IU Indianapolis ScholarWorks :: Browsing by Author "Duke, Jon"

Browsing by Author "Duke, Jon"

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Benchmarks for ethically credible partnerships between industry and academic health centers: beyond disclosure of financial conflicts of interest
(Springer (Biomed Central Ltd.), 2015-12) Meslin, Eric M.; Rager, Joshua B.; Schwartz, Peter H.; Quaid, Kimberly A.; Gaffney, Margaret M.; Duke, Jon; Tierney, William H.; Department of Philosophy, IU School of Liberal Arts
Relationships between industry and university-based researchers have been commonplace for decades and have received notable attention concerning the conflicts of interest these relationships may harbor. While new efforts are being made to update conflict of interest policies and make industry relationships with academia more transparent, the development of broader institutional partnerships between industry and academic health centers challenges the efficacy of current policy to effectively manage these innovative partnerships. In this paper, we argue that existing strategies to reduce conflicts of interest are not sufficient to address the emerging models of industry-academic partnerships because they focus too narrowly on financial matters and are not comprehensive enough to mitigate all ethical risk. Moreover, conflict-of-interest strategies are not designed to promote best practices nor the scientific and social benefits of academic-industry collaboration. We propose a framework of principles and benchmarks for "ethically credible partnerships" between industry and academic health centers and describe how this framework may provide a practical and comprehensive approach for designing and evaluating such partnerships.
Endorsement, Prior Action, and Language: Modeling Trusted Advice in Computerized Clinical Alerts
(ACM, 2016-05) Chattopadhyay, Debaleena; Duke, Jon; Bolchini, Davide; Human-Centered Computing, School of Informatics and Computing
The safe prescribing of medications via computerized physician order entry routinely relies on clinical alerts. Alert compliance, however, remains surprisingly low, with up to 95% often ignored. Prior approaches, such as improving presentational factors in alert design, had limited success, mainly due to physicians' lack of trust in computerized advice. While designing trustworthy alert is key, actionable design principles to embody elements of trust in alerts remain little explored. To mitigate this gap, we introduce a model to guide the design of trust-based clinical alerts-based on what physicians value when trusting advice from peers in clinical activities. We discuss three key dimensions to craft trusted alerts: using colleagues' endorsement, foregrounding physicians' prior actions, and adopting a suitable language. We exemplify our approach with emerging alert designs from our ongoing research with physicians and contribute to the current debate on how to design effective alerts to improve patient safety.
Erratum to: Benchmarks for ethically credible partnerships between industry and academic health centers: beyond disclosure of financial conflicts of interest.
(Springer, 2016) Meslin, Eric M.; Rager, Joshua B.; Schwartz, Peter H.; Quaid, Kimberly A.; Gaffney, Margaret M.; Duke, Jon; Tierney, William M.; Department of Philosophy, IU School of Liberal Arts
From Critique to Collaboration: Rethinking Computerized Clinical Alerts
(Office of the Vice Chancellor for Research, 2016-04-08) Bolchini, Davide; Chattopadhyay, Debaleena; Jia, Yuan; Ghahari, Romisa R.; Duke, Jon
The safe prescribing of medications via computerized physician order entry routinely relies on clinical alerts. Alert compliance, however, remains surprisingly low—with up to 96% of such alerts ignored daily. Prior approaches, such as improving presentational factors in alert design, had limited success, mainly due to physicians’ lack of trust in computerized advice. While designing trustworthy alert is key, actionable design principles to embody elements of trust in alerts remain little explored. To address this issue, we focus on improving the trust between physicians and computerized advice by examining why physicians trust their medical colleagues. To understand trusted advice among physicians, we conducted three contextual inquiries in a hospital setting (n = 22) and corroborated our findings with a survey (n = 37). Drivers that guided physicians in trusting peer advice included: timeliness of the advice, collaborative language, empathy, level of specialization, and medical hierarchy. Based on these findings, we introduced seven design directions for trust-based alerts: endorsement, transparency, team sensing, collaborative, empathic, conflict mitigating, and agency laden. Grounded in these results, we then proposed a model to guide the design of trust-based clinical alerts. Our model constitutes of three key dimensions, using colleagues’ endorsement, foregrounding physicians’ prior actions, and adopting a suitable language. Using this model, we iteratively designed, pruned, and validated a set of novel alert designs. We are currently evaluating eleven alert designs in an online survey with physicians. The ongoing survey evaluates the likelihood of alert compliance and the perceived value of our proposed trust-based alerts. Next, we are planning in-lab studies to evaluate physicians’ cognitive load during decision making and measure attention to different trust cues using gaze duration and trajectories. Our work contributes to the current debate on how to design effective alerts to improve patient safety. Acknowledgements. This research material is based on work supported by the National Science Foundation under Grant #1343973. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the NSF.
How Good Are Provider Annotations?: A Machine Learning Approach
(Wiley, 2017-01) Malas, M. Said; Kasthurirathne, Suranga; Moe, Sharon; Duke, Jon; Department of Medicine, IU School of Medicine
Introduction: CMS-2728 form (Medical Evidence Report) assesses 23 comorbidities chosen to reflect poor outcomes and increased mortality risk. Previous studies questioned the validity of physician reporting on forms CMS-2728. We hypothesize that reporting of comorbidities by computer algorithms identifies more comorbidities than physician completion, and, therefore, is more reflective of underlying disease burden. Methods: We collected data from CMS-2728 forms for all 296 patients who had incident ESRD diagnosis and received chronic dialysis from 2005 through 2014 at Indiana University outpatient dialysis centers. We analyzed patients' data from electronic medical records systems that collated information from multiple health care sources. Previously utilized algorithms or natural language processing was used to extract data on 10 comorbidities for a period of up to 10 years prior to ESRD incidence. These algorithms incorporate billing codes, prescriptions, and other relevant elements. We compared the presence or unchecked status of these comorbidities on the forms to the presence or absence according to the algorithms. Findings: Computer algorithms had higher reporting of comorbidities compared to forms completion by physicians. This remained true when decreasing data span to one year and using only a single health center source. The algorithms determination was well accepted by a physician panel. Importantly, algorithms use significantly increased the expected deaths and lowered the standardized mortality ratios. Discussion: Using computer algorithms showed superior identification of comorbidities for form CMS-2728 and altered standardized mortality ratios. Adapting similar algorithms in available EMR systems may offer more thorough evaluation of comorbidities and improve quality reporting.
Integration of FHIR to Facilitate Electronic Case Reporting: Results from a Pilot Study
(IOS, 2019) Dixon, Brian E.; Taylor, David E.; Choi, Myung; Riley, Michael; Schneider, Trey; Duke, Jon; Epidemiology, School of Public Health
Current approaches to gathering sexually transmitted infection (STI) case information for surveillance efforts are inefficient and lead to underreporting of disease burden. Electronic health information systems offer an opportunity to improve how STI case information can be gathered and reported to public health authorities. To test the feasibility of a standards-based application designed to automate STI case information collection and reporting, we conducted a pilot study where electronic laboratory messages triggered a FHIR-based application to query a patient’s electronic health record for details needed for an electronic case report (eCR). Out of 214 cases observed during a one week period, 181 (84.6%) could be successfully confirmed automatically using the FHIR-based application. Data quality and information representation challenges were identified that will require collaborative efforts to improve the structure of electronic clinical messages as well as the robustness of the FHIR application.
A Novel Visualization Tool for Evaluating Medication Side-Effects in Multi-drug Regimens
(2009) Duke, Jon; Faiola, Anthony; Kharrazi, Hadi
The evaluation and management of medication side-effects is a common and complex task for physicians. Information visualization has the potential to increase the efficiency and reduce the cognitive load involved in this process. We describe the design and development of Rxplore, a novel tool for assessing medication side-effects. Rxplore supports simultaneous lookup of multiple medications and an intuitive visual representation of query results. In a pilot study of Rxplore’s usability and utility, physicians rated the system highly for efficiency, intuitiveness, and clinical value.
Predictive Modeling of Hypoglycemia for Clinical Decision Support in Evaluating Outpatients with Diabetes Mellitus
(Taylor & Francis, 2019) Li, Xiaochun; Yu, Shengsheng; Zhang, Zuoyi; Radican, Larry; Cummins, Jonathan; Engel, Samuel S.; Iglay, Kristy; Duke, Jon; Baker, Jarod; Brodovicz, Kimberly G.; Naik, Ramachandra G.; Leventhal, Jeremy; Chatterjee, Arnaub K.; Rajpathak, Swapnil; Weiner, Michael; Biostatistics, School of Public Health
Objective: Hypoglycemia occurs in 20–60% of patients with diabetes mellitus. Identifying at-risk patients can facilitate interventions to lower risk. We sought to develop a hypoglycemia prediction model. Methods: In this retrospective cohort study, urban adults prescribed a diabetes drug between 2004 and 2013 were identified. Demographic and clinical data were extracted from an electronic medical record (EMR). Laboratory tests, diagnostic codes and natural language processing (NLP) identified hypoglycemia. We compared multiple logistic regression, classification and regression trees (CART), and random forest. Models were evaluated on an independent test set or through cross-validation. Results: The 38,780 patients had mean age 57 years; 56% were female, 40% African-American and 39% uninsured. Hypoglycemia occurred in 8128 (539 identified only by NLP). In logistic regression, factors positively associated with hypoglycemia included infection, non-long-acting insulin, dementia and recent hypoglycemia. Negatively associated factors included long-acting insulin plus sulfonylurea, and age 75 or older. The models’ area under curve was similar (logistic regression, 89%; CART, 88%; random forest, 90%, with ten-fold cross-validation). Conclusions: NLP improved identification of hypoglycemia. Non-long-acting insulin was an important risk factor. Decreased risk with age may reflect treatment or diminished awareness of hypoglycemia. More complex models did not improve prediction.
Understanding Advice Sharing among Physicians: Towards Trust-Based Clinical Alerts
(Oxford, 2016-06) Chattopadhyay, Debaleena; Ghahari, Romisa Rohani; Duke, Jon; Bolchini, Davide; Department of Human-Centered Computing, School of Informatics and Computing
Safe prescribing of medications relies on drug safety alerts, but up to 96% of such warnings are ignored by physicians. Prior research has proposed improvements to the design of alerts, but with limited increase in adherence. We propose a different perspective: before re-designing alerts, we focus on improving the trust between physicians and computerized advice by examining why physicians trust their medical colleagues. To understand trusted advice among physicians, we conducted three contextual inquiries in a hospital setting (22 participants), and corroborated our findings with a survey (37 participants). Drivers that guide physicians in trusting peer advice include: timeliness of the advice, collaborative language, empathy, level of specialization and medical hierarchy. Based on these findings, we introduce seven design directions for trust-based alerts: endorsement, transparency, team sensing, collaborative, empathic, conflict mitigating and agency laden. Our work contributes to novel alert design strategies to improve the effectiveness of drug safety advice.

Browsing by Author "Duke, Jon"

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