About

Marcelline Harris is an Associate Professor Emerita in the Department of Systems, Populations and Leadership at the University of Michigan School of Nursing. Her research primarily focuses on clinical research informatics and infrastructure, biomedical terminologies, clinical informatics, and nursing health services research. The central focus of her work involves informatics methodologies, including terminology systems and standards that facilitate data integration and interoperability. She has extensive practical experience related to clinical systems, data integration, modeling, and reuse within clinical information systems and for large-scale research. Her research has been funded by prominent organizations such as NIH, AHRQ, CDC, RWJF, PCORI, Mayo Clinic, and the University of Michigan. Harris teaches courses across informatics, research, and leadership at the School of Nursing, addressing both clinical and research informatics, evaluation, and sociotechnical system components. She has mentored numerous students and international scholars, contributing significantly to the academic community in nursing informatics.

Research topics

• Computer science
• Medicine
• Nursing
• Data science
• Natural language processing

Selected publications

• CRENO: An ontology to model concepts relating to culture, race, ethnicity, and nationality for health data.

  PubMed · 2023-01-01 · 4 citations

  articleOpen access

  Generating categories and classifications is a common function in life science research; however, categorizing the human population based on "race" remains controversial. There is an awareness and recognition of social-economic disparities with respect to health which are sometimes impacted by someone's ethnicity or race. This work describes an endeavor to develop a computable ontology model to represent a standardization of the concepts surrounding culture, race, ethnicity, and nationality - concepts misrepresented widely. We constructed an OWL ontology based on reliable resources with iterative human expert evaluations and aligned it to existing biomedical ontological models. The effort produced a preliminary ontology that expresses concepts related to classes of ethnic, racial, national, and cultural identities and showcases how health disparity data can be linked and expressed within our ontological framework. Future work will explore automated methods to expand the ontology and its utilization for clinical informatics.

  PublisherOA PDF

• Machine learning-based donor permission extraction from informed consent documents

  BMC Bioinformatics · 2023-12-15 · 5 citations

  articleOpen access

  BACKGROUND: With more clinical trials are offering optional participation in the collection of bio-specimens for biobanking comes the increasing complexity of requirements of informed consent forms. The aim of this study is to develop an automatic natural language processing (NLP) tool to annotate informed consent documents to promote biorepository data regulation, sharing, and decision support. We collected informed consent documents from several publicly available sources, then manually annotated them, covering sentences containing permission information about the sharing of either bio-specimens or donor data, or conducting genetic research or future research using bio-specimens or donor data. RESULTS: We evaluated a variety of machine learning algorithms including random forest (RF) and support vector machine (SVM) for the automatic identification of these sentences. 120 informed consent documents containing 29,204 sentences were annotated, of which 1250 sentences (4.28%) provide answers to a permission question. A support vector machine (SVM) model achieved a F-1 score of 0.95 on classifying the sentences when using a gold standard, which is a prefiltered corpus containing all relevant sentences. CONCLUSIONS: This study provides the feasibility of using machine learning tools to classify permission-related sentences in informed consent documents.

  PublisherOA PDFDOI

• The case for expressing nursing theories using ontologies

  Journal of the American Medical Informatics Association · 2023-06-12 · 3 citations

  articleOpen accessSenior author

  Nursing and informatics share a common strength in their use of structured representations of domains, specifically the underlying notion of 'things' (ie, concepts, constructs, or named entities) and the relationships among those things. Accurate representation of nursing knowledge in machine-interpretable formats is a necessary next step for leveraging contemporary technologies. Expressing validated nursing theories in ontologies, and in particular formal ontologies, would serve not only nursing, but also investigators from other domains, clinical information system developers, and the users of advanced technologies such as artificial intelligence that seek to learn from the real-world data and evidence generated by nurses and others. Such efforts will enable sharing knowledge and conceptualizations about phenomena across the domains of nursing and generating, testing, revising, and providing theoretically-based perspectives when leveraging contemporary technologies. Nursing is well situated for this work, leveraging intentional and focused collaborations among nurse informaticists, scientists, and theorists.

  PublisherOA PDFDOI

• Evaluating and extending the Informed Consent Ontology for representing permissions from the clinical domain

  Applied Ontology · 2022-01-07 · 5 citations

  articleOpen accessSenior author

  The purpose of this study was to evaluate, revise, and extend the Informed Consent Ontology (ICO) for expressing clinical permissions, including reuse of residual clinical biospecimens and health data. This study followed a formative evaluation design and used a bottom-up modeling approach. Data were collected from the literature on US federal regulations and a study of clinical consent forms. Eleven federal regulations and fifteen permission-sentences from clinical consent forms were iteratively modeled to identify entities and their relationships, followed by community reflection and negotiation based on a series of predetermined evaluation questions. ICO included fifty-two classes and twelve object properties necessary when modeling, demonstrating appropriateness of extending ICO for the clinical domain. Twenty-six additional classes were imported into ICO from other ontologies, and twelve new classes were recommended for development. This work addresses a critical gap in formally representing permissions clinical permissions, including reuse of residual clinical biospecimens and health data. It makes missing content available to the OBO Foundry, enabling use alongside other widely-adopted biomedical ontologies. ICO serves as a machine-interpretable and interoperable tool for responsible reuse of residual clinical biospecimens and health data at scale.

  PublisherOA PDFDOI

• Tolerance to oral anticancer agent treatment in older adults with cancer: A secondary analysis of data from EHRs and a pilot study of patient-reported outcomes

  Research Square · 2022-03-09

  preprintOpen access

  Abstract Background: More than 60% of cancer cases occur in older adults, and many are treated with oral anticancer agents (OAAs). Yet, OAA treatment tolerability in older adults has not been fully understood due to their underrepresentation in oncology clinical trials, creating challenges for treatment decision-making and symptom management. The objective of this study was to investigate the tolerance of capecitabine, an OAA, in older adults with cancer and explore factors associated with capecitabine-related side effects and treatment changes, to enhance supportive care. Methods : A secondary analysis used combined data from electronic health records and a pilot study of patient-reported outcomes, with a total of 97 adult patients taking capecitabine during 2016-2017, including older adult patients aged 65 years or older (n=43). The data extracted included patient socio-demographics, capecitabine information, side effects, and capecitabine treatment changes (dose reductions and dose interruptions). Bivariate correlations, negative binomial regression, and multiple linear regression were conducted for data analysis. Results : Older adults were more likely to experience fatigue (86% vs. 51%, p =.001) and experienced more severe fatigue ( β =0.44, p =.03) and HFS ( β =1.15, p =.004) than younger adults. The severity of fatigue and HFS were associated with the number of outpatient medications ( β =0.06, p =.006) and the duration of treatment ( β =0.50, p= 0.009), respectively. Correlations among side effects presented differently between younger and older adults. Older adults also tended to be more likely to experience dose reductions (21% vs. 13%) and dose interruptions (33% vs. 28%) than younger adults. Females, breast cancer diagnosis, capecitabine monotherapy, and severe HFS were found to be associated with dose reductions ( ps< 0.05). Conclusions : Older adults were less likely to tolerate OAA treatment and had different co-occurring side effects compared to younger adults. While dose reductions are common among older adults, age 65 years or older may not be an independent factor of treatment changes. Other socio-demographic and clinical factors may be more likely to be associated. Future studies can be conducted to further explore older adults’ tolerance to a variety of OAAs to generate more evidence to support optimal OAA treatment decision-making and symptom management among older adults.

  PublisherOA PDFDOI

• Technologies for Medication Adherence Monitoring and Technology Assessment Criteria: Narrative Review

  JMIR mhealth and uhealth · 2022-01-28 · 142 citations

  reviewOpen access

  BACKGROUND: Accurate measurement and monitoring of patient medication adherence is a global challenge because of the absence of gold standard methods for adherence measurement. Recent attention has been directed toward the adoption of technologies for medication adherence monitoring, as they provide the opportunity for continuous tracking of individual medication adherence behavior. However, current medication adherence monitoring technologies vary according to their technical features and data capture methods, leading to differences in their respective advantages and limitations. Overall, appropriate criteria to guide the assessment of medication adherence monitoring technologies for optimal adoption and use are lacking. OBJECTIVE: This study aims to provide a narrative review of current medication adherence monitoring technologies and propose a set of technology assessment criteria to support technology development and adoption. METHODS: A literature search was conducted on PubMed, Scopus, CINAHL, and ProQuest Technology Collection (2010-present) using the combination of keywords medication adherence, measurement technology, and monitoring technology. The selection focused on studies related to medication adherence monitoring technology and its development and use. The technological features, data capture methods, and potential advantages and limitations of the identified technology applications were extracted. Methods for using data for adherence monitoring were also identified. Common recurring elements were synthesized as potential technology assessment criteria. RESULTS: Of the 3865 articles retrieved, 98 (2.54%) were included in the final review, which reported a variety of technology applications for monitoring medication adherence, including electronic pill bottles or boxes, ingestible sensors, electronic medication management systems, blister pack technology, patient self-report technology, video-based technology, and motion sensor technology. Technical features varied by technology type, with common expectations for using these technologies to accurately monitor medication adherence and increase adoption in patients' daily lives owing to their unobtrusiveness and convenience of use. Most technologies were able to provide real-time monitoring of medication-taking behaviors but relied on proxy measures of medication adherence. Successful implementation of these technologies in clinical settings has rarely been reported. In all, 28 technology assessment criteria were identified and organized into the following five categories: development information, technology features, adherence to data collection and management, feasibility and implementation, and acceptability and usability. CONCLUSIONS: This narrative review summarizes the technical features, data capture methods, and various advantages and limitations of medication adherence monitoring technology reported in the literature and the proposed criteria for assessing medication adherence monitoring technologies. This collection of assessment criteria can be a useful tool to guide the development and selection of relevant technologies, facilitating the optimal adoption and effective use of technology to improve medication adherence outcomes. Future studies are needed to further validate the medication adherence monitoring technology assessment criteria and construct an appropriate technology assessment framework.

  PublisherOA PDFDOI

• Tolerance to oral anticancer agent treatment in older adults with cancer: a secondary analysis of data from electronic health records and a pilot study of patient-reported outcomes

  BMC Cancer · 2022-09-03 · 14 citations

  articleOpen access

  BACKGROUND: More than 60% of cancer cases occur in older adults, and many are treated with oral anticancer agents. Yet, the treatment tolerability in older adults has not been fully understood due to their underrepresentation in oncology clinical trials, creating challenges for treatment decision-making and symptom management. The objective of this study was to investigate the tolerance of capecitabine, an example of oral chemotherapy, among older adults with cancer and explore factors associated with capecitabine-related side effects and treatment changes, to enhance supportive care. METHODS: A secondary analysis used combined data from electronic health records and a pilot study of patient-reported outcomes, with a total of 97 adult patients taking capecitabine during 2016-2017, including older adult patients aged 65 years or older (n = 43). The data extracted included patient socio-demographics, capecitabine information, side effects, and capecitabine treatment changes (dose reductions and dose interruptions). Bivariate correlations, negative binomial regression, and multiple linear regression were conducted for data analysis. RESULTS: Older adults were more likely to experience fatigue (86% vs. 51%, p = .001) and experienced more severe fatigue (β = 0.44, p = 0.03) and hand-foot syndrome (HFS) (β = 1.15, p = 0.004) than younger adults. The severity of fatigue and HFS were associated with the number of outpatient medications (β = 0.06, p = 0.006) and the duration of treatment (β = 0.50, p = 0.009), respectively. Correlations among side effects presented different patterns between younger and older adults. Although more older adults experienced dose reductions (21% vs. 13%) and dose interruptions (33% vs. 28%) than younger adults, the differences were not statistically different. Female sex, breast cancer diagnosis, capecitabine monotherapy, and severe HFS were found to be associated with dose reductions (p-values < 0.05). CONCLUSIONS: Older adults were less likely to tolerate capecitabine treatment and had different co-occurring side effects compared to younger adults. While dose reductions are common among older adults, age 65 years or older may not be an independent factor of treatment changes. Other socio-demographic and clinical factors may be more likely to be associated. Future studies can be conducted to further explore older adults' tolerance to a variety of oral anticancer agents to generate more evidence to support optimal treatment decision-making and symptom management.

  PublisherOA PDFDOI

• Lessons Learned for Identifying and Annotating Permissions in Clinical Consent Forms

  Applied Clinical Informatics · 2021-05-01 · 2 citations

  articleOpen accessSenior author

  Abstract Background The lack of machine-interpretable representations of consent permissions precludes development of tools that act upon permissions across information ecosystems, at scale. Objectives To report the process, results, and lessons learned while annotating permissions in clinical consent forms. Methods We conducted a retrospective analysis of clinical consent forms. We developed an annotation scheme following the MAMA (Model-Annotate-Model-Annotate) cycle and evaluated interannotator agreement (IAA) using observed agreement (A o), weighted kappa (κw ), and Krippendorff's α. Results The final dataset included 6,399 sentences from 134 clinical consent forms. Complete agreement was achieved for 5,871 sentences, including 211 positively identified and 5,660 negatively identified as permission-sentences across all three annotators (A o = 0.944, Krippendorff's α = 0.599). These values reflect moderate to substantial IAA. Although permission-sentences contain a set of common words and structure, disagreements between annotators are largely explained by lexical variability and ambiguity in sentence meaning. Conclusion Our findings point to the complexity of identifying permission-sentences within the clinical consent forms. We present our results in light of lessons learned, which may serve as a launching point for developing tools for automated permission extraction.

  PublisherOA PDFDOI

• Technologies for Medication Adherence Monitoring and Technology Assessment Criteria: Narrative Review (Preprint)

  2021-11-23

  preprintOpen access

  <sec> <title>BACKGROUND</title> Accurate measurement and monitoring of patient medication adherence is a global challenge because of the absence of &lt;i&gt;gold standard&lt;/i&gt; methods for adherence measurement. Recent attention has been directed toward the adoption of technologies for medication adherence monitoring, as they provide the opportunity for continuous tracking of individual medication adherence behavior. However, current medication adherence monitoring technologies vary according to their technical features and data capture methods, leading to differences in their respective advantages and limitations. Overall, appropriate criteria to guide the assessment of medication adherence monitoring technologies for optimal adoption and use are lacking. </sec> <sec> <title>OBJECTIVE</title> This study aims to provide a narrative review of current medication adherence monitoring technologies and propose a set of technology assessment criteria to support technology development and adoption. </sec> <sec> <title>METHODS</title> A literature search was conducted on PubMed, Scopus, CINAHL, and ProQuest Technology Collection (2010-present) using the combination of keywords &lt;i&gt;medication adherence&lt;/i&gt;, &lt;i&gt;measurement technology&lt;/i&gt;, and &lt;i&gt;monitoring technology&lt;/i&gt;. The selection focused on studies related to medication adherence monitoring technology and its development and use. The technological features, data capture methods, and potential advantages and limitations of the identified technology applications were extracted. Methods for using data for adherence monitoring were also identified. Common recurring elements were synthesized as potential technology assessment criteria. </sec> <sec> <title>RESULTS</title> Of the 3865 articles retrieved, 98 (2.54%) were included in the final review, which reported a variety of technology applications for monitoring medication adherence, including electronic pill bottles or boxes, ingestible sensors, electronic medication management systems, blister pack technology, patient self-report technology, video-based technology, and motion sensor technology. Technical features varied by technology type, with common expectations for using these technologies to accurately monitor medication adherence and increase adoption in patients’ daily lives owing to their unobtrusiveness and convenience of use. Most technologies were able to provide real-time monitoring of medication-taking behaviors but relied on proxy measures of medication adherence. Successful implementation of these technologies in clinical settings has rarely been reported. In all, 28 technology assessment criteria were identified and organized into the following five categories: &lt;i&gt;development information&lt;/i&gt;, &lt;i&gt;technology features&lt;/i&gt;, &lt;i&gt;adherence to data collection and management&lt;/i&gt;, &lt;i&gt;feasibility and implementation&lt;/i&gt;, &lt;i&gt;and acceptability and usability&lt;/i&gt;. </sec> <sec> <title>CONCLUSIONS</title> This narrative review summarizes the technical features, data capture methods, and various advantages and limitations of medication adherence monitoring technology reported in the literature and the proposed criteria for assessing medication adherence monitoring technologies. This collection of assessment criteria can be a useful tool to guide the development and selection of relevant technologies, facilitating the optimal adoption and effective use of technology to improve medication adherence outcomes. Future studies are needed to further validate the medication adherence monitoring technology assessment criteria and construct an appropriate technology assessment framework. </sec>

  PublisherDOI

• Sagittal abdominal diameter and its socioeconomic correlates: perspective of sex differences

  BMC Public Health · 2021-03-11 · 17 citations

  articleOpen access

  BACKGROUND: Sagittal abdominal diameter (SAD) is an anthropometric index associated with visceral adiposity. It remains unclear whether SAD and its socio-economic correlates differ in women and men, which limits the epidemiological and clinical applications of the SAD measurement. The aims of this study are to examine the sex differences in SAD and its socio-economic correlates. METHODS: A complex stratified multistage clustered sampling design was used to select 6975 men and 7079 women aged 18 years or more from the National Health Nutrition and Examination Survey 2011-2016, representative of the US civilian non-institutionalized population. SAD was measured in accordance to the standard protocols using a two-arm abdominal caliper. The sex differences in SAD and its socio-economic correlates were evaluated by performing weighted independent t tests and weighted multiple regression. RESULTS: SAD was lower in women than in men in the entire sample, as well as in all the subgroups characterized by age, race, birth place, household income, and body mass index except for non-Hispanic blacks and those with household income < $20,000. Adjusted for other characteristics, age, birth place, household income, and body mass index were associated with SAD in both women and men. Black women were associated with higher SAD then white women (p < .0001), and Hispanic and Asian men were associated with lower SAD than white men (both p < .01). Women born in other countries were more likely to have lower SAD than women born in the US (p < .0001), and so were men (p = .0118). Both women and men with a household income of <$75,000 had higher SAD than those with an income of over $75,000. The associations of age, race, and household income with SAD differed in women and men. CONCLUSION: SAD is lower in women than in men, in the general population as well as in the most socio-economic subgroups. While socio-economic correlates of SAD are similar in women and men, the associations of age, race, and household income with SAD vary across sex.

  PublisherOA PDFDOI

Recent grants

• Analyzing Nurses' Impact on Outcomes Using Detailed Data

  NIH · $883k · 2006–2010

• NIH Grant R01LM007453

  NIH · $718k · 2006

Frequent coauthors

• Frank J. Manion

  Intelligent Medical Objects (United States)

  23 shared

• Yun Jiang

  University of Michigan–Ann Arbor

  17 shared

• Elizabeth E. Umberfield

  Mayo Clinic in Florida

  16 shared

• Cui Tao

  Fuyang Second People's Hospital

  13 shared

• Christopher G. Chute

  Johns Hopkins University

  12 shared

• Yongqun He

  University of Michigan–Ann Arbor

  8 shared

• Muhammad Amith

  The University of Texas Medical Branch at Galveston

  8 shared

• Cooper Stansbury

  University of Michigan–Ann Arbor

  7 shared

Labs

• University of Michigan School of NursingPI

Awards & honors

• Fellow, American Medical Informatics Association, 2019
• Nurse Researcher of the Year Award, Minnesota Nurses Associa…
• Ruth Lilly Informatics Scholar, Sigma Theta Tau Internationa…
• National Institutes of Health/National Library of Medicine F…
• Research Award, American Organization of Nurse Executives, 1…