The CONCERN Study:
Communicating Narrative Concerns Entered by RNs

The Communicating Narrative Concerns Entered by RNs (CONCERN) system is an expert-knowledge, machine learning, predictive model (CONCERN-PM) that produces an early warning score (CONCERN-EWS) and is implemented as clinical decision support (CONCERN-CDS) to alert nurses and physicians about hospitalized patients who are at risk of decompensation. CONCERN-PM leverages nursing documentation patterns as a proxy measure of nurses’ decisions to increase surveillance and related interventions which are an indicator of nurse concern about hospitalized patients – a key early indicator of decompensation. 

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CONCERN GRANTS and AIMS

CONCERN GRANTS and AIMS

NINR CONCERN 2.0 (4/25/2023 – 1/31/2027)

Title: Communicating Narrative Concerns Entered by RNs (CONCERN)

Funder: National Institute of Nursing Research (NINR)

Grant number: 2R01NR016941-07

MPI: Rossetti (contact) (Other MPI: Cato)

About CONCERN

video courtesy of The American Nursing Foundation (read full overview)

Grant overview: This study will evaluate the implementation of the CONCERN predictive model and clinical decision support across 5 geographically diverse health systems and expand CONCERN to new specialty areas, emergency department and inpatient pediatrics. This project includes a particular focus on incorporating social determinants of health into the CONCERN model and will also explore approaches to engaging caregivers (e.g., parents, guardians) directly in the use of CONCERN in the inpatient pediatric setting.

Specific Aims:

Aim 1: Translate the CONCERN-PM to 2 additional specialty patient populations (emergency department and inpatient pediatrics) and with the addition of social determinants of health (SDOH) data to evaluate model calibration and performance across different patient populations and health systems.

Aim 2: Evaluate CONCERN-CDS implementation across all adult inpatient units at 5 health systems (CUIMC, MGB, VUMC, WUMC, AMC), in inpatient pediatric units at 3 health systems (CUIMC, AMC, and VUMC) and in the adult emergency department at 2 health systems (CUIMC and AMC) to understand health system and patient population characteristics associated with a successful implementation using the Reach, Effectiveness, Adoption, Implementation and Maintenance(RE-AIM) and Consolidated Framework for Implementation Research (CFIR)Frameworks.

Aim 3: Using a patient-centered design approach, explore the creation of a patient-facing CONCERN-PM and CDS for caregivers of inpatient pediatric patients, to compare predictive model performance with additional caregiver feedback to the clinician-only data-driven model.

ANF (2/1/2022 – 1/31/2025)

Title: CONCERN Implementation Toolkit: Advancing technology-enabled nursing expertise and equitable predictions

Funder: American Nurses Foundation (ANF), The Reimagining Nursing Initiative

MPI: Rossetti (Other MPI: Cato)

Grant overview: This 3-year study will develop and validate the CONCERN implementation toolkit and spread the CONCERN clinical decision support tool to 7 other hospitals with diverse patient and nurse populations.  We will also evaluate for biases in EHR data that impact the equitable use of the CONCERN implementation and develop scalable solutions to mitigate those biases.  Evaluation metrics are based on the RE-AIM framework and will guide the optimization of the toolkit, cost-benefit analyses, and opportunity for further spread.

 Specific Aims:

Aim 1: Develop and validate the CONCERN Early Warning Score Implementation toolkit with all key stakeholders (e.g., nurses, physicians, hospital leadership, information technology analysts) involved in its implementation at current sites. Anticipated toolkit components are: 

  • Readiness assessment tools
  • Nursing practice and documentation tools
  • Technical Implementation tools
  • Governance tools

Meet The CONCERN Team

CONCERN Team Members

Columbia University
• Sarah Rossetti, PhD, RN 
• Kenrick Cato, PhD, RN
• Haomiao Jia, PhD
• Mohtashim Bokhari, PhD, Dr.-Ing
• Jennifer Withall, PhD, RN
• Mollie Hobensack, BSN, RN
• Kriste Krstovski, PhD
• Rachel Lee, PhD, RN
• Mai Tran
• Shalom Omollo
• Temmi Daramola

Mass General Brigham
• Patricia Dykes, PhD, MA, RN
• Sandy Cho, MPH, BSN, RN-BC
• Graham Lowenthal, BA

Washington University
• Po-Yin Yen, RN, FAMIA, FAAN
• Albert Lai, PhD, FACMI, FAMIA
• Adam Wilcox, PhD, FACMI
• Lisa Kidin, PhD, RN
• Michele Butkiewicz, MSN, RN

Vanderbilt University
• Catherine Ivory, PhD, RN-BC, RNC-OB, FAAN
• Brian Douthit, PhD, RN-BC

University of Colorado
• David Albers, PhD

Advisory Board

Informatics Experts
 • Suzanne Bakken, PhD, RN, FAAN, FACMI – Columbia University
• David W. Bates, MD, MS, FACMI – Brigham and Women’s Hospital
• Bonnie L. Westra, PhD, RN, FAAN, FACMI – University of Minnesota

Clinical Nursing Subject Matter Experts
 NYP Site
 • Monika Tukacs, BSN, RN, CCRN
• Robert Schroeder, RN
• Amy Moynihan, RN

MGB Site
 • Sarah Beth Thomas, BSN, RN

NWH Site
 • Hailey Poole, RN

  • Model calibration and bias mitigation tools
  • Evaluation measure tools
  • Sustainability and Spread tools

Aim 2: Spread the CONCERN predictive model and CDS intervention using the CONCERN toolkit to three other healthcare systems: remaining hospitals within Mass General Brigham; Vanderbilt University Medical Center; and Washington University Medical Campus.

Aim 3: Apply the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework to evaluate and refine the CONCERN toolkit by analyzing its perceived usefulness from key stakeholders, its cost-benefit analysis, and its impact on nursing practice and patient outcomes.

  • Sub-aim 3a: Evaluate how variability in nursing practice and documentation influences the equitable use of CONCERN and other early warning scores.

NINR CONCERN 1.0 (2017-2023)

Title: Communicating Narrative Concerns Entered by RNs (CONCERN)

Funder: National Institute of Nursing Research (NINR)

Grant number: 5R01NR016941-05

MPI: Rossetti (contact) (Other MPI: Cato)

Grant overview: The aim of this project is to design and evaluate Electronic Health Record (EHR) clinical decision support (CDS) tools across two large academic medical centers that expose to physicians and nurses our new predictive data source from nursing documentation to increase care team situational awareness of at-risk patient to decrease mortality and 30-day readmissions.

Specific Aims:

Aim 1. Perform analytics of existing nursing data and documentation patterns to confirm predictive factors and notification thresholds for patients at risk of adverse outcomes in the hospital

  • Natural Language Processing, Machine Learning, Predictive analytics

 Aim 2. User-centered design and testing of CONCERN SMART App; Prototype development and simulation testing

Aim 3. Implementation and evaluation of the impact of the CONCERN SMART App on patient outcomes

    • Primary outcomes: in-hospital mortality and length of stay
    • Secondary outcomes: cardiac arrest, unanticipated transfers to the intensive care unit, and 30-day hospital readmission rates.

Video: About The CONCERN Initiative

CONCERN Journal Publications

  1. Moy AJ, Hobensack M, Marshall K, Vawdrey DK, Cato KD, Rossetti SC. Understanding the perceived role of electronic health records and workflow fragmentation on clinician documentation burden in emergency departments. J Am Med Inform Assoc, 2023 Mar 11; ocad038. https://doi.org/10.1093/jamia/ocad038.
  2. Hu Y, Cato KD, Chan CW, Dong J, Gavin N, Rossetti SC, Chang BP. Use of Real-Time Information to Predict Future Arrivals in the Emergency Department. Ann Emerg Med, 2023 Jan 18; S0196-0644(22)01269-0. doi:10.1016/j.annemergmed.2022.11.005.
  3. Song J, Ojo M, Bowles KH, McDonald MV, Cato KC, Rossetti SC, Adams V, Chae S, Hobensack M, Kennedy E, Tark A, Kang MJ, Woo K, Barron Y, Sridharan S, Topaz M. Detecting Language Associated with Home Healthcare Patient’s Risk for Hospitalization and Emergency Department Visit. Nurs Res, 2022 Jul-Aug; 71(4):285-294. doi:10.1097/NNR.0000000000000586.
  4. Schwartz J, George M, Rossetti SC, Dykes P, Minshall S, Lucas E, Cato KD. Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study. JMIR Hum Factors, 2022 May 12; 9(2):e33960. doi:10.2196/33960.
  5. Hobensack M, Ojo M, Barrón Y, Bowles KH, Cato KD, Chae S, Kennedy E, McDonald MV, Rossetti SC, Song J, Sridharan, Topaz M. Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians. J Am Med Inform Assoc, 2022 Apr 13; 29(5):805-812. doi:10.1093/jamia/ocac023.
  6. Schwartz JM, George M, Rossetti SC, Dykes PC, Minshall SR, Lucas E, Cato KD. Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study. JMIR Hum Factors. 2022 May 12;9(2):e33960. doi: 10.2196/33960. PMID: 35550304; PMCID: PMC9136656.
  7. Song J, Hobensack M, Bowles KH, McDonald MV, Cato K, Rossetti SC, Chae S, Kennedy E, Barron Y, Sridharan S, Topaz M. Clinical Notes: An Untapped Opportunity for Improving Risk Prediction for Hospitalization and Emergency Department Visit during Home Health Care. J Biomed Inform, 2022 Apr; 129:104309. doi:10.1016/j.jbi.2022.104039.
  8. Hobensack M, Levy DR, Cato KD, Detmer DE, Johnson KB, Williamson J, Murphy J, Moy AJ, Withall J, Lee R, Rossetti SC, Rosenbloom ST. 25 × 5 Symposium to Reduce Documentation Burden: Report-out and Call for Action. Appl Clin Inform, 2022 Mar; 13(2):439-466. doi:10.1055/s-0042-1746169.
  9. Rossetti SC, Dykes PC, Knaplund C, Kang MJ, Schnock K, Garcia JP Jr, Fu LH, Chang F, Thai T, Fred M, Korach TZ, Zhou L, Klann JG, Albers D, Schwartz J, Lowenthal G, Jia H, Liu F, Cato K. The Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) Clinical Decision Support Early Warning System: Protocol for a Cluster Randomized Pragmatic Clinical Trial. JMIR Res Protoc. 2021 Dec 10;10(12):e30238. doi: 10.2196/30238. PMID: 34889766; PMCID: PMC8709914.
  10. Moy AJ, Schwartz JM, Withall J, Lucas E, Cato KD, Rosenbloom ST, Johnson K, Murphy J, Detmer DE, Rossetti SC. Clinician and Health Care Leaders’ Experiences with-and Perceptions of COVID-19 Documentation Reduction Policies and Practices. Appl Clin Inform, 2021 Oct; 12(5):1061-1073. doi:10.1055/s-0041-1739518.
  11. Fu LH, Knaplund C, Cato K, Perotte A, Kang MJ, Dykes PC, Albers D, Collins Rossetti S. Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events. J Am Med Inform Assoc. 2021 Aug 13;28(9):1955-1963. doi: 10.1093/jamia/ocab111. PMID: 34270710; PMCID: PMC8363809.
  12. Rossetti SC, Knaplund C, Albers D, Dykes PC, Kang MJ, Korach TZ, Zhou L, Schnock K, Garcia J, Schwartz J, Fu LH, Klann JG, Lowenthal G, Cato K. Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework. J Am Med Inform Assoc. 2021 Jun 12;28(6):1242-1251. doi: 10.1093/jamia/ocab006. PMID: 33624765; PMCID: PMC8200261.
  13. Schnock KO, Kang MJ, Rossetti SC, Garcia J, Lowenthal G, Knaplund C, Chang F, Albers D, Korach TZ, Zhou L, Klann JG, Cato K, Bates DW, Dykes PC. Identifying nursing documentation patterns associated with patient deterioration and recovery from deterioration in critical and acute care settings. Int J Med Inform. 2021 Sep;153:104525. doi: 10.1016/j.ijmedinf.2021.104525. Epub 2021 Jun 9. PMID: 34171662; PMCID: PMC8390439.
  14. Kang MJ, Rossetti SC, Knaplund C, Chang FY, Schnock KO, Whalen K, Gesner EJ, Garcia JP, Cato KD, Dykes PC. Nursing Documentation Variation Across Different Medical Facilities Within an Integrated Healthcare System. Comput Inform Nurs. 2021 May 3;39(12):845-850. doi:
  15. Song W, Kang MJ, Zhang L, Jung W, Song J, Bates DW, Dykes PC. Predicting pressure injury using nursing assessment phenotypes and machine learning methods. J Am Med Inform Assoc. 2021 Mar 18;28(4):759-765. doi: 10.1093/jamia/ocaa336. PMID: 33517452; PMCID: PMC7973453.
  16. Topaz M, Woo K, Ryvicker M, Zolnoori M, Cato K. Home Healthcare Clinical Notes Predict Patient Hospitalization and Emergency Department Visits. Nurs Res. 2020 Nov/Dec;69(6):448-454. doi: 10.1097/NNR.0000000000000470. PMID: 32852359; PMCID: PMC7606545.
  17. Cato KD, McGrow K, Rossetti SC. Transforming clinical data into wisdom: Artificial intelligence implications for nurse leaders. Nurs Manage. 2020 Nov;51(11):24-30. doi: 10.1097/01.NUMA.0000719396.83518.d6. PMID: 33086364; PMCID: PMC8018525.
  18. Fu LH, Schwartz J, Moy A, Knaplund C, Kang MJ, Schnock KO, Garcia JP, Jia H, Dykes PC, Cato K, Albers D, Rossetti SC. Development and validation of early warning score system: A systematic literature review. J Biomed Inform. 2020 May;105:103410. doi: 10.1016/j.jbi.2020.103410. Epub 2020 Apr 8. PMID: 32278089; PMCID: PMC7295317.
  19. Korach ZT, Cato KD, Collins SA, Kang MJ, Knaplund C, Dykes PC, Wang L, Schnock KO, Garcia JP, Jia H, Chang F, Schwartz JM, Zhou L. Unsupervised Machine Learning of Topics Documented by Nurses about Hospitalized Patients Prior to a Rapid-Response Event. Appl Clin Inform. 2019 Oct;10(5):952-963. doi: 10.1055/s-0039-3401814. Epub 2019 Dec 18. PMID: 31853936; PMCID: PMC6920051.
  20. Korach ZT, Yang J, Rossetti SC, Cato KD, Kang MJ, Knaplund C, Schnock KO, Garcia JP, Jia H, Schwartz JM, Zhou L. Mining clinical phrases from nursing notes to discover risk factors of patient deterioration. Int J Med Inform. 2020 Mar;135:104053. doi: 10.1016/j.ijmedinf.2019.104053. Epub 2019 Dec 14. PMID: 31884312; PMCID: PMC7103062.
  21. Kang MJ, Dykes PC, Korach TZ, Zhou L, Schnock KO, Thate J, Whalen K, Jia H, Schwartz J, Garcia JP, Knaplund C, Cato KD, Rossetti SC. Identifying nurses’ concern concepts about patient deterioration using a standard nursing terminology. Int J Med Inform. 2020 Jan;133:104016. doi: 10.1016/j.ijmedinf.2019.104016. Epub 2019 Oct 31. PMID: 31707264; PMCID: PMC6957124.
  22. Garci JP Jr, Collins SA, Cato KD, Bakken S, Jia H, Kang MJ, Knaplund C, Schnock KO, Dykes PC. CONCERN Factorial Design Survey (FDS) Methods Test: Using REDCap as a Survey Platform. Stud Health Technol Inform. 2019 Aug 21;264:1462-1463. doi: 10.3233/SHTI190485. PMID: 31438182; PMCID: PMC7020103.
  23. Collins SA, Cato K, Albers D, Scott K, Stetson PD, Bakken S, Vawdrey DK. Relationship between nursing documentation and patients’ mortality. Am J Crit Care. 2013 Jul;22(4):306-13. doi: 10.4037/ajcc2013426. PMID: 23817819; PMCID: PMC3771321.
  24. Collins SA, Vawdrey DK. “Reading between the lines” of flow sheet data: nurses’ optional documentation associated with cardiac arrest outcomes. Appl Nurs Res. 2012 Nov;25(4):251-7. doi: 10.1016/j.apnr.2011.06.002. Epub 2011 Nov 12. PMID: 22079746; PMCID: PMC3288795.

CONCERN Conference Abstracts

  1. Hobensack, M., Withall, J., Cato, K., Dykes, P., Lowenthal, G., Cho, S., Ivory, C., Yen, P.Y., Rossetti, S. (July 2023). Understanding the technical implementation of a clinical decision support: A qualitative analysis. [Poster Presentation]. MedInfo2023, Sydney, Australia.
  2. Von Gerich, H., Dowding, D., Hobensack, M., Peltonen, L-M, Sequiera, L., Withall, J. (July 2023) Learning from experts on the evolution of decisions support systems – a career progression panel. [Panel Presentation]. MedInfo2023, Sydney, Australia.
  3. Hobensack, M., Withall, J., Dykes, P.C., Lowenthal, G., Rossetti, S., Cato, K. (May, 2023). Key takeaways from technical experts on the implementation of a clinical decision support smart app: A qualitative analysis. [Podium Presentation]. AMIA Clinical Informatics Conference, Chicago, IL. 
  4. Withall, J., Hobensack, M., Rossetti, S., Dykes, P.C., Cato, K. (May, 2023). Implementation of a Nurse-Driven Early Warning System on Inpatient Clinical Nursing Units: Lessons Learned from a Pragmatic Clinical Trial. [Poster Presentation]. AMIA Clinical Informatics Conference, Chicago, IL. 
  5. Yen, P. Fast Healthcare Interoperability Resources (FHIR) for CONCERN – technical discussion; Barnes-Jewish Hospital Clinical Decision Support; March 27, 2023
  6. Rita Kobb Nursing & Health Informatics Symposium on 2/24/23, https://nursing.ufl.edu/2023-rita-kobb-nursing-and-health-informatics-symposium-speakers/.
  7. Ivory, C. Vanderbilt University Medical Center Executive Nursing Committee, 2022
  8. Cato,K. Harnessing Clinical Nursing Data for Predictive Analytics and Decision Support in the Hospital Setting. Veteran’s Administration Nursing Informatics Continuing Education Series. December 2022
  9. Yen, P. CONCERN Implementation – nurse leaders; Barnes-Jewish Hospital Interprofessional Practice, Education, Innovation and Research, November 11, 2022
  10. Chae S, Song J, Barrón Y, Bowles KH, McDonald MV, Rossetti SC, Cato KD, Hobensack M, Evans L, Topaz M. Heart Failure Patient Characteristics and Symptoms Documented in Home Health Care Clinical Notes Associated with Emergency Department Visits and Hospitalizations. Podium presentation at the AMIA 2022 Annual Symposium; Nov 5-9, 2022; Washington, DC.
  11. Moy AJ, Withall J, Hobensack M, Lee RY, Levy D, Rosenbloom ST, Rossetti SC, Johnson KB, Cato KC. Using Topic Modeling to Elicit Insights from the 25×5 Symposium to Reduce Documentation Burden Chat Logs. Podium presentation at the AMIA 2022 Annual Symposium; Nov 5-9, 2022; Washington, DC.
  12. Rossetti SC, Dykes P, Sengstack P, Murphy J. Technology Will Resolve the Nursing Workforce Shortage Within 5 Years. Informatics Debate at the AMIA 2022 Annual Symposium; Nov 5-9, 2022; Washington, DC.
  13. Taylor BN, Rossetti SC, Cato KD. Deterioration Events in Patients With Depression. Poster presentation at the AMIA 2022 Annual Symposium; Nov 5-9, 2022; Washington, DC.
  14. Lee RY, Rossetti SC, Cato KD. Differences in Frequencies of Nursing Flowsheet Documentation by Patients’ Primary Language. Poster presentation at the AMIA 2022 Annual Symposium; Nov 5-9, 2022; Washington, DC.
  15. Lee, R.Y., Rossetti, S.C., Cato, K.D. Differences in frequencies of nursing flowsheet documentation by patients’ primary language. Poster session to be presented at: AMIA 2022 Annual Symposium; 2022 Nov 5-9; Washington D.C.
  16. Taylor, B.N, Collins Rossetti, S., & Cato, K.D. (2022). Deterioration Events in Patients With Depression. Poster Presentation at the AMIA 2022 Annual Symposium; October 2022; Washington, DC.
  17. Yen, P. CONCERN Implementation – obtaining approval; Washington University/Barnes-Jewish Hospital Research Steering Committee; September 23, 2022
  18. Yen, P. Fast Healthcare Interoperability Resources (FHIR) for CONCERN – technical discussion; Barnes-Jewish Hospital Clinical Decision Support; August 16, 2022
  19. Cato, K. Modeling Clinician Behavior to Support Clinical Decision Making and Improve Patient Outcomes:  The CONCERN Study. Dynamics and Data Assimilation, Physiology and Bioinformatics: Mathematics at the Interface of Theory and Clinical Application. 2022. Banff International Research Station for Mathematical Innovation and Discovery. Banff, Canada
  20. Rossetti S, Albers D, Dykes P, Sengstack P, Del Fiol G. 2022. Informatics Research and Implementation During COVID: Challenges, Opportunities and Recommendations for Building a Sustainable Infrastructure. AMIA Annual Symposium, Washington, DC.
  21. Cao H, Megihani M, Nametz DC, Lorenzi V, Mamykina L, Meyers R, Rossetti SC, Park S. Machine Learning Model Deployment Using Real-Time Physiological Monitoring: Use Case of Detecting Delayed Cerebral Ischemia. 2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT), Houston, TX, USA, 2022 March 10-11; pp. 42-45. doi: 10.1109/HI-POCT54491.2022.9744076.
  22. Cato, K. 2021. Articulating Judgment Into AI: The CONCERN Study. NINR AI Boot Camp – Clinical Applications. Virtual Meeting
  23. Cato, K. What the CONCERN Study Has Taught Me about Racial Bias in Nursing Workflow. 2021. New England HIMSS 2021 Annual Spring Conference. Podium Presentation. Boston, MA
  24. Schwartz, J., Knapland, C., Withall, J., Rossetti, S.C., Kang, M-J, Dykes, P., Schnock, K.O., Zhou, L., Lucas, E., Minshall, S., Cato, K. Clinicians’ perspectives on factors influencing trust in machine-learning-based predictive clinical decision support. AMIA CIC 2021
  25. Fu, L-H., Knaplund, C., Cato, K. Albers, D., Rossetti, SC. Utilizing Timestamps of Longitudinal Data from Electronic Health Record to Predict Clinical Deterioration Events. AMIA 2020 Annual Symposium, Virtual Conference, Nov 14-18 2020
  26. Schwartz J, Knaplund C, Rossetti SC, Kang MJ, Dykes P, Korach TZ, Schnock K, Zhou L, Cato KD. Nurse Use of Vital Sign Flowsheet Comments: Shared or Individual Practice? Poster session presented at: 2020 American Medical Informatics Association (AMIA) Annual Symposium; 2020 Nov 14-18; Virtual Conference
  27. Dykes, P., Rossetti, SC., Cato, K., Dinov, I. Nursing Science: Stronger Together. Columbia University and University of Michigan Schools of Nursing and the Center for Nursing Excellence at Brigham and Women’s Hospital. Virtual Panel. Jul 14 2020
  28. Rossetti SC, Knaplund C, Albers D, Tariq A, Tang K, Vawdrey D, Yip NH, Dykes PC, Klann JG, Kang MJ, Garcia J, Fu LH, Schnock K, Cato K. Leveraging Clinical Expertise as a Feature – not an Outcome – of Predictive Models: Evaluation of an Early Warning System Use Case. AMIA Annu Symp Proc. 2020 Mar 4;2019:323-332. PMID: 32308825; PMCID: PMC7153052.
  29. Cato K., Albers, J., Freeman, R., Hoeksema, L., Womack, D. Jeffery, A. Bench to Bedside: The Power of Nursing Data for Prediction. 2020, Annual Meeting American Medical Informatics Association, Pre-Conference All-Day Workshop. Virtual Meeting. 
  30. Rossetti, S., Moy, A., Kang, M. Schwartz, J., Cato K., Mixed-Methods Approaches to Understanding, Measuring, and Reducing Clinical Documentation Burden. 2020, Annual Meeting American Medical Informatics Association, Podium Presentation. Virtual Meeting. 
  31. J. Schwartz and K. Cato, Machine Learning Based Clinical Decision Support and Clinician Trust, 2020 IEEE International Conference on Healthcare Informatics (ICHI)
  32. Garcia J, Collins S, Cato K, et al. CONCERN factorial design survey (FDS) methods test: Using REDCap as a survey platform. Poster Presentation at MedInfo World Congress 2019 Aug 25-30, Lyon, France
  33. Kang MJ, Zhou L Chang FY, Knaplund C, Garcia JP, Cato KD, Rossetti SC, Dykes P. 2019. Methodology of Sepsis Prognosis Prediction Model Tailored Clinical Practice. Podium abstract presentation at MedInfo 2019 World Congress of Medical Health Informatics; 2019 Aug 25-30; Lyon, France.
  34. Cato K, Knaplund C, Klann J, Dykes P, Albers D, Kang MJ, Schnock K, Zhou L, Korach T, Collins S. 2019. Nursing medication administration data quality: assessment for reuse in analytics. Presentation at AMIA 2019 Clinical Informatics Conference; 2019 Apr 30-May 2; Atlanta, GA.
  35. Kang MJ, Zhou L, Chang F, Knaplund C, Garcia JP, Cato K, Collins S, Dykes P. 2019. Methodology of Sepsis Prognosis Prediction Model Tailored Clinical Practice. Presentation at AMIA 2019 Clinical Informatics Conference; 2019 Apr 30-May 2; Atlanta, GA
  36. Klann J, Kang MJ, Schnock K, Knaplund C, Dykes P, Collins S, Cato K. 2019. A Data Warehousing Design for Multi-Domain Inpatient Nursing Data in i2b2. Abstract at AMIA 2019 Clinical Informatics Conference; 2019 Apr 30-May 2; Atlanta, GA.
  37. Korach ZT, Collins SA, Cato KD, Kang MJ, Schnock KO, Couture B, Knaplund C, Whalen K., Thate J, Dykes P, Zhou L. 2019. Active Learning for the Identification of Nurses’ Concerns from Nursing Notes. Presentation at AMIA 2019 Clinical Informatics Conference; 2019 Apr 30-May 2; Atlanta, GA.
  38. Korach T, Cato K, Collins S, Kang MJ, Knaplund C, Dykes P, Wang L, Schnock K, Garcia JP, Jia H, Chang F, Schwartz J, Zhou L. 2019. Unsupervised-learning of concern topics documented by nurses about hospitalized patients prior to a rapid-response event. Presentation at AMIA 2019 Clinical Informatics Conference; 2019 Apr 30-May 2; Atlanta, GA.
  39. Cato K, Knaplund C, Klann J, Dykes P, Albers D, Kang MJ, Schnock K, Zhou L, Korach T, Collins S. 2019. Signals of Nurse Workarounds in Electronic Medication Administration Record Data. Abstract at AMIA 2019 Informatics Summits; 2019 Mar 25-28; San Francisco, CA.
  40. Collins S, Dykes P, Albers D, Klann J, Kang MJ, Schnock K, Zhou L, Korach T, Knaplund C, Cato K. 2019. EHR Surveillance Patterns to Compute when A “Patient does not look right”. Ignite Talk Abstract at AMIA 2019 Informatics Summits; 2019 Mar 25-28; San Francisco, CA.
  41. Kang MJ, Dykes P, Schnock KO, Korach ZT, Zhou L, Thate J, Whalen K, Garcia JP, Knaplund C, Cato KD, Collins SA. 2019. Identifying and Grading Nurses’ Concern Concepts about Patient Deterioration Using a Standard Nursing Terminology. Podium abstract at AMIA 2019 Informatics Summits; 2019 Mar 25-28; San Francisco, CA.
  42. Klann JG, Kang MJ, Schnock KO, Knaplund C, Dykes P, Collins SA, Cato KD. 2019. A Data Warehousing Design for Multi-Domain Inpatient Nursing Data in i2b2. Ignite Talk Abstract at AMIA 2019 Informatics Summits; 2019 Mar 25-28; San Francisco, CA.
  43. Schnock KO, Kang MJ, Chang FY, Collins SA, Knaplund C, Garcia JP, Cato KD, Dykes P. 2019. Nursing Flowsheet Elements Across Integrated Health Care System. Podium abstract at AMIA 2019 Informatics Summits; 2019 Mar 25-28; San Francisco, CA.
  44. Cato, K. CONCERN: A paradigm shift in nursing. 2019. Brocher Foundation Workshop: Artificial Intelligence for Nursing: Ethical, Legal, and Social Implications. Podium Presentation. Hermance, Switzerland.
  45. Cato, K. The CONCERN Study: Analyzing Inpatient Nursing Data to Develop Healthcare Process Models of Clinical Concern. 2019. 55th Annual Isabel Maitland Stewart Conference on Research in Nursing. End-Note Speaker. New York, NY.
  46. Collins S, Cato K. The CONCERN Study: Clinical Decision Support Communication for Risky Patient States. 2019. New York Academy of Medicine Urban Health Informatics Innovation Conference. Podium Presentation. New York, NY.
  47. Collins SA, Couture B, Kang MJ, Dykes P, Schnock K, Knaplund C, Chang F, Cato K. Quantifying and Visualizing Nursing Flowsheet Documentation Burden in Acute and Critical Care. AMIA Annu Symp Proc. 2018 Dec 5;2018:348-357. PMID: 30815074; PMCID: PMC6371331.
  48. Couture B, Klann J, Cato K, Knaplund C, Kang MJ, Dykes P, Collins S. 2018. Harmonizing Flowsheet Datasets Across EHRs for a Multi-Site Study. Poster at AMIA 2018 Annual Fall Symposium; 2018 Nov 3-7; San Francisco, CA.
  49. Kang MJ, Dykes P, Korach T, Zhou L, Thate J, Whalen K, Schnock K, Knaplund C, Couture B, Cato K, Collins S. 2018. Identifying Concepts of Nurses’ Concerns Using a Standard Nursing Terminology. Poster at AMIA 2018 Annual Fall Symposium; 2018 Nov 3-7; San Francisco, CA.
  50. Korach, Z. T., Collins, S. A., Cato, K. D., Kang, J., Kumiko, O., Couture, B., Knaplund, C., Whalen, K., Thate, J., Dykes, P., Zhou, L. Active Learning for the Identification of Nurses’ Concerns from Nursing Notes. AMIA 2018 Annual Fall Symposium; 2018 Nov 3-7; San Francisco, CA.
  51. Klann, J.G., Collins S, Cato K, Waitman LR, Westra B. 2018. Nursing Documentation and the Clinical Research Informatics Pipeline. Abstract and Panel Presentation at AMIA 2018 Informatics Summits; 2018 March 12-15; San Francisco, CA.

Contact Us

Contact Our Co-Investigators 

Sarah Collins Rossetti RN, PhD: sac2125@cumc.columbia.edu
Kenrick Cato RN, PhD: kcato@nursing.upenn.edu