AITRP eHIT 101 tutorial

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  • Incorporate research lingo
  • Effects on treatment outcome? On treatment adherence?
  • AITRP eHIT 101 tutorial

    1. 1. Utilizing Electronic Health Information Technology to Conduct HIV/AIDS Clinical Pharmacology and Implementation Research: Introductory Module<br />Samuel Gavi, MSc – AITRP Fellow, UZ<br />Charles C. Maponga PharmD, AITRP Co-Principal Investigator, UZ<br />Cara Felton, PharmD – AITRP Mentor, UB<br />Farzia Sayidine- AITRP Research Support, UB<br />Pulkit Bhuptani PharmD - Research Student, UB <br />Kathleen M. Tooley , MSEd- AITRP Program Administrator, UB<br />Robin DiFrancesco, MBA – AITRP Mentor, UB<br />Gene D. Morse, PharmD – AITRP Principal Investigator, UB<br />1<br />University at Buffalo/University of Zimbabwe <br />AIDS International Training And Research Program<br />A Collaboration of the University at Buffalo, University of Zimbabwe, <br />NYS Center of Excellence in Bioinformatics and Life Sciences, and the Institute for Healthcare Informatics<br />
    2. 2. Introduction<br />Goal:<br />To become familiar with applications of electronic Health Information Technology (eHIT) in HIV/AIDS, TB and other infectious disease research<br />This module will explore the following:<br />Overview of electronic health information technology<br />Advantages and disadvantages of using electronic health information technology<br />Specialized fields of application of health information technology<br />Applications of eHIT in HIV/AIDS therapeutics research and implementation science in clinical practice<br />2<br />
    3. 3. Overview of Electronic Health Information Technology<br />3<br />
    4. 4. Electronic Health Information Technology (eHIT)<br />eHIT may consist of:<br />Electronic medical records<br />Electronic health records<br />Electronic prescribing<br />Personal Health records<br />eHIT can:<br />Comprehensively manage patient medical records <br />Allow for secure exchange of information between health care providers<br />Provide patient-centered health system that continuously improves health outcomes for individuals and the population<br />4<br />
    5. 5. eHIT System <br />5<br />Clinical Presentation and Diagnosis <br />Diagnostics – Laboratory Data Management System<br />Electronic Health Record<br />Electronic Medical Records<br />Electronic Prescribing<br />Medication Management<br />Patient Centered outcomes<br />Treatment Biomarkers <br />Translational applications in clinical research, clinical practice, pharmacoepidemiology, pharmacoinformatics, pharmacogenomics and implementation science<br />
    6. 6. eHIT System <br />6<br />Clinical Presentation and Diagnosis <br />Diagnostics – Laboratory Data Management System<br />Electronic Health Record<br />Electronic Medical Records<br />Electronic Prescribing<br />Medication Management<br />Patient Centered outcomes<br />Treatment Biomarkers <br />Translational applications in clinical research, clinical practice, pharmacoepidemiology, pharmacoinformatics, pharmacogenomics and implementation science<br />
    7. 7. Electronic Medical Records (EMR)<br />An application environment composed of:<br />Clinical data repository<br />Clinical decision support<br />Controlled medical vocabulary<br />Computerized provider order entry, pharmacy, and clinical documentation<br />EMRs are used by healthcare practitioners to document, monitor, and manage health care delivery <br />Used within an institution or system<br />7<br />
    8. 8. Components of an EMR<br />Ancillary clinical services, including:<br />Laboratory<br />Radiology<br />Pharmacy records<br />Computerized physician order entry (CPOE)<br />Error checking capabilities<br />Order entry<br />Report and document adverse events<br />Clinical documentation, including:<br />Nursing notes<br />Consult notes<br />8<br />
    9. 9. eHIT System <br />9<br />Clinical Presentation and Diagnosis <br />Diagnostics – Laboratory Data Management System<br />Electronic Health Record<br />Electronic Medical Records<br />Electronic Prescribing<br />Medication Management<br />Patient Centered outcomes<br />Treatment Biomarkers <br />Translational applications in clinical research, clinical practice, pharmacoepidemiology, pharmacoinformatics, pharmacogenomics and implementation science<br />
    10. 10. Electronic Health Records (EHR)<br />Distinguished from EMR as it may be shared among different organizations<br />Can be seen as a constellation of a patient’s EMRs accumulated from the same or various health care institutions over a period of time<br />10<br />
    11. 11. Components of an EHR<br />Patient demographics <br />History and physical<br />Allergies <br />Immunizations<br />Laboratory tests<br />Medical imaging<br />Medication history <br />Pharmacy records<br />Information from multiple providers/ institutions<br />11<br />
    12. 12. eHIT System <br />12<br />Clinical Presentation and Diagnosis <br />Diagnostics – Laboratory Data Management System<br />Electronic Health Record<br />Electronic Medical Records<br />Electronic Prescribing<br />Medication Management<br />Patient Centered outcomes<br />Treatment Biomarkers <br />Translational applications in clinical research, clinical practice, pharmacoepidemiology, pharmacoinformatics, pharmacogenomics and implementation science<br />
    13. 13. Electronic Prescribing<br />ePrescribing is the process whereby an authorized health care prescriber generates a computer-based prescription and transmits it electronically to a pharmacy<br />Computer to computer<br />ePrescribing is not:<br />Computer to fax<br />Computer-generated paper prescription<br />This system can be integrated into an EMR <br />ePrescribing can also be used for research protocols<br />13<br />
    14. 14. Types of ePrescribing Systems <br />Stand-alone system: <br />Simpler and cheaper to install as compared to a complete EMR package<br />Integrated system: <br />Consists of an electronic prescribing module integrated into an EMR system<br />Contains all pertinent medical information within one comprehensive system <br />14<br />
    15. 15. eHIT System <br />15<br />Clinical Presentation and Diagnosis <br />Diagnostics – Laboratory Data Management System<br />Electronic Health Record<br />Electronic Medical Records<br />Electronic Prescribing<br />Medication Management<br />Patient Centered outcomes<br />Treatment Biomarkers <br />Translational applications in clinical research, clinical practice, pharmacoepidemiology, pharmacoinformatics, pharmacogenomics and implementation science<br />
    16. 16. Using eHIT for Medication Management<br />Management of HIV/AIDS and co-infections presents medication complexities <br />Ex. Interactions, adverse events, increased dosing frequency and pill burden<br />eHIT can be used to identify precautionary and prohibited medications during screening and protocol implementation<br />Innovative programming within EHRs could allow for calculating the impact of medication complexities on ARV treatment outcomes<br />16<br />
    17. 17. Using eHIT for Medication Management<br />Medication information: <br />Automated alert systems for:<br />Adverse events<br />Drug-drug and drug-disease interactions<br />Special instructions:<br />Food and timing considerations<br />Administration<br />Drug formulation <br />Route of administration<br />Frequency<br />17<br />
    18. 18. eHIT System <br />18<br />Clinical Presentation and Diagnosis <br />Diagnostics – Laboratory Data Management System<br />Electronic Health Record<br />Electronic Medical Records<br />Electronic Prescribing<br />Medication Management<br />Patient Centered outcomes<br />Treatment Biomarkers <br />Translational applications in clinical research, clinical practice, pharmacoepidemiology, pharmacoinformatics, pharmacogenomics and implementation science<br />
    19. 19. Patient Centered Outcomes<br />Patient outcomes:<br />Result of clinical decision making from evidence based best practices<br />Evidence based best practices: <br />Derived from clinical research results<br />Clinical research results:<br />Studies designed around research hypothesis<br />Research hypothesis:<br />Research questions arise from patient outcomes<br />Adverse drug reactions and toxicities can be monitored to enhance subject safety during protocols<br />19<br />
    20. 20. Using eHIT to Manage Disease Complexity<br />Disease complexity in an HIV/AIDS patient can be determined using all available clinical and laboratory patient data<br />Data from EHRs can be used to monitor disease complexity and progression<br />Patient demographics <br />HIV serology<br />Complete blood count with differential<br />Blood chemistries<br />Resistance tests (genotypic and phenotypic assays)<br />Additional serologic screening<br />Including disease complexity may allow for the detection of individual drug efficacy or toxicity among subjects<br />20<br />
    21. 21. eHIT System <br />21<br />Clinical Presentation and Diagnosis <br />Diagnostics – Laboratory Data Management System<br />Electronic Health Record<br />Electronic Medical Records<br />Electronic Prescribing<br />Medication Management<br />Patient Centered outcomes<br />Treatment Biomarkers <br />Translational applications in clinical research, clinical practice, pharmacoepidemiology, pharmacoinformatics, pharmacogenomics and implementation science<br />
    22. 22. Applications of eHIT in Clinical Pharmacology and Implementation Research<br />Identification of potential research opportunities<br />Identification of potential trial participants<br />Facilitate screening based on inclusion/exclusion criteria<br />Creation of trial-specific data fields<br />Facilitation of data submission <br />Eliminate redundancy and increase accuracy<br />22<br />
    23. 23. Applications of eHIT in Clinical Pharmacology and Implementation Research<br />More efficient and extensive patient data collection<br />Improved external validity <br />Representative of the source population <br />Cost considerations:<br />Lower patient monitoring costs during the trial<br />Lower start up time and costs of clinical research<br />Existing network infrastructure<br />23<br />
    24. 24. Applications of eHIT in Clinical Pharmacology and Implementation Research<br />EHRs provide:<br />Improved participant recruitment and enrollment<br />Participant identification<br />Broad, diverse population<br />Population health surveillance<br />Across exposures, interventions, and outcomes<br />Exposure/outcome associations<br />Outcomes research<br />24<br />
    25. 25. EHR Data in Clinical Pharmacology and Implementation Research <br />Engage more people in research<br />More robust information<br />Enable access to advanced treatment<br />Reduce errors in data<br />Collective data across trials<br />Efficient data collection<br />Eliminate redundancy in data collection<br />Safety reporting – real time data<br />Facilitate sharing of information<br />Improve communication between partnering organizations<br />25<br />
    26. 26. EHR Data in Clinical Pharmacology and Implementation Research<br />How data are collected/generated<br />Strengthen provider/investigator relationship<br />Patient input<br />Consider health literacy, access to care<br />Different purposes for data collection/generation<br />26<br />
    27. 27. EHR Data in Clinical Pharmacology and Implementation Research<br />Quality of data collection<br />What to collect<br />Where to collect <br />How to collect <br />When to collect <br />Why to collect<br />27<br />
    28. 28. Data Collection - Case Report Forms <br />Official data-reporting document used in clinical trials<br />Collects protocol-required information<br />CRFs allow for:<br />Standardization of information <br />Sharing of data among investigators<br />EHR data may be used to pre-populate CRFs<br />Eliminate transcription errors<br />Eliminate redundancy in data collection<br />28<br />
    29. 29. EHR Data in Clinical Pharmacology and Implementation Research<br />Exchange of data<br />Encourage sharing of data<br />Responsible use of data by all participating organizations<br />Encourage standardization of data<br />Collection, storage, retrieval, and analysis <br />29<br />
    30. 30. Additional Considerations Regarding eHIT Systems<br />Security of patient data:<br />Different levels of access for different users<br />Compatibility:<br />Multiple institutions using different eHIT systems<br />Regulatory bodies:<br />Issues concerning security, sharing, and use of patient data <br />Data requirements:<br />Differing requirements between the EHR and a clinical trial<br />30<br />
    31. 31. eHIT in HIV/AIDS Treatment and Research<br />Use of eHIT systems has the potential to improve HIV/AIDS treatment by:<br />Increasing adherence to treatment regimens<br />Minimizing patients lost to follow-up<br />Supporting clinical decisions <br />Minimizing medication errors (interactions, duplications, additive adverse events)<br />31<br />
    32. 32. Information Technology Used in HIV Treatment and Research<br />Information technology can be used for effective management of HIV, TB, and other infectious diseases<br />Networked computers<br />Small basic databases using Microsoft Excel or Access for large databases<br />Internet-based databases <br />Laboratory data management systems<br />Use of portable electronic devices in medication management<br />32<br />
    33. 33. Electronic health information technology applications provide a continuous feedback loop to optimize patient health outcomes and drive clinical research<br />Model for eHIT Facilitated Clinical Pharmacology and Implementation Research<br />
    34. 34. Supplemental Reading Materials<br />FDA guidelines for drug development<br />HIPAA and clinical research<br />Johnson George et al developed the Medication Regimen Complexity Index, a useful tool that can be useful for patients on ARV therapy to determine impact of medication complexities on treatment outcomes <br />CollenDilorio et al tested the antiretroviral medication complexity index (AMCI) to assess the psychometric properties of the AMCI<br />34<br />
    35. 35. Acknowledgments<br />National Institutes of Health Fogarty International Center 1D43TW007991-01A2<br />National Institutes of Health, Fogarty International Center 3D43TW007991-01A2S1<br />We would like to acknowledge Ms. Amal Harb and Mr. Francesco Lliguicota for their informatics assistance on this project. We would also like to acknowledge the Adherence Pharmacology Unit at the Erie County Medical Center for their clinical mentorship to AITRP fellows.<br />35<br />
    36. 36. References<br />1. Farrokh A. Components of EHR. In.<br />2. Hersh W. Health Care Information Technology: Progress and Barriers. Journal of American Medical Association 2004;292(18):2273-2274.<br />3. Hersh W. Electronic health records facilitate development of disease registries and more. Clin J Am Soc Nephrol 2011;6(1):5-6.<br />4. Hersh W. Adding Value to the Electronic Health Record Through Secondary Use of Data for Quality Assurance, Research, and Surveillance. The American Journal of Managed Care 2007;13(6):277-278.<br />5. Hillestad R, Bigelow J, Bower A, et al. Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Aff (Millwood) 2005;24(5):1103-17.<br />6. Kuo GM, Mullen PD, McQueen A, Swank PR, Rogers JC. Cross-sectional comparison of electronic and paper medical records on medication counseling in primary care clinics: a Southern Primary-Care Urban Research Network (SPUR-Net) study. J Am Board Fam Med 2007;20(2):164-73.<br />7. Otero P, Gonzalez Bernaldo de Quiros F, Hersh W. Competencies for a well-trained biomedical and health informatics workforce. Methods Inf Med 2010;49(3):297-8.<br />8. Taylor R, Bower A, Girosi F, Bigelow J, Fonkych K, Hillestad R. Promoting health information technology: is there a case for more-aggressive government action? Health Aff (Millwood) 2005;24(5):1234-45.<br />9. Thomas Handler RH, Jane Metzger, Marc Overhage, Sheryl Taylor, Charlene Underwood, . HIMSS Electronic Health Record Definitional Model Version 1.0. In: EHR Definition, Attributes and Essential Requirements Version 1.0; 2003.<br />10. Martin S, Wolters PL, Calabrese SK, et al. The Antiretroviral Regimen Complexity Index. A novel method of quantifying regimen complexity. J Acquir Immune DeficSyndr 2007;45(5):535-44.<br />11. Farris KD, Kelly MW, Tryon J. Clock drawing test and medication complexity index as indicators of medication management capacity: a pilot study. J Am Pharm Assoc (Wash) 2003;43(1):78-81.<br />12. DiIorio C, McDonnell M, McCarty F, Yeager K. Initial testing of the Antiretroviral Medication Complexity Index. J Assoc Nurses AIDS Care 2006;17(1):26-36.<br />13. Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006;144(10):742-52.<br />14. Bernstam EV, Hersh WR, Sim I, et al. Unintended consequences of health information technology: a need for biomedical informatics. J Biomed Inform 2010;43(5):828-30.<br />15. Benjamin DM. Reducing medication errors and increasing patient safety: case studies in clinical pharmacology. J ClinPharmacol 2003;43(7):768-83.<br />16. Kahn, MG. (2006). Integrating Electronic Health Records and Clinical Trials, An Examination of Pragmatic Issues [Powerpoint]. Retrieved from ESI: www.esi-bethesda.com/ncrrworkshops/clinicalresearch/pdf/MichaelKahnPaper.pdf.<br />17. Ensuring the Inclusion of Clinical Research in the National Health Information Network (2006)<br /> http://www.fastercures.org/index.cfm/OurPrograms/PatientsHelpingDoctors/Nationwide_Health_Information_Network_%28NHIN%29<br />36<br />

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