. RESULTS: Attendees favored a public-private coordination group to guide NHII activities, provide education, share resources, and monitor relevant metrics to mark progress. They identified financial incentives, health information standards, and overcoming a few important legal obstacles as key NHII enablers. Community and regional implementation projects, including consumer access to a personal health record, were seen as necessary to demonstrate comprehensive functional systems that can serve as models for the entire nation. Finally, the participants identified the need for increased funding for research on the impact of health information technology on patient safety and quality of care. Individuals, organizations, and federal agencies are using these consensus recommendations to guide NHII efforts.
Distance-weighted Sinkhorn loss for Alzheimer's disease classification
(Elsevier, 2024-02-12) Wang, Zexuan; Zhan, Qipeng; Tong, Boning; Yang, Shu; Hou, Bojian; Huang, Heng; Saykin, Andrew J.; Thompson, Paul M.; Davatzikos, Christos; Shen, Li; Radiology and Imaging Sciences, School of Medicine
Traditional loss functions such as cross-entropy loss often quantify the penalty for each mis-classified training sample without adequately considering its distance from the ground truth class distribution in the feature space. Intuitively, the larger this distance is, the higher the penalty should be. With this observation, we propose a penalty called distance-weighted Sinkhorn (DWS) loss. For each mis-classified training sample (with predicted label A and true label B), its contribution to the DWS loss positively correlates to the distance the training sample needs to travel to reach the ground truth distribution of all the A samples. We apply the DWS framework with a neural network to classify different stages of Alzheimer's disease. Our empirical results demonstrate that the DWS framework outperforms the traditional neural network loss functions and is comparable or better to traditional machine learning methods, highlighting its potential in biomedical informatics and data science.
EHR-based cohort assessment for multicenter RCTs: a fast and flexible model for identifying potential study sites
(Oxford University Press, 2022) Nelson, Sarah J.; Drury, Bethany; Hood, Daniel; Harper, Jeremy; Bernard, Tiffany; Weng, Chunhua; Kennedy, Nan; LaSalle, Bernie; Gouripeddi, Ramkiran; Wilkins, Consuelo H.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
Objective: The Recruitment Innovation Center (RIC), partnering with the Trial Innovation Network and institutions in the National Institutes of Health-sponsored Clinical and Translational Science Awards (CTSA) Program, aimed to develop a service line to retrieve study population estimates from electronic health record (EHR) systems for use in selecting enrollment sites for multicenter clinical trials. Our goal was to create and field-test a low burden, low tech, and high-yield method. Materials and methods: In building this service line, the RIC strove to complement, rather than replace, CTSA hubs' existing cohort assessment tools. For each new EHR cohort request, we work with the investigator to develop a computable phenotype algorithm that targets the desired population. CTSA hubs run the phenotype query and return results using a standardized survey. We provide a comprehensive report to the investigator to assist in study site selection. Results: From 2017 to 2020, the RIC developed and socialized 36 phenotype-dependent cohort requests on behalf of investigators. The average response rate to these requests was 73%. Discussion: Achieving enrollment goals in a multicenter clinical trial requires that researchers identify study sites that will provide sufficient enrollment. The fast and flexible method the RIC has developed, with CTSA feedback, allows hubs to query their EHR using a generalizable, vetted phenotype algorithm to produce reliable counts of potentially eligible study participants. Conclusion: The RIC's EHR cohort assessment process for evaluating sites for multicenter trials has been shown to be efficient and helpful. The model may be replicated for use by other programs.
Enhancing narrative clinical guidance with computer-readable artifacts: Authoring FHIR implementation guides based on WHO recommendations
(Elsevier, 2021) Shivers, Jennifer; Amlung, Joseph; Ratanaprayul, Natschja; Rhodes, Bryn; Biondich, Paul; Herron School of Art and Design
Introduction: Narrative clinical guidelines often contain assumptions, knowledge gaps, and ambiguities that make translation into an electronic computable format difficult. This can lead to divergence in electronic implementations, reducing the usefulness of collected data outside of that implementation setting. This work set out to evolve guidelines-based data dictionaries by mapping to HL7 Fast Health Interoperability Resources (FHIR) and semantic terminology, thus progressing toward machine-readable guidelines that define the minimum data set required to support family planning and sexually transmitted infections. Material and methods: The data dictionaries were first structured to facilitate mapping to FHIR and semantic terminologies, including ICD-10, SNOMED-CT, LOINC, and RxNorm. FHIR resources and codes were assigned to data dictionary terms. The data dictionary and mappings were used as inputs for a newly developed tool to generate FHIR implementation guides. Results: Implementation guides for core data requirements for family planning and sexually transmitted infections were created. These implementation guides display data dictionary content as FHIR resources and semantic terminology codes. Challenges included the use of a two-dimensional spreadsheet to facilitate mapping, the need to create FHIR profiles and resource extensions, and applying FHIR to a data dictionary that was created with a user interface in mind. Conclusions: Authoring FHIR implementation guides is a complex and evolving practice, and there are limited examples for this groundbreaking work. Moving toward machine-readable guidelines by mapping to FHIR and semantic terminologies requires a thorough understanding of the context and use of terminology, an applied information model, and other strategies for optimizing the creation and long-term management of implementation guides. Next steps for this work include validation and, eventually, real-world application. The process for creating the data dictionary and for generating implementation guides should also be improved to prepare for this expanding work.

Browsing by Subject "Medical informatics"

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