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Item Predictive Modeling for Appointment No-show in Community Health Centers(2016) Mohammadi, Iman; Turkcan, Ayten; Toscos, Tammy; Wu, Huanmei; Doebbeling, Brad N.Reducing no-show rates is one of the most important measures of access to care in Community Health Centers (CHCs). We used EMR and scheduling data to develop no-show prediction models to help design effective scheduling processes and system redesign for greater access in CHCs. Patient and provider characteristics and visit features are key elements for predicting patient adherence with an appointment.Item Dependability and Security in Medical Information System(Springer Nature, 2007) Zou, Xukai; Dai, Yuan-Shun; Doebbeling, Bradley; Qi, Mingrui; Department of Computer and Information Science, School of ScienceMedical Information Systems (MIS) help medical practice and health care significantly. Security and dependability are two increasingly important factors for MIS nowadays. In one hand, people would be willing to step into the MIS age only when their privacy and integrity can be protected and guaranteed with MIS systems. On the other hand, only secure and reliable MIS systems would provide safe and solid medical and health care service to people. In this paper, we discuss some new security and reliability technologies which are necessary for and can be integrated with existing MISs and make the systems highly secure and dependable. We also present an implemented Middleware architecture which has been integrated with the existing VISTA/CPRS system in the U.S. Department of Veterans Affairs seamlessly and transparently.Item 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 ComputingWe 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.Item 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 ComputingObjectives: 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.Item 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 ComputingCommunity 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.Item Applying human factors principles to alert design increases efficiency and reduces prescribing errors in a scenario-based simulation(Oxford University Press, 2014-10) Russ, Alissa L.; Zillich, Alan J.; Melton, Brittany L.; Russell, Scott A.; Chen, Siying; Spina, Jeffrey R.; Weiner, Michael; Johnson, Elizabette G.; Daggy, Joanne K.; McAnas, M. Sue; Hawsey, Jason M.; Puleo, Anthony G.; Doebbeling, Bradley N.; Saleem, Jason J.; Medicine Faculty Volunteers, IU School of MedicineOBJECTIVE: To apply human factors engineering principles to improve alert interface design. We hypothesized that incorporating human factors principles into alerts would improve usability, reduce workload for prescribers, and reduce prescribing errors. MATERIALS AND METHODS: We performed a scenario-based simulation study using a counterbalanced, crossover design with 20 Veterans Affairs prescribers to compare original versus redesigned alerts. We redesigned drug-allergy, drug-drug interaction, and drug-disease alerts based upon human factors principles. We assessed usability (learnability of redesign, efficiency, satisfaction, and usability errors), perceived workload, and prescribing errors. RESULTS: Although prescribers received no training on the design changes, prescribers were able to resolve redesigned alerts more efficiently (median (IQR): 56 (47) s) compared to the original alerts (85 (71) s; p=0.015). In addition, prescribers rated redesigned alerts significantly higher than original alerts across several dimensions of satisfaction. Redesigned alerts led to a modest but significant reduction in workload (p=0.042) and significantly reduced the number of prescribing errors per prescriber (median (range): 2 (1-5) compared to original alerts: 4 (1-7); p=0.024). DISCUSSION: Aspects of the redesigned alerts that likely contributed to better prescribing include design modifications that reduced usability-related errors, providing clinical data closer to the point of decision, and displaying alert text in a tabular format. Displaying alert text in a tabular format may help prescribers extract information quickly and thereby increase responsiveness to alerts. CONCLUSIONS: This simulation study provides evidence that applying human factors design principles to medication alerts can improve usability and prescribing outcomes.Item Optimizing Perioperative Decision Making: Improved Information for Clinical Workflow Planning(2012-11) Doebbeling, Bradley N.; Burton, Matthew M.; Wiebke, Eric A.; Miller, Spencer; Baxter, Laurence; Miller, Donald; Alvarez, Jorge; Pekny, JosephPerioperative care is complex and involves multiple interconnected subsystems. Delayed starts, prolonged cases and overtime are common. Surgical procedures account for 40–70% of hospital revenues and 30–40% of total costs. Most planning and scheduling in healthcare is done without modern planning tools, which have potential for improving access by assisting in operations planning support. We identified key planning scenarios of interest to perioperative leaders, in order to examine the feasibility of applying combinatorial optimization software solving some of those planning issues in the operative setting. Perioperative leaders desire a broad range of tools for planning and assessing alternate solutions. Our modeled solutions generated feasible solutions that varied as expected, based on resource and policy assumptions and found better utilization of scarce resources. Combinatorial optimization modeling can effectively evaluate alternatives to support key decisions for planning clinical workflow and improving care efficiency and satisfaction.Item Multihospital Infection Prevention Collaborative: Informatics Challenges and Strategies to Prevent MRSA(2013-11) Doebbeling, Bradley N.; Flanagan, Mindy E.; Nall, Glenna; Hoke, Shawn; Rosenman, Marc; Kho, AbelWe formed a collaborative to spread effective MRSA prevention strategies. We conducted a two-phase, multisite, quasi-experimental study of seven hospital systems (11 hospitals) in IN, MT, ME and Ontario, Canada over six years. Patients with prior MRSA were identified at admission using regional health information exchange data. We developed a system to return an alert message indicating a prior history of MRSA, directed to infection preventionists and admissions. Alerts indicated the prior anatomic site, and the originating institution. The combined approach of training and coaching, implementation of MRSA registries, notifying hospitals on admission of previously infected or colonized patients, and change strategies was effective in reducing MRSA infections over 80%. Further research and development of electronic surveillance tools is needed to better integrate the varied data source and support preventing MRSA infections. Our study supports the importance of hospitals collaborating to share data and implement effective strategies to prevent MRSA.Item Provider Perceptions of Colorectal Cancer Screening Clinical Decision Support at Three Benchmark Institutions(2009-11) Saleem, Jason J.; Militello, Laura G.; Arbuckle, Nicole; Flanagan, Mindy; Haggstrom, David A.; Linder, Jeffrey A.; Doebbeling, Bradley N.Implementation of computerized clinical decision support (CDS), and its integration into workflow has not reached its potential. To better understand the use of CDS for colorectal cancer (CRC) screening at benchmark institutions for health information technology (HIT), we conducted direct observation, including opportunistic interviews of primary care providers, as well as key informant interviews and focus groups, to document current challenges to CRC screening and follow-up at clinics affiliated with the Veterans Heath Administration, Regenstrief Institute, and Partners HealthCare System. Analysis revealed six common barriers across institutions from the primary care providers’ perspective: receiving and documenting “outside” exam results, inaccuracy of the CDS, compliance issues, poor usability, lack of coordination between primary care and gastroenterology, and the need to attend to more urgent patient issues. Strategies should be developed to enhance current HIT to address these challenges and better support primary care providers and staff.Item OpenMRS, A Global Medical Records System Collaborative: Factors Influencing Successful Implementation(2011-10) Mohammed-Rajput, Nareesa A.; Smith, Dawn C.; Mamlin, Burke; Biondich, Paul; Doebbeling, Bradley N.OpenMRS is an open-source, robust electronic health record (EHR) platform that is supported by a large global network and used in over forty countries. We explored what factors lead to successful implementation of OpenMRS in resource constrained settings. Data sources included in-person and telephone key informant interviews, focus groups and responses to an electronic survey from 10 sites in 7 countries. Qualitative data was coded through independent coding, discussion and consensus. The most common perceived benefits of implementation were for providing clinical care, reporting to funders, managing operations and research. Successful implementation factors include securing adequate infrastructure, and sociotechnical system factors, particularly adequate staffing, computers, and ability to use software. Strategic and tactical planning were successful strategies, including understanding and addressing the infrastructure and human costs involved, training or hiring personnel technically capable of modifying the software and integrating it into the daily work flow to meet clinicians’ needs.
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