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.

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).

Well-designed electronic health records (EHRs) must integrate a variety of accurate information to support efforts to improve quality of care, particularly equity-in-care initiatives. This case study provides insight into the challenges those initiatives may face in collecting accurate race, ethnicity, and language (REAL) information in the EHR. We present the experience of an academic medical center strengthening its EHR for better collection of REAL data with funding from the EHR Incentive Programs for meaningful use of health information technology and the Texas Medicaid 1115 Waiver program. We also present a plan to address some of the challenges that arose during the course of the project. Our experience at an academic medical center can provide guidance about the likely challenges similar institutions may expect when they implement new initiatives to collect REAL data, particularly challenges regarding scope, personnel, and other resource needs.

Following the passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, electronic health records were widely adopted by eligible physicians and hospitals in the United States. Stage 2 meaningful use menu objectives include a digital family history but no stipulation as to how that information should be used. A variety of data mining techniques now exist for these data, which include artificial neural networks (ANNs) for supervised or unsupervised machine learning. In this pilot study, we applied an ANN-based simulation to a previously reported digital family history to mine the database for trends. A graphical user interface was created to display the input of multiple conditions in the parents and output as the likelihood of diabetes, hypertension, and coronary artery disease in male and female offspring. The results of this pilot study show promise in using ANNs to data mine digital family histories for clinical and research purposes.

Objective: Hospital readmissions are a large source of wasteful healthcare spending, and current care transition models are too expensive to be sustainable. One way to circumvent cost-prohibitive care transition programs is complement nurse-staffed care transition programs with those staffed by less expensive nonmedical workers. A major barrier to utilizing nonmedical workers is determining the appropriate time to escalate care to a clinician with a wider scope of practice. The objective of this study is to show how mobile technology can use the observations of nonmedical workers to stratify patients on the basis of their hospital readmission risk.

Materials and methods: An area agency on aging in Massachusetts implemented a quality improvement project with the aim of reducing 30-day hospital readmission rates using a modified care transition intervention supported by mobile predictive analytics technology. Proprietary readmission risk prediction algorithms were used to predict 30-, 60-, 90-, and 120-day readmission risk.

Results: The risk score derived from the nonmedical workers' observations had a significant association with 30-day readmission rate with an odds ratio (OR) of 1.12 (95 percent confidence interval [CI], 1 .09-1.15) compared to an OR of 1.25 (95 percent CI, 1.19-1.32) for the risk score using nurse observations. Risk scores using nurse interpretation of nonmedical workers' observations show that patients in the high-risk category had significantly higher readmission rates than patients in the baseline-risk and mild-risk categories at 30, 60, 90, and 120 days after discharge. Of the 1,064 elevated-risk alerts that were triaged, 1,049 (98.6 percent) involved the nurse care manager, 804 (75.6 percent) involved the patient, 768 (72.2 percent) involved the health coach, 461 (43.3 percent) involved skilled nursing, and 235 (22.1 percent) involved the outpatient physician in the coordination of care in response to the alert.

Discussion: The predictive nature of the 30-day readmission risk scores is influenced by both nurse and nonmedical worker input, and both are required to adequately triage the needs of the patient.

Conclusion: Although this preliminary study is limited by a modest effect size, it demonstrates one approach to using technology to contribute to delivery model innovation that could curb wasteful healthcare spending by tapping into an existing underutilized workforce.

Electronic health record (EHR) adoption among office-based physician practices in the United States has increased significantly in the past decade. However, the challenges of using EHRs have resulted in growing dissatisfaction with the systems among many of these physicians. The purpose of this qualitative multiple-case study was to increase understanding of physician perceptions regarding the value of using EHR technology. Important findings included the belief among physicians that EHR systems need to be more user-friendly and adaptable to individual clinic workflow preferences, physician beliefs that lack of interoperability among EHRs is a major barrier to meaningful use of the systems, and physician beliefs that EHR use does not improve the quality of care provided to patients. These findings suggest that although government initiatives to encourage EHR adoption among office-based physician practices have produced positive results, additional support may be required in the future to maintain this momentum.

Introduction: In clinical practices, the use of information technology, especially computerized provider order entry (CPOE) systems, has been found to be an effective strategy to improve patient care. This study aimed to compare physicians' and nurses' views about the impact of CPOE on their workflow.

Methods: This case study was conducted in 2012. The potential participants included all physicians (n = 28) and nurses (n = 145) who worked in a teaching hospital. Data were collected using a five-point Likert-scale questionnaire and were analyzed using SPSS version 18.0.

Results: The results showed a significant difference between physicians' and nurses' views about the impact of the system on interorganizational workflow (p = .001) and working relationships between physicians and nurses (p = .017).

Conclusion: Interorganizational workflow and working relationships between care providers are important issues that require more attention. Before a CPOE system is designed, it is necessary to identify workflow patterns and hidden structures to avoid compromising quality of care and patient safety.

Background: The United States is one of the last countries to change from ICD-9-CM to ICD-10-CM/PCS. The compliance date for implementation of ICD-10-CM/PCS is expected to fall on October 1, 2015.

Objectives: Evaluate physicians' perceptions on the change from ICD-9-CM to ICD-10-CM/PCS and its effect on their practice, determine how HIM professionals can assist in this transition, and assess what resources are needed to aid in the transition.

Results: Twenty physicians were asked to participate in one of three focus groups. Twelve physicians (60 percent) agreed to participate. Top concerns included electronic health record software readiness, increase in documentation specificity and time, ability of healthcare professionals to learn a new language, and inadequacy of current training methods and content.

Conclusion: Physicians expressed that advantages of ICD-10-CM/PCS were effective data analytics and complexity of patient cases with more specific codes. Health information management professionals were touted as needed during the transition to create simple, clear specialty guides and crosswalks as well as education and training tools specific for physicians.