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

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.

Purpose: Because user acceptance and resistance to the use of health information technology (HIT) affects system utilization and previous studies in this area have typically excluded pharmacists, this study specifically addresses the response of institutional pharmacists to HIT.

Methods: A survey investigating pharmacists' responses to electronic medical record (EMR) system use was developed using questions modified from previously validated research. The survey was distributed electronically to the mailing list for pharmacy preceptors for the University of Tennessee College of Pharmacy. Descriptive statistics and univariate and multivariate analyses were used to analyze the collected data based on a previously validated dual-factor model.

Results: Of the 96 responses from institutional pharmacists, 64 responses (66.7 percent) were complete and usable. Of the acceptance and resistance constructs evaluated, only attitude and perceived behavior control were found to be significantly associated with acceptance of use (p = .036 and p = .025, respectively), and only transition cost was found to be significantly associated with resistance to use (p = .018). System vendor and interface integration were also significantly associated with acceptance of use. These findings suggest that attitude, perceived behavior control, and transition costs may have the most impact on pharmacists' responses to the use of EMR systems.

Conclusion: It is reasonable for hospitals to focus efforts on specific factors influencing acceptance of and resistance to EMR use and, before a system is selected, to consider the effects of vendor selection and level of interface integration on acceptance of use.

On October 1, 2015, the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) was incorporated into the US public health system. Because of significant opposition and reservations expressed by stakeholders, while the proposed rule for ICD-10-CM adoption was issued in 2009, the transition did not occur until October 2015. The purpose of this study was to identify conversion initiatives used by a public health institution during the initial and subsequent stages of ICD-10-CM implementation, to help similar institutions address future unfunded healthcare data infrastructure mandates. The data collection for this study occurred from 2015 to 2018, encompassing 20 semistructured interviews with 13 department heads, managers, physicians, and coders. Research findings from this study identified several trends, disruptions, challenges, and lessons learned that might support the industry with strategies to foster success for the transition to future coding revisions (i.e., ICD-11).

Objectives: Soft-tissue sarcoma (STS) is a heterogeneous group of rare solid tumors that arise from various soft tissues in the body, such as muscle, fat, nerves, and blood vessels. Current International Classification of Diseases (ICD) coding systems include a set of nonspecific codes for malignancies of connective and soft tissue (ICD-9-CM code 171 and ICD-10-CM code C49). The goal of this study was to evaluate the use of these codes for health services research involving patients with a diagnosis of this rare malignancy.

Methods: Two databases were utilized to explore ICD coding for STS: claims data from Truven MarketScan and electronic medical records (EMRs) from Flatiron Health. Eligible patients from claims data were those with at least two ICD-9-CM codes of 171.x on two different days between July 1, 2004, and March 30, 2014. The treatment patterns of these cases were evaluated for consistency with known therapeutic approaches for STS. Eligible patients from the Flatiron EMR system were those who received olaratumab (a drug indicated only for use in patients diagnosed with STS) after its US Food and Drug Administration approval in October 2016 through the end of the data set (November 2017). ICD-10-CM codes were evaluated for this known STS cohort.

Results: In claims data, 4,159 patients were eligible for inclusion. Although national treatment guidelines include only a limited number of drugs used to treat STS, 98 unique anticancer drugs were identified as being used to treat patients in a claims data cohort. Only 7.7 percent of patients had claims for doxorubicin-based therapy and 3.8 percent had claims for ifosfamide-based therapy as initial treatment for STS, despite these being a standard of care. In the EMR data, 350 patients were eligible; only 170 patients (48.6 percent) had any evidence in the database of a connective or soft-tissue ICD-10-CM malignancy code within 60 days before or after initiation of olaratumab.

Conclusions: ICD coding for STS using the "Malignant neoplasm of connective and soft tissue" code is not reliable as a method to identify patients diagnosed with STS. Although codes reflecting the primary site of disease may have clinical relevance, lack of consistency in ICD coding for the diagnosis and treatment of this disease is a limiting factor in the ability to conduct real-world observational research of this rare disease. In the absence of consistent use of this code, an algorithm needs to be developed and validated to accurately identify patients with STS in these databases.

Introduction: Emergency care is usually conducted within limited time and with limited resources. During emergency care processes, data quality issues should be taken into account. The aim of this study was to assess the quality of emergency care data from the perspectives of different data stakeholders.

Method: This survey study was conducted in 2017. In this research, the viewpoints of three groups of data stakeholders, including data producers, data collectors, and data consumers, were collected regarding data quality in emergency care services. Data were collected by using a standard information quality assessment questionnaire.

Results: The mean values for each dimension of data quality were as follows: sound data (6.23), dependable data (6.28), useful data (6.30), and usable data (6.35), with 0 being the lowest possible score and 10 being the highest. The role gap analysis suggested a clear gap between data producers and data customers at the university level.

Conclusion: Overall, data quality in emergency medical services was not at a high level. Although data quality was improving, the levels of data completeness, compatibility, and usability were low. To improve the usability of emergency medical service data, more attention should be paid to the dimensions of accuracy, completeness, and consistency of data sources.

Physicians spend a large portion of their time documenting patient encounters using electronic health records (EHRs). Meaningful Use guidelines have made EHR systems widespread, but they have not been shown to save time. This study compared the time required to complete an emergency department note in two different EHR systems for three separate video-recorded standardized simulated patient encounters. The total time needed to complete documentation, including the time to write and order the initial history, physical exam, and diagnostic studies, and the time to provide medical decision making and disposition, were recorded and compared by trainee across training levels. The only significant difference in documentation time was by classification, with second- and third-year trainees being significantly faster in documenting on the Cerner system than fourth-year medical student and first-year trainees (F = 8.36, p < .001). Level of training and experience with a system affected documentation time.

The future of clinical documentation improvement (CDI) will require expanding the reach of CDI programs into new areas of expertise because the traditional realms of CDI work are increasingly becoming automated. CDI-based research and analytics can serve as a means for demonstrating continued value to an institution. We present four studies as examples of these efforts. We explored the use of claims data to determine whether a clinical condition meets the criteria for a secondary diagnosis and to evaluate whether a clinical problem should be elevated to the status of a comorbid or complicating condition. We demonstrated a way in which CDI professionals can evaluate the impacts of changes in clinical definitions, and we explored how CDI can work with other institutional programs to decrease length of stay. We believe that these models may serve as a springboard within institutions and among the larger CDI community to make research and analytics a foundation of future CDI activities.

As healthcare systems continue to expand their use of electronic health records (EHRs), barriers to robust and successful engagement with such systems by stakeholders remain tenacious. To this effect, this research presents the results of a survey tool utilizing both original and modified constructs from the Consolidated Framework for Implementation Research to assess key points of engagement barriers and potential points of intervention for stakeholders of EHRs in a large-scale healthcare organization (500-bed level II regional trauma center). Based on the extensive assessment, the paper presents recommendations for the utility of engagement process modeling and discusses how intervention opportunities can be used to mitigate engagement barriers.