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Dai P, Zou T, Cheng H, Xin Z, Ouyang W, Peng X, Luo A, Xie W. Multidimensional analysis of job advertisements for medical record information managers. Front Public Health 2022; 10:905054. [PMID: 36408003 PMCID: PMC9674350 DOI: 10.3389/fpubh.2022.905054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022] Open
Abstract
Objective The rapid growth of the medical industry has resulted in a tremendous increase in medical record data, which can be utilized for hospital management, aiding in diagnosis and treatment, medical research, and other purposes. For data management and analysis, medical institutions require more qualified medical record information managers. In light of this, we conducted an analysis of the qualifications, abilities, and job emphasis of medical record information managers in order to propose training recommendations. Materials and methods From online job posting sites, a sample of 241 job advertisements for medical record information management positions posted by Chinese healthcare institutions were collected. We conducted word frequency and keyword co-occurrence analysis to uncover overall demands at the macro level, and job analysis to investigate job-specific disparities at the micro level. Based on content analysis and job analysis, a competency framework was designed for medical record information managers. Results The most frequent keywords were "code," "job experience," and "coding certification," according to the word frequency analysis. The competency framework for managers of medical record information is comprised of seven domains: essential knowledge, medical knowledge, computer expertise, problem-solving skills, leadership, innovation, and attitude and literacy. One of the fundamental skills required of medical record information managers is coordination and communication. Similarly, knowledge and skill requirements emphasize theoretical knowledge, managerial techniques, performance enhancement, and innovation development. Conclusion According to organization type and job differences, the most crucial feature of the job duties of medical record information managers is cross-fertilization. The findings can be utilized by various healthcare organizations for strategic talent planning, by the field of education for medical record information managers for qualification and education emphasis adjustment, and by job seekers to enhance their grasp of the profession and self-evaluation.
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Affiliation(s)
- Pingping Dai
- Third Xiangya Hospital, Central South University, Changsha, China,Department of Medical Information, School of Life Science, Central South University, Changsha, China,Key Laboratory of Medical Information Research (Central South University), College of Hunan Province, Changsha, China,Clinical Research Center for Cardiovascular Intelligent Healthcare in Hunan Province, Changsha, China
| | - Tongkang Zou
- Third Xiangya Hospital, Central South University, Changsha, China,Department of Medical Information, School of Life Science, Central South University, Changsha, China,Key Laboratory of Medical Information Research (Central South University), College of Hunan Province, Changsha, China,Clinical Research Center for Cardiovascular Intelligent Healthcare in Hunan Province, Changsha, China,Second Xiangya Hospital, Central South University, Changsha, China
| | - Haiwei Cheng
- Third Xiangya Hospital, Central South University, Changsha, China,Department of Sociology, Central South University, Changsha, China
| | - Zirui Xin
- Department of Medical Information, School of Life Science, Central South University, Changsha, China,Key Laboratory of Medical Information Research (Central South University), College of Hunan Province, Changsha, China,Clinical Research Center for Cardiovascular Intelligent Healthcare in Hunan Province, Changsha, China,Second Xiangya Hospital, Central South University, Changsha, China
| | - Wei Ouyang
- Third Xiangya Hospital, Central South University, Changsha, China,Department of Medical Information, School of Life Science, Central South University, Changsha, China,Key Laboratory of Medical Information Research (Central South University), College of Hunan Province, Changsha, China,Clinical Research Center for Cardiovascular Intelligent Healthcare in Hunan Province, Changsha, China
| | - Xiaoqing Peng
- Third Xiangya Hospital, Central South University, Changsha, China,Department of Medical Information, School of Life Science, Central South University, Changsha, China,Key Laboratory of Medical Information Research (Central South University), College of Hunan Province, Changsha, China,Clinical Research Center for Cardiovascular Intelligent Healthcare in Hunan Province, Changsha, China
| | - Aijing Luo
- Department of Medical Information, School of Life Science, Central South University, Changsha, China,Key Laboratory of Medical Information Research (Central South University), College of Hunan Province, Changsha, China,Clinical Research Center for Cardiovascular Intelligent Healthcare in Hunan Province, Changsha, China,Second Xiangya Hospital, Central South University, Changsha, China,*Correspondence: Aijing Luo
| | - Wenzhao Xie
- Third Xiangya Hospital, Central South University, Changsha, China,Department of Medical Information, School of Life Science, Central South University, Changsha, China,Key Laboratory of Medical Information Research (Central South University), College of Hunan Province, Changsha, China,Clinical Research Center for Cardiovascular Intelligent Healthcare in Hunan Province, Changsha, China,Wenzhao Xie
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Saxe GN, Bickman L, Ma S, Aliferis C. Mental health progress requires causal diagnostic nosology and scalable causal discovery. Front Psychiatry 2022; 13:898789. [PMID: 36458123 PMCID: PMC9705733 DOI: 10.3389/fpsyt.2022.898789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 10/10/2022] [Indexed: 11/17/2022] Open
Abstract
Nine hundred and seventy million individuals across the globe are estimated to carry the burden of a mental disorder. Limited progress has been achieved in alleviating this burden over decades of effort, compared to progress achieved for many other medical disorders. Progress on outcome improvement for all medical disorders, including mental disorders, requires research capable of discovering causality at sufficient scale and speed, and a diagnostic nosology capable of encoding the causal knowledge that is discovered. Accordingly, the field's guiding paradigm limits progress by maintaining: (a) a diagnostic nosology (DSM-5) with a profound lack of causality; (b) a misalignment between mental health etiologic research and nosology; (c) an over-reliance on clinical trials beyond their capabilities; and (d) a limited adoption of newer methods capable of discovering the complex etiology of mental disorders. We detail feasible directions forward, to achieve greater levels of progress on improving outcomes for mental disorders, by: (a) the discovery of knowledge on the complex etiology of mental disorders with application of Causal Data Science methods; and (b) the encoding of the etiological knowledge that is discovered within a causal diagnostic system for mental disorders.
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Affiliation(s)
- Glenn N Saxe
- Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, New York, NY, United States
| | - Leonard Bickman
- Ontrak Health, Inc., Henderson, NV, United States.,Department of Psychology, Florida International University, Miami, FL, United States
| | - Sisi Ma
- Program in Data Science, Department of Medicine, Clinical and Translational Science Institute, Institute for Health Informatics, School of Medicine, University of Minnesota, Minneapolis, MN, United States
| | - Constantin Aliferis
- Program in Data Science, Department of Medicine, Clinical and Translational Science Institute, Institute for Health Informatics, School of Medicine, University of Minnesota, Minneapolis, MN, United States
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