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

Introduction: 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.

Objectives: 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).

Method: 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.

Results: 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).

Conclusion: 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.

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.

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.

This is the second part in a two-part research study on clinical data abstraction.¹ Clinical data abstraction is the process of capturing key administrative and clinical data elements from a medical record. Very little is known about how the abstraction function is organized and managed today. A research study to gather data on how the clinical data abstraction function is managed in healthcare organizations across the country was performed. Results show that the majority of the healthcare organizations surveyed have a decentralized system, still perform the abstraction in-house as part of the coding workflow, and use manual abstraction followed by natural language processing (NLP) and simple query. The qualifications and training of abstractors varied across abstraction functions, however coders followed by nurses and health information management (HIM) professionals were the three top performers in abstraction. While, in general, abstraction is decentralized in most enterprises, two enterprise-wide abstraction models emerged from our study. In Model 1, the HIM department is responsible for coding, as well as all of the abstraction functions except the cancer registry and trauma registry abstraction. In Model 2, the quality department is responsible for all of the abstraction functions except the cancer registry, trauma registry, and coding function.

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.

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.

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.

Background: 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.

Methods: 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.

Results: 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).

Conclusions: 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.

Erroneous electronic health record (EHR) data capture is a barrier to preserving data integrity. We assessed the impact of an interdisciplinary process in minimizing EHR data loss from prescription orders. We implemented a three-step approach to reduce data loss due to missing medication doses: Step 1-A data analyst updated the request code to optimize data capture; Step 2-A pharmacist and physician identified variations in EHR prescription workflows; and Step 3-The clinician team determined daily doses for patients with multiple prescriptions in the same encounter. The initial report contained 1421 prescriptions, with 377 (26.5 percent) missing dosages. Missing dosages reduced to 361 (26.3 percent) prescriptions following Step 1, and twenty-three (1.7 percent) records after Step 2. After Step 3, 1210 prescriptions remained, including 16 (1.3 percent) prescriptions missing doses. Prescription data is susceptible to missing values due to multiple data capture workflows. Our approach minimized data loss, improving its validity in retrospective research.