About

Katherine A. Sward is a professor involved in the Center of Excellence for Exposure Health Informatics at the University of Utah. Her work focuses on exposure health research, with an emphasis on health informatics, applied data science, and telemedicine. She co-leads initiatives such as the NIEHS-funded R24 SMARTER project, which aims to augment the University of Utah's Exposure Health Informatics Ecosystem with sensor metadata, and serves as the site Principal Investigator for the PCORnet RECOVER EHR study, a multi-site project on digital phenotyping of post-acute sequelae of SARS-CoV-2. Her research includes leading national studies on telemedicine trends and outcomes, utilizing large-scale analysis of industry and electronic health record data.

Research topics

• Computer Science
• Medicine
• Artificial Intelligence
• Knowledge management
• Data Mining
• Process management
• Database
• Medical education
• Nursing
• Psychiatry
• Gerontology
• Social psychology
• Developmental psychology
• Clinical psychology
• Business
• Psychology
• Data science

Selected publications

• Needs, Preferences, and Challenges of Environmental Health Researchers in Using Sensors: A Content Analysis (Preprint)

  2026-01-12

  articleOpen access

  <sec> <title>BACKGROUND</title> Environmental exposures can influence human health in complex ways. It remains difficult for researchers to integrate exposure data, partly due to an unmet need for informatics and metadata tools. </sec> <sec> <title>OBJECTIVE</title> The purpose of this study was to understand the needs, preferences, and pain points of environmental health researchers regarding the selection, deployment, and integration of sensors for their research studies, to inform user requirements for a sensor metadata repository. </sec> <sec> <title>METHODS</title> We purposively recruited six exposure health researchers with expertise entailing sensors, corresponding to one of eight role types, and conducted semi-structured interviews between February 7-26, 2025. Interviews centered on understanding the needs, preferences, and pain points of environmental health researchers seeking to use sensors for their research projects. We conducted deductive content analysis of interview transcripts, guided by the HITREF framework. </sec> <sec> <title>RESULTS</title> The participants held primary roles of primary investigator, study coordinator, sensor developer, biomedical informaticist, and study sponsor. Content analysis revealed that participants consider multiple characteristics of sensors when selecting sensors for studies, including cost, physical parameters and limitations of the sensors, reliability, suitable environments for deployment, and software and processes required for data acquisition, transfer, integration, and analysis. User training and interaction are important considerations, often conceptualized as burdens on study participants that research teams seek to minimize. Participants described a desire for adequate support from sensor developers and flexibility in data transfer and analysis. </sec> <sec> <title>CONCLUSIONS</title> Participants in varied roles described many similar themes regarding considerations for sensor selection, deployment, and integration as well as desired features for a sensor metadata repository. </sec>

  PublisherDOI

• Inflection Point: Artificial Intelligence, Digital Health, and the Evolution of Pre-Licensure Nursing Informatics Competencies

  CIN Computers Informatics Nursing · 2026-03-17

  articleOpen accessSenior author

  Recent advancements in artificial intelligence, telehealth, and sensor technologies have fundamentally transformed health care, creating an urgent need to update and revise nursing informatics competencies for pre-licensure education. The purpose of this narrative, non-systematic review is to (1) describe and characterize the evolution of pre-licensure nursing informatics competencies; (2) discuss the central phenomena driving current digital transformation in health care; and (3) discuss future directions to evolve pre-licensure nursing informatics competency frameworks in an era of digital health. We conducted multiple literature searches in support of the narrative review. Current pre-licensure nursing informatics competency frameworks require evolution, given the new knowledge and skills needed for nurses to engage with rapidly advancing digital health technologies. The review reveals the need for a fifth wave of competency development that prepares nurses as informed users, contributors, evaluators, and stewards of digital health tools. We must advance nursing informatics education to meet the workforce needs of the digital health era by building on a robust foundation of past competency development efforts.

  PublisherDOI

• Exploring Factors That Impact Genetic Counseling Referral and Uptake Using Learning Health Approaches

  Learning Health Systems · 2025-12-04

  articleOpen access

  ABSTRACT Introduction Germline testing and pretest genetic counseling are advised for many cancer patients, yet not all receive these services. Electronic Health Records (EHRs) offer a valuable resource to measure referral to genetic counseling (referral receipt) and uptake (completion of counseling). This study uses EHR data to assess demographic factors influencing genetic counseling referral and uptake among prostate cancer patients, serving as a learning health system model. Methods We included prostate cancer patients who met germline testing and counseling criteria at an NCI‐designated cancer center from January 1, 2018, to June 30, 2022. Demographic factors—age at diagnosis, race, employment, insurance, and geographic region—were assessed for associations with genetic counseling referral and uptake. Analyses involved descriptive statistics, two‐group comparisons, and regression models. Results Among 356 prostate cancer patients, only 34.2% received genetic counseling referrals, and of these, 73% completed a counseling visit. Older patients were less likely to receive referrals (OR = 0.93, 95% CI [0.89–0.97]) and complete visits (OR = 0.92, 95% CI [0.87–0.96]). Patients employed full‐time were more likely to receive referrals (39.2% vs. 23.1%; p = 0.01), while White (93% vs. 81%; p = 0.047) and rural patients (42.7% vs. 6.1%; p = 0.02) had higher uptake. Insurance status did not significantly affect referral or uptake. Conclusion This study demonstrates the potential of EHRs to identify demographic disparities in genetic counseling services. Using a learning health system approach, healthcare institutions can leverage EHR data to design targeted interventions aimed at improving access and reducing disparities in genetic services, ultimately enhancing patient outcomes.

  PublisherOA PDFDOI

• Patterns of self-reported diarrhea in patients with cancer receiving chemotherapy

  Supportive Care in Cancer · 2025-02-06

  article
  PublisherDOI

• Validity of International Classification of Diseases Codes for Identifying Alcohol Use and Repeated or Harm-associated Alcohol Use in Pregnancy

  Journal of Addiction Medicine · 2025-09-16

  article

  OBJECTIVES: To describe the validity of alcohol use disorder (AUD), the International Classification of Diseases (ICD) codes for alcohol use and repeated or harm-associated use in pregnancy. METHODS: We conducted a retrospective study of pregnancies in individuals who sought care at a medical center from May 2014 to August 2023. We selected all pregnancies with an ICD 9th (ICD-9) or 10th (ICD-10) revision AUD code (ICD-9: 303.9x and 305.x; ICD-10: F10.x and O99.31x) and calculated their positive predictive value (PPV) for capturing alcohol use and repeated or harm-associated use in pregnancy using review of health record as the reference standard. We defined alcohol use as any alcohol consumption during pregnancy and repeated or harm-associated alcohol use as a repeated pattern associated with adverse consequences. RESULTS: AUD ICD codes were associated with 305 unique pregnancies. The most common AUD ICD code group was 305.x (n=177, 56.4%), followed by F10.x (n=105, 33.4%), O99.31x (n=25, 8.0%), and 303.9x (n=7, 2.2%). The PPV of AUD codes for capturing alcohol use in pregnancy ranged from 10.0% (95% confidence interval [CI], 8.9%-11.2%) for 305.x to 100% (95% CI, 80.9%-99.5%) for O99.31x. The PPV of AUD codes for capturing repeated or harm-associated use in pregnancy ranged from 1.7% (95% CI, 1.6%-1.8%) for 305.x to 28.0% (95% CI, 21.3%-35.9%) for O99.31x. CONCLUSIONS: The PPV of AUD codes for identifying alcohol use and repeated or harm-associated use in pregnancy varies widely across ICD-9 and ICD-10 code groups. These results should be considered when estimating alcohol exposure in pregnancy from administrative data.

  PublisherDOI

• Patient and parent perspectives on an academic rheumatology transition clinic

  Health Care Transitions · 2025-01-01 · 2 citations

  articleOpen access

  Objectives: To better define components of successful Health Care Transition (HCT) we surveyed patients in an academic Rheumatology Transition clinic at the University of Utah. Results can be used to improve HCT over time. Methodology: We asked patients and parents to complete Mind the Gap and the Transition Feedback survey as part of a larger registry dataset collected from said Rheumatology Transition clinic. Results from Mind the Gap and the Transition Feedback survey were analyzed. Survey responses are presented as averages. Results: Sixty-five patients and 42 parents completed Mind the Gap. Patients report that the clinic is outperforming their expectations in 20 of 22 variables. Parents report that the clinic is underperforming their expectations in 23 of 27 variables. Parents value these 22 variables more than the patients. Twenty-four patients and 15 parents completed the Transition Feedback survey. More than 50 % of patients and parents state that the components of HCT curriculum were addressed. 58 % of patients (14 out of 24) reported feeling "very ready" to move to an adult doctor or other health care provider. 53 % of parents (8 out of 15) felt their child was "very ready" to move to an adult doctor or other health care provider. Conclusion: A difficulty in defining a successful transfer is how to simultaneously integrate the perspective and needs of the patient and parents. This research shows that the values of patients and their parents generally align. However, there are important disparities between these groups. We demonstrate that even in a dedicated Transition clinic, not all components of HCT are being administered and that only half of patients and parents feel prepared to transfer.

  PublisherDOI

• 384 A systematic approach to understanding nursing documentation tasks

  Journal of Clinical and Translational Science · 2025-03-25

  articleOpen accessSenior author

  Objectives/Goals: Healthcare organizations must track electronic health record (EHR) activity at the user level, including logons, accessed records, and viewed or entered documentation. There is little standardization in EHR audit logs and nurse workload has not been explored using these data. In this project, we characterized nurse actions from EHR audit logs. Methods/Study Population: We performed an analysis of EHR audit log data collected from 8,149 nurses over 5 years at University of Utah Health. We preprocessed nursing-centric EHR audit logs from the Epic EHR by cleaning and preparing the data for analysis. We calculated basic statistics for the variables labeled user_id (nurse) and metric_id (action). We reviewed the actions used by nurses and categories the actions as navigation, view, and entry. To capture the clinical context of the actions, two nurses categorized each action. A third nurse resolved any discrepancies. Results/Anticipated Results: We found that of the 4,419 available metrics, nurses used 1,461 unique metrics during the timeframe. The actions most used by nurses were 1) report with patient data viewed, 2) inpatient system list, and 3) storyboard viewed. Most of the metrics were categorized as navigation. The number of nurses interacting with the EHR increased each year and on average, we found that 1000 unique metrics were used by each nurse user in a 24-hour period. The expected outcome is a set of actions that can be mapped to higher level nursing interventions and in the future contribute to models for nursing workload measurement. Discussion/Significance of Impact: We found great value in using EHR audit logs to provide insights into nursing actions. Information gleaned can benefit organizations that are crafting interventions to decrease workload. Ultimately, the goal is to ensure that nurses have an appropriate workload allowing for safe and high-quality patient care while maintaining their well-being.

  PublisherOA PDFDOI

• Standardizing research methods for opioid dose comparison: the NIH HEAL morphine milligram equivalent calculator

  Pain · 2025-02-03 · 30 citations

  articleOpen access

  ABSTRACT: We developed the National Institutes of Health helping to end addiction long-term initiative morphine milligram equivalent (MME) calculator to standardize MME calculations across pain research studies, addressing a critical barrier to effective research synthesis and meta-analysis. The tool provides evidence-based mapping factors for 29 opioids through a research electronic data capture-based calculator and companion Web site ( research-mme.wakehealth.edu ). Development involved systematic evidence evaluation of literature from 1949 to March 2024, following PRISMA guidelines. From an initial screening of over 170,050 articles, we identified 24 studies providing evidence for conversion factors. The calculator incorporates 4 standardized time-window calculation methods aligned with current research approaches and includes traditional full agonists, partial agonists, and mixed-mechanism agents. Using modified GRADE methodology, we evaluated evidence quality for each conversion factor, documenting levels from high-quality randomized controlled trials to pharmacokinetic extrapolation. Our tool replicates most existing Centers for Disease Control and Prevention (CDC) conversion factors while expanding coverage to 7 additional opioids and 6 formulations not included in the 2022 CDC conversion table. The calculator features options to analyze results with or without buprenorphine, accommodating its emerging role in pain research. This standardized framework enables researchers to map opioid doses using consistent, evidence-based ratios and harmonize data collection across research networks. While the tool represents a significant advance in standardizing MME calculations for research, limitations in the underlying evidence base highlight the need for continued validation through clinical research.

  PublisherDOI

• USE OF CLUSTER ANALYSIS TECHNIQUES TO EXPLORE ICD-10-CM CODE DATA IN A LONGITUDINAL BIRTHING PERSON-CHILD LINKED SURVEILLANCE SYSTEM OF PREGNANCIES COMPLICATED BY OPIOID USE DISORDER

  Drug and Alcohol Dependence · 2024-07-01

  article
  PublisherDOI

• A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation

  JMIR Nursing · 2024-06-02 · 5 citations

  articleOpen access

  BACKGROUND: Increased workload, including workload related to electronic health record (EHR) documentation, is reported as a main contributor to nurse burnout and adversely affects patient safety and nurse satisfaction. Traditional methods for workload analysis are either administrative measures (such as the nurse-patient ratio) that do not represent actual nursing care or are subjective and limited to snapshots of care (eg, time-motion studies). Observing care and testing workflow changes in real time can be obstructive to clinical care. An examination of EHR interactions using EHR audit logs could provide a scalable, unobtrusive way to quantify the nursing workload, at least to the extent that nursing work is represented in EHR documentation. EHR audit logs are extremely complex; however, simple analytical methods cannot discover complex temporal patterns, requiring use of state-of-the-art temporal data-mining approaches. To effectively use these approaches, it is necessary to structure the raw audit logs into a consistent and scalable logical data model that can be consumed by machine learning (ML) algorithms. OBJECTIVE: We aimed to conceptualize a logical data model for nurse-EHR interactions that would support the future development of temporal ML models based on EHR audit log data. METHODS: We conducted a preliminary review of EHR audit logs to understand the types of nursing-specific data captured. Using concepts derived from the literature and our previous experience studying temporal patterns in biomedical data, we formulated a logical data model that can describe nurse-EHR interactions, the nurse-intrinsic and situational characteristics that may influence those interactions, and outcomes of relevance to the nursing workload in a scalable and extensible manner. RESULTS: We describe the data structure and concepts from EHR audit log data associated with nursing workload as a logical data model named RNteract. We conceptually demonstrate how using this logical data model could support temporal unsupervised ML and state-of-the-art artificial intelligence (AI) methods for predictive modeling. CONCLUSIONS: The RNteract logical data model appears capable of supporting a variety of AI-based systems and should be generalizable to any type of EHR system or health care setting. Quantitatively identifying and analyzing temporal patterns of nurse-EHR interactions is foundational for developing interventions that support the nursing documentation workload and address nurse burnout.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R21HD061870

  NIH · $450k · 2014

Frequent coauthors

• Mollie Cummins

  40 shared

• Julio C. Facelli

  36 shared

• Alan H. Morris

  Lancaster University

  34 shared

• Ramkiran Gouripeddi

  34 shared

• Jia‐Wen Guo

  Huaqiao University

  23 shared

• Victoria L. Tiase

  21 shared

• Susan L. Beck

  University of Utah

  21 shared

• Nancy Staggers

  University of Utah

  20 shared

Labs

• Center of Excellence for Exposure Health InformaticsPI

  The Center of Excellence for Exposure Health Informatics focuses on research related to exposure health informatics.

Education

• PhD, College of Nursing

  University of Utah

  2007