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Ameen Abu-Hanna

Publications and source records attributed to Ameen Abu-Hanna.

9 recordsLinked to original sources
PubMed2006

Errors associated with applying decision support by suggesting default doses for aminoglycosides.

BACKGROUND: Medication errors, and the resultant adverse drug events (ADEs), are one of the main preventable causes of morbidity and mortality. Computerised physician order entry (CPOE) is reported to reduce the frequency of these errors. However, CPOE systems themselves may be associated with errors. The aim of this study was to investigate the effects of a CPOE system that displays an initial default dose for gentamycin and tobramycin administration on the frequency of medication errors and potential ADEs in patients with renal insufficiency. METHODS: Gentamycin and tobramycin prescriptions from the CPOE records of a Dutch tertiary adult intensive care unit were retrospectively compared with doses recommended by a locally developed guideline. The default dose for gentamycin and tobramycin in the CPOE system is 240 mg/day. A dose prescribing error was defined as an administered dose that exceeded the recommended dose by >10%. RESULTS: Three hundred and ninty two prescriptions, relating to 253 patients (of whom 184 had renal insufficiency), were analysed. There was a high frequency (58%, 227 of 392) of prescriptions that used the CPOE system's default dose of 240 mg/day. The dose was wrong in 73% (165) of these orders. Default orders for patients with renal insufficiency amounted to 52% (132 of 259). A total of 86% (113 of 132) of these resulted in potential ADEs compared with 53% (66 of 124) for the rest of orders (p < 0.0001). DISCUSSION: A markedly high frequency of prescriptions followed the default dose value and, in patients with renal insufficiency, there was a high frequency of doses exceeding the guideline recommendation (+10%), amounting to potential ADEs. CONCLUSION: Initial CPOE dose values for prescribing gentamycin and tobramycin, which are based on a fixed default value, form a source of potential ADEs for patients with renal insufficiency.

Adult↗
PubMed2005

Factors that predict outcome of intensive care treatment in very elderly patients: a review.

INTRODUCTION: Advanced age is thought to be associated with increased mortality in critically ill patients. This report reviews available data on factors that determine outcome, on the value of prognostic models, and on preferences regarding life-sustaining treatments in (very) elderly intensive care unit (ICU) patients. METHODS: We searched the Medline database (January 1966 to January 2005) for English language articles. Selected articles were cross-checked for other relevant publications. RESULTS: Mortality rates are higher in elderly ICU patients than in younger patients. However, it is not age per se but associated factors, such as severity of illness and premorbid functional status, that appear to be responsible for the poorer prognosis. Patients' preferences regarding life-sustaining treatments are importantly influenced by the likelihood of a beneficial outcome. Commonly used prognostic models have not been calibrated for use in the very elderly. Furthermore, they do not address long-term survival and functional outcome. CONCLUSION: We advocate the development of new prognostic models, validated in elderly ICU patients, that predict not only survival but also functional and cognitive status after discharge. Such a model may support informed decision making with respect to patients' preferences.

Activities of Daily Living↗
PubMed2005

Description logic-based methods for auditing frame-based medical terminological systems.

OBJECTIVE: Medical terminological systems (TSs) play an increasingly important role in health care by supporting recording, retrieval and analysis of patient information. As the size and complexity of TSs are growing, the need arises for means to audit them, i.e. verify and maintain (logical) consistency and (semantic) correctness of their contents. This is not only important for the management of TSs but also for providing their users with confidence about the reliability of their contents. Formal methods have the potential to play an important role in the audit of TSs, although there are few empirical studies to assess the benefits of using these methods. METHODS AND MATERIAL: In this paper we propose a method based on description logics (DLs) for the audit of TSs. This method is based on the migration of the medical TS from a frame-based representation to a DL-based one. Our method is characterized by a process in which initially stringent assumptions are made about concept definitions. The assumptions allow the detection of concepts and relations that might comprise a source of logical inconsistency. If the assumptions hold then definitions are to be altered to eliminate the inconsistency, otherwise the assumptions are revised. RESULTS: In order to demonstrate the utility of the approach in a real-world case study we audit a TS in the intensive care domain and discuss decisions pertaining to building DL-based representations. This case study demonstrates that certain types of inconsistencies can indeed be detected by applying the method to a medical terminological system. CONCLUSION: The added value of the method described in this paper is that it provides a means to evaluate the compliance to a number of common modeling principles in a formal manner. The proposed method reveals potential modeling inconsistencies, helping to audit and (if possible) improve the medical TS. In this way, it contributes to providing confidence in the contents of the terminological system.

Algorithms↗
PubMed2005

Two DL-based methods for auditing medical terminological systems.

Medical terminological systems (TSs) play an increasingly important role in health care by supporting recording, retrieval and analysis of patient information. As the size and complexity of TSs are growing, the need arises for means to audit them, i.e. verify and maintain (logical) consistency and (semantic) correctness of their contents. In this paper we describe two methods based on description logics (DLs) for the audit of TSs. One method uses non-primitive definitions to detect concepts with equivalent definitions. The other method is characterized by stringent assumptions that are made about concept definitions, in order to detect inconsistent definitions. We discuss the possibility of applying these methods to the Foundational Model of Anatomy (FMA) to demonstrate the potentials and pitfalls of these methods. We show that the methods are complementary, and can indeed improve the contents of medical TSs.

Anatomy↗
PubMed2004

Fifteen years medical information sciences: the Amsterdam curriculum.

OBJECTIVES: To inform the medical informatics community on the rational, goals, evolution and present contents of the Medical Information Sciences program of the University of Amsterdam and our achievements. METHODS: We elaborate on the history of our program, the philosophy, contents and organizational structure of the present-day curriculum. Besides, we describe the various didactic approaches in the program and the rational for these. Finally, we analyze the contents of our program in respect to the IMIA recommendations for dedicated programs in health and medical informatics. RESULTS AND CONCLUSIONS: Since its foundation in 1987, the program has undergone several major modifications. From a degree program following medical school it developed into a full-fledged, dedicated 4-year program on medical information sciences training high-school graduates for a master degree. The curriculum has been based from its outset within the University of Amsterdam-Faculty of Medicine. This organizational structure leaves ample opportunity for integration of the informatics-oriented components with the medical and health care-oriented components in the program. Student-centered approaches are heavily employed in the program, emphasizing students' critical appraisal and a style of life-long learning. Overall, our program follows the IMIA recommendations with slightly more focus on medicine and health care organization.

Education, Graduate↗
PubMed2004

The specification of a frame-based medical terminological system in Protégé.

A medical terminological system (TS) is essentially a representation of concepts, attributes and relationships pertaining to medical terms. Although the requirements and structures of TSs have enjoyed some attention in the literature, the actual implementation of TSs consisted so far of ad-hoc approaches starting from scratch. Recently, Protégé has been suggested as a software environment for the development of knowledge-based systems. In Protégé various component types interact, and knowledge can be represented at various levels. This paper investigates how to specify a frame-based TS in Protégé and demonstrates this in a specific application in the intensive care. Our approach is characterized by the utilization of a conceptual framework for understanding TSs and mapping its components onto Protégé constructs. This results in specifications of knowledge components for the implementation of terminological systems. The significance of the work stems from the generality of the specifications, thus facilitating the principled design and development of terminological systems in various medical domains

Artificial Intelligence↗
PubMed2002

Evaluation of a frame-based ontology: a formalization-oriented approach.

This paper describes the evaluation of our current frame-based ontology in Intensive Care. Our approach to the evaluation consists of formalizing the ontology. The motivation for formalization is twofold: a. The formalization process itself can shed light on the implicit ambiguities in the ontology and b. The result of this process is a representation that facilitates automatic inference. The evaluation has revealed various ambiguities in the current ontology, and has clarified the path for migration towards a formal representation supporting automatic inference.

Critical Care↗
PubMedDate not supplied

Integrating classification trees with local logistic regression in Intensive Care prognosis.

Health care effectiveness and efficiency are under constant scrutiny especially when treatment is quite costly as in the Intensive Care (IC). Currently there are various international quality of care programs for the evaluation of IC. At the heart of such quality of care programs lie prognostic models whose prediction of patient mortality can be used as a norm to which actual mortality is compared. The current generation of prognostic models in IC are statistical parametric models based on logistic regression. Given a description of a patient at admission, these models predict the probability of his or her survival. Typically, this patient description relies on an aggregate variable, called a score, that quantifies the severity of illness of the patient. The use of a parametric model and an aggregate score form adequate means to develop models when data is relatively scarce but it introduces the risk of bias. This paper motivates and suggests a method for studying and improving the performance behavior of current state-of-the-art IC prognostic models. Our method is based on machine learning and statistical ideas and relies on exploiting information that underlies a score variable. In particular, this underlying information is used to construct a classification tree whose nodes denote patient sub-populations. For these sub-populations, local models, most notably logistic regression ones, are developed using only the total score variable. We compare the performance of this hybrid model to that of a traditional global logistic regression model. We show that the hybrid model not only provides more insight into the data but also has a better performance. We pay special attention to the precision aspect of model performance and argue why precision is more important than discrimination ability.

Artificial Intelligence↗