Papers by Arlene Smaldone
Humana Press eBooks, Nov 5, 2007
In recent years, clinicians have begun to recognize the impact that educational, psychosocial, an... more In recent years, clinicians have begun to recognize the impact that educational, psychosocial, and behavioral factors have on treatment success for leg and foot wounds. Furthermore, many now consider quality of life as an important outcome of treatment for those suffering from neuropathy, foot ulcerations, and amputations. However, although interest is increasing, behavioral aspects of the diabetic foot remain fledging
International Journal of Medical Informatics, Jul 1, 2020
Introduction:Self-monitoring technologies produce patient-generated data that could be leveraged ... more Introduction:Self-monitoring technologies produce patient-generated data that could be leveraged to personalize nutritional goal setting to improve population health; however, most computational approaches are limited when applied to individual-level personalization with sparse and irregular self-monitoring data. We applied informatics methods from expert suggestion systems to a challenging clinical problem: generating personalized nutrition goals from patient-generated diet and blood glucose data.Materials and Methods:We applied qualitative process coding and decision tree modeling to understand how registered dietitians translate patient-generated data into recommendations for dietary self-management of diabetes (i.e., knowledge model). We encoded this process in a set of functions that take diet and blood glucose data as an input and output diet recommendations (i.e., inference engine). Dietitians assessed face validity. Using four patient datasets, we compared our inference engine’s output to clinical narratives and gold standards developed by expert clinicians.Results:To dietitians, the knowledge model represented how recommendations from patient data are made. Inference engine recommendations were 63% consistent with the gold standard (range=42%–75%) and 74% consistent with narrative clinical observations (range=63%–83%).Discussion:Qualitative modeling and automating how dietitians reason over patient data resulted in a knowledge model representing clinical knowledge. However, our knowledge model was less consistent with gold standard than narrative clinical recommendations, raising questions about how best to evaluate approaches that integrate patient-generated data with expert knowledge.Conclusion:New informatics approaches that integrate data-driven methods with expert decision making for personalized goal setting, such as the knowledge base and inference engine presented here, demonstrate the potential to extend the reach of patient-generated data by synthesizing it with clinical knowledge. However, important questions remain about the strengths and weaknesses of computer algorithms developed to discern signal from patient-generated data compared to human experts.
Pediatric Pulmonology, May 26, 2023
PLOS ONE, May 4, 2023
Introduction Behavioral-education interventions have the potential to improve quality of life and... more Introduction Behavioral-education interventions have the potential to improve quality of life and self-care for patients on hemodialysis (HD) but have not been incorporated into routine clinical practice. The purpose of this pilot study was to determine the feasibility of delivering a simple behavioral-education intervention using cognitive behavioral strategies in patients receiving HD with poor quality of life. Methods In this mixed methods study, HD patients were randomly assigned to the study intervention (8 behavioral-education sessions delivered over 12 weeks) or a control group of dialysis education alone. Kidney disease quality of life (KDQOL)-36 scores, depressive symptoms and self-care behaviors were measured at weeks 0, 8, and 16. Following study completion, participants, social workers, and physicians provided their perspectives about the intervention via qualitative interviews. Findings Forty-five participants were randomized. Due, in part, to social worker attrition from the intervention arm, 34 participants (76%) completed at least 1 study session and were included in the analysis. The intervention led to modest, but non-significant, increase in KDQOLphysical component summary scores (+3.1±1.2 points) from week 0 to week 16. There were small, non-significant decreases in interdialytic weight gain and pre-dialysis phosphorus levels in the intervention group. Participants felt that chair-side delivery was practical and efficient, and that content related to the impact of dialysis on daily life was unique and
Cin-computers Informatics Nursing, Nov 4, 2022
Natural language processing includes a variety of techniques that help to extract meaning from na... more Natural language processing includes a variety of techniques that help to extract meaning from narrative data. In healthcare, medical natural language processing has been a growing field of study; however, little is known about its use in nursing. We searched PubMed, EMBASE, and CINAHL and found 689 studies, narrowed to 43 eligible studies using natural language processing in nursing notes. Data related to the study purpose, patient population, methodology, performance evaluation metrics, and quality indicators were extracted for each study. The majority (86%) of the studies were conducted from 2015 to 2021. Most of the studies (58%) used inpatient data. One of four studies used data from open-source databases. The most common standard terminologies used were the Unified Medical Language System and Systematized Nomenclature of Medicine, whereas nursing-specific standard terminologies were used only in eight studies. Full system performance metrics (eg, F score) were reported for 61% of applicable studies. The overall number of nursing natural language processing publications remains relatively small compared with the other medical literature. Future studies should evaluate and report appropriate performance metrics and use existing standard nursing terminologies to enable future scalability of the methods and findings.
In this paper, we describe two case studies of research projects that attempt to scale up HCI res... more In this paper, we describe two case studies of research projects that attempt to scale up HCI research beyond traditional small evaluation studies. The first of these projects focused on evaluating an interactive web application for promoting problem-solving in selfmanagement of type 2 diabetes mellitus (T2DM) in a randomized clinical trial; the second one included deployment in the wild of a smartphone app that provided individuals with T2DM with personalized predictions for changes in blood glucose levels in response to meals. We highlight lessons learned during these two projects and describe four different design considerations important for large scale studies. These include designing for longevity, diversity, adoption, and abandonment. We then discuss implications for future research that targets large scale deployment studies. CCS CONCEPTS • Human-Centered Computing; • Human Computer Interaction (HCI); • HCI design and evaluation methods; • User studies;
Journal of transition medicine, 2023
Transition from pediatric to adult care for adolescents and young adults (AYAs) with chronic illn... more Transition from pediatric to adult care for adolescents and young adults (AYAs) with chronic illness affects the entire family. However, little research has compared AYA and parent experiences of transition. Using Sandelowski and Barroso's method, the aim of this metasynthesis was to summarize findings of qualitative studies focusing on the transition experiences of AYAs and their parents across different chronic physical illnesses. PubMed, EMBASE and CINAHL were searched followed by forward and backward citation searching. Two authors completed a two-step screening process. Quality was appraised using Guba's criteria for qualitative rigor. Study characteristics and second order constructs were extracted by two authors and an iterative codebook guided coding and data synthesis. Of 1,644 records identified, 63 studies met inclusion criteria and reflect data from 1,106 AYAs and 397 parents across 18 diagnoses. Three themes were synthesized: transition is dynamic and experienced differently (differing perceptions of role change and growth during emerging adulthood), need for a supported and gradual transition (transition preparation and the factors which influence it) and liminal space (feeling stuck between pediatric and adult care). While AYAs and parents experience some aspects of transition differently, themes were similar across chronic illnesses which supports the development of disease agnostic transition preparation interventions. Transition preparation should support shifting family roles and responsibilities and offer interventions which align with AYA and family preferences.
Journal of Emergency Nursing, Apr 1, 2023
IntroductionThere was a significant decrease in emergency department encounters during the COVID-... more IntroductionThere was a significant decrease in emergency department encounters during the COVID-19 pandemic. Our large urban emergency department observed decreased encounters and admissions by youths with chronic health conditions. This study aimed to compare the frequency of emergency department encounters for certain young adults before the pandemic and during the COVID-19 pandemic.MethodsA retrospective cohort study using medical records of patients ages 20 to 26 years from October 2018 to September 2019 and February 2020 to February 2021. Files set for inclusion were those with a primary diagnosis of human immunodeficiency virus, diabetes mellitus, epilepsy, cerebral palsy, sickle cell disease, asthma, and certain psychiatric disorders for potentially preventable health events.ResultsWe included 1203 total encounters (853 before the pandemic and 350 during the pandemic), with the total number of subjects included in the study 568 (293 before the pandemic to 239 during the pandemic). During the pandemic, young adults with mental health conditions (53.1%) accounted for most encounters. Encounters requiring hospital admissions increased from 27.4% to 52.5% during the pandemic, primarily among patients with diabetes (41.8% vs 61.1%) and mental health conditions (50% vs 73.3%).DiscussionThe number of young adults with certain chronic health conditions decreased during COVID-19, with encounters for subjects with mental health conditions increasing significantly. The proportion of admissions increased during the pandemic with increases for subjects with mental health disorders and diabetes. The number of frequent users decreased during COVID-19. Future research is needed to understand better the causes for these disparities in young adults with chronic conditions who use the emergency department as a source of care.
Research in Nursing & Health, Oct 12, 2021
Data-driven characterization of symptom clusters in chronic conditions is essential for shared cl... more Data-driven characterization of symptom clusters in chronic conditions is essential for shared cluster detection and physiological mechanism discovery. This study aims to computationally describe symptom documentation from electronic nursing notes and compare symptom clusters among patients diagnosed with four chronic conditions–chronic obstructive pulmonary disease (COPD), heart failure, type 2 diabetes mellitus, and cancer. Nursing notes (N=504,395; 133,977 patients) were obtained for the 2016 calendar year. We used NimbleMiner, a natural language processing application, to identify the presence of 56 symptoms. We calculated symptom documentation prevalence by note and patient for the corpus. Then, we visually compared documentation for a subset of patients (N=22,657) diagnosed with COPD (n=3,339), heart failure (n=6,587), diabetes (n=12,139), and cancer (n=7,269) and conducted multiple correspondence analysis and hierarchical clustering to discover underlying groups of patients who have similar symptom profiles (i.e., symptom clusters) for each condition. As expected, pain was the most frequently documented symptom. All conditions had a group of patients characterized by no symptoms. Shared clusters included cardiovascular symptoms for heart failure and diabetes; pain and other symptoms for COPD, diabetes, and cancer; and a newly-identified cognitive and neurological symptom cluster for heart failure, diabetes, and cancer. Cancer (gastrointestinal symptoms and fatigue) and COPD (mental health symptoms) each contained a unique cluster. In summary, we report both shared and distinct, as well as established and novel, symptom clusters across chronic conditions. Findings support the use of electronic health record-derived notes and NLP methods to study symptoms and symptom clusters to advance symptom science.
Proceedings of the ACM on human-computer interaction, Apr 13, 2021
Health coaching can be an effective intervention to support self-management of chronic conditions... more Health coaching can be an effective intervention to support self-management of chronic conditions like diabetes, but there are not enough coaching practitioners to reach the growing population in need of support. Conversational technology, like chatbots, presents an opportunity to extend health coaching support to broader and more diverse populations. However, some have suggested that the human element is essential to health coaching and cannot be replicated with technology. In this research, we examine automated health coaching using a theory-grounded, wizard-of-oz chatbot, in comparison with text-based virtual coaching from human practitioners who start with the same protocol as the chatbot but have the freedom to embellish and adjust as needed. We found that even a scripted chatbot can create a coach-like experience for participants. While human coaches displayed advantages expressing empathy and using probing questions to tailor their support, they also encountered tremendous barriers and frustrations adapting to text-based virtual coaching. The chatbot coach had advantages in being persistent, as well as more consistently giving choices and options to foster client autonomy. We discuss implications for the design of virtual health coaching interventions.
Nursing Outlook, May 1, 2021
Background:Nurses often document patient symptoms in narrative notes.Purpose:This study used a te... more Background:Nurses often document patient symptoms in narrative notes.Purpose:This study used a technique called natural language processing (NLP) to: (1) Automatically identify documentation of seven common symptoms (anxiety, cognitive disturbance, depressed mood, fatigue, sleep disturbance, pain, and well-being) in homecare narrative nursing notes, and (2) examine the association between symptoms and emergency department visits or hospital admissions from homecare.Method:NLP was applied on a large subset of narrative notes (2.5 million notes) documented for 89,825 patients admitted to one large homecare agency in the Northeast United States.Findings:NLP accurately identified symptoms in narrative notes. Patients with more documented symptom categories had higher risk of emergency department visit or hospital admission.Discussion:Further research is needed to explore additional symptoms and implement NLP systems in the homecare setting to enable early identification of concerning patient trends leading to emergency department visit or hospital admission.
arXiv (Cornell University), Feb 23, 2018
Objective: To evaluate unsupervised clustering methods for identifying individual-level behaviora... more Objective: To evaluate unsupervised clustering methods for identifying individual-level behavioral-clinical phenotypes that relate personal biomarkers and behavioral traits in type 2 diabetes (T2DM) self-monitoring data. Materials and Methods: We used hierarchical clustering (HC) to identify groups of meals with similar nutrition and glycemic impact for 6 individuals with T2DM who collected self-monitoring data. We evaluated clusters on: 1) correspondence to gold standards generated by certified diabetes educators (CDEs) for 3 participants; 2) face validity, rated by CDEs, and 3) impact on CDEs' ability to identify patterns for another 3 participants. Results: Gold standard (GS) included 9 patterns across 3 participants. Of these, all 9 were rediscovered using HC: 4 GS patterns were consistent with patterns identified by HC (over 50% of meals in a cluster followed the pattern); another 5 were included as subgroups in broader clusers. 50% (9/18) of clusters were rated over 3 on 5-point Likert scale for validity, significance, and being actionable. After reviewing clusters, CDEs identified patterns that were more consistent with data (70% reduction in contradictions between patterns and participants' records). Discussion: Hierarchical clustering of blood glucose and macronutrient consumption appears suitable for discovering behavioral-clinical phenotypes in T2DM. Most clusters corresponded to gold standard and were rated positively by CDEs for face validity. Cluster visualizations helped CDEs identify more robust patterns in nutrition and glycemic impact, creating new possibilities for visual analytic solutions. Conclusion: Machine learning methods can use diabetes self-monitoring data to create personalized behavioral-clinical phenotypes, which may prove useful for delivering personalized medicine.
American Journal of Infection Control, May 1, 2017
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Contemporary diabetes, 2018
A96. ENHANCING SUPPORT FOR PATIENTS AND FAMILIES EXPERIENCING SERIOUS ILLNESS
A96. ENHANCING SUPPORT FOR PATIENTS AND FAMILIES EXPERIENCING SERIOUS ILLNESS
Nursing Outlook
BACKGROUND The training and mentoring of pre- and post-doctoral trainees in nursing research is e... more BACKGROUND The training and mentoring of pre- and post-doctoral trainees in nursing research is essential to feed the pipeline of nurses prepared to launch an independent program of research. PURPOSE The purpose of this report is to describe a one-on-one grant writing Partnership developed in a school of nursing targeting pre- and post-doctoral trainees and quantify its impact on funding rates. METHODS The Partnership includes four key elements: regular meetings, setting a timeline with milestones, writing and editing support, and attention to administrative documents. Forty grant applications by pre- and post-doctoral trainees were developed and submitted from 2011 to 2020. FINDINGS Among Partnership participants, 81.0% (17/21) received funding as compared with 42.1% (8/19) who did not participate, p = .02. DISCUSSION Schools of nursing and other disciplines should consider investing in a Partnership to provide grant writing support their pre- and post-doctoral trainees and increase their overall research capacity.
Uploads
Papers by Arlene Smaldone