Medical informatics is "the application of information science and information technology to the theoretical and practical problems of biomedical research, clinical practice, and medical education." A key difference between the two streams lies in their perspectives of "What Is Important in MI to Me?" MI may be seen as the marketplace where biomedicine consumes products and services provided by information science and information technology.
{"title":"Medical informatics as a market for IS/IT","authors":"T. Morris","doi":"10.1002/meet.1450390104","DOIUrl":"https://doi.org/10.1002/meet.1450390104","url":null,"abstract":"Medical informatics is \"the application of information science and information technology to the theoretical and practical problems of biomedical research, clinical practice, and medical education.\" A key difference between the two streams lies in their perspectives of \"What Is Important in MI to Me?\" MI may be seen as the marketplace where biomedicine consumes products and services provided by information science and information technology.","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2005-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/meet.1450390104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51382057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Privacy protection is an important consideration when releasing medical databases to the research community. We show that while recent advances in anonymization algorithms provide increased levels of protection, it is still possible to calculate approximations to the original data set. In some cases, one can even uniquely reconstruct entries in a table before anonymization. In this paper, we demonstrate how knowledge of an anonymization algorithm based on ambiguating data cell entries can be used to undo the anonymization process. We investigate the effect of this algorithm and its reversal on data sets of varying sizes and distributions. It is shown that by using a computationally complex disambiguation process, information on individuals can be extracted from an anonymized data set.
{"title":"Disambiguation Data: Extracting Information from Anonymized Sources","authors":"S. Dreiseitl, S. Vinterbo, L. Ohno-Machado","doi":"10.1197/JAMIA.M1240","DOIUrl":"https://doi.org/10.1197/JAMIA.M1240","url":null,"abstract":"Privacy protection is an important consideration when releasing medical databases to the research community. We show that while recent advances in anonymization algorithms provide increased levels of protection, it is still possible to calculate approximations to the original data set. In some cases, one can even uniquely reconstruct entries in a table before anonymization. In this paper, we demonstrate how knowledge of an anonymization algorithm based on ambiguating data cell entries can be used to undo the anonymization process. We investigate the effect of this algorithm and its reversal on data sets of varying sizes and distributions. It is shown that by using a computationally complex disambiguation process, information on individuals can be extracted from an anonymized data set.","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1197/JAMIA.M1240","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66391336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Protecting individual data in disclosed databases is essential. Data anonymization strategies can produce table ambiguation by suppression of selected cells. Using table ambiguation, different degrees of anonymization can be achieved, depending on the number of individuals that a particular case must become indistinguishable from. This number defines the level of anonymization. Anonymization by cell suppression does not necessarily prevent inferences from being made from the disclosed data. Preventing inferences may be important to preserve confidentiality. We show that anonymized data sets can preserve descriptive characteristics of the data, but might also be used for making inferences on particular individuals, which is a feature that may not be desirable. The degradation of predictive performance is directly proportional to the degree of anonymity. As an example, we report the effect of anonymization on the predictive performance of a model constructed to estimate the probability of disease given clinical findings.
{"title":"Effects of Data Anonymization by Cell Suppression on Descriptive Statistics and Predictive Modeling Performance","authors":"L. Ohno-Machado, S. Vinterbo, S. Dreiseitl","doi":"10.1197/jamia.M1241","DOIUrl":"https://doi.org/10.1197/jamia.M1241","url":null,"abstract":"Protecting individual data in disclosed databases is essential. Data anonymization strategies can produce table ambiguation by suppression of selected cells. Using table ambiguation, different degrees of anonymization can be achieved, depending on the number of individuals that a particular case must become indistinguishable from. This number defines the level of anonymization. Anonymization by cell suppression does not necessarily prevent inferences from being made from the disclosed data. Preventing inferences may be important to preserve confidentiality. We show that anonymized data sets can preserve descriptive characteristics of the data, but might also be used for making inferences on particular individuals, which is a feature that may not be desirable. The degradation of predictive performance is directly proportional to the degree of anonymity. As an example, we report the effect of anonymization on the predictive performance of a model constructed to estimate the probability of disease given clinical findings.","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1197/jamia.M1241","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66391408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patient safety assessment in anaesthesia increasingly relies on the monitoring of frequent but merely undesirable events, like hypotension. We report on the design and implementation of such a monitoring system, where 8032 patients were included over a three years period. Thirty two 'Significant Anaesthetic Events' were defined and their occurrence was routinely collected for each patient. A total of 2106 significant anaesthetic events were reported. The data were analysed using control charts, which showed that an undesirable event was recorded in 1 out of 4 interventions. The control chart showed that the incidence of significant anaesthetic events was out of the expected boundaries during one month. The system sensitivity to change in the frequency of significant anaesthetic events was investigated by a controlled intervention, designed to increase the incidence of bradycardia by changing anxyolitic medication. During the intervention, the incidence of bradycardia doubled, while the incidence of other undesirable events was not affected. The system described for the collection of significant anaesthetic events was easy to set up, sensitive to changes and provided valuable tools in performance monitoring.
{"title":"An Integrated System for Significant Anaesthetic Events Monitoring","authors":"P. Böelle, F. Bonnet, A. Valleron","doi":"10.1197/JAMIA.M1220","DOIUrl":"https://doi.org/10.1197/JAMIA.M1220","url":null,"abstract":"Patient safety assessment in anaesthesia increasingly relies on the monitoring of frequent but merely undesirable events, like hypotension. We report on the design and implementation of such a monitoring system, where 8032 patients were included over a three years period. Thirty two 'Significant Anaesthetic Events' were defined and their occurrence was routinely collected for each patient. A total of 2106 significant anaesthetic events were reported. The data were analysed using control charts, which showed that an undesirable event was recorded in 1 out of 4 interventions. The control chart showed that the incidence of significant anaesthetic events was out of the expected boundaries during one month. The system sensitivity to change in the frequency of significant anaesthetic events was investigated by a controlled intervention, designed to increase the incidence of bradycardia by changing anxyolitic medication. During the intervention, the incidence of bradycardia doubled, while the incidence of other undesirable events was not affected. The system described for the collection of significant anaesthetic events was easy to set up, sensitive to changes and provided valuable tools in performance monitoring.","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1197/JAMIA.M1220","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66391566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CONTEXT Detection of outbreaks of infection in the hospital typically requires daily manual review of microbiology laboratory test results. This process is time-consuming, tedious, prone to error and may miss trends in infection. A standard formalism for procedural knowledge representation, the Arden Syntax, provides a vehicle for implementing algorithms for detecting such infections. OBJECTIVE To design and implement a computer-based system for detection of concerning patterns of infection or antibiotic resistance. SETTING Computer-based event monitor and central patient data repository at the Columbia-Presbyterian Medical Center (CPMC). RESULTS We designed a two-phase system, including initial filtering of individual patient laboratory results by Arden Syntax Medical Logic Modules (MLMs) and subsequent aggregation and analysis across patients and locations using a statistical monitor. Preliminary data for the filtration phase demonstrate a 94.8% reduction in the volume of messages that must be considered in surveillance. CONCLUSIONS Filtering raw laboratory results using a standard formalism eases the process of aggregating data across patients and sites as well as detecting trends in infection. There is a need for augmenting such formalisms in order to enable population-based decision support.
{"title":"Challenges in Using the Arden Syntax for Computer-Based Nosocomial Infection Surveillance","authors":"R. Jenders, Anuj P. Shah","doi":"10.1197/JAMIA.M1237","DOIUrl":"https://doi.org/10.1197/JAMIA.M1237","url":null,"abstract":"CONTEXT\u0000Detection of outbreaks of infection in the hospital typically requires daily manual review of microbiology laboratory test results. This process is time-consuming, tedious, prone to error and may miss trends in infection. A standard formalism for procedural knowledge representation, the Arden Syntax, provides a vehicle for implementing algorithms for detecting such infections.\u0000\u0000\u0000OBJECTIVE\u0000To design and implement a computer-based system for detection of concerning patterns of infection or antibiotic resistance.\u0000\u0000\u0000SETTING\u0000Computer-based event monitor and central patient data repository at the Columbia-Presbyterian Medical Center (CPMC).\u0000\u0000\u0000RESULTS\u0000We designed a two-phase system, including initial filtering of individual patient laboratory results by Arden Syntax Medical Logic Modules (MLMs) and subsequent aggregation and analysis across patients and locations using a statistical monitor. Preliminary data for the filtration phase demonstrate a 94.8% reduction in the volume of messages that must be considered in surveillance.\u0000\u0000\u0000CONCLUSIONS\u0000Filtering raw laboratory results using a standard formalism eases the process of aggregating data across patients and sites as well as detecting trends in infection. There is a need for augmenting such formalisms in order to enable population-based decision support.","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1197/JAMIA.M1237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66391664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automated physiologic event detection and alerting is a challenging task in the ICU. Ideally care providers should be alerted only when events are clinically significant and there is opportunity for corrective action. However, the concepts of clinical significance and opportunity are difficult to define in automated systems, and effectiveness of alerting algorithms is difficult to measure. This paper describes recent efforts on the Simon project to capture information from ICU care providers about patient state and therapy in response to alerts, in order to assess the value of event definitions and progressively refine alerting algorithms. Event definitions for intracranial pressure and cerebral perfusion pressure were studied by implementing a reliable system to automatically deliver alerts to clinical users alphanumeric pagers, and to capture associated documentation about patient state and therapy when the alerts occurred. During a 6-month test period in the trauma ICU at Vanderbilt University Medical Center, 530 alerts were detected in 2280 hours of data spanning 14 patients. Clinical users electronically documented 81% of these alerts as they occurred. Retrospectively classifying documentation based on therapeutic actions taken, or reasons why actions were not taken, provided useful information about ways to potentially improve event definitions and enhance system utility.
{"title":"Closing the Loop in ICU Decision Support: Physiologic Event Detection, Alerts, and Documentation","authors":"Patrick R. Norris, B. Dawant","doi":"10.1197/JAMIA.M1238","DOIUrl":"https://doi.org/10.1197/JAMIA.M1238","url":null,"abstract":"Automated physiologic event detection and alerting is a challenging task in the ICU. Ideally care providers should be alerted only when events are clinically significant and there is opportunity for corrective action. However, the concepts of clinical significance and opportunity are difficult to define in automated systems, and effectiveness of alerting algorithms is difficult to measure. This paper describes recent efforts on the Simon project to capture information from ICU care providers about patient state and therapy in response to alerts, in order to assess the value of event definitions and progressively refine alerting algorithms. Event definitions for intracranial pressure and cerebral perfusion pressure were studied by implementing a reliable system to automatically deliver alerts to clinical users alphanumeric pagers, and to capture associated documentation about patient state and therapy when the alerts occurred. During a 6-month test period in the trauma ICU at Vanderbilt University Medical Center, 530 alerts were detected in 2280 hours of data spanning 14 patients. Clinical users electronically documented 81% of these alerts as they occurred. Retrospectively classifying documentation based on therapeutic actions taken, or reasons why actions were not taken, provided useful information about ways to potentially improve event definitions and enhance system utility.","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1197/JAMIA.M1238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66391778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To assess the value of ICD-9 coded chief complaints for early detection of epidemics, we measured sensitivity, positive predictive value, and timeliness of Influenza detection using a respiratory set (RS) of ICD-9 codes and an Influenza set (IS). We also measured inherent timeliness of these data using the cross-correlation function. We found that, for a one-year period, the detectors had sensitivity of 100% (1/1 epidemic) and positive predictive values of 50% (1/2) for RS and 25% (1/4) for IS. The timeliness of detection using ICD-9 coded chief complaints was one week earlier than the detection using Pneumonia and Influenza deaths (the gold standard). The inherent timeliness of ICD-9 data measured by the cross-correlation function was two weeks earlier than the gold standard.
{"title":"Value of ICD-9-Coded Chief Complaints for Detection of Epidemics","authors":"F. Tsui, M. Wagner, V. Dato, C. Chang","doi":"10.1197/JAMIA.M1224","DOIUrl":"https://doi.org/10.1197/JAMIA.M1224","url":null,"abstract":"To assess the value of ICD-9 coded chief complaints for early detection of epidemics, we measured sensitivity, positive predictive value, and timeliness of Influenza detection using a respiratory set (RS) of ICD-9 codes and an Influenza set (IS). We also measured inherent timeliness of these data using the cross-correlation function. We found that, for a one-year period, the detectors had sensitivity of 100% (1/1 epidemic) and positive predictive values of 50% (1/2) for RS and 25% (1/4) for IS. The timeliness of detection using ICD-9 coded chief complaints was one week earlier than the detection using Pneumonia and Influenza deaths (the gold standard). The inherent timeliness of ICD-9 data measured by the cross-correlation function was two weeks earlier than the gold standard.","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1197/JAMIA.M1224","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66391708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Nebeker, John F. Hurdle, Jennifer M. Hoffman, Beverly Roth, C. Weir, M. Samore
Computerized decision support and order entry shows great promise for reducing adverse drug events (ADEs). The evaluation of these solutions depends on a framework of definitions and classifications that is clear and practical. Unfortunately the literature does not always provide a clear path to defining and classifying adverse drug events. While not a systematic review, this paper uses examples from the literature to illustrate problems that investigators will confront as they develop a conceptual framework for their research. It also proposes a targeted taxonomy that can facilitate a clear and consistent approach to the research of ADEs and aid in the comparison to results of past and future studies. The taxonomy addresses the definition of ADE, types, seriousness, error, and causality.
{"title":"Developing a taxonomy for research in adverse drug events: potholes and signposts","authors":"J. Nebeker, John F. Hurdle, Jennifer M. Hoffman, Beverly Roth, C. Weir, M. Samore","doi":"10.1197/jamia.M1234","DOIUrl":"https://doi.org/10.1197/jamia.M1234","url":null,"abstract":"Computerized decision support and order entry shows great promise for reducing adverse drug events (ADEs). The evaluation of these solutions depends on a framework of definitions and classifications that is clear and practical. Unfortunately the literature does not always provide a clear path to defining and classifying adverse drug events. While not a systematic review, this paper uses examples from the literature to illustrate problems that investigators will confront as they develop a conceptual framework for their research. It also proposes a targeted taxonomy that can facilitate a clear and consistent approach to the research of ADEs and aid in the comparison to results of past and future studies. The taxonomy addresses the definition of ADE, types, seriousness, error, and causality.","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1197/jamia.M1234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66391424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of routinely collected data, such as absenteeism, to provide an early warning of an epidemic will depend on better understanding of the effects of epidemics on such data. We reviewed studies in behavioral medicine and health psychology in order to build a model relating known factors related to human health information and treatment seeking behavior and effects on routinely collected data. This review and modeling effort may be useful to researchers in early detection, simulation, and response policy analysis.
{"title":"Modeling the Effects of Epidemics on Routinely Collected Data","authors":"Xiaoming Zeng, M. Wagner","doi":"10.1197/JAMIA.M1219","DOIUrl":"https://doi.org/10.1197/JAMIA.M1219","url":null,"abstract":"The use of routinely collected data, such as absenteeism, to provide an early warning of an epidemic will depend on better understanding of the effects of epidemics on such data. We reviewed studies in behavioral medicine and health psychology in order to build a model relating known factors related to human health information and treatment seeking behavior and effects on routinely collected data. This review and modeling effort may be useful to researchers in early detection, simulation, and response policy analysis.","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1197/JAMIA.M1219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66391518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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