Big data (BD) is of high interest for research and practice purposes because it has the potential to provide insights into the population served and healthcare practices. Much progress has been made in collecting BD and creating tools for big data analytics (BDA). However, healthcare organizations continue to experience challenges associated with BD characteristics and BDA tools. Utilization of BD impacts current decision-making, planning, and future use of artificial intelligence (AI) tools, which are trained on BD. This qualitative study focused on better understanding the reality of BD and BDA management and usage by healthcare organizations. Six structured interviews were conducted with individuals who work with healthcare BD and BDA. Findings confirmed the known challenges associated with BD/BDA and added rich insights into the structural, operational and utilization aspects, as well as future directions. Such perspectives are valuable for education and improvements in BD/BDA management and development.

Due to recent regulations and the COVID-19 pandemic, patient portals have increased in use and importance as a tool for both patients and providers. While patient portals have many benefits, the recent increase in use has resulted in additional complexities in managing these portals. Health information (HI) professionals are ideally suited to manage these tools. While past efforts may have focused on increasing portal use, current efforts must include ensuring patient access, data quality, portal policies and procedures, and more. This study was designed to explore the experiences and perspectives of a group of HI directors and patient portal managers who are deeply involved in portal use and management. The findings of this study are used to assess the patient portal management role that HI professionals currently play and could play in the future, develop guidelines for best practices, and determine educational needs for both higher and professional education.

The 11th Revision of the International Classification of Diseases (ICD-11) with its informatics-based infrastructure has transformed an antiquated classification system into a suite of 21st century computer applications. This manuscript proposes an innovation model to facilitate the implementation of ICD-11 by the US. The model introduces ICD-11 Comprehensive Clinical Linearization, Evolution and Response, or C-CLEAR, a fully coded comprehensive clinical linearization and syntactical rules for combining these codes. These enhancements can be incorporated into electronic coding tools that enable clinical reporters to transmit complex clinical concepts expressed in detailed natural clinical language by means of standardized clusters of ICD-11 stem and extension codes. The model can support rich clinical data captures such as condition acuity and severity, as well as pharmacological treatments. This approach shows promise to accelerate ICD-11 implementation with minimal disruption and maximal net benefits but will require vetting, testing and input from expert stakeholders.

Moral sensitivity is one criterion for competent professional ethics. This sensitivity can be reinforced by specific educational practices. The purpose of this study was to investigate the impact of professional ethics-based education on the ethical sensitivity of health information technology students.

Assessing for positive deviance is one method of identifying individuals, teams, or organizations that perform substantially better than their peers. This approach has been used to support quality-of-care improvement processes in healthcare settings by identifying healthcare team members who perform comparatively well within a given environment and sharing their opinions, actions, and practices with others. This case study presents an adaptable, straightforward framework for identifying positive deviance, or strong performers, within the healthcare setting and is intended for any primary care health system tracking quality measures and aiming to understand the performance of their providers, clinic sites, or organization. Moreover, this protocol does not require the use of more time-consuming methods, such as interviews, and is instead based on repurposing data already being documented in the electronic health record.

Bexar Data Dive, an online data platform, was created to increase accessibility and use of health and social determinants of health data, such as education, economic barriers to healthcare, and hospitalization rates, to decrease racial/ethnic health disparities throughout Bexar County. A model of user-centered design helped us incorporate community input into the platform. We conducted four interviews and five focus groups to gather information on how people use data - specifically beginner and intermediate-level data users from various educational, governmental, and nonprofit organizations. Then, we launched a community survey to assess specific data needs. Lastly, once the alpha version of Bexar Data Dive was ready, we conducted user testing sessions to measure usability, identify bugs, and gather final feedback before launch. Our findings included many recommendations for incorporating user-centered design in health data management. Participants wanted a health data tool that was easy to use, had the indicators they commonly need, and would provide visualizations for presentations, grants, and other projects.

This systematic literature review seeks to collate the evidence of the evolution of the role of healthcare information systems (HIS) executive in the United States (US) and to identify the significant events which have influenced the development of this role and its impact on the transformation of healthcare organizations. The HIS executive has evolved over time from the manager responsible for in-house computers, advanced data processing (ADP), communication systems, and system conversions to a participatory member of the executive leadership team responsible for delivering technology solutions which transform the delivery of healthcare. The changes in the responsibilities and the attributes of HIS executives have been driven by changes in technology, standardization of clinical data, government regulation, and the ever-changing reimbursement and business environment. The responsibilities and titles of the HIS executive will evolve and adapt as the business environment and the expectations of consumers and payers change.

Clinicians dedicate significant time to clinical documentation, incurring opportunity cost. Artificial Intelligence (AI) tools promise to improve documentation quality and efficiency. This systematic review overviews peer-reviewed AI tools to understand how AI may reduce opportunity cost. PubMed, Embase, Scopus, and Web of Science databases were queried for original, English language research studies published during or before July 2024 that report a new development, application, and validation of an AI tool for improving clinical documentation. 129 studies were extracted from 673 candidate studies. AI tools improve documentation by structuring data, annotating notes, evaluating quality, identifying trends, and detecting errors. Other AI-enabled tools assist clinicians in real-time during office visits, but moderate accuracy precludes broad implementation. While a highly accurate end-to-end AI documentation assistant is not currently reported in peer-reviewed literature, existing techniques such as structuring data offer targeted improvements to clinical documentation workflows.

Healthcare organizations rely on skilled health data professionals to enhance organizational effectiveness and patient care. This study analyzes recent job postings to identify the prevalent skills, competencies, and technical skills that healthcare organizations are looking for when hiring health data professionals. A content analysis of 34 unique job postings provides key insights into the skill sets and knowledge necessary to fulfill these roles. The findings revealed a diverse range of skills, including analytics, SQL proficiency, business acumen, data visualization, and essential soft skills such as problem solving, interpersonal communication, and project management. Additionally, the education requirements indicate a need for bachelor's degrees or higher for these positions. These findings serve as a valuable resource for both educators and employers in guiding curriculum development and refining hiring practices.

Computerized clinical decision support systems (CDSS) are increasingly being used to facilitate the role of clinicians in complex decision-making processes. This systematic review evaluates evidence of the available CDSS developed and tested to support the decision-making process in primary healthcare for stroke prevention and barriers to practical implementations in primary care settings. A systematic search of Web of Science, Medline Ovid, Embase Ovid, and Cinahl was done. A total of five studies, experimental and observational, were synthesised in this review. This review found that CDSS facilitate decision-making processes in primary health care settings in stroke prevention options. However, barriers were identified in designing, implementing, and using the CDSS.

The transition to a new electronic health record (EHR) system requires an understanding of how the new system addresses the needs, business processes, and current activities of a healthcare system. To address such requirements, a multidisciplinary team conducted a current state workflow assessment (CSWFA) of clinical and administrative functions to elicit and document business processes (via process diagrams), requirements, workarounds, and process issues (i.e., user interface issues, training gaps) at one healthcare facility. We provided a novel method of evaluating the implementation process to ensure that a CSWFA was documented with key stakeholders. In this analysis, we describe the CSWFA approach and expected outcomes with a specific emphasis on how a qualitative approach can be integrated to explore underlying patterns and relationships in the data. Overall, this methodology enables practitioners to deliver data-driven support initiatives that optimize EHR implementation while considering user experience, productivity, and patient safety.