Concept: Clinical decision support system
BACKGROUND: Pain management is a critical but complex issue for the relief of acute pain, particularly for postoperative pain and severe pain in cancer patients. It also plays important roles in promoting quality of care. The introduction of pain management decision support systems (PM-DSS) is considered a potential solution for addressing the complex problems encountered in pain management. This study aims to investigate factors affecting acceptance of PM-DSS from a nurse anesthetist perspective. METHODS: A questionnaire survey was conducted to collect data from nurse anesthetists in a case hospital. A total of 113 questionnaires were distributed, and 101 complete copies were returned, indicating a valid response rate of 89.3 %. Collected data were analyzed by structure equation modeling using the partial least square tool. RESULTS: The results show that perceived information quality (gamma=.451, p<.001), computer self-efficacy (gamma=.315, p<.01), and organizational structure (gamma=.210, p<.05), both significantly impact nurse anesthetists' perceived usefulness of PM-DSS. Information quality (gamma=.267, p<.05) significantly impacts nurse anesthetists' perceptions of PM-DSS ease of use. Furthermore, both perceived ease of use (beta=.436, p<.001, R2=.487) and perceived usefulness (beta=.443, p<.001, R2=.646) significantly affected nurse anesthetists' PM-DSS acceptance (R2=.640). Thus, the critical role of information quality in the development of clinical decision support system is demonstrated. CONCLUSIONS: The findings of this study enable hospital managers to understand the important considerations for nurse anesthetists in accepting PM-DSS, particularly for the issues related to the improvement of information quality, perceived usefulness and perceived ease of use of the system. In addition, the results also provide useful suggestions for designers and implementers of PM-DSS in improving system development.
The objective was to find evidence to substantiate assertions that electronic applications for medication management in ambulatory care (electronic prescribing, clinical decision support (CDSS), electronic health record, and computer generated paper prescriptions), while intended to reduce prescribing errors, can themselves result in errors that might harm patients or increase risks to patient safety.
Clinical decision support systems (CDSSs) are an integral component of today’s health information technologies. They assist with interpretation, diagnosis, and treatment. A CDSS can be embedded throughout the patient safety continuum providing reminders, recommendations, and alerts to health care providers. Although CDSSs have been shown to reduce medical errors and improve patient outcomes, they have fallen short of their full potential. User acceptance has been identified as one of the potential reasons for this shortfall.
- Medical decision making : an international journal of the Society for Medical Decision Making
- Published almost 8 years ago
. To better understand 1) why patients have a negative perception of the use of computerized clinical decision support systems (CDSSs) and 2) what contributes to the documented heterogeneity in the evaluations of physicians who use a CDSS.
Clinical decision support systems have the potential to improve patient care in a multitude of ways. Clinical decision support systems can aid in the reduction of medical errors and reduction in adverse drug events, ensure comprehensive treatment of patient illnesses and conditions, encourage the adherence to guidelines, shorten patient length of stay, and decrease expenses over time. A clinical decision support system is one of the key components for reaching compliance for Meaningful Use. In this article, the advantages, potential drawbacks, and clinical decision support system adoption barriers are discussed, followed by an in-depth review of the characteristics that make a clinical decision support system successful. The legal and ethical issues that come with the implementation of a clinical decision support system within an organization and the future expectations of clinical decision support system are reviewed.
This study aims to determine what the initial disposition of physicians towards the use of Clinical Decision Support Systems (CDSS) based on Computerised Clinical Guidelines and Protocols (CCGP) is; and whether their prolonged utilisation has a positive effect on their intention to adopt them in the future. For a period of 3 months, 8 volunteer paediatricians monitored each up to 10 asthmatic patients using two CCGPs deployed in thee-GuidesMed CDSS. A Technology Acceptance Model (TAM) questionnaire was supplied to them before and after using the system. Results from both questionnaires are analysed searching for significant improvements in opinion between them. An additional survey was performed to analyse the usability of the system. It was found that initial disposition of physicians towards e-Guidesmed is good. Improvement between the pre and post iterationsof the TAM questionnaire has been found to be statistically significant. Nonetheless, slightly lower values in the Compatibility and Habit variables show that participants perceive possible difficulties to integrate e-GuidesMed into their daily routine. The variable Facilitators shows the highest correlation with the Intention to Use. Usabilityof the system has also been rated very high and, in this regard, no fundamental flaw has been detected. Initial views towards e-GuidesMed are positive, and become reinforced after continued utilisation of the system. In order to achieve an effective implementation, it becomes essential to facilitate conditions to integrate the system intothe physician’s daily routine.
Background: The relationship between clinical decision support systems (CDSS) and quality is a relatively new, and in light of the new health information technology (HIT) legislation, policy-relevant area. Moreover, very few studies exist examining the link between HIT and healthcare disparities. The purpose of this article is to examine the association between CDSS and the treatment of pneumonia care within high-minority (≥29.1% non-white, non-Hispanic) and low-minority (<29.1%) Zip Code Tabulation Areas (ZCTAs). Research design: This study employed a cross-sectional design and used 2009 data from the American Hospital Association, the Centers for Medicare and Medicaid Services and the Research Triangle Institute. Adjusted analysis controlled for a hospital's propensity to use CDSS. Results: In the unadjusted analysis, hospitals in high-minority ZCTAs had lower pneumonia quality composite scores than their low-minority counterparts. When adjusting for other hospital and ZCTA-level variables, we found that CDSS use had stronger positive associations with quality in high-minority hospitals. Conclusions: Results support policy directives may support higher quality improvements by focusing CDSS adoption in high-minority hospitals.
This Position Statement represents a consensus of an expert committee convened by the European Society of Endodontology (ESE) on the use of Cone Beam Computed Tomography (CBCT). The statement is based on the current scientific evidence, and provides the clinician with evidence-based criteria on when to use CBCT in Endodontics. Given the dynamic and changing nature of research, development of new devices and clinical practice relating to CBCT, this Position Statement will be updated within 3 years, or before that time should new evidence become available.
As a result of the disease’s high prevalence, chronic kidney disease (CKD) has become a global public health problem. A clinical decision support system that integrates with computer-interpretable guidelines (CIGs) should improve clinical outcomes and help to ensure patient safety.
The broad adoption of clinical decision support systems within clinical practice has been hampered mainly by the difficulty in expressing domain knowledge and patient data in a unified formalism. This paper presents a semantic-based approach to the unified representation of healthcare domain knowledge and patient data for practical clinical decision making applications.