Health and Clinical Informatics Research

HCIN icon for health and clinical informatics

Health and clinical informatics transforms medicine and health care by analyzing, designing, implementing, and evaluating systems and interventions to improve patient care, enhance access to care, advance individual and population health outcomes, and strengthen the clinician-patient relationship.

Among the areas of research expertise in health and clinical include:

  • Re-use of clinical data
  • Text mining and natural language processing
  • Information retrieval (search)
  • People and organizational issues
  • Health information exchange
  • Telemedicine/telehealth
  • Data quality
  • Patient decision-making and decision aids
  • Informatics for chronic disease management
  • Electronic Health Record simulation for patient safety

Some grant-funded projects include:

Clinical and Genetic Analysis of Retinopathy of Prematurity    
Michael Chiang, Jayashree Kalpathy-Cramer
The goals of this project are to develop and validate artificial intelligence methods for diagnosis of retinopathy of prematurity (ROP) in at-risk infants, to identify genetic factors related to ROP pathogenesis, and to develop predictive risk models that integrate clinical, quantitative imaging, and genetic factors.
Funder: National Eye Institute, National Institutes of Health

Semi-structured Information Retrieval in Clinical Text for Cohort Identification
William Hersh, Steven Bedrick
The major goals of this project are to develop information retrieval techniques for cohort identification based on clinical text. A collaboration with Mayo Clinic, both sites use an extract of electronic health record data from 100,000 patients, including text reports, to identify the best approaches to correctly identifying patient cohorts for possible inclusion in 56 different clinical studies.
Funder: National Library of Medicine, National Institutes of Health  

Measuring and Improving Data Quality for Clinical Quality Measure Reliability
Nicole Weiskopf
The goal of this project is to understand the relationships between clinical data quality and the evaluation of healthcare quality, develop methods for estimating the reliability of these evaluations, and thereby enable the overall improvement the reliability of quality of care measurement. 
Funder: National Library of Medicine, National Institutes of Health

Nicole Weiskopf
Through a combination of systematic review of the literature and interviews with EHR data consumers, this project developed a simple framework and associated recommendations to guide data quality assessment.  3x3 DQA will lead data consumers through a logical and coherent exploration of the quality of a dataset, making it easier to decide whether or not the dataset is of sufficient quality.

Provider Order Entry Team: Computerized Provider Order Entry
Joan Ash
The Provider Order Entry Team (POET), a multidisciplinary team of clinicians, informaticians, and social scientists, has, since 2000, conducted a series of grant and contract funded projects on computerized provider order entry, the unintended consequences of health information technology (HIT), clinical decision support, and HIT safety.

Modeling and Optimization of Clinical Processes Using EHR Data
Michelle Hribar, Michael Chiang
This project analyzes clinical workflow in high-volume outpatient ophthalmology clinics using secondary EHR data and positioning data, create simulation models to optimize efficiency based on these data, and apply these methods to medical domains outside of ophthalmology. 
Funder: National Library of Medicine, National Institutes of Health

EHR Solutions for Accurate Reporting of Data on Interprofessional ICU Rounds
Jeff Gold, Vishnu Mohan, Joan Ash
A vast amount of information must be collected and effectively communicated during interprofessional ICU rounds to allow effective clinical decision making. The Electronic Health Record (EHR) is at the nexus of this issue as the source of clinical data and the device utilized to generate tools for reporting it. This project aims to define the full extent to which clinical data are miscommunicated on rounds and through the use of a controlled ICU rounds simulation, develop a series of toolboxes to ensure accurate data reporting during, and effective integration of the EHR into, interprofessional ICU rounds.
Funder: Agency for Healthcare Research and Quality

Creation and Validation of a Training Toolkit to Ensure Safe and Proficient Use of EHR by Medical Scribes
Jeff Gold, Vishnu Mohan, Joan Ash
With the widespread adoption of Electronic Health Records (EHRs) there has been a growing appreciation of the unintended consequences associated with their adoption and specifically the negative impacts on productivity and workflow. Consequently, there has been dramatic growth in the use of medical scribes to aid providers by, in essence, “untethering the provider from the EHR”. In spite of this rapid growth, and the purported benefits on improving physician efficiency and improved billing, there is little to no regulation on standardization of scribe training, nor any assessment of their ability to safely interface with the EHR. This study aims to create a web based, comprehensive toolkit to allow for real-world assessment of safe and accurate use of EHR by medical scribes across a range of EHR functional levels and a variety of specialties and environments.
Funder: Agency for Healthcare Research and Quality

Text Mining Pipeline to Accelerate Systematic Reviews in Evidence-Based Medicine
Aaron Cohen
Systematic reviews are essential for determining which treatments and interventions are safe and effective. At present, systematic reviews are written largely by laborious manual methods. The proposed studies will reduce the time and effort needed to write systematic reviews, and thereby enhance evidence-based medicine and the incorporation of best practices into clinical care.
Funder: National Library of Medicine, National Institutes of Health