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Pharmacy informatics

Health Informatics
Simulation models

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Pharmacy informatics

  1. 1. Therapy Simulation Models in Health Informatics Ankit Dhaundiyal , M.S.(Pharm.) 3rd Semester, Dept. of Pharmacoinformatics, NIPER, S.A.S. Nagar, Punjab (160062)
  2. 2. Outline • Brief Introduction of Pharmacy Informatics • Use of Computers in simulating different pathological states of human body • Diabetes Treatment Simulation Model • Virtual Reality Therapy 2
  3. 3. What is Pharmacy Informatics/Health Informatics ? • Pharmacy informatics is the scientific field that focuses on medication-related data and knowledge within the continuum of healthcare systems - including its acquisition, storage, analysis, use and dissemination - in the delivery of optimal medication- related patient care and health outcomes (HIMSS October 2006) 3
  4. 4. Health Informatics • What is Health Informatics? – A discipline at the intersection of information science, computer science, and health care – Is the interdisciplinary study of the design, development, adoption and application of IT-based innovations in healthcare services delivery, management and planning. – Deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of information in health and biomedicine
  5. 5. Health Informatics • Applications of Health Informatics – Translational Bioinformatics – Clinical Research Informatics – Clinical Informatics – Consumer Health Informatics – Public Health Informatics
  6. 6. • Health Informatics is, in part, a mathematics and statistics-based approach to understanding health information
  7. 7. What is a Model ? • A model is a logical mathematical framework that permits the integration of facts and values and that links these data to outcomes that are of interest to health-care decision makers. • End result of a model is often an estimate of cost per quality-adjusted life year (QALY) gained or other measure of value for money. 7
  8. 8.  Conditions for a Good Model • Ability to reveal the logical connection between inputs (i.e., data and assumptions) and outputs in the form of valued consequences and costs • should be transparent to the end user 8
  9. 9.  Components of a Model  Model definition and evaluation  Data  Data identification  Data modeling  Data incorporation  Validation  Internal validation  Between model validation  External and predictive validation 9
  10. 10. • Various Therapeutic Simulation Models 1. Diabetes mellitus 2. HIV-AIDS 3. Radiation therapy simulation 4. Post traumatic disorder 10
  11. 11. Diabetes Treatment Simulation Model • Assess alternative treatment intensification strategies on survival and diabetes-related complications • The objective of this analysis is to project the long-term impacts on life expectancy and occurrence over 5, 10, and 40 years of microvascular and macrovascular complications of diabetes. • Different haemoglobin A1c (HbA1c) thresholds used for intensifying treatment of type 2 diabetes. 11
  12. 12. • In the present analysis, the model is used to investigate the impact of alternative HbA1c thresholds for treatment intensification ranging from 7.0 to 9.0% . • Model is run using 80 simulated patients for each of 1224 patient profiles from the Real-Life effectiveness and Care Patterns of Diabetes Management study (for a total of 97 920 simulated patients) to project the number of patients who will experience diabetes-related complications over time 12
  13. 13. • Enables health-care researchers, providers and decision makers to project the long-term consequences of provider behaviours on various diabetes related complications as well as patients’ quality of life (QOL; quality-adjusted life years) • Januvia Diabetes Economic (JADE) Model, to evaluate the effects of using different HbA1c thresholds for intensifying pharmacotherapy on long-term health outcomes in patients with type 2 diabetes. 13
  14. 14. • Model Structure of JADE :  The model builds on the landmark United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model, incorporating its integrated system of event risk equations and simulation algorithm to predict the occurrence and timing of seven diabetes- related complications and death.  Permits the calculation of cost-effectiveness interventions to help determine cost-effective treatment strategies for patients with type 2 diabetes. 14
  15. 15.  The JADE Model has been written using Microsoft Visual Basic 6.3 [Visual Basics for Applicants (VBA)] with Microsoft Office Excel 2003.  JADE Model comprises five related modules :  Initial conditions  Treatment conditions  Risk factor/adverse events  Diabetes-related events and  Cost/QOL 15
  16. 16.  Initial Conditions : • Purpose of the Initial Conditions module is to establish the baseline profile for each patient whose life course with diabetes will be simulated. • Each profile contains information on the patient’s current risk factor status, history of pre-existing diabetes-related complications and current treatment regimen. 16
  17. 17.  Treatment Module : • Used to simulate the sequence of up to six treatment regimens prescribed over a patient’s lifetime. • Specific sequence or path a patient takes through the treatment algorithm depends on the patient’s simulated HbA1c response to, and toleration of, specific regimens and the development of potential contraindications over time 17
  18. 18. Fig 1. Treatment pathways evaluated in the treatment-intensification example. Basal, basal insulin; MDI, multiple-dose insulin; MF, metformin; Rosi, rosiglitazone; SU, sulphonylurea. 18
  19. 19. Fig 2. Structure of the Januvia Diabetes Economic Model. QOL, quality of life. 19
  20. 20.  Risk Factor/Adverse Events Module • When a patient initiates a new oral AHA or insulin regimen, changes in HbA1c are assumed to follow three stages over time:  a drop in HbA1c during the first cycle,  followed by a stable HbA1c period  then increases in HbA1c at a fixed rate over time. 20
  21. 21. • Other time-varying factors : 1. Total cholesterol (TC) to high-density lipoprotein cholesterol (HDL) ratio, 2. Systolic blood pressure (SBP) 3. Body mass index 21
  22. 22.  Diabetes-related Events Module • Ischaemic heart disease (IHD) • Myocardial infarction (MI) • Congestive heart failure (CHF) • Stroke • Amputation • Renal failure • Blindness • Death 22
  23. 23.  Cost/QOL Module • Accumulates the costs, survival time and quality- adjusted survival time for a patient. • The user can specify whether diabetes-related complications have either a short-term (single-cycle) or long-term (lifetime) impact on the patient’s QOL. 23
  24. 24. Virtual Reality Therapy 24
  25. 25. • Method of psychotherapy that uses virtual reality technology to treat patients with anxiety disorders and phobias • https://www.youtube.com/watch?v=Oe_3uL4JxEc • https://www.youtube.com/watch?v=bIj26r4VPaA 25
  26. 26. Various Universities offering study courses in Health informatics 26
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  28. 28. References • Principles of Good Practice for Decision Analytic Modeling in Health-Care Evaluation: Report of the ISPOR Task Force on Good Research Practices—Modeling Studies Milton C.Weinstein, PhD, Bernie O’Brien, PhD, John Hornberger, MD, MS, Joseph Jackson, PhD, Volume 6 • Number 1 • 2003 VA L U E I N H E A LT H. • THE USE OF COMPUTER SIMULATION IN HEALTH CARE FACILITY DESIGN O. George Kennedy, Ph.D. Manager, Management Systems - Midwestern Region MEDICUS Systems • Development of a diabetes treatment simulation model: with application to assessing alternative treatment intensification strategies on survival and diabetes-related complications J. Chen,1 E. Alemao,2 D. Yin2 and J. Cook Diabetes, Obesity and Metabolism, 10 (Suppl. 1), 2008, 33–42 28
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