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

Assessing ICD Data Quality and Its Impact on DRG Payments: Evidence from a Women and Children Special Hospital in China.

Perspectives in health information management / AHIMA, American Health Information Management Association Pub Date : 2024-06-01

Ying Zhang, Han Dong, Shu-Yi Xu, Chen Lyu, Ling-Yun Wei

{"title":"Assessing ICD Data Quality and Its Impact on DRG Payments: Evidence from a Women and Children Special Hospital in China.","authors":"Ying Zhang, Han Dong, Shu-Yi Xu, Chen Lyu, Ling-Yun Wei","doi":"","DOIUrl":"","url":null,"abstract":"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.","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"21 2","pages":"1b"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Training Competent Health Information Management Professionals: Based on A Survey of Field Experts.

Perspectives in health information management / AHIMA, American Health Information Management Association Pub Date : 2024-06-01

Hyunkyung Lee, Sangok Cho

引用次数: 0

Essential Skill and Knowledge Required for Health Data Professionals: A Content Analysis of Job Advertisements.

Perspectives in health information management / AHIMA, American Health Information Management Association Pub Date : 2024-06-01

Cathy A Flite, Susan Foster, Shannon H Houser, Thomas J Hunt, Lakesha Kinnerson, Angela Morey, Jennifer Peterson, Roberta Darnez Pope

引用次数: 0

Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.

Perspectives in health information management / AHIMA, American Health Information Management Association Pub Date : 2024-06-01

Scott W Perkins, Justin C Muste, Taseen Alam, Rishi P Singh

{"title":"Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.","authors":"Scott W Perkins, Justin C Muste, Taseen Alam, Rishi P Singh","doi":"","DOIUrl":"","url":null,"abstract":"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.","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"21 2","pages":"1d"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Significant Events Influencing the Evolution of the Role of the Healthcare Information Systems Executive.

Perspectives in health information management / AHIMA, American Health Information Management Association Pub Date : 2024-06-01

Douglas A Jones, Nancy Borkowski, Christy Harris Lemak, Dae Hyun Daniel Kim, Dalton E Pena

{"title":"Significant Events Influencing the Evolution of the Role of the Healthcare Information Systems Executive.","authors":"Douglas A Jones, Nancy Borkowski, Christy Harris Lemak, Dae Hyun Daniel Kim, Dalton E Pena","doi":"","DOIUrl":"","url":null,"abstract":"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.","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"21 2","pages":"1f"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605377/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.

Perspectives in health information management / AHIMA, American Health Information Management Association Pub Date : 2024-06-01

Scott W Perkins, Justin C Muste, Taseen A Alam, Rishi P Singh

{"title":"Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.","authors":"Scott W Perkins, Justin C Muste, Taseen A Alam, Rishi P Singh","doi":"","DOIUrl":"","url":null,"abstract":"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.","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"21 2","pages":"1g"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Validation Of COVID-19 Information In The Pharmacoepidemiological Research Database of Spain's Public Health System Data by Vaccination Status.

Perspectives in health information management / AHIMA, American Health Information Management Association Pub Date : 2024-03-01 eCollection Date: 2024-12-01

Oliver Astasio, Belén Castillo-Cano, Beatriz Sánchez Delgado, Fabio Riefolo, Rosa Gini, Elisa Martín-Merino

{"title":"The Validation Of COVID-19 Information In The Pharmacoepidemiological Research Database of Spain's Public Health System Data by Vaccination Status.","authors":"Oliver Astasio, Belén Castillo-Cano, Beatriz Sánchez Delgado, Fabio Riefolo, Rosa Gini, Elisa Martín-Merino","doi":"","DOIUrl":"","url":null,"abstract":"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.","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"21 1","pages":"1i"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11102056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unlocking Patient Portals: Health Information Professionals Navigating Challenges and Shaping the Future.

Perspectives in health information management / AHIMA, American Health Information Management Association Pub Date : 2024-03-01 eCollection Date: 2024-12-01

Jennifer L Peterson, Shannon H Houser

引用次数: 0

Using Electronic Health Records Data to Identify Strong Performers in Healthcare Quality Improvement.

Perspectives in health information management / AHIMA, American Health Information Management Association Pub Date : 2024-03-01 eCollection Date: 2024-12-01

Adam Baus, Andrea Calkins, Cecil Pollard, Craig Robinson, Robin Seabury, Marcus Thygeson, Curt Lindberg, Andrya Durr

引用次数: 0

Perspectives on Big Data and Big Data Analytics in Healthcare.

Perspectives in health information management / AHIMA, American Health Information Management Association Pub Date : 2024-03-01 eCollection Date: 2024-12-01

Egondu R Onyejekwe, Dasantila Sherifi, Hung Ching

引用次数: 0