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Ramaswamy T, Sparling JL, Chang MG, Bittner EA. Ten misconceptions regarding decision-making in critical care. World J Crit Care Med 2024; 13:89644. [PMID: 38855268 PMCID: PMC11155500 DOI: 10.5492/wjccm.v13.i2.89644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/25/2024] [Accepted: 03/01/2024] [Indexed: 06/03/2024] Open
Abstract
Diagnostic errors are prevalent in critical care practice and are associated with patient harm and costs for providers and the healthcare system. Patient complexity, illness severity, and the urgency in initiating proper treatment all contribute to decision-making errors. Clinician-related factors such as fatigue, cognitive overload, and inexperience further interfere with effective decision-making. Cognitive science has provided insight into the clinical decision-making process that can be used to reduce error. This evidence-based review discusses ten common misconceptions regarding critical care decision-making. By understanding how practitioners make clinical decisions and examining how errors occur, strategies may be developed and implemented to decrease errors in Decision-making and improve patient outcomes.
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Affiliation(s)
- Tara Ramaswamy
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States
| | - Jamie L Sparling
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
| | - Marvin G Chang
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
| | - Edward A Bittner
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
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Zahra MA, Al-Taher A, Alquhaidan M, Hussain T, Ismail I, Raya I, Kandeel M. The synergy of artificial intelligence and personalized medicine for the enhanced diagnosis, treatment, and prevention of disease. Drug Metab Pers Ther 2024; 39:47-58. [PMID: 38997240 DOI: 10.1515/dmpt-2024-0003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 06/17/2024] [Indexed: 07/14/2024]
Abstract
INTRODUCTION The completion of the Human Genome Project in 2003 marked the beginning of a transformative era in medicine. This milestone laid the foundation for personalized medicine, an innovative approach that customizes healthcare treatments. CONTENT Central to the advancement of personalized medicine is the understanding of genetic variations and their impact on drug responses. The integration of artificial intelligence (AI) into drug response trials has been pivotal in this domain. These technologies excel in handling large-scale genomic datasets and patient histories, significantly improving diagnostic accuracy, disease prediction and drug discovery. They are particularly effective in addressing complex diseases such as cancer and genetic disorders. Furthermore, the advent of wearable technology, when combined with AI, propels personalized medicine forward by offering real-time health monitoring, which is crucial for early disease detection and management. SUMMARY The integration of AI into personalized medicine represents a significant advancement in healthcare, promising more accurate diagnoses, effective treatment plans and innovative drug discoveries. OUTLOOK As technology continues to evolve, the role of AI in enhancing personalized medicine and transforming the healthcare landscape is expected to grow exponentially. This synergy between AI and healthcare holds great promise for the future, potentially revolutionizing the way healthcare is delivered and experienced.
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Affiliation(s)
- Mohammad Abu Zahra
- Department of Biomolecular Sciences, College of Veterinary Medicine, 114800 King Faisal University , Al-Hofuf, Al-Ahsa, Saudi Arabia
| | - Abdulla Al-Taher
- Department of Biomolecular Sciences, College of Veterinary Medicine, 114800 King Faisal University , Al-Hofuf, Al-Ahsa, Saudi Arabia
| | - Mohamed Alquhaidan
- Department of Biomolecular Sciences, College of Veterinary Medicine, 114800 King Faisal University , Al-Hofuf, Al-Ahsa, Saudi Arabia
| | - Tarique Hussain
- Animal Sciences Division, Nuclear Institute for Agriculture and Biology (NIAB), Faisalabad, Pakistan
| | - Izzeldin Ismail
- Department of Biomolecular Sciences, College of Veterinary Medicine, 114800 King Faisal University , Al-Hofuf, Al-Ahsa, Saudi Arabia
| | - Indah Raya
- Department of Chemistry, Faculty of Mathematics, and Natural Science, Hasanuddin University, Makassar, Indonesia
| | - Mahmoud Kandeel
- Department of Biomolecular Sciences, College of Veterinary Medicine, 114800 King Faisal University , Al-Hofuf, Al-Ahsa, Saudi Arabia
- Department of Pharmacology, Faculty of Veterinary Medicine, Kafrelshikh University, Kafrelshikh, Egypt
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Auerbach AD, Lee TM, Hubbard CC, Ranji SR, Raffel K, Valdes G, Boscardin J, Dalal AK, Harris A, Flynn E, Schnipper JL. Diagnostic Errors in Hospitalized Adults Who Died or Were Transferred to Intensive Care. JAMA Intern Med 2024; 184:164-173. [PMID: 38190122 PMCID: PMC10775080 DOI: 10.1001/jamainternmed.2023.7347] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/07/2023] [Indexed: 01/09/2024]
Abstract
Importance Diagnostic errors contribute to patient harm, though few data exist to describe their prevalence or underlying causes among medical inpatients. Objective To determine the prevalence, underlying cause, and harms of diagnostic errors among hospitalized adults transferred to an intensive care unit (ICU) or who died. Design, Setting, and Participants Retrospective cohort study conducted at 29 academic medical centers in the US in a random sample of adults hospitalized with general medical conditions and who were transferred to an ICU, died, or both from January 1 to December 31, 2019. Each record was reviewed by 2 trained clinicians to determine whether a diagnostic error occurred (ie, missed or delayed diagnosis), identify diagnostic process faults, and classify harms. Multivariable models estimated association between process faults and diagnostic error. Opportunity for diagnostic error reduction associated with each fault was estimated using the adjusted proportion attributable fraction (aPAF). Data analysis was performed from April through September 2023. Main Outcomes and Measures Whether or not a diagnostic error took place, the frequency of underlying causes of errors, and harms associated with those errors. Results Of 2428 patient records at 29 hospitals that underwent review (mean [SD] patient age, 63.9 [17.0] years; 1107 [45.6%] female and 1321 male individuals [54.4%]), 550 patients (23.0%; 95% CI, 20.9%-25.3%) had experienced a diagnostic error. Errors were judged to have contributed to temporary harm, permanent harm, or death in 436 patients (17.8%; 95% CI, 15.9%-19.8%); among the 1863 patients who died, diagnostic error was judged to have contributed to death in 121 (6.6%; 95% CI, 5.3%-8.2%). In multivariable models examining process faults associated with any diagnostic error, patient assessment problems (aPAF, 21.4%; 95% CI, 16.4%-26.4%) and problems with test ordering and interpretation (aPAF, 19.9%; 95% CI, 14.7%-25.1%) had the highest opportunity to reduce diagnostic errors; similar ranking was seen in multivariable models examining harmful diagnostic errors. Conclusions and Relevance In this cohort study, diagnostic errors in hospitalized adults who died or were transferred to the ICU were common and associated with patient harm. Problems with choosing and interpreting tests and the processes involved with clinician assessment are high-priority areas for improvement efforts.
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Affiliation(s)
- Andrew D. Auerbach
- Division of Hospital Medicine, Department of Medicine, University of California San Francisco
| | - Tiffany M. Lee
- Division of Hospital Medicine, Department of Medicine, University of California San Francisco
| | - Colin C. Hubbard
- Division of Hospital Medicine, Department of Medicine, University of California San Francisco
| | - Sumant R. Ranji
- Division of Hospital Medicine, Zuckerberg San Francisco General Hospital, San Francisco, California
| | - Katie Raffel
- Department of Medicine, University of Colorado School of Medicine, Denver
| | - Gilmer Valdes
- Department of Radiation Oncology, University of California San Francisco
| | - John Boscardin
- Division of Geriatrics, Department of Medicine, University of California San Francisco
| | - Anuj K. Dalal
- Hospital Medicine Unit, Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts
| | | | | | - Jeffrey L. Schnipper
- Hospital Medicine Unit, Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts
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Garber A, Garabedian P, Wu L, Lam A, Malik M, Fraser H, Bersani K, Piniella N, Motta-Calderon D, Rozenblum R, Schnock K, Griffin J, Schnipper JL, Bates DW, Dalal AK. Developing, pilot testing, and refining requirements for 3 EHR-integrated interventions to improve diagnostic safety in acute care: a user-centered approach. JAMIA Open 2023; 6:ooad031. [PMID: 37181729 PMCID: PMC10172040 DOI: 10.1093/jamiaopen/ooad031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 01/04/2023] [Accepted: 04/20/2023] [Indexed: 05/16/2023] Open
Abstract
Objective To describe a user-centered approach to develop, pilot test, and refine requirements for 3 electronic health record (EHR)-integrated interventions that target key diagnostic process failures in hospitalized patients. Materials and Methods Three interventions were prioritized for development: a Diagnostic Safety Column (DSC) within an EHR-integrated dashboard to identify at-risk patients; a Diagnostic Time-Out (DTO) for clinicians to reassess the working diagnosis; and a Patient Diagnosis Questionnaire (PDQ) to gather patient concerns about the diagnostic process. Initial requirements were refined from analysis of test cases with elevated risk predicted by DSC logic compared to risk perceived by a clinician working group; DTO testing sessions with clinicians; PDQ responses from patients; and focus groups with clinicians and patient advisors using storyboarding to model the integrated interventions. Mixed methods analysis of participant responses was used to identify final requirements and potential implementation barriers. Results Final requirements from analysis of 10 test cases predicted by the DSC, 18 clinician DTO participants, and 39 PDQ responses included the following: DSC configurable parameters (variables, weights) to adjust baseline risk estimates in real-time based on new clinical data collected during hospitalization; more concise DTO wording and flexibility for clinicians to conduct the DTO with or without the patient present; and integration of PDQ responses into the DSC to ensure closed-looped communication with clinicians. Analysis of focus groups confirmed that tight integration of the interventions with the EHR would be necessary to prompt clinicians to reconsider the working diagnosis in cases with elevated diagnostic error (DE) risk or uncertainty. Potential implementation barriers included alert fatigue and distrust of the risk algorithm (DSC); time constraints, redundancies, and concerns about disclosing uncertainty to patients (DTO); and patient disagreement with the care team's diagnosis (PDQ). Discussion A user-centered approach led to evolution of requirements for 3 interventions targeting key diagnostic process failures in hospitalized patients at risk for DE. Conclusions We identify challenges and offer lessons from our user-centered design process.
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Affiliation(s)
- Alison Garber
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Pamela Garabedian
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Lindsey Wu
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Alyssa Lam
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Maria Malik
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Hannah Fraser
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Kerrin Bersani
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Nicholas Piniella
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Daniel Motta-Calderon
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Ronen Rozenblum
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Kumiko Schnock
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | | | - Jeffrey L Schnipper
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - David W Bates
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Anuj K Dalal
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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Malik MA, Motta-Calderon D, Piniella N, Garber A, Konieczny K, Lam A, Plombon S, Carr K, Yoon C, Griffin J, Lipsitz S, Schnipper JL, Bates DW, Dalal AK. A structured approach to EHR surveillance of diagnostic error in acute care: an exploratory analysis of two institutionally-defined case cohorts. Diagnosis (Berl) 2022; 9:446-457. [PMID: 35993878 PMCID: PMC9651987 DOI: 10.1515/dx-2022-0032] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 07/12/2022] [Indexed: 12/29/2022]
Abstract
OBJECTIVES To test a structured electronic health record (EHR) case review process to identify diagnostic errors (DE) and diagnostic process failures (DPFs) in acute care. METHODS We adapted validated tools (Safer Dx, Diagnostic Error Evaluation Research [DEER] Taxonomy) to assess the diagnostic process during the hospital encounter and categorized 13 postulated e-triggers. We created two test cohorts of all preventable cases (n=28) and an equal number of randomly sampled non-preventable cases (n=28) from 365 adult general medicine patients who expired and underwent our institution's mortality case review process. After excluding patients with a length of stay of more than one month, each case was reviewed by two blinded clinicians trained in our process and by an expert panel. Inter-rater reliability was assessed. We compared the frequency of DE contributing to death in both cohorts, as well as mean DPFs and e-triggers for DE positive and negative cases within each cohort. RESULTS Twenty-seven (96.4%) preventable and 24 (85.7%) non-preventable cases underwent our review process. Inter-rater reliability was moderate between individual reviewers (Cohen's kappa 0.41) and substantial with the expert panel (Cohen's kappa 0.74). The frequency of DE contributing to death was significantly higher for the preventable compared to the non-preventable cohort (56% vs. 17%, OR 6.25 [1.68, 23.27], p<0.01). Mean DPFs and e-triggers were significantly and non-significantly higher for DE positive compared to DE negative cases in each cohort, respectively. CONCLUSIONS We observed substantial agreement among final consensus and expert panel reviews using our structured EHR case review process. DEs contributing to death associated with DPFs were identified in institutionally designated preventable and non-preventable cases. While e-triggers may be useful for discriminating DE positive from DE negative cases, larger studies are required for validation. Our approach has potential to augment institutional mortality case review processes with respect to DE surveillance.
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Affiliation(s)
- Maria A. Malik
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Daniel Motta-Calderon
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Nicholas Piniella
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Alison Garber
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Kaitlyn Konieczny
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Alyssa Lam
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Savanna Plombon
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Kevin Carr
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Catherine Yoon
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | | | - Stuart Lipsitz
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jeffrey L. Schnipper
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - David W. Bates
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Anuj K. Dalal
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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