{"id":2924,"date":"2026-01-27T18:14:11","date_gmt":"2026-01-27T18:14:11","guid":{"rendered":"https:\/\/research.uga.edu\/team-research\/?post_type=projects&p=2924"},"modified":"2026-01-28T15:37:28","modified_gmt":"2026-01-28T15:37:28","slug":"drive-data-driven-icu-volume-intervention-an-interdisciplinary-approach-for-evidence-based-staffing-and-workload-optimization-in-the-critical-care-setting","status":"publish","type":"projects","link":"https:\/\/research.uga.edu\/team-research\/projects\/drive-data-driven-icu-volume-intervention-an-interdisciplinary-approach-for-evidence-based-staffing-and-workload-optimization-in-the-critical-care-setting\/","title":{"rendered":"DRIVE (Data-dRiven ICU Volume IntErvention): An Interdisciplinary approach for evidence-based staffing and workload optimization in the critical care setting"},"content":{"rendered":"

[vc_row disable_element=”yes” css=”.vc_custom_1596458870957{margin-top: 0px !important;margin-right: 0px !important;margin-bottom: 0px !important;margin-left: 0px !important;padding-top: 0px !important;padding-right: 0px !important;padding-bottom: 0px !important;padding-left: 0px !important;}”][vc_column css=”.vc_custom_1579011887505{margin-top: 0px !important;margin-bottom: 0px !important;margin-left: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;padding-left: 0px !important;}”][vc_column_text css=”.vc_custom_1579011932922{margin-top: 0px !important;margin-left: 0px !important;padding-top: 0px !important;padding-left: 0px !important;}”]<\/p>\n

Teaming for Interdisciplinary Research Pre-Seed Program<\/a><\/h1>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row bg_type=”bg_color” css=”.vc_custom_1642004953070{margin-right: 0px !important;margin-bottom: 0px !important;border-right-width: 0px !important;border-bottom-width: 0px !important;padding-right: 0px !important;padding-bottom: 0px !important;}” bg_color_value=”#004e60″][vc_column width=”1\/2″ css=”.vc_custom_1578671862861{background-color: #004e60 !important;}”][vc_column_text css=”.vc_custom_1763404934609{margin-left: 10px !important;}”]<\/p>\n

DRIVE (Data-dRiven ICU Volume intErvention): An Interdisciplinary approach for evidence-based staffing and workload optimization in the critical care setting
\n<\/span><\/h3>\n

[\/vc_column_text][\/vc_column][vc_column width=”1\/2″ css=”.vc_custom_1642018498769{margin-right: 0px !important;border-right-width: 0px !important;padding-right: 0px !important;}”][vc_single_image image=”2925″ img_size=”445×300″ alignment=”right” css=”.vc_custom_1769537649592{margin-top: 0px !important;margin-right: 0px !important;margin-bottom: 0px !important;margin-left: 0px !important;border-right-width: 0px !important;padding-top: 0px !important;padding-right: 0px !important;padding-bottom: 0px !important;padding-left: 0px !important;background-color: #004e60 !important;}”][\/vc_column][\/vc_row][vc_row][vc_column css=”.vc_custom_1595855041843{margin-top: 0px !important;padding-top: 0px !important;}”][vc_row_inner][vc_column_inner width=”2\/3″][vc_empty_space][vc_column_text css=””]The\u00a0intensive\u00a0care\u00a0unit\u00a0(ICU)\u00a0is\u00a0one\u00a0of\u00a0the\u00a0most\u00a0high-stakes\u00a0environments\u00a0in\u00a0healthcare,\u00a0characterized\u00a0by complex patient acuity and constant, time-sensitive decision-making. These demands place high workloads on healthcare workers (HCWs), which directly\u00a0impact\u00a0HCW well-being and compromise patient safety. Burnout rates of the ICU team members are among the highest in healthcare, at nearly 70%, with compelling evidence linking excessive workload to the 1.5 million adverse drug events that occur annually in the U.S. Current staffing procedures rely on unvalidated metrics, such as doses dispensed,\u00a0medications\u00a0verified,\u00a0or\u00a0administrations\u00a0completed,\u00a0which\u00a0capture\u00a0only\u00a0isolated\u00a0tasks\u00a0and\u00a0fail to account for the complexities of team-based bedside care, the goal standard for safe and effective ICU\u00a0practice.<\/span>\u00a0<\/span><\/p>\n

To\u00a0address\u00a0this\u00a0gap,\u00a0we\u00a0propose\u00a0the\u00a0development\u00a0of\u00a0<\/span>DRIVE\u00a0(Data-dRiven\u00a0ICU\u00a0Volume\u00a0intErvention)<\/span><\/b>,\u00a0an evidence-based, real-time dashboard that integrates HCW staffing data with patient care needs to improve staffing decisions and\u00a0optimize\u00a0workloads. This interdisciplinary project brings together\u00a0expertise\u00a0from the College of Pharmacy, College of Education, College of Public Health, College of Veterinary Medicine, and School of Computing.<\/span>\u00a0<\/span><\/p>\n

The project has both practical and scholarly goals. From a practical perspective, DRIVE will provide the ICU\u00a0team\u00a0members\u00a0and\u00a0administrators\u00a0with\u00a0real-time,\u00a0patient-level\u00a0workload\u00a0measures\u00a0to\u00a0guide\u00a0staffing decisions that improve patient safety and quality of care. From a research perspective, DRIVE will serve as a platform for studying the impact of workload on HCW well-being and patient outcomes.<\/span>\u00a0<\/span><\/p>\n

Workload\u00a0factors\u00a0to\u00a0be\u00a0included\u00a0in\u00a0DRIVE\u00a0will\u00a0be\u00a0identified\u00a0using\u00a0the\u00a0TURF\u00a0(Task,\u00a0User,\u00a0Representation, and Function) framework. We will ask ICU HCWs and administrators to\u00a0participate\u00a0in semi-structured interviews and collect perspectives on (1) safe care goals and tasks, (2)\u00a0perceptions\u00a0of AI-driven workload models, (3) design requirements for information presentation, and (4) existing workflows.<\/span>\u00a0<\/span><\/p>\n

These\u00a0insights\u00a0will\u00a0guide\u00a0the\u00a0development\u00a0of\u00a0a\u00a0prototype\u00a0DRIVE\u00a0dashboard,\u00a0a\u00a0wireframe\u00a0of\u00a0which\u00a0can\u00a0be seen in the accompanying figure. Usability testing will then be conducted, using simulated cases and think-aloud protocols to assess transparency (including predictability and observability),\u00a0augment\u00a0cognition, and trustworthiness. Robust statistical approaches will be applied, with iterative refinements based on feedback and observations. The final DRIVE design will be implemented in the ICU setting and formally evaluated in a workflow comparison study using validated measures, including (1) the Situational Awareness Global Assessment Technique (SAGAT) for situation awareness<\/span>,\u00a0<\/span><\/b>(2) accuracy, (3) time on task, and (4) the System Usability Scale for usability metrics.<\/span>\u00a0<\/span><\/p>\n

Through interdisciplinary collaboration, DRIVE will enhance ICU staffing practices and workload allocation.\u00a0This\u00a0innovation\u00a0will\u00a0reduce\u00a0HCW\u00a0burnout\u00a0while\u00a0enhancing\u00a0patient\u00a0safety\u00a0and\u00a0quality\u00a0of\u00a0care. As a pre-seed effort, team development will be supported to\u00a0establish\u00a0a strong foundation\u00a0for competitive applications to internal UGA seed grants and extramural funding opportunities.<\/span>\u00a0<\/span>[\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/3″][vc_column_text css=””]<\/p>\n

Team Lead<\/h4>\n

Smith, Susan<\/strong>
\nsusan.smith@uga.edu<\/a><\/u><\/span>
\nCollege: College of Pharmacy<\/span><\/span>
\nDepartment: Clinical and Administrative Pharmacy<\/p>\n

Team Members<\/h4>\n

Chin, XianYan<\/strong>
\nxchen@uga.edu<\/a><\/u>
\nCollege: College of Public Health<\/span>
\nDepartment: Epidemiology & Biostatistics<\/span><\/p>\n

Hill, Janette<\/strong>
\njanette@uga.edu<\/a><\/u>
\nCollege: Mary Frances Early College of Education<\/span>
\nDepartment: Workforce Education and Instructional Technology<\/span><\/p>\n

Keedy, Chelsea<\/strong>
\nchelsea.keedy@uga.edu<\/a><\/u>
\nCollege: College of Pharmacy)<\/span>
\nDepartment: Clinical & Administrative Pharmacy<\/span><\/p>\n

Kim, Eunice<\/strong>
\nsj.eunicekim@uga.edu<\/a><\/u>
\nCollege: College of Pharmacy \/ College of Veterinary Medicine<\/span>
\nDepartment: Clinical & Administrative Pharmacy \/ Pathology<\/span><\/p>\n

Sikora, Andrea<\/strong>
\nandrea.sikora@cuanschutz.edu<\/a><\/u>
\nCollege: College of Pharmacy<\/span>
\nDepartment: Clinical & Administrative Pharmacy<\/span>\u00a0<\/span><\/p>\n

Xiang, Zhen<\/strong>
\nzsiangaa@uga.edu<\/a><\/u>
\nCollege: Franklin College of Arts and Science \/ College of Engineering<\/span>
\nDepartment: School of Computing<\/span>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"featured_media":2925,"menu_order":0,"template":"","format":"standard","meta":{"_links_to":"","_links_to_target":""},"categories":[30],"class_list":["post-2924","projects","type-projects","status-publish","format-standard","has-post-thumbnail","hentry","category-2025-projects"],"_links":{"self":[{"href":"https:\/\/research.uga.edu\/team-research\/wp-json\/wp\/v2\/projects\/2924"}],"collection":[{"href":"https:\/\/research.uga.edu\/team-research\/wp-json\/wp\/v2\/projects"}],"about":[{"href":"https:\/\/research.uga.edu\/team-research\/wp-json\/wp\/v2\/types\/projects"}],"version-history":[{"count":0,"href":"https:\/\/research.uga.edu\/team-research\/wp-json\/wp\/v2\/projects\/2924\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/research.uga.edu\/team-research\/wp-json\/wp\/v2\/media\/2925"}],"wp:attachment":[{"href":"https:\/\/research.uga.edu\/team-research\/wp-json\/wp\/v2\/media?parent=2924"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/research.uga.edu\/team-research\/wp-json\/wp\/v2\/categories?post=2924"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}