Josette Jones

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Developing Solution-Focused Partnerships with Industry Using Data Analytics-Patient Centered Informatics (PCI): Connecting Consumers and Providers

Dr. Josette Jones is the program director of the Department of BioHealth Informatics at the Indiana University School of Informatics and Computing (SoIC) in Indianapolis (IUPUI), where she prepares students with skills they will need as health informatics professionals. These competencies include patient-centered care, interdisciplinary teamwork, evidence-based practice, and the ability to use informatics to improve and expand the delivery and quality of care. Additionally, at IUPUI, she has been instrumental in developing the graduate curricula in nursing and health informatics and health information technology.

Her current research program focuses on analyzing, formalizing and representing (ontology) how health care providers, including nurses, and health care consumers collect and manage data, process data into information and knowledge, and make knowledge-based decisions and inferences for health care. This empirical and experential knowledge is used in order to broaden the scope and enhance the quality of professional practice as well as interactive patient self-management support. Her research also capitalizes on Internet technology and its widespread acceptance as an information resource for providers and consumers alike.

Dr. Jones' use of data to improve consumer and provider experiences is another example of how IUPUI faculty are TRANSLATING RESEARCH INTO PRACTICE.


Recent Submissions

Now showing 1 - 10 of 52
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    PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature
    (Springer, 2022) Binkheder, Samar; Wu, Heng-Yi; Quinney, Sara K.; Zhang, Shijun; Zitu, Md. Muntasir; Chiang, Chien-Wei; Wang, Lei; Jones, Josette; Li, Lang; BioHealth Informatics, School of Informatics and Computing
    Background Adverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for establishing a cohort of patients. However, phenotype definitions are not readily available for all phenotypes. One of the first steps of developing automated text mining tools is building a corpus. Therefore, this study aimed to develop annotation guidelines and a gold standard corpus to facilitate building future automated approaches for mining phenotype definitions contained in the literature. Furthermore, our aim is to improve the understanding of how these published phenotype definitions are presented in the literature and how we annotate them for future text mining tasks. Results Two annotators manually annotated the corpus on a sentence-level for the presence of evidence for phenotype definitions. Three major categories (inclusion, intermediate, and exclusion) with a total of ten dimensions were proposed characterizing major contextual patterns and cues for presenting phenotype definitions in published literature. The developed annotation guidelines were used to annotate the corpus that contained 3971 sentences: 1923 out of 3971 (48.4%) for the inclusion category, 1851 out of 3971 (46.6%) for the intermediate category, and 2273 out of 3971 (57.2%) for exclusion category. The highest number of annotated sentences was 1449 out of 3971 (36.5%) for the “Biomedical & Procedure” dimension. The lowest number of annotated sentences was 49 out of 3971 (1.2%) for “The use of NLP”. The overall percent inter-annotator agreement was 97.8%. Percent and Kappa statistics also showed high inter-annotator agreement across all dimensions. Conclusions The corpus and annotation guidelines can serve as a foundational informatics approach for annotating and mining phenotype definitions in literature, and can be used later for text mining applications.
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    Transfer Center Functions: Describing Changes to Nursing Roles Through Goal-Directed Task Analysis
    Lenox, Michelle M.; Jones, Josette
    Patient transfer centers offer a centralized point of contact for transferring a patient into or between the in-patient care settings that comprise any one particular healthcare system. There are case studies on the improvements seen in patient throughput (Ayers, 2012) and physician satisfaction (Amedee, Maronge, & Pinsky, 2012) when these centers are introduced, but benchmarks, standards, staffing requirements or process guidelines were found to be lacking. Variations exist in the types of transfers supported (intra-facility, inter-facility, or both; within healthcare system vs. external to healthcare system) as well as additional services provided (e.g., transportation).
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    Patient-Centered Decision Support for Pediatric Asthma Screening: A Web-based Interface for Parents
    Zolnoori, Maryam; Schilling, Katherine; Jones, Josette
    Asthma in children is a global health crisis. Differential diagnosis of asthma is a complex process. Significant disconnects exist between disease prevalence vs. diagnoses. Families ignore or misunderstand asthma signs and symptoms. Families experience barriers to screening and diagnosis: health literacy, costs, travel and time, limited access to expert care, etc. Under-diagnosis results in significant individual and societal burdens. Early diagnosis leads to more effective disease management.
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    U.S. Hospitals' Web-Based Patient Engagement Activities
    Jones, Josette; Zolnoori, Maryam; Binkheder, Samar; Schilling, Katherine; Lenox, Michelle; Pondugala, Lakshmi Ravali
    The purpose of this poster is to describe how U.S. Hospitals use their websites to meet the National e-Health Collaborative (NeHC) patient engagement criteria and to explore trends, challenges, opportunities for hospitals when it comes to leveraging websites for patient engagement.
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    Analysis of Co-Indicators and Counter-Indicators Among Patients Using Coding Algorithms: Learning Phenotype study
    Reddy, Nagarjuna; Jones, Josette; Kanakasabai, Saravanan; Klapper, Gregory
    Chronic complications associated with the diabetes are responsible for increase in mortality and morbidity rate. The main aim of the project is to analyze the co-indicators and counter-indicators among the patients by mapping the conditions with ICD codes and developing an algorithm. A positive and strong correlation is identified with respect to BMI, Poverty, Education, Age and T2DM cohorts and it's comorbidities.
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    A Predictive Modelling Approach in the Diagnosis of Parkinson's Disease Using Cerebrospinal Fluid Biomarkers
    Lakmala, Prathima; Jones, Josette; Lai, Patrick
    The research in Parkinson's disease {PD) using biomarkers has long been dominated by measuring dopamine metabolites or alpha-Synuclein in cerebrospinal fluid. However, these markers do not allow early detection or monitoring of disease progression. In the recent years, metabolic profiling of body fluids has become powerful and promising tools in identification of the novel biomarkers in the diagnosis of the disease. While not much research has been done using machine learning techniques and predictive modeling to predict the severity of Parkinson's disease. The purpose of this project is to apply a predictive modeling approach in the diagnosis of Parkinson's disease using Cerebrospinal Fluid Biomarkers. The dataset for this study was collected from the PPMI website which comprises of 360 - Parkinson's patient, 220 - Control and 20 - SWEDD (Scans without evidence for dopaminergic deficit). Various predictive models were developed in order to classify the disease based on its severity. The various machine learning algorithms used in this process are Decision tree, Random forest, Support Vector Machine {SVM), K- Nearest Neighbor (KNN), and Gradient boosting. Feature scaling and Mean normalization was applied to standardize the dataset. The above mentioned machine learning algorithms were applied on the Parkinson's Progression Markers Initiative (PPMI) data and accuracy for each algorithm was calculated. Out of all the models, Random forest and Gradient Boosting gave the best classification accuracy of 66.67%. In conclusion, the main factors that might have affected accuracy of the model are dataset size, missing data and number of features. To sum up, while the results show some predictive power, we conclude negative results and hence these models are not Clinically significant.
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    Assessment of Parkinson's Disease Progression by Feature Relevance Analysis and Regression Techniques Using Machine Learning Algorithms
    Gullapelli, Rakesh; Jones, Josette; Lai, Patrick T. S.
    Remote patient tracking has been gaining increased attention due to its low-cost non-invasive methods. Unified Parkinson's Disease Rating Scale (UPDRS) is used often to track Parkinson's Disease (PD) symptoms which requires the patient's visit to the clinic and time consuming medical tests that may not be feasible for most of the elderly PD patients. One of the major concerns to predict the PD in early stages is that PD symptoms overlap with the symptoms of other diseases such as Multiple Sclerosis, Alzheimer's disease. Moreover, most of the current methods used for tracking PD rely on expert clinical raters, from which PD symptoms assessment may be difficult due to inter-individual variability. Predicting relevant features using machine learning algorithms is helpful in providing the scientific decision-making classification rules necessary to assess the disease progression in early stages.
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    An Expandable Drug Information Retrieval System for Oncology (EDIRS-FO)
    Jain, Lakshika; Jones, Josette; Kulanthaivel, Anand
    Breast cancer was life threating a decade ago, however, now with the improvement in treatment the survival rate has increased considerably. Avoidable adverse effects accompany these treatments. The risk of acute and chronic adverse effects caused due to treatment greatly influence the quality of life in breast cancer survivors. An information system can reduce the avoidable ADR, thus, improving the decision making during patient encounters. It also makes the access to the information easier for the clinicians.
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    Factor Analysis of Sepsis and Other Bacterial Infections in Congestive Heart Failure Patients
    Barma, Ramachandrarao; Jones, Josette; Klopper, Gregory
    The main objective of this study is to identify co-factors that are responsible for sepsis and other bacterial infections in congestive heart failure patients. Our second objective is to build a predictive learning model to identify the probability of sepsis and other bacterial infections based on past 30 days diagnosis history and past 15 days procedures history.
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    Development and Evaluation of a Natural Language Conversational Bot for Identifying Appropriate Clinician Referral from Patient Narratives
    Tummala, Sriharsha; Purkayastha, Saptarshi; Jones, Josette
    Recent years have seen a significant increase in automated conversational agent chatbots. Conversational agents like chatbots for health may provide timely and cost-effective support in clinical care. Some studies show that chatbots could have an impact on patient engagement. Additionally, health systems are attempting to connect with patients over social networks, mainly where specialists are limited. By 2025, the Association of American Medical Colleges estimates that the United States will have a shortfall of 61,700-94,700 physicians and critical shortage in many specialties, delaying available appointments by months in many cases. Thus, we need innovative solutions that can manage the time of limited specialists appropriately. Recent research has demonstrated that deep learning methods are superior for natural language classification tasks compared to other machine learning methods. The primary objective of this study was to develop a telegram chatbot which reads patient narratives and acts as a conversational agent by redirecting the case to the appropriate specialist. Besides simply working on improving conversational capabilities of chatbots, we developed a novel method for referring the cases to specialists based on their responses to previous cases on a social network group. As far as we know, no other chatbot has the level of accuracy or referral system like our developed chatbot.