Background: Pneumonia is the most common infectious cause of death in the United States, with an estimated 50,000 deaths and $8-10 billion in costs annually.12 Twenty-five thousand Veterans are seen per year in the Emergency Department, and 20,000 are hospitalized.13 Risk adjusted 30-day mortality rates range among VA facilities from 11% to 18%. The resources utilized for pneumonia also vary widely, with the cost of hospital care estimated at up to 25 times that of outpatient care.20 To improve the standard of pneumonia care, intense research and quality improvement efforts have been focused upon establishing evidence-based practice guidelines for pneumonia that represent a consensus of research and opinions generated from our best academic hospitals and pneumonia experts. However, we continue to see variation in both management and outcomes across the VA as in other systems, suggesting that guidelines may be difficult to apply to real practice. The recent advancement of our electronic health record allows us to measure actual practice at both the population and the individual level. By studying variation in triage and antibiotic decisions for patients with pneumonia, we can learn from our own population to generate evidence that includes the previously underrepresented patients, settings and scenarios, bringing it closer to real practice.
Objectives: This proposal aims to understand and improve the care of Veterans treated for pneumonia through informatics through the following three aims:
(1) Examine sources of variation in sit-of-care and antibiotic selection decisions for pneumonia across VA emergency departments.
(2) Characterize the cognitive processes influencing decision-making in pneumonia among providers at emergency departments demonstrating variation in decision-making.
(3) Design and test an informatics tool that supports decision-making, integrates with workflow, and allows us to learn from clinical experience.
Methods: Aim 1 A. will develop and test models of practice variation for triage and antibiotic selection and b. will identify emergency departments with high and low decision-making thresholds across the VA system. Aim 2 will use mixed methods to characterize the cognitive process of clinical reasoning and contextual influences impact decision-making through interviews with providers. Aim 3 will utilize a real-time informatics tool to test current best practice recommendations against physician decisions by providing physicians the opportunity to disagree with any recommendations, and collect information on reasons for disagreement.
Not yet available.
Impact: This work will directly inform the development of informatics tools for pneumonia for the VA. Additionally, it will advance my career goals to develop skills in advanced population analytics, gain a foundation in behavioral sciences, and advance my understanding in informatics. While my clinical interest is in pneumonia, the skills I develop will be applied to many clinical problems in medicine where decision-making occurs in the setting of uncertainty.
- Jones BE, Collingridge DS, Vines CG, Post H, Holmen J, Allen TL, Haug P, Weir CR, Dean NC. CDS in a Learning Health Care System: Identifying Physicians' Reasons for Rejection of Best-Practice Recommendations in Pneumonia through Computerized Clinical Decision Support. Applied clinical informatics. 2019 Jan 2; 10(1):1-9.
- Jones BE, Haroldsen C, Madaras-Kelly K, Goetz MB, Ying J, Sauer B, Jones MM, Leecaster M, Greene T, Fridkin SK, Neuhauser MM, Samore MH. In Data We Trust? Comparison of Electronic Versus Manual Abstraction of Antimicrobial Prescribing Quality Metrics for Hospitalized Veterans With Pneumonia. Medical care. 2018 Jul 1; 56(7):626-633.
- Jones BE, South BR, Shao Y, Lu CC, Leng J, Sauer BC, Gundlapalli AV, Samore MH, Zeng Q. Development and Validation of a Natural Language Processing Tool to Identify Patients Treated for Pneumonia across VA Emergency Departments. Applied clinical informatics. 2018 Feb 21; 9(1):122-128.
- Jones BE, Samore MH. Antibiotic Overuse: Clinicians Are the Solution. Annals of internal medicine. 2017 Jun 6; 166(11):844-845.
Technology Development and Assessment