AI-Based Processing of Hospital-Based Nursing Data (SC2K)
Columbia University
Eligibility
All Students
Accepts Applications Until
Dec 20, 2025
Project Duration
Flexible
Description
This project will harness nursing knowledge in a systematic way to better capture the nuances of nursing data, leading to more comprehensive, accurate, and transparent algorithms. Additionally, the study seeks to develop scalable computational approaches to evaluate and improve the quality of data recorded by inpatient nurses and used in AI algorithms. Advanced AI methods will increasingly use data documented by nurses. Insufficient knowledge of nursing practice, nurse decision-making, and nursing workflows risks both inaccurate and undiscovered data signals.
The goals of this project are to:
Test and validate different computational methods (e.g., LLM, logistic regression, neural network) within a healthcare process modeling (HPM) framework applied to two AI-based use cases (classifying missing data versus missed care; classifying implicit biases) that leverage inpatient nursing and multi-modal data ready for integration with knowledge graphs. The HPM framework moves data science methods beyond transactional data analytics to model clinical knowledge, decision making, and behavior to classify and make predictions about patients that are consistent with and can enhance the quality of the data captured used to discover previously unknown patterns.
Generate and validate a set of applicable knowledge graphs related to HPMs that are generalizable and valuable for the two AI-based use cases that leverage inpatient nursing and multi-modal data.
Extend multi-modal approaches to HPM informed scalable computational processes combined with knowledge graphs across five additional AI-based use cases that leverage inpatient nursing and multi-modal data.
Build an open-source pipeline to share and reuse the HPM informed scalable computational processes combined with knowledge graphs.
Required Skills
- Knowledge of programming languages used for data science, such as R or Python. Experience with statistical software, such as R, SAS or SPSS, Matlab and big data management processes. - Knowledge of relational and other databases (Oracle, SQL, PL/SQ) is a plus. - Ability to work cross-functionally as part of a team, as well as an individual contributor. -Ability to think independently as well as perform tasks according to the directions of faculty researchers and their project managers on specific projects. -Effective communication and writing skills. -Must demonstrate good decision-making skills, be able to call out conflicting issues when they arise, and possess knowledge of how and when to follow through on issues. -Good interpersonal skills and the ability to exercise leadership in data analysis activities as part of a research project team
Additional Information
Lab/Building Location: PH-20, CUIMC
Hours per week: 10-20, flexible
The students responsibilities will include any of following tasks that align with students experience and skill:
Data specifications, data queries, data collection, data curation/cleaning and harmonization across study sites
Computational modeling tasks under direction of PI and co-investigators and data engineer
De-identification of study data sets
Recruitment activities for qualitative data collection
Qualitative data collection and analysis