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Vasudevan A, Plombon S, Piniella N, Garber A, Malik M, O'Fallon E, Goyal A, Gershanik E, Kumar V, Fiskio J, Yoon C, Lipsitz SR, Schnipper JL, Dalal AK. Effect of digital tools to promote hospital quality and safety on adverse events after discharge. J Am Med Inform Assoc 2024:ocae176. [PMID: 39013194 DOI: 10.1093/jamia/ocae176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 06/10/2024] [Accepted: 06/26/2024] [Indexed: 07/18/2024] Open
Abstract
OBJECTIVES Post-discharge adverse events (AEs) are common and heralded by new and worsening symptoms (NWS). We evaluated the effect of electronic health record (EHR)-integrated digital tools designed to promote quality and safety in hospitalized patients on NWS and AEs after discharge. MATERIALS AND METHODS Adult general medicine patients at a community hospital were enrolled. We implemented a dashboard which clinicians used to assess safety risks during interdisciplinary rounds. Post-implementation patients were randomized to complete a discharge checklist whose responses were incorporated into the dashboard. Outcomes were assessed using EHR review and 30-day call data adjudicated by 2 clinicians and analyzed using Poisson regression. We conducted comparisons of each exposure on post-discharge outcomes and used selected variables and NWS as independent predictors to model post-discharge AEs using multivariable logistic regression. RESULTS A total of 260 patients (122 pre, 71 post [dashboard], 67 post [dashboard plus discharge checklist]) enrolled. The adjusted incidence rate ratios (aIRR) for NWS and AEs were unchanged in the post- compared to pre-implementation period. For patient-reported NWS, aIRR was non-significantly higher for dashboard plus discharge checklist compared to dashboard participants (1.23 [0.97,1.56], P = .08). For post-implementation patients with an AE, aIRR for duration of injury (>1 week) was significantly lower for dashboard plus discharge checklist compared to dashboard participants (0 [0,0.53], P < .01). In multivariable models, certain patient-reported NWS were associated with AEs (3.76 [1.89,7.82], P < .01). DISCUSSION While significant reductions in post-discharge AEs were not observed, checklist participants experiencing a post-discharge AE were more likely to report NWS and had a shorter duration of injury. CONCLUSION Interventions designed to prompt patients to report NWS may facilitate earlier detection of AEs after discharge. CLINICALTRIALS.GOV NCT05232656.
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Affiliation(s)
- Anant Vasudevan
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Savanna Plombon
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
- Mass General Brigham, Boston, MA 02145, United States
| | - Nicholas Piniella
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
| | - Alison Garber
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
| | - Maria Malik
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
| | - Erin O'Fallon
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Abhishek Goyal
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Esteban Gershanik
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Vivek Kumar
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Julie Fiskio
- Mass General Brigham, Boston, MA 02145, United States
| | - Cathy Yoon
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
| | - Stuart R Lipsitz
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Jeffrey L Schnipper
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Anuj K Dalal
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States
- Harvard Medical School, Boston, MA 02115, United States
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Fleisher LA, Economou-Zavlanos NJ. Artificial Intelligence Can Be Regulated Using Current Patient Safety Procedures and Infrastructure in Hospitals. JAMA HEALTH FORUM 2024; 5:e241369. [PMID: 38941085 DOI: 10.1001/jamahealthforum.2024.1369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
This Viewpoint describes the potential benefits and harms of using artificial intelligence (AI) in health care decision-making processes.
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Affiliation(s)
- Lee A Fleisher
- Perelman School of Medicine at University of Pennsylvania, Philadelphia
- Bipartisan Policy Center
| | - Nicoleta J Economou-Zavlanos
- Duke Health AI Evaluation and Governance, Algorithm-Based Clinical Decision Support Oversight, Duke University School of Medicine, Durham, North Carolina
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Larsen EP, Schaeubinger MM, Won J, Sze RW, Anupindi S. Integrating human factors engineering into your pediatric radiology practice. Pediatr Radiol 2024; 54:936-943. [PMID: 38483592 DOI: 10.1007/s00247-024-05903-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 03/01/2024] [Accepted: 03/04/2024] [Indexed: 05/24/2024]
Abstract
Human factors engineering involves the study and development of methods aimed at enhancing performance, improving safety, and optimizing user satisfaction. The focus of human factors engineering encompasses the design of work environments and an understanding of human mental processes to prevent errors. In this review, we summarize the history, applications, and impacts of human factors engineering on the healthcare field. To illustrate these applications and impacts, we provide several examples of how successful integration of a human factors engineer in our pediatric radiology department has positively impacted various projects. The successful integration of human factors engineering expertise has contributed to projects including improving response times for portable radiography requests, deploying COVID-19 response resources, informing the redesign of scheduling workflows, and implementation of a virtual ergonomics program for remote workers. In sum, the integration of human factors engineering insight into our department has resulted in tangible benefits and has also positioned us as proactive contributors to broader hospital-wide improvements.
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Affiliation(s)
- Ethan P Larsen
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, 19104, Philadelphia, PA, USA.
- Center for Healthcare Quality and Analytics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
| | - Monica Miranda Schaeubinger
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, 19104, Philadelphia, PA, USA
| | - James Won
- Center for Healthcare Quality and Analytics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Raymond W Sze
- Department of Radiology, UCSF Benihoff Children's Hospital Oakland, Oakland, CA, USA
| | - Sudha Anupindi
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, 19104, Philadelphia, PA, USA
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Dalal AK, Schnipper JL, Raffel K, Ranji S, Lee T, Auerbach A. Identifying and classifying diagnostic errors in acute care across hospitals: Early lessons from the Utility of Predictive Systems in Diagnostic Errors (UPSIDE) study. J Hosp Med 2024; 19:140-145. [PMID: 37211760 DOI: 10.1002/jhm.13136] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 04/20/2023] [Accepted: 05/02/2023] [Indexed: 05/23/2023]
Affiliation(s)
- Anuj K Dalal
- Hospital Medicine Unit, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jeffrey L Schnipper
- Hospital Medicine Unit, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Katie Raffel
- Division of Hospital Medicine, University of Colorado Anschutz Medical Campus, Denver, Colorado, USA
| | - Sumant Ranji
- Division of Hospital Medicine, University of California San Francisco, San Francisco, California, USA
| | | | - Andrew Auerbach
- Division of Hospital Medicine, University of California San Francisco, San Francisco, California, USA
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Wang Y, Jiang M, He M, Du M. Design and Implementation of an Inpatient Fall Risk Management Information System. JMIR Med Inform 2024; 12:e46501. [PMID: 38165733 DOI: 10.2196/46501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 08/15/2023] [Accepted: 11/29/2023] [Indexed: 01/04/2024] Open
Abstract
BACKGROUND Falls had been identified as one of the nursing-sensitive indicators for nursing care in hospitals. With technological progress, health information systems make it possible for health care professionals to manage patient care better. However, there is a dearth of research on health information systems used to manage inpatient falls. OBJECTIVE This study aimed to design and implement a novel hospital-based fall risk management information system (FRMIS) to prevent inpatient falls and improve nursing quality. METHODS This implementation was conducted at a large academic medical center in central China. We established a nurse-led multidisciplinary fall prevention team in January 2016. The hospital's fall risk management problems were summarized by interviewing fall-related stakeholders, observing fall prevention workflow and post-fall care process, and investigating patients' satisfaction. The FRMIS was developed using an iterative design process, involving collaboration among health care professionals, software developers, and system architects. We used process indicators and outcome indicators to evaluate the implementation effect. RESULTS The FRMIS includes a fall risk assessment platform, a fall risk warning platform, a fall preventive strategies platform, fall incident reporting, and a tracking improvement platform. Since the implementation of the FRMIS, the inpatient fall rate was significantly lower than that before implementation (P<.05). In addition, the percentage of major fall-related injuries was significantly lower than that before implementation. The implementation rate of fall-related process indicators and the reporting rate of high risk of falls were significantly different before and after system implementation (P<.05). CONCLUSIONS The FRMIS provides support to nursing staff in preventing falls among hospitalized patients while facilitating process control for nursing managers.
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Affiliation(s)
- Ying Wang
- School of Management, Wuhan University of Technology, Wuhan, China
- Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mengyao Jiang
- Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mei He
- Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Meijie Du
- Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Oniani D, Hilsman J, Peng Y, Poropatich RK, Pamplin JC, Legault GL, Wang Y. Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare. NPJ Digit Med 2023; 6:225. [PMID: 38042910 PMCID: PMC10693640 DOI: 10.1038/s41746-023-00965-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/15/2023] [Indexed: 12/04/2023] Open
Abstract
In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the "GREAT PLEA" ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Eutonomy, for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has been useful in the military and can be applied to healthcare for generative AI, based on contrasting their ethical concerns and risks. Ultimately, we aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI into healthcare practice.
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Affiliation(s)
- David Oniani
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jordan Hilsman
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yifan Peng
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Ronald K Poropatich
- Division of Pulmonary, Allergy, Critical Care & Sleep Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Military Medicine Research, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jeremy C Pamplin
- Telemedicine & Advanced Technology Research Center, US Army, Fort Detrick, Frederick, MD, USA
| | - Gary L Legault
- Department of Surgery, Uniformed Services University, Bethesda, MD, USA
- Virtual Medical Center, Brooke Army Medical Center, San Antonio, TX, USA
| | - Yanshan Wang
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA.
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
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Deimazar G, Sheikhtaheri A. Machine learning models to detect and predict patient safety events using electronic health records: A systematic review. Int J Med Inform 2023; 180:105246. [PMID: 37837710 DOI: 10.1016/j.ijmedinf.2023.105246] [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: 04/15/2023] [Revised: 10/02/2023] [Accepted: 10/08/2023] [Indexed: 10/16/2023]
Abstract
INTRODUCTION Identifying patient safety events using electronic health records (EHRs) and automated machine learning-based detection methods can help improve the efficiency and quality of healthcare service provision. OBJECTIVE This study aimed to systematically review machine learning-based methods and techniques, as well as their results for patient safety event management using EHRs. METHODS We reviewed the studies that focused on machine learning techniques, including automatic prediction and detection of patient safety events and medical errors through EHR analysis to manage patient safety events. The data were collected by searching Scopus, PubMed (Medline), Web of Science, EMBASE, and IEEE Xplore databases. RESULTS After screening, 41 papers were reviewed. Support vector machine (SVM), random forest, conditional random field (CRF), and bidirectional long short-term memory with conditional random field (BiLSTM-CRF) algorithms were mostly applied to predict, identify, and classify patient safety events using EHRs; however, they had different performances. BiLSTM-CRF was employed in most of the studies to extract and identify concepts, e.g., adverse drug events (ADEs) and adverse drug reactions (ADRs), as well as relationships between drug and severity, drug and ADEs, drug and ADRs. Recurrent neural networks (RNN) and BiLSTM-CRF had the best results in detecting ADEs compared to other patient safety events. Linear classifiers and Naive Bayes (NB) had the highest performance for ADR detection. Logistic regression had the best results in detecting surgical site infections. According to the findings, the quality of articles has non-significantly improved in recent years, but they had low average scores. CONCLUSIONS Machine learning can be useful in automatic detection and prediction of patient safety events. However, most of these algorithms have not yet been externally validated or prospectively tested. Therefore, further studies are required to improve the performance of these automated systems.
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Affiliation(s)
- Ghasem Deimazar
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Abbas Sheikhtaheri
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
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Mueller SK, Garabedian P, Goralnick E, Bates DW, Samal L. Advancing health information during interhospital transfer: An interrupted time series. J Hosp Med 2023; 18:1063-1071. [PMID: 37846028 DOI: 10.1002/jhm.13221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 09/21/2023] [Accepted: 09/27/2023] [Indexed: 10/18/2023]
Abstract
INTRODUCTION Although the transfer of patients between acute care hospitals (interhospital transfer, IHT) is common, health information exchange (HIE) during IHT remains inadequate, with fragmented communication and unreliable access to clinical information. This study aims to design, implement, and rigorously evaluate the implementation of a HIE platform to improve data access during IHT. METHODS AND ANALYSIS Study subjects include patients aged >18 transferred to the medical, cardiology, oncology, or intensive care unit (ICU) services at an 800-bed quaternary care hospital; and healthcare workers involved in their care. The first aim of this study is to optimize clinician workflow, data visualization, and interoperability through user-centered design sessions for HIE platform development. The second aim is to evaluate the impact of the intervention on clinician-reported medical errors among 500 pre- and 500 postintervention IHT patients using interrupted time series methodology, adjusting for confounding variables and temporal trends. The third aim is to evaluate intervention fidelity, use and perceived usability of the platform, and barriers and facilitators of implementation from interprofessional stakeholder input, using mixed-methods evaluation. The fourth aim is to consolidate key findings to create a toolkit for spread and sustainability. ETHICS AND DISSEMINATION We will track patient safety endpoints and clinician workflow burdens and ensure the protection of patient data throughout the study. We will disseminate our findings via the creation of a toolkit for spread and sustainability, partnering with our funder (AHRQ) for dissemination, and communicating our results via abstracts and publications.
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Affiliation(s)
- Stephanie K Mueller
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | | | - Eric Goralnick
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - David W Bates
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Lipika Samal
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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Chalasani SH, Syed J, Ramesh M, Patil V, Pramod Kumar T. Artificial intelligence in the field of pharmacy practice: A literature review. EXPLORATORY RESEARCH IN CLINICAL AND SOCIAL PHARMACY 2023; 12:100346. [PMID: 37885437 PMCID: PMC10598710 DOI: 10.1016/j.rcsop.2023.100346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 10/06/2023] [Accepted: 10/07/2023] [Indexed: 10/28/2023] Open
Abstract
Artificial intelligence (AI) is a transformative technology used in various industrial sectors including healthcare. In pharmacy practice, AI has the potential to significantly improve medication management and patient care. This review explores various AI applications in the field of pharmacy practice. The incorporation of AI technologies provides pharmacists with tools and systems that help them make accurate and evidence-based clinical decisions. By using AI algorithms and Machine Learning, pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations tailored to individual patient requirements. Various AI models have been developed to predict and detect adverse drug events, assist clinical decision support systems with medication-related decisions, automate dispensing processes in community pharmacies, optimize medication dosages, detect drug-drug interactions, improve adherence through smart technologies, detect and prevent medication errors, provide medication therapy management services, and support telemedicine initiatives. By incorporating AI into clinical practice, health care professionals can augment their decision-making processes and provide patients with personalized care. AI allows for greater collaboration between different healthcare services provided to a single patient. For patients, AI may be a useful tool for providing guidance on how and when to take a medication, aiding in patient education, and promoting medication adherence and AI may be used to know how and where to obtain the most cost-effective healthcare and how best to communicate with healthcare professionals, optimize the health monitoring using wearables devices, provide everyday lifestyle and health guidance, and integrate diet and exercise.
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Affiliation(s)
- Sri Harsha Chalasani
- Dept. of Pharmacy Practice, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Mysuru 15, Karnataka, India
| | - Jehath Syed
- Dept. of Pharmacy Practice, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Mysuru 15, Karnataka, India
| | - Madhan Ramesh
- Dept. of Pharmacy Practice, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Mysuru 15, Karnataka, India
| | - Vikram Patil
- Dept. of Radiology, JSS Medical College & Hospital, JSS Academy of Higher Education & Research, Mysuru 15, Karnataka, India
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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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Li Z, Marshall AP, Lin F, Ding Y, Chaboyer W. Registered nurses' approach to pressure injury prevention: A descriptive qualitative study. J Adv Nurs 2022; 78:2575-2585. [PMID: 35307866 PMCID: PMC9545357 DOI: 10.1111/jan.15218] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 01/20/2022] [Accepted: 02/13/2022] [Indexed: 02/02/2023]
Abstract
AIMS To explore Registered Nurses' approaches to pressure injury prevention, including how they perceive their roles, how they prioritize pressure injury prevention and factors influencing prevention in the Chinese context. DESIGN A qualitative descriptive study. METHODS Audio-recorded, face-to-face, semi-structured individual interviews were conducted with Registered Nurses in a large tertiary hospital in China from August to December 2020. Using the System Engineering Initiative for Patient Safety Model, the interview guide was developed to describe the work system, processes and outcomes (three domains) associated with nurses' pressure injury prevention practices. Deductive and inductive content analyses were used. FINDINGS Twenty-seven nurses participated in the interviews. Four themes related to two domains of the model emerged: Work system: (i) Nurses lead and coordinate pressure injury prevention; Work processes: (ii) Individualized pressure injury prevention is founded on comprehensive patient assessment; (iii) Collaborating ensures patients receive appropriate pressure injury prevention; and (iv) Competing factors influence the delivery of appropriate pressure injury prevention. One category emerged about work outcome: Nurses strive to do their best in pressure injury prevention but hold major concerns when pressure injuries occur. CONCLUSIONS Nurses play a leading role in pressure injury prevention delivery but require appropriate resources and assistance and support from other healthcare personnel, patients and carers. Understaffing, lack of resources, complex reporting and poor patient compliance challenge nurses in their delivery of pressure injury prevention. IMPACT Pressure injury prevention is primarily a nursing responsibility therefore nurses' approaches to prevention were explored. Nurses rely on collaboration with others and access to various resources to provide pressure injury prevention. They recognize the patients' and carers' roles and acknowledge the importance of accessing guidance and support from nursing leaders and wound experts. Acknowledging nurses leading role in prevention and ensuring they have adequate resources are important for quality care.
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Affiliation(s)
- Zhaoyu Li
- School of Nursing and Midwifery, Menzies Health Institute QueenslandGriffith UniversityGriffithQueenslandAustralia
| | - Andrea P. Marshall
- School of Nursing and Midwifery, Menzies Health Institute QueenslandGriffith UniversityGriffithQueenslandAustralia,Nursing and Midwifery Education and Research Unit, Gold Coast HealthGold Coast University HospitalGold CoastQueenslandAustralia
| | - Frances Lin
- School of Nursing and Midwifery, Menzies Health Institute QueenslandGriffith UniversityGriffithQueenslandAustralia,School of Nursing, Midwifery and ParamedicineUniversity of the Sunshine CoastSunshine CoastQueenslandAustralia
| | - Yanming Ding
- Nursing DepartmentPeking University First HospitalBeijingChina
| | - Wendy Chaboyer
- NHMRC Centre of Research Excellence in Wiser Wound Care, Menzies Health Institute QueenslandGriffith UniversityGriffithQueenslandAustralia
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12
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Choudhury A. Toward an Ecologically Valid Conceptual Framework for the Use of Artificial Intelligence in Clinical Settings: Need for Systems Thinking, Accountability, Decision-making, Trust, and Patient Safety Considerations in Safeguarding the Technology and Clinicians. JMIR Hum Factors 2022; 9:e35421. [PMID: 35727615 PMCID: PMC9257623 DOI: 10.2196/35421] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 03/26/2022] [Accepted: 05/20/2022] [Indexed: 01/29/2023] Open
Abstract
The health care management and the medical practitioner literature lack a descriptive conceptual framework for understanding the dynamic and complex interactions between clinicians and artificial intelligence (AI) systems. As most of the existing literature has been investigating AI's performance and effectiveness from a statistical (analytical) standpoint, there is a lack of studies ensuring AI's ecological validity. In this study, we derived a framework that focuses explicitly on the interaction between AI and clinicians. The proposed framework builds upon well-established human factors models such as the technology acceptance model and expectancy theory. The framework can be used to perform quantitative and qualitative analyses (mixed methods) to capture how clinician-AI interactions may vary based on human factors such as expectancy, workload, trust, cognitive variables related to absorptive capacity and bounded rationality, and concerns for patient safety. If leveraged, the proposed framework can help to identify factors influencing clinicians' intention to use AI and, consequently, improve AI acceptance and address the lack of AI accountability while safeguarding the patients, clinicians, and AI technology. Overall, this paper discusses the concepts, propositions, and assumptions of the multidisciplinary decision-making literature, constituting a sociocognitive approach that extends the theories of distributed cognition and, thus, will account for the ecological validity of AI.
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Affiliation(s)
- Avishek Choudhury
- Industrial and Management Systems Engineering, Benjamin M Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, United States
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13
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Dalal AK, Piniella N, Fuller TE, Pong D, Pardo M, Bessa N, Yoon C, Lipsitz S, Schnipper JL. Evaluation of electronic health record-integrated digital health tools to engage hospitalized patients in discharge preparation. J Am Med Inform Assoc 2021; 28:704-712. [PMID: 33463681 PMCID: PMC7973476 DOI: 10.1093/jamia/ocaa321] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 12/01/2020] [Indexed: 01/26/2023] Open
Abstract
OBJECTIVE To evaluate the effect of electronic health record (EHR)-integrated digital health tools comprised of a checklist and video on transitions-of-care outcomes for patients preparing for discharge. MATERIALS AND METHODS English-speaking, general medicine patients (>18 years) hospitalized at least 24 hours at an academic medical center in Boston, MA were enrolled before and after implementation. A structured checklist and video were administered on a mobile device via a patient portal or web-based survey at least 24 hours prior to anticipated discharge. Checklist responses were available for clinicians to review in real time via an EHR-integrated safety dashboard. The primary outcome was patient activation at discharge assessed by patient activation (PAM)-13. Secondary outcomes included postdischarge patient activation, hospital operational metrics, healthcare resource utilization assessed by 30-day follow-up calls and administrative data and change in patient activation from discharge to 30 days postdischarge. RESULTS Of 673 patients approached, 484 (71.9%) enrolled. The proportion of activated patients (PAM level 3 or 4) at discharge was nonsignificantly higher for the 234 postimplementation compared with the 245 preimplementation participants (59.8% vs 56.7%, adjusted OR 1.23 [0.38, 3.96], P = .73). Postimplementation participants reported 3.75 (3.02) concerns via the checklist. Mean length of stay was significantly higher for postimplementation compared with preimplementation participants (10.13 vs 6.21, P < .01). While there was no effect on postdischarge outcomes, there was a nonsignificant decrease in change in patient activation within participants from pre- to postimplementation (adjusted difference-in-difference of -16.1% (9.6), P = .09). CONCLUSIONS EHR-integrated digital health tools to prepare patients for discharge did not significantly increase patient activation and was associated with a longer length of stay. While issues uncovered by the checklist may have encouraged patients to inquire about their discharge preparedness, other factors associated with patient activation and length of stay may explain our observations. We offer insights for using PAM-13 in context of real-world health-IT implementations. TRIAL REGISTRATION NIH US National Library of Medicine, NCT03116074, clinicaltrials.gov.
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Affiliation(s)
- Anuj K Dalal
- Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | | | | | - Denise Pong
- Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Michael Pardo
- Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | | | - Catherine Yoon
- Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Stuart Lipsitz
- Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Jeffrey L Schnipper
- Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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14
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Choudhury A, Renjilian E, Asan O. Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review. JAMIA Open 2020; 3:459-471. [PMID: 33215079 PMCID: PMC7660963 DOI: 10.1093/jamiaopen/ooaa034] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 06/26/2020] [Accepted: 07/11/2020] [Indexed: 12/13/2022] Open
Abstract
Objectives Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients’ (age 65 years and above) functional ability, physical health, and cognitive well-being. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases. Materials and Methods We restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE, ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research articles published in English between January 2010 and June 2019. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions. Results We identified 35 eligible studies and classified in three groups: psychological disorder (n = 22), eye diseases (n = 6), and others (n = 7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications. Conclusion More studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.
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Affiliation(s)
- Avishek Choudhury
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Emily Renjilian
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
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15
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Choudhury A, Asan O. Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR Med Inform 2020; 8:e18599. [PMID: 32706688 PMCID: PMC7414411 DOI: 10.2196/18599] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/26/2020] [Accepted: 06/13/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. OBJECTIVE The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. METHODS We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. RESULTS We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. CONCLUSIONS This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.
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Affiliation(s)
- Avishek Choudhury
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
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16
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Fuller TE, Garabedian PM, Lemonias DP, Joyce E, Schnipper JL, Harry EM, Bates DW, Dalal AK, Benneyan JC. Assessing the cognitive and work load of an inpatient safety dashboard in the context of opioid management. APPLIED ERGONOMICS 2020; 85:103047. [PMID: 32174343 DOI: 10.1016/j.apergo.2020.103047] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 12/19/2019] [Accepted: 01/09/2020] [Indexed: 06/10/2023]
Abstract
For health information technology to realize its potential to improve flow, care, and patient safety, applications should be intuitive to use and burden neutral for frontline clinicians. We assessed the impact of a patient safety dashboard on clinician cognitive and work load within a simulated information-seeking task for safe inpatient opioid medication management. Compared to use of an electronic health record for the same task, the dashboard was associated with significantly reduced time on task, mouse clicks, and mouse movement (each p < 0.001), with no significant increases in cognitive load nor task inaccuracy. Cognitive burden was higher for users with less experience, possibly partly attributable to usability issues identified during this study. Findings underscore the importance of assessing the usability, cognitive, and work load analysis during the design and implementation of health information technology applications.
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Affiliation(s)
- Theresa E Fuller
- Healthcare Systems Engineering Institute, Northeastern University, Boston, MA, USA; Brigham and Women's Hospital, Boston, MA, USA
| | | | - Demetri P Lemonias
- Healthcare Systems Engineering Institute, Northeastern University, Boston, MA, USA
| | - Erin Joyce
- Healthcare Systems Engineering Institute, Northeastern University, Boston, MA, USA
| | - Jeffrey L Schnipper
- Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Elizabeth M Harry
- Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - David W Bates
- Brigham and Women's Hospital, Boston, MA, USA; Partners Healthcare, Incorporated, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Anuj K Dalal
- Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - James C Benneyan
- Healthcare Systems Engineering Institute, Northeastern University, Boston, MA, USA; College of Engineering, Northeastern University, Boston, MA, USA.
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17
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Fuller TE, Pong DD, Piniella N, Pardo M, Bessa N, Yoon C, Boxer RB, Schnipper JL, Dalal AK. Interactive Digital Health Tools to Engage Patients and Caregivers in Discharge Preparation: Implementation Study. J Med Internet Res 2020; 22:e15573. [PMID: 32343248 PMCID: PMC7218608 DOI: 10.2196/15573] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 12/16/2019] [Accepted: 02/04/2020] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Poor discharge preparation during hospitalization may lead to adverse events after discharge. Checklists and videos that systematically engage patients in preparing for discharge have the potential to improve safety, especially when integrated into clinician workflow via the electronic health record (EHR). OBJECTIVE This study aims to evaluate the implementation of a suite of digital health tools integrated with the EHR to engage hospitalized patients, caregivers, and their care team in preparing for discharge. METHODS We used the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework to identify pertinent research questions related to implementation. We iteratively refined patient and clinician-facing intervention components using a participatory process involving end users and institutional stakeholders. The intervention was implemented at a large academic medical center from December 2017 to July 2018. Patients who agreed to participate were coached to watch a discharge video, complete a checklist assessing discharge readiness, and request postdischarge text messaging with a physician 24 to 48 hours before their expected discharge date, which was displayed via a patient portal and bedside display. Clinicians could view concerns reported by patients based on their checklist responses in real time via a safety dashboard integrated with the EHR and choose to open a secure messaging thread with the patient for up to 7 days after discharge. We used mixed methods to evaluate our implementation experience. RESULTS Of 752 patient admissions, 510 (67.8%) patients or caregivers participated: 416 (55.3%) watched the video and completed the checklist, and 94 (12.5%) completed the checklist alone. On average, 4.24 concerns were reported per each of the 510 checklist submissions, most commonly about medications (664/2164, 30.7%) and follow-up (656/2164, 30.3%). Of the 510 completed checklists, a member of the care team accessed the safety dashboard to view 210 (41.2%) patient-reported concerns. For 422 patient admissions where postdischarge messaging was available, 141 (33.4%) patients requested this service; of these, a physician initiated secure messaging for 3 (2.1%) discharges. Most patient survey participants perceived that the intervention promoted self-management and communication with their care team. Patient interview participants endorsed gaps in communication with their care team and thought that the video and checklist would be useful closer toward discharge. Clinicians participating in focus groups perceived the value for patients but suggested that low awareness and variable workflow regarding the intervention, lack of technical optimization, and inconsistent clinician leadership limited the use of clinician-facing components. CONCLUSIONS A suite of EHR-integrated digital health tools to engage patients, caregivers, and clinicians in discharge preparation during hospitalization was feasible, acceptable, and valuable; however, important challenges were identified during implementation. We offer strategies to address implementation barriers and promote adoption of these tools. TRIAL REGISTRATION ClinicalTrials.gov NCT03116074; https://clinicaltrials.gov/ct2/show/NCT03116074.
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Affiliation(s)
| | - Denise D Pong
- Brigham and Women's Hospital, Boston, MA, United States
| | | | - Michael Pardo
- Brigham and Women's Hospital, Boston, MA, United States
| | - Nathaniel Bessa
- Brigham and Women's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | | | - Robert B Boxer
- Brigham and Women's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Jeffrey Lawrence Schnipper
- Brigham and Women's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Anuj K Dalal
- Brigham and Women's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
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18
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Carayon P, Hoonakker P, Hundt AS, Salwei M, Wiegmann D, Brown RL, Kleinschmidt P, Novak C, Pulia M, Wang Y, Wirkus E, Patterson B. Application of human factors to improve usability of clinical decision support for diagnostic decision-making: a scenario-based simulation study. BMJ Qual Saf 2020; 29:329-340. [PMID: 31776197 PMCID: PMC7490974 DOI: 10.1136/bmjqs-2019-009857] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 10/11/2019] [Accepted: 11/05/2019] [Indexed: 12/14/2022]
Abstract
OBJECTIVE In this study, we used human factors (HF) methods and principles to design a clinical decision support (CDS) that provides cognitive support to the pulmonary embolism (PE) diagnostic decision-making process in the emergency department. We hypothesised that the application of HF methods and principles will produce a more usable CDS that improves PE diagnostic decision-making, in particular decision about appropriate clinical pathway. MATERIALS AND METHODS We conducted a scenario-based simulation study to compare a HF-based CDS (the so-called CDS for PE diagnosis (PE-Dx CDS)) with a web-based CDS (MDCalc); 32 emergency physicians performed various tasks using both CDS. PE-Dx integrated HF design principles such as automating information acquisition and analysis, and minimising workload. We assessed all three dimensions of usability using both objective and subjective measures: effectiveness (eg, appropriate decision regarding the PE diagnostic pathway), efficiency (eg, time spent, perceived workload) and satisfaction (perceived usability of CDS). RESULTS Emergency physicians made more appropriate diagnostic decisions (94% with PE-Dx; 84% with web-based CDS; p<0.01) and performed experimental tasks faster with the PE-Dx CDS (on average 96 s per scenario with PE-Dx; 117 s with web-based CDS; p<0.001). They also reported lower workload (p<0.001) and higher satisfaction (p<0.001) with PE-Dx. CONCLUSIONS This simulation study shows that HF methods and principles can improve usability of CDS and diagnostic decision-making. Aspects of the HF-based CDS that provided cognitive support to emergency physicians and improved diagnostic performance included automation of information acquisition (eg, auto-populating risk scoring algorithms), minimisation of workload and support of decision selection (eg, recommending a clinical pathway). These HF design principles can be applied to the design of other CDS technologies to improve diagnostic safety.
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Affiliation(s)
- Pascale Carayon
- Department of Industrial and Systems Engineering, Wisconsin Institute for Healthcare Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Peter Hoonakker
- Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ann Schoofs Hundt
- Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Megan Salwei
- Department of Industrial and Systems Engineering, Wisconsin Institute for Healthcare Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Douglas Wiegmann
- Department of Industrial and Systems Engineering, Wisconsin Institute for Healthcare Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Roger L Brown
- School of Nursing, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Peter Kleinschmidt
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | | | - Michael Pulia
- Department of Emergency Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Yudi Wang
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Emily Wirkus
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Brian Patterson
- Department of Emergency Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
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19
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Businger AC, Fuller TE, Schnipper JL, Rossetti SC, Schnock KO, Rozenblum R, Dalal AK, Benneyan J, Bates DW, Dykes PC. Lessons learned implementing a complex and innovative patient safety learning laboratory project in a large academic medical center. J Am Med Inform Assoc 2020; 27:301-307. [PMID: 31794030 PMCID: PMC7647251 DOI: 10.1093/jamia/ocz193] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/30/2019] [Accepted: 11/14/2019] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE The objective of this paper is to share challenges, recommendations, and lessons learned regarding the development and implementation of a Patient Safety Learning Laboratory (PSLL) project, an innovative and complex intervention comprised of a suite of Health Information Technology (HIT) tools integrated with a newly implemented Electronic Health Record (EHR) vendor system in the acute care setting at a large academic center. MATERIALS AND METHODS The PSLL Administrative Core engaged stakeholders and study personnel throughout all phases of the project: problem analysis, design, development, implementation, and evaluation. Implementation challenges and recommendations were derived from direct observations and the collective experience of PSLL study personnel. RESULTS The PSLL intervention was implemented on 12 inpatient units during the 18-month study period, potentially impacting 12,628 patient admissions. Challenges to implementation included stakeholder engagement, project scope/complexity, technology/governance, and team structure. Recommendations to address each of these challenges were generated, some enacted during the trial, others as lessons learned for future iterative refinements of the intervention and its implementation. CONCLUSION Designing, implementing, and evaluating a suite of tools integrated within a vendor EHR to improve patient safety has a variety of challenges. Keys to success include continuous stakeholder engagement, involvement of systems and human factors engineers within a multidisciplinary team, an iterative approach to user-centered design, and a willingness to think outside of current workflows and processes to change health system culture around adverse event prevention.
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Affiliation(s)
- Alexandra C Businger
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Theresa E Fuller
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Healthcare Systems Engineering Institute, Northeastern University, Boston, MA, USA
| | - Jeffrey L Schnipper
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Sarah Collins Rossetti
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Columbia University Medical Center, New York, New York, USA
| | - Kumiko O Schnock
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Ronen Rozenblum
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, 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
| | - James Benneyan
- Healthcare Systems Engineering Institute, Northeastern University, Boston, MA, USA
| | - David W Bates
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Patricia C Dykes
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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20
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Bakken S. Breadth and Diversity in Biomedical and Health Informatics. J Am Med Inform Assoc 2019. [DOI: 10.1093/jamia/ocz055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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