Small nonprofit medical practices lack the technical expertise to implement electronic medical records (EMRs) that are consistent with federal meaningful-use requirements. Failure to comply with meaningful-use EMR requirements affects nonprofit community health care leaders' ability to receive reimbursement for care. Complexity theory was the conceptual framework used in this exploratory single case study. The purpose of the study was to explore the strategies nonprofit community health care leaders in Washington, DC used to implement EMRs in order to comply with the meaningful-use requirements. Data were collected via in-depth interviews with 7 purposively-selected health care leaders in a nonprofit clinic and were supplemented with archival records from the organization's policies and legislated mandates. Participants' responses were coded into invariant constituents, single concepts, and ideas to develop theme clusters. Member checking was used to validate the transcribed data which was subsequently coded into 4 themes that included: access to information, quality of care, training, and reporting implications. Recommendations include increased effectiveness of training provided to health care leaders or the perceptions of the patients as stakeholders in EMR implementation. By using strategies that facilitate seamless movement of information within a digital health care infrastructure, business leaders could benefit from improved reimbursement for services. Implications for social change include progress and transformation in the way health care access is provided.

<p dir="ltr">This study aims to predict the environmental, organizational, and managerial factors that determine the adoption of cloud computing in U.S. healthcare delivery systems. The premise of the analysis is that several internal and external factors determine a health provider’s transition to cloud computing. The U.S. government has funded healthcare providers through HITECH <a href="" target="_blank">(Health Information Technology for Economic and Clinical Health) </a>to implement electronic health records (EHR) which is considered as an important first step in transitioning to cloud computing. This study investigated whether there is a significant difference between hospitals and providers that received HITECH funding to enhance their EHR infrastructure and those that did not in terms of their external environmental complexities, internal organizational structure, and quality of healthcare services they provide. A stratified random sample was applied to select a cohort of 3,385 hospitals from the American Hospital Association (AHA) 2022 roster for the period 2018- 2021 to test the study hypothesis. The sampled hospitals were linked with claim, administrative, cost, and ICD-10 clinical data files to capture variables of interest repeatedly over the study period. The analysis modeled for selected external (location, market concentration as measured by Herfindahl Index), internal (number and composition of staff – physicians, nurses, technicians, etc.) demographic, clinical and financial factors. Quantitative methods such as generalized estimating equations (GEE), logistic regression, and generalized linear mixed model (GLMM) were applied within the framework of unified technology acceptance theory (UTAT), accounting for both discrete and continuous response variables while modeling for possible between-subject heterogeneity and within-subject correlations. The analysis is based on publicly available data sources that are systematically linked to address the research questions. The portion of the HITECH funding that is applied for cloud computing is calculated from the hospital’s EHR funding. This is one of the very few longitudinal time series studies of cloud computing in healthcare since almost all previous studies on American hospitals are cross-sectional. The findings of this study show statistically significant differences between hospitals that received government funding in terms of internal organizational structure, environmental complexity, and quality of healthcare provided. The analysis identified management and quality metrics that help to gauge continuously changing organizational needs and identify emerging trends. This study proposes specific topics that future researchers can consider promoting a successful implementation of cloud computing.</p>

No description available.

In 2009, the American Recovery and Reinvestment Act was signed into law. This legislation provided for monetary rewards for those acute-care hospitals that meet "meaningful use" computerization and reporting criteria. The study used a descriptive, nonexperimental design to answer three research questions (1) What is the level of readiness to meet "meaningful use" criteria in the Tennessee Hospital Association (THA) member hospitals; (2) What is the level of readiness to meet "meaningful use" criteria in the rural THA member hospitals; and (3) Is there a difference in the readiness to meet "meaningful use" criteria between rural and urban THA member hospitals?. A survey was sent to 115 THA member hospital, with a return rate of 83% (N=95). The inclusion criteria focused on acute-care hospitals, with rehabilitation, psychiatric and long-term care hospitals falling into the exclusion criteria. The Readiness Score was determined for the total survey respondents (N=95), as well as for the rural (N=41) hospitals and urban (N=54) hospitals in the Tennessee Hospital Association member hospitals meeting the inclusion criteria. Z-scores of the readiness score were examined and indicated that there was one outlier with z>3.0. Therefore, that case was removed from the comparison in the t-test (N=94). The t-test comparison of rural and urban hospital found a significant difference at (p=.002), two tailed. To ensure that the slightly nonnormal distribution of the readiness scores did not explain the difference found with the t-test, an additional nonparametric test was also conducted. The Mann Whitney U-test showed that even with the assumption of a normal distribution is not made, the difference in readiness between urban and rural hospitals is still statistically significant at p=0.026.

Since the HITECH (Health Information Technology for Economic and Clinical Health) Act was enacted, healthcare providers are required to achieve “Meaningful Use.” CPOE (Clinical Provider Order Entry), is one such requirement. Many providers prefer to dictate their orders rather than typing them. Medical vocabulary is wrought with its own terminology and department-specific acronyms, and many ASR (Automated Speech Recognition) systems are not trained to interpret this language. The purpose of this thesis research was to investigate the use and effectiveness of ASR in the healthcare industry. Multiple hospitals and multiple clinicians agreed to be followed through their use of an ASR system to enter patient data into the record. As a result of this research, the effectiveness and use of the ASR was examined, and multiple issues with the use and accuracy of the system were uncovered.

This paper seeks to break down how a large scale Electronic Health Records system could improve quality of care and reduce monetary waste in the healthcare system. The paper further explores issues regarding regulations to data exchange and data interoperability. Due to the massive size of healthcare data, the exponential increase in the speed of data generation through innovative technologies, and the complexity of healthcare data types, the widespread of a large-scale EHR system has hit barriers. Much of the data available is unstructured or contained within a singular healthcare provider’s systems. To fully utilize all the data available, methods for making data interoperable and regulations for data exchange to protect and support patients must be made. Through angles addressing data exchange and interoperability, we seek to break down the constraints and issues that EHR systems still face and gain an understanding of the regulatory landscape.