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

{"title":"Functions and Outcomes of Personal Health Records for Patients with Chronic Diseases: A Systematic Review.","authors":"Somayeh Paydar,&nbsp;Hassan Emami,&nbsp;Farkhondeh Asadi,&nbsp;Hamid Moghaddasi,&nbsp;Azamossadat Hosseini","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>The personal health record (PHR) makes it possible for patients to access, manage, track, and share their health information. By engaging patients in chronic disease care, they will be active members in decision-making and healthcare management.</p><p><strong>Objectives: </strong>This study aimed to identify the functions and outcomes of PHR for patients with four major groups of chronic diseases (cardiovascular diseases, cancers, diabetes, and chronic respiratory diseases).</p><p><strong>Method: </strong>A systematic review was conducted on studies published in PubMed, Scopus, Web of Science, and Embase. Searching and screening were performed using the keyword of \"Personal Health Record\" without time limitation, and ended in August 2018.</p><p><strong>Results: </strong>In total, 3742 studies were retrieved, 35 of which met the inclusion criteria. Out of these 35, 18 studies were conducted in the United States, 24 studies were related to patients with diabetes, and 32 studies focused on tethered PHRs. Moreover, in 25 studies, the function of viewing and reading medical records and personal health information was provided for three groups of chronic patients. Results showed that the use of PHRs helps the management and control of chronic diseases (10 studies).</p><p><strong>Conclusion: </strong>It is recommended that integrated PHRs with comprehensive functions and features were designed in order to support patient independence and empowerment in self-management, decrease the number of referrals to health centers, and reduce the costs imposed on families and society.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"18 Spring","pages":"1l"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8314040/pdf/phim0018-0001m.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39273152","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}

{"title":"Examining Individual Transition from Healthcare to Information Technology Roles Using the Theory of Planned Behavior.","authors":"Rebecca Johnston,&nbsp;Barbara HewittHewitt,&nbsp;Alexander McLeod,&nbsp;Jackie Moczygemba","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Many health information management (HIM) positions, including coders and transcriptionists, are evolving due to the widespread adoption of electronic health records (EHR) and other automated entry systems. Thus, focus for roles associated with those positions are changing and new positions to manage and manipulate the data collected in the new systems. This study seeks to identify which factors influence HIM professionals' decision to transition from a traditional HIM role to an information technology (IT) position. An online survey was sent to these individuals to determine which factors influenced their decision to consider a transition from healthcare roles to information technology using the theory of planned behavior. In other words, this study explored whether these individuals were influenced by attitudes, normative beliefs, and self-efficacy to consider transitioning from healthcare roles to information technology positions. In order to better understand whether education played a role in this behavior, an additional element, education efficacy was added. The findings revealed that these health information management professionals are not considering a transition from healthcare positions to IT roles.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"18 Spring","pages":"1b"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120678/pdf/phim0018-0001b.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39016987","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}

{"title":"Electronic Health Record (EHR) Abstraction.","authors":"Amal A Alzu'bi,&nbsp;Valerie J M Watzlaf,&nbsp;Patty Sheridan","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The purpose of electronic health record (EHR) abstraction includes collection of data related to administrative coding functions, quality improvement, clinical registry functions and clinical research. This article examines the different abstraction methods, such as manual abstraction, simple query, and natural language processing (NLP). It also discusses the advantages and disadvantages of each of those methods. The process used for successful EHR abstraction is also discussed and includes the scope and resources needed (time, budget, type of healthcare professionals RHIA, RHIT, etc.). The relationship between EHRs and the clinical registry is also examined with a focus on validity of the data extracted. Future research in this area to examine abstraction methods across hospitals who do data abstraction are being finalized for a future publication.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"18 Spring","pages":"1g"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120673/pdf/phim0018-0001g.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39018380","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}

{"title":"Privacy Officers: Who They are and Where They Work.","authors":"Amanda Walden,&nbsp;Kendall Cortelyou-Ward,&nbsp;Alice Noblin","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The study's objective is to examine the role of healthcare privacy officers, including their personal and organizational knowledge, and the facilities where they work. A survey was conducted of privacy officers that are members of the American Health Information Management Association (AHIMA). This resulted in 123 responses that were analyzed for this study. Descriptive statistics were used to characterize factors. The results showed the characteristics predominant among privacy officers are female, higher age, employed in healthcare for numerous years, mostly hold credentials, higher educated, with higher self-reported knowledge levels. Privacy officers are housed in several departments, with the majority within health information management (HIM). Their facilities are typically acute-care hospitals or healthcare systems located in states without additional privacy laws and are primarily non-profit.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"18 Spring","pages":"1i"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120679/pdf/phim0018-0001i.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39018382","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}

{"title":"A Virtual Leadership Program's Impact on Employee Leadership Development at a Healthcare Organization.","authors":"Charlene Banta,&nbsp;Kelly Doran,&nbsp;Erin Duncan,&nbsp;Patty Heiderscheit,&nbsp;Rhonda Jensen,&nbsp;Jenny Jorgenson,&nbsp;Barb Rechtzigel,&nbsp;Sarah Shtylla","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>In this study, we explored the effectiveness of the virtual organizational leadership development program at Mayo Clinic. The purpose of this study was to explain how a virtual leadership development program impacted employee leadership efficacy. The research questions addressed how the program affected participant promotions, how the program learning objectives were implemented by participants, and how the program impacted participants. Collection tools included satisfaction surveys, interviews, and data reflecting promotion rates. Participants appreciated the advantages of the virtual format of the program and the quality of the instructors. They completed the program with enhanced communication skills, the ability to influence positive change, and increased self-awareness. Opportunities for program improvement included incorporating real-world projects to give participants the ability to practice the leadership skills taught, the ability to be paired with a mentor, and a second part to the program to explore the leadership competencies at a more advanced level.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"18 Spring","pages":"1c"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120676/pdf/phim0018-0001c.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39016988","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}

{"title":"Informatics-Supported Diabetes Prevention Programming in West Virginia.","authors":"Adam Baus,&nbsp;Samantha Shawley-Brzoska,&nbsp;Jessica Wright,&nbsp;Sheryn Carey,&nbsp;Erikah DeFrehn Berry,&nbsp;Sandra Burrell,&nbsp;Megan Ross,&nbsp;Cecil Pollard,&nbsp;Audrey Semel,&nbsp;Andrea Calkins,&nbsp;Divya Gadde,&nbsp;Traci Jarrett","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Addressing diabetes, prediabetes, and related health conditions such as high blood pressure, high cholesterol, obesity, and physical inactivity are critical public health priorities for the United States, particularly West Virginia. Preventing chronic conditions through early identification of risk and intervention to reduce risk is essential. Primary care and community-based programs need a more connected informatics system by which they work in tandem to identify, refer, treat, and track target populations. This case study in quality improvement examines the effectiveness of national diabetes prevention programming in West Virginia via the West Virginia Health Connection initiative, which was designed to provide such an informatics structure. Cohort analysis reveals an average weight loss of 13.6 pounds-or 6.3 percent total body weight loss-per person. These changes represent decreased risk of diabetes incidence and increased healthcare savings. Lessons learned are applicable to other areas aiming to build and sustain a data-informed health analytics network.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"18 Spring","pages":"1l"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120671/pdf/phim0018-0001l.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39018385","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}

{"title":"Predictive Model Based on Health Data Analysis for Risk of Readmission in Disease-Specific Cohorts.","authors":"Md Shahid Ansari,&nbsp;Abhay Kumar Alok,&nbsp;Dinesh Jain,&nbsp;Santu Rana,&nbsp;Sunil Gupta,&nbsp;Roopa Salwan,&nbsp;Svetha Venkatesh","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>Intervention planning to reduce 30-day readmission post-acute myocardial infarction (AMI) in an environment of resource scarcity can be improved by readmission prediction score. The aim of study is to derive and validate a prediction model based on routinely collected hospital data for identification of risk factors for all-cause readmission within zero to 30 days post discharge from AMI.</p><p><strong>Methods: </strong>Our study includes 2,849 AMI patient records (January 2005 to December 2014) from a tertiary care facility in India. EMR with ICD-10 diagnosis, admission, pathological, procedural and medication data is used for model building. Model performance is analyzed for different combination of feature groups and diabetes sub-cohort. The derived models are evaluated to identify risk factors for readmissions.</p><p><strong>Results: </strong>The derived model using all features has the highest discrimination in predicting readmission, with AUC as 0.62; (95 percent confidence interval) in internal validation with 70/30 split for derivation and validation. For the sub-cohort of diabetes patients (1359) the discrimination is slightly better with AUC 0.66; (95 percent CI;). Some of the positively associated predictive variables, include age group 80-90, medicine class administered during index admission (Anti-ischemic drugs, Alpha 1 blocker, Xanthine oxidase inhibitors), additional procedure in index admission (Dialysis). While some of the negatively associated predictive variables, include patient demography (Male gender), medicine class administered during index admission (Betablocker, Anticoagulant, Platelet inhibitors, Anti-arrhythmic).</p><p><strong>Conclusions: </strong>Routinely collected data in the hospital's clinical and administrative data repository can identify patients at high risk of readmission following AMI, potentially improving AMI readmission rate.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"18 Spring","pages":"1j"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120669/pdf/phim0018-0001j.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39018383","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}

{"title":"An Exploration of Global Leadership Behavior and Job Satisfaction in Health Information Management.","authors":"Patricia S DeVoy","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Health information management (HIM) professionals are a vital component of a global network of healthcare specialists who assure quality documentation, data governance, analysis of data, and medical coding of vital healthcare statistics.¹ These healthcare professionals make up a globally diverse community² which demands leaders with globally transferable leadership skills. The goal of this study was to explore the application of Servant Leadership Theory³ to job satisfaction through globally applicable and transferable leadership behavior. A case study approach of semi-structured interviews and blog posting entries were examined through the principles of a global mindset.^4. Results of this study are applicable to the community of practicing HIM professionals through the identification and examples of the application of effective and globally transferable leadership behavior.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"18 Spring","pages":"1d"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120670/pdf/phim0018-0001d.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39016989","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}

{"title":"Comparison of ICD-9-CM to ICD-10-CM Crosswalks Derived by Physician and Clinical Coder vs. Automated Methods.","authors":"Jason C Simeone,&nbsp;Xinyue Liu,&nbsp;Tarun Bhagnani,&nbsp;Matthew W Reynolds,&nbsp;Jenna Collins,&nbsp;Edward A Bortnichak","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate whether automated methods are sufficient for deriving ICD-10-CM algorithms by comparing ICD-9-CM to ICD-10-CM crosswalks from general equivalence mappings (GEMs) with physician/clinical coder-derived crosswalks.</p><p><strong>Patients and methods: </strong>Forward mapping was used to derive ICD-10-CM crosswalks for 10 conditions. As a sensitivity analysis, forward-backward mapping (FBM) was also conducted for three clinical conditions. The physician/coder independently developed crosswalks for the same conditions. Differences between the crosswalks were summarized using the Jaccard similarity coefficient (JSC).</p><p><strong>Results: </strong>Physician/coder crosswalks were typically far more inclusive than GEMs crosswalks. Crosswalks for peripheral artery disease were most dissimilar (JSC: 0.06), while crosswalks for mild cognitive impairment (JSC: 1) and congestive heart failure (0.85) were most similar. FBM added ICD-10-CM codes for all three conditions but did not consistently increase similarity between crosswalks.</p><p><strong>Conclusion: </strong>The GEMs and physician/coder algorithms rarely aligned fully; human review is still required for ICD-9-CM to ICD-10-CM crosswalk development.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"18 Spring","pages":"1e"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120674/pdf/phim0018-0001e.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39016991","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}

{"title":"Assessing the Prevalence of Ahima-Identified Health Informatics and Information Management Careers and Related Skills: A Cross-Sectional Study.","authors":"Charisse R Madlock-Brown,&nbsp;Marcia Y Sharp,&nbsp;Rebecca B Reynolds","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This study's objective was to identify the prevalence of the American Health Information Management Association (AHIMA) career map jobs and determine which job categories, degrees, and skills are associated with higher pay. We extracted data from SimplyHired, a major employment website, from December 2018 to December 2019. We retrieved 12,688 career posts. We found differences in average salary by career category (p-value 0.00). Most jobs were in coding and revenue cycle (CRC) and information governance (IG) categories. The highest average salaries were in data analytics (DA) and informatics (IN). Each career category had a unique set of skills associated with the highest paying jobs. Eighty-two percent of CRC, 67 percent of IG, 65 percent of IN, and 83 percent of DA jobs listed in the AHIMA career map were present in the extracted dataset. These results can help employees, academics, and industry leaders understand the health informatics and information management (HIM) workforce landscape.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"18 Spring","pages":"1k"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120672/pdf/phim0018-0001k.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39018384","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}