Maxim Topaz, PhD, MA, RN, FAAN

  • Elizabeth Standish Gill Associate Professor of Nursing
Profile Headshot


Academic Appointments

  • Elizabeth Standish Gill Associate Professor of Nursing


  • Hebrew
  • Russian


  • Male

Credentials & Experience

Education & Training

  • BA, 2007 Nursing , Technion-Israel Institute of Technology (Israel)
  • PhD, 2014 Nursing Informatics , University of Pennsylvania, Philadelphia
  • 2016 Health Informatics (Postdoctoral fellowship), Harvard Medical School

Honors & Awards

  • 2022: Research in Nursing and Health 2021 Best Paper Award
  • 2021: IMIA Working Group of the Year Award
  • 2021: AMIA New Investigator Award
  • 2020: IMIA Working Group of the Year Award
  • 2016: 1st place- Best Homecare and Cancer Hack Award
  • 2015: Theresa J. Lynch PhD Graduation Award
  • 2015: Finalist: VA Care Coordination for Improved Outcomes Challenge
  • 2013: Best Student Paper Competition Finalist
  • 2013: PhD Student Methodologist Award
  • 2010: Fulbright Israel- Alumni Prize
  • 2010 - 2014: Fulbright Fellowship (PhD studies)
  • 2008: Academic Excellence Award for M.A. Students


Health DATA for GOOD

My research focuses on improving human health via cutting-edge technologies. My team develops artificial intelligence solutions that help health providers to provide best care for their patients. Specifically, we developed an open source natural language processing software called NimlbeMiner that clinicians and researchers can use to mine millions of patient records. In addition, my team is developing and implementing several clinical decision support tools. For example, we are currently testing a patient prioritization tool PREVENT that assists with identifying high risk patients during hospital to homecare transitions. In another study, we are using Artificial Intelligence to create personalized models of risk for preventable hospitalization and emergency department visits in homecare. Overall, I published more than 100 articles on topics related to data science and informatics. 

Research Interests

  • Clinical decision support
  • Critical care outcomes after hospital discharge
  • health informatics
  • Home care
  • hospital
  • Machine learning and artificial intelligence
  • Natural Language Processing
  • text mining



2022-2024 Using machine learning to measure racial/ethnic bias in obstetric settings
  Columbia Data Science Institute
  This innovative project applies data science to the study of pregnancy-related morbidity for a systems-level approach to reducing health inequities. The overall objective of this project is to examine the association between linguistic bias and pregnancy-related morbidity documented in the electronic health records (EHR). Our central hypothesis is that Black and Latinx birthing people will have more biased language in the EHR and more pregnancy-related morbidity than those of other races/ethnicities. Specific aims: Aim 1: Develop and test an natural language processing (NLP) system designed to identify stigmatizing language in clinical notes using established machine learning methods. Aim 2: Apply the NLP system on a large subset of clinical notes for all birthing people admitted for birth from 2017-2019. Aim 3: Examine the associations between stigmatizing language and pregnancy-related outcomes associated with risk for morbidity (i.e., unplanned cesarean birth, hemorrhage, and infection) by race/ethnicity.
2020-2024 Homecare-CONCERN: Building risk models for preventable hospitalizations and emergency department visits in homecare
  Agency for Health Research and Quality
  Primary Investigator
  This study brings together an interdisciplinary team of experts in homecare, data science, nursing and risk model development to explore whether cutting-edge data science approaches can improve timely identification of patients at risk in homecare. Specific aims are to: 1. Further develop and validate a preventable hospitalization or ED visit risk prediction model (Homecare-CONCERN). We will apply traditional (time varying Cox regression) and cutting-edge time-sensitive analytical methods (Deep Survival Analysis and Long-Short Term Memory Neural Network) for risk model development. 2. Prepare Homecare-CONCERN for clinical trial via pilot testing. We will apply user centered design to develop Homecare-CONCERN clinical decision support tool and pilot test the tool for clinical validity and acceptability. 3. Inform the future implementation of Homecare-CONCERN clinical decision support tool in the homecare setting. We will examine if all risk elements can be mapped to a data standard (Fast Healthcare Interoperability Resources - FHIR) and conduct interviews with key informants across the US about current readiness, barriers and facilitators, and implementation strategies for adopting such tools in homecare setting.
2020-2022 Using natural language processing to improve identification and prediction of Alzheimer’s disease and other dementias
  National Institute of Aging
  Multiple-Principal Investigator, Contact PI M. Ryvicker
  Nurses' documentation of patient diagnoses, symptoms and interventions for home care patients with Alzheimer's Disease and related dementias: A natural language processing study Alzheimer's disease and related dementias (ADRD) affect about 5 million people in the U.S. Home health care nurses provide care for many people with ADRD and document what they observe about their patients’ needs in the form of free-text notes. This study will use a method known as ‘natural language processing’ to gain new knowledge from nurses’ notes and identify ways to better support people with ADRD and their caregivers.
2020-2022 Using artificial intelligence to identify homecare patient’s risk of hospitalization and emergency department visits: speech-recognition feasibility study
  Amazon Sponsored Research Proposal, Columbia Center of AI Technology (CAIT); Columbia Nursing Pilot Grant; Eugenie and Joseph Doyle Research Fund (VNSNY)
  Multiple-Principal Investigator, Contact PI M. Zolnoori (Topaz’s postdoctoral fellow)
  This work explores the feasibility of improving homecare patient risk prediction by using a critical but underexplored data stream: verbal communication between nurses and patients. This study applies advanced artificial intelligence methods to explore whether audio recorded nurse-patient communication during routine homecare visits can inform identification of patients at risk for hospitalization or ED visits. Aims : 1) Assess the feasibility of a nurse-patient encounter audio recording during homecare encounters; 2) Evaluate the accuracy of two automated speech recognition methods (commercial software versus open source software); and 3) Explore whether features of audio recorded nurse-patient encounters can be used to predict (via machine learning) patient hospitalizations or ED visits.
2019-2022 Artificial Intelligence-Assisted Identification of Child Abuse and Neglect in Hospital Settings with Implications for Bias Reduction and Future Interventions
  Columbia Data Science Institute
  Contact-Principal Investigator, Multiple-PI A. Landau
  Child abuse and neglect is a social problem that has reached epidemic proportions. The broad adoption of electronic health records in clinical settings offers a new avenue for addressing this epidemic. This grant develops an innovative artificial intelligence system to detect and assess risk for child abuse and neglect within hospital settings. Our algorithm incorporates elements that would prioritize the prevention and reduction of bias against Black and Latinx communities.
2019-2020 Advancing Symptom Science in Home Care Through Natural Language Processing and Patient-Reported Outcomes
Precision in Symptom Self-Management (PriSSM) Center at Columbia University
Primary Investigator
The goals of the project are:1) to create and test a natural language processing algorithm, 2) examine the prevalence of symptoms by race and ethnicity, 3) examine associations between symptoms and ED or hospital admission, and 4) explore the potential contribution of patient-reported symptoms to refine a symptom algorithm.
2019-2022 Improving patient prioritization during hospital-homecare transition: A mixed methods study of a clinical decision support tool (R01 NR018831-01)
National Institute of Nursing Research
Primary Investigator
We developed an innovative decision support tool called “Priority for the First Nursing Visit Tool” (PREVENT) to assist nurses in prioritizing patients in need of immediate first homecare nursing visits. The proposed study assembles a strong interdisciplinary team of experts in health informatics, nursing, homecare, and sociotechnical disciplines to evaluate PREVENT in a pre-post intervention efficacy study. Specifically, the study aims are: Aim 1) Evaluate the effectiveness of the PREVENT tool on process and patient outcomes. Using survival analysis and logistic regression with propensity score matching we will test the following hypotheses: Compared to not using the tool in the pre-intervention phase, when homecare clinicians use the PREVENT tool, high risk patients in the intervention phase will: a) receive more timely first homecare visits and b) have decreased incidence of rehospitalization and have decreased emergency department (ED) use within 60 days. Aim 2) Examine aspects of PREVENT’s reach, adoption, and implementation. Aim 2 will be assessed using mixed methods including homecare admission staff interviews, think-aloud observations, and analysis of staffing and other relevant data.
2019-2020 Exploring prevalence of wound infections and related patient characteristics in home care using NLP
Eugenie and Joseph Doyle Research Partnership Fund at the Visiting Nurse Service of New York & Columbia Nursing Pilot Grants
The proposed study uses natural language processing to identify patients with wound infections in homecare settings. The study will also explore patient level characteristics associated with developing a wound infection.
2019 Data science for better health outcomes: A nursing perspective (Course development grant)
Columbia Collaboratory Fund
Co-Primary Investigator
The proposed pioneering course will expose nursing students at the Columbia University School of Nursing to the fascinating world of data science. Tailored to nursing, course topics will employ interactive flipped classroom learning of fundamental data science technologies (e.g., machine learning and text mining), discussion of ethical aspects of data science, and a hands-on data science project in collaboration with Columbia University Data Science Institute students.
2019 Automated Data Abstraction for Accreditations (Partnership with American College of Cardiology)
Consultant, (Patient Insight Inc.), (Contribution: build natural language processing pipeline)
National Institute of Health, Small Business Innovation Research Grants (SBIR, R43 LM012955-01)
Health service accreditation and certification programs are a critical mechanism to direct quality improvements and ensure compliance with regulations. Reporting on requisite accreditation measures currently requires human data abstractors to interpret heterogeneous and disparately presented data elements in an electronic health record, both structured and unstructured, which is resource intensive for hospitals. Replacement of human elements using evolving automated data abstraction methods and a novel data visualization to improve their workflow would solve an important, unmet need and offer a dramatic improvement on the status quo.
2018- P20: Center for Improving End-of-Life Care for Vulnerable Adults with multiple chronic conditions (MCC)
Site Primary Investigator (VNSNY)
National Institute of Nursing Research
The Center for Improving Palliative Care for Vulnerable Adults with MCC (CIPC) core mission includes three objectives: 1. Develop a sustainable infrastructure that supports interdisciplinary researchers to develop into transdisciplinary teams that conduct biobehavioral, palliative care research across health care settings for vulnerable adults with MCC. 2. Develop new programs of biobehavioral, palliative care research for vulnerable adults with MCC led by nurse scientists. 3. Enhance the knowledge and skills of participating investigators on transdisciplinary, biobehavioral, palliative care research methods across health care settings for vulnerable adults with MCC and disseminate new knowledge to relevant stakeholders.

Selected Publications

  1. Flaks-Manov, N., Topaz, M., Hoshen, M., Balicer, R. D., & Shadmi, E. (2019). Identifying patients at highest-risk: the best timing to apply a readmission predictive model. BMC Medical Informatics and Decision Making, 19(1), 118.
  2. Peltonen, L. M., Nibber, R., Lewis, A., Block, L., Pruinelli, L., Topaz, M., ... & Ronquillo, C. (2019). Emerging Professionals' Observations of Opportunities and Challenges in Nursing Informatics. Nursing leadership (Toronto, Ont.), 32(2), 8-18.
  3. O'Connor S, Chu CH, Thilo F, Lee JJ, Mather C, Topaz M. (2019). Professionalism in a digital and mobile world: A way forward for nursing. J Adv Nurs. doi: 10.1111/jan.14224. [Epub ahead of print] PubMed PMID: 31588582.
  4. Topaz M, Naylor MD, Holmes JH, Bowles KH. Factors Affecting Patient Prioritization Decisions at Admission to Home Healthcare: A Predictive Study to Develop a Risk Screening Tool. Comput Inform Nurs. 2019 Nov 21. doi: 10.1097/CIN.0000000000000576. [Epub ahead of print] PubMed PMID: 31804243.
  5. Topaz M, Murga L, Bar-Bachar O, McDonald M, Bowles K. NimbleMiner: An Open-Source Nursing-Sensitive Natural Language Processing System Based on Word Embedding. Comput Inform Nurs. 2019 Aug 30. doi: 10.1097/CIN.0000000000000557. [Epub ahead of print] PubMed PMID: 31478922.
  6. Peltonen, L.-M., Pruinelli, L., Ronquillo, C., Nibber, R., Peresmitre, E. L., Block, L., … Topaz, M. (2019). The current state of Nursing Informatics - An international cross-sectional survey. Finnish Journal of EHealth and EWelfare, 11(3), 220-231.
  7. Topaz M, Bar-Bachar O, Admi H, Denekamp Y, Zimlichman E. Patient-centered care via health information technology: a qualitative study with experts from Israel and the U.S. Informatics Heal Soc Care. March 2019:1-12. doi:10.1080/17538157.2019.1582055
  8. Topaz M, Murga L, Bar-Bachar O, Cato K, Collins S. Extracting Alcohol and Substance Abuse Status from Clinical Notes: The Added Value of Nursing Data. Stud Health Technol Inform. 2019 Aug 21;264:1056-1060. doi: 10.3233/SHTI190386. PubMed PMID: 31438086
  9. Topaz M, Murga L, Grossman C, Daliyot D, Jacobson S, Rozendorn N, Zimlichman E, Furie N. Identifying Diabetes in Clinical Notes in Hebrew: A Novel Text Classification Approach Based on Word Embedding. Stud Health Technol Inform. 2019 Aug 21;264:393-397. doi: 10.3233/SHTI190250. PubMed PMID: 31437952.
  10. Kwon JY, Karim ME, Topaz M, Currie LM. Nurses "Seeing Forest for the Trees" in the Age of Machine Learning: Using Nursing Knowledge to Improve Relevance and Performance. Comput Inform Nurs. 2019 Jan 25. doi: 10.1097/CIN.0000000000000508. [Epub ahead of print] PubMed PMID: 30688670.
  11. Topaz M, Murga L, Gaddis KM, McDonald MV, Bar-Bachar O, Goldberg Y, Bowles K. (2019). Mining fall-related information in clinical notes: comparison of rule-based and novel word embedding-based machine learning approaches. J Biomed Inform.:103103. doi: 10.1016/j.jbi.2019.103103. [Epub ahead of print] PubMed PMID: 30639392.
  12. Blumenthal KG, Topaz M (Co-first-author), Zhou L, Harkness T, Sa'adon R, Bar-Bachar O, Long AA. Mining Social Media Data to Assess the Risk of Skin and Soft Tissue Infections from Allergen Immunotherapy. J Allergy Clin Immunol. 2019 Feb 2. pii: S0091-6749(19)30186-1. doi: 10.1016/j.jaci.2019.01.029. [Epub ahead of print] PubMed PMID: 30721764.
  13. Topaz M, Trifilio M, Maloney D, Bar-Bachar O, Bowles KH. (2018). Improving patient prioritization during hospital-homecare transition: A pilot study of a clinical decision support tool. Res Nurs Health. 2018 Sep 11. doi: 10.1002/nur.21907. [Epub ahead of print] PubMed PMID: 30203417.
  14. Dhopeshwarkar N, Sheikh A, Doan R, Topaz M, Bates DW, Blumenthal KG, Zhou L. (2018). Drug-induced Anaphylaxis Documented in Electronic Health Records. J Allergy Clin Immunol Pract. 2018 Jun 30. pii: S2213-2198(18)30411-2. doi: 10.1016/j.jaip.2018.06.010. [Epub ahead of print] PubMed PMID: 29969686.
  15. Topaz M, Schaffer A, Lai KH, Korach ZT, Einbinder J, Zhou L. (2018). Medical Malpractice Trends: Errors in Automated Speech Recognition. J Med Syst. 2018 Jul 9;42(8):153. doi: 10.1007/s10916-018-1011-9. PubMed PMID: 29987660.
  16. M. Topaz., Schaffer, A., Lai, K., Korach, T., Einbinder, J., Zhou, L. (2018). Malpractice cases involving allergy information in the electronic health records: implications for safer systems. Perspectives of Health Information Management (Summer 2018): 1-9.
  17. Al Assad, W., Topaz, M., Tu, J., Zhou, L. (2017). Novel Application of Machine Learning to Evaluate the Adequacy of Information in Radiology Orders. IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
  18. Goss FR, Lai KH, Topaz M, Acker WW, Kowalski L, Plasek JM, Blumenthal KG, Seger DL, Slight SP, Wah Fung K, Chang FY, Bates DW, Zhou L. (2018). A value set for documenting adverse reactions in electronic health records. J Am Med Inform Assoc. 2018 Jun 1;25(6):661-669. doi: 10.1093/jamia/ocx139. PubMed PMID: 29253169..
  19. Acker WW, Plasek JM, Blumenthal KG, Lai KH, Topaz M, Seger DL, Goss FR, Slight SP, Bates DW, Zhou L. (2017). Prevalence of food allergies and intolerances documented in electronic health records. J Allergy Clin Immunol. 2017 Dec;140(6):1587-1591.e1. doi: 10.1016/j.jaci.2017.04.006. Epub 2017 May 31. PubMed PMID: 28577971.
  20. O'Connor, M., Hanlon, A., Mauer, E.,…Topaz M, Naylor M. Identifying distinct risk profiles to predict adverse events among community-dwelling older adults. Geriatr Nurs. 2017 May 4. pii: S0197-4572(17)30067-8. doi: 10.1016/j.gerinurse.2017.03.013. [Epub ahead of print] PubMed PMID: 28479081.
  21. Navathe, A., … Topaz, M., Zhou, L. (2017) Hospital Readmission and Social Risk Factors Identified from Physician Notes. Health Services Research
  22. Topaz, M., Goss, F., Blumenthal, K., Lai, K., Seger, DL., Slight, SP., Wickner ,PG., Robinson, GA., Fung, KW., McClure, RC., Spiro, S., Acker, WW., Bates, DW., Zhou, L. (2016) Towards improved drug allergy alerts: Multidisciplinary expert recommendations. Int J Med Inform. 2017 Jan;97:353-355. doi: 10.1016/j.ijmedinf.2016.10.006. PubMed PMID: 27729200
  23. Topaz, M., Lai, K., Dowding, D., Lei, VJ., Zisberg, A., Bowles, KH., Zhou, L. (2016) Automated identification of wound information in clinical notes of patients with heart diseases: Developing and validating a natural language processing application. Int J Nurs Stud. 2016 Sep 19;64:25-31. doi: 10.1016/j.ijnurstu.2016.09.013. [Epub ahead of print] PubMed PMID: 27668855.
  24. Wang, L., Topaz, M., Plasek, J., Zhou, L. (2017). Content and Trends in Medical Informatics Publications Over the Past Two Decades. Bi-annual World Congress on Medical and Health Informatics, (MEDINFO).
  25. Topaz M, Ronquillo C, Peltonen LM, Pruinelli L, Sarmiento RF, Badger MK, Ali S, Lewis A, Georgsson M, Jeon E, Tayaben JL, Kuo CH, Islam T, Sommer J, Jung H, Eler GJ, Alhuwail D, Lee YL. Nurse Informaticians Report Low Satisfaction and Multi-level Concerns with Electronic Health Records: Results from an International Survey. AMIA Annu Symp Proc. 2017 Feb 10;2016:2016-2025. eCollection 2016. PubMed PMID: 28269961; PubMed Central PMCID: PMC5333337. [Nominated for best paper award]
  26. Peltonen, L., Topaz, M., Ronquillo, C., Pruinelli, L., Sarmiento, R., Badger, M., Ali, S., Lewis, A., Georgsson, M., Jeon, E., Tayaben, J., Kuo, C., Islam, T., Sommer, J., Jung, H., Eler, J., Alhuwail, D. (2016). Nursing Informatics Research Priorities for the Future: Recommendations from an International Survey. The Bi-annual Congress in Nursing Informatics 2016, (NI 2016). Stud Health Technol Inform. 2016;225:222-6. PubMed PMID: 27332195. [Nominated for best paper award]
  27. Topaz, M., Ronquillo, C., Peltonen, L., Pruinelli, L., Sarmiento, R., Badger, M., Ali, S., Lewis, A., Georgsson, M., Jeon, E., Tayaben, J., Kuo, C., Islam, T., Sommer, J., Jung, H., Eler, J., Alhuwail, D. Advancing nursing informatics in the next decade: recommendations from an international survey. The Bi-annual Congress in Nursing Informatics 2016, (NI 2016). Stud Health Technol Inform. 2016;225:123-7. PubMed PMID: 27332175.
  28. Topaz M, Radhakrishnan K, Blakley S, Lei V, Lai K, Zhou L. (2016). Studying associations between heart failure self-management and rehospitalizations using natural language processing. Western Journal of Nursing Research. pii: 0193945916668493.
  29. Huang KP, Joyce CJ, Topaz M, Guo Y, Mostaghimi A. Cardiovascular risk in patients with alopecia areata (AA): A propensity-matched retrospective analysis. J Am Acad Dermatol. 2016 May 13. pii: S0190-9622(16)01504-8. doi: 10.1016/j.jaad.2016.02.1234. [Epub ahead of print] PubMed PMID: 27183846.
  30. Plasek JM, Goss FR, Lai KH, Lau JJ, Seger DL, Blumenthal KG, Wickner PG, Slight SP, Chang FY, Topaz M, Bates DW, Zhou L. Food entries in a large allergy data repository. J Am Med Inform Assoc. 2015. pii: ocv128. doi: 10.1093/jamia/ocv128. [Epub ahead of print] PubMed PMID: 26384406.
  31. Koru G, AlHuwail D, Topaz M, Norcio AF, Mills ME. Investigating the challenges and opportunities in home care to facilitate effective information technology adoption. Journal of the American Medical Directors Association [In Press, Fall 2015].
  32. Topaz M, Ronquillo C, Pruinelli L, Ramos R, Peltonen LM, Siirala E, Atique S, Hamann G, Badger MK. Central trends in nursing informatics: students' reflections from International Congress on Nursing Informatics 2014 (Taipei, Taiwan). Comput Inform Nurs 2015 Mar;33(3):85-9. doi: 10.1097/CIN.0000000000000139. PubMed PMID: 25793554.
  33. Nandigam H, Topaz M. Mapping Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) to International Classification of Diseases 10 (ICD-10-CM): Lessons Learned from Applying National Library of Medicine's Mappings. Perspectives in Health Information Management, July 2016.
  34. Topaz M, Seger DL, Slight SP, Goss F, Lai K, Wickner PG, Blumenthal K, Dhopeshwarkar N, Chang F, Bates DW, Zhou L. Rising drug allergy alert overrides in electronic health records: an observational retrospective study of a decade of experience. J Am Med Inform Assoc. 2016 May;23(3):601-8. doi: 10.1093/jamia/ocv143. Epub 2015 Nov 17. PubMed PMID: 26578227
  35. Topaz M. Benefits and Lessons Learned from Organizing and Managing an International Nursing Students' Group in Informatics, Journal of Nursing Doctoral Students Scholarship 2014;1: 4-7.
  36. Topaz M, Lisby M, Morrison CR, Levtzion-Korach O, Hockey PM, Salzberg CA, Efrati N, Lipsitz S, Bates DW, Rozenblum R. (2016). Nurses' Perspectives on Patient Satisfaction and Expectations: An International Cross-Sectional Multicenter Study With Implications for Evidence-Based Practice. Worldviews Evid Based Nurs. 2016 Jun;13(3):185-96. doi: 10.1111/wvn.12143. PMID: 26840190.
  37. Zhou L, Baughman AW, Lei VJ, Lai KH, Navathe AS, Chang F, Sordo Sanchez M, Topaz M, Zhong F, Murrali M, Navathe S, Rocha RA. Identifying Patients with Depression Using Free-text Clinical Documents. MEDINFO 2105 proceedings [Best Paper Award Nominee].
  38. Topaz M, Seger D, Lai K, Wickner P, Goss F, Dhopeshwarkar N, Chang F, Bates W D, Zhou L. High Override Rate for Opioid Drug-allergy Interaction Alerts: Current Trends and Recommendations for Future. MEDINFO 2105 proceedings.
  39. Lai KH, Topaz M, Goss FR, Zhou L. Automated Misspelling Detection and Correction in Clinical Free-Text Records. J Biomed Inform 2015; S1532-0464(15)00075-1.
  40. Bowles KH, Chittams J, Heil E, Topaz M, Rickard K, Bhasker M, Tanzer M, Behta M, Hanlon AL. Successful electronic implementation of discharge referral decision support has a positive impact on 30- and 60-day readmissions. Res Nurs Health. 2015 Apr;38(2):102-14. doi: 10.1002/nur.21643. Epub 2015 Jan 25. PubMed PMID: 25620675; PubMed Central PMCID: PMC4363131..
  41. Topaz M, Kang Y, Holland D, Bowles KH. Higher 30 and 60-day readmissions among patients who refuse post acute care serves. American Journal of Managed Care [In Press, May 2015].
  42. George, M., Topaz, M., Rand, C., Mao, C. & Shea, J.A. Negative inhaled corticosteroid beliefs and complementary and alternative medicine use in urban adults with asthma. Journal of Allergy and Clinical Immunology 2014; 134(6):1252-9.
  43. Zisberg A, Topaz M, Winterstein T. Effect of nursing education on students' knowledge, attitudes and preferences to work with older adults: An Israeli perspective, Journal of Transcultural Nursing 2014: pii:1043659614526252. [Epub ahead of print] PubMed PMID: 24848351.
  44. Topaz M, Golfenshtein N, Bowles KH. The Omaha System: a systematic review of the recent literature, J Am Med Inform Assoc 2014;21:163-170.
  45. Bowles KH, Hanlon A, Holland D, Potashnik SL, Topaz M. Impact of discharge planning decision support on time to readmission among older adult medical patients, Prof Case Manag 2014;19:29-38.
  46. Gerber A, Topaz M. Promoting Meaningful Use of Health Information Technology in Israel: Ministry of Health Vision. Nursing Informatics Bi-Annual International Congress Proceedings 2014:568-575.
  47. Topaz M, Ash N. Overview of the US policies for health information technology and lessons learned for Israel [In Hebrew], Harefuah 2013;152:262-6, 310, 309.
  48. Topaz M. The Hitchhiker's Guide to nursing informatics theory: using the Data-Knowledge-Information-Wisdom framework to guide informatics research, Online Journal of Nursing Informatics 2013;13.
  49. Topaz M, Shalom E, Masterson-Creber R, Rhadakrishnan K, Monsen KA, Bowles KH. Developing nursing computer interpretable guidelines: a feasibility study of heart failure guidelines in homecare, AMIA Annu Symp Proc 2013; 1353-1361. [Nominated for best paper award]
  50. Topaz M. Developing a Clinical Decision Support Tool for Patient Prioritization at Admission to Home Health Care, AMIA Annu Symp Proc 2013 [Section: Doctoral Consortium on Sociotechnical Issues in Biomedical Informatics] 2013:681-686.
  51. Topaz M, Rao A, Masterson Creber R, Bowles KH. Educating clinicians on new elements incorporated into the electronic health record: theories, evidence, and one educational project, Comput Inform Nurs 2013;31:375-9.
  52. Topaz M, Doron I. Nurses' attitudes toward older patients in acute care in Israel, Online Journal of Issues in Nursing 2013;18.
  53. Topaz M, Troutman (Flood) M, MacKenzie M. Construction, Deconstruction and Reconstruction: A history and evolution of theories of aging. Nursing Science Quarterly 2013;19;27(3):226-233.
  54. Radhakrishnan K,* Topaz M,* Creber RM. Adapting Heart Failure Guidelines for Nursing Care in Home Health Settings: Challenges and Solutions, J Cardiovasc Nurs 2013 [Epub ahead of print].
  55. Radhakrishnan K, Bowles K, Hanlon A, Topaz M, Chittams J. A retrospective study on patient characteristics and telehealth alerts indicative of key medical events for heart failure patients at a home health agency, Telemed J E Health 2013;19:664-670.
  56. Masterson Creber R, Topaz M, Lennie TA, Lee CS, Puzantian H, Riegel B. Identifying predictors of high sodium excretion in patients with heart failure: A mixed effect analysis of longitudinal data, Eur J Cardiovasc Nurs 2013 [Epub ahead of print].
  57. George M,* Topaz M.* A systematic review of complementary and alternative medicine for asthma self-management, Nurs Clin North Am 2013;48:53-149.
  58. Creber RM, Lee CS, Lennie TA, Topaz M, Riegel B. Using Growth Mixture Modeling to Identify Classes of Sodium Adherence in Adults With Heart Failure, J Cardiovasc Nurs 2013 [Epub ahead of print].
  59. Bowles KH, Potashnik S, Ratcliffe SJ, Rosenberg M, Shih NW, Topaz M, Holmes JH, Naylor MD. Conducting research using the electronic health record across multi-hospital systems: semantic harmonization implications for administrators, J Nurs Adm 2013;43:355-360.
  60. Topaz M, Johnson A, Pinilla R, Rand C, George M. Primary Care Providers' Attitudes and Beliefs About Patients' Complementary and Alternative Medicine Use for Asthma Self-Management: An Exploratory Study, Journal of Asthma & Allergy Educators 2012;3:255-263.
  61. Riegel B, Dickson VV, Topaz M. Qualitative Analysis of Naturalistic Decision Making in Adults with Chronic Heart Failure, Nurs Res 2012;62:91-8.
  62. Topaz M. One paper's journey: from class assignment to journal publication, Journal of Nursing Doctoral Students Scholarship 2013;1:54-57.
  63. Topaz M, Shafran-Topaz L, Bowles KH. ICD-9 to ICD-10: Evolution, Revolution and Current Debates in the US, Perspectives in Health Information Management 2012.
  64. Topaz M, Bowles KH. Electronic Health Records and Quality of Care: Mixed Results and Emerging Debates. Online Journal of Nursing Informatics 2012;16.
  65. Topaz M, Radhakrishnan K, Masterson-Creber R, Bowles KH. Putting evidence to work: Using standardized terminologies to incorporate clinical practice guidelines within homecare electronic health records. Online Journal of Nursing Informatics 2012;16:1694-1699.
  66. Masterson Creber RM, Kang Y, Topaz M, Lennie TA, Riegel B. Discordance between Self-Reported and 24-Hour Urine Sodium Intake and Predictors of Sodium Non-Adherence, J Card Fail 2012;18-22.
  67. Meleis AI, Topaz M. Nursing theory of the future: situation-specific theories, Pflege 2011;24:345-347.