Josette Jones

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https://hdl.handle.net/1805/25234

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.

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Now showing 1 - 10 of 25

Impact of document consolidation on healthcare providers’ perceived workload and information reconciliation tasks: a mixed methods study
(Oxford University Press, 2019-02) Hosseini, Masoud; Faiola, Anthony; Jones, Josette; Vreeman, Daniel J.; Wu, Huanmei; Dixon, Brian E.; Medicine, School of Medicine
Background Information reconciliation is a common yet complex and often time-consuming task performed by healthcare providers. While electronic health record systems can receive “outside information” about a patient in electronic documents, rarely does the computer automate reconciling information about a patient across all documents. Materials and Methods Using a mixed methods design, we evaluated an information system designed to reconcile information across multiple electronic documents containing health records for a patient received from a health information exchange (HIE) network. Nine healthcare providers participated in scenario-based sessions in which they manually consolidated information across multiple documents. Accuracy of consolidation was measured along with the time spent completing 3 different reconciliation scenarios with and without support from the information system. Participants also attended an interview about their experience. Perceived workload was evaluated quantitatively using the NASA-TLX tool. Qualitative analysis focused on providers’ impression of the system and the challenges faced when reconciling information in practice. Results While 5 providers made mistakes when trying to manually reconcile information across multiple documents, no participants made a mistake when the system supported their work. Overall perceived workload decreased significantly for scenarios supported by the system (37.2% in referrals, 18.4% in medications, and 31.5% in problems scenarios, P < 0.001). Information reconciliation time was reduced significantly when the system supported provider tasks (58.8% in referrals, 38.1% in medications, and 65.1% in problem scenarios). Conclusion Automating retrieval and reconciliation of information across multiple electronic documents shows promise for reducing healthcare providers’ task complexity and workload.
Measuring and Visualizing Chlamydia and Gonorrhea Inequality: An Informatics Approach Using Geographical Information Systems
(University of Illinois at Chicago, 2019-09-19) Lai, Patrick T.S.; Wilson, Jeffrey; Wu, Huanmei; Jones, Josette; Dixon, Brian E.; Geography, School of Liberal Arts
Background: Health inequality measurements are vital in understanding disease patterns in identifying high-risk patients and implementing effective intervention programs to treat and manage sexually transmitted diseases. Objectives: To measure and identify inequalities among chlamydia and gonorrhea rates using Gini coefficient measurements and spatial visualization mapping from geographical information systems. Additionally, we seek to examine trends of disease rate distribution longitudinally over a ten-year period for an urbanized county. Methods: Chlamydia and gonorrhea data from January 2005 to December 2014 were collected from the Indiana Network for Patient Care, a health information exchange system that gathers patient data from electronic health records. The Gini coefficient was used to calculate the magnitude of inequality in disease rates. Spatial visualization mapping and decile categorization of disease rates were conducted to identify locations where high and low rates of disease persisted and to visualize differences in inequality. A multiple comparisons ANOVA test was conducted to determine if Gini coefficient values were statistically different between townships and time periods during the study. Results: Our analyses show that chlamydia and gonorrhea rates are not evenly distributed. Inequalities in disease rates existed for different areas of the county with higher disease rates occurring near the center of the county. Inequality in gonorrhea rates were higher than chlamydia rates. Disease rates were statistically different when geographical locations or townships were compared to each other (p < 0.0001) but not for different years or time periods (p = 0.5152). Conclusion: The ability to use Gini coefficients combined with spatial visualization techniques presented a valuable opportunity to analyze information from health information systems in investigating health inequalities. Knowledge from this study can benefit and improve health quality, delivery of services, and intervention programs while managing healthcare costs.
Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach
(JMIR Publications, 2019-07-22) Kasthurirathne, Suranga N.; Biondich, Paul G.; Grannis, Shaun J.; Purkayastha, Saptarshi; Vest, Joshua R.; Jones, Josette F.; Epidemiology, School of Public Health
BACKGROUND: As the most commonly occurring form of mental illness worldwide, depression poses significant health and economic burdens to both the individual and community. Different types of depression pose different levels of risk. Individuals who suffer from mild forms of depression may recover without any assistance or be effectively managed by primary care or family practitioners. However, other forms of depression are far more severe and require advanced care by certified mental health providers. However, identifying cases of depression that require advanced care may be challenging to primary care providers and health care team members whose skill sets run broad rather than deep. OBJECTIVE: This study aimed to leverage a comprehensive range of patient-level diagnostic, behavioral, and demographic data, as well as past visit history data from a statewide health information exchange to build decision models capable of predicting the need of advanced care for depression across patients presenting at Eskenazi Health, the public safety net health system for Marion County, Indianapolis, Indiana. METHODS: Patient-level diagnostic, behavioral, demographic, and past visit history data extracted from structured datasets were merged with outcome variables extracted from unstructured free-text datasets and were used to train random forest decision models that predicted the need of advanced care for depression across (1) the overall patient population and (2) various subsets of patients at higher risk for depression-related adverse events; patients with a past diagnosis of depression; patients with a Charlson comorbidity index of ≥1; patients with a Charlson comorbidity index of ≥2; and all unique patients identified across the 3 above-mentioned high-risk groups. RESULTS: The overall patient population consisted of 84,317 adult (aged ≥18 years) patients. A total of 6992 (8.29%) of these patients were in need of advanced care for depression. Decision models for high-risk patient groups yielded area under the curve (AUC) scores between 86.31% and 94.43%. The decision model for the overall patient population yielded a comparatively lower AUC score of 78.87%. The variance of optimal sensitivity and specificity for all decision models, as identified using Youden J Index, is as follows: sensitivity=68.79% to 83.91% and specificity=76.03% to 92.18%. CONCLUSIONS: This study demonstrates the ability to automate screening for patients in need of advanced care for depression across (1) an overall patient population or (2) various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors, and past visit history. Furthermore, these results show considerable potential to enable preventative care and can be easily integrated into existing clinical workflows to improve access to wraparound health care services.
Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer Forum
(JMIR, 2018) Jones, Josette; Pradhan, Meeta; Hosseini, Masoud; Kulanthaivel, Anand; Hosseini, Mahmood; Biohealth Informatics, School of Informatics and Computing
Background: The increasing use of social media and mHealth apps has generated new opportunities for health care consumers to share information about their health and well-being. Information shared through social media contains not only medical information but also valuable information about how the survivors manage disease and recovery in the context of daily life. Objective: The objective of this study was to determine the feasibility of acquiring and modeling the topics of a major online breast cancer support forum. Breast cancer patient support forums were selected to discover the hidden, less obvious aspects of disease management and recovery. Methods: First, manual topic categorization was performed using qualitative content analysis (QCA) of each individual forum board. Second, we requested permission from the Breastcancer.org Community for a more in-depth analysis of the postings. Topic modeling was then performed using open source software Machine Learning Language Toolkit, followed by multiple linear regression (MLR) analysis to detect highly correlated topics among the different website forums. Results: QCA of the forums resulted in 20 categories of user discussion. The final topic model organized >4 million postings into 30 manageable topics. Using qualitative analysis of the topic models and statistical analysis, we grouped these 30 topics into 4 distinct clusters with similarity scores of ≥0.80; these clusters were labeled Symptoms & Diagnosis, Treatment, Financial, and Family & Friends. A clinician review confirmed the clinical significance of the topic clusters, allowing for future detection of actionable items within social media postings. To identify the most significant topics across individual forums, MLR demonstrated that 6 topics—based on the Akaike information criterion values ranging from −642.75 to −412.32—were statistically significant. Conclusions: The developed method provides an insight into the areas of interest and concern, including those not ascertainable in the clinic. Such topics included support from lay and professional caregivers and late side effects of therapy that consumers discuss in social media and may be of interest to clinicians. The developed methods and results indicate the potential of social media to inform the clinical workflow with regards to the impact of recovery on daily life. [JMIR Med Inform 2018;6(4):e45]
Digital Cohorts Within the Social Mediome: An Approach to Circumvent Conventional Research Challenges?
(Elsevier, 2017-05) Kulanthaivel, Anand; Fogel, Rachel; Jones, Josette; Lammert, Craig; Biohealth Informatics, School of Informatics and Computing
Hypothesis Generation Using Network Structures on Community Health Center Cancer-Screening Performance
(Elsevier, 2015-10) Carney, Timothy Jay; Morgan, Geoffrey P.; Jones, Josette; McDaniel, Anna M.; Weaver, Michael; Weiner, Bryan; Haggstrom, David A.; BioHealth Informatics, School of Informatics and Computing
RESEARCH OBJECTIVES: Nationally sponsored cancer-care quality-improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer-screening rates among vulnerable populations. Despite several immediate and short-term gains, screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science. METHODS: To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects on the relationship between clinical decision support capabilities and improvement in cancer-screening rate improvement, we employed Construct-TM to create simulated community health centers using previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement. RESULTS: This virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge-absorption rates, and fewer agents that were unconnected to key knowledge resources. Conclusions and research implications: Using the point-in-time survey data outlining community health center cancer-screening practices, our computational model successfully distinguished between high and low performers. Results indicated that high-performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network-specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time, thereby enhancing the sustainability of long-term strategic-improvement efforts. Our results revealed a strategic profile for community health center cancer-screening improvement via simulation over a projected 10-year period. The use of computational modeling allows additional inferential knowledge to be drawn from existing data when examining organizational performance in increasingly complex environments.
What Do They Mean by "Health Informatics"? Health Informations Posts Compared to Program Standards
(IOS Press, 2017) Jones, Josette F.; Zhang, Enming; Kulanthaivel, Anand; Katta, Shilpa; BioHealth Informatics, School of Informatics and Computing
There is a lack of alignment between and within the competencies and skills required by health informatics (HI) related jobs and those present in academic curriculum frameworks. This study uses computational topic modeling for gap analysis of career needs vs. curriculum objectives. The seven AMIA-CAHIIM-accepted core knowledge domains were used to categorize a corpus of HI-related job postings (N = 475) from a major United States-based job posting website. Computational modeling-generated topics were created and then compared and matched to the seven core knowledge domains. The HI-defining core domain, representing the intersection of health, technology and social/behavioral sciences matched only 45.9% of job posting content. Therefore, the authors suggest that bidirectional communication between academia and industry is needed in order to better align educational objectives to the demands of the job market.
Toward Timely Data for Cancer Research: Assessment and Reengineering of the Cancer Reporting Process
(JMIR Publications, 2018-03-01) Jabour, Abdulrahman M.; Dixon, Brian E.; Jones, Josette F.; Haggstrom, David A.; BioHealth Informatics, School of Informatics and Computing
Background Cancer registries systematically collect cancer-related data to support cancer surveillance activities. However, cancer data are often unavailable for months to years after diagnosis, limiting its utility. Objective The objective of this study was to identify the barriers to rapid cancer reporting and identify ways to shorten the turnaround time. Methods Certified cancer registrars reporting to the Indiana State Department of Health cancer registry participated in a semistructured interview. Registrars were asked to describe the reporting process, estimate the duration of each step, and identify any barriers that may impact the reporting speed. Qualitative data analysis was performed with the intent of generating recommendations for workflow redesign. The existing and redesigned workflows were simulated for comparison. Results Barriers to rapid reporting included access to medical records from multiple facilities and the waiting period from diagnosis to treatment. The redesigned workflow focused on facilitating data sharing between registrars and applying a more efficient queuing technique while registrars await the delivery of treatment. The simulation results demonstrated that our recommendations to reduce the waiting period and share information could potentially improve the average reporting speed by 87 days. Conclusions Knowing the time elapsing at each step within the reporting process helps in prioritizing the needs and estimating the impact of future interventions. Where some previous studies focused on automating some of the cancer reporting activities, we anticipate much shorter reporting by leveraging health information technologies to target this waiting period.
Dietary intake of isoflavones and coumestrol and the risk of prostate cancer in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial
(Wiley, 2018-02) Reger, Michael K.; Zollinger, Terrell W.; Liu, Ziyue; Jones, Josette F.; Zhang, Jianjun; Epidemiology, School of Public Health
Experimental studies have revealed that phytoestrogens may modulate the risk of certain sites of cancer due to their structural similarity to 17β‐estradiol. The present study investigates whether intake of these compounds may influence prostate cancer risk in human populations. During a median follow up of 11.5 years, 2,598 cases of prostate cancer (including 287 advanced cases) have been identified among 27,004 men in the intervention arm of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Dietary intake of phytoestrogens (excluding lignans) was assessed with a food frequency questionnaire. Cox proportional hazards regression analysis was performed to estimate hazard ratios (HRs) and 95% confidence intervals (CI) for dietary isoflavones and coumestrol in relation to prostate cancer risk. After adjustment for confounders, an increased risk of advanced prostate cancer [HR (95% CI) for quintile (Q) 5 vs. Q1] was found for the dietary intake of total isoflavones [1.91 (1.25–2.92)], genistein [1.51 (1.02–2.22), daidzein [1.80 (1.18–2.75) and glycitein [1.67 (1.15–2.43)] (p‐trend for all associations ≤0.05). For example, HR (95% CI) for comparing the Q2, Q3, Q4 and Q5 with Q1 of daidzein intake was 1.45 (0.93–2.25), 1.65 (1.07–2.54), 1.73 (1.13–2.66) and 1.80 (1.18–2.75), respectively (p‐trend: 0.013). No statistically significant associations were observed between the intake of total isoflavones and individual phytoestrogens and non‐advanced and total prostate cancer after adjustment for confounders. This study revealed that dietary intake of isoflavones was associated with an elevated risk of advanced prostate cancer.
Association between Urinary Phytoestrogens and C-reactive Protein in the Continuous National Health and Nutrition Examination Survey
(Taylor & Francis, 2017) Reger, Michael K.; Zollinger, Terrell W.; Liu, Ziyue; Jones, Josette; Zhang, Jianjun; Epidemiology, School of Public Health
Objective: A reduced risk of some cancers and cardiovascular disease associated with phytoestrogen intake may be mediated through its effect on serum C-reactive protein (CRP; an inflammation biomarker). Therefore, this study examined the associations between urinary phytoestrogens and serum CRP. Methods: Urinary phytoestrogen and serum CRP data obtained from 6009 participants aged ≥ 40 years in the continuous National Health and Nutrition Examination Survey during 1999–2010 were analyzed. Results: After adjustment for confounders, urinary concentrations of total and all individual phytoestrogens were inversely associated with serum concentrations of CRP (all p < 0.004). The largest reductions in serum CRP (mg/L) per interquartile range increase in urinary phytoestrogens (ng/mL) were observed for total phytoestrogens (β = −0.18; 95% confidence interval [CI], −0.22, −0.15), total lignan (β = −0.15; 95% CI, −0.18, −0.12), and enterolactone (β = −0.15; 95% CI, −0.19, −0.12). A decreased risk of having high CRP concentrations (≥3.0 mg/L) for quartile 4 vs quartile 1 was also found for total phytoestrogens (OR = 0.63; 95% CI, 0.53, 0.73), total lignan (OR = 0.64; 95% CI, 0.54, 0.75), and enterolactone (OR = 0.59; 95% CI, 0.51, 0.69). Conclusion: Urinary total and individual phytoestrogens were significantly inversely associated with serum CRP in a nationally representative sample of the U.S. population.

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