The Experts below are selected from a list of 81930 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, 2012
    Co-Authors: Michael Klompas, Gillian Haney, Ross Lazarus, Jason Mcvetta, Emma M Eggleston, Benjamin A Kruskal, Katherine W Yih, Patricia Daly, Paul Oppedisano, Brianne Beagan
    Abstract:

    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, 2012
    Co-Authors: Michael Klompas, Gillian Haney, Ross Lazarus, Jason Mcvetta, Emma M Eggleston, Benjamin A Kruskal, Katherine W Yih, Patricia Daly, Paul Oppedisano, Brianne Beagan
    Abstract:

    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, 2008
    Co-Authors: Michael Klompas, Gillian Haney, Daniel R Church, Ross Lazarus, Xuanlin Hou, Richard Platt
    Abstract:

    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.

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, 2012
    Co-Authors: Michael Klompas, Gillian Haney, Ross Lazarus, Jason Mcvetta, Emma M Eggleston, Benjamin A Kruskal, Katherine W Yih, Patricia Daly, Paul Oppedisano, Brianne Beagan
    Abstract:

    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, 2012
    Co-Authors: Michael Klompas, Gillian Haney, Ross Lazarus, Jason Mcvetta, Emma M Eggleston, Benjamin A Kruskal, Katherine W Yih, Patricia Daly, Paul Oppedisano, Brianne Beagan
    Abstract:

    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.

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, 2008
    Co-Authors: Michael Klompas, Gillian Haney, Daniel R Church, Ross Lazarus, Xuanlin Hou, Richard Platt
    Abstract:

    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.

Ross Lazarus - 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, 2012
    Co-Authors: Michael Klompas, Gillian Haney, Ross Lazarus, Jason Mcvetta, Emma M Eggleston, Benjamin A Kruskal, Katherine W Yih, Patricia Daly, Paul Oppedisano, Brianne Beagan
    Abstract:

    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, 2012
    Co-Authors: Michael Klompas, Gillian Haney, Ross Lazarus, Jason Mcvetta, Emma M Eggleston, Benjamin A Kruskal, Katherine W Yih, Patricia Daly, Paul Oppedisano, Brianne Beagan
    Abstract:

    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, 2008
    Co-Authors: Michael Klompas, Gillian Haney, Daniel R Church, Ross Lazarus, Xuanlin Hou, Richard Platt
    Abstract:

    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.

Gillian Haney - 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, 2012
    Co-Authors: Michael Klompas, Gillian Haney, Ross Lazarus, Jason Mcvetta, Emma M Eggleston, Benjamin A Kruskal, Katherine W Yih, Patricia Daly, Paul Oppedisano, Brianne Beagan
    Abstract:

    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, 2012
    Co-Authors: Michael Klompas, Gillian Haney, Ross Lazarus, Jason Mcvetta, Emma M Eggleston, Benjamin A Kruskal, Katherine W Yih, Patricia Daly, Paul Oppedisano, Brianne Beagan
    Abstract:

    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, 2008
    Co-Authors: Michael Klompas, Gillian Haney, Daniel R Church, Ross Lazarus, Xuanlin Hou, Richard Platt
    Abstract:

    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.