Using data warehousing and OLAP in public health care.
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The paper describes the possibilities of using data warehousing and OLAP technologies in public health care in general and then our own experience with these technologies gained during the implementation of a data warehouse of outpatient data at the national level. Such a data warehouse serves as a basis for advanced decision support systems based on statistical, OLAP or data mining methods. We used OLAP to enable interactive exploration and analysis of the data. We found out that data warehousing and OLAP are suitable for the domain of public health and that they enable new analytical possibilities in addition to the traditional statistical approaches. (+info)
A randomized controlled trial of the accuracy of clinical record retrieval using SNOMED-RT as compared with ICD9-CM.
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BACKGROUND: Concept-based Indexing is purported to provide more granular data representation for clinical records.1,2 This implies that a detailed clinical terminology should be able to provide improved access to clinical records. To date there is no data to show that a clinical reference terminology is superior to a precoordinated terminology in its ability to provide access to the clinical record. Today, ICD9-CM is the most commonly used method of retrieving clinical records. OBJECTIVE: In this study, we compare the sensitivity, specificity, positive likelihood ratio, positive predictive value and accuracy of SNOMED-RT vs. ICD9-CM in retrieving ten diagnoses from a random sample of 2,022 episodes of care. METHOD: We randomly selected 1,014 episodes of care from the inpatient setting and 1,008 episodes of care from the outpatient setting. Each record had associated with it, the free text final diagnoses from the Master Sheet Index at the Mayo Clinic and the ICD9-CM codes used to bill for the encounters within the episode of care. The free text diagnoses were coded by two expert indexers (disagreements were addressed by a Staff Clinician) as to whether queries regarding one of 5 common or 5 uncommon diagnoses should return this encounter. The free text entries were automatically coded using the Mayo Vocabulary Processor. Each of the ten diagnoses was exploded in both SNOMED-RT and ICD9-CM and using these entry points, a retrieval set was generated from the underlying corpus of records. Each retrieval set was compared with the Gold Standard created by the expert indexers. RESULTS: SNOMED-RT produced significantly greater specificity in its retrieval sets (99.8% vs. 98.3%, p<0.001 McNemar Test). The positive likelihood ratios were significantly better for SNOMED-RT retrieval sets (264.9 vs. 33.8, p<0.001 McNemar Test). The positive predictive value of a SNOMED-RT retrieval was also significantly better than ICD9-CM (92.9% vs. 62.4%, p<0.001 McNemar Test). The accuracy defined as 1 (the total error rate (FP+FN) / Total # episodes queried (20,220)) was significantly greater for SNOMED-RT (98.2% vs. 96.8%, p=0.002 McNemar Test). Interestingly, the sensitivity of the SNOMED-RT generated retrieval set was not significantly different from ICD9-CM, but there was a trend toward significance (60.4% vs. 57.6%, p=0.067 McNemar Test). However, if we examine only the outpatient practice SNOMED-RT produced a more sensitive retrieval set than ICD9-CM (54.8% vs. 46.4%, p=0.002 McNemar Test). CONCLUSIONS: Our data clearly shows that information regarding both common and rare disorders is more accurately identified with automated SNOMED-RT indexing using the Mayo Vocabulary Processor than it is with traditional hand picked constellations of codes using ICD9-CM. SNOMED-RT provided more sensitive retrievals of outpatient episodes of care than ICD9-CM. (+info)
A framework for best practices in the deployment of departmental information systems.
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In previous work, we conceptualized a departmental information system as embodying a flexible, but limited, model for the operation of a department, such as a laboratory or diagnostic imaging service. We further recognized these systems as tools that enable data-driven departmental management and function as feeder systems to enterprise management decision-support systems, and also embody a departmental and components of an enterprise management model. Finally, for systems that interact with professionals related to decision support, we note that such systems embody partial cognitive models that must be congruent with the professionals cognitive processes (or professionals cognitive behaviors must alternatively be congruent with the systems cognitive model), if these systems are to be supportive of the professionals thinking and decision making. In this paper, we review this thinking and use it to derive proposed "best practices" in IS management, and system planning, procurement, implementation, and use. (+info)
Information systems for health sector monitoring in Papua New Guinea.
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This paper describes (i). how a national health information System was designed, tested and implemented in Papua New Guinea, (ii). how the system was integrated with other management information systems, and (iii). how information has been used to support decision-making. It concludes that central coordination of systems design is essential to make sure that information systems are aligned with government priorities and can deliver the information required by managers. While there is often scope for improving the performance of existing information systems, too much emphasis can be placed on revising data collection procedures and creating the perfect information system. Data analysis, even from imperfect systems, can stimulate greater interest in information, which can improve the quality and completeness of reporting and encourage a more methodical approach to planning and monitoring services. Our experience suggests that senior decision-makers and political leaders can play an important role in creating a culture of information use. By demanding health information, using it to formulate policy, and disseminating it through the channels open to them, they can exert greater influence in negotiations with donors and other government departments, encourage a more rational approach to decision-making that will improve the operation of health services, and stimulate greater use of information at lower levels of the health system. The ability of information systems to deliver these benefits is critical to their sustainability. (+info)
Enhancing malaria control using a computerised management system in southern Africa.
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BACKGROUND: Malaria control programmes utilising indoor residual spraying are only effective if a high coverage of targeted structures is achieved and an insecticide that is effective against the specific mosquito vector is correctly applied. Ongoing monitoring of spraying operations is essential to assure optimal programme performance and early corrective action, where indicated. METHODS: Successful development and application of a computerised spraying operations management system in Mpumalanga Province, South Africa during 1998 resulted in its adaptation and introduction in neighbouring Maputo Province, southern Mozambique during 2000. The structure and components of this computerised management system are described, and its' operational benefit in southern Mozambique, where community-based spray operators apply intradomiciliary insecticide, are reviewed. CONCLUSIONS: The computerised management system allowed malaria programme management and field supervisors to monitor spraying coverage, insecticide consumption and application rates on an ongoing basis. The system supported a successful transition to community-based spraying, while assuring correct insecticide application and spraying completion according to schedule. (+info)
Challenges and opportunities for Medicare's original prospective payment system.
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The Medicare program initiated prospective payment for inpatient hospital services in 1983. Although the payment system has achieved many of its goals, changes in the health care market and the public nature of the program will continue to present both challenges and opportunities for improvement. Looking forward, policymakers must consider how to balance paying accurately for services with using Medicare to achieve broader policy objectives. Paying for new technologies, responding to market segmentation and specialization, and encouraging quality improvement must also be addressed. To successfully navigate these issues, policymakers and program administrators need accurate and timely information. (+info)
A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samples.
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Currently, the best way to reduce the mortality of cancer is to detect and treat it in the earliest stages. Technological advances in genomics and proteomics have opened a new realm of methods for early detection that show potential to overcome the drawbacks of current strategies. In particular, pattern analysis of mass spectra of blood samples has attracted attention as an approach to early detection of cancer. Mass spectrometry provides rapid and precise measurements of the sizes and relative abundances of the proteins present in a complex biological/chemical mixture. This article presents a review of the development of clinical decision support systems using mass spectrometry from a machine learning perspective. The literature is reviewed in an explicit machine learning framework, the components of which are preprocessing, feature extraction, feature selection, classifier training, and evaluation. (+info)
Automatic generation of spoken dialogue from medical plans and ontologies.
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This paper presents some research undertaken as part of the EU-funded HOMEY project, into the application of intelligent dialogue systems to healthcare systems. The work presented here concentrates on the ways in which knowledge of underlying task structure (e.g., a medical guideline) can be combined with ontological knowledge (e.g., medical semantic dictionaries) to provide a basis for the automatic generation of flexible and re-configurable dialogue. This approach is next evaluated via a specific application that provides decision support to general practitioners to help determine whether or not a patient should be referred to a cancer specialist. The competence of the resulting dialogue application, its speech recognition performance, and dialogue performance are all evaluated to determine the applicability of this approach. (+info)