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

While nearly all hospitals have adopted electronic health record (EHR) systems, some are dissatisfied and considering replacement systems to better address unique organizational needs and priorities. With more than 4,000 certified health information technology products available, comparing the vast number of EHR options is complex. This study tested the hypothesis that various EHR systems demonstrate different financial and quality performance and presented a framework for comparison. Using a subscribed database containing US hospitals' observations from 2011 to 2016, we estimated an ordinary least squares regression model with robust standard errors and clustered by year. We regressed the selected finance and quality measures as dependent variables with the vendors' indicators as independent variables, with control variables. This study demonstrated an approach for analyzing performance data to help hospitals distinguish EHR systems on the basis of several organizational outcomes: return on assets, bed utilization rate, Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) summary star rating, and value-based purchasing Total Performance Score. This framework will help EHR acquisition teams make informed decisions.

Technology is intended to assist with diagnosing, treating, and monitoring patients remotely. Little is known of its impact on health outcomes or how it is used for obesity management. This study reviewed the literature to identify the different types of technologies used for obesity management and their outcomes. A literature search strategy using PubMed, CINAHL, Scopus, Embase, and ABI/Inform was developed and then was vetted by two pairs of researchers. Twenty-three studies from 2010 to 2017 were identified as relevant. Mobile health, eHealth, and telehealth/telemedicine are among the most popular technologies used. Study outcome measurements include association between technology use and weight loss, changes in body mass index, dietary habits, physical activities, self-efficacy, and engagement. All studies reported positive findings between technology use and weight loss; 60 percent of the studies found statistically significant relationships. Knowledge gaps persist regarding opportunities for technology use in obesity management. Future research needs to include patient-level outcomes, cost-effectiveness, and user engagement to fully evaluate the feasibility of continued and expanded use of technology in obesity management.

During residency training, one of the tools residents learn to use is the electronic health record (EHR). The EHR contains up-to-date medical data that are crucial to the care of the patient; thus the provider must know what is pertinent, where to locate it, and how to efficiently document the data for ongoing communication of patient care. Because institutions may have different EHR vendors, EHR workflow study data are often obtained in single institutions, with a limited number of participants and specialties. Increasing our understanding of the subtleties of residents' EHR usage not only can help educators understand how residents use the EHR but also may provide information on another cognitive factor to assess residents' performance. This, however, will only occur when EHR skills are considered an important part of residency training and we ask our EHR vendors to help us develop validated electronic tools to assess EHR performance.

This is a case study of the evidence-based management practices of a centralized health information management (HIM) department in a large integrated healthcare delivery system. The case study used interviews and focus groups, as well as de-identified dashboards, to explore the impact of reporting on the organization. The dashboards and key performance indicators (KPIs) were initially developed in 2012 and have continued to evolve. The themes that resulted include the following: (1) evidence-based management is integral to the culture of the organization; (2) communicating regularly via dashboards and KPIs is key to transmitting the value of HIM to the entire organization; and (3) staff not only report the required measures for the dashboard but also take pride in it and often develop methods for tracking their individual performance. Most evidence supporting HIM operations management is related to coding and clinical documentation improvement, but even in those areas, national benchmarks are missing. It is important for the HIM profession to develop national and regional benchmarks to assist professionals in managing operations effectively and communicating their value to the healthcare industry.

As health information management (HIM) shifts from paper-based medical records to electronic medical documentation, HIM professionals must appropriately manage their resources to produce higher results for their organization's operational and financial indicators. This case study highlights the experience of the HIM department in a small Florida community hospital in analyzing existing productivity standards and developing new standards with the purpose of improving the document imaging process. The research produced new productivity standards that more accurately represent the time HIM technicians spend performing their everyday tasks. The data collected during this period indicate that the average HIM technician was prepping 844 images an hour, scanning 601 images an hour, and indexing 482 images an hour. While a trend in productivity cannot be identified because different types of data were collected, the department's standards are now based on more consistently measurable output. The data collected during this study were used to manage the continuously changing workflow processes; improve the staff's knowledge, skills, and abilities; and identify potential areas of process improvement.

Although the federal electronic health record (EHR) incentive program has ended, the need to effectively implement and use EHRs has not. The advent of the federal Quality Payment Program (QPP) has made effective use of EHRs more critical than ever, especially for clinical quality measurement and improvement. However, practices continue to face challenges in successfully implementing and using EHRs to achieve these aims. We used a multiple case study approach to understand how physician practices were using EHR data to measure and improve quality. We interviewed a variety of physicians and staff at multiple practices of diverse sizes and settings. Our findings suggest specific approaches that can help practices better harness their EHR data to measure and improve the quality of care while reducing or preventing staff dissatisfaction and burnout. These lessons can help practices better leverage their EHRs to succeed in the QPP.

On October 1, 2012, as part of the Affordable Care Act, the Centers for Medicare and Medicaid Services began to reduce payments to hospitals with excessive rehospitalization rates through the Hospital Readmissions Reduction Program. These financial penalties have intensified hospital leaders' efforts to implement strategies to reduce readmission rates. The purpose of this multiple case study was to explore organizational strategies that leaders use to reduce readmission rates in hospitals located in a non-Medicaid-expansion state. The data collection included semistructured interviews with 15 hospital leaders located in five metropolitan and rural hospitals in southwest Missouri. Consistent with prior research, the use of predictive analytics stratified by patient population was acknowledged as a key strategy to help reduce avoidable rehospitalization. Study findings suggest that leveraging data from the electronic health records to identify at-risk patients provides comprehensive health information to reduce readmissions. Hospital leaders also revealed the need to understand and address the health needs of their local population, including social determinants such as lack of access to transportation as well as food and housing.

In this study, the relationship between data breach characteristics and the number of individuals affected by these violations was considered. Data were acquired from the Department of Health and Human Services breach reporting database and analyzed using SPSS. Regression analyses revealed that the hacking/IT incident breach type and network server breach location were the most significant predictors of the number of individuals affected; however, they were not predictive when combined. Moreover, network server location and unauthorized access/disclosure breach type were predictive when combined. Additional analyses of variance revealed that covered entity type and business associate presence were significant predictors, while the geographic region of a breach occurrence was insignificant. The results of this study revealed several associations between healthcare breach characteristics and the number of individuals affected, suggesting that more individuals are affected in hacking/IT incidents and network server breaches independently and that network server breach location and unauthorized access/disclosure breach type were predictive in combination.

The shortage of data scientists has restricted the implementation of big data analytics in healthcare facilities. This survey study explores big data tool and technology usage, examines the gap between the supply and the demand for data scientists through Diffusion of Innovations theory, proposes engaging academics to accelerate knowledge diffusion, and recommends adoption of curriculum-building models. For this study, data were collected through a national survey of healthcare managers. Results provide practical data on big data tool and technology skills utilized in the workplace. This information is valuable for healthcare organizations, academics, and industry leaders who collaborate to implement the necessary infrastructure for content delivery and for experiential learning. It informs academics working to reengineer their curriculum to focus on big data analytics. The paper presents numerous resources that provide guidance for building knowledge. Future research directions are discussed.

The time and costs associated with the sequencing of a human genome have decreased significantly in recent years. Many people have chosen to have their genomes sequenced to receive genomics-based personalized healthcare services. To reach the goal of genomics-based precision medicine, health information management (HIM) professionals need to manage and analyze patients' genomic data. Two important pieces of information from the genome sequence are the risk of genetic diseases and the specific medication or pharmacogenomic results for the individual patient, both of which are linked to a patient's genetic variations. In this review article, we introduce genetic variations, including their data types, relevant databases, and some currently available analysis methods and systems. HIM professionals can choose to use these databases, methods, and systems in the management and analysis of patients' genomic data.