Brad Doebbeling

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https://hdl.handle.net/1805/6678

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Browsing Brad Doebbeling by Author "BioHealth Informatics, School of Informatics and Computing"

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    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.
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    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.
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    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.