Introduction: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. Method:This quasi-experimental pre-post study was conducted in 2022 with 49 students. A researcher-created questionnaire based on Lutzen was used for data collection. Data were analyzed using descriptive statistics and paired t-tests. Findings:Students' moral sensitivity score was 7.4 ± 0.7 before and 7.6 ± 0.8 after, a significant increase in post scores (P=0.031). The moral sensitivity score of students who had not previously received professional ethics training was statistically significantly increased by case-based learning. Results:The professional ethics-based teaching method was effective in increasing the moral sensitivity of health information technology students, so it is recommended to use this method of teaching medical ethics courses.
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.
Purpose:The study purpose was to describe the availability of sex, gender identity, and sexual orientation (SOGI) data in a large, Catholic health system. Methods:A retrospective chart review on the Sisters of St. Mary (SSM) Health database was conducted from January 1, 2012, to March 27, 2024. The availability of SOGI data and number of sexual and gender minority patients was reported. Results:Among the 5,759,869 records, data on sex was available for the majority of the population (99.9 percent); data on gender identity and sexual orientation were reported for smaller proportions (7.4 percent and 4.5 percent, respectively). Sex and gender were reported among 7.4 percent of the population. A total of 4,567 gender minority and 14,644 sexual minority patients were seen. Conclusion:Though SOGI data were largely unavailable in the SSM Health database, the system has the capacity to separately record sex, gender, and sexual orientation, with a range of response options to capture gender and sexual orientation diversity.
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.
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.
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.
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.
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.
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.
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.
Primary care physicians (PCPs) have an important role in the identification and management of Attention Deficit Hyperactivity Disorder (ADHD). There is a paucity of research on PCPs' practices related to the discussion of educational interventions. We conducted a retrospective chart review using Natural Language Processing to extract data on how often PCPs in an outpatient clinic: 1) discuss educational support with patients and caregivers; and 2) obtain educational records. About three-quarters of patients had at least one term related to educational support included in at least one note, but only 13 percent of patients had at least one educational record uploaded into the electronic health record (EHR). There was no association between having an educational document uploaded into the EHR and inclusion of a term related to educational support in a note. Almost half (48 percent) of these records were unclearly labeled. Further education of PCPs is warranted to increase discussions of educational support and obtaining educational records, as is collaboration with health information management professionals around labeling.
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 objective of the study is to identify challenges and associated factors for privacy and security related to telehealth visits during the COVID-19 pandemic. The systematic search strategy used the databases of PubMed, ScienceDirect, ProQuest, Embase, CINAHL, and COCHRANE, with the search terms of telehealth/telemedicine, privacy, security, and confidentiality. Reviews included peer-reviewed empirical studies conducted from January 2020 to February 2022. Studies conducted outside of the US, non-empirical, and non-telehealth related were excluded. Eighteen studies were included in the final analysis. Three risk factors associated with privacy and security in telehealth practice included: environmental factors (lack of private space for vulnerable populations, difficulty sharing sensitive health information remotely), technology factors (data security issues, limited access to the internet, and technology), and operational factors (reimbursement, payer denials, technology accessibility, training, and education). Findings from this study can assist governments, policymakers, and healthcare organizations in developing best practices in telehealth privacy and security strategies.
Studies have quantified various specific benefits related to the use of medical scribes, finding physician workflow and productivity improvements, with some demonstrating marginal value or detrimental impact. However, this evidence base misses a critical underlying issue with the expanding number of physicians using medical scribes routinely. There are an estimated 28,000-33,000 peer reviewed biomedical journals worldwide, currently publishing an estimated 1.8-2 million scientific articles every year. Over a typical physician's career from the 11-13 years of undergraduate through medical school and specialty/residency training as well as 34-36 practice/care delivery years beyond (to age 65), this yields 84-94+ million peer reviewed journal articles that are published in the global medical literature and to be potentially consumed/ considered over a roughly 47-year career. Clinical trial results in various stages of peer review, with 409,000 clinical trials registered in 2022, augment this massive volume of new clinical and bioscience information that clinicians might utilize to advance their care delivery by over 19 million bioscientific reports over a lifetime of training and care delivery. Inclusive of clinical trial reports and peer reviewed journal articles, a physician might derive clinical care value from an expanding career-long evidence base of 103-113+ million scientific communications. Even if only 0.1 percent of the global output of biomedical science has clinical relevance to a highly specialized physician, the narrowed career-long total remains a staggering 103,000 journal publications and clinical trial reports. For physicians with a more general and diverse clinical focus such as family medicine, emergency medicine physicians, and hospitalists, if 1 percent of newly published evidence-based literature is pertinent, the total career-long estimate is over 1 million journal articles and clinical trials to be reviewed and clinically integrated. As a result, a challenging issue created by the increasing role of medical scribes is not just evaluating their value (or lack thereof) for practicing physicians in their workflows and productivity. Rather it concerns the impact that medical scribes may be having by decoupling physicians from the iterative technological and cognitive progression of the electronic health record (EHR) and its evolving artificial intelligence (AI), which can facilitate the integration of the year-over-year proliferation of clinically pertinent new scientific evidence into a physician's practice of medicine. This commentary addresses the challenge to the evolution of the AI of the EHR posed by physicians' increasing use of and reliance upon medical scribes, and highlights how medical scribes may also, inadvertently, isolate and insulate physicians from their essential role in continuous refinement and advancement of EHR AI. Consideration is given to the broader challenge of inadequate focus and resources needed across sectors to drive the evolution of AI in the EHR, and associated health informatics research, as a US national priority.
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.
The World Health Organization's International Classification of Diseases (ICD) has become the international standard diagnostic classification for reporting morbidity and mortality. In 2015, the United States transitioned from the 9th to 10th Revision. The update was necessary due to major structural limitations of the ICD-9 system. Concerns of the transition mainly centered around clinical usage and cost; however, there were concerns for overlapping codes with the same classification but different meanings between the two versions. Duplicate codes could pose an issue for big data retrospective studies that overlap between the two systems. Therefore, the goals of this study are to further explore and identify duplicate ICD codes between the systems. ICD-9-CM and ICD-10-CM code files were obtained from the Centers for Medicare & Medicaid Services. There were 14,567 ICD-9-CM codes and 91,737 unique ICD-10-CM codes tabulated. Duplicated items between the files were isolated. Four hundred sixty-nine duplicate codes were identified, consisting of 39 E Codes and 430 V Codes. These twin codes contain classifications for external causes of injury and factors influencing health status and contact with health services. Therefore, special attention should be drawn to retrospective research involving methods of injury spanning ICD-9 and ICD-10 systems.
Since 2020, health informaticians have developed and enhanced public-facing COVID-19 dashboards worldwide. The improvement of dashboards implemented by health informaticians will ultimately benefit the public in making better healthcare decisions and improve population-level healthcare outcomes. The authors evaluated 100 US city, county, and state government COVID-19 health dashboards and identified the top 10 best practices to be considered when creating a public health dashboard. These features include 1) easy navigation, 2) high usability, 3) use of adjustable thresholds, 4) use of diverse chart selection, 5) compliance with the Americans with Disabilities Act, 6) use of charts with tabulated data, 7) incorporated user feedback, 8) simplicity of design, 9) adding clear descriptions for charts, and 10) comparison data with other entities. To support their findings, the authors also conducted a survey of 118 randomly selected individuals in six states and the District of Columbia that supports these top 10 best practices for the design of health dashboards.
