Artificial Intelligence to Enhance Precision Medicine in Cardio-Oncology: A Scientific Statement From the American Heart Association.

Artificial intelligence is poised to transform cardio-oncology by enabling personalized care for patients with cancer, who are at a heightened risk of cardiovascular disease due to both the disease and its treatments. The rising prevalence of cancer and the availability of multiple new therapeutic options has resulted in improved survival among patients with cancer and has expanded the scope of cardio-oncology to not only short-term but also long-term cardiovascular risks resulting from both cancer and its treatments. However, there is considerable heterogeneity in cardiovascular risk, driven by the nature of the malignancy as well as each individual's unique characteristics.

Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine.

Artificial intelligence (AI) applied to medicine offers immense promise, in addition to safety and regulatory concerns. Traditional AI produces a core algorithm result, typically without a measure of statistical confidence or an explanation of its biological-theoretical basis. Efforts are underway to develop explainable AI (XAI) algorithms that not only produce a result but also an explanation to support that result.

Artificial intelligence modelling to assess the risk of cardiovascular disease in oncology patients.

Aims: There are no comprehensive machine learning (ML) tools used by oncologists to assist with risk identification and referrals to cardio-oncology. This study applies ML algorithms to identify oncology patients at risk for cardiovascular disease for referrals to cardio-oncology and to generate risk scores to support quality of care.

Methods And Results: De-identified patient data were obtained from Vanderbilt University Medical Center.

Grounded in reality: artificial intelligence in medical education.

Background: In a recent survey, medical students expressed eagerness to acquire competencies in the use of artificial intelligence (AI) in medicine. It is time that undergraduate medical education takes the lead in helping students develop these competencies. We propose a solution that integrates competency-driven AI instruction in medical school curriculum.

Innovation of health data science curricula.

Objective: There is a growing need for innovation to prepare a well-trained health informatics workforce with data science and digital technology skills. To meet the workforce demands and prepare students for a career in health informatics, a Health Data Science (HDS) concentration was added to the Master's in Health Informatics (MSHI) program at the University of Illinois at Chicago.

Methods: Four levels of learning were incorporated into the curriculum to prepare students for highly complex jobs in health informatics.

From Testing to Decision-Making: A Data-Driven Analytics COVID-19 Response.

In March 2020, NorthShore University Health System laboratories mobilized to develop and validate polymerase chain reaction based testing for detection of SARS-CoV-2. Using laboratory data, NorthShore University Health System created the Data Coronavirus Analytics Research Team to track activities affected by SARS-CoV-2 across the organization. Operational leaders used data insights and predictions from Data Coronavirus Analytics Research Team to redeploy critical care resources across the hospital system, and real-time data were used daily to make adjustments to staffing and supply decisions.

ICD-10 procedure codes produce transition challenges.

The transition of procedure coding from ICD-9-CM-Vol-3 to ICD-10-PCS has generated problems for the medical community at large resulting from the lack of clarity required to integrate two non-congruent coding systems. We hypothesized that quantifying these issues with network topology analyses offers a better understanding of the issues, and therefore we developed solutions (online tools) to empower hospital administrators and researchers to address these challenges. Five topologies were identified: "identity"(I), "class-to-subclass"(C2S), "subclass-toclass"(S2C), "convoluted(C)", and "no mapping"(NM).

Effectiveness of Evidence-based Pneumonia CPOE Order Sets Measured by Health Outcomes.

Objective: Evidence-based sets of medical orders for the treatment of patients with common conditions have the potential to induce greater efficiency and convenience across the system, along with more consistent health outcomes. Despite ongoing utilization of order sets, quantitative evidence of their effectiveness is lacking. In this study, conducted at Advocate Health Care in Illinois, we quantitatively analyzed the benefits of community acquired pneumonia order sets as measured by mortality, readmission, and length of stay (LOS) outcomes.

Patient Outcomes as Transformative Mechanisms to Bring Health Information Technology Industry and Research Informatics Closer Together.

Despite the fast pace of recent innovation within the health information technology and research informatics domains, there remains a large gap between research and academia, while interest in translating research innovations into implementations in the patient care settings is lacking. This is due to absence of common outcomes and performance measurement targets, with health information technology industry employing financial and operational measures and academia focusing on patient outcome concerns. The paper introduces methodology for and roadmap to introduction of common objectives as a way to encourage better collaboration between industry and academia using patient outcomes as a composite measure of demonstrated success from health information systems investments.

The complexity and challenges of the International Classification of Diseases, Ninth Revision, Clinical Modification to International Classification of Diseases, 10th Revision, Clinical Modification transition in EDs.

Beginning October 2015, the Center for Medicare and Medicaid Services will require medical providers to use the vastly expanded International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) system. Despite wide availability of information and mapping tools for the next generation of the ICD classification system, some of the challenges associated with transition from ICD-9-CM to ICD-10-CM are not well understood. To quantify the challenges faced by emergency physicians, we analyzed a subset of a 2010 Illinois Medicaid database of emergency department ICD-9-CM codes, seeking to determine the accuracy of existing mapping tools in order to better prepare emergency physicians for the change to the expanded ICD-10-CM system.

Effectiveness of Evidence-Based Congestive Heart Failure (CHF) CPOE Order Sets Measured by Health Outcomes.

Objective: Evidence-based order sets for treatment of patients with common conditions promise ordering efficiency and more consistent health outcomes. Despite ongoing utilization of order sets, quantitative evidence of their effectiveness is lacking. This study quantitatively analyzed benefits of CHF order sets as measured by mortality, readmission, and length of stay (LOS) outcomes.

Building effective workforce management practices through shared governance and technology systems integration.

In integrated delivery networks (IDNs) with complex management structures, shared governance in nursing is a proven model for health care delivery. After Advocate Health Care, the largest IDN in Illinois, implemented shared governance in its nursing, clinical, and non-clinical departments and restructured the organization's technology use, it benefited greatly from a new, shared decision-making process. After listening to business consultants, clinical professionals, and information technology experts, hospitals should take the blended, or comprehensive, approach to new projects.