Grants & Contracts

  • Electronic Health Record Safety: Developing Framework-Based Measures and Identifying Best Practices
    R21 LM 012448 (PI – Allison B. McCoy, PhD 2016-2018) Billions of dollars have been spent on implementing health information technology (HIT), including electronic health records (EHRs) with the promise of improved patient safety and reduction of healthcare costs, but recent reports have highlighted potential challenges and unintended consequences of these technologies. Effective measurement of of potential hazards can overcome existing barriers to detecting potentially unsafe system conditions and implementing interventions within these systems to prevent medical errors. We propose to define a pilot set of measures, based on a National Quality Forum endorsed HIT safety framework, for assessing EHR safety and identify barriers and facilitators to implementing the measures and reporting best practices. For more information see project profile on NIH REPORTER.
  • InSPECt: Interactive Surveillance Portal for Evaluating Clinical Decision Support
    K22 LM011430 (PI – Allison B. McCoy, PhD 2013-2016) Effectively evaluating the appropriateness of clinical decision support alerts and responses is critical to improving patient safety through health information technology. This proposal will develop novel, semi- automated methods to facilitate such evaluations in both ambulatory and community hospital settings with commercial electronic health records. For more information see project profile on NIH REPORTER.
  • Improving Clinical Decision Support Reliability using Anomaly Detection Methods
    R01 LM011966 (PI – Adam Wright, PhD  2014-2018) Clinical decision support systems, such as drug-interaction alerts and preventive care reminders, when used effectively, have been shown to the quality, safety and efficiency of care. However, such systems are complex and sometimes fail – these failures are often not noticed for a long period of time and can lead to patient harm. In the proposed project, we will study the causes of such failures and develop and test anomaly detection systems to detect such failures and alert knowledge engineers about them with the goal of improving the safety and reliability of clinical decision support systems. For more information see project profile on NIH REPORTER.
  • Decision Making and Clinical Work of Test Result Follow-up in Health IT Settings
    AHRQ R01HS022087 (PI – Hardeep Singh, MD, MPH 2013-2017) A significant number of patients with abnormal test results “fall through the cracks” of the health care system and experience delays in diagnosis and treatment. Although electronic health records enhance the communication of abnormal test results, they do not guarantee the prompt follow-up that is required for timely care. We propose to study test result follow-up practices across healthcare institutions that use various electronic health record systems to understand why abnormal test results are missed. For more information see project profile on NIH REPORTER.
  • Improving Quality by Maintaining Accurate Problem Lists in the EHR (IQ-MAPLE)
    R01HL122225 (PI – Adam Wright, PhD  2014-2018) An accurate, complete clinical problem list the cornerstone of a problem-oriented medical record, and research shows that accurate problem lists improve healthcare quality; however, problem lists are often incomplete. In this study, we will develop and evaluate an intervention to identify gaps in problem lists for patients with heart, lung, and blood conditions. If successful, this system should improve problem list completeness and, ultimately, the quality of care delivered to patients. For more information see project profile on NIH REPORTER.