This textbook introduces students to the application methods of control charts to improve quality in health care. The textbook is written to be accessible to any student in the areas of health information management, health care informatics, and health care industrial engineering. Having a basic background in statistics would be beneficial, but such training is not a prerequisite to understanding how to apply the techniques discussed here. Several How-To sections are included to demonstrate the implementation of the given control charts using software such as Minitab and Excel. Additionally, samples of a Python code are included and can directly be accessed in a Jupyter Notebook at https://github.com/JeromeNN/Applications-Control-Charts-Quality-Improvement-Health-Care
This textbook introduces students to the essential tools of quality improvement. The emphasis is placed on health care informatics, as reflected in the several examples contained in the text. The book is written to be accessible to any student in the areas of health information management, health care informatics, and health care industrial engineering. Although having some statistical background would be a plus, such knowledge is not a prerequisite to understanding and applying the tools presented here. Several How-To sections are included to demonstrate the hands-on implementation of the discussed concepts using software such as Minitab, Visio, and Excel.
This unit depicts the medical model of healthcare in the US, with an overview of the organization of healthcare and the physical structure of healthcare delivery in the outpatient, inpatient and long-term care settings, including an overview of the organization of the Veterans Affairs (VA) system. This unit is intended primarily for the student who does not have a background in healthcare, though the topics of this unit will be described at a relatively advanced level.
This unit describes the traditions and values that guide physicians, nurses, and allied health professionals. It explores medical ethics, professionalism and legal duties and applies ethics and professionalism to specific topics, including health informatics.
This course provides a comprehensive review of interoperability, health data standards, and other advanced topics including Substitutable Medical Applications, Reusable Technologies (SMART) and Fast Healthcare Interoperability Resources® (FHIR), also known as SMART-on-FHIR applications and Accelerator projects. This course will use Interoperability Land™ to provide learners with a hands-on experience using FHIR resources. Upon successful completion of this course, learners will be able to: explain interoperability and use cases; locate information within JSON and XML files; Create queries in IOL; understand SMART application authorization. Interoperability Land can be purchased on AWS Marketplace at the followinglink: https://aws.amazon.com/marketplace/pp/prodview-f34r2uj3naohe For information regarding education pricing please email firstname.lastname@example.org.
This introductory unit covers definitions of terms used in the component, with an emphasis on paradigm shifts in healthcare, including the transition from physician-centric to patient-centric care, the transition from individual care to interdisciplinary team-based care, and the central role of technology in healthcare delivery. This unit also emphasizes the core values in US healthcare.
In this unit, students will read and interpret primary sources to address the question “How do we measure the attainment of human rights?” By exploring the Universal Declaration of Human Rights, the UN’s Guide to Indicators of Human Rights, and data about development indicators from multiple databases, students will unpack the complexities of using indicators to measure human rights.
This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:
"Artificial intelligence is making rapid advances in medicine. Already, there are machine learning algorithms that can outperform doctors in some medical fields. There’s only one fairly big problem: experts aren’t quite sure how these algorithms work. While designers know full well what goes into the A-I systems they build and what comes out, the learning part in between is often too complex to comprehend. To their users, machine learning algorithms are effectively black boxes. Now, researchers from the RIKEN Center for Advanced Intelligence Project in Japan are lifting the lid. They’ve developed a deep-learning system that can outperform human experts in predicting whether prostate cancer will reoccur within one year. More importantly, the deep learning system they developed can acquire human-understandable features from unannotated pathology images to offer up critical clues that could help humans make better diagnoses themselves..."
The rest of the transcript, along with a link to the research itself, is available on the resource itself.