Effect of Automation of Communicable Disease Reports on Public Health Surveillance Uzay Krbyk, Brian E. Dixon, Shaun J. Grannis introduction This a is Regenstrief Institute study on the automation of communicable disease reports (CDR). It is funded by the U.S. Agency for Healthcare Research and Quality (AHRQ). It is a 5-year 3-phase project : Baseline Phase I (pre-populated CDR forms) Phase II (customized pre-populated CDR forms) background Surveillance of communicable diseases is a core public health (PH) function. To manage and adjudicate cases of suspected communicable disease, PH workers gather data elements from a variety of sources. Underreporting by providers makes gathering details on suspected cases challenging for public health departments. background Adoption of electronic lab reporting (ELR) has dominated Health Informatics Information Technology (HIIT), but not much has been done to improve provider reporting. Overhage JM, Grannis S, McDonald CJ. Am J Public Health Feb methods Most of the reports to PH originate from laboratories. We aim to increase reporting rates for providers using an automated process where CDR fields are pre- populated using electronic health records (EHRs). current CDR forms patient Information Name Address Phone# DOB Gender Race/ethnicity lab Information Etiologic agent Test name Test date Treatment initiation date Treatment (drugs) provider Information Physician name Physician address Phone# Reported by Report date pre-populated CDR forms A pre-populated CDR in PDF form will be send to providers electronically. communicable disease reporting Reportable condition HIE pre-populated form reporting NCDFormsD4D Health Information Exchange Electronic lab reports trigger the pre-population of CDR forms, which are routed electronically to providers for completion. fax electronic methods The intervention is focused on 7 representative reportable conditions: Hepatitis C, Acute hepatitis B, Salmonellosis, Histoplasmosis, Gonorrhea, Chlamydia And syphilis. methods We are implementing a mixed methods; complementing quantitative and qualitative research methodologies. Reporting rates, timeliness and completeness of the information in CDRs from providers before and after are being collected for time- series analysis. Pre- & post-intervention surveys for qualitative evaluation are also underway. preliminary results We are at year 4 (of 5): Baseline Phase I (pre-populated CDR forms) Phase II (customized pre-populated CDR forms) Communication with clinical and public health professionals suggest the automated process is perceived positively. preliminary results Report type and count per case Unpublished data; (c) 2014 preliminary results With NCD Unpublished data; (c) 2014 discussion An automated, EHR-based intervention is promising to increase the efficiency of reporting processes given the increase in EHR availability in clinical settings. Effective interventions will be those which can leverage new clinical workflows using EHR systems to improve reporting without increasing burden on clinical or public health staff. future directions A thorough analysis when Phase I is complete Comparison of baseline and phase I Reporting rates, timeliness and completeness Interrupted time analysis Qualitative analysis of the intervention Phase II pre-populated forms customized for each disease Acknowledgements We thank the staff at Marion County Public Health Department (MCPHD) for their help with this research. I would like to thank Dr. Brian Dixon, Dr. Shaun Grannis and the other members of our research team for their invaluable support. I am also grateful to the Agency for Healthcare Research and Quality (AHRQ) for funding my graduate assistantship and education. * This project is supported by grant number R01HS from AHRQ. The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ. C C ?, Contact Information: Uzay KIRBIYIK IU Richard M. Fairbanks School of Public Heath at IUPUI 714 N. Senate Ave EF250 Indianapolis, IN 46202