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Ferreira LD, McCants D, Velamuri S. Using machine learning for process improvement in sepsis management. J Healthc Qual Res 2023; 38:304-311. [PMID: 36319584 DOI: 10.1016/j.jhqr.2022.09.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/18/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
INTRODUCTION In the U.S., sepsis afflicts 1.7 million adults, causing 270,000 deaths each year. Early detection of sepsis could decrease the number of deaths by 92,000 annually and decrease hospital expenditures by 1.5 billion USD. Few prior studies and reviews have presented a holistic understanding of the relationship between machine learning and existing process improvement measures. This study, in addition to discussing machine learning and existing process improvements measures, elaborates on the disadvantages and the barriers to integrating machine learning into the clinic. This article synthesizes previous studies to educate healthcare professionals on effectively managing sepsis by leveraging the benefits of machine learning. METHODS This study used the PubMed database. Search terms include sepsis antibiotics, sepsis process improvement, sepsis machine learning. Our search criteria included previous studies published between January 1, 2017, and February 1, 2022. RESULTS/DISCUSSION Although machine learning algorithms have better predictive capabilities, their effectiveness in the clinical setting is limited as studies show mixed results because the medical staff often fails to intervene. To overcome poor interventional response, clinicians need to work with the facility's IT department to ensure integration into clinical workflow and minimize alert-fatigue. Algorithms should enhance the productivity of clinical teams, not attempt to replace them entirely. CONCLUSION Hospitals can employ process improvement measures that effectively utilize machine learning algorithms to ensure integration into clinical workflows. Healthcare professionals can utilize workflow tools in addition to the predictive capabilities of machine learning to enhance clinical decisions in sepsis.
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Affiliation(s)
- L D Ferreira
- Department of Student Affairs, Baylor College of Medicine, United States.
| | - D McCants
- Department of Internal Medicine, Baylor College of Medicine, United States
| | - S Velamuri
- Department of Internal Medicine, Baylor College of Medicine, United States; Luminare, Inc. United States
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2
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Abraham J, Meng A, Montes de Oca A, Politi M, Wildes T, Gregory S, Henrichs B, Kannampallil T, Avidan MS. An ethnographic study on the impact of a novel telemedicine-based support system in the operating room. J Am Med Inform Assoc 2022; 29:1919-1930. [PMID: 35985294 PMCID: PMC10161534 DOI: 10.1093/jamia/ocac138] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/07/2022] [Accepted: 08/04/2022] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE The Anesthesiology Control Tower (ACT) for operating rooms (ORs) remotely assesses the progress of surgeries and provides real-time perioperative risk alerts, communicating risk mitigation recommendations to bedside clinicians. We aim to identify and map ACT-OR nonroutine events (NREs)-risk-inducing or risk-mitigating workflow deviations-and ascertain ACT's impact on clinical workflow and patient safety. MATERIALS AND METHODS We used ethnographic methods including shadowing ACT and OR clinicians during 83 surgeries, artifact collection, chart reviews for decision alerts sent to the OR, and 10 clinician interviews. We used hybrid thematic analysis informed by a human-factors systems-oriented approach to assess ACT's role and impact on safety, conducting content analysis to assess NREs. RESULTS Across 83 cases, 469 risk alerts were triggered, and the ACT sent 280 care recommendations to the OR. 135 NREs were observed. Critical factors facilitating ACT's role in supporting patient safety included providing backup support and offering a fresh-eye perspective on OR decisions. Factors impeding ACT included message timing and ACT and OR clinician cognitive lapses. Suggestions for improvement included tailoring ACT message content (structure, timing, presentation) and incorporating predictive analytics for advanced planning. DISCUSSION ACT served as a safety net with remote surveillance features and as a learning healthcare system with feedback/auditing features. Supporting strategies include adaptive coordination and harnessing clinician/patient support to improve ACT's sustainability. Study insights inform future intraoperative telemedicine design considerations to mitigate safety risks. CONCLUSION Incorporating similar remote technology enhancement into routine perioperative care could markedly improve safety and quality for millions of surgical patients.
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Affiliation(s)
- Joanna Abraham
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
- Institute for Informatics, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
- Division of Biology and Biomedical Sciences, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Alicia Meng
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Arianna Montes de Oca
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Mary Politi
- Department of Surgery, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Troy Wildes
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Stephen Gregory
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Bernadette Henrichs
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
- Goldfarb School of Nursing, Barnes-Jewish College, St. Louis, Missouri, USA
| | - Thomas Kannampallil
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
- Institute for Informatics, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
- Division of Biology and Biomedical Sciences, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Computer Science & Engineering, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Michael S Avidan
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
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3
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Ackermann K, Baker J, Green M, Fullick M, Varinli H, Westbrook J, Li L. Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review. J Med Internet Res 2022; 24:e31083. [PMID: 35195528 PMCID: PMC8908200 DOI: 10.2196/31083] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/23/2021] [Accepted: 10/29/2021] [Indexed: 12/21/2022] Open
Abstract
Background Sepsis is a significant cause of morbidity and mortality worldwide. Early detection of sepsis followed promptly by treatment initiation improves patient outcomes and saves lives. Hospitals are increasingly using computerized clinical decision support (CCDS) systems for the rapid identification of adult patients with sepsis. Objective This scoping review aims to systematically describe studies reporting on the use and evaluation of CCDS systems for the early detection of adult inpatients with sepsis. Methods The protocol for this scoping review was previously published. A total of 10 electronic databases (MEDLINE, Embase, CINAHL, the Cochrane database, LILACS [Latin American and Caribbean Health Sciences Literature], Scopus, Web of Science, OpenGrey, ClinicalTrials.gov, and PQDT [ProQuest Dissertations and Theses]) were comprehensively searched using terms for sepsis, CCDS, and detection to identify relevant studies. Title, abstract, and full-text screening were performed by 2 independent reviewers using predefined eligibility criteria. Data charting was performed by 1 reviewer with a second reviewer checking a random sample of studies. Any disagreements were discussed with input from a third reviewer. In this review, we present the results for adult inpatients, including studies that do not specify patient age. Results A search of the electronic databases retrieved 12,139 studies following duplicate removal. We identified 124 studies for inclusion after title, abstract, full-text screening, and hand searching were complete. Nearly all studies (121/124, 97.6%) were published after 2009. Half of the studies were journal articles (65/124, 52.4%), and the remainder were conference abstracts (54/124, 43.5%) and theses (5/124, 4%). Most studies used a single cohort (54/124, 43.5%) or before-after (42/124, 33.9%) approach. Across all 124 included studies, patient outcomes were the most frequently reported outcomes (107/124, 86.3%), followed by sepsis treatment and management (75/124, 60.5%), CCDS usability (14/124, 11.3%), and cost outcomes (9/124, 7.3%). For sepsis identification, the systemic inflammatory response syndrome criteria were the most commonly used, alone (50/124, 40.3%), combined with organ dysfunction (28/124, 22.6%), or combined with other criteria (23/124, 18.5%). Over half of the CCDS systems (68/124, 54.8%) were implemented alongside other sepsis-related interventions. Conclusions The current body of literature investigating the implementation of CCDS systems for the early detection of adult inpatients with sepsis is extremely diverse. There is substantial variability in study design, CCDS criteria and characteristics, and outcomes measured across the identified literature. Future research on CCDS system usability, cost, and impact on sepsis morbidity is needed. International Registered Report Identifier (IRRID) RR2-10.2196/24899
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Affiliation(s)
- Khalia Ackermann
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia
| | - Jannah Baker
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia
| | | | - Mary Fullick
- Clinical Excellence Commission, Sydney, Australia
| | | | - Johanna Westbrook
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia
| | - Ling Li
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia
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4
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Mohr NM, Harland KK, Okoro UE, Fuller BM, Campbell K, Swanson MB, Simpson SQ, Parker EA, Mack LJ, Bell A, DeJong K, Faine B, Zepeski A, Mueller K, Chrischilles E, Carpenter CR, Jones MP, Ward MM. TELEmedicine as an intervention for sepsis in emergency departments: a multicenter, comparative effectiveness study (TELEvISED Study). J Comp Eff Res 2021; 10:77-91. [PMID: 33470848 DOI: 10.2217/cer-2020-0141] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Sepsis is a life-threatening infection that affects over 1.7 million Americans annually. Low-volume rural hospitals have worse sepsis outcomes, and emergency department (ED)-based telemedicine (tele-ED) has been one promising strategy for improving rural sepsis care. The objective of this study is to evaluate the impact of tele-ED consultation on sepsis care and outcomes in rural ED patients. The TELEvISED study is a multicenter (n = 25) retrospective propensity-matched comparative effectiveness study of tele-ED care for rural sepsis patients in a mature tele-ED network. Telemedicine-exposed patients will be matched with non telemedicine patients using a propensity score to predict tele-ED use. The primary outcome is 28-day hospital free days, and secondary outcomes include adherence with guidelines, mortality and organ failure. ClinicalTrials.gov: NCT04441944.
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Affiliation(s)
- Nicholas M Mohr
- Department of Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA.,Division of Critical Care, Department of Anesthesia, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA.,Department of Epidemiology, University of Iowa College of Public Health, Iowa City, IA 52242, USA
| | - Karisa K Harland
- Department of Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA
| | - Uche E Okoro
- Department of Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA
| | - Brian M Fuller
- Division of Critical Care, Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Kalyn Campbell
- Department of Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA.,Department of Surgery, Hennepin County Medical Center, Minneapolis, MN 55415, USA
| | - Morgan B Swanson
- Department of Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA.,Department of Epidemiology, University of Iowa College of Public Health, Iowa City, IA 52242, USA
| | - Stephen Q Simpson
- Department of Pulmonary, Critical Care, & Sleep Medicine, University of Kansas, Kansas City, KS 66160, USA.,Biomedical Advanced Research & Development Authority, US Department of Health & Human Services, Washington, DC 20201, USA
| | - Edith A Parker
- Department of Community & Behavioral Health, University of Iowa College of Public Health, Iowa City, IA 52242, USA
| | - Luke J Mack
- Avera eCARE, Sioux Falls, SD 57104, USA.,Department of Family Medicine, University of South Dakota School of Medicine, Sioux Falls, SD 57105, USA
| | | | | | - Brett Faine
- Department of Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA.,Department of Pharmacy Practice & Science, College of Pharmacy, University of Iowa, Iowa City, IA 52242, USA.,Department of Pharmaceutical Care, University of Iowa Hospitals & Clinics, Department of Health Management & Policy, Iowa City, IA 52242, USA
| | - Anne Zepeski
- Department of Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA.,Department of Pharmaceutical Care, University of Iowa Hospitals & Clinics, Department of Health Management & Policy, Iowa City, IA 52242, USA
| | - Keith Mueller
- Department of Health Management and Policy, University of Iowa College of Public Health, Iowa City, IA 52242, USA
| | - Elizabeth Chrischilles
- Department of Epidemiology, University of Iowa College of Public Health, Iowa City, IA 52242, USA
| | - Christopher R Carpenter
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Michael P Jones
- Department of Biostatistics, University of Iowa College of Public Health, Iowa City, IA 52242, USA
| | - Marcia M Ward
- Department of Health Management and Policy, University of Iowa College of Public Health, Iowa City, IA 52242, USA
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Lilly CM, Mickelson JT. Evolution of the Intensive Care Unit Telemedicine Value Proposition. Crit Care Clin 2019; 35:463-477. [PMID: 31076046 DOI: 10.1016/j.ccc.2019.02.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Advances in clinical information sciences, telecommunication technologies, electronic health records, early warning systems, automated acuity assessment, and clinician communication support systems have allowed current-generation intensive care (ICU) telemedicine systems to address the inefficiencies of the failed advice-upon-request ICU telemedicine model. Value is related to the ability of health care systems to leverage ICU telemedicine resources to provide care. Local financial benefits of ICU telemedicine program implementation depend on changing behavior to better focus on activities that reduce the duration of critical illness and length of stay.
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Affiliation(s)
- Craig M Lilly
- Department of Medicine, University of Massachusetts Medical School, Graduate School of Biomedical Sciences, UMass Memorial Health Care, Memorial Medical Center, 55 Lake Avenue North, Worcester, MA 01655, USA; Department of Anesthesiology, and Surgery, Clinical and Population Health Research Program, University of Massachusetts Medical School, Graduate School of Biomedical Sciences, UMass Memorial Health Care, Memorial Medical Center, 281 Lincoln Street, Worcester, MA 01605, USA.
| | - Jared T Mickelson
- Department of Medicine, University of Massachusetts Medical School, Graduate School of Biomedical Sciences, UMass Memorial Health Care, Memorial Medical Center, 55 Lake Avenue North, Worcester, MA 01655, USA
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Rincon TA, Bakshi V, Beninati W, Carpenter D, Cucchi E, Davis TM, Dreher J, Hiddleson C, Johansson MK, Katz AW, Olff C, Wansor EA, Ward D, Washington V, WinterBottom F, Kleinpell RM. Describing advanced practice provider roles within critical care teams with Tele-ICUs: Exemplars from seven US health systems. Nurs Outlook 2019; 68:5-13. [PMID: 31376986 DOI: 10.1016/j.outlook.2019.06.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 05/11/2019] [Accepted: 06/15/2019] [Indexed: 11/19/2022]
Abstract
Telehealth is an acknowledged strategy to meet patient healthcare needs. In critical care settings, Tele-ICU's are expanding to deliver clinical services across a diverse spectrum of critically ill patients. The expansion of telehealth provides increased opportunities for advanced practice providers including advanced practice nurses and physician assistants; however, limited information on roles and models of care for advanced practice providers in telehealth exist. This article reviews current and evolving roles for advanced practice providers in telehealth in acute and critical care settings across 7 healthcare systems in the United States. The health system exemplars described in this article identify the important role of advanced practice providers in providing patient care oversight and in improving outcomes for acute and critically ill patients. As telehealth continues to expand, additional opportunities will lead to novel roles for advanced practice providers in the field of telehealth to assist with patient care management for subacute, acute, and critically ill patients.
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Affiliation(s)
- Teresa A Rincon
- Virtual Medicine Department, UMass Memorial Health Care, Worcester, MA.
| | - Vishal Bakshi
- Emory Critical Care Center, Emory Healthcare, Atlanta, GA
| | - William Beninati
- Intermountain Healthcare Connect, TeleCritical Care, Salt Lake City, UT
| | - Dawn Carpenter
- Graduate School of Nursing, UMass Medical School, Worcester, MA
| | - Eric Cucchi
- UMass Memorial Health Care, Critical Care Operations, Worcester, MA
| | | | - Jennifer Dreher
- Veteran's Health Administration, Veterans Integrated Services Network (VISN 10 Tele-ICU), Cincinnati, OH
| | | | | | - Adam W Katz
- UMass Memorial Health Care, Critical Care Operations, Worcester, MA
| | - Carol Olff
- John Muir Health, Concord Medical Center, Concord, CA
| | | | - Denise Ward
- Intermountain Healthcare Connect, TeleCritical Care, Salt Lake City, UT
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Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems. Crit Care Clin 2019; 35:483-495. [PMID: 31076048 DOI: 10.1016/j.ccc.2019.02.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
This article examines the history of the telemedicine intensive care unit (tele-ICU), the current state of clinical decision support systems (CDSS) in the tele-ICU, applications of machine learning (ML) algorithms to critical care, and opportunities to integrate ML with tele-ICU CDSS. The enormous quantities of data generated by tele-ICU systems is a major driver in the development of the large, comprehensive, heterogeneous, and granular data sets necessary to develop generalizable ML CDSS algorithms, and deidentification of these data sets expands opportunities for ML CDSS research.
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Nurse Influence in Meeting Compliance With the Centers for Medicare and Medicaid Services Quality Measure. Dimens Crit Care Nurs 2019; 38:70-82. [DOI: 10.1097/dcc.0000000000000340] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
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