The Experts below are selected from a list of 442440 Experts worldwide ranked by ideXlab platform
Michael Klompas - One of the best experts on this subject based on the ideXlab platform.
-
integrating clinical practice and public health surveillance using electronic Medical Record systems
American Journal of Public Health, 2012Co-Authors: Michael Klompas, Gillian Haney, Ross Lazarus, Jason Mcvetta, Emma M Eggleston, Benjamin A Kruskal, Katherine W Yih, Patricia Daly, Paul Oppedisano, Brianne BeaganAbstract:Electronic Medical Record (EMR) systems have rich potential to improve integration between primary care and the public health system at the point of care. EMRs make it possible for clinicians to contribute timely, clinically detailed surveillance data to public health practitioners without changing their existing workflows or incurring extra work. New surveillance systems can extract raw data from providers’ EMRs, analyze them for conditions of public health interest, and automatically communicate results to health departments. We describe a model EMR-based public health surveillance platform called Electronic Medical Record Support for Public Health (ESP). The ESP platform provides live, automated surveillance for notifiable diseases, influenza-like illness, and diabetes prevalence, care, and complications. Results are automatically transmitted to state health departments.
-
integrating clinical practice and public health surveillance using electronic Medical Record systems
American Journal of Preventive Medicine, 2012Co-Authors: Michael Klompas, Gillian Haney, Ross Lazarus, Jason Mcvetta, Emma M Eggleston, Benjamin A Kruskal, Katherine W Yih, Patricia Daly, Paul Oppedisano, Brianne BeaganAbstract:Electronic Medical Record (EMR) systems have rich potential to improve integration between primary care and the public health system at the point of care. EMRs make it possible for clinicians to contribute timely, clinically detailed surveillance data to public health practitioners without changing their existing workflows or incurring extra work. New surveillance systems can extract raw data from providers' EMRs, analyze them for conditions of public health interest, and automatically communicate results to health departments. The current paper describes a model EMR-based public health surveillance platform called Electronic Medical Record Support for Public Health (ESP). The ESP platform provides live, automated surveillance for notifiable diseases, influenza-like illness, and diabetes prevalence, care, and complications. Results are automatically transmitted to state health departments.
-
automated identification of acute hepatitis b using electronic Medical Record data to facilitate public health surveillance
PLOS ONE, 2008Co-Authors: Michael Klompas, Gillian Haney, Daniel R Church, Ross Lazarus, Xuanlin Hou, Richard PlattAbstract:Background Automatic identification of notifiable diseases from electronic Medical Records can potentially improve the timeliness and completeness of public health surveillance. We describe the development and implementation of an algorithm for prospective surveillance of patients with acute hepatitis B using electronic Medical Record data. Methods Initial algorithms were created by adapting Centers for Disease Control and Prevention diagnostic criteria for acute hepatitis B into electronic terms. The algorithms were tested by applying them to ambulatory electronic Medical Record data spanning 1990 to May 2006. A physician reviewer classified each case identified as acute or chronic infection. Additional criteria were added to algorithms in serial fashion to improve accuracy. The best algorithm was validated by applying it to prospective electronic Medical Record data from June 2006 through April 2008. Completeness of case capture was assessed by comparison with state health department Records. Findings A final algorithm including a positive hepatitis B specific test, elevated transaminases and bilirubin, absence of prior positive hepatitis B tests, and absence of an ICD9 code for chronic hepatitis B identified 112/113 patients with acute hepatitis B (sensitivity 97.4%, 95% confidence interval 94–100%; specificity 93.8%, 95% confidence interval 87–100%). Application of this algorithm to prospective electronic Medical Record data identified 8 cases without false positives. These included 4 patients that had not been reported to the health department. There were no known cases of acute hepatitis B missed by the algorithm. Conclusions An algorithm using codified electronic Medical Record data can reliably detect acute hepatitis B. The completeness of public health surveillance may be improved by automatically identifying notifiable diseases from electronic Medical Record data.
Richard Platt - One of the best experts on this subject based on the ideXlab platform.
-
automated identification of acute hepatitis b using electronic Medical Record data to facilitate public health surveillance
PLOS ONE, 2008Co-Authors: Michael Klompas, Gillian Haney, Daniel R Church, Ross Lazarus, Xuanlin Hou, Richard PlattAbstract:Background Automatic identification of notifiable diseases from electronic Medical Records can potentially improve the timeliness and completeness of public health surveillance. We describe the development and implementation of an algorithm for prospective surveillance of patients with acute hepatitis B using electronic Medical Record data. Methods Initial algorithms were created by adapting Centers for Disease Control and Prevention diagnostic criteria for acute hepatitis B into electronic terms. The algorithms were tested by applying them to ambulatory electronic Medical Record data spanning 1990 to May 2006. A physician reviewer classified each case identified as acute or chronic infection. Additional criteria were added to algorithms in serial fashion to improve accuracy. The best algorithm was validated by applying it to prospective electronic Medical Record data from June 2006 through April 2008. Completeness of case capture was assessed by comparison with state health department Records. Findings A final algorithm including a positive hepatitis B specific test, elevated transaminases and bilirubin, absence of prior positive hepatitis B tests, and absence of an ICD9 code for chronic hepatitis B identified 112/113 patients with acute hepatitis B (sensitivity 97.4%, 95% confidence interval 94–100%; specificity 93.8%, 95% confidence interval 87–100%). Application of this algorithm to prospective electronic Medical Record data identified 8 cases without false positives. These included 4 patients that had not been reported to the health department. There were no known cases of acute hepatitis B missed by the algorithm. Conclusions An algorithm using codified electronic Medical Record data can reliably detect acute hepatitis B. The completeness of public health surveillance may be improved by automatically identifying notifiable diseases from electronic Medical Record data.
David W. Bates - One of the best experts on this subject based on the ideXlab platform.
-
can comorbidity be measured by questionnaire rather than Medical Record review
Medical Care, 1996Co-Authors: Jeffrey N Katz, Lily C Chang, O Sangha, Anne H Fossel, David W. BatesAbstract:Comorbidity generally is measured by Medical Record abstraction, which is expensive and often impractical. The aim of this study was to assess the reproducibility and validity of a comorbidity questionnaire. The authors developed a brief comorbidity questionnaire that included items corresponding to
-
can comorbidity be measured by questionnaire rather than Medical Record review
Medical Care, 1996Co-Authors: Jeffrey N Katz, Lily C Chang, O Sangha, Anne H Fossel, David W. BatesAbstract:Comorbidity generally is measured by Medical Record abstraction, which is expensive and often impractical. The aim of this study was to assess the reproducibility and validity of a comorbidity questionnaire. The authors developed a brief comorbidity questionnaire that included items corresponding to each element of the Medical Record-based Charlson index. The questionnaire was administered to 170 inpatients. Charlson scores were abstracted from these patients' Medical Records. We assessed test-retest reliability of the questionnaire and the Charlson index, the correlation between the questionnaire and the Charlson index, and correlations between each comorbidity measure and indicators of health resource utilization including medication use, hospitalizations in the past year, and hospital charges. Test-retest reliability, assessed with the intraclass correlation coefficient, was 0.91 for the questionnaire and 0.92 for the chart-based Charlson index. The Spearman correlation between these two measures was 0.63. The correlation between comorbidity measures was weaker in less educated patients. Correlations with indicators of resource utilization were similar for the two comorbidity instruments. The authors found that a questionnaire version of the Charlson index is reproducible, valid, and offers practical advantages over Medical Record-based assessments.
Troyen A Brennan - One of the best experts on this subject based on the ideXlab platform.
-
the reliability of Medical Record review for estimating adverse event rates
Annals of Internal Medicine, 2002Co-Authors: Eric J Thomas, David M. Studdert, Troyen A BrennanAbstract:Background: The data used by the U.S. Institute of Medicine to estimate deaths from Medical errors come from a study that relied on nurse and physician reviews of Medical Records to detect the errors. Objective: To measure the reliability of Medical Record review for detecting adverse events and negligent adverse events. Design: Medical Record review. Setting: Hospitalizations in Utah and Colorado in 1992. Measurements: After three independent reviews of 500 Medical Records, the following were measured: reliability and the effect of varying criteria for reviewer confidence in and reviewer agreement about the presence of adverse events. Results: For agreements in judgments of adverse events among the three sets of reviews, the K statistics ranged from 0.40 to 0.41 (95% CIs ranged from 0.30 to 0.51) for adverse events and from 0.19 to 0.23 (CIs, 0.05 to 0.37) for negligent adverse events. Rates for adverse events and for negligent adverse events varied substantially depending on the degree of agreement and the level of confidence that was required among reviewers. Conclusion: Estimates of adverse event rates from Medical Record review, including those reported by the Institute of Medicine in its 2000 report on Medical errors, are highly sensitive to the degree of consensus and confidence among reviewers.
-
the reliability of Medical Record review for estimating adverse event rates
Annals of Internal Medicine, 2002Co-Authors: Eric J Thomas, David M. Studdert, Stuart R Lipsitz, Troyen A BrennanAbstract:Estimates of adverse event rates from Medical Record review, including those reported by the U.S. Institute of Medicine in its 2000 report on Medical errors, are highly sensitive to the degree of c...
Brianne Beagan - One of the best experts on this subject based on the ideXlab platform.
-
integrating clinical practice and public health surveillance using electronic Medical Record systems
American Journal of Public Health, 2012Co-Authors: Michael Klompas, Gillian Haney, Ross Lazarus, Jason Mcvetta, Emma M Eggleston, Benjamin A Kruskal, Katherine W Yih, Patricia Daly, Paul Oppedisano, Brianne BeaganAbstract:Electronic Medical Record (EMR) systems have rich potential to improve integration between primary care and the public health system at the point of care. EMRs make it possible for clinicians to contribute timely, clinically detailed surveillance data to public health practitioners without changing their existing workflows or incurring extra work. New surveillance systems can extract raw data from providers’ EMRs, analyze them for conditions of public health interest, and automatically communicate results to health departments. We describe a model EMR-based public health surveillance platform called Electronic Medical Record Support for Public Health (ESP). The ESP platform provides live, automated surveillance for notifiable diseases, influenza-like illness, and diabetes prevalence, care, and complications. Results are automatically transmitted to state health departments.
-
integrating clinical practice and public health surveillance using electronic Medical Record systems
American Journal of Preventive Medicine, 2012Co-Authors: Michael Klompas, Gillian Haney, Ross Lazarus, Jason Mcvetta, Emma M Eggleston, Benjamin A Kruskal, Katherine W Yih, Patricia Daly, Paul Oppedisano, Brianne BeaganAbstract:Electronic Medical Record (EMR) systems have rich potential to improve integration between primary care and the public health system at the point of care. EMRs make it possible for clinicians to contribute timely, clinically detailed surveillance data to public health practitioners without changing their existing workflows or incurring extra work. New surveillance systems can extract raw data from providers' EMRs, analyze them for conditions of public health interest, and automatically communicate results to health departments. The current paper describes a model EMR-based public health surveillance platform called Electronic Medical Record Support for Public Health (ESP). The ESP platform provides live, automated surveillance for notifiable diseases, influenza-like illness, and diabetes prevalence, care, and complications. Results are automatically transmitted to state health departments.
