Electronic Health Records and Clinical Decision Support Systems Impact on National Ambulatory Care Quality

Program on Prevention Outcomes and Practices, Stanford Prevention Research Center, Stanford University School of Medicine, USA.
Archives of internal medicine (Impact Factor: 17.33). 05/2011; 171(10):897-903. DOI: 10.1001/archinternmed.2010.527
Source: PubMed


Electronic health records (EHRs) are increasingly used by US outpatient physicians. They could improve clinical care via clinical decision support (CDS) and electronic guideline-based reminders and alerts. Using nationally representative data, we tested the hypothesis that a higher quality of care would be associated with EHRs and CDS.
We analyzed physician survey data on 255,402 ambulatory patient visits in nonfederal offices and hospitals from the 2005-2007 National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey. Based on 20 previously developed quality indicators, we assessed the relationship of EHRs and CDS to the provision of guideline-concordant care using multivariable logistic regression.
Electronic health records were used in 30% of an estimated 1.1 billion annual US patient visits. Clinical decision support was present in 57% of these EHR visits (17% of all visits). The use of EHRs and CDS was more likely in the West and in multiphysician settings than in solo practices. In only 1 of 20 indicators was quality greater in EHR visits than in non-EHR visits (diet counseling in high-risk adults, adjusted odds ratio, 1.65; 95% confidence interval, 1.21-2.26). Among the EHR visits, only 1 of 20 quality indicators showed significantly better performance in visits with CDS compared with EHR visits without CDS (lack of routine electrocardiographic ordering in low-risk patients, adjusted odds ratio, 2.88; 95% confidence interval, 1.69-4.90). There were no other significant quality differences.
Our findings indicate no consistent association between EHRs and CDS and better quality. These results raise concerns about the ability of health information technology to fundamentally alter outpatient care quality.

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Available from: Max J Romano, Mar 02, 2015
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    • "Medical decision support systems are designed to assist physicians or other professionals making more informed clinical decisions, helping in the diagnosis, and analysis (Romano & Stafford, 2011). Furthermore, a decision support system can support the control of costs, for example, monitoring medication orders, managing clinical complexity, performing a preventive care, and supporting administrative tasks. "
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    ABSTRACT: The analysis of medical records is a major challenge, considering they are generally presented in plain text, have a very specific technical vocabulary, and are nearly always unstructured. It is an interdisciplinary work that requires knowledge from several fields. The analysis may have several goals, such as assistance on clinical decision, classification of medical procedures, and to support hospital management decisions. This work presents the concepts involved, the relevant existent related work and the main open issues for future research within the analysis of electronic medical records, using data and text mining techniques. It provides a comprehensive contextualization to all those who wish to perform an analytical work of medical records, enabling the identification of fruitful research fields. With the digitalization of medical records and the large amount of medical data available, this is an area of wide research potential.
    Full-text · Article · Jul 2015 · International Journal of E-Health and Medical Communications
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    • "Computers have helped implement clinical guidelines and improved clinical care [11] [12]. They have supported clinical decision-making [13] [14]. A systematic review reported a significant improvement in care after the implementation of computerized clinical guidelines [15]. "
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    ABSTRACT: Background: Despite the widely held belief that the computerization of hospital medical systems contributes to improved patient care management, especially in the context of ordering medications and record keeping, extensive study of the attitudes of medical staff to computerization has found them to be negative. The views of nursing staff have been barely studied and so are unclear. The study reported here investigated the association between nurses' current computer use and skills, the extent of their involvement in quality control and improvement activities on the ward and their perception of the contribution of computerization to improving nursing care. The study was made in the context of a Joint Commission International Accreditation (JCIA) in a large tertiary medical center in Israel. The perception of the role of leadership commitment in the success of a quality initiative was also tested for. Methods: Two convenience samples were drawn from 33 clinical wards and units of the medical center. They were questioned at two time points, one before the JCIA and a second after JCIA completion. Of all nurses (N=489), 89 were paired to allow analysis of the study data in a before-and-after design. Thus, this study built three data sets: a pre-JCIA set, a post-JCIA set and a paired sample who completed the questionnaire both before and after JCIA. Data were collected by structured self-administered anonymous questionnaire. Results: After the JCIA the participants ranked the role of leadership in quality improvement, the extent of their own quality control activity, and the contribution of computers to quality improvement higher than before the JCIA. Significant Pearson correlations were found showing that the higher the rating given to quality improvement leadership the more nurses reported quality improvement activities undertaken by them and the higher nurses rated the impact of computerization on the quality of care. In a regression analysis quality improvement leadership and computer use/skills accounted for 30% of the variance in the perceived contribution of computerization to quality improvement. Conclusions: (a) The present study is the first to show a relationship between organizational leadership and computer use by nurses for the purpose of improving clinical care. (b) The nurses' appreciation of the contribution computerization can make to data management and to clinical care quality improvement were both increased by the JCI accreditation process.
    Full-text · Article · Dec 2014 · International Journal of Medical Informatics
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    • "This large corpus of population-based records is increasingly used in the context of clinical decision making for the individual patient [10]. Nevertheless, there still seems to be no consistent association between EHRs and clinical decision support systems (CDSS) and better quality of care [11]. "
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    ABSTRACT: Clinical decision support systems assist physicians in interpreting complex patient data. However, they typically operate on a per-patient basis and do not exploit the extensive latent medical knowledge in electronic health records (EHRs). The emergence of large EHR systems offers the opportunity to integrate population information actively into these tools. Here, we assess the ability of a large corpus of electronic records to predict individual discharge diagnoses. We present a method that exploits similarities between patients along multiple dimensions to predict the eventual discharge diagnoses. Using demographic, initial blood and electrocardiography measurements, as well as medical history of hospitalized patients from two independent hospitals, we obtained high performance in cross-validation (area under the curve >0.88) and correctly predicted at least one diagnosis among the top ten predictions for more than 84% of the patients tested. Importantly, our method provides accurate predictions (>0.86 precision in cross validation) for major disease categories, including infectious and parasitic diseases, endocrine and metabolic diseases and diseases of the circulatory systems. Our performance applies to both chronic and acute diagnoses. Our results suggest that one can harness the wealth of population-based information embedded in electronic health records for patient-specific predictive tasks.
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