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Lacroix, Jacques

Mayo Clinic

Université de Montréal

Citations 3

Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy
Morris, Alan H. , Horvat, Christopher M. , Grainger, David W. , Orme, James F., Clemmer, Terry P., Weaver, Lindell K., Thomas, Frank O, Grissom, Colin K. , Hirshberg, Ellie , East, Thomas D. , Wallace, C. Jane, Young, Michael , Sittig, Dean F. , Pearl, James E., Pesenti, Antinio, Bombino, Michela, Beck, Eduardo, Sward, Katherine , Phansalkar, Shobha, Bernard, Gordon R. , Brower, Roy G., Truwit, Jonathon D. , Steingrub, Jay S. , Hiten, R Duncan, Willson, Douglas F., Randolph, Adrienne G. , Curley, Martha A. Q. , Newth, Christopher J. L. , Lacroix, Jacques, Agus, Michael S. D. , Lee, Kang Hoe, deBoisblanc, Bennett P., Moore, Frederick A. , Evans, R. Scott, Sorenson, Dean, Wong, Anthony, Boland, Michael V. , Dere, W. , Crandall, Alan S. , Huff, Stanley M. , Haug, Peter J. , Pielmeier, Ulrike , Rees, Stephen Edward , Karbing, Dan Stieper , Berger, Kenneth I. , Oppenheimer, Beno W. , Pickering, Brian W. , Schoenfeld, David , Tocino, Irena , Savitz, Lucy A., Dreyfuss, Didier, Slutsky, Arthur S. , Crapo, James D. , Pinsky, Michael R. , James, Brent C., Berwick, Donald M. , Sittig, Dean F , Sward, Katherine A , Weir, Charlene , Huff, Stanley M , Gonnering, Russell S , Randolph, Adrienne G , Stagg, Brian C. , Lanspa, Michael J. , Suchyta, Mary R. , Thompson, Bruce , Zimmerman, Jerry J. , Nadkarni, Vinay , Facelli, Julio Cesar , Andreassen, Steen , Fan, Eddy , Goldring, Roberta M. , Ely, Eugene Wesley , Pronovost, Peter J.

Journal of the American Medical Informatics Association | September 20, 2022 | Volume: 30 | Issue: 1 | Pages: 178-194

10.1093/jamia/ocac143 Get It

AbstractHow to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.
Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions
Morris, Alan H. , Orme, James F., Clemmer, Terry P., Weaver, Lindell K., Thomas, Frank, Grissom, Colin K. , Hirshberg, Ellie , East, Thomas D. , Wallace, C. Jane, Young, Michael , Sittig, Dean F. , Bombino, Michela, Beck, Eduardo, Sward, Katherine , Phansalkar, Shobha, Bernard, Gordon R. , Brower, Roy G., Truwit, Jonathon D. , Steingrub, Jay, Willson, Douglas F., Randolph, Adrienne G. , Curley, Martha A. Q. , Newth, Christopher J. L. , Lacroix, Jacques, Agus, Michael S. D. , Lee, Kang H., deBoisblanc, Bennett P., Evans, R. Scott, Sorenson, Dean, Wong, Anthony, Boland, Michael V. , Grainger, David W. , Dere, W. , Crandall, Alan S. , Huff, Stanley M. , Haug, Peter J. , Pielmeier, Ulrike , Rees, Stephen Edward , Karbing, Dan Stieper , Goldring, Roberta M. , Berger, Kenneth I. , Oppenheimer, Beno W. , Pickering, Brian W. , Schoenfeld, David , Tocino, Irena , Savitz, Lucy A., Dreyfuss, Didier, Slutsky, Arthur S. , Crapo, James D. , Pinsky, Michael R. , James, Brent C., Berwick, Donald M. , Sittig, Dean F , Weir, Charlene , Randolph, Adrienne G , Sward, Katherine A , Stagg, Brian C. , Lanspa, Michael J. , Pesenti, Antonio Maria , Thompson, Bruce , Hite, R. Duncan , Zimmerman, Jerry J. , Nadkarni, Vinay , Facelli, Julio Cesar , Andreassen, Steen , Fan, Eddy , Ely, Eugene Wesley , Gajic, Ognjen , Gonnering, Russell S , Pronovost, Peter J. , Angus, Derek C.

Journal of the American Medical Informatics Association | November 11, 2020 | Volume: 28 | Issue: 6 | Pages: 1330-1344

10.1093/jamia/ocaa294 Get It

AbstractClinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention—the starting point for delivery of “All the right care, but only the right care,” an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.
Epidemiology and Outcomes of Pediatric Multiple Organ Dysfunction Syndrome
Watson, R. Scott , Crow, Sheri, Hartman, Mary E. , Lacroix, Jacques, Odetola, Folafoluwa O.

Pediatric Critical Care Medicine | March 1, 2017 | Volume: 18 | Issue: 3 | Pages: S4-S16

10.1097/pcc.0000000000001047 Get It

Objective: To summarize the epidemiology and outcomes of children with multiple organ dysfunction syndrome as part of the Eunice Kennedy Shriver National Institute of Child Health and Human Development multiple organ dysfunction syndrome workshop (March 26–27, 2015). Data Sources: Literature review, research data, and expert opinion. Study Selection: Not applicable. Data Extraction: Moderated by an experienced expert from the field, issues relevant to the epidemiology and outcomes of children with multiple organ dysfunction syndrome were presented, discussed, and debated with a focus on identifying knowledge gaps and research priorities. Data Synthesis: Summary of presentations and discussion supported and supplemented by the relevant literature. Conclusions: A full understanding the epidemiology and outcome of multiple organ dysfunction syndrome in children is limited by inconsistent definitions and populations studied. Nonetheless, pediatric multiple organ dysfunction syndrome is common among PICU patients, occurring in up to 57% depending on the population studied; sepsis remains its leading cause. Pediatric multiple organ dysfunction syndrome leads to considerable short-term morbidity and mortality. Long-term outcomes of multiple organ dysfunction syndrome in children have not been well studied; however, studies of adults and children with other critical illnesses suggest that the risk of long-term adverse sequelae is high. Characterization of the long-term outcomes of pediatric multiple organ dysfunction syndrome is crucial to identify opportunities for improved treatment and recovery strategies that will improve the quality of life of critically ill children and their families. The workshop identified important knowledge gaps and research priorities intended to promote the development of standard definitions and the identification of modifiable factors related to its occurrence and outcome.