IU Indianapolis ScholarWorks :: Browsing by Author "Toscos, Tammy"

Browsing by Author "Toscos, Tammy"

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A big data augmented analytics platform to operationalize efficiencies at community clinics
(2016-04-15) Kunjan, Kislaya; Jones, Josette F.; Toscos, Tammy; Wu, Huanmei; Holden, Richard
Community Health Centers (CHCs) play a pivotal role in delivery of primary healthcare to the underserved, yet have not benefited from a modern data analytics platform that can support clinical, operational and financial decision making across the continuum of care. This research is based on a systems redesign collaborative of seven CHC organizations spread across Indiana to improve efficiency and access to care. Three research questions (RQs) formed the basis of this research, each of which seeks to address known knowledge gaps in the literature and identify areas for future research in health informatics. The first RQ seeks to understand the information needs to support operations at CHCs and implement an information architecture to support those needs. The second RQ leverages the implemented data infrastructure to evaluate how advanced analytics can guide open access scheduling – a specific use case of this research. Finally, the third RQ seeks to understand how the data can be visualized to support decision making among varying roles in CHCs. Based on the unique work and information flow needs uncovered at these CHCs, an end to-end analytics solution was designed, developed and validated within the framework of a rapid learning health system. The solution comprised of a novel heterogeneous longitudinal clinic data warehouse augmented with big data technologies and dashboard visualizations to inform CHCs regarding operational priorities and to support engagement in the systems redesign initiative. Application of predictive analytics on the health center data guided the implementation of open access scheduling and up to a 15% reduction in the missed appointment rates. Performance measures of importance to specific job profiles within the CHCs were uncovered. This was followed by a user-centered design of an online interactive dashboard to support rapid assessments of care delivery. The impact of the dashboard was assessed over time and formally validated through a usability study involving cognitive task analysis and a system usability scale questionnaire. Wider scale implementation of the data aggregation and analytics platform through regional health information networks could better support a range of health system redesign initiatives in order to address the national ‘triple aim’ of healthcare.
Clinician use of data elements from cardiovascular implantable electronic devices in clinical practice
(Elsevier, 2023-01-20) Daley, Carly; Coupe, Amanda; Allmandinger, Tina; Shirazi, Jonathan; Wagner, Shauna; Drouin, Michelle; Ahmed, Ryan; Toscos, Tammy; Mirro, Michael; BioHealth Informatics, School of Informatics and Computing
Background: Cardiovascular implantable electronic devices (CIEDs) capture an abundance of data for clinicians to review and integrate into the clinical decision-making process. The multitude of data from different device types and vendors presents challenges for viewing and using the data in clinical practice. Efforts are needed to improve CIED reports by focusing on key data elements used by clinicians. Objective: The purpose of this study was to uncover the extent to which clinicians use the specific types of data elements from CIED reports in clinical practice and explore clinicians' perceptions of CIED reports. Methods: A brief, web-based, cross-sectional survey study was deployed using snowball sampling from March 2020 through September 2020 to clinicians who are involved in the care of patients with CIEDs. Results: Among 317 clinicians, the majority specialized in electrophysiology (EP) (80.1%), were from North America (88.6%), and were white (82.2%). Over half (55.3%) were physicians. Arrhythmia episodes and ventricular therapies rated the highest among 15 categories of data presented, and nocturnal or resting heart rate and heart rate variability were rated the lowest. As anticipated, clinicians specializing in EP reported using the data significantly more than other specialties across nearly all categories. A subset of respondents offered general comments describing preferences and challenges related to reviewing reports. Conclusion: CIED reports contain an abundance of information that is important to clinicians; however, some data are used more frequently than others, and reports could be streamlined for users to improve access to key information and facilitate more efficient clinical decision making.
Data Analytics and Modeling for Appointment No-show in Community Health Centers
(SAGE, 2018) Mohammadi, Iman; Wu, Huanmei; Turkcan, Ayten; Toscos, Tammy; Doebbeling, Bradley N.; BioHealth Informatics, School of Informatics and Computing
Objectives: Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. Methods and Materials: We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models’ ability to identify patients missing their appointments. Results: Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier). Discussion: Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues. Conclusion: EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions.
Data Integration and Interoperability for Patient-Centered Remote Monitoring of Cardiovascular Implantable Electronic Devices
(MDPI, 2019-03-17) Daley, Carly; Toscos, Tammy; Mirro, Michael; BioHealth Informatics, School of Informatics and Computing
The prevalence of cardiovascular implantable electronic devices with remote monitoring capabilities continues to grow, resulting in increased volume and complexity of biomedical data. These data can provide diagnostic information for timely intervention and maintenance of implanted devices, improving quality of care. Current remote monitoring procedures do not utilize device diagnostics to their potential, due to the lack of interoperability and data integration among proprietary systems and electronic medical record platforms. However, the development of a technical framework that standardizes the data and improves interoperability shows promise for improving remote monitoring. Along with encouraging the implementation of this framework, we challenge the current paradigm and propose leveraging the framework to provide patients with their remote monitoring data. Patient-centered remote monitoring may empower patients and improve collaboration and care with health care providers. In this paper, we describe the implementation of technology to deliver remote monitoring data to patients in two recent studies. Our body of work explains the potential for developing a patent-facing information display that affords the meaningful use of implantable device data and enhances interactions with providers. This paradigm shift in remote monitoring-empowering the patient with data-is critical to using the vast amount of complex and clinically relevant biomedical data captured and transmitted by implantable devices to full potential.
Health Care Needs of Underserved Populations in the City of Indianapolis
(2016) Mohammadi, Iman; Hashemikhabir, Seyedsasan; Toscos, Tammy; Wu, Huanmei
Meeting the health care needs for underserved populations is crucial. We used EMR data to investigate the relationship between diagnoses and patient characteristics to help providers redesign healthcare systems that can meet the needs of underserved patients.
Impact of electronic personal health record use on engagement and intermediate health outcomes among cardiac patients: a quasi-experimental study
(Oxford University Press, 2016-01) Toscos, Tammy; Daley, Carly; Heral, Lisa; Doshi, Riddhi; Chen, Yu-Chieh; Eckert, George J.; Plant, Robert L.; Mirro, Michael J.; Biostatistics, School of Public Health
Objectives: To determine the impact of tethered personal health record (PHR) use on patient engagement and intermediate health outcomes among patients with coronary artery disease (CAD). Methods: Adult CAD patients (N = 200) were enrolled in this prospective, quasi-experimental observational study. Each patient received a PHR account and training on its use. PHRs were populated with information from patient electronic medical records, hosted by a Health Information Exchange. Intermediate health outcomes including blood pressure, body mass index, and hemoglobin A1c (HbA1c) were evaluated through electronic medical record review or laboratory tests. Trends in patient activation measure® (PAM) were determined through three surveys conducted at baseline, 6 and 12 months. Frequency of PHR use data was collected and used to classify participants into groups for analysis: Low, Active, and Super users. Results: There was no statistically significant improvement in patient engagement as measured by PAM scores during the study period. HbA1c levels improved significantly in the Active and Super user groups at 6 months; however, no other health outcome measures improved significantly. Higher PAM scores were associated with lower body mass index and lower HbA1c, but there was no association between changes in PAM scores and changes in health outcomes. Use of the PHR health diary increased significantly following PHR education offered at the 6-month study visit and an elective group refresher course. Conclusions: The study findings show that PHR use had minimal impact on intermediate health outcomes and no significant impact on patient engagement among CAD patients.
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 Computing
Background: 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.
Missing links: challenges in engaging the underserved with health information and communication technology
(ACM, 2016-05) Wright, Maria D.; Flanagan, Mindy E.; Kunjan, Kislaya; Doebbeling, Bradley N.; Toscos, Tammy; BioHealth Informatics, School of Informatics and Computing
We sought to understand underserved patients' preferences for health information technology (HIT) and examine the current use of personal health records (PHRs) in Community Health Centers (CHCs) serving low-income, uninsured, and underinsured patients. Forty-three patients and 49 clinic staff, administrators, and providers from these CHC systems were interviewed using open-ended questions assessing patient experience, perceptions of the CHC, access barriers, strategies used to overcome access barriers, technology access and use, and clinic operations and workflow. All seven CHC systems were at some stage of implementing PHRs, with two clinics having already completed implementation. Indiana CHCs have experienced barriers to implementing and using PHRs in a way that provides value for patients or providers/staff There was a general lack of awareness among patients regarding the existence of PHRs, their benefits and a lack of effective promotion to patients. Most patients have access to the internet, primarily through mobile phones, and desire greater functionality in order to communicate with CHCs and manage their health conditions. Despite decades of research, there remain barriers to the adoption and use of PHRs. Novel approaches must be developed to achieve the desired impact of PHRs on patient engagement, communication and satisfaction. Our findings provide a roadmap to greater engagement of patients via PHRs by expanding functionality, training both patients and clinic providers/staff, and incorporating adult learning strategies.
A Multidimensional Data Warehouse for Community Health Centers
(2015-11-05) Kunjan, Kislaya; Toscos, Tammy; Turkcan, Ayten; Doebbeling, Brad N.; BioHealth Informatics, School of Informatics and Computing
Community health centers (CHCs) play a pivotal role in healthcare delivery to vulnerable populations, but have not yet benefited from a data warehouse that can support improvements in clinical and financial outcomes across the practice. We have developed a multidimensional clinic data warehouse (CDW) by working with 7 CHCs across the state of Indiana and integrating their operational, financial and electronic patient records to support ongoing delivery of care. We describe in detail the rationale for the project, the data architecture employed, the content of the data warehouse, along with a description of the challenges experienced and strategies used in the development of this repository that may help other researchers, managers and leaders in health informatics. The resulting multidimensional data warehouse is highly practical and is designed to provide a foundation for wide-ranging healthcare data analytics over time and across the community health research enterprise.
"Notjustgirls": Exploring Male-related Eating Disordered Content across Social Media Platforms
(ACM, 2019-05) Pater, Jessica A.; Reining, Lauren E.; Miller, Andrew D.; Toscos, Tammy; Mynatt, Elizabeth D.; Human-Centered Computing, School of Informatics and Computing
Eating disorders (EDs) are a worldwide public health concern that impact approximately 10% of the U.S. population. Our previous research characterized these behaviors across online spaces. These characterizations have used clinical terminology, and their lexical variants, to identify ED content online. However, previous HCI research on EDs (including our own) suffers from a lack of gender and cultural diversity. In this paper, we designed a follow-up study of online ED characterizations, extending our previous methodologies to focus specifically on male/masculine-related content. We highlight the similarities and differences found in the terminology utilized and media archetypes associated with the social media content. Finally, we discuss other considerations highlighted through our analysis of the male-related content that is missing from the previous research.

Browsing by Author "Toscos, Tammy"

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