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Brian E. Dixon, MPA, PhD, FHIMSS

Assistant Professor, Department of Epidemiology, Richard M. Fairbanks School of Public Health at Indiana University-Purdue University of Indianapolis
Research Scientist, Indiana University Center for Health Services and Outcomes Research
Research Scientist, Centers for Health Services Research and Biomedical Informatics, Regenstrief Institute, Inc.
Affiliate Investigator, VA HSR&D Center for Health Information and Communication

     Dr. Dixon is an Assistant Professor in the Department of Epidemiology at the Richard M. Fairbanks School of Public Health at Indiana University-Purdue University Indianapolis (IUPUI). His primary research focus is on the use of information and computing systems to improve public health practice and clinical outcomes. This research has included the development, testing, and implementation of measures that leverage secondary data captured from electronic health records.  Research platforms include Regenstrief Institute, Inc.'s Center for Health Services Research and Center for Biomedical Informatics - and the VA Health Services Research & Development  Center for Health Information and Communication where he is an Investigator in Residence. 

 

     Dr. Dixon is also involved with the Indiana Health Information Exchange (IHIE) and national HIE initiatives that aim to promote bi-directional exchange of health information to improve clinical and population health quality, effectiveness, and efficiency. His has contributed to the development and implementation of health information applications and systems, including tools supporting the standard clinical vocabulary LOINC®, technology supporting the automated electronic reporting of notifiable conditions, and tools for querying large clinical data repositories.


     Recent work includes leveraging clinical and administrative data in a health information exchange to improve public health reporting processes, disability determination, surveillance activities, continuity of care for Veterans, and community assessment. Dr. Dixon’s research also involves the evolution of information infrastructure as well as data quality to support continuous use of clinical data in support of efforts to improve health care quality, safety, and efficiency.

Active Projects

Exploring the Utilization of and Outcomes from Health Information Exchange in Emergency Settings
2017-19, Principal Investigator
Solicitation: NIH 1R21 HS025502-01 
Aims: Health information exchange (HIE) involves the electronic exchange of clinical or administrative health data across the boundaries of health care institutions, health data repositories, and states. Widespread HIE has the potential to improve health care, and HIE has been described as a critical component of recent health reform initiatives including value-based care and patient centered medical homes. Despite its potential, the evidence base for HIE is weak. Additional studies employing robust methods are necessary to strengthen the evidence base and enable examination of HIE across the myriad environments in which it is used. The purpose of the proposed study is to examine HIE use among emergency department staff using longitudinal log files from an operational HIE network involving dozens of hospitals. The specific aims focus on: 1) characterizing HIE usage patterns across multiple organizations; 2) interviewing emergency department staff about the facilitators and barriers to using HIE in the context of patient care; and 3) evaluating the relationship between HIE use and outcomes (e.g., admissions, laboratory testing) over time. Knowledge gained from the proposed study will provide evidence for or against the use of HIE. Furthermore, the research will create generalizable methods and a theoretical framework for understanding HIE use and the impact of use on health care delivery as well as clinical and population health outcomes.

 

Advancing Methods to Measure and Improve the Quality of Large Scale Health Data
2015-18, Principal Investigator
Solicitation: NIH 5R21 LM012219 
Aims: ? Clinical and public health are as much information sciences as they are biomedical sciences, and data are their lifeblood. Nearly all of the myriad activities (or use cases) in clinical and public health (e.g., patient care, surveillance, community health assessment, policy) involve generating, collecting, storing, analyzing, or sharing data about individual patients or populations. Effective clinical and public health practice in the twenty-first century requires access to data from an increasing array of information systems, including but not limited to electronic health records. However, the quality of data in electronic health record systems has been shown to be poor or "unfit for use" across a number of use cases like surveillance and policy. In addition, methods for measuring the quality of data in information systems are nascent. This presents an opportunity for the development of improved methods for assessing and improving the quality of data in electronic systems. In the proposed project, we will use a Health Data Stewardship framework to guide the development and testing of methods that measure data quality in large observational health data sets. Specifically, we will 1) extend the Automated Characterization of Health Information at Large-scale Longitudinal Evidence Systems (ACHILLES) software to measure the quality of data electronically reported from disparate information systems to public health agencies for disease surveillance; and 2) apply the ACHILLES extensions to explore the quality of data captured from multiple real-world health systems, hospitals, laboratories, and clinics. We will further demonstrate the extended software to public health professionals, gathering feedback on the ability of the methods and software tool to support public health agencies' efforts to routinely monitor the quality of data received for surveillance of disease prevalence and burden. Furthermore, because the ACHILLES software is available as open-source and supported by multiple health systems, our work may be applicable to other organizations that seek to characterize the quality of large scale observational data sets for other use cases including comparative effectiveness research (CER), patient centered outcomes research (PCOR), and pharmacoepidemiology.

 

The Indiana Training Program in Public & Population Health Informatics
2017-18, Principal Investigator
Solicitation: NIH 3T15LM012502-01 
Aims: Major trends in health and healthcare make a significant investment in training public and population health informatics (PHI) researchers particularly compelling. These trends include (1) the increasing realization of the importance of social, environmental, and behavioral factors as determinants of human health; (2) the requirement to train experts who can support local, state, and federal health agencies with informatics research and development; and (3) the urgent need to help the healthcare system develop capacity in community and population health management, as well as data analytics, to help it transition to a model emphasizing prevention and wellness rather than intervention. Therefore we propose a multi-disciplinary training program in PHI. Through access to unparalleled population-level data, education in population health and informatics methods, and experiences working on a broad range of innovative PHI research projects, we will help trainees become independent scientists capable of designing, developing, implementing and evaluating advanced information systems in clinical and public health settings. Our program will incrementally grow from an initial set of 12 trainees (6 predoctoral, 6 postdoctoral) to 17 total trainees (9 predoctoral, 8 postdoctoral) in the fifth year. Predoctoral trainees will complete a doctoral degree in either Epidemiology or Health Policy & Management, with a concentration in PHI. Postdoctoral trainees will complete their MPH with a concentration in PHI. Furthermore, expert faculty will actively mentor trainees in hands-on research in areas such as health information exchange (HIE), detection of notifiable diseases from HIE data streams, rapid identification and notification of emerging health threats (syndromic surveillance), HIE- based computer-based decision support to improve primary care, and informatics methods to help improve chronic pain care and prevent prescription drug abuse. Research experiences will include both retrospective, observational studies involving health data from multiple health systems, as well as prospective experiences involving a local health system partner. Trainees will also complete a broad set of workshops and seminars that will equip them with knowledge and skills essential to BMI research, such as the responsible conduct of research, team science, and research ethics. Building upon a strong legacy of research and educational collaborations, we will successfully train a cadre of researchers capable of leading the design, development and evaluation of next generation PHI systems. Collectively our faculty and trainees will work on research that can be translated to public health agencies and health care delivery systems to transform care delivery as well as outcomes.


Selected Publications                          |                          Go To PubMed List >>

  
1. Dixon BE, Zhang Z, Lai PTS, Kirbiyik U, Williams J, Hills R, Revere D, Gibson PJ, Grannis SJ. Completeness and timeliness of notifiable disease reporting: a comparison of laboratory and provider reports submitted to a large county health department. BMC Med Inform Decis Mak. 2017 Jun 23;17(1):87. doi:10.1186/s12911-017-0491-8. PubMed PMID: 28645285; PubMed Central PMCID:PMC5481902.

2. Kasthurirathne SN, Dixon BE, Gichoya J, Xu H, Xia Y, Mamlin B, Grannis SJ. Toward better public health reporting using existing off the shelf approaches: The value of medical dictionaries in automated cancer detection using plain text medical data. J Biomed Inform. 2017 May;69:160-176. doi:10.1016/j.jbi.2017.04.008. Epub 2017 Apr 12. PubMed PMID: 28410983.

3.Revere D, Hills RH, Dixon BE, Gibson PJ, Grannis SJ. Notifiable condition reporting practices: implications for public health agency participation in a health information exchange. BMC Public Health. 2017 Mar 11;17(1):247. doi:10.1186/s12889-017-4156-4. PubMed PMID: 28284190; PubMed Central PMCID:PMC5346201.

4. Dixon BE, Barboza K, Jensen AE, Bennett KJ, Sherman SE, Schwartz MD. Measuring Practicing Clinicians' Information Literacy. An Exploratory Analysis in the Context of Panel Management. Appl Clin Inform. 2017 Feb 15;8(1):149-161. doi:10.4338/ACI-2016-06-RA-0083. PubMed PMID: 28197620; PubMed Central PMCID:PMC5373760.

5. Kasting ML, Wilson S, Zollinger TW, Dixon BE, Stupiansky NW, Zimet GD. Differences in cervical cancer screening knowledge, practices, and beliefs: An examination of survey responses. Prev Med Rep. 2016 Dec 21;5:169-174. doi:10.1016/j.pmedr.2016.12.013. eCollection 2017 Mar. PubMed PMID: 28050339; PubMed Central PMCID: PMC5200875.

6. Vest JR, Harle CA, Schleyer T, Dixon BE, Grannis SJ, Halverson PK, Menachemi N. Getting from here to there: health IT needs for population health. Am J Manag Care. 2016 Dec;22(12):827-829. PubMed PMID: 27982666.

7. Kasting ML, Wilson S, Dixon BE, Downs SM, Kulkarni A, Zimet GD. Healthcare providers' beliefs and attitudes regarding risk compensation following HPV vaccination. Papillomavirus Res. 2016 Dec;2:116-121. PubMed PMID: 27441302; PubMed Central PMCID: PMC4946644.

8. Dixon BE, Ofner S, Perkins SM, Myers LJ, Rosenman MB, Zillich AJ, French DD, Weiner M, Haggstrom DA. Which veterans enroll in a VA health information exchange program? J Am Med Inform Assoc. 2017 Jan;24(1):96-105. doi: 10.1093/jamia/ocw058. Epub 2016 Jun 6. PubMed PMID: 27274014.

9. Kasting ML, Wilson S, Dixon BE, Downs SM, Kulkarni A, Zimet GD. A qualitative study of healthcare provider awareness and informational needs regarding the nine-valent HPV vaccine. Vaccine. 2016 Mar 8;34(11):1331-4. doi:10.1016/j.vaccine.2016.01.050. Epub 2016 Feb 7. PubMed PMID: 26859240; PubMedCentral PMCID: PMC4995443.

10. Dixon BE, Alzeer AH, Phillips EO, Marrero DG. Integration of Provider, Pharmacy, and Patient-Reported Data to Improve Medication Adherence for Type 2 Diabetes: A Controlled Before-After Pilot Study. JMIR Med Inform. 2016 Feb8;4(1):e4. doi: 10.2196/medinform.4739. PubMed PMID: 26858218; PubMed CentralPMCID: PMC4763113.


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