Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions

Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review

OBJECTIVE

In order to comprehensively understand the characteristics of Adaptive Business Intelligence (ABI) in Healthcare, this study is structured to provide insights into the common features and evolving patterns within this domain. Applying the Sheridan's Classification as a framework, we aim to assess the degree of autonomy exhibited by various ABI components. Together, these objectives will contribute to a deeper understanding of ABI implementation and its implications within the Healthcare context.

METHODS

A comprehensive search of academic databases was conducted to identify relevant studies, selecting AIS e-library (AISel), Decision Support Systems Journal (DSSJ), Nature, The Lancet Digital Health (TLDH), PubMed, Expert Systems with Application (ESWA) and npj Digital Medicine as information sources. Studies from 2006 to 2022 were included based on predefined eligibility criteria. PRISMA statements were used to report this study.

RESULTS

The outcomes showed that ABI systems present distinct levels of development, autonomy and practical deployment. The high levels of autonomy were essentially associated with predictive components. However, the possibility of completely autonomous decisions by these systems is totally excluded. Lower levels of autonomy are also observed, particularly in connection with prescriptive components, granting users responsibility in the generation of decisions.

CONCLUSION

The study presented emphasizes the vital connection between desired outcomes and the inherent autonomy of these solutions, highlighting the critical need for additional research on the consequences of ABI systems and their constituent elements. Organizations should deploy these systems in a way consistent with their objectives and values, while also being mindful of potential adverse effects. Providing valuable insights for researchers, practitioners, and policymakers aiming to comprehend the diverse levels of ABI systems implementation, it contributes to well-informed decision-making in this dynamic field.

Hospital Adaptions to Mitigate the COVID-19 Pandemic Effects on MARQUIS Toolkit Implementation and Sustainability

OBJECTIVE

To explore the perceived effects of COVID-19 on MARQUIS toolkit implementation and sustainability, challenges faced by hospitals in sustaining medication reconciliation efforts, and the strategies used to mitigate the negative effects of the pandemic.

DATA SOURCES AND STUDY SETTINGS

Primary qualitative data were extracted from a Web-based survey. Data were collected from hospitals that participated in MARQUIS2 ( n = 18) and the MARQUIS Collaborative ( n = 5).

STUDY DESIGN

A qualitative, cross-sectional study was conducted.

DATA COLLECTION/DATA EXTRACTION

Qualitative data were extracted from a Research Electronic Data Capture survey databased and uploaded into an Excel data analysis template. Two coders independently coded the data with a third coder resolving discrepancies.

PRINCIPAL FINDINGS

Thirty-one team members participated, including pharmacists ( n = 20; 65%), physicians ( n = 9; 29%), or quality-improvement (QI) specialists ( n = 2; 6%) with expertise in medication reconciliation (MedRec) (14; 45%) or QI (10; 32%). Organizational resources were limited, including funding, staffing, and access to pharmacy students. To support program continuation, hospitals reallocated staff and used new MedRec order sets. Telemedicine, workflow adaptations, leadership support, QI team involvement, and ongoing audits and feedback promoted toolkit sustainability.

CONCLUSIONS

COVID-19 affected the capacity of hospitals to sustain the MARQUIS toolkit. However, hospitals adapted various strategies to sustain the toolkit.

Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings

AIMS AND OBJECTIVES

To examine the nature and use of automation in contemporary clinical information systems by reviewing studies reporting the implementation and evaluation of artificial intelligence (AI) technologies in healthcare settings.

METHOD

PubMed/MEDLINE, Web of Science, EMBASE, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies reporting evaluation of AI in clinical settings from January 2021 to December 2022. We documented the clinical application areas and tasks supported, and the level of system autonomy. Reported effects on user experience, decision-making, care delivery and outcomes were summarised.

RESULTS

AI technologies are being applied in a wide variety of clinical areas. Most contemporary systems utilise deep learning, use routinely collected data, support diagnosis and triage, are assistive (requiring users to confirm or approve AI provided information or decisions), and are used by doctors in acute care settings in high-income nations. AI systems are integrated and used within existing clinical information systems including electronic medical records. There is limited support for One Health goals. Evaluation is largely based on quantitative methods measuring effects on decision-making.

CONCLUSION

AI systems are being implemented and evaluated in many clinical areas. There remain many opportunities to understand patterns of routine use and evaluate effects on decision-making, care delivery and patient outcomes using mixed-methods. Support for One Health including integrating data about environmental factors and social determinants needs further exploration.

Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data

OBJECTIVES

As the real-world electronic health record (EHR) data continue to grow exponentially, novel methodologies involving artificial intelligence (AI) are becoming increasingly applied to enable efficient data-driven learning and, ultimately, to advance healthcare. Our objective is to provide readers with an understanding of evolving computational methods and help in deciding on methods to pursue.

TARGET AUDIENCE

The sheer diversity of existing methods presents a challenge for health scientists who are beginning to apply computational methods to their research. Therefore, this tutorial is aimed at scientists working with EHR data who are early entrants into the field of applying AI methodologies.

SCOPE

This manuscript describes the diverse and growing AI research approaches in healthcare data science and categorizes them into 2 distinct paradigms, the bottom-up and top-down paradigms to provide health scientists venturing into artificial intelligent research with an understanding of the evolving computational methods and help in deciding on methods to pursue through the lens of real-world healthcare data.

Risk-factor analysis for extended-spectrum beta-lactamase-producing Enterobacterales colonization or infection: Evaluation of a novel approach to assess local prevalence as a risk factor

OBJECTIVE

To explore an approach to identify the risk of local prevalence of extended-spectrum β-lactamase-producing Enterobacterales (ESBL-E) on ESBL-E colonization or infection and to reassess known risk factors.

DESIGN

Case-control study.

SETTING

Johns Hopkins Health System emergency departments (EDs) in the Baltimore-Washington, DC, region.

PATIENTS

Patients aged ≥18 years with a culture growing Enterobacterales between April 2019 and December 2021. Cases had a culture growing an ESBL-E.

METHODS

Addresses were linked to Census Block Groups and placed into communities using a clustering algorithm. Prevalence in each community was estimated using the proportion of ESBL-E among Enterobacterales isolates. Logistic regression was used to determine risk factors for ESBL-E colonization or infection.

RESULTS

ESBL-E were detected in 1,167 of 11,224 patients (10.4%). Risk factors included a history of ESBL-E in the prior 6 months (aOR, 20.67; 95% CI, 13.71-31.18), exposure to a skilled nursing or long-term care facility (aOR, 1.64; 95% CI, 1.37-1.96), exposure to a third-generation cephalosporin (aOR, 1.79; 95% CI, 1.46-2.19), exposure to a carbapenem (aOR, 2.31; 95% CI, 1.68-3.18), or exposure to a trimethoprim-sulfamethoxazole (aOR, 1.54; 95% CI, 1.06-2.25) within the prior 6 months. Patients were at lower risk if their community had a prevalence <25th percentile in the prior 3 months (aOR, 0.83; 95% CI, 0.71-0.98), 6 months (aOR, 0.83; 95% CI, 0.71-0.98), or 12 months (aOR, 0.81; 95% CI, 0.68-0.95). There was no association between being in a community in the >75th percentile and the outcome.

CONCLUSIONS

This method of defining the local prevalence of ESBL-E may partially capture differences in the likelihood of a patient having an ESBL-E.