Perspectives in health information management / AHIMA, American Health Information Management Association最新文献

Background: The International Statistical Classification of Diseases and Related Health Problems (ICD) codes play a critical role as fundamental data for hospital management and can significantly impact diagnosis-related groups (DRGs). This study investigated the quality issues associated with ICD data and their impact on improper DRG payments.

Methods: Our study analyzed data from a Chinese hospital from 2016-2017 to evaluate the impact of ICD data quality on Chinese Diagnosis-related Group (CN-DRG) evaluation variables and payments. We assessed different stages of the ICD generation process and established a standardized process for evaluating ICD data quality and relevant indicators. The validation of the data quality assessment (DQA) was confirmed through sampling data.

Results: This study of 85,522 inpatient charts found that gynecology had the highest and obstetrics had the lowest diagnosis agreement rates. Pediatrics had the highest agreement rates for MDC and DRG, while neonatal pediatrics had the lowest. The Case Mix Index (CMI) of Coder-coded data showed to be more reasonable than physician-coded data, with increased DRG payments in obstetrics and gynecology. The DQA model revealed coding errors ranging from 40.3 percent to 65.1 percent for physician and 12.2 percent to 23.6 percent for coder. Payment discrepancies were observed, with physicians resulting in underpayment and coders displaying overpayment in some cases.

Conclusion: ICD data is crucial for effective healthcare management, and implementing standardized and automated processes to assess ICD data quality can improve data accuracy. This enhances the ability to make reasonable DRG payments and accurately reflects the quality of healthcare management.

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.

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.

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.

Purpose: To validate COVID-19 information records in The Pharmacoepidemiological Research Database for Public Health System (BIFAP) of Spain.

Methods: The recorded COVID-19 cases in primary care or positive test registries (gold-standard) were identified among vaccinated patients against COVID-19 infection and their matched unvaccinated controls, between December 2020 and October 2021. The sensitivity, specificity, positive (PPV) and negative (NPV) predictive values were estimated for primary care records.

Results: Among 21,702 patients with positive tests and 20,866 with recorded COVID-19 diagnoses, the sensitivity, specificity, PPV and NPV were, respectively, 79.98 percent, 99.95 percent, 80.24 percent, and 99.94 percent among vaccinated, and 78.67 percent, 99.96 percent, 84.51 percent and 99.94 percent among controls.

Conclusions: Primary care COVID-19 diagnosis recorded in BIFAP showed that sensitivity was similar and PPV was slightly lower among vaccinated than unvaccinated controls. Among the elderly, COVID-19 diagnosis was less recorded. These findings permit the design of informed algorithms for performing COVID-19-related studies.

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.

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.

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.