Browsing by Subject "Health informatics"
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Item A pragmatic, stepped-wedge, hybrid type II trial of interoperable clinical decision support to improve venous thromboembolism prophylaxis for patients with traumatic brain injury(Springer Nature, 2024-08-05) Tignanelli, Christopher J.; Shah, Surbhi; Vock, David; Siegel, Lianne; Serrano, Carlos; Haut, Elliott; Switzer, Sean; Martin, Christie L.; Rizvi, Rubina; Peta, Vincent; Jenkins, Peter C.; Lemke, Nicholas; Thyvalikakath, Thankam; Osheroff, Jerome A.; Torres, Denise; Vawdrey, David; Callcut, Rachael A.; Butler, Mary; Melton, Genevieve B.; Surgery, School of MedicineBackground: Venous thromboembolism (VTE) is a preventable medical condition which has substantial impact on patient morbidity, mortality, and disability. Unfortunately, adherence to the published best practices for VTE prevention, based on patient centered outcomes research (PCOR), is highly variable across U.S. hospitals, which represents a gap between current evidence and clinical practice leading to adverse patient outcomes. This gap is especially large in the case of traumatic brain injury (TBI), where reluctance to initiate VTE prevention due to concerns for potentially increasing the rates of intracranial bleeding drives poor rates of VTE prophylaxis. This is despite research which has shown early initiation of VTE prophylaxis to be safe in TBI without increased risk of delayed neurosurgical intervention or death. Clinical decision support (CDS) is an indispensable solution to close this practice gap; however, design and implementation barriers hinder CDS adoption and successful scaling across health systems. Clinical practice guidelines (CPGs) informed by PCOR evidence can be deployed using CDS systems to improve the evidence to practice gap. In the Scaling AcceptabLE cDs (SCALED) study, we will implement a VTE prevention CPG within an interoperable CDS system and evaluate both CPG effectiveness (improved clinical outcomes) and CDS implementation. Methods: The SCALED trial is a hybrid type 2 randomized stepped wedge effectiveness-implementation trial to scale the CDS across 4 heterogeneous healthcare systems. Trial outcomes will be assessed using the RE2-AIM planning and evaluation framework. Efforts will be made to ensure implementation consistency. Nonetheless, it is expected that CDS adoption will vary across each site. To assess these differences, we will evaluate implementation processes across trial sites using the Exploration, Preparation, Implementation, and Sustainment (EPIS) implementation framework (a determinant framework) using mixed-methods. Finally, it is critical that PCOR CPGs are maintained as evidence evolves. To date, an accepted process for evidence maintenance does not exist. We will pilot a "Living Guideline" process model for the VTE prevention CDS system. Discussion: The stepped wedge hybrid type 2 trial will provide evidence regarding the effectiveness of CDS based on the Berne-Norwood criteria for VTE prevention in patients with TBI. Additionally, it will provide evidence regarding a successful strategy to scale interoperable CDS systems across U.S. healthcare systems, advancing both the fields of implementation science and health informatics.Item Analyzing Chlamydia and Gonorrhea Health Disparities from Health Information Systems: A Closer Examination Using Spatial Statistics and Geographical Information Systems(2022-05) Lai, Patrick T. S.; Jones, Josette; Dixon, Brian E.; Wilson, Jeffrey; Wu, Huanmei; Shih, PatrickThe emergence and development of electronic health records have contributed to an abundance of patient data that can greatly be used and analyzed to promote health outcomes and even eliminate health disparities. However, challenges exist in the data received with factors such as data inconsistencies, accuracy issues, and unstructured formatting being evident. Furthermore, the current electronic health records and clinical information systems that are present do not contain the social determinants of health that may enhance our understanding of the characteristics and mechanisms of disease risk and transmission as well as health disparities research. Linkage to external population health databases to incorporate these social determinants of health is often necessary. This study provides an opportunity to identify and analyze health disparities using geographical information systems on two important sexually transmitted diseases in chlamydia and gonorrhea using Marion County, Indiana as the geographical location of interest. Population health data from the Social Assets and Vulnerabilities Indicators community information system and electronic health record data from the Indiana Network for Patient Care will be merged to measure the distribution and variability of greatest chlamydia and gonorrhea risk and to determine where the greatest areas of health disparities exist. A series of both statistical and spatial statistical methods such as a longitudinal measurement of health disparity through the Gini index, a hot-spot and cluster analysis, and a geographically weighted regression will be conducted in this study. The outcome and broader impact of this research will contribute to enhanced surveillance and increased effective strategies in identifying the level of health disparities for sexually transmitted diseases in vulnerable localities and high-risk communities. Additionally, the findings from this study will lead to improved standardization and accuracy in data collection to facilitate subsequent studies involving multiple disparate data sources. Finally, this study will likely introduce ideas for potential social determinants of health to be incorporated into electronic health records and clinical information systems.Item Asynchronous Conferencing Through a Secure Messaging Application Increases Reporting of Medical Errors in a Mature Trauma Center(Sage, 2023) Lee, Joy L.; Isenberg, Scott; Adams, Georgann; Thurston, Maria; Hammer, Peter M.; Mohanty, Sanjay K.; Jenkins, Peter C.; Surgery, School of MedicineBackground: Medical errors occur frequently, yet they are often under-reported and strategies to increase the reporting of medical errors are lacking. In this work, we detail how a level 1 trauma center used a secure messaging application to track medical errors and enhance its quality improvement initiatives. Methods: We describe the formulation, implementation, evolution, and evaluation of a chatroom integrated into a secure texting system to identify performance improvement and patient safety (PIPS) concerns. For evaluation, we used descriptive statistics to examine PIPS reporting by the reporting method over time, the incidence of mortality and unplanned ICU readmissions tracked in the hospital trauma registry over the same, and time-to-loop closure over the study period to quantify the impact of the processes instituted by the PIPS team. We also categorized themes of reported events. Results: With the implementation of a PIPS chatroom, the number of events reported each month increased and texting became the predominant way for users to report trauma PIPS events. This increase in PIPS reporting did not appear to be accompanied by an increase in mortality and unplanned ICU readmissions. The PIPS team also improved the tracking and timely resolution of PIPS events and observed a decrease in time-to-loop closure with the implementation of the PIPS chatroom. Conclusions: The adoption of clinical texting as a way to report PIPS events was associated with increased reporting of such events and more timely resolution of concerns regarding patient safety and healthcare quality.Item Decisional Informatics for Psychosocial Rehabilitation: A Feasibility Pilot on Tailored and Fluid Treatment Algorithms for Serious Mental Illness(Wolters Kluwer, 2017-11) Choi, Jimmy; Lysaker, Paul H.; Bell, Morris D.; Dixon, Lisa; Margolies, Paul; Gold, Matthew; Golden-Roose, Elizabeth; Thime, Warren; Haber, Lawrence C.; Dewberry, Michael J.; Stevens, Michael; Pearlson, Godfrey D.; Fiszdon, Joanna M.; Medicine, School of MedicineThis study introduces a computerized clinical decision-support tool, the Fluid Outpatient Rehabilitation Treatment (FORT), that incorporates individual and ever-evolving patient needs to guide clinicians in developing and updating treatment decisions in real-time. In this proof-of-concept feasibility pilot, FORT was compared against traditional treatment planning using similar behavioral therapies in 52 adults with severe mental illness attending community-based day treatment. At posttreatment and follow-up, group differences and moderate-to-large effect sizes favoring FORT were detected in social function, work readiness, self-esteem, working memory, processing speed, and mental flexibility. Of participants who identified obtaining a General Education Diploma as their goal, 73% in FORT passed the examination compared with 18% in traditional treatment planning. FORT was also associated with higher agency cost-effectiveness and a better average benefit-cost ratio, even when considering diagnosis, baseline symptoms, and education. Although the comparison groups were not completely equivalent, the findings suggest computerized decision support systems that collaborate with human decision-makers to personalize psychiatric rehabilitation and address critical decisions may have a role in improving treatment effectiveness and efficiency.Item Developing a medication adherence technologies repository: proposed structure and protocol for an online real-time Delphi study(BMJ, 2022-04-22) Nabergoj Makovec, Urska; Goetzinger, Catherine; Ribaut, Janette; Barnestein-Fonseca, Pilar; Haupenthal, Frederik; Herdeiro, Maria Teresa; Grant, Sean Patrick; Jácome, Cristina; Roque, Fatima; Smits, Dins; Tadic, Ivana; Dima, Alexandra L.; European Network to Advance Best practices and technoLogy on medication adherencE (ENABLE); Epidemiology, Richard M. Fairbanks School of Public HealthIntroduction: An online interactive repository of available medication adherence technologies may facilitate their selection and adoption by different stakeholders. Developing a repository is among the main objectives of the European Network to Advance Best practices and technoLogy on medication adherencE (ENABLE) COST Action (CA19132). However, meeting the needs of diverse stakeholders requires careful consideration of the repository structure. Methods and analysis: A real-time online Delphi study by stakeholders from 39 countries with research, practice, policy, patient representation and technology development backgrounds will be conducted. Eleven ENABLE members from 9 European countries formed an interdisciplinary steering committee to develop the repository structure, prepare study protocol and perform it. Definitions of medication adherence technologies and their attributes were developed iteratively through literature review, discussions within the steering committee and ENABLE Action members, following ontology development recommendations. Three domains (product and provider information (D1), medication adherence descriptors (D2) and evaluation and implementation (D3)) branching in 13 attribute groups are proposed: product and provider information, target use scenarios, target health conditions, medication regimen, medication adherence management components, monitoring/measurement methods and targets, intervention modes of delivery, target behaviour determinants, behaviour change techniques, intervention providers, intervention settings, quality indicators and implementation indicators. Stakeholders will evaluate the proposed definition and attributes' relevance, clarity and completeness and have multiple opportunities to reconsider their evaluations based on aggregated feedback in real-time. Data collection will stop when the predetermined response rate will be achieved. We will quantify agreement and perform analyses of process indicators on the whole sample and per stakeholder group. Ethics and dissemination: Ethical approval for the COST ENABLE activities was granted by the Malaga Regional Research Ethics Committee. The Delphi protocol was considered compliant regarding data protection and security by the Data Protection Officer from University of Basel. Findings from the Delphi study will form the basis for the ENABLE repository structure and related activities.Item Involving patients as key stakeholders in the design of cardiovascular implantable electronic device data dashboards: Implications for patient care(Elsevier, 2020-05-11) Daley, Carly; Ghahari, Romisa Rohani; Drouin, Michelle; Ahmed, Ryan; Wagner, Shauna; Reining, Lauren; Coupe, Amanda; Toscos, Tammy; Mirro, Michael; BioHealth Informatics, School of Informatics and ComputingBackground: Data from remote monitoring (RM) of cardiovascular implantable electronic devices (CIEDs) currently are not accessible to patients despite demand. The typical RM report contains multiple pages of data for trained technicians to read and interpret and requires a patient-centered approach to be curated to meet individual user needs. Objective: The purpose of this study was to understand which RM data elements are important to patients and to gain design insights for displaying meaningful data in a digital dashboard. Methods: Adults with implantable cardioverter-defibrillators (ICDs) and pacemakers (PMs) participated in this 2-phase, user-centered design study. Phase 1 included a card-sorting activity to prioritize device data elements. Phase 2 included one-on-one design sessions to gather insights and feedback about a visual display (labels and icons). Results: Twenty-nine adults (mean age 71.8 ± 11.6 years; 51.7% female; 89.7% white) participated. Priority data elements for both ICD and PM groups in phase 1 (n = 19) were related to cardiac episodes, device activity, and impedance values. Recommended replacement time for battery was high priority for the PM group but not the ICD group. Phase 2 (n = 10) revealed that patients would like descriptive, nontechnical terms to depict the data and icons that are intuitive and informative. Conclusion: This user-centered design study demonstrated that patients with ICDs and PMs were able to prioritize specific data from a comprehensive list of data elements that they had never seen before. This work contributes to the goal of sharing RM data with patients in a way that optimizes the RM feature of CIEDs for improving patient outcomes and clinical care.Item Strategies prescribers and pharmacists use to identify and mitigate adverse drug reactions in inpatient and outpatient care: a cognitive task analysis at a US Veterans Affairs Medical Center(BMJ, 2022-02-21) Nguyen, Khoa Anh; Militello, Laura G.; Ifeachor, Amanda; Arthur, Karen J.; Glassman, Peter A.; Zillich, Alan J.; Weiner, Michael; Russ-Jara, Alissa L.; Medicine, School of MedicineObjective: To develop a descriptive model of the cognitive processes used to identify and resolve adverse drug reactions (ADRs) from the perspective of healthcare providers in order to inform future informatics efforts SETTING: Inpatient and outpatient care at a tertiary care US Veterans Affairs Medical Center. Participants: Physicians, nurse practitioners and pharmacists who report ADRs. Outcomes: Descriptive model and emerging themes from interviews. Results: We conducted critical decision method interviews with 10 physicians and 10 pharmacists. No nurse practitioners submitted ADR incidents. We generated a descriptive model of an ADR decision-making process and analysed emerging themes, categorised into four stages: detection of potential ADR, investigation of the problem's cause, risk/benefit consideration, and plan, action and follow-up. Healthcare professionals (HCPs) relied on several confirmatory or disconfirmatory cues to detect and investigate potential ADRs. Evaluating risks and benefits of related medications played an essential role in HCPs' pursuits of solutions CONCLUSIONS: This study provides an illustrative model of how HCPs detect problems and make decisions regarding ADRs. The design of supporting technology for potential ADR problems should align with HCPs' real-world cognitive strategies, to assist fully in detecting and preventing ADRs for patients.Item Supporting Collaborative Health Tracking in the Hospital: Patients' Perspectives(Association for Computing Machinery, 2018-04-21) Mishra, Sonali R.; Miller, Andrew D.; Haldar, Shefali; Khelifi, Maher; Eschler, Jordan; Elera, Rashmi G.; Pollack, Ari H.; Pratt, Wanda; Human-Centered Computing, School of Informatics and ComputingThe hospital setting creates a high-stakes environment where patients' lives depend on accurate tracking of health data. Despite recent work emphasizing the importance of patients' engagement in their own health care, less is known about how patients track their health and care in the hospital. Through interviews and design probes, we investigated hospitalized patients' tracking activity and analyzed our results using the stage-based personal informatics model. We used this model to understand how to support the tracking needs of hospitalized patients at each stage. In this paper, we discuss hospitalized patients' needs for collaboratively tracking their health with their care team. We suggest future extensions of the stage-based model to accommodate collaborative tracking situations, such as hospitals, where data is collected, analyzed, and acted on by multiple people. Our findings uncover new directions for HCI research and highlight ways to support patients in tracking their care and improving patient safety.Item Supporting Collaborative Health Tracking in the Hospital: Patients’ Perspectives(ACM, 2018) Mishra, Sonali R.; Miller, Andrew D.; Haldar, Shefali; Khelifi, Maher; Eschler, Jordan; Elera, Rashmi G.; Pollack, Ari H; Pratt, Wanda; Human-Centered Computing, School of Informatics and ComputingThe hospital setting creates a high-stakes environment where patients’ lives depend on accurate tracking of health data. Despite recent work emphasizing the importance of patients’ engagement in their own health care, less is known about how patients track their health and care in the hospital. Through interviews and design probes, we investigated hospitalized patients’ tracking activity and analyzed our results using the stage-based personal informatics model. We used this model to understand how to support the tracking needs of hospitalized patients at each stage. In this paper, we discuss hospitalized patients’ needs for collaboratively tracking their health with their care team. We suggest future extensions of the stage-based model to accommodate collaborative tracking situations, such as hospitals, where data is collected, analyzed, and acted on by multiple people. Our findings uncover new directions for HCI research and highlight ways to support patients in tracking their care and improving patient safety.Item Using machine learning to detect sarcopenia from electronic health records(Sage, 2023-08-29) Luo, Xiao; Ding, Haoran; Broyles, Andrea; Warden, Stuart J.; Moorthi, Ranjani N.; Imel, Erik A.; Physical Therapy, School of Health and Human SciencesIntroduction: Sarcopenia (low muscle mass and strength) causes dysmobility and loss of independence. Sarcopenia is often not directly coded or described in electronic health records (EHR). The objective was to improve sarcopenia detection using structured data from EHR. Methods: Adults undergoing musculoskeletal testing (December 2017-March 2020) were classified as meeting sarcopenia thresholds for 0 (controls), ≥1 (Sarcopenia-1), or ≥2 (Sarcopenia-2) tests. Electronic health record diagnoses, medications, and laboratory testing were extracted from the Indiana Network for Patient Care. Five machine learning models were applied to EHR data for predicting sarcopenia. Results: Of 1304 participants, 1055 were controls, 249 met Sarcopenia-1 and 76 met Sarcopenia-2. Sarcopenic participants were older, with higher fat mass, Charlson Comorbidity Index, and more chronic diseases. All models performed better for Sarcopenia-2 than Sarcopenia-1. The top performing models for Sarcopenia-1 were Logistic Regression [area under the curve (AUC) 71.59 (95% confidence interval [CI], 71.51-71.66)] and Multi-Layer Perceptron [AUC 71.48 (95%CI, 71.00-71.97)]. The top performing models for Sarcopenia-2 were Logistic Regression [AUC 91.44 (95%CI, 91.28-91.60)] and Support Vector Machine [AUC 90.81 (95%CI, 88.41-93.20)]. For the best Logistic Regression Model, important sarcopenia predictors included diabetes mellitus, digestive system complaints, signs and symptoms involving the nervous, musculoskeletal and respiratory systems, metabolic disorders, and kidney or urinary tract disorders. Opioids, corticosteroids, and antihyperlipidemic drugs were also more common among sarcopenic participants. Conclusions: Applying machine learning models, sarcopenia can be predicted from structured data in EHR, which may be developed through future studies to facilitate large-scale early detection and intervention in clinical populations.