Browsing by Author "Mamlin, Burke"

Now showing 1 - 5 of 5
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    Determinants of Long Immunization Clinic Wait Times in a Sub-Saharan African Country
    (Sage, 2021-06-29) Ekhaguere, Osayame Austine; Oluwafemi, Rosena Olubanke; Oyo-Ita, Angela; Mamlin, Burke; Bondich, Paul; Mendonca, Eneida A.; Rollins, Angela L.; Pediatrics, School of Medicine
    The wait time clients spend during immunization clinic visits in low- and middle-income countries is a not well-understood reported barrier to vaccine completion. We used a prospective, observational design to document the total time from client arrival-to-discharge and all sequential provider-client activities in 1 urban, semi-urban, and rural immunization clinic in Nigeria. We also conducted caregiver and provider focus group discussions to identify perceived determinants of long clinic wait times. Our findings show that the time from arrival-to-discharge varied significantly by the clinic and ranged between 57 and 235 minutes, as did arrival-to-all providers-client activities. Focus group data attributed workflow delays to clinic staff waiting for a critical mass of clients to arrive for their immunization appointment before starting the essential health education talk or opening specific vaccine vials. Additionally, respondents indicated that complex documentation processes caused system delays. Research on clinic workflow transformation and simplification of immunization documentation is needed.
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    An Incremental Adoption Pathway for Developing Precision Medicine Based Healthcare Infrastructure for Underserved Settings
    (Medinfo 2017 Conference proceedings, 2017-08) Kasthurirathne, Suranga; Biondich, Paul; Mamlin, Burke; Cullen, Theresa; Grannis, Shaun
    Recent focus on Precision medicine (PM) has led to a flurry of research activities across the developed world. understaffed and underfunded health care systems in the US and elsewhere evolve to adapt PM to address pressing But how can healthcare needs? We offer guidance on a wide range of sources of healthcare data / knowledge sources as well as other infrastructure / tools that could inform PM initiatives, and may serve as low hanging fruit easily adapted on the incremental pathway towards a PM based healthcare system. Using these resources and tools, we propose an incremental adoption pathway to inform implementers working in underserved communities around the world on how they should position themselves to gradually embrace the concepts of PM with minimal interruption to existing care delivery.
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    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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    Toward better public health reporting using existing off the shelf approaches: The value of medical dictionaries in automated cancer detection using plaintext medical data
    (Elsevier, 2017-05) Kasthurirathne, Suranga N.; Dixon, Brian E.; Gichoya, Judy; Xu, Huiping; Xia, Yuni; Mamlin, Burke; Grannis, Shaun J.; Department of Epidemiology, Richard M. Fairbanks School of Public Health
    Objectives Existing approaches to derive decision models from plaintext clinical data frequently depend on medical dictionaries as the sources of potential features. Prior research suggests that decision models developed using non-dictionary based feature sourcing approaches and “off the shelf” tools could predict cancer with performance metrics between 80% and 90%. We sought to compare non-dictionary based models to models built using features derived from medical dictionaries. Materials and methods We evaluated the detection of cancer cases from free text pathology reports using decision models built with combinations of dictionary or non-dictionary based feature sourcing approaches, 4 feature subset sizes, and 5 classification algorithms. Each decision model was evaluated using the following performance metrics: sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. Results Decision models parameterized using dictionary and non-dictionary feature sourcing approaches produced performance metrics between 70 and 90%. The source of features and feature subset size had no impact on the performance of a decision model. Conclusion Our study suggests there is little value in leveraging medical dictionaries for extracting features for decision model building. Decision models built using features extracted from the plaintext reports themselves achieve comparable results to those built using medical dictionaries. Overall, this suggests that existing “off the shelf” approaches can be leveraged to perform accurate cancer detection using less complex Named Entity Recognition (NER) based feature extraction, automated feature selection and modeling approaches.
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    Towards Standardized Patient Data Exchange: Integrating a FHIR Based API for the Open Medical Record System
    (IOS, 2015) Kasthurirathne, Suranga Nath; Mamlin, Burke; Grieve, Grahame; Biondich, Paul; Department of BioHealth Informatics, School of Informatics and Computing
    Interoperability is essential to address limitations caused by the ad hoc implementation of clinical information systems and the distributed nature of modern medical care. The HL7 V2 and V3 standards have played a significant role in ensuring interoperability for healthcare. FHIR is a next generation standard created to address fundamental limitations in HL7 V2 and V3. FHIR is particularly relevant to OpenMRS, an Open Source Medical Record System widely used across emerging economies. FHIR has the potential to allow OpenMRS to move away from a bespoke, application specific API to a standards based API. We describe efforts to design and implement a FHIR based API for the OpenMRS platform. Lessons learned from this effort were used to define long term plans to transition from the legacy OpenMRS API to a FHIR based API that greatly reduces the learning curve for developers and helps enhance adhernce to standards.