This study investigates how hospital electronic medical record (EMR) use influences quality performance. Data include nonfederal acute care hospitals in the United States. Sources of the data include the American Hospital Association, Hospital Quality Alliance, the Healthcare Information and Management Systems Society, and the Centers for Medicare and Medicaid Services case-mix index sets. The authors use a retrospective cross-sectional format with linear regression to assess the relationship between hospital EMR use and quality performance. Quality performance is measured using 10 process indicators related to 3 clinical conditions: acute myocardial infarction, congestive heart failure, and pneumonia. The authors also use a propensity score adjustment to control for possible selection bias. After this adjustment, the authors identify a positive significant relationship between EMR use and 4 of the 10 quality indicators. They conclude that there is limited evidence of the relationship between hospital EMR use and quality.
"Another area non-directly related to clinical skills that we are interested in is education and information in e-health for general practitioners. Services and systems proposed by ehealth including EHR(Electronic health records), e-prescribing, and Healthcare Information Systems, would allow the health care provider to be better informed, and facilitate remote medical collaboration and resource management, which helps to deliver higher quality care  . "
"All mortality measures are reported as part of this research, with the exception of carotid endarterectomy, hip fracture, and hip replacement because of the low volume of such procedures performed in our sample from the state of Texas. Data are not considered valid if a hospital treats fewer than 25 qualifying patients (Jha, Li et al. 2005; Kazley and Ozcan 2008). The recognition of data measures with fewer than 25 cases as being potentially unreliable and invalid is consistent with the Centers for Medicare & Medicate Services (CMS) recommendation for use of these data stating, " …that the number of cases is too small (fewer than 25) to reliably tell how well the hospital is performing " (CMS 2009). "
[Show abstract][Hide abstract] ABSTRACT: Hospitals invest in information technology to lower costs and to improve quality of care. With presidential leaders backing an in place policy that requires Electronic Health Records (EHRs) to be implemented in all hospitals by 2014 and the unveiling of a $1.2 billion grant for these systems, it is essential to understand the operational impacts of EHRs. This study explores EHRs in a hospital environment and investigates their relationship to quality of care and patient safety. EHRs are categorised into four functional groups: patient information data, results management, order entry, and decision support. This new knowledge will provide a better understanding of the relationship between EHRs and operational outcomes by showing the impact of various EHR functions on patient safety and quality of care.
International Journal of Electronic Healthcare 10/2012; 7(2):125-40. DOI:10.1504/IJEH.2012.049874
"Although research examining the impact of HIT adoption on patient satisfaction has shown increased patient satisfaction, most research has concentrated primarily on HIT adoption in physician offices , adoption of only one system, such as a computer-based patient record system   or special patient treatment . Most related studies have concluded that further research and new development methods are required to understand the effects that may be achieved through the successful implementation and use of HIT    . "
[Show abstract][Hide abstract] ABSTRACT: Objectives:
To develop and explore the predictability of patient perceptions of satisfaction through the hospital adoption of health information technology (HIT), leading to a better understanding of the benefits of increased HIT investment.
Data and methods:
The solution proposed is based on comparing the predictive capability of artificial neural networks (ANNs) with the adaptive neuro-fuzzy inference system (ANFIS). The latter integrates artificial neural networks and fuzzy logic and can handle certain complex problems that include fuzziness in human perception, and non-normal and non-linear data. Secondary data from two surveys were combined to develop the model. Hospital HIT adoption capability and use indicators in the Canadian province of Ontario were used as inputs, while patient satisfaction indicators of healthcare services in acute hospitals were used as outputs.
Eight different types of models were trained and tested for each of four patient satisfaction dimensions. The accuracy of each predictive model was evaluated through statistical performance measures, including root mean square error (RMSE), and adjusted coefficient of determination R(2)(Adjusted). For all four patient satisfaction indicators, the performance of ANFIS was found to be more effective (R(Adjusted)(2)=0.99) when compared with the results of ANN modeling in predicting the impact of HIT adoption on patient satisfaction (R(Adjusted)(2)=0.86-0.88).
The impact of HIT adoption on patient satisfaction was obtained for different HIT adoption scenarios using ANFIS simulations. The results through simulation scenarios revealed that full implementation of HIT in hospitals can lead to significant improvement in patient satisfaction. We conclude that the proposed ANFIS modeling technique can be used as a decision support mechanism to assist government and policy makers in predicting patient satisfaction resulting from the implementation of HIT in hospitals.
Artificial intelligence in medicine 09/2012; 56(2). DOI:10.1016/j.artmed.2012.08.001 · 2.02 Impact Factor
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