IU Indianapolis ScholarWorks :: Browsing by Author "Allen, Katie S."

Browsing by Author "Allen, Katie S."

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Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review
(JMIR Public Health and Surveillance, 2019) Golembiewski, Elizabeth; Allen, Katie S.; Blackmon, Amber M.; Hinrichs, Rachel J.; Vest, Joshua R.
Background: Nonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients’ nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health. Objective: This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources. Methods: We conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported. Results: A total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location. Conclusions: A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.
Enhancing the nation’s public health information infrastructure: a report from the ACMI symposium
(Oxford University Press, 2023) Dixon, Brian E.; Staes, Catherine; Acharya, Jessica; Allen, Katie S.; Hartsell, Joel; Cullen, Theresa; Lenert, Leslie; Rucker, Donald W.; Lehmann, Harold; Community and Global Health, Richard M. Fairbanks School of Public Health
The COVID-19 pandemic exposed multiple weaknesses in the nation's public health system. Therefore, the American College of Medical Informatics selected "Rebuilding the Nation's Public Health Informatics Infrastructure" as the theme for its annual symposium. Experts in biomedical informatics and public health discussed strategies to strengthen the US public health information infrastructure through policy, education, research, and development. This article summarizes policy recommendations for the biomedical informatics community postpandemic. First, the nation must perceive the health data infrastructure to be a matter of national security. The nation must further invest significantly more in its health data infrastructure. Investments should include the education and training of the public health workforce as informaticians in this domain are currently limited. Finally, investments should strengthen and expand health data utilities that increasingly play a critical role in exchanging information across public health and healthcare organizations.
Evaluating a Prototype Clinical Decision Support Tool for Chronic Pain Treatment in Primary Care
(Thieme, 2022) Allen, Katie S.; Danielson, Elizabeth C.; Downs, Sarah M.; Mazurenko, Olena; Diiulio, Julie; Salloum, Ramzi G.; Mamlin, Burke W.; Harle, Christopher A.; Health Policy and Management, School of Public Health
Objectives: The Chronic Pain Treatment Tracker (Tx Tracker) is a prototype decision support tool to aid primary care clinicians when caring for patients with chronic noncancer pain. This study evaluated clinicians' perceived utility of Tx Tracker in meeting information needs and identifying treatment options, and preferences for visual design. Methods: We conducted 12 semi-structured interviews with primary care clinicians from four health systems in Indiana. The interviews were conducted in two waves, with prototype and interview guide revisions after the first six interviews. The interviews included exploration of Tx Tracker using a think-aloud approach and a clinical scenario. Clinicians were presented with a patient scenario and asked to use Tx Tracker to make a treatment recommendation. Last, participants answered several evaluation questions. Detailed field notes were collected, coded, and thematically analyzed by four analysts. Results: We identified several themes: the need for clinicians to be presented with a comprehensive patient history, the usefulness of Tx Tracker in patient discussions about treatment planning, potential usefulness of Tx Tracker for patients with high uncertainty or risk, potential usefulness of Tx Tracker in aggregating scattered information, variability in expectations about workflows, skepticism about underlying electronic health record data quality, interest in using Tx Tracker to annotate or update information, interest in using Tx Tracker to translate information to clinical action, desire for interface with visual cues for risks, warnings, or treatment options, and desire for interactive functionality. Conclusion: Tools like Tx Tracker, by aggregating key information about past, current, and potential future treatments, may help clinicians collaborate with their patients in choosing the best pain treatments. Still, the use and usefulness of Tx Tracker likely relies on continued improvement of its functionality, accurate and complete underlying data, and tailored integration with varying workflows, care team roles, and user preferences.
Findings From a Scoping Review: Presumptive Treatment for Chlamydia trachomatis and Neisseria gonorrhoeae in the United States, 2006–2021
(American Sexually Transmitted Diseases Association, 2023-04) Allen, Katie S.; Hinrichs, Rachel J.; Heumann, Christine L.; Titus, Melissa K.; Duszynski, Thomas J.; Valvi, Nimish R.; Wiensch, Ashley; Tao, Guoyu; Dixon, Brian E.
Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (GC) are the 2 most common reported sexually transmitted infections in the United States. Current recommendations are to presumptively treat CT and/or GC in persons with symptoms or known contact. This review characterizes the literature around studies with presumptive treatment, including identifying rates of presumptive treatment and overtreatment and undertreatment rates. Of the 18 articles that met our inclusion criteria, 6 pertained to outpatient settings. In the outpatient setting, presumptive treatment rates, for both asymptomatic and symptomic patients, varied from 12% to 100%, and the percent positive of those presumptively treated ranged from 25% to 46%. Three studies also reported data on positive results in patients not presumptively treated, which ranged from 2% to 9%. Two studies reported median follow-up time for untreated, which was roughly 9 days. The remaining 12 articles pertained to the emergency setting where presumptive treatment rates, for both asymptomatic and symptomic patients, varied from 16% to 91%, the percent positive following presumptive treatment ranged from 14% to 59%. Positive results without presumptive treatment ranged from 4% to 52%. Two studies reported the percent positive without any treatment (6% and 32%, respectively) and one reported follow-up time for untreated infections (median, 4.8 days). Rates of presumptive treatment, as well as rates of overtreatment or undertreatment vary widely across studies and within care settings. Given the large variability in presumptive treatment, the focus on urban settings, and minimal focus on social determinants of health, additional studies are needed to guide treatment practices for CT and GC in outpatient and emergency settings.
Findings From a Scoping Review: Presumptive Treatment for Chlamydiatrachomatis and Neisseria gonorrhoeae in the United States, 2006-2021
(Wolters Kluwer, 2023) Allen, Katie S.; Hinrichs, Rachel; Heumann, Christine L.; Titus, Melissa K.; Duszynski, Thomas J.; Valvi, Nimish R.; Wiensch, Ashley; Tao, Guoyu; Dixon, Brian E.; University Library
Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (GC) are the two most common reported sexually transmitted infections in the USA. Current recommendations are to presumptively treat CT and/or GC in persons with symptoms or known contact. This review characterizes the literature around studies with presumptive treatment, including identifying rates of presumptive treatment and over- and under-treatment rates. Of the 18 articles that met our inclusion criteria, six pertained to outpatient settings. In the outpatient setting, presumptive treatment rates, for both asymptomatic and symptomic patients, varied from 12% - 100%, and the percent positive of those presumptively treated ranged from 25% - 46%. Three studies also reported data on positive results in patients not presumptively treated, which ranged from 2% - 9%. Two studies reported median follow-up time for untreated, which was roughly nine days. The remaining 12 articles pertained to the emergency setting where presumptive treatment rates, for both asymptomatic and symptomic patients, varied from 16% - 91%, the percent positive following presumptive treatment ranged from 14% - 59%. Positive results without presumptive treatment ranged from 4% - 52%. Two studies reported the percent positive without any treatment (6% and 32% respectively) and one reported follow-up time for untreated infections (median: 4.8 days). Rates of presumptive treatment, as well as rates of over- or under- treatment vary widely across studies and within care settings. Given large variability in presumptive treatment, the focus on urban settings, and minimal focus on social determinants of health, additional studies are needed to guide treatment practices for CT and GC in outpatient and emergency settings.
Generalizability and portability of natural language processing system to extract individual social risk factors
(Elsevier, 2023) Magoc, Tanja; Allen, Katie S.; McDonnell, Cara; Russo, Jean-Paul; Cummins, Jonathan; Vest, Joshua R.; Harle, Christopher A.; Emergency Medicine, School of Medicine
Objective: The objective of this study is to validate and report on portability and generalizability of a Natural Language Processing (NLP) method to extract individual social factors from clinical notes, which was originally developed at a different institution. Materials and methods: A rule-based deterministic state machine NLP model was developed to extract financial insecurity and housing instability using notes from one institution and was applied on all notes written during 6 months at another institution. 10% of positively-classified notes by NLP and the same number of negatively-classified notes were manually annotated. The NLP model was adjusted to accommodate notes at the new site. Accuracy, positive predictive value, sensitivity, and specificity were calculated. Results: More than 6 million notes were processed at the receiving site by the NLP model, which resulted in about 13,000 and 19,000 classified as positive for financial insecurity and housing instability, respectively. The NLP model showed excellent performance on the validation dataset with all measures over 0.87 for both social factors. Discussion: Our study illustrated the need to accommodate institution-specific note-writing templates as well as clinical terminology of emergent diseases when applying NLP model for social factors. A state machine is relatively simple to port effectively across institutions. Our study. showed superior performance to similar generalizability studies for extracting social factors. Conclusion: Rule-based NLP model to extract social factors from clinical notes showed strong portability and generalizability across organizationally and geographically distinct institutions. With only relatively simple modifications, we obtained promising performance from an NLP-based model.
Natural language processing-driven state machines to extract social factors from unstructured clinical documentation
(Oxford University Press, 2023-04-18) Allen, Katie S.; Hood, Dan R.; Cummins, Jonathan; Kasturi, Suranga; Mendonca, Eneida A.; Vest, Joshua R.; Health Policy and Management, School of Public Health
Objective: This study sought to create natural language processing algorithms to extract the presence of social factors from clinical text in 3 areas: (1) housing, (2) financial, and (3) unemployment. For generalizability, finalized models were validated on data from a separate health system for generalizability. Materials and methods: Notes from 2 healthcare systems, representing a variety of note types, were utilized. To train models, the study utilized n-grams to identify keywords and implemented natural language processing (NLP) state machines across all note types. Manual review was conducted to determine performance. Sampling was based on a set percentage of notes, based on the prevalence of social need. Models were optimized over multiple training and evaluation cycles. Performance metrics were calculated using positive predictive value (PPV), negative predictive value, sensitivity, and specificity. Results: PPV for housing rose from 0.71 to 0.95 over 3 training runs. PPV for financial rose from 0.83 to 0.89 over 2 training iterations, while PPV for unemployment rose from 0.78 to 0.88 over 3 iterations. The test data resulted in PPVs of 0.94, 0.97, and 0.95 for housing, financial, and unemployment, respectively. Final specificity scores were 0.95, 0.97, and 0.95 for housing, financial, and unemployment, respectively. Discussion: We developed 3 rule-based NLP algorithms, trained across health systems. While this is a less sophisticated approach, the algorithms demonstrated a high degree of generalizability, maintaining >0.85 across all predictive performance metrics. Conclusion: The rule-based NLP algorithms demonstrated consistent performance in identifying 3 social factors within clinical text. These methods may be a part of a strategy to measure social factors within an institution.
SARS-CoV-2 Infection, Hospitalization, and Death in Vaccinated and Infected Individuals by Age Groups in Indiana, 2021‒2022
(American Public Health Association, 2023) Tu, Wanzhu; Zhang, Pengyue; Roberts, Anna; Allen, Katie S.; Williams, Jennifer; Embi, Peter; Grannis, Shaun; Biostatistics and Health Data Science, School of Medicine
Objectives: To assess the effectiveness of vaccine-induced immunity against new infections, all-cause emergency department (ED) and hospital visits, and mortality in Indiana. Methods: Combining statewide testing and immunization data with patient medical records, we matched individuals who received at least 1 dose of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines with individuals with previous SARS-CoV-2 infection on index date, age, gender, race/ethnicity, zip code, and clinical diagnoses. We compared the cumulative incidence of infection, all-cause ED visits, hospitalizations, and mortality. Results: We matched 267 847 pairs of individuals. Six months after the index date, the incidence of SARS-CoV-2 infection was significantly higher in vaccine recipients (6.7%) than the previously infected (2.9%). All-cause mortality in the vaccinated, however, was 37% lower than that of the previously infected. The rates of all-cause ED visits and hospitalizations were 24% and 37% lower in the vaccinated than in the previously infected. Conclusions: The significantly lower rates of all-cause ED visits, hospitalizations, and mortality in the vaccinated highlight the real-world benefits of vaccination. The data raise questions about the wisdom of reliance on natural immunity when safe and effective vaccines are available.
Strengths, weaknesses, opportunities, and threats for the nation’s public health information systems infrastructure: synthesis of discussions from the 2022 ACMI Symposium
(Oxford University Press, 2023-05-05) Acharya, Jessica C.; Staes, Catherine; Allen, Katie S.; Hartsell, Joel; Cullen, Theresa A.; Lenert, Leslie; Rucker, Donald W.; Lehmann, Harold P.; Dixon, Brian E.; Health Policy and Management, Richard M. Fairbanks School of Public Health
Objective: The annual American College of Medical Informatics (ACMI) symposium focused discussion on the national public health information systems (PHIS) infrastructure to support public health goals. The objective of this article is to present the strengths, weaknesses, threats, and opportunities (SWOT) identified by public health and informatics leaders in attendance. Materials and methods: The Symposium provided a venue for experts in biomedical informatics and public health to brainstorm, identify, and discuss top PHIS challenges. Two conceptual frameworks, SWOT and the Informatics Stack, guided discussion and were used to organize factors and themes identified through a qualitative approach. Results: A total of 57 unique factors related to the current PHIS were identified, including 9 strengths, 22 weaknesses, 14 opportunities, and 14 threats, which were consolidated into 22 themes according to the Stack. Most themes (68%) clustered at the top of the Stack. Three overarching opportunities were especially prominent: (1) addressing the needs for sustainable funding, (2) leveraging existing infrastructure and processes for information exchange and system development that meets public health goals, and (3) preparing the public health workforce to benefit from available resources. Discussion: The PHIS is unarguably overdue for a strategically designed, technology-enabled, information infrastructure for delivering day-to-day essential public health services and to respond effectively to public health emergencies. Conclusion: Most of the themes identified concerned context, people, and processes rather than technical elements. We recommend that public health leadership consider the possible actions and leverage informatics expertise as we collectively prepare for the future.

Browsing by Author "Allen, Katie S."

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